diff --git a/.gitignore b/.gitignore index 62273454..2151ab46 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,2 @@ -# Google App Engine generated folder -appengine-generated/ +.DS_Store +.ipynb_checkpoints diff --git "a/JupyterNotebooks/Cap\303\255tulo01/DSA-Python-Cap\303\255tulo1-Como utilizar o Jupyter Notebook.ipynb" b/Cap01/DSA-Python-Cap01-ComoUtilizarJupyterNotebook.ipynb similarity index 57% rename from "JupyterNotebooks/Cap\303\255tulo01/DSA-Python-Cap\303\255tulo1-Como utilizar o Jupyter Notebook.ipynb" rename to Cap01/DSA-Python-Cap01-ComoUtilizarJupyterNotebook.ipynb index ca607ca8..e692a12e 100644 --- "a/JupyterNotebooks/Cap\303\255tulo01/DSA-Python-Cap\303\255tulo1-Como utilizar o Jupyter Notebook.ipynb" +++ b/Cap01/DSA-Python-Cap01-ComoUtilizarJupyterNotebook.ipynb @@ -12,35 +12,37 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Como utilizar o Jupyer Notebook" + "## Como Utilizar o Jupyter Notebook" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "')" ] }, { @@ -61,9 +63,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -75,9 +77,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo01/Jupyter Notebook-Manual de Usu\303\241rio.ipynb" b/Cap01/JupyterNotebook-ManualUsuario.ipynb similarity index 100% rename from "JupyterNotebooks/Cap\303\255tulo01/Jupyter Notebook-Manual de Usu\303\241rio.ipynb" rename to Cap01/JupyterNotebook-ManualUsuario.ipynb diff --git a/Cap01/hello.py b/Cap01/hello.py new file mode 100644 index 00000000..8e235769 --- /dev/null +++ b/Cap01/hello.py @@ -0,0 +1 @@ +print("Hello World") \ No newline at end of file diff --git a/Cap02/Lab01/game_v1.py b/Cap02/Lab01/game_v1.py new file mode 100644 index 00000000..5d3329cf --- /dev/null +++ b/Cap02/Lab01/game_v1.py @@ -0,0 +1,145 @@ +# Game Ping-Pong + +from tkinter import * +import random +import time + +level = int(input("Qual nível você gostaria de jogar? 1/2/3/4/5 \n")) +length = 500/level + + +root = Tk() +root.title("Ping Pong") +root.resizable(0,0) +root.wm_attributes("-topmost", -1) + +canvas = Canvas(root, width=800, height=600, bd=0,highlightthickness=0) +canvas.pack() + +root.update() + +# Variável +count = 0 +lost = False + +class Bola: + def __init__(self, canvas, Barra, color): + self.canvas = canvas + self.Barra = Barra + self.id = canvas.create_oval(0, 0, 15, 15, fill=color) + self.canvas.move(self.id, 245, 200) + + starts_x = [-3, -2, -1, 1, 2, 3] + random.shuffle(starts_x) + + self.x = starts_x[0] + self.y = -3 + + self.canvas_height = self.canvas.winfo_height() + self.canvas_width = self.canvas.winfo_width() + + + def draw(self): + self.canvas.move(self.id, self.x, self.y) + + pos = self.canvas.coords(self.id) + + if pos[1] <= 0: + self.y = 3 + + if pos[3] >= self.canvas_height: + self.y = -3 + + if pos[0] <= 0: + self.x = 3 + + if pos[2] >= self.canvas_width: + self.x = -3 + + self.Barra_pos = self.canvas.coords(self.Barra.id) + + + if pos[2] >= self.Barra_pos[0] and pos[0] <= self.Barra_pos[2]: + if pos[3] >= self.Barra_pos[1] and pos[3] <= self.Barra_pos[3]: + self.y = -3 + global count + count +=1 + score() + + + if pos[3] <= self.canvas_height: + self.canvas.after(10, self.draw) + else: + game_over() + global lost + lost = True + + +class Barra: + def __init__(self, canvas, color): + self.canvas = canvas + self.id = canvas.create_rectangle(0, 0, length, 10, fill=color) + self.canvas.move(self.id, 200, 400) + + self.x = 0 + + self.canvas_width = self.canvas.winfo_width() + + self.canvas.bind_all("", self.move_left) + self.canvas.bind_all("", self.move_right) + + def draw(self): + self.canvas.move(self.id, self.x, 0) + + self.pos = self.canvas.coords(self.id) + + if self.pos[0] <= 0: + self.x = 0 + + if self.pos[2] >= self.canvas_width: + self.x = 0 + + global lost + + if lost == False: + self.canvas.after(10, self.draw) + + def move_left(self, event): + if self.pos[0] >= 0: + self.x = -3 + + def move_right(self, event): + if self.pos[2] <= self.canvas_width: + self.x = 3 + + +def start_game(event): + global lost, count + lost = False + count = 0 + score() + canvas.itemconfig(game, text=" ") + + time.sleep(1) + Barra.draw() + Bola.draw() + + +def score(): + canvas.itemconfig(score_now, text="Pontos: " + str(count)) + +def game_over(): + canvas.itemconfig(game, text="Game over!") + + +Barra = Barra(canvas, "orange") +Bola = Bola(canvas, Barra, "purple") + + +score_now = canvas.create_text(430, 20, text="Pontos: " + str(count), fill = "green", font=("Arial", 16)) +game = canvas.create_text(400, 300, text=" ", fill="red", font=("Arial", 40)) + + +canvas.bind_all("", start_game) + +root.mainloop() \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-N\303\272meros.ipynb" b/Cap02/Notebooks/DSA-Python-Cap02-01-Numeros.ipynb similarity index 86% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-N\303\272meros.ipynb" rename to Cap02/Notebooks/DSA-Python-Cap02-01-Numeros.ipynb index d94bfcf3..81e996d5 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-N\303\272meros.ipynb" +++ b/Cap02/Notebooks/DSA-Python-Cap02-01-Numeros.ipynb @@ -13,29 +13,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Números " + "## Números e Operações Matemáticas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Operações Básicas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pressione as teclas shift e enter para executar o código em uma célula" + "# Pressione as teclas shift e enter para executar o código em uma célula ou pressione o botão Play no menu superior" ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -56,9 +47,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -79,9 +68,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -102,9 +89,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -125,9 +110,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -148,9 +131,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -178,9 +159,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -200,9 +179,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -222,9 +199,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -252,9 +227,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -274,9 +247,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -296,9 +267,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -318,9 +287,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -341,9 +308,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -364,9 +329,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -386,9 +349,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -415,9 +376,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -437,9 +396,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -459,9 +416,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -488,9 +443,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -510,9 +463,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -532,9 +483,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -554,9 +503,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -583,9 +530,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -606,9 +551,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -629,9 +572,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -652,9 +593,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -675,9 +614,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -713,9 +650,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -727,9 +664,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Vari\303\241veis.ipynb" b/Cap02/Notebooks/DSA-Python-Cap02-02-Variaveis.ipynb similarity index 86% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Vari\303\241veis.ipynb" rename to Cap02/Notebooks/DSA-Python-Cap02-02-Variaveis.ipynb index 9895358a..562f186d 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Vari\303\241veis.ipynb" +++ b/Cap02/Notebooks/DSA-Python-Cap02-02-Variaveis.ipynb @@ -31,9 +31,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -54,9 +52,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -74,9 +70,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "NameError", @@ -109,9 +103,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -131,9 +123,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -164,9 +154,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -197,9 +185,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -237,9 +223,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -259,9 +243,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -281,9 +263,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -314,9 +294,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -336,9 +314,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -355,6 +331,29 @@ "fruta2" ] }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Fruta2' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFruta2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'Fruta2' is not defined" + ] + } + ], + "source": [ + "# Fique atento!!! Python é case-sensitive. Criamos a variável fruta2, mas não a variável Fruta2.\n", + "# Letras maiúsculas e minúsculas tem diferença no nome da variável.\n", + "Fruta2" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -364,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -375,10 +374,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -386,7 +383,7 @@ "50" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -397,17 +394,15 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "ename": "SyntaxError", - "evalue": "invalid syntax (, line 2)", + "evalue": "invalid syntax (, line 2)", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m 1x = 50\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m 1x = 50\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], @@ -459,17 +454,15 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "ename": "SyntaxError", - "evalue": "invalid syntax (, line 2)", + "evalue": "invalid syntax (, line 2)", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m break = 1\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m break = 1\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], @@ -487,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -498,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "collapsed": true }, @@ -509,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "collapsed": true }, @@ -520,10 +513,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -531,7 +522,7 @@ "8" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -542,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": { "collapsed": true }, @@ -553,10 +544,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { @@ -564,7 +553,7 @@ "12" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -575,9 +564,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -587,10 +576,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { @@ -598,7 +585,7 @@ "32" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -627,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": { "collapsed": true }, @@ -638,10 +625,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "data": { @@ -649,7 +634,7 @@ "60" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -660,10 +645,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "data": { @@ -671,7 +654,7 @@ "10" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -682,10 +665,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "data": { @@ -693,7 +674,7 @@ "875" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -704,10 +685,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "data": { @@ -715,7 +694,7 @@ "1.4" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -726,10 +705,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [ { "data": { @@ -737,7 +714,7 @@ "10" ] }, - "execution_count": 37, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -755,29 +732,29 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "nome = \"Luis\"" + "nome = \"Steve\"" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "sobrenome = \"Suarez\"" + "sobrenome = \"Jobs\"" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -788,18 +765,16 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, + "execution_count": 42, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Luis Suarez'" + "'Steve Jobs'" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -826,9 +801,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -840,9 +815,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Strings.ipynb" b/Cap02/Notebooks/DSA-Python-Cap02-03-Strings.ipynb similarity index 87% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Strings.ipynb" rename to Cap02/Notebooks/DSA-Python-Cap02-03-Strings.ipynb index 18a93489..ccfb5e4a 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Strings.ipynb" +++ b/Cap02/Notebooks/DSA-Python-Cap02-03-Strings.ipynb @@ -27,14 +27,12 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Olá'" + "'Oi'" ] }, "execution_count": 1, @@ -44,20 +42,18 @@ ], "source": [ "# Uma única palavra\n", - "'Olá'" + "'Oi'" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Isto é uma string em Python'" + "'Criando uma string em Python'" ] }, "execution_count": 2, @@ -67,20 +63,18 @@ ], "source": [ "# Uma frase\n", - "'Isto é uma string em Python'" + "'Criando uma string em Python'" ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Isto é uma string em Python, usando aspas duplas'" + "'Podemos usar aspas duplas ou simples para strings em Python'" ] }, "execution_count": 3, @@ -90,15 +84,13 @@ ], "source": [ "# Podemos usar aspas duplas\n", - "\"Isto é uma string em Python, usando aspas duplas\"" + "\"Podemos usar aspas duplas ou simples para strings em Python\"" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -126,9 +118,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -145,9 +135,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -167,9 +155,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -206,9 +192,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -225,9 +209,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -248,9 +230,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -270,9 +250,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -299,9 +277,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -315,7 +291,7 @@ } ], "source": [ - "# Retorna todos os elementos da string, começando pela posição (lembre que Python começa a indexação pela posição 0),\n", + "# Retorna todos os elementos da string, começando pela posição (lembre-se que Python começa a indexação pela posição 0),\n", "# até o fim da string.\n", "s[1:]" ] @@ -323,9 +299,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -346,9 +320,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -369,9 +341,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -391,9 +361,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -414,9 +382,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -444,9 +410,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -466,9 +430,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -488,9 +450,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -517,9 +477,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -539,9 +497,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -563,9 +519,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -597,9 +551,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -628,9 +580,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -657,9 +607,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -679,9 +627,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -702,9 +648,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -725,9 +669,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -766,9 +708,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -802,20 +742,18 @@ }, "outputs": [], "source": [ - "s = 'olá! seja bem vindo ao universo de python'" + "s = 'seja bem vindo ao universo de python'" ] }, { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Olá! seja bem vindo ao universo de python'" + "'Seja bem vindo ao universo de python'" ] }, "execution_count": 35, @@ -830,9 +768,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -852,14 +788,12 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "35" + "30" ] }, "execution_count": 37, @@ -874,14 +808,12 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'olá! seja bem vindo ao universo de python'" + "'seja bem vindo ao universo de python'" ] }, "execution_count": 38, @@ -896,9 +828,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -918,9 +848,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -940,9 +868,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -962,9 +888,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -984,9 +908,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1006,14 +928,12 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "('olá', '!', ' seja bem vindo ao universo de python')" + "('seja bem vindo ao universo de python', '', '')" ] }, "execution_count": 44, @@ -1035,9 +955,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1054,9 +972,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1088,9 +1004,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1102,9 +1018,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Listas.ipynb" b/Cap02/Notebooks/DSA-Python-Cap02-04-Listas.ipynb similarity index 91% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Listas.ipynb" rename to Cap02/Notebooks/DSA-Python-Cap02-04-Listas.ipynb index ce551c4b..3fbfedbc 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Listas.ipynb" +++ b/Cap02/Notebooks/DSA-Python-Cap02-04-Listas.ipynb @@ -31,9 +31,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -63,9 +61,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -95,9 +91,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -116,7 +110,7 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -140,9 +134,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -167,9 +159,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -202,9 +192,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -232,9 +220,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "IndexError", @@ -268,9 +254,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -311,9 +295,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -346,9 +328,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -379,9 +359,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -412,9 +390,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -445,9 +421,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -478,9 +452,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -511,9 +483,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -544,9 +514,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -585,9 +553,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -619,9 +585,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -652,9 +616,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -685,9 +647,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -718,9 +678,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -751,9 +709,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -791,9 +747,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -824,9 +778,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -858,9 +810,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -899,9 +849,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -919,9 +867,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -946,9 +892,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -969,9 +913,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -992,9 +934,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1039,9 +979,7 @@ { "cell_type": "code", "execution_count": 58, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1072,9 +1010,7 @@ { "cell_type": "code", "execution_count": 60, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1094,9 +1030,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1128,9 +1062,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1147,9 +1079,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1180,9 +1110,7 @@ { "cell_type": "code", "execution_count": 66, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1213,9 +1141,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1270,9 +1196,7 @@ { "cell_type": "code", "execution_count": 72, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1325,9 +1249,7 @@ { "cell_type": "code", "execution_count": 76, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1347,9 +1269,7 @@ { "cell_type": "code", "execution_count": 77, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1369,9 +1289,7 @@ { "cell_type": "code", "execution_count": 78, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1390,9 +1308,7 @@ { "cell_type": "code", "execution_count": 79, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1419,9 +1335,7 @@ { "cell_type": "code", "execution_count": 80, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "ValueError", @@ -1442,9 +1356,7 @@ { "cell_type": "code", "execution_count": 81, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1465,7 +1377,7 @@ "cell_type": "code", "execution_count": 82, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -1475,9 +1387,7 @@ { "cell_type": "code", "execution_count": 83, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1509,9 +1419,7 @@ { "cell_type": "code", "execution_count": 85, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1543,9 +1451,7 @@ { "cell_type": "code", "execution_count": 87, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1577,9 +1483,7 @@ { "cell_type": "code", "execution_count": 89, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1611,9 +1515,7 @@ { "cell_type": "code", "execution_count": 91, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1648,9 +1550,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1662,9 +1564,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Dicion\303\241rios.ipynb" b/Cap02/Notebooks/DSA-Python-Cap02-05-Dicionarios.ipynb similarity index 86% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Dicion\303\241rios.ipynb" rename to Cap02/Notebooks/DSA-Python-Cap02-05-Dicionarios.ipynb index 7107af78..7eda25a2 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Dicion\303\241rios.ipynb" +++ b/Cap02/Notebooks/DSA-Python-Cap02-05-Dicionarios.ipynb @@ -25,20 +25,18 @@ "outputs": [], "source": [ "# Isso é uma lista\n", - "estudantes_lst = [\"Pedro\", 24, \"Fernando\", 22, \"Tania\", 26, \"Cris\", 25] " + "estudantes_lst = [\"Mateus\", 24, \"Fernanda\", 22, \"Tamires\", 26, \"Cristiano\", 25] " ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Pedro', 24, 'Fernando', 22, 'Tania', 26, 'Cris', 25]" + "['Mateus', 24, 'Fernanda', 22, 'Tamires', 26, 'Cristiano', 25]" ] }, "execution_count": 2, @@ -59,20 +57,18 @@ "outputs": [], "source": [ "# Isso é um dicionário\n", - "estudantes_dict = {\"Pedro\":24, \"Fernando\":22, \"Tania\":26, \"Cris\":25}" + "estudantes_dict = {\"Mateus\":24, \"Fernanda\":22, \"Tamires\":26, \"Cristiano\":25}" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Cris': 25, 'Fernando': 22, 'Pedro': 24, 'Tania': 26}" + "{'Cristiano': 25, 'Fernanda': 22, 'Mateus': 24, 'Tamires': 26}" ] }, "execution_count": 4, @@ -87,9 +83,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -103,7 +97,7 @@ } ], "source": [ - "estudantes_dict[\"Pedro\"]" + "estudantes_dict[\"Mateus\"]" ] }, { @@ -120,9 +114,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -142,9 +134,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -158,7 +148,7 @@ } ], "source": [ - "estudantes_dict[\"Tania\"]" + "estudantes_dict[\"Tamires\"]" ] }, { @@ -175,9 +165,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -208,9 +196,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "NameError", @@ -236,20 +222,18 @@ }, "outputs": [], "source": [ - "estudantes = {\"Pedro\":24, \"Fernando\":22, \"Tania\":26, \"Cris\":25}" + "estudantes = {\"Mateus\":24, \"Fernanda\":22, \"Tamires\":26, \"Cristiano\":25}" ] }, { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Cris': 25, 'Fernando': 22, 'Pedro': 24, 'Tania': 26}" + "{'Cristiano': 25, 'Fernanda': 22, 'Mateus': 24, 'Tamires': 26}" ] }, "execution_count": 14, @@ -264,9 +248,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -286,14 +268,12 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_keys(['Cris', 'Pedro', 'Tania', 'Fernando'])" + "dict_keys(['Mateus', 'Fernanda', 'Tamires', 'Cristiano'])" ] }, "execution_count": 16, @@ -308,14 +288,12 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_values([25, 24, 26, 22])" + "dict_values([24, 22, 26, 25])" ] }, "execution_count": 17, @@ -330,14 +308,12 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_items([('Cris', 25), ('Pedro', 24), ('Tania', 26), ('Fernando', 22)])" + "dict_items([('Mateus', 24), ('Fernanda', 22), ('Tamires', 26), ('Cristiano', 25)])" ] }, "execution_count": 18, @@ -363,9 +339,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -396,20 +370,18 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Cris': 25,\n", + "{'Cristiano': 25,\n", " 'Erika': 28,\n", - " 'Fernando': 22,\n", + " 'Fernanda': 22,\n", " 'Maria': 27,\n", + " 'Mateus': 24,\n", " 'Milton': 26,\n", - " 'Pedro': 24,\n", - " 'Tania': 26}" + " 'Tamires': 26}" ] }, "execution_count": 22, @@ -435,9 +407,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -468,9 +438,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -498,9 +466,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -525,20 +491,18 @@ }, "outputs": [], "source": [ - "dic1[8.2] = \"Olá\"" + "dic1[8.2] = \"Python\"" ] }, { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'key_one': 2, 8.2: 'Olá', 10: 5}" + "{'key_one': 2, 10: 5, 8.2: 'Python'}" ] }, "execution_count": 30, @@ -564,14 +528,12 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'key_one': 2, 8.2: 'Olá', 10: 5, 'teste': 5}" + "{'key_one': 2, 10: 5, 8.2: 'Python', 'teste': 5}" ] }, "execution_count": 32, @@ -597,9 +559,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -641,9 +601,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -708,9 +666,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -763,9 +719,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -797,9 +751,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -819,9 +771,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -841,9 +791,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -876,9 +824,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -910,9 +856,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -951,9 +895,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -973,9 +915,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1010,9 +950,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1024,9 +964,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Tuplas.ipynb" b/Cap02/Notebooks/DSA-Python-Cap02-06-Tuplas.ipynb similarity index 91% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Tuplas.ipynb" rename to Cap02/Notebooks/DSA-Python-Cap02-06-Tuplas.ipynb index 1e4d6ff0..54c2e7ea 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Tuplas.ipynb" +++ b/Cap02/Notebooks/DSA-Python-Cap02-06-Tuplas.ipynb @@ -25,20 +25,18 @@ "outputs": [], "source": [ "# Criando uma tupla\n", - "tupla1 = (\"Matemática\", 23, \"Gatos\")" + "tupla1 = (\"Geografia\", 23, \"Elefantes\")" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "('Matemática', 23, 'Gatos')" + "('Geografia', 23, 'Elefantes')" ] }, "execution_count": 2, @@ -54,9 +52,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "AttributeError", @@ -78,9 +74,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -114,9 +108,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -141,20 +133,18 @@ }, "outputs": [], "source": [ - "tupla1 = (\"Matemática\", 23, \"Gatos\")" + "tupla1 = (\"Geografia\", 23, \"Elefantes\")" ] }, { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Matemática'" + "'Geografia'" ] }, "execution_count": 8, @@ -169,9 +159,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -192,14 +180,12 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(23, 'Gatos')" + "(23, 'Elefantes')" ] }, "execution_count": 10, @@ -215,9 +201,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -231,15 +215,13 @@ } ], "source": [ - "tupla1.index('Gatos')" + "tupla1.index('Elefantes')" ] }, { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -273,9 +255,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "NameError", @@ -308,9 +288,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -330,9 +308,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -366,9 +342,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -411,9 +385,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -448,9 +420,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -462,9 +434,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap02/Notebooks/DSA-Python-Cap02-Exercicios-Solucao.ipynb b/Cap02/Notebooks/DSA-Python-Cap02-Exercicios-Solucao.ipynb new file mode 100644 index 00000000..fe10febb --- /dev/null +++ b/Cap02/Notebooks/DSA-Python-Cap02-Exercicios-Solucao.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Science Academy - Python Fundamentos - Capítulo 2\n", + "\n", + "## Download: http://github.com/dsacademybr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercícios Cap02 - Soluções" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n" + ] + } + ], + "source": [ + "# Exercício 1 - Imprima na tela os números de 1 a 10. Use uma lista para armazenar os números.\n", + "lista = [1,2,3,4,5,6,7,8,9,10]\n", + "print(lista)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chocolate', 'banana', 'abacaxi', 'uva', 'morango']\n" + ] + } + ], + "source": [ + "# Exercício 2 - Crie uma lista de 5 objetos e imprima na tela\n", + "lista = ['chocolate', 'banana', 'abacaxi', 'uva', 'morango']\n", + "print(lista)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Se beber não dirija!\n" + ] + } + ], + "source": [ + "# Exercício 3 - Crie duas strings e concatene as duas em uma terceira string\n", + "frase1 = 'Se beber '\n", + "frase2 = 'não dirija!'\n", + "frase_final = frase1 + frase2\n", + "print(frase_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercício 4 - Crie uma tupla com os seguintes elementos: 1, 2, 2, 3, 4, 4, 4, 5 e depois utilize a função count do \n", + "# objeto tupla para verificar quantas vezes o número 4 aparece na tupla\n", + "tup1 = (1, 2, 2, 3, 4, 4, 4, 5)\n", + "tup1.count(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{}\n" + ] + } + ], + "source": [ + "# Exercício 5 - Crie um dicionário vazio e imprima na tela\n", + "dict3 = {}\n", + "print(dict3)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'k1': 'martelo', 'k2': 'serrote', 'k3': 'machado'}\n" + ] + } + ], + "source": [ + "# Exercício 6 - Crie um dicionário com 3 chaves e 3 valores e imprima na tela\n", + "dict = {'k1':'martelo', 'k2':'serrote', 'k3':'machado'}\n", + "print(dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'k1': 'martelo', 'k2': 'serrote', 'k3': 'machado', 'k4': 'parafuso'}\n" + ] + } + ], + "source": [ + "# Exercício 7 - Adicione mais um elemento ao dicionário criado no exercício anterior e imprima na tela\n", + "dict['k4'] = 'parafuso'\n", + "print(dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'chave1': 'Geografia', 'chave2': 'Biologia', 'chave3': [70, 90]}\n" + ] + } + ], + "source": [ + "# Exercício 8 - Crie um dicionário com 3 chaves e 3 valores. Um dos valores deve ser uma lista de 2 elementos numéricos. \n", + "# Imprima o dicionário na tela.\n", + "dict2 = {'chave1':'Geografia', 'chave2':'Biologia', 'chave3':[70, 90]}\n", + "print(dict2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['String1', (90, 92), {'k1': 'v1', 'k2': 'v2'}, 99.98]\n" + ] + } + ], + "source": [ + "# Exercício 9 - Crie uma lista de 4 elementos. O primeiro elemento deve ser uma string, \n", + "# o segundo uma tupla de 2 elementos, o terceiro um dcionário com 2 chaves e 2 valores e \n", + "# o quarto elemento um valor do tipo float.\n", + "# Imprima a lista na tela.\n", + "lst = ['String1', (90, 92), {'k1':'v1', 'k2':'v2'}, 99.98]\n", + "print(lst)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Cientista de Dados'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercício 10 - Considere a string abaixo. Imprima na tela apenas os caracteres da posição 1 a 18.\n", + "frase = 'Cientista de Dados é o profissional mais sexy do século XXI'\n", + "frase[0:18]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obrigado - Data Science Academy - facebook.com/dsacademybr" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Exerc\303\255cios.ipynb" b/Cap02/Notebooks/DSA-Python-Cap02-Exercicios.ipynb similarity index 61% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Exerc\303\255cios.ipynb" rename to Cap02/Notebooks/DSA-Python-Cap02-Exercicios.ipynb index f4f7e176..0fa62962 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Exerc\303\255cios.ipynb" +++ b/Cap02/Notebooks/DSA-Python-Cap02-Exercicios.ipynb @@ -13,123 +13,103 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercícios" + "## Exercícios Cap02" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 1 - Imprima na tela os números de 1 a 10\n" + "# Exercício 1 - Imprima na tela os números de 1 a 10. Use uma lista para armazenar os números." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 2 - Crie uma lista de 5 objetos e imprima na tela\n" + "# Exercício 2 - Crie uma lista de 5 objetos e imprima na tela" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 3 - Crie duas strings e concatene as duas em uma terceira string\n" + "# Exercício 3 - Crie duas strings e concatene as duas em uma terceira string" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Exercício 4 - Crie uma tupla com os seguintes elementos: 1, 2, 2, 3, 4, 4, 4, 5 e depois utilize a função count do \n", - "# objeto tupla para verificar quantas vezes o número 4 aparece na tupla\n" + "# objeto tupla para verificar quantas vezes o número 4 aparece na tupla" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 5 - Crie um dicionário com 3 chaves e 3 valores e imprima na tela\n" + "# Exercício 5 - Crie um dicionário vazio e imprima na tela" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 6 - Adicione mais uma ferramenta ao dicionário criado no exercício anterior e imprima na tela\n" + "# Exercício 6 - Crie um dicionário com 3 chaves e 3 valores e imprima na tela" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 7 - Crie um arquivo chamado nomes.txt no diretório onde está este notebook, grave 5 nomes no arquivo e \n", - "# feche-o.\n" + "# Exercício 7 - Adicione mais um elemento ao dicionário criado no exercício anterior e imprima na tela" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 8 - Abra o arquivo criado no item anterior e leia o arquivo na tela.\n" + "# Exercício 8 - Crie um dicionário com 3 chaves e 3 valores. Um dos valores deve ser uma lista de 2 elementos numéricos. \n", + "# Imprima o dicionário na tela." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 9 - Crie um dicionário vazio e imrpima na tela\n" + "# Exercício 9 - Crie uma lista de 4 elementos. O primeiro elemento deve ser uma string, \n", + "# o segundo uma tupla de 2 elementos, o terceiro um dcionário com 2 chaves e 2 valores e \n", + "# o quarto elemento um valor do tipo float.\n", + "# Imprima a lista na tela." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Exercício 10 - Complelete o trecho de código abaixo criado com Pandas e use a função tail() para ler os últimos \n", - "# elementos do dataset.\n", - "import pandas as pd\n", - "file_name = \"http://www.ats.ucla.edu/stat/data/binary.csv\"\n", - "df = pd.read_csv(file_name)" + "# Exercício 10 - Considere a string abaixo. Imprima na tela apenas os caracteres da posição 1 a 18.\n", + "frase = 'Cientista de Dados é o profissional mais sexy do século XXI'" ] }, { @@ -163,9 +143,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap03/Lab01/game_v2.py b/Cap03/Lab01/game_v2.py new file mode 100644 index 00000000..63f6c122 --- /dev/null +++ b/Cap03/Lab01/game_v2.py @@ -0,0 +1,208 @@ +# Game Ping-Pong + +# Imports +from tkinter import * +import random +import time + +# Variável recebendo o valor digitado pelo usuário +level = int(input("Qual nível você gostaria de jogar? 