google-location-history-cluster - Copy.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DBSCAN clustering to reduce Google location history data set\n",
    "\n",
    "See [this blog post](http://geoffboeing.com/2016/06/mapping-google-location-history-python/) for my full write-up of this project, or [this one](http://geoffboeing.com/2014/08/clustering-to-reduce-spatial-data-set-size/) for more about using Python's scikit-learn implementation of DBSCAN to reduce the size of a spatial data set.\n",
    "\n",
    "This notebook reduces the size of a Google location history data set down to a set of spatially representative points, with DBSCAN clustering. Visit https://accounts.google.com/ServiceLogin?service=backup to download your Google location history as a JSON file called LocationHistory.json."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
<<<<<<< HEAD
   "outputs": [],
=======
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m   1352\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1353\u001b[0;31m     \u001b[0mfontManager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_fmcache\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   1354\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py\u001b[0m in \u001b[0;36mjson_load\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    923\u001b[0m     \"\"\"\n\u001b[0;32m--> 924\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\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    925\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_json_decode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/cassandrabalbuena/.matplotlib/fontlist-v310.json'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-17ccdbea69eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# import necessary modules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDBSCAN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgeopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistance\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgreat_circle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mshapely\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMultiPoint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig_init\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m from pandas.core.api import (\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;31m# dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mInt8Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/api.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m )\n\u001b[1;32m     23\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNamedAgg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mset_eng_float_format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m from pandas.core.index import (\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m from pandas.core.groupby.generic import (  # noqa: F401\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mDataFrameGroupBy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mNamedAgg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mSeriesGroupBy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m )\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommon\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_shared_docs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    113\u001b[0m )\n\u001b[1;32m    114\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdispatch_fill_zeros\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseries\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconsole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0m__all__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Series\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0mhttps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mgithub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcom\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mpandas\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdev\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mpandas\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0missues\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m26747.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \"\"\"\n\u001b[0;32m---> 59\u001b[0;31m from pandas.plotting._core import (\n\u001b[0m\u001b[1;32m     60\u001b[0m     \u001b[0mPlotAccessor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0mboxplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# we can lazily import matplotlib.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m  \u001b[0;31m# noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_config\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m from pandas.plotting._matplotlib.boxplot import (\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mBoxPlot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mboxplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint_thing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMPLPlot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_get_standard_colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_flatten\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_subplots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_mpl_ge_3_0_0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_get_standard_colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m from pandas.plotting._matplotlib.tools import (\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0m_flatten\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0m_get_all_lines\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mticker\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mticker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/table.