From b52b55c7d1b111ea2869b2bfeab6b8b066010ade Mon Sep 17 00:00:00 2001 From: stemgene <35020077+stemgene@users.noreply.github.com> Date: Fri, 16 Feb 2024 11:55:28 -0500 Subject: [PATCH] change --- Location.ipynb | 544 ++++++++++++++++++++++++------------------------- 1 file changed, 271 insertions(+), 273 deletions(-) diff --git a/Location.ipynb b/Location.ipynb index 065d222..c8f0d27 100644 --- a/Location.ipynb +++ b/Location.ipynb @@ -1,24 +1,10 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Location.ipynb", - "provenance": [], - "collapsed_sections": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ "\"Open" @@ -26,15 +12,25 @@ }, { "cell_type": "code", + "execution_count": 147, "metadata": { - "id": "kloBqxSaruPd", - "colab_type": "code", - "outputId": "fd033e89-1483-4a50-a980-cb7a7f1e865d", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - } + }, + "colab_type": "code", + "id": "kloBqxSaruPd", + "outputId": "fd033e89-1483-4a50-a980-cb7a7f1e865d" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n" + ] + } + ], "source": [ "# Import PyDrive and associated libraries.\n", "# This only needs to be done once per notebook.\n", @@ -51,25 +47,17 @@ "import pandas_profiling\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" - ], - "execution_count": 147, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n" - ], - "name": "stdout" - } ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "SMK6NBiYsCr3", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "SMK6NBiYsCr3" }, + "outputs": [], "source": [ "attributes = ['PatientId', 'AppointmentID','Gender', 'ScheduledDay', 'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension', 'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'No-show']\n", "with open('/gdrive/My Drive/UR/DSC481/Project/noshowdata.csv') as csv_file:\n", @@ -80,24 +68,24 @@ "towns_keys = [el for el in patient_loc.groups.keys()] # approach of get keys after 'groupby'\n", "patient_count = [patient_loc.count().iloc[i,0] for i in range(len(patient_loc.count()))]\n", "towns = dict(zip(towns_keys, patient_count))" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "RIMiua0xsHWk", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "RIMiua0xsHWk" }, + "outputs": [], "source": [ "import urllib.parse\n", "import requests\n", "import pprint\n", "class Get_GIS():\n", " def __init__(self):\n", - " self.__user_key = 'AIzaSyBU7HVfmG9LOhzZYtL4uaCHrll8llnekgM'\n", + " self.__user_key = 'Google_API_KEY'\n", " self.google_url = 'https://maps.googleapis.com/maps/api/geocode/json?'\n", " def get_json(self, address):\n", " self.address = address\n", @@ -119,44 +107,42 @@ " #print(\"{ location: new google.maps.LatLng\" + str(gis.parse_json()) + \", weight:\" + str(values) + \"},\")\n", "\n", "#{ location: new google.maps.LatLng(-20.287964, -40.3056718), weight: 2262 }" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "kHbQVqDBsSIM", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "kHbQVqDBsSIM" }, + "outputs": [], "source": [ "# delete extreme values\n", "del towns['ILHAS OCEÂNICAS DE TRINDADE']\n", "del towns['PARQUE INDUSTRIAL']" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "JLKhwyIisbIM", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "JLKhwyIisbIM" }, + "outputs": [], "source": [ "# Adjust wrong gps\n", "towns['SANTO ANDRÉ'][1], towns['SANTO ANDRÉ'][2] = -20.278602, -40.332620" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { - "id": "EBJ8EasBwhMp", - "colab_type": "text" + "colab_type": "text", + "id": "EBJ8EasBwhMp" }, "source": [ "#### Export and read towns" @@ -164,27 +150,29 