1/2/3/4/5 \n")) + +# Variável +length = 500/level + +# Instância do Objeto Tk +root = Tk() +root.title("Ping Pong") +root.resizable(0,0) +root.wm_attributes("-topmost", -1) + +# Variável recebendo o resultado da função Canvas +canvas = Canvas(root, width=800, height=600, bd=0,highlightthickness=0) +canvas.pack() +root.update() + +# Variável +count = 0 + +# Variável +lost = False + +# Classe +class Bola: + def __init__(self, canvas, Barra, color): + + # Variáveis + self.canvas = canvas + self.Barra = Barra + self.id = canvas.create_oval(0, 0, 15, 15, fill=color) + self.canvas.move(self.id, 245, 200) + + # Lista + starts_x = [-3, -2, -1, 1, 2, 3] + random.shuffle(starts_x) + + # Variáveis + self.x = starts_x[0] + self.y = -3 + self.canvas_height = self.canvas.winfo_height() + self.canvas_width = self.canvas.winfo_width() + + # Função + def draw(self): + + # Variáveis + self.canvas.move(self.id, self.x, self.y) + pos = self.canvas.coords(self.id) + + # Condicional if + if pos[1] <= 0: + + # Variável + self.y = 3 + + # Condicional if + if pos[3] >= self.canvas_height: + + # Variável + self.y = -3 + + # Condicional if + if pos[0] <= 0: + + # Variável + self.x = 3 + + # Condicional if + if pos[2] >= self.canvas_width: + + # Variável + self.x = -3 + + # Variável + self.Barra_pos = self.canvas.coords(self.Barra.id) + + # Condicional if aninhado + if pos[2] >= self.Barra_pos[0] and pos[0] <= self.Barra_pos[2]: + if pos[3] >= self.Barra_pos[1] and pos[3] <= self.Barra_pos[3]: + + # Variáveis + self.y = -3 + global count + count +=1 + + # Chamada à função + score() + + # Condicional if + if pos[3] <= self.canvas_height: + + # Variável + self.canvas.after(10, self.draw) + else: + + # Chamada à função + game_over() + + # Variáveis + global lost + lost = True + +# Classe +class Barra: + def __init__(self, canvas, color): + + # Variáveis + self.canvas = canvas + self.id = canvas.create_rectangle(0, 0, length, 10, fill=color) + self.canvas.move(self.id, 200, 400) + self.x = 0 + self.canvas_width = self.canvas.winfo_width() + self.canvas.bind_all("", self.move_left) + self.canvas.bind_all("", self.move_right) + + # Função + def draw(self): + + # Chamada ao método + self.canvas.move(self.id, self.x, 0) + + # Variável + self.pos = self.canvas.coords(self.id) + + # Condicional if + if self.pos[0] <= 0: + + # Variável + self.x = 0 + + # Condicional if + if self.pos[2] >= self.canvas_width: + + # Variável + self.x = 0 + + global lost + + # Condicional if + if lost == False: + self.canvas.after(10, self.draw) + + # Função + def move_left(self, event): + + # Condicional if + if self.pos[0] >= 0: + + # Variável + self.x = -3 + + # Função + def move_right(self, event): + + # Condicional if + if self.pos[2] <= self.canvas_width: + + # Variável + self.x = 3 + +# Função +def start_game(event): + + # Variáveis + global lost, count + lost = False + count = 0 + + # Chamada à função + score() + + # Variável que recebe o resultado da função + canvas.itemconfig(game, text=" ") + + # Métodos dos objetos + time.sleep(1) + Barra.draw() + Bola.draw() + +# Função +def score(): + canvas.itemconfig(score_now, text="Pontos: " + str(count)) + +# Função +def game_over(): + canvas.itemconfig(game, text="Game over!") + +# Instâncias dos objetos Barra e Bola +Barra = Barra(canvas, "orange") +Bola = Bola(canvas, Barra, "purple") + +# Variáveis que recebem o resultado das funções +score_now = canvas.create_text(430, 20, text="Pontos: " + str(count), fill = "green", font=("Arial", 16)) +game = canvas.create_text(400, 300, text=" ", fill="red", font=("Arial", 40)) +canvas.bind_all("", start_game) + +# Executa o programa +root.mainloop() + + diff --git a/Cap03/Lab02/calculadora_v1.py b/Cap03/Lab02/calculadora_v1.py new file mode 100644 index 00000000..097a578a --- /dev/null +++ b/Cap03/Lab02/calculadora_v1.py @@ -0,0 +1,8 @@ +# Calculadora em Python + +# Desenvolva uma calculadora em Python com tudo que você aprendeu nos capítulos 2 e 3. +# A solução será apresentada no próximo capítulo! +# Assista o vídeo com a execução do programa! + +print("\n******************* Python Calculator *******************") + diff --git "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-If-Elif-Else.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-01-If-Elif-Else.ipynb similarity index 83% rename from "JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-If-Elif-Else.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-01-If-Elif-Else.ipynb index 76b5cd27..0007b264 100644 --- "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-If-Elif-Else.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-01-If-Elif-Else.ipynb @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -34,16 +32,13 @@ "source": [ "# Condicional If\n", "if 5 > 2:\n", - " print(\"Python funciona!\")\n", - " " + " print(\"Python funciona!\")" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -64,9 +59,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -86,9 +79,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -108,9 +99,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -130,9 +119,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -152,9 +139,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -172,9 +157,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -192,9 +175,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "SyntaxError", @@ -211,19 +192,37 @@ " print(\"Tudo funciona!\")" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "expected an indented block (, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m print(\"Tudo funciona!\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n" + ] + } + ], + "source": [ + "# Atenção com a sintaxe\n", + "if 4 > 3:\n", + "print(\"Tudo funciona!\")" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Condicionais aninhados" + "### Condicionais Aninhados" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -241,10 +240,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -265,10 +262,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -287,10 +282,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -316,10 +309,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -339,10 +330,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -370,10 +359,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -392,10 +379,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -413,17 +398,15 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Digite o nome da disciplina: Matemática\n", - "Digite a nota final (entre 0 e 100): 90\n", + "Digite o nome da disciplina: Geografia\n", + "Digite a nota final (entre 0 e 100): 76\n", "Você foi aprovado!\n" ] } @@ -434,7 +417,7 @@ "disciplina = input('Digite o nome da disciplina: ')\n", "nota_final = input('Digite a nota final (entre 0 e 100): ')\n", "\n", - "if disciplina == 'Matemática' and nota_final >= '50':\n", + "if disciplina == 'Geografia' and nota_final >= '70':\n", " print('Você foi aprovado!')\n", "else:\n", " print('Lamento, acho que você precisa estudar mais!')" @@ -442,19 +425,17 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Digite o nome da disciplina: Matemática\n", - "Digite a nota final (entre 0 e 100): 90\n", - "Digite o semestre (1 a 4): 3\n", - "Você foi aprovado em Matemática com média final '90'!\n" + "Digite o nome da disciplina: Geografia\n", + "Digite a nota final (entre 0 e 100): 87\n", + "Digite o semestre (1 a 4): 2\n", + "Você foi aprovado em Geografia com média final '87'!\n" ] } ], @@ -465,7 +446,7 @@ "nota_final = input('Digite a nota final (entre 0 e 100): ')\n", "semestre = input('Digite o semestre (1 a 4): ')\n", "\n", - "if disciplina == 'Matemática' and nota_final >= '50' and int(semestre) != 1:\n", + "if disciplina == 'Geografia' and nota_final >= '50' and int(semestre) != 1:\n", " print('Você foi aprovado em %s com média final %r!' %(disciplina, nota_final))\n", "else:\n", " print('Lamento, acho que você precisa estudar mais!')" @@ -475,7 +456,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### --> Fique atento aos espaços entre a margem e cada um dos seus comandos. Falaremos mais sobre indentação ao longo do curso." + "### --> Fique atento aos espaços entre a margem e cada um dos seus comandos. Falaremos mais sobre indentação ao longo do curso. A indentação faz parte da sintaxe da linguagem Python." ] }, { @@ -509,9 +490,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-For.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-02-For.ipynb similarity index 91% rename from "JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-For.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-02-For.ipynb index 5e410751..6a68cfd3 100644 --- "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-For.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-02-For.ipynb @@ -20,9 +20,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -36,17 +34,15 @@ ], "source": [ "# Criando uma tupla e imprimindo cada um dos valores\n", - "tupla = (2,3,4)\n", - "for i in tupla:\n", + "tp = (2,3,4)\n", + "for i in tp:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -68,9 +64,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -93,9 +87,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -120,9 +112,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -191,9 +181,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -266,9 +254,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -311,10 +297,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -338,9 +322,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -362,9 +344,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -387,9 +367,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -413,9 +391,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -438,9 +414,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -462,9 +436,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -513,9 +485,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-While.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-03-While.ipynb similarity index 94% rename from "JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-While.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-03-While.ipynb index 5e1ae173..b6b635b6 100644 --- "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-While.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-03-While.ipynb @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -51,9 +49,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -106,9 +102,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -135,9 +129,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -168,9 +160,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -199,6 +189,7 @@ " j = j + 1\n", " else:\n", " j = j + 1\n", + " \n", " if counter == 0:\n", " print(str(i) + \" é um número primo\")\n", " counter = 0\n", @@ -237,9 +228,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Range.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-04-Range.ipynb similarity index 89% rename from "JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Range.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-04-Range.ipynb index b97fd3c9..79104d2e 100644 --- "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Range.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-04-Range.ipynb @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -65,9 +63,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -87,9 +83,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -116,9 +110,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -126,13 +118,13 @@ "text": [ "Morango\n", "Banana\n", - "Maça\n", + "Abacaxi\n", "Uva\n" ] } ], "source": [ - "lista = ['Morango', 'Banana', 'Maça', 'Uva']\n", + "lista = ['Morango', 'Banana', 'Abacaxi', 'Uva']\n", "lista_tamanho = len(lista)\n", "for i in range(0, lista_tamanho):\n", " print(lista[i])" @@ -141,9 +133,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -192,9 +182,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-M\303\251todos.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-05-Metodos.ipynb similarity index 93% rename from "JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-M\303\251todos.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-05-Metodos.ipynb index 25628b2f..8b6f0445 100644 --- "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-M\303\251todos.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-05-Metodos.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 4\n", + "# Data Science Academy - Python Fundamentos - Capítulo 3\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -43,9 +43,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -66,9 +64,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -89,9 +85,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -113,9 +107,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -137,6 +129,7 @@ " '__iadd__',\n", " '__imul__',\n", " '__init__',\n", + " '__init_subclass__',\n", " '__iter__',\n", " '__le__',\n", " '__len__',\n", @@ -191,9 +184,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -239,9 +230,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Fun\303\247\303\265es.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-06-Funcoes.ipynb similarity index 72% rename from "JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Fun\303\247\303\265es.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-06-Funcoes.ipynb index b7b8cd38..db73984a 100644 --- "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Fun\303\247\303\265es.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-06-Funcoes.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 4\n", + "# Data Science Academy - Python Fundamentos - Capítulo 3\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -26,21 +26,19 @@ "source": [ "# Definindo uma função\n", "def primeiraFunc():\n", - " print ('Olá')" + " print('Hello World')" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Olá\n" + "Hello World\n" ] } ], @@ -58,21 +56,19 @@ "source": [ "# Definindo uma função com parâmetro\n", "def primeiraFunc(nome):\n", - " print ('Olá %s' %(nome))" + " print('Hello %s' %(nome))" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Olá Aluno\n" + "Hello Aluno\n" ] } ], @@ -96,9 +92,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -119,30 +113,28 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Função para somar números\n", "def addNum(firstnum, secondnum):\n", " print(\"Primeiro número: \" + str(firstnum))\n", - " print(\"Segundo número: \" + str(secondnum))" + " print(\"Segundo número: \" + str(secondnum))\n", + " print(\"Soma: \", firstnum + secondnum)" ] }, { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Primeiro número: 45\n", - "Segundo número: 3\n" + "Segundo número: 3\n", + "Soma: 48\n" ] } ], @@ -162,12 +154,13 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Variável Global\n", "var_global = 10 # Esta é uma variável global\n", + "\n", "def multiply(num1, num2):\n", " var_global = num1 * num2 # Esta é uma variável local\n", " print(var_global)" @@ -176,28 +169,24 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "230\n" + "125\n" ] } ], "source": [ - "multiply(10, 23)" + "multiply(5, 25)" ] }, { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -215,42 +204,38 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Variável Local\n", - "var_global = 10\n", + "var_global = 10 # Esta é uma variável global\n", "def multiply(num1, num2):\n", - " var_local = num1 * num2\n", + " var_local = num1 * num2 # Esta é uma variável local\n", " print(var_local)" ] }, { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "230\n" + "125\n" ] } ], "source": [ - "multiply(10, 23)" + "multiply(5, 25)" ] }, { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "NameError", @@ -278,9 +263,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -300,9 +283,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -322,9 +303,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -344,9 +323,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -363,192 +340,6 @@ "bool(1)" ] }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Criando um string\n", - "Frase = \"Eu estou aprendendo Python\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Tipo de variável Frase\n", - "type(Frase)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['__add__',\n", - " '__class__',\n", - " '__contains__',\n", - " '__delattr__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__eq__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__getitem__',\n", - " '__getnewargs__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__iter__',\n", - " '__le__',\n", - " '__len__',\n", - " '__lt__',\n", - " '__mod__',\n", - " '__mul__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__rmod__',\n", - " '__rmul__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " 'capitalize',\n", - " 'casefold',\n", - " 'center',\n", - " 'count',\n", - " 'encode',\n", - " 'endswith',\n", - " 'expandtabs',\n", - " 'find',\n", - " 'format',\n", - " 'format_map',\n", - " 'index',\n", - " 'isalnum',\n", - " 'isalpha',\n", - " 'isdecimal',\n", - " 'isdigit',\n", - " 'isidentifier',\n", - " 'islower',\n", - " 'isnumeric',\n", - " 'isprintable',\n", - " 'isspace',\n", - " 'istitle',\n", - " 'isupper',\n", - " 'join',\n", - " 'ljust',\n", - " 'lower',\n", - " 'lstrip',\n", - " 'maketrans',\n", - " 'partition',\n", - " 'replace',\n", - " 'rfind',\n", - " 'rindex',\n", - " 'rjust',\n", - " 'rpartition',\n", - " 'rsplit',\n", - " 'rstrip',\n", - " 'split',\n", - " 'splitlines',\n", - " 'startswith',\n", - " 'strip',\n", - " 'swapcase',\n", - " 'title',\n", - " 'translate',\n", - " 'upper',\n", - " 'zfill']" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Função dir() retorna o que funções podem ser usadas com um determinado objeto\n", - "dir(Frase)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on built-in function upper:\n", - "\n", - "upper(...) method of builtins.str instance\n", - " S.upper() -> str\n", - " \n", - " Return a copy of S converted to uppercase.\n", - "\n" - ] - } - ], - "source": [ - "# Função help() mostra para que serve o método que pode ser usado com o objeto\n", - "help(Frase.upper)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on built-in function replace:\n", - "\n", - "replace(...) method of builtins.str instance\n", - " S.replace(old, new[, count]) -> str\n", - " \n", - " Return a copy of S with all occurrences of substring\n", - " old replaced by new. If the optional argument count is\n", - " given, only the first count occurrences are replaced.\n", - "\n" - ] - } - ], - "source": [ - "help(Frase.replace)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -558,10 +349,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -572,13 +361,13 @@ }, { "ename": "TypeError", - "evalue": "unorderable types: str() > int()", + "evalue": "'>' not supported between instances of 'str' and 'int'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Erro ao executar por causa da conversão\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0midade\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Digite sua idade: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0midade\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m13\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Você pode acessar o Facebook\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: unorderable types: str() > int()" + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Erro ao executar por causa da conversão\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0midade\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Digite sua idade: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0midade\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m13\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Você pode acessar o Facebook\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'str' and 'int'" ] } ], @@ -591,10 +380,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -614,10 +401,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -625,7 +410,7 @@ "26" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -636,10 +421,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -647,7 +430,7 @@ "123.345" ] }, - "execution_count": 27, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -658,10 +441,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { @@ -669,7 +450,7 @@ "'14'" ] }, - "execution_count": 28, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -680,10 +461,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -691,7 +470,7 @@ "4" ] }, - "execution_count": 29, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -702,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": { "collapsed": true }, @@ -713,10 +492,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { @@ -724,7 +501,7 @@ "'c'" ] }, - "execution_count": 31, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -735,10 +512,8 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -746,7 +521,7 @@ "'a'" ] }, - "execution_count": 32, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -757,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 28, "metadata": { "collapsed": true }, @@ -768,10 +543,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { @@ -779,7 +552,7 @@ "['a', 'b', 'c', 'd', 'A', 'B', 'C', 'D']" ] }, - "execution_count": 34, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -790,10 +563,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { "data": { @@ -801,7 +572,7 @@ "'d'" ] }, - "execution_count": 35, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -812,10 +583,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { @@ -823,7 +592,7 @@ "'A'" ] }, - "execution_count": 36, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -834,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -845,10 +614,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "data": { @@ -856,7 +623,7 @@ "125" ] }, - "execution_count": 38, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -874,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -897,10 +664,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "data": { @@ -908,7 +673,7 @@ "'Este número é primo'" ] }, - "execution_count": 40, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -926,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -939,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 37, "metadata": { "collapsed": true }, @@ -950,10 +715,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -970,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 39, "metadata": { "collapsed": true }, @@ -982,10 +745,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "data": { @@ -1003,7 +764,7 @@ " 'dados.']" ] }, - "execution_count": 45, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1014,7 +775,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -1025,7 +786,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 42, "metadata": { "collapsed": true }, @@ -1037,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -1048,10 +809,8 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false - }, + "execution_count": 44, + "metadata": {}, "outputs": [ { "data": { @@ -1059,7 +818,7 @@ "'este texto deveria estar todo em lowercase'" ] }, - "execution_count": 49, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1070,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 45, "metadata": { "collapsed": true }, @@ -1089,10 +848,8 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, + "execution_count": 46, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1104,15 +861,13 @@ ], "source": [ "# Fazendo chamada à função usando apenas 1 argumento\n", - "printVarInfo(10)\n" + "printVarInfo(10)" ] }, { "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, + "execution_count": 47, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1159,9 +914,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Lambda.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-07-Lambda.ipynb similarity index 87% rename from "JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Lambda.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-07-Lambda.ipynb index 42703f5a..84a24831 100644 --- "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Lambda.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-07-Lambda.ipynb @@ -33,9 +33,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -68,9 +66,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -91,7 +87,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -102,9 +98,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -124,29 +118,36 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, + "outputs": [], + "source": [ + "# Definindo uma expressão lambda\n", + "potencia = lambda num: num**2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - ">" + "25" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Definindo uma expressão lambda\n", - "lambda num: num**2" + "potencia(5)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -158,10 +159,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { @@ -169,7 +168,7 @@ "False" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -180,10 +179,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { @@ -191,7 +188,7 @@ "True" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -202,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -213,10 +210,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { @@ -224,7 +219,7 @@ "'P'" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -235,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -246,10 +241,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { @@ -257,7 +250,7 @@ "'nohtyP'" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -268,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -279,10 +272,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { @@ -290,7 +281,7 @@ "5" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -330,9 +321,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Funcoes-Solucao.ipynb b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Funcoes-Solucao.ipynb new file mode 100644 index 00000000..3177df81 --- /dev/null +++ b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Funcoes-Solucao.ipynb @@ -0,0 +1,627 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Science Academy - Python Fundamentos - Capítulo 3\n", + "\n", + "## Download: http://github.com/dsacademybr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercícios - Métodos e Funções" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "4\n", + "6\n", + "8\n", + "10\n", + "12\n", + "14\n", + "16\n", + "18\n", + "20\n" + ] + } + ], + "source": [ + "# Exercício 1 - Crie uma função que imprima a sequência de números pares entre 1 e 20 (a função não recebe parâmetro) e \n", + "# depois faça uma chamada à função para listar os números\n", + "def listaPar():\n", + " for i in range(2, 21, 2): \n", + " print(i)\n", + " \n", + "listaPar() " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RUMO À ANÁLISE DE DADOS\n" + ] + } + ], + "source": [ + "# Exercício 2 - Crie uam função que receba uma string como argumento e retorne a mesma string em letras maiúsculas.\n", + "# Faça uma chamada à função, passando como parâmetro uma string\n", + "def listaString(texto):\n", + " print(texto.upper())\n", + " return\n", + "\n", + "listaString('Rumo à Análise de Dados')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + "None\n", + "[1, 2, 3, 4, 5, 6]\n" + ] + } + ], + "source": [ + "# Exercício 3 - Crie uma função que receba como parâmetro uma lista de 4 elementos, adicione 2 elementos a lista e \n", + "# imprima a lista\n", + "def novaLista(lista):\n", + " print(lista.append(5))\n", + " print(lista.append(6))\n", + " \n", + "lista1 = [1, 2, 3, 4] \n", + "novaLista(lista1)\n", + "print(lista1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100\n", + "A\n", + "B\n", + "C\n" + ] + } + ], + "source": [ + "# Exercício 4 - Crie uma função que receba um argumento formal e uma possível lista de elementos. Faça duas chamadas \n", + "# à função, com apenas 1 elemento e na segunda chamada com 4 elementos\n", + "def printNum( arg1, *lista ):\n", + " print (arg1)\n", + " for i in lista:\n", + " print (i)\n", + " return;\n", + "\n", + "# Chamada à função\n", + "printNum( 100 )\n", + "printNum( 'A', 'B', 'C' )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A soma é : 750\n" + ] + } + ], + "source": [ + "# Exercício 5 - Crie uma função anônima e atribua seu retorno a uma variável chamada soma. A expressão vai receber 2 \n", + "# números como parâmetro e retornar a soma deles\n", + "soma = lambda arg1, arg2: arg1 + arg2\n", + "print (\"A soma é : \", soma( 452, 298 ))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dentro da função o total é: 30\n", + "Fora da função o total é: 0\n" + ] + } + ], + "source": [ + "# Exercício 6 - Execute o código abaixo e certifique-se que compreende a diferença entre variável global e local\n", + "\n", + "total = 0\n", + "def soma( arg1, arg2 ):\n", + " total = arg1 + arg2; \n", + " print (\"Dentro da função o total é: \", total)\n", + " return total;\n", + "\n", + "\n", + "soma( 10, 20 );\n", + "print (\"Fora da função o total é: \", total)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[102.56, 97.7, 99.14, 100.03999999999999]\n" + ] + } + ], + "source": [ + "# Exercício 7 - Abaixo você encontra uma lista com temperaturas em graus Celsius\n", + "# Crie uma função anônima que converta cada temperatura para Fahrenheit\n", + "# Dica: para conseguir realizar este exercício, você deve criar sua função lambda, dentro de uma função \n", + "# (que será estudada no próximo capítulo). Isso permite aplicar sua função a cada elemento da lista\n", + "# Como descobrir a fórmula matemática que converte de Celsius para Fahrenheit? Pesquise!!!\n", + "Celsius = [39.2, 36.5, 37.3, 37.8]\n", + "Fahrenheit = map(lambda x: (float(9)/5)*x + 32, Celsius)\n", + "print (list(Fahrenheit))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__contains__',\n", + " '__delattr__',\n", + " '__delitem__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getitem__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__iter__',\n", + " '__le__',\n", + " '__len__',\n", + " '__lt__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__setitem__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " 'clear',\n", + " 'copy',\n", + " 'fromkeys',\n", + " 'get',\n", + " 'items',\n", + " 'keys',\n", + " 'pop',\n", + " 'popitem',\n", + " 'setdefault',\n", + " 'update',\n", + " 'values']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercício 8\n", + "# Crie um dicionário e liste todos os métodos e atributos do dicionário\n", + "dic = {'k1': 'Natal', 'k2': 'Recife'}\n", + "dir(dic)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Categorical',\n", + " 'CategoricalIndex',\n", + " 'DataFrame',\n", + " 'DateOffset',\n", + " 'DatetimeIndex',\n", + " 'ExcelFile',\n", + " 'ExcelWriter',\n", + " 'Expr',\n", + " 'Float64Index',\n", + " 'Grouper',\n", + " 'HDFStore',\n", + " 'Index',\n", + " 'IndexSlice',\n", + " 'Int64Index',\n", + " 'Interval',\n", + " 'IntervalIndex',\n", + " 'MultiIndex',\n", + " 'NaT',\n", + " 'Panel',\n", + " 'Panel4D',\n", + " 'Period',\n", + " 'PeriodIndex',\n", + " 'RangeIndex',\n", + " 'Series',\n", + " 'SparseArray',\n", + " 'SparseDataFrame',\n", + " 'SparseList',\n", + " 'SparseSeries',\n", + " 'Term',\n", + " 'TimeGrouper',\n", + " 'Timedelta',\n", + " 'TimedeltaIndex',\n", + " 'Timestamp',\n", + " 'UInt64Index',\n", + " 'WidePanel',\n", + " '_DeprecatedModule',\n", + " '__builtins__',\n", + " '__cached__',\n", + " '__doc__',\n", + " '__docformat__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__path__',\n", + " '__spec__',\n", + " '__version__',\n", + " '_hashtable',\n", + " '_lib',\n", + " '_libs',\n", + " '_np_version_under1p10',\n", + " '_np_version_under1p11',\n", + " '_np_version_under1p12',\n", + " '_np_version_under1p13',\n", + " '_np_version_under1p14',\n", + " '_np_version_under1p8',\n", + " '_np_version_under1p9',\n", + " '_tslib',\n", + " '_version',\n", + " 'api',\n", + " 'bdate_range',\n", + " 'compat',\n", + " 'concat',\n", + " 'core',\n", + " 'crosstab',\n", + " 'cut',\n", + " 'date_range',\n", + " 'datetime',\n", + " 'datetools',\n", + " 'describe_option',\n", + " 'errors',\n", + " 'eval',\n", + " 'ewma',\n", + " 'ewmcorr',\n", + " 'ewmcov',\n", + " 'ewmstd',\n", + " 'ewmvar',\n", + " 'ewmvol',\n", + " 'expanding_apply',\n", + " 'expanding_corr',\n", + " 'expanding_count',\n", + " 'expanding_cov',\n", + " 'expanding_kurt',\n", + " 'expanding_max',\n", + " 'expanding_mean',\n", + " 'expanding_median',\n", + " 'expanding_min',\n", + " 'expanding_quantile',\n", + " 'expanding_skew',\n", + " 'expanding_std',\n", + " 'expanding_sum',\n", + " 'expanding_var',\n", + " 'factorize',\n", + " 'get_dummies',\n", + " 'get_option',\n", + " 'get_store',\n", + " 'groupby',\n", + " 'infer_freq',\n", + " 'interval_range',\n", + " 'io',\n", + " 'isnull',\n", + " 'json',\n", + " 'lib',\n", + " 'lreshape',\n", + " 'match',\n", + " 'melt',\n", + " 'merge',\n", + " 'merge_asof',\n", + " 'merge_ordered',\n", + " 'notnull',\n", + " 'np',\n", + " 'offsets',\n", + " 'option_context',\n", + " 'options',\n", + " 'ordered_merge',\n", + " 'pandas',\n", + " 'parser',\n", + " 'period_range',\n", + " 'pivot',\n", + " 'pivot_table',\n", + " 'plot_params',\n", + " 'plotting',\n", + " 'pnow',\n", + " 'qcut',\n", + " 'read_clipboard',\n", + " 'read_csv',\n", + " 'read_excel',\n", + " 'read_feather',\n", + " 'read_fwf',\n", + " 'read_gbq',\n", + " 'read_hdf',\n", + " 'read_html',\n", + " 'read_json',\n", + " 'read_msgpack',\n", + " 'read_pickle',\n", + " 'read_sas',\n", + " 'read_sql',\n", + " 'read_sql_query',\n", + " 'read_sql_table',\n", + " 'read_stata',\n", + " 'read_table',\n", + " 'reset_option',\n", + " 'rolling_apply',\n", + " 'rolling_corr',\n", + " 'rolling_count',\n", + " 'rolling_cov',\n", + " 'rolling_kurt',\n", + " 'rolling_max',\n", + " 'rolling_mean',\n", + " 'rolling_median',\n", + " 'rolling_min',\n", + " 'rolling_quantile',\n", + " 'rolling_skew',\n", + " 'rolling_std',\n", + " 'rolling_sum',\n", + " 'rolling_var',\n", + " 'rolling_window',\n", + " 'scatter_matrix',\n", + " 'set_eng_float_format',\n", + " 'set_option',\n", + " 'show_versions',\n", + " 'stats',\n", + " 'test',\n", + " 'testing',\n", + " 'timedelta_range',\n", + " 'to_datetime',\n", + " 'to_msgpack',\n", + " 'to_numeric',\n", + " 'to_pickle',\n", + " 'to_timedelta',\n", + " 'tools',\n", + " 'tseries',\n", + " 'tslib',\n", + " 'unique',\n", + " 'util',\n", + " 'value_counts',\n", + " 'wide_to_long']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercício 9\n", + "# Abaixo você encontra a importação do Pandas, um dos principais pacotes Python para análise de dados.\n", + "# Analise atentamente todos os métodos disponíveis. Um deles você vai usar no próximo exercício.\n", + "import pandas as pd\n", + "dir(pd)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " admit gre gpa rank\n", + "count 5.000000 5.000000 5.000000 5.000000\n", + "mean 0.600000 616.000000 3.480000 3.000000\n", + "std 0.547723 185.148589 0.421307 1.224745\n", + "min 0.000000 380.000000 2.930000 1.000000\n", + "25% 0.000000 520.000000 3.190000 3.000000\n", + "50% 1.000000 640.000000 3.610000 3.000000\n", + "75% 1.000000 660.000000 3.670000 4.000000\n", + "max 1.000000 880.000000 4.000000 4.000000" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ************* Desafio ************* (pesquise na documentação Python)\n", + "\n", + "# Exercício 10 - Crie uma função que receba o arquivo abaixo como argumento e retorne um resumo estatístico descritivo \n", + "# do arquivo. Dica, use Pandas e um de seus métodos, describe()\n", + "# Arquivo: \"binary.csv\"\n", + "import pandas as pd\n", + "file_name = \"binary.csv\"\n", + "\n", + "def retornaArq(file_name):\n", + " df = pd.read_csv(file_name)\n", + " return df.describe()\n", + " \n", + "retornaArq(file_name) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obrigado - Data Science Academy - facebook.com/dsacademybr" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Exerc\303\255cios.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Funcoes.ipynb similarity index 52% rename from "JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Exerc\303\255cios.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Funcoes.ipynb index 68a63dec..2c45f023 100644 --- "a/JupyterNotebooks/Cap\303\255tulo04/DSA-Python-Cap\303\255tulo4-Exerc\303\255cios.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Funcoes.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 4\n", + "# Data Science Academy - Python Fundamentos - Capítulo 3\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -13,92 +13,149 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercícios" + "## Exercícios - Métodos e Funções" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 1 - Crie uma função que imprima a sequência de números pares entre 1 e 20 (a função não recebe parâmetro) e \n", - "# depois faça uma chamada à função para listar os números\n", - " " + "# depois faça uma chamada à função para listar os números " ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 2 - Crie uam função que receba uma string como argumento e retorne a mesma string em letras maiúsculas.\n", - "# Faça uma chamada à função, passando como parâmetro uma string\n" + "# Faça uma chamada à função, passando como parâmetro uma string" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 3 - Crie uma função que receba como parâmetro uma lista de 4 elementos, adicione 2 elementos a lista e \n", - "# imprima a lista\n" + "# imprima a lista" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 4 - Crie uma função que receba um argumento formal e uma possível lista de elementos. Faça duas chamadas \n", - "# à função, com apenas 1 elemento e na segunda chamada com 4 elementos\n" + "# à função, com apenas 1 elemento e na segunda chamada com 4 elementos" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 5 - Crie uma função anônima e atribua seu retorno a uma variável chamada soma. A expressão vai receber 2 \n", - "# números como parâmetro e retornar a soma deles\n" + "# números como parâmetro e retornar a soma deles" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "# Exercício 6 - Execute o código abaixo e certifique-se que compreende a diferença entre variável global e local\n" + "# Exercício 6 - Execute o código abaixo e certifique-se que compreende a diferença entre variável global e local\n", + "total = 0\n", + "def soma( arg1, arg2 ):\n", + " total = arg1 + arg2; \n", + " print (\"Dentro da função o total é: \", total)\n", + " return total;\n", + "\n", + "\n", + "soma( 10, 20 );\n", + "print (\"Fora da função o total é: \", total)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Exercício 7 - Abaixo você encontra uma lista com temperaturas em graus Celsius\n", + "# Crie uma função anônima que converta cada temperatura para Fahrenheit\n", + "# Dica: para conseguir realizar este exercício, você deve criar sua função lambda, dentro de uma função \n", + "# (que será estudada no próximo capítulo). Isso permite aplicar sua função a cada elemento da lista\n", + "# Como descobrir a fórmula matemática que converte de Celsius para Fahrenheit? Pesquise!!!