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0martist\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mArtist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_rasterization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRectangle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mText\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0martist\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mArtist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfont_manager\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFontProperties\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLine2D\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFancyArrowPatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFancyBboxPatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRectangle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m   1353\u001b[0m     \u001b[0mfontManager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_fmcache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1354\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1355\u001b[0;31m     \u001b[0m_rebuild\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   1356\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1357\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontManager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_version'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mFontManager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py\u001b[0m in \u001b[0;36m_rebuild\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1346\u001b[0m     \u001b[0mfontManager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFontManager\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   1347\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_fmcache\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[0;32m-> 1348\u001b[0;31m         \u001b[0mjson_dump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontManager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_fmcache\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   1349\u001b[0m     \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generated new fontManager\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py\u001b[0m in \u001b[0;36mjson_dump\u001b[0;34m(data, filename)\u001b[0m\n\u001b[1;32m    909\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m             \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mJSONEncoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\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    912\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m             \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Could not save font_manager cache {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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[0;32m~/opt/anaconda3/lib/python3.7/json/__init__.py\u001b[0m in \u001b[0;36mdump\u001b[0;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[0;31m# could accelerate with writelines in some versions of Python, at\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;31m# a debuggability cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\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    180\u001b[0m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36m_iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m    437\u001b[0m                 \u001b[0mmarkers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmarkerid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0mo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_iterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\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    440\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmarkers\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmarkerid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36m_iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_iterencode_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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[0;32m--> 431\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_iterencode_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\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    432\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmarkers\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36m_iterencode_dict\u001b[0;34m(dct, _current_indent_level)\u001b[0m\n\u001b[1;32m    403\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m                     \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_iterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 405\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mchunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    406\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnewline_indent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m             \u001b[0m_current_indent_level\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36m_iterencode_list\u001b[0;34m(lst, _current_indent_level)\u001b[0m\n\u001b[1;32m    323\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m                     \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_iterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mchunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnewline_indent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m             \u001b[0m_current_indent_level\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36m_iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m    