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "M7uxucjjsTh1", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "M7uxucjjsTh1" }, + "outputs": [], "source": [ "#export dataframe to google drive\n", "#towns_df = pd.DataFrame(towns.values(), index=towns.keys(), columns=['Frequency', \"lan\", \"lon\"])\n", "matrix.dist_series().to_csv('dist_series.csv')\n", "!cp dist_series.csv /gdrive/My\\ Drive/UR/DSC481/Project/dist_series.csv" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "O-uaHPLCwIu7", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "O-uaHPLCwIu7" }, + "outputs": [], "source": [ "with open('/gdrive/My Drive/UR/DSC481/Project/towns.csv') as csv_file:\n", " next(csv_file)\n", @@ -192,15 +180,13 @@ " towns = {row[0]:[int(row[1]),float(row[2]),float(row[3])] for row in csv_reader}\n", "## towns = pd.DataFrame(csv_reader4, columns=attributes)\n", "towns" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { - "id": "xEw-Eecuw2PR", - "colab_type": "text" + "colab_type": "text", + "id": "xEw-Eecuw2PR" }, "source": [ "### Calculate distance" @@ -208,24 +194,18 @@ }, { "cell_type": "code", + "execution_count": 154, "metadata": { - "id": "kM0su53cw7Df", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 357 }, + "colab_type": "code", + "id": "kM0su53cw7Df", "outputId": "ccbdd10d-4dba-42a6-809c-498f487b664b" }, - "source": [ - "small = ['AEROPORTO', 'ANDORINHAS', 'ANTÔNIO HONÓRIO', 'ARIOVALDO FAVALESSA', 'BARRO VERMELHO', 'BELA VISTA', 'BENTO FERREIRA', 'BOA VISTA', 'BONFIM', 'CARATOÍRA', 'CENTRO', 'COMDUSA', 'CONQUISTA', 'CONSOLAÇÃO', 'CRUZAMENTO', 'DA PENHA', 'DE LOURDES', 'DO CABRAL', 'DO MOSCOSO', 'DO QUADRO']\n", - "smalltowns = {k:towns[k] for k in small }\n", - "smalltowns" - ], - "execution_count": 154, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "{'AEROPORTO': [8, -20.2616507, -40.2833315],\n", @@ -250,20 +230,28 @@ " 'DO QUADRO': [849, -20.3170592, -40.35000830000001]}" ] }, + "execution_count": 154, "metadata": { "tags": [] }, - "execution_count": 154 + "output_type": "execute_result" } + ], + "source": [ + "small = ['AEROPORTO', 'ANDORINHAS', 'ANTÔNIO HONÓRIO', 'ARIOVALDO FAVALESSA', 'BARRO VERMELHO', 'BELA VISTA', 'BENTO FERREIRA', 'BOA VISTA', 'BONFIM', 'CARATOÍRA', 'CENTRO', 'COMDUSA', 'CONQUISTA', 'CONSOLAÇÃO', 'CRUZAMENTO', 'DA PENHA', 'DE LOURDES', 'DO CABRAL', 'DO MOSCOSO', 'DO QUADRO']\n", + "smalltowns = {k:towns[k] for k in small }\n", + "smalltowns" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "Xm22l8lOxLYi", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "Xm22l8lOxLYi" }, + "outputs": [], "source": [ "import math\n", "from geopy.distance import distance\n", @@ -328,45 +316,43 @@ "#print(matrix.cal_dist().iloc[0])\n", "#threshold = 350 # 1% of whole data\n", "#matrix.merge_data(smalltowns, threshold)\n" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "2rj1RBn8Yngz", - "colab_type": "code", - "outputId": "31eca4ee-f32a-4c07-d52b-f0f74e06aa05", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - } + }, + "colab_type": "code", + "id": "2rj1RBn8Yngz", + "outputId": "31eca4ee-f32a-4c07-d52b-f0f74e06aa05" }, - "source": [ - "(79+1)*79/2 - 80 # the last one is both 0 and self*self" - ], - "execution_count": 0, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "3080.0" ] }, + "execution_count": 106, "metadata": { "tags": [] }, - "execution_count": 106 + "output_type": "execute_result" } + ], + "source": [ + "(79+1)*79/2 - 80 # the last one is both 0 and self*self" ] }, { "cell_type": "markdown", "metadata": { - "id": "YeWRqRgvzltn", - "colab_type": "text" + "colab_type": "text", + "id": "YeWRqRgvzltn" }, "source": [ "### Draw heatmap" @@ -374,11 +360,13 