\n", + "Celsius = [39.2, 36.5, 37.3, 37.8]\n", + "Fahrenheit = map(coloque_aqui_sua_função_lambda)\n", + "print (list(Fahrenheit))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Exercício 8\n", + "# Crie um dicionário e liste todos os métodos e atributos do dicionário" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "# Exercício 7 - Crie uma função que receba o arquivo abaixo como argumento e retorne um resumo estatístico descritivo \n", + "# Exercício 9\n", + "# Abaixo você encontra a importação do Pandas, um dos principais pacotes Python para análise de dados.\n", + "# Analise atentamente todos os métodos disponíveis. Um deles você vai usar no próximo exercício.\n", + "import pandas as pd\n", + "dir(pd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ************* Desafio ************* (pesquise na documentação Python)\n", + "\n", + "# Exercício 10 - Crie uma função que receba o arquivo abaixo como argumento e retorne um resumo estatístico descritivo \n", "# do arquivo. Dica, use Pandas e um de seus métodos, describe()\n", - "# Arquivo: \"http://www.ats.ucla.edu/stat/data/binary.csv\"\n", + "# Arquivo: \"binary.csv\"\n", + "import pandas as pd\n", + "file_name = \"binary.csv\"\n", " " ] }, @@ -133,9 +190,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Exerc\303\255cios-Solu\303\247\303\243o.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Loops-Condiconais-Solucao.ipynb similarity index 88% rename from "JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Exerc\303\255cios-Solu\303\247\303\243o.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Loops-Condiconais-Solucao.ipynb index 4265233e..5a2b5885 100644 --- "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Exerc\303\255cios-Solu\303\247\303\243o.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Loops-Condiconais-Solucao.ipynb @@ -13,22 +13,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercícios" + "## Exercícios - Loops e Condiconais - Solução" ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Digite o dia da semana: Sábado\n", - "Hoje é dia de descanso\n" + "Digite o dia da semana: segunda\n", + "Você precisa trabalhar!\n" ] } ], @@ -45,9 +43,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -68,9 +64,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -94,9 +88,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -140,9 +132,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -168,9 +158,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -205,20 +193,18 @@ "source": [ "# Exercício 6 - Crie uma variável chamada contador = 0. Enquanto counter for menor que 100, imprima os valores na tela,\n", "# mas quando for encontrado o valor 23, interrompa a execução do programa\n", - "counter = 0\n", - "while counter < 100:\n", - " if counter == 23:\n", + "contador = 0\n", + "while contador < 100:\n", + " if contador == 23:\n", " break\n", - " print(counter)\n", - " counter += 1" + " print(contador)\n", + " contador += 1" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -242,9 +228,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -263,9 +247,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -288,20 +270,18 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "O caracter a aparece 9 vezes na frase.\n" + "O caracter r aparece 6 vezes na frase.\n" ] } ], "source": [ - "# Exercício 10 - Faça um programa que conte quantas vezes a letra \"a\" aparece na frase abaixo. Use um placeholder na \n", + "# Exercício 10 - Faça um programa que conte quantas vezes a letra \"r\" aparece na frase abaixo. Use um placeholder na \n", "# sua instrução de impressão\n", "\n", "# “É melhor, muito melhor, contentar-se com a realidade; se ela não é tão brilhante como os sonhos, tem pelo menos a \n", @@ -310,9 +290,9 @@ "frase = \"É melhor, muito melhor, contentar-se com a realidade; se ela não é tão brilhante como os sonhos, tem pelo menos a vantagem de existir.\" \n", "count = 0\n", "for caracter in frase:\n", - " if caracter == 'a':\n", + " if caracter == 'r':\n", " count += 1\n", - "print(\"O caracter a aparece %s vezes na frase.\" %(count))" + "print(\"O caracter r aparece %s vezes na frase.\" %(count))" ] }, { @@ -346,9 +326,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Exerc\303\255cios.ipynb" b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Loops-Condiconais.ipynb similarity index 79% rename from "JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Exerc\303\255cios.ipynb" rename to Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Loops-Condiconais.ipynb index bf185807..aa7d8146 100644 --- "a/JupyterNotebooks/Cap\303\255tulo03/DSA-Python-Cap\303\255tulo3-Exerc\303\255cios.ipynb" +++ b/Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Loops-Condiconais.ipynb @@ -13,132 +13,132 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercícios" + "## Exercícios - Loops e Condiconais" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 1 - Crie uma estrutura que pergunte ao usuário qual o dia da semana. Se o dia for igual a Domingo ou \n", - "# igual a sábado, imprima na tela \"Hoje é dia de descanso\", caso contrário imprima na tela \"Você precisa trabalhar!\"\n" + "# igual a sábado, imprima na tela \"Hoje é dia de descanso\", caso contrário imprima na tela \"Você precisa trabalhar!\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "# Exercício 2 - Crie uma lista de 5 frutas e verifique se a fruta 'Morango' faz parte da lista\n" + "# Exercício 2 - Crie uma lista de 5 frutas e verifique se a fruta 'Morango' faz parte da lista" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 3 - Crie uma tupla de 4 elementos, multiplique cada elemento da tupla por 2 e guarde os resultados em uma \n", - "# lista\n" + "# lista" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "# Exercício 4 - Crie uma sequência de números pares entre 100 e 150 e imprima na tela\n" + "# Exercício 4 - Crie uma sequência de números pares entre 100 e 150 e imprima na tela" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 5 - Crie uma variável chamada temperatura e atribua o valor 40. Enquanto temperatura for maior que 35, \n", - "# imprima as temperaturas na tela\n" + "# imprima as temperaturas na tela" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 6 - Crie uma variável chamada contador = 0. Enquanto counter for menor que 100, imprima os valores na tela,\n", - "# mas quando for encontrado o valor 23, interrompa a execução do programa\n" + "# mas quando for encontrado o valor 23, interrompa a execução do programa" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 7 - Crie uma lista vazia e uma variável com valor 4. Enquanto o valor da variável for menor ou igual a 20, \n", - "# adicione à lista, apenas os valores pares e imprima a lista\n" + "# adicione à lista, apenas os valores pares e imprima a lista" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "# Exercício 8 - Transforme o resultado desta função range em uma lista: range(5, 45, 2)\n" + "# Exercício 8 - Transforme o resultado desta função range em uma lista: range(5, 45, 2)\n", + "nums = range(5, 45, 2)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Exercício 9 - Faça a correção dos erros no código abaixo e execute o programa. Dica: são 3 erros.\n", - "temperatura = float(input('Qual a temperatura? ')\n", - " if temperatura > 30\n", - " print('Vista roupas leves.')\n", - " else:\n", - " print('Busque seus casacos.')\n" + "temperatura = float(input('Qual a temperatura? '))\n", + "if temperatura > 30\n", + "print('Vista roupas leves.')\n", + "else\n", + " print('Busque seus casacos.')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "# Exercício 10 - Faça um programa que conte quantas vezes a letra \"a\" aparece na frase abaixo. Use um placeholder na \n", + "# Exercício 10 - Faça um programa que conte quantas vezes a letra \"r\" aparece na frase abaixo. Use um placeholder na \n", "# sua instrução de impressão\n", "\n", "# “É melhor, muito melhor, contentar-se com a realidade; se ela não é tão brilhante como os sonhos, tem pelo menos a \n", "# vantagem de existir.” (Machado de Assis)\n", - "\n" + "\n", + "frase = \"É melhor, muito melhor, contentar-se com a realidade; se ela não é tão brilhante como os sonhos, tem pelo menos a vantagem de existir.\" " ] }, { @@ -172,9 +172,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap03/Notebooks/binary.csv b/Cap03/Notebooks/binary.csv new file mode 100755 index 00000000..b2488b11 --- /dev/null +++ b/Cap03/Notebooks/binary.csv @@ -0,0 +1,6 @@ +admit,gre,gpa,rank +0,380,3.61,3 +1,660,3.67,3 +1,880,4.00,1 +1,640,3.19,4 +0,520,2.93,4 diff --git a/Cap04/Lab02/calculadora_v2.py b/Cap04/Lab02/calculadora_v2.py new file mode 100644 index 00000000..f1506e9c --- /dev/null +++ b/Cap04/Lab02/calculadora_v2.py @@ -0,0 +1,51 @@ +# Calculadora em Python + +print("\n******************* Python Calculator *******************") + +def add(x, y): + return x + y + +def subtract(x, y): + return x - y + +def multiply(x, y): + return x * y + +def divide(x, y): + return x / y + +print("\nSelecione o número da operação desejada: \n") +print("1 - Soma") +print("2 - Subtração") +print("3 - Multiplicação") +print("4 - Divisão") + +escolha = input("\nDigite sua opção (1/2/3/4): ") + +num1 = int(input("\nDigite o primeiro número: ")) +num2 = int(input("\nDigite o segundo número: ")) + +if escolha == '1': + print("\n") + print(num1, "+", num2, "=", add(num1, num2)) + print("\n") + +elif escolha == '2': + print("\n") + print(num1, "-", num2, "=", subtract(num1, num2)) + print("\n") + +elif escolha == '3': + print("\n") + print(num1, "*", num2, "=", multiply(num1, num2)) + print("\n") + +elif escolha == '4': + print("\n") + print(num1, "/", num2, "=", divide(num1, num2)) + print("\n") + +else: + print("\nOpção Inválida!") + + \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Arquivos.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-01-Arquivos-Parte1.ipynb similarity index 99% rename from "JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Arquivos.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-01-Arquivos-Parte1.ipynb index af80c93e..e285c23a 100644 --- "a/JupyterNotebooks/Cap\303\255tulo02/DSA-Python-Cap\303\255tulo2-Arquivos.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-01-Arquivos-Parte1.ipynb @@ -4,11 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 2\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ************ ATENÇÃO ************\n", + "Caso você tenha problemas com acentos nos arquivos:\n", + "\n", + "Primeiro, recomendamos a leitura do material sobre Formato Unicode, ao final do capítulo 4.\n", + "\n", + "Uma forma de resolver esse problema, é abrir o arquivo em um editor de texto como o Sublime Text, clicar em File - Save with Encoding e então salvar com encoding UTF-8.\n", + "\n", + "Outra opção é incluir o parâmetro encoding='utf8' ao abrir o arquivo para leitura ou escrita.\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -20,7 +34,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Obs: Crie um arquivo txt, chamado arquivo-dsc.txt no mesmo diretório onde está seu Jupyter Notebook, digite a frase: Python é uma linguagem poderosa! e salve o arquivo." + "Obs: Crie um arquivo txt, chamado arquivo1.txt no diretório \"arquivos\" na pasta onde está seu Jupyter Notebook, digite a frase: Python é uma linguagem poderosa! \n", + "e salve o arquivo." ] }, { @@ -39,21 +54,19 @@ "outputs": [], "source": [ "# Abrindo o arquivo para leitura\n", - "arq1 = open(\"arquivo-dsc.txt\", \"r\")" + "arq1 = open(\"arquivos/arquivo1.txt\", \"r\")" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Testando gravação de arquivos em Python Acrescentando conteúdo\n" + "Python é uma linguagem poderosa!\n" ] } ], @@ -65,15 +78,13 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "65\n" + "33\n" ] } ], @@ -85,9 +96,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -105,15 +114,13 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Testando g\n" + "Python é u\n" ] } ], @@ -138,15 +145,13 @@ "outputs": [], "source": [ "# Abrindo arquivo para gravação\n", - "arq2 = open(\"arquivo-dsc.txt\", \"w\")" + "arq2 = open(\"arquivos/arquivo1.txt\", \"w\")" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "UnsupportedOperation", @@ -168,9 +173,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -208,15 +211,13 @@ "outputs": [], "source": [ "# Lendo arquivo gravado\n", - "arq2 = open(\"arquivo-dsc.txt\", \"r\")" + "arq2 = open(\"arquivos/arquivo1.txt\", \"r\")" ] }, { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -239,20 +240,18 @@ "outputs": [], "source": [ "# Acrescentando conteúdo\n", - "arq2 = open(\"arquivo-dsc.txt\", \"a\")" + "arq2 = open(\"arquivos/arquivo1.txt\", \"a\")" ] }, { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "22" + "23" ] }, "execution_count": 13, @@ -261,7 +260,7 @@ } ], "source": [ - "arq2.write(\"Acrescentando conteúdo\")" + "arq2.write(\" Acrescentando conteúdo\")" ] }, { @@ -283,21 +282,19 @@ }, "outputs": [], "source": [ - "arq2 = open(\"arquivo-dsc.txt\", \"r\")" + "arq2 = open(\"arquivos/arquivo1.txt\", \"r\")" ] }, { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Testando gravação de arquivos em Python Acrescentando conteúdo\n" + "Testando gravação de arquivos em Python Acrescentando conteúdo\n" ] } ], @@ -308,9 +305,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -331,15 +326,13 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Testando gravação de arquivos em Python Acrescentando conteúdo\n" + "Testando gravação de arquivos em Python Acrescentando conteúdo\n" ] } ], @@ -357,9 +350,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -398,9 +389,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -421,7 +410,7 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -432,7 +421,7 @@ "cell_type": "code", "execution_count": 24, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -442,9 +431,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -480,9 +467,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Faça download do arquivo salarios.csv em nosso repositório no github. Este arquivo foi obtido no sites de dados abertos do governo da cidade de Chicago, nos EUA: https://data.cityofchicago.org/\n", - "\n", - "Download od arquivo: http://github.com/dsacademybr/JupyterNotebooks/Capitulo02/salarios.csv" + "Faça download do arquivo salarios.csv em nosso repositório no github. Este arquivo foi obtido no site de dados abertos do governo da cidade de Chicago, nos EUA: https://data.cityofchicago.org/" ] }, { @@ -493,7 +478,7 @@ }, "outputs": [], "source": [ - "f = open('salarios.csv', 'r')" + "f = open('arquivos/salarios.csv', 'r')" ] }, { @@ -521,9 +506,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -552,7 +535,7 @@ }, "outputs": [], "source": [ - "f = open('salarios.csv', 'r')" + "f = open('arquivos/salarios.csv', 'r')" ] }, { @@ -592,7 +575,7 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -604,9 +587,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -635,7 +616,7 @@ }, "outputs": [], "source": [ - "f = open('salarios.csv', 'r')" + "f = open('arquivos/salarios.csv', 'r')" ] }, { @@ -700,9 +681,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -731,7 +710,7 @@ }, "outputs": [], "source": [ - "f = open('salarios.csv', 'r')\n", + "f = open('arquivos/salarios.csv', 'r')\n", "data = f.read()\n", "rows = data.split('\\n')\n", "full_data = []" @@ -761,15 +740,17 @@ "outputs": [], "source": [ "for column in first_row:\n", - " count = count + 1" + " count = count + 1\n", + " \n", + "# Outra solução possível\n", + "# for column in full_data[0]:\n", + "# count = count + 1" ] }, { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -793,20 +774,18 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Overwriting teste.txt\n" + "Overwriting arquivos/teste.txt\n" ] } ], "source": [ - "%%writefile teste.txt \n", + "%%writefile arquivos/teste.txt \n", "Olá este arquivo foi gerado pelo próprio Jupyter Notebook.\n", "Podemos gerar quantas linhas quisermos e o Jupyter gera o arquivo final." ] @@ -815,19 +794,17 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "arq4 = open(\"teste.txt\", 'r')" + "arq4 = open(\"arquivos/teste.txt\", 'r')" ] }, { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -847,9 +824,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -870,9 +845,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -893,9 +866,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -917,9 +888,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -933,7 +902,7 @@ ], "source": [ "# Podemos usar um loop for para ler o arquivo\n", - "for line in open('teste.txt'):\n", + "for line in open('arquivos/teste.txt'):\n", " print(line)" ] }, @@ -963,14 +932,14 @@ }, "outputs": [], "source": [ - "file_name = \"http://www.ats.ucla.edu/stat/data/binary.csv\"" + "file_name = \"arquivos/binary.csv\"" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -980,14 +949,25 @@ { "cell_type": "code", "execution_count": 58, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -1016,7 +996,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1042,7 +1022,7 @@ " admit gre gpa rank\n", "0 0 380 3.61 3\n", "1 1 660 3.67 3\n", - "2 1 800 4.00 1\n", + "2 1 880 4.00 1\n", "3 1 640 3.19 4\n", "4 0 520 2.93 4" ] @@ -1060,11 +1040,11 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "file2 = \"salarios.csv\"" + "file2 = \"arquivos/salarios.csv\"" ] }, { @@ -1081,14 +1061,25 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
218008804.001
\n", " \n", " \n", @@ -1181,9 +1172,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1195,9 +1186,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Manipula\303\247\303\243o de Arquivos.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-02-Arquivos-Parte2.ipynb similarity index 71% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Manipula\303\247\303\243o de Arquivos.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-02-Arquivos-Parte2.ipynb index 5ff7e898..bfe345b7 100755 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Manipula\303\247\303\243o de Arquivos.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-02-Arquivos-Parte2.ipynb @@ -4,11 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ************ ATENÇÃO ************\n", + "Caso você tenha problemas com acentos nos arquivos:\n", + "\n", + "Primeiro, recomendamos a leitura do material sobre Formato Unicode, ao final do capítulo 4.\n", + "\n", + "Uma forma de resolver esse problema, é abrir o arquivo em um editor de texto como o Sublime Text, clicar em File - Save with Encoding e então salvar com encoding UTF-8.\n", + "\n", + "Outra opção é incluir o parâmetro encoding='utf8' ao abrir o arquivo para leitura ou escrita.\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -20,9 +34,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- Text files\n", - "- CSV files\n", - "- JSON" + "- Arquivos TXT\n", + "- Arquivos CSV \n", + "- Arquivos JSON" ] }, { @@ -40,41 +54,39 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "texto = \"Cientista de Dados é a profissão que mais tem crescido ultimamente.\\n\"\n", - "texto = texto + \"Esses profissionais precisam se especializar em Estatística, Programação e Machine Learning.\\n\"\n", + "texto = \"Cientista de Dados é a profissão que mais tem crescido em todo mundo.\\n\"\n", + "texto = texto + \"Esses profissionais precisam se especializar em Programação, Estatística e Machine Learning.\\n\"\n", "texto += \"E claro, em Big Data.\"" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cientista de Dados é a profissão que mais tem crescido ultimamente.\n", - "Esses profissionais precisam se especializar em Estatística, Programação e Machine Learning.\n", + "Cientista de Dados é a profissão que mais tem crescido em todo mundo.\n", + "Esses profissionais precisam se especializar em Programação, Estatística e Machine Learning.\n", "E claro, em Big Data.\n" ] } ], "source": [ - "print (texto)" + "print(texto)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -86,19 +98,19 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "# Criando um arquivo (no mesmo diretório onde está o Jupyter Notebook)\n", - "arquivo = open(os.path.join('cientista.txt'),'w')" + "# Criando um arquivo \n", + "arquivo = open(os.path.join('arquivos/cientista.txt'),'w')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -111,7 +123,7 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -122,44 +134,23 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cientista de Dados é a profissão que mais tem crescido ultimamente. Esses profissionais precisam se especializar em Estatística, Programação e Machine Learning. E claro, em Big Data. " - ] - } - ], - "source": [ - "!cat cientista.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cientista de Dados é a profissão que mais tem crescido ultimamente. Esses profissionais precisam se especializar em Estatística, Programação e Machine Learning. E claro, em Big Data. \n" + "Cientista de Dados é a profissão que mais tem crescido em todo mundo. Esses profissionais precisam se especializar em Programação, Estatística e Machine Learning. E claro, em Big Data. \n" ] } ], "source": [ "# Lendo o arquivo\n", - "arquivo = open('cientista.txt','r')\n", - "texto = arquivo.read()\n", + "arquivo = open('arquivos/cientista.txt','r')\n", + "conteudo = arquivo.read()\n", "arquivo.close()\n", "\n", - "print (texto)" + "print(conteudo)" ] }, { @@ -177,63 +168,59 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "with open('cientista.txt','r') as arquivo:\n", - " texto = arquivo.read()" + "with open('arquivos/cientista.txt','r') as arquivo:\n", + " conteudo = arquivo.read()" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "183\n" + "185\n" ] } ], "source": [ - "print (len(texto))" + "print(len(conteudo))" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cientista de Dados é a profissão que mais tem crescido ultimamente. Esses profissionais precisam se especializar em Estatística, Programação e Machine Learning. E claro, em Big Data. \n" + "Cientista de Dados é a profissão que mais tem crescido em todo mundo. Esses profissionais precisam se especializar em Programação, Estatística e Machine Learning. E claro, em Big Data. \n" ] } ], "source": [ - "print (texto)" + "print(conteudo)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "with open('cientista.txt','w') as arquivo:\n", + "with open('arquivos/cientista.txt','w') as arquivo:\n", " arquivo.write(texto[:21])\n", " arquivo.write('\\n')\n", " arquivo.write(texto[:33])" @@ -241,22 +228,25 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cientista de Dados é \r\n", - "Cientista de Dados é a profissão " + "Cientista de Dados é \n", + "Cientista de Dados é a profissão \n" ] } ], "source": [ - "!cat cientista.txt" + "# Lendo o arquivo\n", + "arquivo = open('arquivos/cientista.txt','r')\n", + "conteudo = arquivo.read()\n", + "arquivo.close()\n", + "\n", + "print (conteudo)" ] }, { @@ -272,9 +262,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -284,13 +274,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "with open('numeros.csv','w') as arquivo:\n", + "with open('arquivos/numeros.csv','w') as arquivo:\n", " writer = csv.writer(arquivo)\n", " writer.writerow(('primeira','segunda','terceira'))\n", " writer.writerow((55,93,76)) \n", @@ -299,34 +289,35 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "primeira,segunda,terceira\r", - "\r\n", - "55,93,76\r", - "\r\n", - "62,14,86\r", - "\r\n" + "Número de colunas: 3\n", + "['primeira', 'segunda', 'terceira']\n", + "Número de colunas: 3\n", + "['55', '93', '76']\n", + "Número de colunas: 3\n", + "['62', '14', '86']\n" ] } ], "source": [ - "!cat numeros.csv" + "# Leitura de arquivos csv\n", + "with open('arquivos/numeros.csv','r') as arquivo:\n", + " leitor = csv.reader(arquivo)\n", + " for x in leitor:\n", + " print ('Número de colunas:', len(x))\n", + " print(x)" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -342,8 +333,8 @@ } ], "source": [ - "# Leitura de arquivos csv\n", - "with open('numeros.csv','r') as arquivo:\n", + "# Código alternativo para eventuais problemas com linhas em branco no arquivo\n", + "with open('arquivos/numeros.csv','r', encoding='utf8', newline = '\\r\\n') as arquivo:\n", " leitor = csv.reader(arquivo)\n", " for x in leitor:\n", " print ('Número de colunas:', len(x))\n", @@ -352,10 +343,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -367,7 +356,7 @@ ], "source": [ "# Gerando uma lista com dados do arquivo csv\n", - "with open('numeros.csv','r') as arquivo:\n", + "with open('arquivos/numeros.csv','r') as arquivo:\n", " leitor = csv.reader(arquivo)\n", " dados = list(leitor)\n", " \n", @@ -377,10 +366,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -411,9 +398,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -426,18 +413,16 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "similar ['c', 'Modula-3', 'lisp']\n", "nome Guido van Rossum\n", "linguagem Python\n", + "similar ['c', 'Modula-3', 'lisp']\n", "users 1000000\n" ] } @@ -449,9 +434,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -461,18 +446,16 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'{\"similar\": [\"c\", \"Modula-3\", \"lisp\"], \"nome\": \"Guido van Rossum\", \"linguagem\": \"Python\", \"users\": 1000000}'" + "'{\"nome\": \"Guido van Rossum\", \"linguagem\": \"Python\", \"similar\": [\"c\", \"Modula-3\", \"lisp\"], \"users\": 1000000}'" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -484,43 +467,41 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Criando um arquivo Json\n", - "with open('dados.json','w') as arquivo:\n", + "with open('arquivos/dados.json','w') as arquivo:\n", " arquivo.write(json.dumps(dict))" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Leitura de arquivos Json\n", - "with open('dados.json','r') as arquivo:\n", + "with open('arquivos/dados.json','r') as arquivo:\n", " texto = arquivo.read()\n", " data = json.loads(texto)" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'similar': ['c', 'Modula-3', 'lisp'], 'nome': 'Guido van Rossum', 'linguagem': 'Python', 'users': 1000000}\n" + "{'nome': 'Guido van Rossum', 'linguagem': 'Python', 'similar': ['c', 'Modula-3', 'lisp'], 'users': 1000000}\n" ] } ], @@ -530,10 +511,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -549,9 +528,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -564,19 +543,17 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Título: The Good Man trailer\n", - "URL: http://vimeo.com/57733101\n", + "URL: https://vimeo.com/57733101\n", "Duração: 143\n", - "Número de Visualizações: 5206\n" + "Número de Visualizações: 5514\n" ] } ], @@ -589,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -598,15 +575,15 @@ "# Copiando o conteúdo de um arquivo para outro\n", "import os\n", "\n", - "arquivo_fonte = 'dados.json'\n", - "arquivo_destino = 'json_data.txt'" + "arquivo_fonte = 'arquivos/dados.json'\n", + "arquivo_destino = 'arquivos/json_data.txt'" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -619,10 +596,8 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { @@ -630,7 +605,7 @@ "107" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -642,21 +617,33 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { - "collapsed": false + "collapsed": true }, + "outputs": [], + "source": [ + "# Leitura de arquivos Json\n", + "with open('arquivos/json_data.txt','r') as arquivo:\n", + " texto = arquivo.read()\n", + " data = json.loads(texto)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{\"similar\": [\"c\", \"Modula-3\", \"lisp\"], \"nome\": \"Guido van Rossum\", \"linguagem\": \"Python\", \"users\": 1000000}" + "{'nome': 'Guido van Rossum', 'linguagem': 'Python', 'similar': ['c', 'Modula-3', 'lisp'], 'users': 1000000}\n" ] } ], "source": [ - "!cat json_data.txt" + "print(data)" ] }, { @@ -697,9 +684,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap04/Notebooks/DSA-Python-Cap04-03-Modulos-e-Pacotes.ipynb b/Cap04/Notebooks/DSA-Python-Cap04-03-Modulos-e-Pacotes.ipynb new file mode 100755 index 00000000..b18f853e --- /dev/null +++ b/Cap04/Notebooks/DSA-Python-Cap04-03-Modulos-e-Pacotes.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", + "\n", + "## Download: http://github.com/dsacademybr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Módulos e Pacotes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Importando um módulo em Python\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " 'acos',\n", + " 'acosh',\n", + " 'asin',\n", + " 'asinh',\n", + " 'atan',\n", + " 'atan2',\n", + " 'atanh',\n", + " 'ceil',\n", + " 'copysign',\n", + " 'cos',\n", + " 'cosh',\n", + " 'degrees',\n", + " 'e',\n", + " 'erf',\n", + " 'erfc',\n", + " 'exp',\n", + " 'expm1',\n", + " 'fabs',\n", + " 'factorial',\n", + " 'floor',\n", + " 'fmod',\n", + " 'frexp',\n", + " 'fsum',\n", + " 'gamma',\n", + " 'gcd',\n", + " 'hypot',\n", + " 'inf',\n", + " 'isclose',\n", + " 'isfinite',\n", + " 'isinf',\n", + " 'isnan',\n", + " 'ldexp',\n", + " 'lgamma',\n", + " 'log',\n", + " 'log10',\n", + " 'log1p',\n", + " 'log2',\n", + " 'modf',\n", + " 'nan',\n", + " 'pi',\n", + " 'pow',\n", + " 'radians',\n", + " 'sin',\n", + " 'sinh',\n", + " 'sqrt',\n", + " 'tan',\n", + " 'tanh',\n", + " 'tau',\n", + " 'trunc']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verificando todos os métodos disponíveis no módulo\n", + "dir(math)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Usando um dos métodos do módulo math\n", + "math.sqrt(25)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Importando apenas um dos métodos do módulo math\n", + "from math import sqrt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Usando o método\n", + "sqrt(9)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau', 'trunc']\n" + ] + } + ], + "source": [ + "# Imprimindo todos os métodos do módulo math\n", + "print(dir(math))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function sqrt in module math:\n", + "\n", + "sqrt(...)\n", + " sqrt(x)\n", + " \n", + " Return the square root of x.\n", + "\n" + ] + } + ], + "source": [ + "# Help do método sqrt do módulo math\n", + "help(sqrt)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Maça'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random.choice(['Maça', 'Banana', 'Laranja'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[54, 37, 67, 23, 97, 8, 16, 10, 72, 59]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random.sample(range(100), 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dados = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.6071428571428572" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "statistics.mean(dados)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.25" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "statistics.median(dados)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/dmpm/Dropbox/DSA/PythonFundamentos/Cap04/Notebooks'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['CLD_CONTINUED', 'CLD_DUMPED', 'CLD_EXITED', 'CLD_TRAPPED', 'DirEntry', 'EX_CANTCREAT', 'EX_CONFIG', 'EX_DATAERR', 'EX_IOERR', 'EX_NOHOST', 'EX_NOINPUT', 'EX_NOPERM', 'EX_NOUSER', 'EX_OK', 'EX_OSERR', 'EX_OSFILE', 'EX_PROTOCOL', 'EX_SOFTWARE', 'EX_TEMPFAIL', 'EX_UNAVAILABLE', 'EX_USAGE', 'F_LOCK', 'F_OK', 'F_TEST', 'F_TLOCK', 'F_ULOCK', 'MutableMapping', 'NGROUPS_MAX', 'O_ACCMODE', 'O_APPEND', 'O_ASYNC', 'O_CLOEXEC', 'O_CREAT', 'O_DIRECTORY', 'O_DSYNC', 'O_EXCL', 'O_EXLOCK', 'O_NDELAY', 'O_NOCTTY', 'O_NOFOLLOW', 'O_NONBLOCK', 'O_RDONLY', 'O_RDWR', 'O_SHLOCK', 'O_SYNC', 'O_TRUNC', 'O_WRONLY', 'PRIO_PGRP', 'PRIO_PROCESS', 'PRIO_USER', 'P_ALL', 'P_NOWAIT', 'P_NOWAITO', 'P_PGID', 'P_PID', 'P_WAIT', 'PathLike', 'RTLD_GLOBAL', 'RTLD_LAZY', 'RTLD_LOCAL', 'RTLD_NODELETE', 'RTLD_NOLOAD', 'RTLD_NOW', 'R_OK', 'SCHED_FIFO', 'SCHED_OTHER', 'SCHED_RR', 'SEEK_CUR', 'SEEK_END', 'SEEK_SET', 'ST_NOSUID', 'ST_RDONLY', 'TMP_MAX', 'WCONTINUED', 'WCOREDUMP', 'WEXITED', 'WEXITSTATUS', 'WIFCONTINUED', 'WIFEXITED', 'WIFSIGNALED', 'WIFSTOPPED', 'WNOHANG', 'WNOWAIT', 'WSTOPPED', 'WSTOPSIG', 'WTERMSIG', 'WUNTRACED', 'W_OK', 'X_OK', '_Environ', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_execvpe', '_exists', '_exit', '_fspath', '_get_exports_list', '_putenv', '_spawnvef', '_unsetenv', '_wrap_close', 'abc', 'abort', 'access', 'altsep', 'chdir', 'chflags', 'chmod', 'chown', 'chroot', 'close', 'closerange', 'confstr', 'confstr_names', 'cpu_count', 'ctermid', 'curdir', 'defpath', 'device_encoding', 'devnull', 'dup', 'dup2', 'environ', 'environb', 'errno', 'error', 'execl', 'execle', 'execlp', 'execlpe', 'execv', 'execve', 'execvp', 'execvpe', 'extsep', 'fchdir', 'fchmod', 'fchown', 'fdopen', 'fork', 'forkpty', 'fpathconf', 'fsdecode', 'fsencode', 'fspath', 'fstat', 'fstatvfs', 'fsync', 'ftruncate', 'get_blocking', 'get_exec_path', 'get_inheritable', 'get_terminal_size', 'getcwd', 'getcwdb', 'getegid', 'getenv', 'getenvb', 'geteuid', 'getgid', 'getgrouplist', 'getgroups', 'getloadavg', 'getlogin', 'getpgid', 'getpgrp', 'getpid', 'getppid', 'getpriority', 'getsid', 'getuid', 'initgroups', 'isatty', 'kill', 'killpg', 'lchflags', 'lchmod', 'lchown', 'linesep', 'link', 'listdir', 'lockf', 'lseek', 'lstat', 'major', 'makedev', 'makedirs', 'minor', 'mkdir', 'mkfifo', 'mknod', 'name', 'nice', 'open', 'openpty', 'pardir', 'path', 'pathconf', 'pathconf_names', 'pathsep', 'pipe', 'popen', 'pread', 'putenv', 'pwrite', 'read', 'readlink', 'readv', 'remove', 'removedirs', 'rename', 'renames', 'replace', 'rmdir', 'scandir', 'sched_get_priority_max', 'sched_get_priority_min', 'sched_yield', 'sendfile', 'sep', 'set_blocking', 'set_inheritable', 'setegid', 'seteuid', 'setgid', 'setgroups', 'setpgid', 'setpgrp', 'setpriority', 'setregid', 'setreuid', 'setsid', 'setuid', 'spawnl', 'spawnle', 'spawnlp', 'spawnlpe', 'spawnv', 'spawnve', 'spawnvp', 'spawnvpe', 'st', 'stat', 'stat_float_times', 'stat_result', 'statvfs', 'statvfs_result', 'strerror', 'supports_bytes_environ', 'supports_dir_fd', 'supports_effective_ids', 'supports_fd', 'supports_follow_symlinks', 'symlink', 'sync', 'sys', 'sysconf', 'sysconf_names', 'system', 'tcgetpgrp', 'tcsetpgrp', 'terminal_size', 'times', 'times_result', 'truncate', 'ttyname', 'umask', 'uname', 'uname_result', 'unlink', 'unsetenv', 'urandom', 'utime', 'wait', 'wait3', 'wait4', 'waitpid', 'walk', 'write', 'writev']\n" + ] + } + ], + "source": [ + "print(dir(os))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sys" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Teste" + ] + } + ], + "source": [ + "sys.stdout.write('Teste')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.6.4 |Anaconda custom (64-bit)| (default, Mar 12 2018, 20:05:31) \\n[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.version" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__displayhook__', '__doc__', '__excepthook__', '__interactivehook__', '__loader__', '__name__', '__package__', '__spec__', '__stderr__', '__stdin__', '__stdout__', '_clear_type_cache', '_current_frames', '_debugmallocstats', '_getframe', '_git', '_home', '_xoptions', 'abiflags', 'api_version', 'argv', 'base_exec_prefix', 'base_prefix', 'builtin_module_names', 'byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info', 'float_repr_style', 'get_asyncgen_hooks', 'get_coroutine_wrapper', 'getallocatedblocks', 'getcheckinterval', 'getdefaultencoding', 'getdlopenflags', 'getfilesystemencodeerrors', 'getfilesystemencoding', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info', 'intern', 'is_finalizing', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1', 'ps2', 'ps3', 'set_asyncgen_hooks', 'set_coroutine_wrapper', 'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout', 'thread_info', 'version', 'version_info', 'warnoptions']\n" + ] + } + ], + "source": [ + "print(dir(sys))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Importando o módulo request do pacote urllib, usado para trazer url's \n", + "# para dentro do nosso ambiente Python\n", + "import urllib.request" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Variável resposta armazena o objeto de conexão à url passada como \n", + "# parâmetro\n", + "resposta = urllib.request.urlopen('http://python.org')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Objeto resposta\n", + "print(resposta)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Chamando o método read() do objeto resposta e armazenando o código \n", + "# html na variável html\n", + "html = resposta.read()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'\\n\\n\\n\\n \\n\\n\\n \\n \\n\\n \\n\\n \\n \\n \\n \\n \\n\\n \\n \\n \\n \\n \\n\\n \\n\\n \\n \\n \\n\\n \\n\\n \\n \\n \\n \\n \\n \\n \\n\\n \\n \\n \\n \\n\\n Welcome to Python.org\\n\\n \\n \\n\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n\\n \\n\\n \\n \\n \\n \\n\\n \\n\\n \\n \\n\\n \\n \\n \\n\\n\\n\\n\\n
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    # Python 3: Fibonacci series up to n\\r\\n>>> def fib(n):\\r\\n>>>     a, b = 0, 1\\r\\n>>>     while a < n:\\r\\n>>>         print(a, end=\\' \\')\\r\\n>>>         a, b = b, a+b\\r\\n>>>     print()\\r\\n>>> fib(1000)\\r\\n0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
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    Functions Defined