437\u001b[0m                 \u001b[0mmarkers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmarkerid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0mo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_iterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\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    440\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmarkers\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmarkerid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36m_iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_iterencode_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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[0;32m--> 431\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_iterencode_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_current_indent_level\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    432\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmarkers\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
>>>>>>> ec5630674878b36d6914a6fa9cee537b855e6671
   "source": [
    "# import necessary modules\n",
    "import pandas as pd, numpy as np, matplotlib.pyplot as plt, time\n",
    "from sklearn.cluster import DBSCAN\n",
    "# from geopy.distance import great_circle\n",
    "#from shapely.geometry import MultiPoint\n",
    "from datetime import datetime as dt\n",
    "\n",
    "# magic command to display matplotlib plots inline within the ipython notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prep the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1,574,479 rows\n"
     ]
    }
   ],
   "source": [
    "# load the full location history json file downloaded from google\n",
    "df_gps = pd.read_json('data/LocationHistory.json')\n",
    "print('There are {:,} rows'.format(len(df_gps)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# parse lat, lon from the dict inside the locations column and convert to decimalized degrees\n",
    "df_gps['lat'] = df_gps['locations'].map(lambda x: x['latitudeE7']) / 10.**7\n",
    "df_gps['lon'] = df_gps['locations'].map(lambda x: x['longitudeE7']) / 10.**7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# parse timestamp in seconds from locations column and convert to datetime\n",
    "df_gps['timestamp_s'] = (df_gps['locations'].map(lambda x: x['timestampMs']).astype(float) / 1000).astype(int)\n",
    "df_gps['datetime'] = pd.to_datetime(df_gps['timestamp_s'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# filter out points with altitudes above 3000 meters to remove airplane data\n",
    "df_gps['altitude'] = df_gps['locations'].map(lambda x: x['altitude'] if 'altitude' in x else None)\n",
    "mask = (df_gps['altitude'] < 3000) | (pd.isnull(df_gps['altitude']))\n",
    "df_gps = df_gps[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scikit-learn DBSCAN haversine distance metric requires data in the form of [latitude, longitude] and both inputs and outputs are in units of radians."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the number of kilometers in one radian\n",
    "kms_per_radian = 6371.0088"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_gps = df_gps.iloc[range(0, len(df_gps), int(len(df_gps)/5000))] #uncomment to cluster only a sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the clustering functions"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 2,
=======
   "execution_count": 4,
>>>>>>> ec5630674878b36d6914a6fa9cee537b855e6671
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas' has no attribute 'compat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-916ceef513fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairwise\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpairwise_distances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[0;31m# GH 27101\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;31m# TODO: remove Panel compat in 1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPY37\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    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\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[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'compat'"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle as pk\n",
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "# from sklearn.cluster import DBSCAN\n",
    "# from geopy.distance import vincenty\n",
    "\n",
    "with open(\"crime.pk\", \"rb\") as file:\n",
    "    xl = pk.load(file)\n",
    "# except:\n",
    "#     xl = pd.read_csv(\"dc_crime_add_vars.csv\")\n",
    "#     with open(\"crime.pk\", \"wb\") as file:\n",
    "#         pk.dump(xl, file)\n",
    "\n",
    "x1 = xl[xl[\"year\"] == 2017]\n",
    "matrix_dbscan = []\n",
    "for i, j in x1.iterrows():\n",
    "    matrix_dbscan.append((j[\"XBLOCK\"],j[\"YBLOCK\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>REPORT_DAT</th>\n",
       "      <th>SHIFT</th>\n",
       "      <th>OFFENSE</th>\n",
       "      <th>METHOD</th>\n",
       "      <th>BLOCK</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>PSA</th>\n",
       "      <th>WARD</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>EW</th>\n",
       "      <th>NS</th>\n",
       "      <th>quad</th>\n",
       "      <th>crimetype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>34334.0</td>\n",
       "      <td>34334.0</td>\n",
       "      <td>1/1/2017 7:57</td>\n",
       "      <td>DAY</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>500 - 599 BLOCK OF 19TH STREET NW</td>\n",
       "      <td>2.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>West</td>\n",