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "o2HN6z4gz0-J", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "o2HN6z4gz0-J" }, + "outputs": [], "source": [ "import folium\n", "mat = [[v[0], v[1][0], v[1][1]] for v in towns.values()]\n", @@ -405,34 +393,50 @@ "\n", "folium.GeoJson(district23).add_to(hmap)\n", "hmap.add_child(hm_wide)" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "HN90xUGpW8qb", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "HN90xUGpW8qb" }, + "outputs": [], "source": [ "!pip install geopandas pyepsg folium" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "W2wEalCd0EFv", - "colab_type": "code", - "outputId": "6db06dbc-46da-4e29-9aad-7d9e93c57d05", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 - } + }, + "colab_type": "code", + "id": "W2wEalCd0EFv", + "outputId": "6db06dbc-46da-4e29-9aad-7d9e93c57d05" }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 133, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], "source": [ "import geopandas as gpd\n", "import pyepsg\n", @@ -461,44 +465,24 @@ "\n", "#folium.GeoJson(gjson).add_to(mapa)\n", "mapa.add_child(hm_wide)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 133 - } ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "zsG_pMWK0EI3", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "zsG_pMWK0EI3" }, - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] + "outputs": [], + "source": [] }, { "cell_type": "markdown", "metadata": { - "id": "fKwwh4-ZfqqC", - "colab_type": "text" + "colab_type": "text", + "id": "fKwwh4-ZfqqC" }, "source": [ "### K-means" @@ -506,82 +490,74 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "fW0xv02-0EL-", - "colab_type": "code", - "outputId": "a348e413-c570-49d2-a085-c645af8ecc11", "colab": { "base_uri": "https://localhost:8080/", "height": 798 - } + }, + "colab_type": "code", + "id": "fW0xv02-0EL-", + "outputId": "a348e413-c570-49d2-a085-c645af8ecc11" }, - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns; sns.set() # for plot styling\n", - "import numpy as np\n", - "from matplotlib.pyplot import figure\n", - "from sklearn.cluster import KMeans\n", - "\n", - "X = np.array([[v[1], v[2]] for v in towns.values()])\n", - "kmeans = KMeans(n_clusters=30)\n", - "kmeans.fit(X)\n", - "y_kmeans = kmeans.predict(X)\n", - "\n", - "figure(num=None, figsize=(15, 12), dpi=80, facecolor='w', edgecolor='k')\n", - "plt.scatter(X[:, 1], X[:, 0], c=y_kmeans, s=30, cmap='viridis')" - ], - "execution_count": 0, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 132, "metadata": { "tags": [] }, - "execution_count": 132 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "image/png": 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5Lpd3aOY0AAAAwFejrANo8dq2T9PNowcqGKp5T00rYGrslBHuhAIAAAAugLvB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\nAABJRU5ErkJggg==\n", 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", "text/plain": [ "
" ] }, "metadata": { "tags": [] - } + }, + "output_type": "display_data" } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set() # for plot styling\n", + "import numpy as np\n", + "from matplotlib.pyplot import figure\n", + "from sklearn.cluster import KMeans\n", + "\n", + "X = np.array([[v[1], v[2]] for v in towns.values()])\n", + "kmeans = KMeans(n_clusters=30)\n", + "kmeans.fit(X)\n", + "y_kmeans = kmeans.predict(X)\n", + "\n", + "figure(num=None, figsize=(15, 12), dpi=80, facecolor='w', edgecolor='k')\n", + "plt.scatter(X[:, 1], X[:, 0], c=y_kmeans, s=30, cmap='viridis')" ] }, { "cell_type": "code", + "execution_count": 158, "metadata": { - "id": "kKv6Yyeh0EPA", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 435 }, + "colab_type": "code", + "id": "kKv6Yyeh0EPA", "outputId": "1ca90f29-78c1-4002-82df-2ba29dbe3224" }, - "source": [ - "#np.random.multivariate_normal\n", - "cluster_df = df[[\"PatientId\", 'Neighbourhood']]\n", - "cluster_df.drop(cluster_df.index[cluster_df['Neighbourhood'] == 'ILHAS OCEÂNICAS DE TRINDADE'], inplace = True)\n", - "cluster_df.drop(cluster_df.index[cluster_df['Neighbourhood'] == 'PARQUE INDUSTRIAL'], inplace = True)\n", - "patient_gps = [(towns[key][1], towns[key][2]) for key in cluster_df[['Neighbourhood']]]\n", - "patient_gps[:20]\n", - "#cluster_df.insert(3,\"lat\", lat)" - ], - "execution_count": 158, "outputs": [ { + "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4117: SettingWithCopyWarning: \n", @@ -589,13 +565,12 @@ "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " errors=errors,\n" - ], - "name": "stderr" + ] }, { - "output_type": "error", "ename": "KeyError", "evalue": "ignored", + "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", @@ -604,26 +579,31 @@ "\u001b[0;31mKeyError\u001b[0m: 'Neighbourhood'" ] } + ], + "source": [ + "#np.random.multivariate_normal\n", + "cluster_df = df[[\"PatientId\", 'Neighbourhood']]\n", + "cluster_df.drop(cluster_df.index[cluster_df['Neighbourhood'] == 'ILHAS OCEÂNICAS DE TRINDADE'], inplace = True)\n", + "cluster_df.drop(cluster_df.index[cluster_df['Neighbourhood'] == 'PARQUE INDUSTRIAL'], inplace = True)\n", + "patient_gps = [(towns[key][1], towns[key][2]) for key in cluster_df[['Neighbourhood']]]\n", + "patient_gps[:20]\n", + "#cluster_df.insert(3,\"lat\", lat)" ] }, { "cell_type": "code", + "execution_count": 160, "metadata": { - "id": "RuMTSUtLG655", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 419 }, + "colab_type": "code", + "id": "RuMTSUtLG655", "outputId": "71e03517-2e1f-4e78-c0ea-9e3980eb98c5" }, - "source": [ - "cluster_df[['Neighbourhood']]" - ], - "execution_count": 160, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -714,18 +694,22 @@ "[110524 rows x 1 columns]" ] }, + "execution_count": 160, "metadata": { "tags": [] }, - "execution_count": 160 + "output_type": "execute_result" } + ], + "source": [ + "cluster_df[['Neighbourhood']]" ] }, { "cell_type": "markdown", "metadata": { - "id": "ushbDw2Ju7Tv", - "colab_type": "text" + "colab_type": "text", + "id": "ushbDw2Ju7Tv" }, "source": [ "### DBSCAN" @@ -734,8 +718,8 @@ { "cell_type": "markdown", "metadata": { - "id": "QcTOc4VMxsED", - "colab_type": "text" + "colab_type": "text", + "id": "QcTOc4VMxsED" }, "source": [ "Calculate epsilon\n", @@ -748,69 +732,65 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "YHx1RFjqk7P6", - "colab_type": "code", - "outputId": "f246a093-22e2-4c04-fe06-3394069afb7d", "colab": { "base_uri": "https://localhost:8080/", "height": 304 - } + }, + "colab_type": "code", + "id": "YHx1RFjqk7P6", + "outputId": "f246a093-22e2-4c04-fe06-3394069afb7d" }, - "source": [ - "# choose the best esp, but it didn't seem like the paper shown\n", - "dist_array = np.sort([dist_series[\"dist(m)\"].values])\n", - "x = np.linspace(0, len(dist_array), num=3080)\n", - "plt.scatter(x, dist_array)\n", - "dist_array" - ], - "execution_count": 0, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([[ 167.35797784, 205.63066744, 230.95683454, ...,\n", " 11310.29501868, 11359.35771961, 11512.39231567]])" ] }, + "execution_count": 131, "metadata": { "tags": [] }, - "execution_count": 131 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "tags": [] - } + }, + "output_type": "display_data" } + ], + "source": [ + "# choose the best esp, but it didn't seem like the paper shown\n", + "dist_array = np.sort([dist_series[\"dist(m)\"].values])\n", + "x = np.linspace(0, len(dist_array), num=3080)\n", + "plt.scatter(x, dist_array)\n", + "dist_array" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "2fLV8CNou3DK", - "colab_type": "code", - "outputId": "7b0d8413-0c45-4199-fdb5-55f6c84ad4e3", "colab": { "base_uri": "https://localhost:8080/", "height": 419 - } + }, + "colab_type": "code", + "id": "2fLV8CNou3DK", + "outputId": "7b0d8413-0c45-4199-fdb5-55f6c84ad4e3" }, - "source": [ - "for_map" - ], - "execution_count": 0, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -925,24 +905,58 @@ "[79 rows x 3 columns]" ] }, + "execution_count": 134, "metadata": { "tags": [] }, - "execution_count": 134 + "output_type": "execute_result" } + ], + "source": [ + "for_map" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "7RFjPLBct0jM", - "colab_type": "code", - "outputId": "581f7fff-23ec-4d6f-d3d4-a95e9869ea1b", "colab": { "base_uri": "https://localhost:8080/", "height": 771 - } + }, + "colab_type": "code", + "id": "7RFjPLBct0jM", + "outputId": "581f7fff-23ec-4d6f-d3d4-a95e9869ea1b" }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters: 40\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], "source": [ "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", "from sklearn.cluster import DBSCAN\n", @@ -976,78 +990,48 @@ "ax.set_ylabel('Latitude')\n", "ax.legend([df_scatter, rep_points_scatter], ['Full set', 'Reduced set'], loc='upper right')\n", "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", - " \n" - ], - "name": "stderr" - }, - { - "output_type": "stream", - "text": [ - "Number of clusters: 40\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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8WVu3blVOTo4kKSsrS5LU1tYWnLVsbGxUfn7+kOc4evSoVq1apcrKShUVFQ06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\nWfps9fv7FDD9cticynbkWF0aAGCSkuIn1euuu04Ox5kjE6666iq1tbU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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "9OA6vGuQ0ER5", - "colab_type": "code", - "outputId": "de03b8f4-5988-4c1a-a8dd-d14123ce67ba", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - } + }, + "colab_type": "code", + "id": "9OA6vGuQ0ER5", + "outputId": "de03b8f4-5988-4c1a-a8dd-d14123ce67ba" }, - "source": [ - "from sklearn.cluster import DBSCAN\n", - "import numpy as np\n", - "clustering = DBSCAN(eps=3, min_samples=1).fit(X)\n", - "#clustering.labels_\n", - "\n", - "clustering " - ], - "execution_count": 0, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([ 5, 10, 15, 24, 30, 85, 71, 60, 55, 80])" ] }, + "execution_count": 29, "metadata": { "tags": [] }, - "execution_count": 29 + "output_type": "execute_result" } + ], + "source": [ + "from sklearn.cluster import DBSCAN\n", + "import numpy as np\n", + "clustering = DBSCAN(eps=3, min_samples=1).fit(X)\n", + "#clustering.labels_\n", + "\n", + "clustering " ] }, { "cell_type": "markdown", "metadata": { - "id": "4SwwkuYm0MXH", - "colab_type": "text" + "colab_type": "text", + "id": "4SwwkuYm0MXH" }, "source": [ "https://alcidanalytics.com/p/geographic-heatmap-in-python\n", @@ -1058,8 +1042,8 @@ { "cell_type": "markdown", "metadata": { - "id": "-2vaw99v0FRb", - "colab_type": "text" + "colab_type": "text", + "id": "-2vaw99v0FRb" }, "source": [ "### HTML" @@ -1067,11 +1051,13 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "6Dy7_VGNyiIo", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "6Dy7_VGNyiIo" }, + "outputs": [], "source": [ "\n", "\n", @@ -1290,9 +1276,21 @@ " \n", "\n", "\n" - ], - "execution_count": 0, - "outputs": [] + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "name": "Location.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}