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    The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3

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    # Python 3: List comprehensions\\r\\n>>> fruits = [\\'Banana\\', \\'Apple\\', \\'Lime\\']\\r\\n>>> loud_fruits = [fruit.upper() for fruit in fruits]\\r\\n>>> print(loud_fruits)\\r\\n[\\'BANANA\\', \\'APPLE\\', \\'LIME\\']\\r\\n\\r\\n# List and the enumerate function\\r\\n>>> list(enumerate(fruits))\\r\\n[(0, \\'Banana\\'), (1, \\'Apple\\'), (2, \\'Lime\\')]
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    Compound Data Types

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    Lists (known as arrays in other languages) are one of the compound data types that Python understands. Lists can be indexed, sliced and manipulated with other built-in functions. More about lists in Python 3

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    # Python 3: Simple arithmetic\\r\\n>>> 1 / 2\\r\\n0.5\\r\\n>>> 2 ** 3\\r\\n8\\r\\n>>> 17 / 3  # classic division returns a float\\r\\n5.666666666666667\\r\\n>>> 17 // 3  # floor division\\r\\n5
    \\n

    Intuitive Interpretation

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    Calculations are simple with Python, and expression syntax is straightforward: the operators +, -, * and / work as expected; parentheses () can be used for grouping. More about simple math functions in Python 3.

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    # Python 3: Simple output (with Unicode)\\r\\n>>> print(\"Hello, I\\'m Python!\")\\r\\nHello, I\\'m Python!\\r\\n\\r\\n# Input, assignment\\r\\n>>> name = input(\\'What is your name?\\\\n\\')\\r\\n>>> print(\\'Hi, %s.\\' % name)\\r\\nWhat is your name?\\r\\nPython\\r\\nHi, Python.
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    Quick & Easy to Learn

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    Experienced programmers in any other language can pick up Python very quickly, and beginners find the clean syntax and indentation structure easy to learn. Whet your appetite with our Python 3 overview.

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    # For loop on a list\\r\\n>>> numbers = [2, 4, 6, 8]\\r\\n>>> product = 1\\r\\n>>> for number in numbers:\\r\\n...    product = product * number\\r\\n... \\r\\n>>> print(\\'The product is:\\', product)\\r\\nThe product is: 384
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    All the Flow You’d Expect