       "      <td>North</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>34426.0</td>\n",
       "      <td>34426.0</td>\n",
       "      <td>2/6/2017 9:13</td>\n",
       "      <td>DAY</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1200 - 1217 BLOCK OF 18TH STREET NW</td>\n",
       "      <td>2.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>West</td>\n",
       "      <td>North</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>34649.0</td>\n",
       "      <td>34649.0</td>\n",
       "      <td>1/10/2017 20:16</td>\n",
       "      <td>EVENING</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1600 - 1699 BLOCK OF P STREET NW</td>\n",
       "      <td>2.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>West</td>\n",
       "      <td>North</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>34650.0</td>\n",
       "      <td>34650.0</td>\n",
       "      <td>1/11/2017 7:18</td>\n",
       "      <td>DAY</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1600 - 1617 BLOCK OF 14TH STREET NW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>East</td>\n",
       "      <td>North</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>34651.0</td>\n",
       "      <td>34651.0</td>\n",
       "      <td>1/18/2017 20:29</td>\n",
       "      <td>EVENING</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1000 - 1025 BLOCK OF WISCONSIN AVENUE NW</td>\n",
       "      <td>2.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>West</td>\n",
       "      <td>North</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>342862</td>\n",
       "      <td>342863.0</td>\n",
       "      <td>342863.0</td>\n",
       "      <td>3/21/2008 13:00</td>\n",
       "      <td>DAY</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>2200 - 2230 BLOCK OF 6TH STREET NW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>East</td>\n",
       "      <td>North</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>342863</td>\n",
       "      <td>342864.0</td>\n",
       "      <td>342864.0</td>\n",
       "      <td>3/21/2008 21:08</td>\n",
       "      <td>EVENING</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>3300 - 3341 BLOCK OF 16TH STREET NW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>West</td>\n",
       "      <td>North</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>342864</td>\n",
       "      <td>342865.0</td>\n",
       "      <td>342865.0</td>\n",
       "      <td>3/21/2008 16:30</td>\n",
       "      <td>EVENING</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1500 - 1530 BLOCK OF PARK ROAD NW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>East</td>\n",
       "      <td>North</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>342865</td>\n",
       "      <td>342866.0</td>\n",
       "      <td>342866.0</td>\n",
       "      <td>3/21/2008 17:15</td>\n",
       "      <td>EVENING</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>3200 - 3299 BLOCK OF PARK PLACE NW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>East</td>\n",
       "      <td>North</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>Non-Violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>342866</td>\n",
       "      <td>342867.0</td>\n",
       "      <td>342867.0</td>\n",
       "      <td>3/21/2008 18:30</td>\n",
       "      <td>EVENING</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>GUN</td>\n",
       "      <td>2200 - 2299 BLOCK OF GEORGIA AVENUE NW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>East</td>\n",
       "      <td>North</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>Violent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>325340 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0         X       REPORT_DAT    SHIFT              OFFENSE  \\\n",
       "0          34334.0   34334.0    1/1/2017 7:57      DAY          THEFT/OTHER   \n",
       "1          34426.0   34426.0    2/6/2017 9:13      DAY          THEFT/OTHER   \n",
       "2          34649.0   34649.0  1/10/2017 20:16  EVENING          THEFT/OTHER   \n",
       "3          34650.0   34650.0   1/11/2017 7:18      DAY             BURGLARY   \n",
       "4          34651.0   34651.0  1/18/2017 20:29  EVENING         THEFT F/AUTO   \n",
       "...            ...       ...              ...      ...                  ...   \n",
       "342862    342863.0  342863.0  3/21/2008 13:00      DAY  MOTOR VEHICLE THEFT   \n",
       "342863    342864.0  342864.0  3/21/2008 21:08  EVENING          THEFT/OTHER   \n",
       "342864    342865.0  342865.0  3/21/2008 16:30  EVENING          THEFT/OTHER   \n",
       "342865    342866.0  342866.0  3/21/2008 17:15  EVENING  MOTOR VEHICLE THEFT   \n",
       "342866    342867.0  342867.0  3/21/2008 18:30  EVENING              ROBBERY   \n",
       "\n",
       "        METHOD                                     BLOCK  DISTRICT    PSA  \\\n",
       "0       OTHERS         500 - 599 BLOCK OF 19TH STREET NW       2.0  207.0   \n",
       "1       OTHERS       1200 - 1217 BLOCK OF 18TH STREET NW       2.0  208.0   \n",
       "2       OTHERS          1600 - 1699 BLOCK OF P STREET NW       2.0  208.0   \n",
       "3       OTHERS       1600 - 1617 BLOCK OF 14TH STREET NW       3.0  307.0   \n",
       "4       OTHERS  1000 - 1025 BLOCK OF WISCONSIN AVENUE NW       2.0  206.0   \n",
       "...        ...                                       ...       ...    ...   \n",
       "342862  OTHERS        2200 - 2230 BLOCK OF 6TH STREET NW       3.0  305.0   \n",
       "342863  OTHERS       3300 - 3341 BLOCK OF 16TH STREET NW       3.0  302.0   \n",
