darkblue-b/test_join_800.ipynb

## test_join_800.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:a15c442fea31302f6b75c5c7010d1c6b99011222cd1406891212158c8d5b919e"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import geopandas as gpd\n",
      "from geopandas import GeoDataFrame\n",
      "\n",
      "from geopandas.tools import sjoin\n",
      "\n",
      "## Minimal JOIN on GeoDataFrame\n",
      "##  26feb15 --dbb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import psycopg2\n",
      "conn = psycopg2.connect( \"dbname=geo_datamine_f\" )\n",
      "curs = conn.cursor()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#conn.rollback()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rpd = pd.DataFrame( \n",
      "  data=[ (0,'94601',3),(1,'94602',4),(2,'94603',5) ],\n",
      "  columns = ('id','zcta5ce10','val')  \n",
      ")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_sql = ''' SELECT gid, zcta5ce10, geom \n",
      "  FROM zips_2010 WHERE zcta5ce10 ~ '^946';\n",
      "'''\n",
      "gdf = gpd.read_postgis( t_sql, conn, crs=4326)\n",
      "gdf.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>gid</th>\n",
        "      <th>zcta5ce10</th>\n",
        "      <th>geom</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 23247</td>\n",
        "      <td> 94613</td>\n",
        "      <td> (POLYGON ((-122.178271 37.779933, -122.178281 ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 23248</td>\n",
        "      <td> 94618</td>\n",
        "      <td> (POLYGON ((-122.228048 37.830005, -122.228202 ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 23249</td>\n",
        "      <td> 94619</td>\n",
        "      <td> (POLYGON ((-122.193419 37.774343, -122.193507 ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 23250</td>\n",
        "      <td> 94621</td>\n",
        "      <td> (POLYGON ((-122.20084 37.76913, -122.200178 37...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 23236</td>\n",
        "      <td> 94601</td>\n",
        "      <td> (POLYGON ((-122.227171 37.791969, -122.226933 ...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "     gid zcta5ce10                                               geom\n",
        "0  23247     94613  (POLYGON ((-122.178271 37.779933, -122.178281 ...\n",
        "1  23248     94618  (POLYGON ((-122.228048 37.830005, -122.228202 ...\n",
        "2  23249     94619  (POLYGON ((-122.193419 37.774343, -122.193507 ...\n",
        "3  23250     94621  (POLYGON ((-122.20084 37.76913, -122.200178 37...\n",
        "4  23236     94601  (POLYGON ((-122.227171 37.791969, -122.226933 ...\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## calling GeoDataFrame merge does create a GeoDataFrame, of course !!\n",
      "\n",
      "res801 = GeoDataFrame.merge( gdf, rpd )\n",
      "print type(res801)\n",
      "#print res801\n",
      "res801.plot( column='val')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'geopandas.geodataframe.GeoDataFrame'>\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f6d977e2510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tdghyH3lJn203ki4bQEWk1i+1o6kdc3NzEhMT0ky7efMou3b9wty5c4SjCXKE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\nfvllNn8DuQfhbHmciIgIli1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       "text": [
        "<matplotlib.figure.Figure at 0x7f6d9776d990>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## calling PANDAS merge does not create a GeoDataFrame, of course\n",
      "\n",
      "res800 = pd.merge( rpd, gdf )\n",
      "print type(res800)\n",
      "res800"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdf.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}