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\\n\\n\\n\\n\\n \\n\\n \\n \\n\\n \\n \\n\\n\\n \\n \\n\\n \\n \\n\\n \\n\\n \\n \\n \\n\\n \\n\\n \\n \\n\\n \\n\\n \\n\\n \\n\\n \\n \\n\\n\\n\\n'\n" + ] + } + ], + "source": [ + "# Imprimindo html\n", + "print(html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Obrigado - Data Science Academy - facebook.com/dsacademybr" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Datetime.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-04-Datetime.ipynb similarity index 82% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Datetime.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-04-Datetime.ipynb index c34e3c4c..b1ec19ca 100644 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Datetime.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-04-Datetime.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -21,7 +21,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -30,9 +30,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -41,18 +41,16 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "datetime.datetime(2016, 5, 30, 20, 16, 1, 751134)" + "datetime.datetime(2018, 4, 5, 14, 1, 53, 388643)" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -63,9 +61,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -74,10 +72,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -93,10 +89,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -118,10 +112,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -137,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -148,20 +140,18 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2016-05-30\n", - "ctime: Mon May 30 00:00:00 2016\n", - "Ano: 2016\n", - "Mês : 5\n", - "Dia : 30\n" + "2018-04-05\n", + "ctime: Thu Apr 5 00:00:00 2018\n", + "Ano: 2018\n", + "Mês : 4\n", + "Dia : 5\n" ] } ], @@ -175,10 +165,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -195,10 +183,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -215,10 +201,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { @@ -226,13 +210,13 @@ "datetime.timedelta(366)" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Diferença em dias emtre duas datas\n", + "# Diferença em dias entre duas datas\n", "d2 - d1" ] }, @@ -267,9 +251,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Map.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-05-Map.ipynb similarity index 87% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Map.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-05-Map.ipynb index 7ac0d66d..00341262 100644 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Map.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-05-Map.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -20,19 +20,17 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Criando duas funções\n", "\n", - "# Função 1 - Recebe uma temperatura como parâmetro e retorna a \n", - "# temperatura em Fahrenheit\n", + "# Função 1 - Recebe uma temperatura como parâmetro e retorna a temperatura em Fahrenheit\n", "def fahrenheit(T):\n", " return ((float(9)/5)*T + 32)\n", "\n", - "# Função 2 - Recebe uma temperatura como parâmetro e retorna a \n", - "# temperatura em Celsius\n", + "# Função 2 - Recebe uma temperatura como parâmetro e retorna a temperatura em Celsius\n", "def celsius(T):\n", " return (float(5)/9)*(T-32)" ] @@ -52,14 +50,12 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -76,9 +72,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -99,9 +93,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -123,14 +115,12 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -146,9 +136,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -168,14 +156,12 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -191,9 +177,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -214,7 +198,7 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -226,9 +210,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -249,7 +231,7 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -262,9 +244,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -312,9 +292,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Reduce.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-06-Reduce.ipynb similarity index 97% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Reduce.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-06-Reduce.ipynb index 259fb2d7..12a294b0 100644 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Reduce.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-06-Reduce.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -20,7 +20,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -43,9 +43,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -66,7 +64,7 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -79,9 +77,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -95,17 +91,14 @@ } ], "source": [ - "# Usando reduce com uma função e uma lista. A função vai retornar o \n", - "# valor máximo\n", + "# Usando reduce com uma função e uma lista. A função vai retornar o valor máximo\n", "reduce(soma, lista)" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -121,14 +114,14 @@ ], "source": [ "from IPython.display import Image\n", - "Image('http://github.com/dsacademybr/PythonFundamentos/tree/master/JupyterNotebooks/Cap%C3%ADtulo07/reduce.png')" + "Image('arquivos/reduce.png')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -139,9 +132,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -174,9 +165,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -196,9 +185,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -212,8 +199,7 @@ } ], "source": [ - "# Reduzindo a lista até o valor máximo, através da função criada com a \n", - "# expressão lambda\n", + "# Reduzindo a lista até o valor máximo, através da função criada com a expressão lambda\n", "reduce(max_find2, lst)" ] }, @@ -248,9 +234,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Filter.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-07-Filter.ipynb similarity index 89% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Filter.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-07-Filter.ipynb index 0b5a4f37..1491d539 100644 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Filter.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-07-Filter.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -20,7 +20,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -35,9 +35,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -60,7 +58,7 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -70,9 +68,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -92,14 +88,12 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -114,9 +108,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -136,9 +128,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -158,9 +148,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -208,9 +196,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-List Comprehensions.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-08-List Comprehensions.ipynb similarity index 90% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-List Comprehensions.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-08-List Comprehensions.ipynb index f5e2f82b..9a6edd55 100755 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-List Comprehensions.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-08-List Comprehensions.ipynb @@ -6,7 +6,7 @@ "collapsed": true }, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -33,9 +33,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -56,9 +54,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -84,15 +80,13 @@ "outputs": [], "source": [ "# Raiz quadrada e um range de números e retornar uma lista\n", - "lst = [x**2 for x in range(0,11)]" + "lst = [x**2 for x in range(0, 11)]" ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -124,9 +118,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -146,9 +138,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -174,7 +164,7 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -185,9 +175,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -235,9 +223,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Zip.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-09-Zip.ipynb similarity index 87% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Zip.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-09-Zip.ipynb index acdd666a..2ff40df1 100644 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Zip.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-09-Zip.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -20,7 +20,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -32,14 +32,12 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -55,9 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -78,9 +74,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -102,7 +96,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -114,9 +108,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -137,7 +129,7 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -149,14 +141,12 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('a', 'd'), ('b', 'c')]" + "[('a', 'c'), ('b', 'd')]" ] }, "execution_count": 8, @@ -172,14 +162,12 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('a', 5), ('b', 4)]" + "[('a', 4), ('b', 5)]" ] }, "execution_count": 9, @@ -196,7 +184,7 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -213,14 +201,12 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'a': 5, 'b': 4}" + "{'a': 4, 'b': 5}" ] }, "execution_count": 11, @@ -263,9 +249,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Enumerate.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-10-Enumerate.ipynb similarity index 88% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Enumerate.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-10-Enumerate.ipynb index 7d5d792a..68d26f87 100644 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Enumerate.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-10-Enumerate.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -20,7 +20,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -31,14 +31,12 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -53,9 +51,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -75,9 +71,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -90,8 +84,7 @@ } ], "source": [ - "# Imprimindo os valores de uma lista com a função enumerate() e \n", - "# seus respectivos índices\n", + "# Imprimindo os valores de uma lista com a função enumerate() e seus respectivos índices\n", "for indice, valor in enumerate(seq):\n", " print (indice, valor)" ] @@ -99,9 +92,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -134,9 +125,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -156,9 +145,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -192,9 +179,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -249,9 +234,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Erros-e-Exce\303\247\303\265es.ipynb" b/Cap04/Notebooks/DSA-Python-Cap04-11-Erros-e-Excecoes.ipynb similarity index 77% rename from "JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Erros-e-Exce\303\247\303\265es.ipynb" rename to Cap04/Notebooks/DSA-Python-Cap04-11-Erros-e-Excecoes.ipynb index d9b22cd2..ee8aa4ce 100644 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Erros-e-Exce\303\247\303\265es.ipynb" +++ b/Cap04/Notebooks/DSA-Python-Cap04-11-Erros-e-Excecoes.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", "\n", "## Download: http://github.com/dsacademybr" ] @@ -20,7 +20,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "scrolled": true }, "outputs": [ { @@ -54,9 +54,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -74,9 +72,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "ZeroDivisionError", @@ -106,9 +102,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -129,9 +123,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -152,9 +144,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -167,7 +157,7 @@ "source": [ "# Utilizando try, except e else\n", "try:\n", - " f = open('testandoerros.txt','w')\n", + " f = open('arquivos/testandoerros.txt','w')\n", " f.write('Gravando no arquivo')\n", "except IOError:\n", " print (\"Erro: arquivo não encontrado ou não pode ser salvo.\")\n", @@ -179,9 +169,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -194,7 +182,7 @@ "source": [ "# Utilizando try, except e else\n", "try:\n", - " f = open('testandoerros','r')\n", + " f = open('arquivos/testandoerros','r')\n", "except IOError:\n", " print (\"Erro: arquivo não encontrado ou não pode ser lido.\")\n", "else:\n", @@ -205,9 +193,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -220,7 +206,7 @@ ], "source": [ "try:\n", - " f = open('testandoerros.txt','w')\n", + " f = open('arquivos/testandoerros.txt','w')\n", " f.write('Gravando no arquivo')\n", "except IOError:\n", " print (\"Erro: arquivo não encontrado ou não pode ser salvo.\")\n", @@ -233,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -241,8 +227,8 @@ "source": [ "def askint():\n", " try:\n", - " val = int(input(\"Digite um número: \"))\n", - " except:\n", + " val = int((input(\"Digite um número: \")))\n", + " except UnboundLocalError:\n", " print (\"Você não digitou um número!\")\n", " finally:\n", " print (\"Obrigado!\")\n", @@ -251,18 +237,27 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Digite um número: 2\n", - "Obrigado!\n", - "2\n" + "Digite um número: \n", + "Obrigado!\n" + ] + }, + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: ''", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maskint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36maskint\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0maskint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Digite um número: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mUnboundLocalError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Você não digitou um número!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: ''" ] } ], @@ -272,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -291,35 +286,33 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Digite um número: q\n", + "Digite um número: \n", "Você não digitou um número!\n", - "Tente novamente. Digite um número: w\n", + "Tente novamente. Digite um número: \n", "Obrigado!\n" ] }, { "ename": "ValueError", - "evalue": "invalid literal for int() with base 10: 'w'", + "evalue": "invalid literal for int() with base 10: ''", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36maskint\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Digite um número: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'q'", + "\u001b[0;32m\u001b[0m in \u001b[0;36maskint\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Digite um número: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: ''", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maskint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36maskint\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Você não digitou um número!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tente novamente. Digite um número: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Obrigado!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'w'" + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maskint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36maskint\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Você não digitou um número!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tente novamente. Digite um número: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Obrigado!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: ''" ] } ], @@ -329,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -352,25 +345,38 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Digite um número: q\n", + "Digite um número: \n", + "Você não digitou um número!\n", + "Fim da execução!\n", + "Digite um número: \n", + "Você não digitou um número!\n", + "Fim da execução!\n", + "Digite um número: \n", + "Você não digitou um número!\n", + "Fim da execução!\n", + "Digite um número: \n", "Você não digitou um número!\n", "Fim da execução!\n", - "Digite um número: w\n", + "Digite um número: \n", "Você não digitou um número!\n", "Fim da execução!\n", - "Digite um número: r\n", + "Digite um número: \n", "Você não digitou um número!\n", "Fim da execução!\n", - "Digite um número: 4\n", + "Digite um número: \n", + "Você não digitou um número!\n", + "Fim da execução!\n", + "Digite um número: \n", + "Você não digitou um número!\n", + "Fim da execução!\n", + "Digite um número: 87\n", "Obrigado por digitar um número!\n", "Fim da execução!\n" ] @@ -382,10 +388,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -413,7 +417,7 @@ "source": [ "Uma lista completa de exceções em Python, pode ser encontrada aqui:\n", "\n", - "https://docs.python.org/3.5/library/exceptions.html\n" + "https://docs.python.org/3.6/library/exceptions.html\n" ] }, { @@ -447,9 +451,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap04/Notebooks/DSA-Python-Cap04-ExercicioS.ipynb b/Cap04/Notebooks/DSA-Python-Cap04-ExercicioS.ipynb new file mode 100644 index 00000000..90273da1 --- /dev/null +++ b/Cap04/Notebooks/DSA-Python-Cap04-ExercicioS.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", + "\n", + "## Download: http://github.com/dsacademybr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercícios " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 1 - Crie uma lista de 3 elementos e calcule a terceira potência de cada elemento." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 2 - Reescreva o código abaixo, usando a função map(). O resultado final deve ser o mesmo!\n", + "palavras = 'A Data Science Academy oferce os melhores cursos de análise de dados do Brasil'.split()\n", + "resultado = [[w.upper(), w.lower(), len(w)] for w in palavras]\n", + "for i in resultado:\n", + " print (i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 3 - Calcule a matriz transposta da matriz abaixo.\n", + "# Caso não saiba o que é matriz transposta, visite este link: https://pt.wikipedia.org/wiki/Matriz_transposta\n", + "# Matriz transposta é um conceito fundamental na construção de redes neurais artificiais, base de sistemas de IA.\n", + "matrix = [[1, 2],[3,4],[5,6],[7,8]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 4 - Crie duas funções, uma para elevar um número ao quadrado e outra para elevar ao cubo. \n", + "# Aplique as duas funções aos elementos da lista abaixo. \n", + "# Obs: as duas funções devem ser aplicadas simultaneamente.\n", + "lista = [0, 1, 2, 3, 4]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 5 - Abaixo você encontra duas listas. Faça com que cada elemento da listaA seja elevado \n", + "# ao elemento correspondente na listaB.\n", + "listaA = [2, 3, 4]\n", + "listaB = [10, 11, 12]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 6 - Considerando o range de valores abaixo, use a função filter() para retornar apenas os valores negativos.\n", + "range(-5, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 7 - Usando a função filter(), encontre os valores que são comuns às duas listas abaixo.\n", + "a = [1,2,3,5,7,9]\n", + "b = [2,3,5,6,7,8]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 8 - Considere o código abaixo. Obtenha o mesmo resultado usando o pacote time. \n", + "# Não conhece o pacote time? Pesquise!\n", + "import datetime\n", + "print (datetime.datetime.now().strftime(\"%d/%m/%Y %H:%M\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 9 - Considere os dois dicionários abaixo. \n", + "# Crie um terceiro dicionário com as chaves do dicionário 1 e os valores do dicionário 2.\n", + "dict1 = {'a':1,'b':2}\n", + "dict2 = {'c':4,'d':5}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício 10 - Considere a lista abaixo e retorne apenas os elementos cujo índice for maior que 5.\n", + "lista = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obrigado - Data Science Academy - facebook.com/dsacademybr" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Cap04/Notebooks/DSA-Python-Cap04-Exercicios-Solucao.ipynb b/Cap04/Notebooks/DSA-Python-Cap04-Exercicios-Solucao.ipynb new file mode 100644 index 00000000..1e9fbde3 --- /dev/null +++ b/Cap04/Notebooks/DSA-Python-Cap04-Exercicios-Solucao.ipynb @@ -0,0 +1,344 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Science Academy - Python Fundamentos - Capítulo 4\n", + "\n", + "## Download: http://github.com/dsacademybr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercícios " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[27, 64, 125]\n" + ] + } + ], + "source": [ + "# Exercício 1 - Crie uma lista de 3 elementos e calcule a terceira potência de cada elemento.\n", + "list1 = [3,4,5]\n", + "quadrado = [item**3 for item in list1] \n", + "print(quadrado)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'a', 1]\n", + "['DATA', 'data', 4]\n", + "['SCIENCE', 'science', 7]\n", + "['ACADEMY', 'academy', 7]\n", + "['OFERCE', 'oferce', 6]\n", + "['OS', 'os', 2]\n", + "['MELHORES', 'melhores', 8]\n", + "['CURSOS', 'cursos', 6]\n", + "['DE', 'de', 2]\n", + "['ANÁLISE', 'análise', 7]\n", + "['DE', 'de', 2]\n", + "['DADOS', 'dados', 5]\n", + "['DO', 'do', 2]\n", + "['BRASIL', 'brasil', 6]\n" + ] + } + ], + "source": [ + "# Exercício 2 - Reescreva o código abaixo, usando a função map(). O resultado final deve ser o mesmo!\n", + "palavras = 'A Data Science Academy oferce os melhores cursos de análise de dados do Brasil'.split()\n", + "resultado = [[w.upper(), w.lower(), len(w)] for w in palavras]\n", + "for i in resultado:\n", + " print (i)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'a', 1]\n", + "['DATA', 'data', 4]\n", + "['SCIENCE', 'science', 7]\n", + "['ACADEMY', 'academy', 7]\n", + "['OFERCE', 'oferce', 6]\n", + "['OS', 'os', 2]\n", + "['MELHORES', 'melhores', 8]\n", + "['CURSOS', 'cursos', 6]\n", + "['DE', 'de', 2]\n", + "['ANÁLISE', 'análise', 7]\n", + "['DE', 'de', 2]\n", + "['DADOS', 'dados', 5]\n", + "['DO', 'do', 2]\n", + "['BRASIL', 'brasil', 6]\n" + ] + } + ], + "source": [ + "resultado = map(lambda w: [w.upper(), w.lower(), len(w)], palavras)\n", + "for i in resultado:\n", + " print (i)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 3, 5, 7], [2, 4, 6, 8]]\n" + ] + } + ], + "source": [ + "# Exercício 3 - Calcule a matriz transposta da matriz abaixo.\n", + "# Caso não saiba o que é matriz transposta, visite este link: https://pt.wikipedia.org/wiki/Matriz_transposta\n", + "# Matriz transposta é um conceito fundamental na construção de redes neurais artificiais, base de sistemas de IA.\n", + "matrix = [[1, 2],[3,4],[5,6],[7,8]]\n", + "transpose = [[row[i] for row in matrix] for i in range(2)]\n", + "print(transpose)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 0]\n", + "[1, 1]\n", + "[4, 8]\n", + "[9, 27]\n", + "[16, 64]\n" + ] + } + ], + "source": [ + "# Exercício 4 - Crie duas funções, uma para elevar um número ao quadrado e outra para elevar ao cubo. \n", + "# Aplique as duas funções aos elementos da lista abaixo. \n", + "# Obs: as duas funções devem ser aplicadas simultaneamente.\n", + "lista = [0, 1, 2, 3, 4]\n", + "\n", + "def square(x):\n", + " return (x**2)\n", + " \n", + "def cube(x):\n", + " return (x**3)\n", + "\n", + "funcs = [square, cube]\n", + "\n", + "for i in lista:\n", + " valor = map(lambda x: x(i), funcs)\n", + " print(list((valor)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1024, 177147, 16777216]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercício 5 - Abaixo você encontra duas listas. Faça com que cada elemento da listaA seja elevado \n", + "# ao elemento correspondente na listaB.\n", + "listaA = [2, 3, 4]\n", + "listaB = [10, 11, 12]\n", + "list(map(pow, listaA, listaB))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[-5, -4, -3, -2, -1]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercício 6 - Considerando o range de valores abaixo, use a função filter() para retornar apenas os valores negativos.\n", + "range(-5, 5)\n", + "list(filter((lambda x: x < 0), range(-5,5)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 3, 5, 7]\n" + ] + } + ], + "source": [ + "# Exercício 7 - Usando a função filter(), encontre os valores que são comuns às duas listas abaixo.\n", + "a = [1,2,3,5,7,9]\n", + "b = [2,3,5,6,7,8]\n", + "print (list(filter(lambda x: x in a, b)))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "05/04/2018 22:16\n", + "05/04/2018 22:16\n" + ] + } + ], + "source": [ + "# Exercício 8 - Considere o código abaixo. Obtenha o mesmo resultado usando o pacote time. \n", + "# Não conhece o pacote time? Pesquise!\n", + "import datetime\n", + "print (datetime.datetime.now().strftime(\"%d/%m/%Y %H:%M\"))\n", + "\n", + "import time\n", + "print (time.strftime(\"%d/%m/%Y %H:%M\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 4, 'b': 5}\n" + ] + } + ], + "source": [ + "# Exercício 9 - Considere os dois dicionários abaixo. \n", + "# Crie um terceiro dicionário com as chaves do dicionário 1 e os valores do dicionário 2.\n", + "dict1 = {'a':1,'b':2}\n", + "dict2 = {'c':4,'d':5}\n", + "\n", + "def trocaValores(d1, d2):\n", + " dicTemp = {}\n", + " \n", + " for d1key, d2val in zip(d1,d2.values()):\n", + " dicTemp[d1key] = d2val\n", + " \n", + " return dicTemp\n", + "\n", + "dict3 = trocaValores(dict1, dict2)\n", + "print(dict3)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "g\n", + "h\n" + ] + } + ], + "source": [ + "# Exercício 10 - Considere a lista abaixo e retorne apenas os elementos cujo índice for maior que 5.\n", + "lista = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\n", + "for indice, valor in enumerate(lista):\n", + " if indice <= 5:\n", + " continue\n", + " else:\n", + " print (valor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obrigado - Data Science Academy - facebook.com/dsacademybr" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Cap04/Notebooks/arquivos/arquivo1.txt b/Cap04/Notebooks/arquivos/arquivo1.txt new file mode 100644 index 00000000..03265a85 --- /dev/null +++ b/Cap04/Notebooks/arquivos/arquivo1.txt @@ -0,0 +1 @@ +Testando gravação de arquivos em Python Acrescentando conteúdo \ No newline at end of file diff --git a/Cap04/Notebooks/arquivos/binary.csv b/Cap04/Notebooks/arquivos/binary.csv new file mode 100755 index 00000000..b2488b11 --- /dev/null +++ b/Cap04/Notebooks/arquivos/binary.csv @@ -0,0 +1,6 @@ +admit,gre,gpa,rank +0,380,3.61,3 +1,660,3.67,3 +1,880,4.00,1 +1,640,3.19,4 +0,520,2.93,4 diff --git "a/JupyterNotebooks/Cap\303\255tulo07/cientista.txt" b/Cap04/Notebooks/arquivos/cientista.txt similarity index 100% rename from "JupyterNotebooks/Cap\303\255tulo07/cientista.txt" rename to Cap04/Notebooks/arquivos/cientista.txt diff --git a/Cap04/Notebooks/arquivos/dados.json b/Cap04/Notebooks/arquivos/dados.json new file mode 100644 index 00000000..6191d72f --- /dev/null +++ b/Cap04/Notebooks/arquivos/dados.json @@ -0,0 +1 @@ +{"nome": "Guido van Rossum", "linguagem": "Python", "similar": ["c", "Modula-3", "lisp"], "users": 1000000} \ No newline at end of file diff --git a/Cap04/Notebooks/arquivos/file.csv b/Cap04/Notebooks/arquivos/file.csv new file mode 100644 index 00000000..a94fc3b8 --- /dev/null +++ b/Cap04/Notebooks/arquivos/file.csv @@ -0,0 +1,4 @@ +01/01/2016, 4 +02/01/2016, 2 +03/01/2016, 10 +04/01/2016, 8 \ No newline at end of file diff --git a/Cap04/Notebooks/arquivos/json_data.txt b/Cap04/Notebooks/arquivos/json_data.txt new file mode 100644 index 00000000..6191d72f --- /dev/null +++ b/Cap04/Notebooks/arquivos/json_data.txt @@ -0,0 +1 @@ +{"nome": "Guido van Rossum", "linguagem": "Python", "similar": ["c", "Modula-3", "lisp"], "users": 1000000} \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo07/numeros.csv" b/Cap04/Notebooks/arquivos/numeros.csv similarity index 100% rename from "JupyterNotebooks/Cap\303\255tulo07/numeros.csv" rename to Cap04/Notebooks/arquivos/numeros.csv diff --git "a/JupyterNotebooks/Cap\303\255tulo07/reduce.png" b/Cap04/Notebooks/arquivos/reduce.png similarity index 100% rename from "JupyterNotebooks/Cap\303\255tulo07/reduce.png" rename to Cap04/Notebooks/arquivos/reduce.png diff --git "a/JupyterNotebooks/Cap\303\255tulo02/salarios.csv" b/Cap04/Notebooks/arquivos/salarios.csv similarity index 100% rename from "JupyterNotebooks/Cap\303\255tulo02/salarios.csv" rename to Cap04/Notebooks/arquivos/salarios.csv diff --git "a/JupyterNotebooks/Cap\303\255tulo07/testandoerros.txt" b/Cap04/Notebooks/arquivos/testandoerros.txt similarity index 100% rename from "JupyterNotebooks/Cap\303\255tulo07/testandoerros.txt" rename to Cap04/Notebooks/arquivos/testandoerros.txt diff --git a/Cap04/Notebooks/arquivos/teste.txt b/Cap04/Notebooks/arquivos/teste.txt new file mode 100644 index 00000000..674e65e6 --- /dev/null +++ b/Cap04/Notebooks/arquivos/teste.txt @@ -0,0 +1,2 @@ +Olá este arquivo foi gerado pelo próprio Jupyter Notebook. +Podemos gerar quantas linhas quisermos e o Jupyter gera o arquivo final. \ No newline at end of file diff --git a/Cap05/Lab03/forca_v1.py b/Cap05/Lab03/forca_v1.py new file mode 100755 index 00000000..ea3e420c --- /dev/null +++ b/Cap05/Lab03/forca_v1.py @@ -0,0 +1,126 @@ +# Hangman Game (Jogo da Forca) +# Programação Orientada a Objetos + +# Import +import random + +# Board (tabuleiro) +board = [''' + +>>>>>>>>>>Hangman<<<<<<<<<< + ++---+ +| | + | + | + | + | +=========''', ''' + ++---+ +| | +O | + | + | + | +=========''', ''' + ++---+ +| | +O | +| | + | + | +=========''', ''' + + +---+ + | | + O | +/| | + | + | +=========''', ''' + + +---+ + | | + O | +/|\ | + | + | +=========''', ''' + + +---+ + | | + O | +/|\ | +/ | + | +=========''', ''' + + +---+ + | | + O | +/|\ | +/ \ | + | +========='''] + + +# Classe +class Hangman: + + # Método Construtor + def __init__(self, word): + + + # Método para adivinhar a letra + def guess(self, letter): + + + # Método para verificar se o jogo terminou + def hangman_over(self): + + + # Método para verificar se o jogador venceu + def hangman_won(self): + + + # Método para não mostrar a letra no board + def hide_word(self): + + + # Método para checar o status do game e imprimir o board na tela + def print_game_status(self): + + +# Função para ler uma palavra de forma aleatória do banco de palavras +def rand_word(): + with open("palavras.txt", "rt") as f: + bank = f.readlines() + return bank[random.randint(0,len(bank))].strip() + + +# Função Main - Execução do Programa +def main(): + + # Objeto + game = Hangman(rand_word()) + + # Enquanto o jogo não tiver terminado, print do status, solicita uma letra e faz a leitura do caracter + + + # Verifica o status do jogo + game.print_game_status() + + # De acordo com o status, imprime mensagem na tela para o usuário + if game.hangman_won(): + print ('\nParabéns! Você venceu!!') + else: + print ('\nGame over! Você perdeu.') + print ('A palavra era ' + game.word) + + print ('\nFoi bom jogar com você! Agora vá estudar!\n') + +# Executa o programa +if __name__ == "__main__": + main() diff --git a/Cap05/Lab03/palavras.txt b/Cap05/Lab03/palavras.txt new file mode 100644 index 00000000..c5f67c95 --- /dev/null +++ b/Cap05/Lab03/palavras.txt @@ -0,0 +1,3 @@ +bola +casa +massa \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Classes.ipynb" b/Cap05/Notebooks/DSA-Python-Cap05-01-Classes.ipynb similarity index 90% rename from "JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Classes.ipynb" rename to Cap05/Notebooks/DSA-Python-Cap05-01-Classes.ipynb index aeff6fb5..55c74b6d 100644 --- "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Classes.ipynb" +++ b/Cap05/Notebooks/DSA-Python-Cap05-01-Classes.ipynb @@ -38,18 +38,16 @@ " \n", " # Este método vai inicializar cada objeto criado a partir desta classe\n", " # O nome deste método é __init__\n", - " # (self) é uma referência a cada atributo de um objeto criado a partir \n", - " # desta classe\n", + " # (self) é uma referência a cada atributo de um objeto criado a partir desta classe\n", " def __init__(self):\n", " \n", - " # Atributos de cada objeto criado a partir desta classe. O self \n", - " # indica que estes são atributos dos objetos\n", + " # Atributos de cada objeto criado a partir desta classe. \n", + " # O self indica que estes são atributos dos objetos\n", " self.titulo = 'O Monge e o Executivo'\n", " self.isbn = 9988888\n", " print(\"Construtor chamado para criar um objeto desta classe\")\n", " \n", - " # Métodos são funções, que recebem como parâmetro atributos do \n", - " # objeto criado \n", + " # Métodos são funções, que recebem como parâmetro atributos do objeto criado \n", " def imprime(self):\n", " print(\"Foi criado o livro %s e ISBN %d\" %(self.titulo, self.isbn))" ] @@ -57,9 +55,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -77,9 +73,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -100,9 +94,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -123,9 +115,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -144,7 +134,7 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -162,9 +152,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -182,9 +170,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -204,9 +190,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -239,9 +223,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -259,9 +241,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -279,9 +259,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -302,9 +280,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -353,9 +329,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Objetos.ipynb" b/Cap05/Notebooks/DSA-Python-Cap05-02-Objetos.ipynb similarity index 86% rename from "JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Objetos.ipynb" rename to Cap05/Notebooks/DSA-Python-Cap05-02-Objetos.ipynb index 66de4119..65f57fae 100644 --- "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Objetos.ipynb" +++ b/Cap05/Notebooks/DSA-Python-Cap05-02-Objetos.ipynb @@ -20,27 +20,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Em Python, tudo é objeto" + "Em Python, tudo é objeto!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Criando uma lista\n", - "lst_num = [1, 2, 3]" + "lst_num = [\"Data\", \"Science\", \"Academy\", \"Nota\", 10, 10]" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -61,9 +59,27 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst_num.count(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -79,19 +95,17 @@ ], "source": [ "# Usamos a função type, para verificar o tipo de um objeto\n", - "print (type(10))\n", - "print (type([]))\n", - "print (type(()))\n", - "print (type({}))\n", - "print (type('a'))" + "print(type(10))\n", + "print(type([]))\n", + "print(type(()))\n", + "print(type({}))\n", + "print(type('a'))" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -109,14 +123,14 @@ "# Instância do Carro\n", "palio = Carro()\n", "\n", - "print (type(palio))" + "print(type(palio))" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -130,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -142,10 +156,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { @@ -153,7 +165,7 @@ "'Pele'" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -165,10 +177,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { @@ -176,7 +186,7 @@ "12" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -188,10 +198,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { @@ -199,7 +207,7 @@ "9.5" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -211,9 +219,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -229,22 +237,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Criando um objeto chamado Estudante1 a partir da classe Estudantes\n", + "# Criando um objeto chamado Func1 a partir da classe Funcionarios\n", "Func1 = Funcionarios(\"Obama\", 20000)" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -261,10 +267,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -280,10 +284,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { @@ -291,7 +293,7 @@ "True" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -302,10 +304,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "data": { @@ -313,7 +313,7 @@ "True" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -335,10 +335,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { @@ -346,7 +344,7 @@ "True" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -357,10 +355,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "data": { @@ -368,7 +364,7 @@ "4500" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -379,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -390,10 +386,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -401,7 +395,7 @@ "False" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -441,9 +435,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-M\303\251todos.ipynb" b/Cap05/Notebooks/DSA-Python-Cap05-03-Metodos.ipynb similarity index 76% rename from "JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-M\303\251todos.ipynb" rename to Cap05/Notebooks/DSA-Python-Cap05-03-Metodos.ipynb index 8bae504c..9dc8ac6c 100644 --- "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-M\303\251todos.ipynb" +++ b/Cap05/Notebooks/DSA-Python-Cap05-03-Metodos.ipynb @@ -25,18 +25,16 @@ "outputs": [], "source": [ "# Criando uma classe chamada Circulo\n", - "class Circulo(object):\n", + "class Circulo():\n", " \n", " # O valor de pi é constante\n", " pi = 3.14\n", "\n", - " # Quando um objeto desta classe for criado, este método será executado \n", - " # e o valor default do raio será 5.\n", + " # Quando um objeto desta classe for criado, este método será executado e o valor default do raio será 5.\n", " def __init__(self, raio = 5):\n", " self.raio = raio \n", "\n", - " # Esse método calcula a área. Self utiliza os atributos deste mesmo \n", - " # objeto\n", + " # Esse método calcula a área. Self utiliza os atributos deste mesmo objeto\n", " def area(self):\n", " return (self.raio * self.raio) * Circulo.pi\n", "\n", @@ -64,9 +62,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -88,8 +84,40 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": true }, + "outputs": [], + "source": [ + "# Criando outro objeto chamado circ1. Uma instância da classe Circulo()\n", + "# Agora sobrescrevendo o valor do atributo\n", + "circ1 = Circulo(7)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Executando um método da classe Circulo\n", + "circ1.getRaio()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -106,29 +134,27 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Area é: 78.5\n" + "Area igual a: 78.5\n" ] } ], "source": [ "# Imprimindo a area\n", - "print ('Area é: ', circ.area())" + "print('Area igual a: ', circ.area())" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -138,22 +164,20 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "O novo raio é: 3\n" + "Novo raio igual a: 3\n" ] } ], "source": [ "# Imprimindo o novo raio\n", - "print ('O novo raio é: ', circ.getRaio())" + "print ('Novo raio igual a: ', circ.getRaio())" ] }, { @@ -187,9 +211,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Heran\303\247a.ipynb" b/Cap05/Notebooks/DSA-Python-Cap05-04-Heranca.ipynb similarity index 74% rename from "JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Heran\303\247a.ipynb" rename to Cap05/Notebooks/DSA-Python-Cap05-04-Heranca.ipynb index e6ca7f84..cb7f7273 100644 --- "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Heran\303\247a.ipynb" +++ b/Cap05/Notebooks/DSA-Python-Cap05-04-Heranca.ipynb @@ -24,16 +24,17 @@ }, "outputs": [], "source": [ - "# Criando a classe Animal\n", - "class Animal(object):\n", + "# Criando a classe Animal - Super-classe\n", + "class Animal():\n", + " \n", " def __init__(self):\n", - " print (\"Animal criado\")\n", + " print(\"Animal criado\")\n", "\n", " def Identif(self):\n", - " print (\"Animal\")\n", + " print(\"Animal\")\n", "\n", " def comer(self):\n", - " print (\"Comendo\")" + " print(\"Comendo\")" ] }, { @@ -44,25 +45,24 @@ }, "outputs": [], "source": [ - "# Criando a classe Cachorro\n", + "# Criando a classe Cachorro - Sub-classe\n", "class Cachorro(Animal):\n", + " \n", " def __init__(self):\n", " Animal.