       "342864  OTHERS         1500 - 1530 BLOCK OF PARK ROAD NW       3.0  302.0   \n",
       "342865  OTHERS        3200 - 3299 BLOCK OF PARK PLACE NW       3.0  302.0   \n",
       "342866     GUN    2200 - 2299 BLOCK OF GEORGIA AVENUE NW       3.0  306.0   \n",
       "\n",
       "        WARD  ...    year month   day  hour minute  second    EW     NS  \\\n",
       "0        2.0  ...  2017.0   1.0   1.0   7.0   57.0     5.0  West  North   \n",
       "1        2.0  ...  2017.0   2.0   6.0   9.0   13.0    25.0  West  North   \n",
       "2        2.0  ...  2017.0   1.0  10.0  20.0   16.0    16.0  West  North   \n",
       "3        2.0  ...  2017.0   1.0  11.0   7.0   18.0    27.0  East  North   \n",
       "4        2.0  ...  2017.0   1.0  18.0  20.0   29.0    20.0  West  North   \n",
       "...      ...  ...     ...   ...   ...   ...    ...     ...   ...    ...   \n",
       "342862   1.0  ...  2008.0   3.0  21.0  13.0    0.0     0.0  East  North   \n",
       "342863   1.0  ...  2008.0   3.0  21.0  21.0    8.0     0.0  West  North   \n",
       "342864   1.0  ...  2008.0   3.0  21.0  16.0   30.0     0.0  East  North   \n",
       "342865   1.0  ...  2008.0   3.0  21.0  17.0   15.0     0.0  East  North   \n",
       "342866   1.0  ...  2008.0   3.0  21.0  18.0   30.0     0.0  East  North   \n",
       "\n",
       "             quad    crimetype  \n",
       "0       Northwest  Non-Violent  \n",
       "1       Northwest  Non-Violent  \n",
       "2       Northwest  Non-Violent  \n",
       "3       Northeast  Non-Violent  \n",
       "4       Northwest  Non-Violent  \n",
       "...           ...          ...  \n",
       "342862  Northeast  Non-Violent  \n",
       "342863  Northwest  Non-Violent  \n",
       "342864  Northeast  Non-Violent  \n",
       "342865  Northeast  Non-Violent  \n",
       "342866  Northeast      Violent  \n",
       "\n",
       "[325340 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xl = xl.dropna()\n",
    "xl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_centermost_point(cluster):\n",
    "    centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)\n",
    "    centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m)\n",
    "    return tuple(centermost_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "def dbscan_reduce(df, epsilon, x='lon', y='lat'):\n",
    "    start_time = time.time()\n",
    "    # represent points consistently as (lat, lon) and convert to radians to fit using haversine metric\n",
    "    coords = df.as_matrix(columns=[y, x])    \n",
    "    db = DBSCAN(eps=epsilon, min_samples=1, metric='haversine').fit(np.radians(coords))\n",
    "    cluster_labels = db.labels_\n",
    "    num_clusters = len(set(cluster_labels))\n",
    "    print('Number of clusters: {:,}'.format(num_clusters))\n",
    "    \n",
    "    clusters = pd.Series([coords[cluster_labels==n] for n in range(num_clusters)])\n",
    "    \n",
    "    # find the point in each cluster that is closest to its centroid\n",
    "    centermost_points = clusters.map(get_centermost_point)\n",
    "\n",
    "    # unzip the list of centermost points (lat, lon) tuples into separate lat and lon lists\n",
    "    lats, lons = zip(*centermost_points)\n",
    "    rep_points = pd.DataFrame({x:lons, y:lats})\n",
    "    rep_points.tail()\n",
    "    \n",
    "    # pull row from original data set where lat/lon match the lat/lon of each row of representative points\n",
    "    rs = rep_points.apply(lambda row: df[(df[y]==row[y]) & (df[x]==row[x])].iloc[0], axis=1)\n",
    "    \n",
    "    # all done, print outcome\n",
    "    message = 'Clustered {:,} points down to {:,} points, for {:.2f}% compression in {:,.2f} seconds.'\n",
    "    print(message.format(len(df), len(rs), 100*(1 - float(len(rs)) / len(df)), time.time()-start_time))    \n",
    "    return rs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now cluster the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "# first cluster the full gps location history data set coarsely, with epsilon=5km in radians\n",
    "eps_rad = 1 / kms_per_radian\n",
    "df_clustered = dbscan_reduce(xl, epsilon=eps_rad, x='XBLOCK', y='YBLOCK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\czhao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "X = xl.as_matrix(columns=['YBLOCK', 'XBLOCK']) \n",
    "dbscan = KMeans(n_clusters=8, random_state=0).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 38.9154694 , -77.02623922],\n",
       "       [ 38.92513351, -76.98407841],\n",
       "       [ 38.84870222, -76.98711734],\n",
       "       [ 38.95620814, -77.02286581],\n",
       "       [ 38.94634757, -77.07679637],\n",
       "       [ 38.89070668, -76.93880116],\n",
       "       [ 38.91057017, -77.04986661],\n",
       "       [ 38.89175456, -76.99329307]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'faker'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-9392b048bf01>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdbscan\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mfaker\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFactory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mfake\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFactory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# show a map of the worldwide data points\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'faker'"
     ]
    }
   ],
   "source": [
    "dbscan.labels_\n",
    "from faker import Factory\n",
    "fake = Factory.create()\n",
    "# show a map of the worldwide data points\n",
