geographic_viz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geographic Visualization Examples from Zillow Data\n",
    "\n",
    "**Useful Links**\n",
    "http://darribas.org/gds15/slides/lecture_03.html\n",
    "http://darribas.org/gds15/index.html\n",
    "https://www.census.gov/geo/maps-data/maps/datamapper.html\n",
    "https://catalog.data.gov/dataset?res_format=KML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'vincent'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-1efc9e406beb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[1;32mimport\u001b[0m \u001b[0mvincent\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'vincent'"
     ]
    }
   ],
   "source": [
    "import geopandas as gp\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import vincent\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "import sys\n",
    "import os\n",
    "# Importing our contained utilities.\n",
    "from utils import zillow_helpers as zh\n",
    "sys.path.insert(0,'../')\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle('main&propinfo46.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b5d4a8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29695748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('PropertyAddressLatitude', 'PropertyAddressLongitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_lat_long = df[['PropertyAddressLatitude', 'PropertyAddressLongitude']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 439771 entries, 0 to 439770\n",
      "Data columns (total 2 columns):\n",
      "PropertyAddressLatitude     59856 non-null float64\n",
      "PropertyAddressLongitude    59856 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 10.1 MB\n"
     ]
    }
   ],
   "source": [
    "df_lat_long.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 439771 entries, 0 to 439770\n",
      "Columns: 198 entries, TransId to BatchID_y\n",
      "dtypes: float64(84), int64(5), object(109)\n",
      "memory usage: 667.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# conda install -c esri arcgis\n",
    "from arcgis.gis import GIS\n",
    "my_gis = GIS()\n",
    "my_gis.map()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  },
  "widgets": {
   "state": {
    "3a8f774323014454a37f9dc1009de810": {
     "views": [
      {
       "cell_index": 7
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}