__init__(self)\n", - " print (\"Objeto Cachorro criado\")\n", + " print(\"Objeto Cachorro criado\")\n", "\n", " def Identif(self):\n", - " print (\"Cachorro\")\n", + " print(\"Cachorro\")\n", "\n", " def latir(self):\n", - " print (\"Au Au!\")" + " print(\"Au Au!\")" ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -75,15 +75,13 @@ ], "source": [ "# Criando um objeto (Instanciando a classe)\n", - "puppy = Cachorro()" + "rex = Cachorro()" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -94,16 +92,14 @@ } ], "source": [ - "# Executando o método da classe Cachorro (sub classe)\n", - "puppy.Identif()" + "# Executando o método da classe Cachorro (sub-classe)\n", + "rex.Identif()" ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -114,16 +110,14 @@ } ], "source": [ - "# Executando o método da classe Animal (super classe)\n", - "puppy.comer()" + "# Executando o método da classe Animal (super-classe)\n", + "rex.comer()" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -134,8 +128,8 @@ } ], "source": [ - "# Executando o método da classe Cachorro (sub classe)\n", - "puppy.latir()" + "# Executando o método da classe Cachorro (sub-classe)\n", + "rex.latir()" ] }, { @@ -169,9 +163,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-MetodosEspeciais.ipynb" b/Cap05/Notebooks/DSA-Python-Cap05-05-MetodosEspeciais.ipynb similarity index 81% rename from "JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-MetodosEspeciais.ipynb" rename to Cap05/Notebooks/DSA-Python-Cap05-05-MetodosEspeciais.ipynb index 0e21be9c..2c0ebabc 100644 --- "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-MetodosEspeciais.ipynb" +++ b/Cap05/Notebooks/DSA-Python-Cap05-05-MetodosEspeciais.ipynb @@ -4,7 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 5" + "# Data Science Academy - Python Fundamentos - Capítulo 5\n", + "## Download: http://github.com/dsacademybr" ] }, { @@ -23,7 +24,7 @@ "outputs": [], "source": [ "# Criando a classe Livro\n", - "class Livro(object):\n", + "class Livro():\n", " def __init__(self, titulo, autor, paginas):\n", " print (\"Livro criado\")\n", " self.titulo = titulo\n", @@ -35,15 +36,16 @@ " %(self.titulo, self.autor, self.paginas)\n", "\n", " def __len__(self):\n", - " return self.paginas" + " return self.paginas\n", + " \n", + " def len(self):\n", + " return print(\"Páginas do livro com método comum: \", self.paginas)" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -60,9 +62,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -80,9 +80,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -102,9 +100,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -124,8 +120,25 @@ { "cell_type": "code", "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Páginas do livro com método comum: 8816\n" + ] + } + ], + "source": [ + "livro1.len()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -136,10 +149,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { @@ -147,7 +158,7 @@ "False" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -187,9 +198,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Exerc\303\255cios-Solu\303\247\303\243o.ipynb" b/Cap05/Notebooks/DSA-Python-Cap05-Exercicios-Solucao.ipynb similarity index 96% rename from "JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Exerc\303\255cios-Solu\303\247\303\243o.ipynb" rename to Cap05/Notebooks/DSA-Python-Cap05-Exercicios-Solucao.ipynb index 71b52067..d4db98f8 100644 --- "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Exerc\303\255cios-Solu\303\247\303\243o.ipynb" +++ b/Cap05/Notebooks/DSA-Python-Cap05-Exercicios-Solucao.ipynb @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -62,9 +60,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -107,9 +103,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -170,9 +164,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Exerc\303\255cios.ipynb" b/Cap05/Notebooks/DSA-Python-Cap05-Exercicios.ipynb similarity index 94% rename from "JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Exerc\303\255cios.ipynb" rename to Cap05/Notebooks/DSA-Python-Cap05-Exercicios.ipynb index 7a4d31af..55f4549e 100644 --- "a/JupyterNotebooks/Cap\303\255tulo05/DSA-Python-Cap\303\255tulo5-Exerc\303\255cios.ipynb" +++ b/Cap05/Notebooks/DSA-Python-Cap05-Exercicios.ipynb @@ -20,7 +20,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -47,20 +47,20 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "# Exercício 2 - Crie uma classe chamada Pessoa() com os atributos: nome, cidade, telefone e e-mail. Use pelo menos 2\n", "# métodos especiais na sua classe. Crie um objeto da sua classe e faça uma chamada a pelo menos um dos seus métodos\n", - "# especiais\n" + "# especiais.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -99,9 +99,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap06/Lab03/forca_v2.py b/Cap06/Lab03/forca_v2.py new file mode 100755 index 00000000..075883c8 --- /dev/null +++ b/Cap06/Lab03/forca_v2.py @@ -0,0 +1,153 @@ +# Hangman Game (Jogo da Forca) +# Programação Orientada a Objetos + +# Import +import random + +# Board (tabuleiro) +board = [''' + +>>>>>>>>>>Hangman<<<<<<<<<< + ++---+ +| | + | + | + | + | +=========''', ''' + ++---+ +| | +O | + | + | + | +=========''', ''' + ++---+ +| | +O | +| | + | + | +=========''', ''' + + +---+ + | | + O | +/| | + | + | +=========''', ''' + + +---+ + | | + O | +/|\ | + | + | +=========''', ''' + + +---+ + | | + O | +/|\ | +/ | + | +=========''', ''' + + +---+ + | | + O | +/|\ | +/ \ | + | +========='''] + + +# Classe +class Hangman: + + # Método Construtor + def __init__(self, word): + self.word = word + self.missed_letters = [] + self.guessed_letters = [] + + # Método para adivinhar a letra + def guess(self, letter): + if letter in self.word and letter not in self.guessed_letters: + self.guessed_letters.append(letter) + elif letter not in self.word and letter not in self.missed_letters: + self.missed_letters.append(letter) + else: + return False + return True + + # Método para verificar se o jogo terminou + def hangman_over(self): + return self.hangman_won() or (len(self.missed_letters) == 6) + + # Método para verificar se o jogador venceu + def hangman_won(self): + if '_' not in self.hide_word(): + return True + return False + + # Método para não mostrar a letra no board + def hide_word(self): + rtn = '' + for letter in self.word: + if letter not in self.guessed_letters: + rtn += '_' + else: + rtn += letter + return rtn + + # Método para checar o status do game e imprimir o board na tela + def print_game_status(self): + print (board[len(self.missed_letters)]) + print ('\nPalavra: ' + self.hide_word()) + print ('\nLetras erradas: ',) + for letter in self.missed_letters: + print (letter,) + print () + print ('Letras corretas: ',) + for letter in self.guessed_letters: + print (letter,) + print () + +# Método para ler uma palavra de forma aleatória do banco de palavras +def rand_word(): + with open("palavras.txt", "rt") as f: + bank = f.readlines() + return bank[random.randint(0,len(bank))].strip() + +# Método Main - Execução do Programa +def main(): + + # Objeto + game = Hangman(rand_word()) + + # Enquanto o jogo não tiver terminado, print do status, solicita uma letra e faz a leitura do caracter + while not game.hangman_over(): + game.print_game_status() + user_input = input('\nDigite uma letra: ') + game.guess(user_input) + + # Verifica o status do jogo + game.print_game_status() + + # De acordo com o status, imprime mensagem na tela para o usuário + if game.hangman_won(): + print ('\nParabéns! Você venceu!!') + else: + print ('\nGame over! Você perdeu.') + print ('A palavra era ' + game.word) + + print ('\nFoi bom jogar com você! Agora vá estudar!\n') + +# Executa o programa +if __name__ == "__main__": + main() diff --git a/Cap06/Lab03/palavras.txt b/Cap06/Lab03/palavras.txt new file mode 100644 index 00000000..dc6d8f8b --- /dev/null +++ b/Cap06/Lab03/palavras.txt @@ -0,0 +1,5 @@ +bola + +casa + +massa \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte1 - Criando um Banco de Dados SQLite.ipynb" b/Cap06/Notebooks/DSA-Python-Cap06-01-Criando um Banco de Dados SQLite.ipynb similarity index 87% rename from "JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte1 - Criando um Banco de Dados SQLite.ipynb" rename to Cap06/Notebooks/DSA-Python-Cap06-01-Criando um Banco de Dados SQLite.ipynb index 4c20b35c..f720ccd8 100644 --- "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte1 - Criando um Banco de Dados SQLite.ipynb" +++ b/Cap06/Notebooks/DSA-Python-Cap06-01-Criando um Banco de Dados SQLite.ipynb @@ -9,6 +9,13 @@ "## Download: http://github.com/dsacademybr" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Documentação SQLite: http://www.sqlite.org/docs.html" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -20,7 +27,20 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true + }, + "outputs": [], + "source": [ + "# Reemove o arquivo com o banco de dados SQLite (caso exista)\n", + "import os\n", + "os.remove(\"escola.db\") if os.path.exists(\"escola.db\") else None" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true }, "outputs": [], "source": [ @@ -30,9 +50,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -43,10 +63,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { @@ -54,7 +72,7 @@ "sqlite3.Connection" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -65,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -78,10 +96,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -89,7 +105,7 @@ "sqlite3.Cursor" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -100,9 +116,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -115,18 +131,16 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -138,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -150,9 +164,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -164,9 +178,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -177,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -189,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -199,47 +213,21 @@ "sql_select = 'select * from cursos'" ] }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Seleciona todos os registros\n", - "cur.execute(sql_select)" - ] - }, { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# Recupera os resultados\n", + "# Seleciona todos os registros e recupera os registros\n", + "cur.execute(sql_select)\n", "dados = cur.fetchall()" ] }, { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -264,7 +252,7 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -283,9 +271,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -359,9 +345,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte2 - Instru\303\247\303\243o Insert no SQLite.ipynb" b/Cap06/Notebooks/DSA-Python-Cap06-02-Insert no SQLite.ipynb similarity index 82% rename from "JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte2 - Instru\303\247\303\243o Insert no SQLite.ipynb" rename to Cap06/Notebooks/DSA-Python-Cap06-02-Insert no SQLite.ipynb index 3b0d8c6b..ab3735b2 100644 --- "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte2 - Instru\303\247\303\243o Insert no SQLite.ipynb" +++ b/Cap06/Notebooks/DSA-Python-Cap06-02-Insert no SQLite.ipynb @@ -20,7 +20,20 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": true + }, + "outputs": [], + "source": [ + "# Reemove o arquivo com o banco de dados SQLite (caso exista)\n", + "import os\n", + "os.remove(\"dsa.db\") if os.path.exists(\"dsa.db\") else None" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true }, "outputs": [], "source": [ @@ -39,7 +52,7 @@ " \n", "# Função para inserir uma linha\n", "def data_insert():\n", - " c.execute(\"INSERT INTO produtos VALUES(10, '2016-05-02 14:32:11', 'Teclado', 90 )\")\n", + " c.execute(\"INSERT INTO produtos VALUES(10, '2018-05-02 14:32:11', 'Teclado', 90 )\")\n", " conn.commit()\n", " c.close()\n", " conn.close()" @@ -47,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -59,9 +72,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -100,9 +113,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte3 - Instru\303\247\303\243o Insert no SQLite usando Vari\303\241veis.ipynb" b/Cap06/Notebooks/DSA-Python-Cap06-03-Insert no SQLite usando Variaveis.ipynb similarity index 92% rename from "JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte3 - Instru\303\247\303\243o Insert no SQLite usando Vari\303\241veis.ipynb" rename to Cap06/Notebooks/DSA-Python-Cap06-03-Insert no SQLite usando Variaveis.ipynb index 5f3380ee..f919205c 100644 --- "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte3 - Instru\303\247\303\243o Insert no SQLite usando Vari\303\241veis.ipynb" +++ b/Cap06/Notebooks/DSA-Python-Cap06-03-Insert no SQLite usando Variaveis.ipynb @@ -42,7 +42,7 @@ " \n", "# Função para inserir uma linha\n", "def data_insert():\n", - " c.execute(\"INSERT INTO produtos VALUES('2016-05-02 12:34:45', 'Teclado', 130.00 )\")\n", + " c.execute(\"INSERT INTO produtos VALUES('2018-05-02 12:34:45', 'Teclado', 130.00 )\")\n", " conn.commit()\n", " c.close()\n", " conn.close()\n", @@ -52,8 +52,7 @@ " new_date = datetime.datetime.now()\n", " new_prod_name = 'Monitor'\n", " new_valor = random.randrange(50,100)\n", - " c.execute(\"INSERT INTO produtos (date, prod_name, valor) VALUES (?, ?, ?)\", \n", - " (new_date, new_prod_name, new_valor))\n", + " c.execute(\"INSERT INTO produtos (date, prod_name, valor) VALUES (?, ?, ?)\", (new_date, new_prod_name, new_valor))\n", " conn.commit()" ] }, @@ -61,7 +60,7 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -115,9 +114,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte4 - Instru\303\247\303\243o Select no SQLite.ipynb" b/Cap06/Notebooks/DSA-Python-Cap06-04-Select no SQLite.ipynb similarity index 72% rename from "JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte4 - Instru\303\247\303\243o Select no SQLite.ipynb" rename to Cap06/Notebooks/DSA-Python-Cap06-04-Select no SQLite.ipynb index 904be036..cfb366b7 100644 --- "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte4 - Instru\303\247\303\243o Select no SQLite.ipynb" +++ b/Cap06/Notebooks/DSA-Python-Cap06-04-Select no SQLite.ipynb @@ -78,25 +78,23 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(10, '2016-05-02 14:32:11', 'Teclado', 90.0)\n", - "(11, '2016-05-23 14:13:21.779164', 'Monitor', 70.0)\n", - "(12, '2016-05-23 14:13:22.785889', 'Monitor', 60.0)\n", - "(13, '2016-05-23 14:13:23.791211', 'Monitor', 50.0)\n", - "(14, '2016-05-23 14:13:24.796350', 'Monitor', 92.0)\n", - "(15, '2016-05-23 14:13:25.802804', 'Monitor', 91.0)\n", - "(16, '2016-05-23 14:13:26.805365', 'Monitor', 62.0)\n", - "(17, '2016-05-23 14:13:27.810955', 'Monitor', 83.0)\n", - "(18, '2016-05-23 14:13:28.813340', 'Monitor', 82.0)\n", - "(19, '2016-05-23 14:13:29.819722', 'Monitor', 98.0)\n", - "(20, '2016-05-23 14:13:30.825445', 'Monitor', 71.0)\n" + "(10, '2018-05-02 14:32:11', 'Teclado', 90.0)\n", + "(11, '2018-04-18 15:32:15.064730', 'Monitor', 52.0)\n", + "(12, '2018-04-18 15:32:16.074631', 'Monitor', 98.0)\n", + "(13, '2018-04-18 15:32:17.083495', 'Monitor', 94.0)\n", + "(14, '2018-04-18 15:32:18.092251', 'Monitor', 64.0)\n", + "(15, '2018-04-18 15:32:19.100759', 'Monitor', 74.0)\n", + "(16, '2018-04-18 15:32:20.112758', 'Monitor', 62.0)\n", + "(17, '2018-04-18 15:32:21.120451', 'Monitor', 86.0)\n", + "(18, '2018-04-18 15:32:22.130355', 'Monitor', 57.0)\n", + "(19, '2018-04-18 15:32:23.140021', 'Monitor', 53.0)\n", + "(20, '2018-04-18 15:32:24.150483', 'Monitor', 78.0)\n" ] } ], @@ -108,23 +106,20 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(10, '2016-05-02 14:32:11', 'Teclado', 90.0)\n", - "(11, '2016-05-23 14:13:21.779164', 'Monitor', 70.0)\n", - "(14, '2016-05-23 14:13:24.796350', 'Monitor', 92.0)\n", - "(15, '2016-05-23 14:13:25.802804', 'Monitor', 91.0)\n", - "(16, '2016-05-23 14:13:26.805365', 'Monitor', 62.0)\n", - "(17, '2016-05-23 14:13:27.810955', 'Monitor', 83.0)\n", - "(18, '2016-05-23 14:13:28.813340', 'Monitor', 82.0)\n", - "(19, '2016-05-23 14:13:29.819722', 'Monitor', 98.0)\n", - "(20, '2016-05-23 14:13:30.825445', 'Monitor', 71.0)\n" + "(10, '2018-05-02 14:32:11', 'Teclado', 90.0)\n", + "(12, '2018-04-18 15:32:16.074631', 'Monitor', 98.0)\n", + "(13, '2018-04-18 15:32:17.083495', 'Monitor', 94.0)\n", + "(14, '2018-04-18 15:32:18.092251', 'Monitor', 64.0)\n", + "(15, '2018-04-18 15:32:19.100759', 'Monitor', 74.0)\n", + "(16, '2018-04-18 15:32:20.112758', 'Monitor', 62.0)\n", + "(17, '2018-04-18 15:32:21.120451', 'Monitor', 86.0)\n", + "(20, '2018-04-18 15:32:24.150483', 'Monitor', 78.0)\n" ] } ], @@ -136,25 +131,23 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "90.0\n", - "70.0\n", - "60.0\n", - "50.0\n", - "92.0\n", - "91.0\n", - "62.0\n", - "83.0\n", - "82.0\n", + "52.0\n", "98.0\n", - "71.0\n" + "94.0\n", + "64.0\n", + "74.0\n", + "62.0\n", + "86.0\n", + "57.0\n", + "53.0\n", + "78.0\n" ] } ], @@ -207,9 +200,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte5 - Instru\303\247\303\265es Update e Delete no SQLite.ipynb" b/Cap06/Notebooks/DSA-Python-Cap06-05-Update e Delete no SQLite.ipynb similarity index 71% rename from "JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte5 - Instru\303\247\303\265es Update e Delete no SQLite.ipynb" rename to Cap06/Notebooks/DSA-Python-Cap06-05-Update e Delete no SQLite.ipynb index 63607ff5..8065ed49 100644 --- "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte5 - Instru\303\247\303\265es Update e Delete no SQLite.ipynb" +++ b/Cap06/Notebooks/DSA-Python-Cap06-05-Update e Delete no SQLite.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -76,20 +76,20 @@ " \n", "# Update\n", "def atualiza_dados():\n", - " c.execute(\"UPDATE produtos SET valor = 70.00 WHERE valor = 80.0\")\n", + " c.execute(\"UPDATE produtos SET valor = 70.00 WHERE valor = 98.0\")\n", " conn.commit()\n", " \n", "# Delete\n", "def remove_dados():\n", - " c.execute(\"DELETE FROM produtos WHERE valor = 60.0\")\n", + " c.execute(\"DELETE FROM produtos WHERE valor = 62.0\")\n", " conn.commit()" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -98,26 +98,24 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(10, '2016-05-02 14:32:11', 'Teclado', 90.0)\n", - "(11, '2016-05-23 14:13:21.779164', 'Monitor', 70.0)\n", - "(12, '2016-05-23 14:13:22.785889', 'Monitor', 60.0)\n", - "(13, '2016-05-23 14:13:23.791211', 'Monitor', 50.0)\n", - "(14, '2016-05-23 14:13:24.796350', 'Monitor', 92.0)\n", - "(15, '2016-05-23 14:13:25.802804', 'Monitor', 91.0)\n", - "(16, '2016-05-23 14:13:26.805365', 'Monitor', 62.0)\n", - "(17, '2016-05-23 14:13:27.810955', 'Monitor', 83.0)\n", - "(18, '2016-05-23 14:13:28.813340', 'Monitor', 82.0)\n", - "(19, '2016-05-23 14:13:29.819722', 'Monitor', 98.0)\n", - "(20, '2016-05-23 14:13:30.825445', 'Monitor', 71.0)\n" + "(10, '2018-05-02 14:32:11', 'Teclado', 90.0)\n", + "(11, '2018-04-18 15:32:15.064730', 'Monitor', 52.0)\n", + "(12, '2018-04-18 15:32:16.074631', 'Monitor', 70.0)\n", + "(13, '2018-04-18 15:32:17.083495', 'Monitor', 94.0)\n", + "(14, '2018-04-18 15:32:18.092251', 'Monitor', 64.0)\n", + "(15, '2018-04-18 15:32:19.100759', 'Monitor', 74.0)\n", + "(16, '2018-04-18 15:32:20.112758', 'Monitor', 62.0)\n", + "(17, '2018-04-18 15:32:21.120451', 'Monitor', 86.0)\n", + "(18, '2018-04-18 15:32:22.130355', 'Monitor', 57.0)\n", + "(19, '2018-04-18 15:32:23.140021', 'Monitor', 53.0)\n", + "(20, '2018-04-18 15:32:24.150483', 'Monitor', 78.0)\n" ] } ], @@ -127,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -138,25 +136,23 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(10, '2016-05-02 14:32:11', 'Teclado', 90.0)\n", - "(11, '2016-05-23 14:13:21.779164', 'Monitor', 70.0)\n", - "(13, '2016-05-23 14:13:23.791211', 'Monitor', 50.0)\n", - "(14, '2016-05-23 14:13:24.796350', 'Monitor', 92.0)\n", - "(15, '2016-05-23 14:13:25.802804', 'Monitor', 91.0)\n", - "(16, '2016-05-23 14:13:26.805365', 'Monitor', 62.0)\n", - "(17, '2016-05-23 14:13:27.810955', 'Monitor', 83.0)\n", - "(18, '2016-05-23 14:13:28.813340', 'Monitor', 82.0)\n", - "(19, '2016-05-23 14:13:29.819722', 'Monitor', 98.0)\n", - "(20, '2016-05-23 14:13:30.825445', 'Monitor', 71.0)\n" + "(10, '2018-05-02 14:32:11', 'Teclado', 90.0)\n", + "(11, '2018-04-18 15:32:15.064730', 'Monitor', 52.0)\n", + "(12, '2018-04-18 15:32:16.074631', 'Monitor', 70.0)\n", + "(13, '2018-04-18 15:32:17.083495', 'Monitor', 94.0)\n", + "(14, '2018-04-18 15:32:18.092251', 'Monitor', 64.0)\n", + "(15, '2018-04-18 15:32:19.100759', 'Monitor', 74.0)\n", + "(17, '2018-04-18 15:32:21.120451', 'Monitor', 86.0)\n", + "(18, '2018-04-18 15:32:22.130355', 'Monitor', 57.0)\n", + "(19, '2018-04-18 15:32:23.140021', 'Monitor', 53.0)\n", + "(20, '2018-04-18 15:32:24.150483', 'Monitor', 78.0)\n" ] } ], @@ -195,9 +191,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Cap06/Notebooks/DSA-Python-Cap06-06-Criando Graficos com Matplotlib e SQLite.ipynb b/Cap06/Notebooks/DSA-Python-Cap06-06-Criando Graficos com Matplotlib e SQLite.ipynb new file mode 100644 index 00000000..5ed23562 --- /dev/null +++ b/Cap06/Notebooks/DSA-Python-Cap06-06-Criando Graficos com Matplotlib e SQLite.ipynb @@ -0,0 +1,941 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Science Academy - Python Fundamentos - Capítulo 6\n", + "\n", + "## Download: http://github.com/dsacademybr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gráficos" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sqlite3\n", + "import random\n", + "import datetime\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib notebook\n", + " \n", + "# Criando uma conexão\n", + "conn = sqlite3.connect('dsa.db') \n", + "\n", + "# Criando um cursor\n", + "c = conn.cursor()\n", + " \n", + "# Função para criar uma tabela\n", + "def create_table():\n", + " c.execute('CREATE TABLE IF NOT EXISTS produtos(id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, date TEXT, '\\\n", + " 'prod_name TEXT, valor REAL)')\n", + " \n", + "# Função para inserir uma linha\n", + "def data_insert():\n", + " c.execute(\"INSERT INTO produtos VALUES(now, 'Teclado', 130 )\")\n", + " conn.commit()\n", + " c.close()\n", + " conn.close()\n", + " \n", + "# Usando variáveis para inserir dados \n", + "def data_insert_var():\n", + " new_date = datetime.datetime.now()\n", + " new_prod_name = 'monitor'\n", + " new_valor = random.randrange(50,100)\n", + " c.execute(\"INSERT INTO produtos (date, prod_name, valor) VALUES (?, ?, ?, ?)\", \n", + " (new_date, new_prod_name, new_valor))\n", + " conn.commit()\n", + " \n", + "# Leitura de dados\n", + "def leitura_todos_dados():\n", + " c.execute(\"SELECT * FROM PRODUTOS\")\n", + " for linha in c.fetchall():\n", + " print(linha)\n", + " \n", + "# Leitura de registros específicos\n", + "def leitura_registros():\n", + " c.execute(\"SELECT * FROM PRODUTOS WHERE valor > 60.0\")\n", + " for linha in c.fetchall():\n", + " print(linha) \n", + " \n", + "# Leitura de colunas específicos\n", + "def leitura_colunas():\n", + " c.execute(\"SELECT * FROM PRODUTOS\")\n", + " for linha in c.fetchall():\n", + " print(linha[3]) \n", + " \n", + "# Update\n", + "def atualiza_dados():\n", + " c.execute(\"UPDATE produtos SET valor = 70.00 WHERE valor > 80.0\")\n", + " conn.commit()\n", + " \n", + "# Delete\n", + "def remove_dados():\n", + " c.execute(\"DELETE FROM produtos WHERE valor = 62.0\")\n", + " conn.commit()\n", + "\n", + "# Gerar gráfico com os dados no banco de dados\n", + "def dados_grafico():\n", + " c.execute(\"SELECT id, valor FROM produtos\")\n", + " ids = []\n", + " valores = []\n", + " dados = c.fetchall()\n", + " for linha in dados:\n", + " ids.append(linha[0])\n", + " valores.append(linha[1])\n", + " \n", + " plt.bar(ids, valores)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Gerando gráficos\n", - "dados_grafico()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obrigado - Data Science Academy - facebook.com/dsacademybr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte9 - Stream de Dados do Twitter com MongoDB, Pandas e Scikit Learn.ipynb" "b/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte9 - Stream de Dados do Twitter com MongoDB, Pandas e Scikit Learn.ipynb" deleted file mode 100644 index 2f573c1d..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo06/Jupyter Notebook Parte9 - Stream de Dados do Twitter com MongoDB, Pandas e Scikit Learn.ipynb" +++ /dev/null @@ -1,394 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 6\n", - "\n", - "## Download: http://github.com/dsacademybr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stream de Dados do Twitter com MongoDB, Pandas e Scikit Learn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Para instalar o tweepy, abra um prompt de comando e digite: pip install tweepy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando os módulos Tweepy, Datetime e Json\n", - "from tweepy.streaming import StreamListener\n", - "from tweepy import OAuthHandler\n", - "from tweepy import Stream\n", - "from datetime import datetime\n", - "import json" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Veja no E-book como criar sua API no Twitter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Adicione aqui sua Consumer Key\n", - "consumer_key = \"xxxxxxxxxxxxx\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Adicione aqui sua Consumer Secret \n", - "consumer_secret = \"xxxxxxxxxxxxxxx\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Adicione aqui seu Access Token\n", - "access_token = \"xxxxxxxxxxxx\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Adicione aqui seu Access Token Secret\n", - "access_token_secret = \"xxxxxxxxxxxxxx\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Criando as chaves de autenticação\n", - "auth = OAuthHandler(consumer_key, consumer_secret)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "auth.set_access_token(access_token, access_token_secret)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Criando uma classe para capturar os stream de dados do Twitter e \n", - "# armazenar no MongoDB\n", - "class MyListener(StreamListener):\n", - " def on_data(self, dados):\n", - " tweet = json.loads(dados)\n", - " created_at = tweet[\"created_at\"]\n", - " id_str = tweet[\"id_str\"]\n", - " text = tweet[\"text\"]\n", - " obj = {\"created_at\":created_at,\"id_str\":id_str,\"text\":text,}\n", - " tweetind = col.insert_one(obj).inserted_id\n", - " print (obj)\n", - " return True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Criando o objeto mylistener\n", - "mylistener = MyListener()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Criando o objeto mystream\n", - "mystream = Stream(auth, listener = mylistener)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando do PyMongo o módulo MongoClient\n", - "from pymongo import MongoClient " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Criando a conexão ao MongoDB\n", - "client = MongoClient('localhost', 27017)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Criando o banco de dados twitterdb\n", - "db = client.twitterdb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Criando a collection \"col\"\n", - "col = db.tweets " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Criando uma lista de palavras chave para buscar nos Tweets\n", - "keywords = ['Big Data', 'Python', 'Data Mining', 'Data Science']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Iniciando o filtro e gravando os tweets no MongoDB\n", - "mystream.filter(track=keywords)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## --> Pressione o botão Stop na barra de ferramentas para encerrar a captura dos Tweets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "mystream.disconnect()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Verificando um documento no collection\n", - "col.find_one()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# criando um dataset com dados retornados do MongoDB\n", - "dataset = [{\"created_at\": item[\"created_at\"],\n", - " \"text\": item[\"text\"],\n", - " } for item in col.find()]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Importando o módulo Pandas para trabalhar com datasets em Python\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Criando um dataframe a partir do dataset \n", - "df = pd.DataFrame(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Imprimindo o dataframe\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando o módulo Scikit Learn\n", - "from sklearn.feature_extraction.text import CountVectorizer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Usando o método CountVectorizer para criar uma matriz de documentos\n", - "cv = CountVectorizer()\n", - "count_matrix = cv.fit_transform(df.text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Contando o número de ocorrências das principais palavras em nosso dataset\n", - "word_count = pd.DataFrame(cv.get_feature_names(), columns=[\"word\"])\n", - "word_count[\"count\"] = count_matrix.sum(axis=0).tolist()[0]\n", - "word_count = word_count.sort_values(\"count\", ascending=False).reset_index(drop=True)\n", - "word_count[:50]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obrigado - Data Science Academy - facebook.com/dsacademybr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git "a/JupyterNotebooks/Cap\303\255tulo06/dsa.db" "b/JupyterNotebooks/Cap\303\255tulo06/dsa.db" deleted file mode 100644 index 80f9332b..00000000 Binary files "a/JupyterNotebooks/Cap\303\255tulo06/dsa.db" and /dev/null differ diff --git "a/JupyterNotebooks/Cap\303\255tulo06/escola.db" "b/JupyterNotebooks/Cap\303\255tulo06/escola.db" deleted file mode 100644 index 59e28251..00000000 Binary files "a/JupyterNotebooks/Cap\303\255tulo06/escola.db" and /dev/null differ diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-M\303\263dulos e Pacotes.ipynb" "b/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-M\303\263dulos e Pacotes.ipynb" deleted file mode 100644 index 3b8d1a99..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-M\303\263dulos e Pacotes.ipynb" +++ /dev/null @@ -1,1421 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", - "\n", - "## Download: http://github.com/dsacademybr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Módulos e Pacotes\n", - "\n", - "Biblioteca Padrão" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando um módulo em Python\n", - "import math" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['__doc__',\n", - " '__file__',\n", - " '__loader__',\n", - " '__name__',\n", - " '__package__',\n", - " '__spec__',\n", - " 'acos',\n", - " 'acosh',\n", - " 'asin',\n", - " 'asinh',\n", - " 'atan',\n", - " 'atan2',\n", - " 'atanh',\n", - " 'ceil',\n", - " 'copysign',\n", - " 'cos',\n", - " 'cosh',\n", - " 'degrees',\n", - " 'e',\n", - " 'erf',\n", - " 'erfc',\n", - " 'exp',\n", - " 'expm1',\n", - " 'fabs',\n", - " 'factorial',\n", - " 'floor',\n", - " 'fmod',\n", - " 'frexp',\n", - " 'fsum',\n", - " 'gamma',\n", - " 'gcd',\n", - " 'hypot',\n", - " 'inf',\n", - " 'isclose',\n", - " 'isfinite',\n", - " 'isinf',\n", - " 'isnan',\n", - " 'ldexp',\n", - " 'lgamma',\n", - " 'log',\n", - " 'log10',\n", - " 'log1p',\n", - " 'log2',\n", - " 'modf',\n", - " 'nan',\n", - " 'pi',\n", - " 'pow',\n", - " 'radians',\n", - " 'sin',\n", - " 'sinh',\n", - " 'sqrt',\n", - " 'tan',\n", - " 'tanh',\n", - " 'trunc']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Verificando todos os métodos disponíveis no módulo\n", - "dir(math)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Usando um dos métodos do módulo math\n", - "math.sqrt(25)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando apenas um dos métodos do módulo math\n", - "from math import sqrt" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3.0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Usando o método\n", - "sqrt(9)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n" - ] - } - ], - "source": [ - "# Imprimindo todos os métodos do módulo math\n", - "print(dir(math))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on built-in function sqrt in module math:\n", - "\n", - "sqrt(...)\n", - " sqrt(x)\n", - " \n", - " Return the square root of x.\n", - "\n" - ] - } - ], - "source": [ - "# Help do método sqrt do módulo math\n", - "help(sqrt)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Laranja'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random.choice(['Maça', 'Banana', 'Laranja'])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[66, 16, 91, 95, 85, 87, 38, 77, 62, 14]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random.sample(range(100), 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import statistics" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "dados = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1.6071428571428572" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "statistics.mean(dados)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1.25" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "statistics.median(dados)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'/opt/DSA/PythonFundamentos/JupyterNotebooks/Capítulo07'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['CLD_CONTINUED', 'CLD_DUMPED', 'CLD_EXITED', 'CLD_TRAPPED', 'EX_CANTCREAT', 'EX_CONFIG', 'EX_DATAERR', 'EX_IOERR', 'EX_NOHOST', 'EX_NOINPUT', 'EX_NOPERM', 'EX_NOUSER', 'EX_OK', 'EX_OSERR', 'EX_OSFILE', 'EX_PROTOCOL', 'EX_SOFTWARE', 'EX_TEMPFAIL', 'EX_UNAVAILABLE', 'EX_USAGE', 'F_LOCK', 'F_OK', 'F_TEST', 'F_TLOCK', 'F_ULOCK', 'MutableMapping', 'NGROUPS_MAX', 'O_ACCMODE', 'O_APPEND', 'O_ASYNC', 'O_CREAT', 'O_DIRECTORY', 'O_EXCL', 'O_EXLOCK', 'O_NDELAY', 'O_NOCTTY', 'O_NOFOLLOW', 'O_NONBLOCK', 'O_RDONLY', 'O_RDWR', 'O_SHLOCK', 'O_SYNC', 'O_TRUNC', 'O_WRONLY', 'PRIO_PGRP', 'PRIO_PROCESS', 'PRIO_USER', 'P_ALL', 'P_NOWAIT', 'P_NOWAITO', 'P_PGID', 'P_PID', 'P_WAIT', 'RTLD_GLOBAL', 'RTLD_LAZY', 'RTLD_LOCAL', 'RTLD_NODELETE', 'RTLD_NOLOAD', 'RTLD_NOW', 'R_OK', 'SCHED_FIFO', 'SCHED_OTHER', 'SCHED_RR', 'SEEK_CUR', 'SEEK_END', 'SEEK_SET', 'ST_NOSUID', 'ST_RDONLY', 'TMP_MAX', 'WCONTINUED', 'WCOREDUMP', 'WEXITED', 'WEXITSTATUS', 'WIFCONTINUED', 'WIFEXITED', 'WIFSIGNALED', 'WIFSTOPPED', 'WNOHANG', 'WNOWAIT', 'WSTOPPED', 'WSTOPSIG', 'WTERMSIG', 'WUNTRACED', 'W_OK', 'X_OK', '_Environ', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_execvpe', '_exists', '_exit', '_get_exports_list', '_putenv', '_spawnvef', '_unsetenv', '_wrap_close', 'abort', 'access', 'altsep', 'chdir', 'chflags', 'chmod', 'chown', 'chroot', 'close', 'closerange', 'confstr', 'confstr_names', 'cpu_count', 'ctermid', 'curdir', 'defpath', 'device_encoding', 'devnull', 'dup', 'dup2', 'environ', 'environb', 'errno', 'error', 'execl', 'execle', 'execlp', 'execlpe', 'execv', 'execve', 'execvp', 'execvpe', 'extsep', 'fchdir', 'fchmod', 'fchown', 'fdopen', 'fork', 'forkpty', 'fpathconf', 'fsdecode', 'fsencode', 'fstat', 'fstatvfs', 'fsync', 'ftruncate', 'get_blocking', 'get_exec_path', 'get_inheritable', 'get_terminal_size', 'getcwd', 'getcwdb', 'getegid', 'getenv', 'getenvb', 'geteuid', 'getgid', 'getgrouplist', 'getgroups', 'getloadavg', 'getlogin', 'getpgid', 'getpgrp', 'getpid', 'getppid', 'getpriority', 'getsid', 'getuid', 'initgroups', 'isatty', 'kill', 'killpg', 'lchflags', 'lchmod', 'lchown', 'linesep', 'link', 'listdir', 'lockf', 'lseek', 'lstat', 'major', 'makedev', 'makedirs', 'minor', 'mkdir', 'mkfifo', 'mknod', 'name', 'nice', 'open', 'openpty', 'pardir', 'path', 'pathconf', 'pathconf_names', 'pathsep', 'pipe', 'popen', 'pread', 'putenv', 'pwrite', 'read', 'readlink', 'readv', 'remove', 'removedirs', 'rename', 'renames', 'replace', 'rmdir', 'scandir', 'sched_get_priority_max', 'sched_get_priority_min', 'sched_yield', 'sendfile', 'sep', 'set_blocking', 'set_inheritable', 'setegid', 'seteuid', 'setgid', 'setgroups', 'setpgid', 'setpgrp', 'setpriority', 'setregid', 'setreuid', 'setsid', 'setuid', 'spawnl', 'spawnle', 'spawnlp', 'spawnlpe', 'spawnv', 'spawnve', 'spawnvp', 'spawnvpe', 'st', 'stat', 'stat_float_times', 'stat_result', 'statvfs', 'statvfs_result', 'strerror', 'supports_bytes_environ', 'supports_dir_fd', 'supports_effective_ids', 'supports_fd', 'supports_follow_symlinks', 'symlink', 'sync', 'sys', 'sysconf', 'sysconf_names', 'system', 'tcgetpgrp', 'tcsetpgrp', 'terminal_size', 'times', 'times_result', 'truncate', 'ttyname', 'umask', 'uname', 'uname_result', 'unlink', 'unsetenv', 'urandom', 'utime', 'wait', 'wait3', 'wait4', 'waitpid', 'walk', 'write', 'writev']\n" - ] - } - ], - "source": [ - "print(dir(os))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import sys" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Teste" - ] - } - ], - "source": [ - "sys.stdout.write('Teste')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'3.5.1 |Anaconda 4.0.0 (x86_64)| (default, Dec 7 2015, 11:24:55) \\n[GCC 4.2.1 (Apple Inc. build 5577)]'" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sys.version" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['__displayhook__', '__doc__', '__excepthook__', '__interactivehook__', '__loader__', '__name__', '__package__', '__spec__', '__stderr__', '__stdin__', '__stdout__', '_clear_type_cache', '_current_frames', '_debugmallocstats', '_getframe', '_home', '_mercurial', '_xoptions', 'abiflags', 'api_version', 'argv', 'base_exec_prefix', 'base_prefix', 'builtin_module_names', 'byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info', 'float_repr_style', 'get_coroutine_wrapper', 'getallocatedblocks', 'getcheckinterval', 'getdefaultencoding', 'getdlopenflags', 'getfilesystemencoding', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info', 'intern', 'is_finalizing', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1', 'ps2', 'ps3', 'set_coroutine_wrapper', 'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout', 'thread_info', 'version', 'version_info', 'warnoptions']\n" - ] - } - ], - "source": [ - "print(dir(sys))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando o módulo request do pacote urllib, usado para trazer url's \n", - "# para dentro do nosso ambiente Python\n", - "import urllib.request" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Variável resposta armazena o objeto de conexão à url passada como \n", - "# parâmetro\n", - "resposta = urllib.request.urlopen('http://python.org')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# Objeto resposta\n", - "print(resposta)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Chamando o método read() do objeto resposta e armazenando o código \n", - "# html na variável html\n", - "html = resposta.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "b'\\n\\n\\n\\n \\n\\n\\n \\n \\n\\n \\n\\n \\n \\n \\n \\n \\n\\n \\n \\n \\n \\n \\n\\n \\n\\n \\n \\n \\n\\n \\n\\n \\n \\n \\n \\n \\n \\n \\n\\n \\n \\n \\n \\n\\n Welcome to Python.org\\n\\n \\n \\n\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n\\n \\n\\n \\n\\n \\n \\n\\n \\n \\n \\n\\n\\n\\n\\n
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    # Python 3: Fibonacci series up to n\\r\\n>>> def fib(n):\\r\\n>>>     a, b = 0, 1\\r\\n>>>     while a < n:\\r\\n>>>         print(a, end=\\' \\')\\r\\n>>>         a, b = b, a+b\\r\\n>>>     print()\\r\\n>>> fib(1000)\\r\\n0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
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    Functions Defined