    "fig, ax = plt.subplots(figsize=[11, 8])\n",
    "rs_scatter = ax.scatter(xl['XBLOCK'], xl['YBLOCK'], c='m', edgecolor='None', alpha=0.3, s=120)\n",
    "a = [\"#1404d8\", \"7c8180\", \"#a67a80\", \"#3b4f46\", \"#ab44fa\", \"#972120\"]\n",
    "colors = {a:fake.hex_color() for a in set(dbscan.labels_)}\n",
    "colors_list = []\n",
    "for index, row in xl.iterrows():\n",
    "    colors_list.append(colors[dbscan.labels_[index]])\n",
    "    #ax.annotate('%d'%dbscan.labels_[index], xy=(row['XBLOCK'], row['YBLOCK']))\n",
    "# df_scatter = ax.scatter(df_gps['lon'], df_gps['lat'], c='k', alpha=0.5, s=3)\n",
    "ax.set_title(' DBSCAN clustered set')\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "ax.legend([df_scatter, rs_scatter], ['Full set', 'Reduced set'], loc='upper right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous clustering reduces dense clusters of points to a single point. Because min_samples=1, no point is noise - if there was a single gps reading in the middle of nowhere, it will be retained as a cluster/point. If the points were too dense along a linear strip, such as a highway, it will remove the entire line of points and leave just one behind, in the middle. To fix this, let's thin out the data set by retaining every *n*th point as df_sampled. Then, combine df_sampled with df_clustered, and re-cluster once again to reduce the thinned-out data set. The final product will retain lonely points in the middle of nowhere (that would otherwise possibly be lost by merely sampling every *n*th point) and will not strip out long linear corridors of dense points (that were removed in the first clustering step)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78710"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# next, sample every nth row (where n=sample_rate) of the full gps location history data set\n",
    "sample_rate = 20\n",
    "df_sampled = df_gps.iloc[range(0, len(df_gps), sample_rate)]\n",
    "len(df_sampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# combine the clustered and sampled sets\n",
    "df_combined = pd.concat([df_clustered, df_sampled], axis=0)\n",
    "df_combined = df_combined.reset_index().drop(labels='index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters: 4,714\n",
      "Clustered 79,157 points down to 4,714 points, for 94.04% compression in 56.08 seconds.\n"
     ]
    }
   ],
   "source": [
    "# then reduce by clustering again, finely this time with epsilon=0.1km in radians\n",
    "eps_rad = 0.1 / kms_per_radian\n",
    "df_final = dbscan_reduce(df_combined, epsilon=eps_rad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save to csv\n",
    "df_final.to_csv('data/location-history-clustered.csv', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g586dDB8+nKSkJG655RZycnJ45513uOaaaygsLOTWW2/lmGOO4f777+eee+7h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\n/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      "text/plain": [
       "<matplotlib.figure.Figure at 0xaac5438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show a map of the worldwide data points\n",
    "fig, ax = plt.subplots(figsize=[11, 8])\n",
    "rs_scatter = ax.scatter(df_final['lon'], df_final['lat'], c='m', edgecolor='None', alpha=0.3, s=120)\n",
    "df_scatter = ax.scatter(df_gps['lon'], df_gps['lat'], c='k', alpha=0.5, s=3)\n",
    "ax.set_title('Full data set vs DBSCAN reduced set')\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "ax.legend([df_scatter, rs_scatter], ['Full set', 'Reduced set'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PqDxE9i+ysbnrZoj1rvfi3egFHTubrDY1waIgD7/zcDSQjKknCo3GYTgY3n04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B1wbL7n/83Hc/x1+X/BWAw/Y8jMduemz4OWUqak6owQjuCAt8hBBCiNHz1ltvcfDBB8MnSCa3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fSB5cMPXxVC7AWcDbwONAPvjLk7pAPXCyHOkVJuv65tnnfeedTU1JQ9d8opp3DKKads6sOP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107b9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show a map of only the california data points\n",
    "fig, ax = plt.subplots(figsize=[11, 8])\n",
    "rs_scatter = ax.scatter(df_final['lon'], df_final['lat'], c='m', edgecolor='None', alpha=0.3, s=120)\n",
    "df_scatter = ax.scatter(df_gps['lon'], df_gps['lat'], c='k', alpha=0.5, s=3)\n",
    "ax.set_title('Full data set vs DBSCAN reduced set')\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "ax.set_xlim([-125, -113])\n",
    "ax.set_ylim([32, 42])\n",
    "ax.legend([df_scatter, rs_scatter], ['Full set', 'Reduced set'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Due to a memory issue introduced in newer versions of scikit-learn, you may have to downgrade to an earlier version such as 0.15 to get the DBSCAN clustering to run without errors if you have a huge dataset. \n",
    "\n",
    "To do this with Anaconda, run: `conda install scikit-learn=0.15`\n",
    "\n",
    "Or create a virtual environment, like: `conda create -n clusterenv python=3.4 scikit-learn=0.15 matplotlib pandas jupyter`, and then `pip install geopy` and `shapely`. If you're on Windows, pip install the shapely wheel [from Gohlke](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).\n",
    "\n",
    "More info:\n",
    "  - https://stackoverflow.com/questions/24024510/clustering-500-000-geospatial-points-in-python\n",
    "  - https://github.com/scikit-learn/scikit-learn/issues/5275\n",
    "  - https://github.com/scikit-learn/scikit-learn/issues/6256\n",
    "  - http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html\n",
    "  - https://en.wikipedia.org/wiki/Haversine_formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}