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    The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3

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    # Python 3: List comprehensions\\r\\n>>> fruits = [\\'Banana\\', \\'Apple\\', \\'Lime\\']\\r\\n>>> loud_fruits = [fruit.upper() for fruit in fruits]\\r\\n>>> print(loud_fruits)\\r\\n[\\'BANANA\\', \\'APPLE\\', \\'LIME\\']\\r\\n\\r\\n# List and the enumerate function\\r\\n>>> list(enumerate(fruits))\\r\\n[(0, \\'Banana\\'), (1, \\'Apple\\'), (2, \\'Lime\\')]
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    Compound Data Types

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    Lists (known as arrays in other languages) are one of the compound data types that Python understands. Lists can be indexed, sliced and manipulated with other built-in functions. More about lists in Python 3

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    # Python 3: Simple arithmetic\\r\\n>>> 1 / 2\\r\\n0.5\\r\\n>>> 2 ** 3\\r\\n8\\r\\n>>> 17 / 3  # classic division returns a float\\r\\n5.666666666666667\\r\\n>>> 17 // 3  # floor division\\r\\n5
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    Intuitive Interpretation

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    Calculations are simple with Python, and expression syntax is straightforward: the operators +, -, * and / work as expected; parentheses () can be used for grouping. More about simple math functions in Python 3.

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    # Python 3: Simple output (with Unicode)\\r\\n>>> print(\"Hello, I\\'m Python!\")\\r\\nHello, I\\'m Python!\\r\\n\\r\\n# Input, assignment\\r\\n>>> name = input(\\'What is your name?\\\\n\\')\\r\\n>>> print(\\'Hi, %s.\\' % name)\\r\\nWhat is your name?\\r\\nPython\\r\\nHi, Python.
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    Quick & Easy to Learn

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    Experienced programmers in any other language can pick up Python very quickly, and beginners find the clean syntax and indentation structure easy to learn. Whet your appetite with our Python 3 overview.

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    # For loop on a list\\r\\n>>> numbers = [2, 4, 6, 8]\\r\\n>>> product = 1\\r\\n>>> for number in numbers:\\r\\n...    product = product * number\\r\\n... \\r\\n>>> print(\\'The product is:\\', product)\\r\\nThe product is: 384
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\\n\\n \\n \\n \\n\\n \\n\\n \\n \\n\\n \\n\\n \\n\\n \\n\\n \\n \\n\\n\\n\\n'\n" - ] - } - ], - "source": [ - "# Imprimindo html\n", - "print(html)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Importando o arquivo com a cotação das ações da Petrobras, lendo o \n", - "# arquivo com o Pandas e gerando um gráfico com Matplotlib\n", - "from urllib.request import urlopen\n", - "import matplotlib.pyplot as plt\n", - "import pandas\n", - "%matplotlib inline\n", - " \n", - "endereco = 'http://real-chart.finance.yahoo.com/table.csv?s=PETR4.SA&d=9&e=17&f=2015&g=d&a=0&b=3&c=2000&ignore=.csv'\n", - "arquivo = urlopen(endereco)\n", - " \n", - "petrobras = pandas.read_csv(arquivo, index_col=0, parse_dates=True)\n", - "petrobras.plot(y='Adj Close')\n", - " \n", - "plt.xlabel('Ano')\n", - "plt.ylabel('Cotação')\n", - "plt.legend().set_visible(False)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from pandas import DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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OpenHighLowCloseVolumeAdj Close
Date
2015-10-167.998.087.817.95429638007.95
2015-10-158.008.077.678.00614441008.00
2015-10-148.078.237.897.96753391007.96
2015-10-138.368.478.078.13561006008.13
2015-10-128.808.808.808.8008.80
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2015-09-246.610007.140006.600006.96000565773006.96000
2015-09-236.960007.160006.700006.82000647748006.82000
2015-09-227.240007.240006.810006.97000667207006.97000
2015-09-217.660007.680007.300007.30000410636007.30000
2015-09-187.750007.760007.500007.60000680970007.60000
2015-09-178.050008.110007.860007.86000728876007.86000
2015-09-167.770008.240007.740008.14000666134008.14000
2015-09-157.650007.870007.540007.65000351029007.65000
2015-09-147.700007.820007.460007.72000528514007.72000
2015-09-117.940007.990007.590007.66000536473007.66000
2015-09-107.900008.150007.810007.97000628587007.97000
2015-09-098.810008.900008.360008.39000387063008.39000
2015-09-088.700008.790008.580008.64000293830008.64000
2015-09-078.510008.510008.510008.5100008.51000
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"2000-01-04 0.44400 0.44400 0.44400 0.44400 28861440000 4.11892\n", - "2000-01-03 0.47000 0.47000 0.47000 0.47000 35389440000 4.36012\n", - "\n", - "[3874 rows x 6 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fim" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Obrigado - Data Science Academy - facebook.com/dsacademybr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Python-Debugger.ipynb" "b/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Python-Debugger.ipynb" deleted file mode 100644 index e3754872..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Python-Debugger.ipynb" +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", - "\n", - "## Download: http://github.com/dsacademybr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Python Debugger" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a = [4,5,6]\n", - "b = 2\n", - "c = 3" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "resultado = b + c\n", - "print (resultado)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "can only concatenate list (not \"int\") to list", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresultado2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mresultado2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list" - ] - } - ], - "source": [ - "resultado2 = a + b\n", - "print (resultado2)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pdb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n", - "--Return--\n", - "> (8)()->None\n", - "-> pdb.set_trace()\n", - "(Pdb) a + b\n", - "(Pdb) [4,5,6] + 2\n", - "*** TypeError: can only concatenate list (not \"int\") to list\n" - ] - } - ], - "source": [ - "a = [4,5,6]\n", - "b = 2\n", - "c = 3\n", - "\n", - "resultado = b + c\n", - "print (resultado)\n", - "\n", - "pdb.set_trace()\n", - "\n", - "resultado2 = a + b\n", - "print (resultado2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# FIM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obrigado - Data Science Academy - facebook.com/dsacademybr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Python-Express\303\265es-Regulares.ipynb" "b/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Python-Express\303\265es-Regulares.ipynb" deleted file mode 100644 index da2bef6d..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo07/DSA-Python-Cap\303\255tulo7-Python-Express\303\265es-Regulares.ipynb" +++ /dev/null @@ -1,538 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 7\n", - "\n", - "## Download: http://github.com/dsacademybr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expressões Regulares\n", - "\n", - "Uma expressão regular é um método formal de se especificar um padrão de texto.\n", - "\n", - "Mais detalhadamente, é uma composição de símbolos, caracteres com funções especiais, que, agrupados entre si e com caracteres literais, formam uma sequência, uma expressão. Essa expressão é interpretada como uma regra, que indicará sucesso se uma entrada de dados qualquer casar com essa regra, ou seja, obedecer exatamente a todas as suas condições." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando o módulo re (regular expression)\n", - "# Esse módulo fornece operações com expressões regulares (ER)\n", - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Lista de termos para busca\n", - "lista_pesquisa = ['informações', 'Negócios']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Text to parse\n", - "texto = 'Existem muitos desafios para o Big Data. O primeiro deles é a coleta dos dados, pois fala-se aqui de '\\\n", - "'enormes quantidades sendo geradas em uma taxa maior do que um servidor comum seria capaz de processar e armazenar. '\\\n", - "'O segundo desafio é justamente o de processar essas informações. Com elas então distribuídas, a aplicação deve ser '\\\n", - "'capaz de consumir partes das informações e gerar pequenas quantidades de dados processados, que serão calculados em '\\\n", - "'conjunto depois para criar o resultado final. Outro desafio é a exibição dos resultados, de forma que as informações '\\\n", - "'estejam disponíveis de forma clara para os tomadores de decisão.'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Buscando por \"informações\" em: \n", - "\n", - "\"Existem muitos desafios para o Big Data. O primeiro deles é a coleta dos dados, pois fala-se aqui de enormes quantidades sendo geradas em uma taxa maior do que um servidor comum seria capaz de processar e armazenar. O segundo desafio é justamente o de processar essas informações. Com elas então distribuídas, a aplicação deve ser capaz de consumir partes das informações e gerar pequenas quantidades de dados processados, que serão calculados em conjunto depois para criar o resultado final. Outro desafio é a exibição dos resultados, de forma que as informações estejam disponíveis de forma clara para os tomadores de decisão.\"\n", - "\n", - "\n", - "Palavra encontrada. \n", - "\n", - "\n", - "\n", - "Buscando por \"Negócios\" em: \n", - "\n", - "\"Existem muitos desafios para o Big Data. O primeiro deles é a coleta dos dados, pois fala-se aqui de enormes quantidades sendo geradas em uma taxa maior do que um servidor comum seria capaz de processar e armazenar. O segundo desafio é justamente o de processar essas informações. Com elas então distribuídas, a aplicação deve ser capaz de consumir partes das informações e gerar pequenas quantidades de dados processados, que serão calculados em conjunto depois para criar o resultado final. Outro desafio é a exibição dos resultados, de forma que as informações estejam disponíveis de forma clara para os tomadores de decisão.\"\n", - "\n", - "\n", - "Palavra não encontrada.\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# Exemplo básico de Data Mining\n", - "for item in lista_pesquisa:\n", - " print ('Buscando por \"%s\" em: \\n\\n\"%s\"' % (item, texto))\n", - " \n", - " # Verificando se o termo de pesquisa existe no texto\n", - " if re.search(item, texto):\n", - " print ('\\n')\n", - " print ('Palavra encontrada. \\n')\n", - " print ('\\n')\n", - " else:\n", - " print ('\\n')\n", - " print ('Palavra não encontrada.')\n", - " print ('\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Termo usado para dividir uma string\n", - "split_term = '@'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "frase = 'Qual o domínio de alguém com o e-mail: aluno@gmail.com'" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Qual o domínio de alguém com o e-mail: aluno', 'gmail.com']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Dividindo a frase\n", - "re.split(split_term, frase)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def encontra_padrao(lista, frase):\n", - " \n", - " for item in lista:\n", - " print ('Pesquisando na frase: %r' %item)\n", - " print (re.findall(item, frase))\n", - " print ('\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "frase_padrao = 'zLzL..zzzLLL...zLLLzLLL...LzLz...dzzzzz...zLLLL'\n", - "\n", - "lista_padroes = [ 'zL*', # z seguido de zero ou mais L\n", - " 'zL+', # z seguido por um ou mais L\n", - " 'zL?', # z seguido por zero ou um L\n", - " 'zL{3}', # z seguido por três L\n", - " 'zL{2,3}', # z seguido por dois a três L\n", - " ]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pesquisando na frase: 'zL*'\n", - "['zL', 'zL', 'z', 'z', 'zLLL', 'zLLL', 'zLLL', 'zL', 'z', 'z', 'z', 'z', 'z', 'z', 'zLLLL']\n", - "\n", - "\n", - "Pesquisando na frase: 'zL+'\n", - "['zL', 'zL', 'zLLL', 'zLLL', 'zLLL', 'zL', 'zLLLL']\n", - "\n", - "\n", - "Pesquisando na frase: 'zL?'\n", - "['zL', 'zL', 'z', 'z', 'zL', 'zL', 'zL', 'zL', 'z', 'z', 'z', 'z', 'z', 'z', 'zL']\n", - "\n", - "\n", - "Pesquisando na frase: 'zL{3}'\n", - "['zLLL', 'zLLL', 'zLLL', 'zLLL']\n", - "\n", - "\n", - "Pesquisando na frase: 'zL{2,3}'\n", - "['zLLL', 'zLLL', 'zLLL', 'zLLL']\n", - "\n", - "\n" - ] - } - ], - "source": [ - "encontra_padrao(lista_padroes, frase_padrao)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "frase = 'Esta é uma string com pontuação. Isso pode ser um problema quando fazemos mineração de dados em busca '\\\n", - " 'de padrões! Não seria melhor retirar os sinais ao fim de cada frase?'" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Esta',\n", - " 'é',\n", - " 'uma',\n", - " 'string',\n", - " 'com',\n", - " 'pontuação',\n", - " 'Isso',\n", - " 'pode',\n", - " 'ser',\n", - " 'um',\n", - " 'problema',\n", - " 'quando',\n", - " 'fazemos',\n", - " 'mineração',\n", - " 'de',\n", - " 'dados',\n", - " 'em',\n", - " 'busca',\n", - " 'de',\n", - " 'padrões',\n", - " 'Não',\n", - " 'seria',\n", - " 'melhor',\n", - " 'retirar',\n", - " 'os',\n", - " 'sinais',\n", - " 'ao',\n", - " 'fim',\n", - " 'de',\n", - " 'cada',\n", - " 'frase']" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A expressão [^!.? ] verifica por valores que não sejam pontuação \n", - "# (!, ., ?) e o sinal de adição (+) verifica se o item aparece pelo menos \n", - "# uma vez. Traduzindo: esta expressão diz: traga apenas as palavras na \n", - "# frase.\n", - "re.findall('[^!.? ]+', frase)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "frase = 'Esta é uma frase de exemplo. Vamos verificar quais padrões serão encontrados.'\n", - "\n", - "lista_padroes = [ '[a-z]+', # sequência de letras minúsculas\n", - " '[A-Z]+', # sequência de letras maiúsculas\n", - " '[a-zA-Z]+', # sequência de letras minúsculas e maiúsculas\n", - " '[A-Z][a-z]+'] # uma letra maiúscula, seguida de uma letra minúscula\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pesquisando na frase: '[a-z]+'\n", - "['sta', 'uma', 'frase', 'de', 'exemplo', 'amos', 'verificar', 'quais', 'padr', 'es', 'ser', 'o', 'encontrados']\n", - "\n", - "\n", - "Pesquisando na frase: '[A-Z]+'\n", - "['E', 'V']\n", - "\n", - "\n", - "Pesquisando na frase: '[a-zA-Z]+'\n", - "['Esta', 'uma', 'frase', 'de', 'exemplo', 'Vamos', 'verificar', 'quais', 'padr', 'es', 'ser', 'o', 'encontrados']\n", - "\n", - "\n", - "Pesquisando na frase: '[A-Z][a-z]+'\n", - "['Esta', 'Vamos']\n", - "\n", - "\n" - ] - } - ], - "source": [ - "encontra_padrao(lista_padroes, frase)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Escape Codes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "É possível usar códigos específicos para enocntrar padrões nos dados, tais como dígitos, não dígitos, espaços, etc..\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
CódigoSignificado
\\dum dígito
\\Dum não-dígito
\\sespaço (tab, espaço, nova linha, etc.)
\\Snão-espaço
\\walfanumérico
\\Wnão-alfanumérico
\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\\\b'" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# O prefixo r antes da expressão regular evita o pré-processamento da ER \n", - "# pela linguagem. Colocamos o modificador r (do inglês \"raw\", crú) \n", - "# imediatamente antes das aspas\n", - "r'\\b'" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\x08'" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'\\b'" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "frase = 'Esta é uma string com alguns números, como 1287 e um símbolo #hashtag'\n", - "\n", - "lista_padroes = [ r'\\d+', # sequência de dígitos\n", - " r'\\D+', # sequência de não-dígitos\n", - " r'\\s+', # sequência de espaços\n", - " r'\\S+', # sequência de não-espaços\n", - " r'\\w+', # caracteres alfanuméricos\n", - " r'\\W+', # não-alfanumérico\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pesquisando na frase: '\\\\d+'\n", - "['1287']\n", - "\n", - "\n", - "Pesquisando na frase: '\\\\D+'\n", - "['Esta é uma string com alguns números, como ', ' e um símbolo #hashtag']\n", - "\n", - "\n", - "Pesquisando na frase: '\\\\s+'\n", - "[' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\n", - "\n", - "\n", - "Pesquisando na frase: '\\\\S+'\n", - "['Esta', 'é', 'uma', 'string', 'com', 'alguns', 'números,', 'como', '1287', 'e', 'um', 'símbolo', '#hashtag']\n", - "\n", - "\n", - "Pesquisando na frase: '\\\\w+'\n", - "['Esta', 'é', 'uma', 'string', 'com', 'alguns', 'números', 'como', '1287', 'e', 'um', 'símbolo', 'hashtag']\n", - "\n", - "\n", - "Pesquisando na frase: '\\\\W+'\n", - "[' ', ' ', ' ', ' ', ' ', ' ', ', ', ' ', ' ', ' ', ' ', ' #']\n", - "\n", - "\n" - ] - } - ], - "source": [ - "encontra_padrao(lista_padroes, frase)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# FIM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obrigado - Data Science Academy - facebook.com/dsacademybr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git "a/JupyterNotebooks/Cap\303\255tulo07/dados.json" "b/JupyterNotebooks/Cap\303\255tulo07/dados.json" deleted file mode 100644 index b20dc428..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo07/dados.json" +++ /dev/null @@ -1 +0,0 @@ -{"similar": ["c", "Modula-3", "lisp"], "nome": "Guido van Rossum", "linguagem": "Python", "users": 1000000} \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo07/json_data.txt" "b/JupyterNotebooks/Cap\303\255tulo07/json_data.txt" deleted file mode 100644 index b20dc428..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo07/json_data.txt" +++ /dev/null @@ -1 +0,0 @@ -{"similar": ["c", "Modula-3", "lisp"], "nome": "Guido van Rossum", "linguagem": "Python", "users": 1000000} \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo07/logfile.txt" "b/JupyterNotebooks/Cap\303\255tulo07/logfile.txt" deleted file mode 100644 index 1c6fa192..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo07/logfile.txt" +++ /dev/null @@ -1 +0,0 @@ -Gravando no log... \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo07/testfile.txt" "b/JupyterNotebooks/Cap\303\255tulo07/testfile.txt" deleted file mode 100644 index 5bcd3549..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo07/testfile.txt" +++ /dev/null @@ -1 +0,0 @@ -Gravando no arquivo \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Chart-Interativo.html" "b/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Chart-Interativo.html" deleted file mode 100644 index 386e4035..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Chart-Interativo.html" +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - Bokeh Plot - - - - - - - - -
- - - - \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Datetime.html" "b/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Datetime.html" deleted file mode 100644 index 33c38e8a..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Datetime.html" +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - Bokeh Plot - - - - - - - - -
- - - - \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-GeoJSON.html" "b/JupyterNotebooks/Cap\303\255tulo08/Bokeh-GeoJSON.html" deleted file mode 100644 index 22f49f4d..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-GeoJSON.html" +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - Bokeh Plot - - - - - - - - -
- - - - \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Grafico-Interativo.html" "b/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Grafico-Interativo.html" deleted file mode 100644 index c8142c86..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Grafico-Interativo.html" +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - Bokeh Plot - - - - - - - - -
- - - - \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Grafico-Linha.html" "b/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Grafico-Linha.html" deleted file mode 100644 index 4f46e53d..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-Grafico-Linha.html" +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - Bokeh Plot - - - - - - - - -
- - - - \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-ViolinPlot.html" "b/JupyterNotebooks/Cap\303\255tulo08/Bokeh-ViolinPlot.html" deleted file mode 100644 index 6cb60de2..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/Bokeh-ViolinPlot.html" +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - Bokeh Plot - - - - - - - - -
- - - - \ No newline at end of file diff --git "a/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Bokeh.ipynb" "b/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Bokeh.ipynb" deleted file mode 100644 index 4c83423f..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Bokeh.ipynb" +++ /dev/null @@ -1,1381 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 8\n", - "\n", - "## Download: http://github.com/dsacademybr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bokeh" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Caso o Bokeh não esteja instalado, executar:\n", - "### pip install bokeh" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Importando o módulo Bokeh\n", - "import bokeh" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Usando output_file para gravar as visualizações\n", - "from bokeh.plotting import figure, output_file, show\n", - "\n", - "# Usando output_notebook para visualizar no iPython Notebook\n", - "from bokeh.charts import Bar, output_notebook, show" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(global) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", - " window._bokeh_onload_callbacks = [];\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " delete window._bokeh_onload_callbacks\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " window._bokeh_onload_callbacks.push(callback);\n", - " if (window._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " window._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " window._bokeh_is_loading--;\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " Bokeh.$(\"#6cb2911e-4ab9-4f80-b50e-01c5c7f2b681\").text(\"BokehJS successfully loaded\");\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\n", - " }\n", - " }\n", - "\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(this));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Output \n", - "output_notebook() " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Arquivo gerado pela visualização\n", - "output_file(\"Bokeh-Grafico-Interativo.html\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "p = figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "bokeh.plotting.figure.Figure" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_width = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

<Bokeh Notebook handle for In[8]>

" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "show(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Usando output_notebook para visualizar no iPython Notebook\n", - "from bokeh.charts import Bar, output_notebook, show" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Preparando dados\n", - "data = {\"y\": [1, 2, 3, 4, 5]}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(global) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", - " window._bokeh_onload_callbacks = [];\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " delete window._bokeh_onload_callbacks\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " window._bokeh_onload_callbacks.push(callback);\n", - " if (window._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " window._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " window._bokeh_is_loading--;\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " Bokeh.$(\"#98902fd3-4a20-4733-92ac-03c857fd1808\").text(\"BokehJS successfully loaded\");\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\n", - " }\n", - " }\n", - "\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(this));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Output \n", - "output_notebook() " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Criando um novo gráfico\n", - "gr = Bar(data, title = \"Gráfico Bokeh\", xlabel = 'x', ylabel = 'valores', \n", - " width = 400, height = 400)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

<Bokeh Notebook handle for In[13]>

" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Mostrando os resultados\n", - "show(gr)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

<Bokeh Notebook handle for In[14]>

" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construindo um ScatterPlot\n", - "from bokeh.charts import Scatter, output_file, show\n", - "\n", - "# Preparando os dados\n", - "from bokeh.sampledata.autompg import autompg as df\n", - "\n", - "# Criando um Scatter Plot\n", - "p = Scatter(df, x = 'mpg', y = 'hp', color = 'cyl',\n", - " title = \"Consumo x Potência\",\n", - " legend = 'top_right',\n", - " xlabel = \"Km/Litro\",\n", - " ylabel = \"Potência do Motor\")\n", - "\n", - "# Especificando o arquivo de saída\n", - "output_file(\"Bokeh-Chart-Interativo.html\")\n", - "\n", - "# Visualizando o gráfico\n", - "show(p)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

<Bokeh Notebook handle for In[15]>

" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Criando Violin Plots - Seaborn e Bokeh\n", - "import seaborn as sea\n", - "\n", - "from bokeh import mpl\n", - "from bokeh.plotting import output_file, show\n", - "\n", - "tips = sea.load_dataset(\"tips\")\n", - "\n", - "sea.set_style(\"whitegrid\")\n", - "\n", - "ax = sea.violinplot(x = \"day\", y = \"total_bill\", hue = \"sex\", data = tips, \n", - " palette = \"Set2\", split = True, \n", - " scale = \"count\", inner = \"stick\")\n", - "\n", - "output_file(\"Bokeh-ViolinPlot.html\")\n", - "\n", - "show(mpl.to_bokeh())" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

<Bokeh Notebook handle for In[16]>

" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Gráficos de Linha\n", - "from bokeh.plotting import figure, output_file, show\n", - "\n", - "# Outuput\n", - "output_file(\"Bokeh-Grafico-Linha.html\")\n", - "\n", - "p = figure(plot_width = 400, plot_height = 400)\n", - "\n", - "# Adicionando círculos ao gráfico\n", - "p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size = 20, color = \"navy\", alpha = 0.5)\n", - "\n", - "# Mostrando o resultado\n", - "show(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

<Bokeh Notebook handle for In[17]>

" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Geojson\n", - "from bokeh.io import output_file, show\n", - "from bokeh.models import GeoJSONDataSource\n", - "from bokeh.plotting import figure\n", - "from bokeh.sampledata.sample_geojson import geojson\n", - "\n", - "geo_source = GeoJSONDataSource(geojson=geojson)\n", - "\n", - "p = figure()\n", - "p.circle(x = 'x', y = 'y', alpha = 0.9, source = geo_source)\n", - "output_file(\"Bokeh-GeoJSON.html\")\n", - "show(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

<Bokeh Notebook handle for In[18]>

" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Plots de Séries Temporais com Pandas\n", - "import pandas as pd\n", - "from bokeh.plotting import figure, output_file, show\n", - "\n", - "AAPL = pd.read_csv(\n", - " \"http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000&d=0&e=1&f=2010\",\n", - " parse_dates=['Date']\n", - " )\n", - "\n", - "output_file(\"Bokeh-Datetime.html\")\n", - "\n", - "# create a new plot with a datetime axis type\n", - "p = figure(width=800, height=250, x_axis_type=\"datetime\")\n", - "\n", - "p.line(AAPL['Date'], AAPL['Close'], color='navy', alpha=0.5)\n", - "\n", - "show(p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Google Maps\n", - "from bokeh.io import output_file, show\n", - "from bokeh.models import (\n", - " GMapPlot, GMapOptions, ColumnDataSource, Circle, DataRange1d, PanTool, WheelZoomTool, BoxSelectTool\n", - ")\n", - "\n", - "map_options = GMapOptions(lat=-23.5431786, lng=-46.62918450000001, map_type=\"roadmap\", zoom=11)\n", - "\n", - "plot = GMapPlot(\n", - " x_range=DataRange1d(), y_range=DataRange1d(), map_options=map_options, title=\"São Paulo\"\n", - ")\n", - "\n", - "source = ColumnDataSource(\n", - " data=dict(\n", - " lat=[-23.54, -46.62],\n", - " lon=[97.70, 97.74],\n", - " )\n", - ")\n", - "\n", - "circle = Circle(x=\"lon\", y=\"lat\", size=15, fill_color=\"blue\", fill_alpha=0.8, line_color=None)\n", - "plot.add_glyph(source, circle)\n", - "\n", - "plot.add_tools(PanTool(), WheelZoomTool(), BoxSelectTool())\n", - "output_file(\"gmap_plot.html\")\n", - "show(plot)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obrigado - Data Science Academy - facebook.com/dsacademybr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git "a/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Matplotlib-Mapas.ipynb" "b/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Matplotlib-Mapas.ipynb" deleted file mode 100644 index 213da98e..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Matplotlib-Mapas.ipynb" +++ /dev/null @@ -1,280 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 8\n", - "\n", - "## Download: http://github.com/dsacademybr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Criando Mapas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Para usuários do Windows, executar o procedimento abaixo para instalar o basemap." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1- Acessar o site http://www.lfd.uci.edu/~gohlke/pythonlibs/\n", - "### 2- Baixar o arquivo basemap-1.0.8-cp35-none-win_amd64.whl\n", - "### 3- Colocar o arquivo no diretório de instalação do Anaconda\n", - "### 4- No prompt de comando, nevegar até o diretório de instalação do Anaconda e executar\n", - "### 5- pip install basemap-1.0.8-cp35-none-win_amd64.whl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Usuários Mac e Linux, executar: pip install basemap" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from mpl_toolkits.basemap import Basemap\n", - "from mpl_toolkits.mplot3d.axes3d import Axes3D\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Mapa do globo terrestre\n", - "map = Basemap(projection = 'ortho', lat_0 = 0, lon_0 = 0)\n", - "\n", - "# Definindo a cor do globo\n", - "map.drawmapboundary(fill_color = 'aqua')\n", - "\n", - "# Preenchendo a cor dos continentes\n", - "map.fillcontinents(color = 'coral', lake_color = 'aqua')\n", - "\n", - "map.drawcoastlines()\n", - "\n", - "plt.show()\n", - "plt.savefig('Globo.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Mapa do globo terrestre expandido\n", - "from mpl_toolkits.basemap import Basemap\n", - "import matplotlib.pyplot as plt\n", - "\n", - "map = Basemap()\n", - "\n", - "map.drawcoastlines()\n", - "\n", - "# Mostrar e gravar\n", - "plt.show()\n", - "plt.savefig('Globo-expandido.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Cria um mapa usando Basemap\n", - "from mpl_toolkits.basemap import Basemap\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import pyplot\n", - "from numpy import arange\n", - "\n", - "mapa = Basemap(projection = 'robin', lat_0 = -20, lon_0 = -50,\n", - "resolution = 'l', area_thresh = 1e3)\n", - "\n", - "# desenha a costa dos continentes\n", - "mapa.drawcoastlines(color='#777799')\n", - "\n", - "# Desenha as fronteiras\n", - "mapa.drawcountries(color='#ccccee')\n", - "\n", - "# Pinta os continentes\n", - "mapa.fillcontinents(color='#ddddcc')\n", - "\n", - "# Desenha os meridianos\n", - "mapa.drawmeridians(arange(0, 360, 30), color='#ccccee')\n", - "\n", - "# Desenha os paralelos\n", - "mapa.drawparallels(arange(-180, 180, 30), color='#ccccee')\n", - "\n", - "# Desenha os limites do mapa\n", - "mapa.drawmapboundary()\n", - "\n", - "# Mostrar e gravar\n", - "plt.show()\n", - "pyplot.savefig('Mapa.png', dpi=150)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Plot contour\n", - "from mpl_toolkits.basemap import Basemap\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Criando o mapa base\n", - "map = Basemap(projection = 'ortho', lat_0 = 45, lon_0 = -100, resolution = 'l')\n", - "\n", - "# Definindo as linhas dos continentes\n", - "map.drawcoastlines(linewidth = 0.25)\n", - "map.drawcountries(linewidth = 0.25)\n", - "map.fillcontinents(color = 'coral', lake_color = 'aqua')\n", - "\n", - "# Desenhando os limites\n", - "map.drawmapboundary(fill_color = 'aqua')\n", - "\n", - "# Desenhando latitude e longitude\n", - "map.drawmeridians(np.arange(0,360,30))\n", - "map.drawparallels(np.arange(-90,90,30))\n", - "\n", - "# Desenhando os grids\n", - "nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)\n", - "lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])\n", - "lons = (delta*np.indices((nlats,nlons))[1,:,:])\n", - "wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))\n", - "mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)\n", - "\n", - "# Projetando coordeandas.\n", - "x, y = map(lons*180./np.pi, lats*180./np.pi)\n", - "\n", - "# Dados do mapa\n", - "cs = map.contour(x,y,wave+mean,15,linewidths=1.5)\n", - "plt.title('Linhas sobre o continente')\n", - "\n", - "# Mostrar e gravar\n", - "plt.show()\n", - "pyplot.savefig('Continentes.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Mapas dia e noite\n", - "import numpy as np\n", - "from mpl_toolkits.basemap import Basemap\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "\n", - "# Projeção\n", - "map = Basemap(projection='mill',lon_0=180)\n", - "\n", - "# Linhas meridianas e paralelas.\n", - "map.drawcoastlines()\n", - "map.drawparallels(np.arange(-90,90,30),labels=[1,0,0,0])\n", - "map.drawmeridians(np.arange(map.lonmin,map.lonmax+30,60),labels=[0,0,0,1])\n", - "\n", - "# Cores\n", - "map.drawmapboundary(fill_color='aqua')\n", - "map.fillcontinents(color='coral',lake_color='aqua')\n", - "\n", - "# Sombreando as noites. Time em UTC.\n", - "date = datetime.utcnow()\n", - "CS=map.nightshade(date)\n", - "# plt.title('Mapa Dia/Noite %s (UTC)' % date.strftime(\"%d %b %Y %H:%M:%S\"))\n", - "plt.title('Mapa Dia/Noite')\n", - "\n", - "# Mostrar e gravar\n", - "plt.show()\n", - "pyplot.savefig('Dia-Noite.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Acesse esse site para mais exemplos com Basemap\n", - "# http://matplotlib.org/basemap/users/examples.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obrigado - Data Science Academy - facebook.com/dsacademybr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git "a/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Matplotlib-Plots-e-Graficos.ipynb" "b/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Matplotlib-Plots-e-Graficos.ipynb" deleted file mode 100644 index a563b146..00000000 --- "a/JupyterNotebooks/Cap\303\255tulo08/DSA-Python-Cap\303\255tulo8-Matplotlib-Plots-e-Graficos.ipynb" +++ /dev/null @@ -1,11035 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Science Academy - Python Fundamentos - Capítulo 8\n", - "\n", - "## Download: http://github.com/dsacademybr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Construindo Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# O matplotlib.pyplot é uma coleção de funções e estilos que fazem com que o Matplotlib \n", - "# funcione como o Matlab.\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# O método plot() define os eixos do gráfico\n", - "plt.plot([1, 3, 5], [2, 5, 7])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x = [1, 4, 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "y = [3, 7, 2]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, y)\n", - "plt.xlabel('Variável 1')\n", - "plt.ylabel('Variável 2')\n", - "plt.title('Teste Plot')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x2 = [1, 2, 3]\n", - "y2 = [11, 12, 15]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x2, y2, label = 'Primeira Linha')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gráficos de Barras" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x = [2,4,6,8,10]\n", - "y = [6,7,8,2,4]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(x, y, label = 'Barras', color = 'r')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x2 = [1,3,5,7,9]\n", - "y2 = [7,8,2,4,2]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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2wxHxcsa1AQAGKLmlHRHbIuK54vROSS9JmpF1YQCAwSoa07bdKekESeuzKAYAcGDlDI9I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\nhgoywMgAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(x, y, label = 'Barras1', color = 'r')\n", - "plt.bar(x2, y2, label = 'Barras2', color = 'y')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "idades = [22,65,45,55,21,22,34,42,41,4,99,101,120,122,130,111,115,80,75,54,44,64,13,18,48]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "ids = [x for x in range(len(idades))]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(idades, bins, histtype = 'stepfilled', rwidth = 0.8)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scatterplot" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x = [1,2,3,4,5,6,7,8]\n", - "y = [5,2,4,5,6,8,4,8]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(x, y, label = 'Pontos', color = 'r', marker = 'o', s = 100)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stack Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "dias = [1,2,3,4,5]\n", - "dormir = [7,8,6,77,7]\n", - "comer = [2,3,4,5,3]\n", - "trabalhar = [7,8,7,2,2]\n", - "passear = [8,5,7,8,13]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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8yVpUNbv8kpISBg0a1GjnzJeCL0QDNGzYsNjFXimO05roiRE+Bj4yBjMh6qRs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\n1tC+PVx0USgQXrrwRs81pJNev8mJJi2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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.stackplot(dias, dormir, comer, trabalhar, passear, colors = ['m','c','r','k','b'])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pie Chart" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "fatias = [7, 2, 2, 13]\n", - "atividades = ['dormir','comer','trabalhar','passear']\n", - "colunas = ['c','m','r','k']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.pie(fatias, labels = atividades, colors = colunas, startangle = 90, \n", - " shadow = True, explode = (0,0.1,0,0))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Gráficos " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Visualizando os gráfico dentro do Jupyter Notebook\n", - "# O Pylab combina funcionalidades do pyplot com funcionalidades do Numpy\n", - "%matplotlib inline\n", - "from pylab import *" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = linspace(0, 5, 10)\n", - "y = x ** 2\n", - "\n", - "fig = plt.figure()\n", - "\n", - "# Definindo os eixos\n", - "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n", - "\n", - "axes.plot(x, y, 'r')\n", - "\n", - "axes.set_xlabel('x')\n", - "axes.set_ylabel('y')\n", - "axes.set_title('Gráfico de Linha');" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Gráficos com 2 figuras\n", - "x = linspace(0, 5, 10)\n", - "y = x ** 2\n", - "\n", - "fig = plt.figure()\n", - "\n", - "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # eixos da figura principal\n", - "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # eixos da figura secundária\n", - "\n", - "# Figura principal\n", - "axes1.plot(x, y, 'r')\n", - "axes1.set_xlabel('x')\n", - "axes1.set_ylabel('y')\n", - "axes1.set_title('Figura Principal')\n", - "\n", - "# Figura secundária\n", - "axes2.plot(y, x, 'g')\n", - "axes2.set_xlabel('y')\n", - "axes2.set_ylabel('x')\n", - "axes2.set_title('Figura Secundária');" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Gráficos em Paralelo\n", - "fig, axes = plt.subplots(nrows = 1, ncols = 2)\n", - "\n", - "for ax in axes:\n", - " ax.plot(x, y, 'r')\n", - " ax.set_xlabel('x')\n", - " ax.set_ylabel('y')\n", - " ax.set_title('Título')\n", - " \n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gráficos a partir do NumPy" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib notebook\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot e Scatter\n", - "fig = plt.figure()\n", - "\n", - "ax1 = fig.add_subplot(1,2,1)\n", - "ax1.plot(np.random.randn(50), color='red')\n", - "\n", - "ax2 = fig.add_subplot(1,2,2)\n", - "ax2.scatter(np.arange(50), np.random.randn(50))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Controle dos eixos\n", - "fig, axes = plt.subplots(1, 3, figsize = (12, 4))\n", - "\n", - "axes[0].plot(x, x**2, x, x**3)\n", - "axes[0].set_title(\"Eixos com range padrão\")\n", - "\n", - "axes[1].plot(x, x**2, x, x**3)\n", - "axes[1].axis('tight')\n", - "axes[1].set_title(\"Eixos menores\")\n", - "\n", - "axes[2].plot(x, x**2, x, x**3)\n", - "axes[2].set_ylim([0, 60])\n", - "axes[2].set_xlim([2, 5])\n", - "axes[2].set_title(\"Eixos customizados\");" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Grid\n", - "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n", - "\n", - "# Grid padrão\n", - "axes[0].plot(x, x**2, x, x**3, lw = 2)\n", - "axes[0].grid(True)\n", - "\n", - "# Grid customizado\n", - "axes[1].plot(x, x**2, x, x**3, lw = 2)\n", - "axes[1].grid(color = 'b', alpha = 0.5, linestyle = 'dashed', linewidth = 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Diferentes estilos de Plots\n", - "xx = np.linspace(-0.75, 1., 100)\n", - "n = np.array([0,1,2,3,4,5])\n", - "\n", - "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n", - "\n", - "axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n", - "axes[0].set_title(\"scatter\")\n", - "\n", - "axes[1].step(n, n**2, lw=2)\n", - "axes[1].set_title(\"step\")\n", - "\n", - "axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n", - "axes[2].set_title(\"bar\")\n", - "\n", - "axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);\n", - "axes[3].set_title(\"fill_between\");" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Color Map\n", - "alpha = 0.7\n", - "phi_ext = 2 * np.pi * 0.5\n", - "\n", - "def ColorMap(phi_m, phi_p):\n", - " return ( + alpha - 2 * np.cos(phi_p)*cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p))\n", - "\n", - "phi_m = np.linspace(0, 2*np.pi, 100)\n", - "phi_p = np.linspace(0, 2*np.pi, 100)\n", - "X,Y = np.meshgrid(phi_p, phi_m)\n", - "Z = ColorMap(X, Y).T\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "p = ax.pcolor(X/(2*np.pi), Y/(2*np.pi), Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n", - "cb = fig.colorbar(p, ax=ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gráficos 3D" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from mpl_toolkits.mplot3d.axes3d import Axes3D" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Wire frame\n", - "fig = plt.figure(figsize=(8,6))\n", - "\n", - "ax = fig.add_subplot(1, 1, 1, projection = '3d')\n", - "\n", - "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('