bernt nghbd example0

b0.ipynb

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 "metadata": {
  "name": "",
  "signature": "sha256:fcea1b22e187db4c83626fb3ce2d03cdcac3fec4ef3f1434e1c75577e1b25bdf"
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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import psycopg2\n",
      "\n",
      "conn = psycopg2.connect(\"dbname=bernt_work_i7d\")\n",
      "curs = conn.cursor()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn.rollback()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Bernt Neighborhoods - Feb ASK*\n",
      "\n",
      "show some basic information about a table of geometry in Postgres\n",
      "\n",
      "    bernt_work_i7d=# \\d nb_ask_final\n",
      "\n",
      "    gid        | integer               | not null default nextval('ask_final_gid_seq'::regclass)\n",
      "    id         | bigint                | \n",
      "    state      | character varying(4)  | \n",
      "    city       | character varying(40) | \n",
      "    neighborho | character varying(50) | \n",
      "    subneighbo | character varying(40) | \n",
      "    ward       | character varying(40) | \n",
      "    district   | character varying(40) | \n",
      "    metro      | character varying(40) | \n",
      "    submetro   | character varying(40) | \n",
      "    stplace    | character varying(90) | \n",
      "    the_geom   | geometry              | \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_sql = '''\n",
      "Select state, count(*) from nb_ask_final group by state  order by count(*) desc\n",
      "'''\n",
      "curs.execute(t_sql)\n",
      "#print curs.fetchall()\n",
      "t_states = []\n",
      "t_counts = []\n",
      "for elem in curs.fetchall():\n",
      "    t_states.append(elem[0])\n",
      "    t_counts.append(elem[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#plt.barh?\n",
      "#plt.yticks?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'How many states are represented ? ',len(t_states)\n",
      "\n",
      "##plot a few at a time\n",
      "p1 = t_states[0:14]\n",
      "p2 = t_counts[0:14]\n",
      "\n",
      "y_pos = np.arange(len(p1))\n",
      "y_pos += 1\n",
      "\n",
      "plt.barh( y_pos, p2,  align='center', alpha=0.4, height=1.1)\n",
      "plt.yticks(y_pos, t_states, rotation=42, size='small')\n",
      "plt.xlabel('Count')\n",
      "plt.title('Bernt Neighborhoods Count (largest), by State')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "How many states are represented ?  118\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OMQulJNVOJUF+DHAt8Dxx1evJRGt+GTG0cllupZMkVaXcID+FuFHIYUTWyc2S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\nSZIkSZIkSZIkSZIkSZIkSZIq9f8AvDI68D7Lu7sAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe14810a2d0>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_sql = '''\n",
      "Select distinct(metro, state) from nb_ask_final order by (metro,state)\n",
      "'''\n",
      "curs.execute(t_sql)\n",
      "print curs.fetchall()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_sql = '''\n",
      "Select metro,state,count(*) from nb_ask_final group by metro,state order by count(*) desc\n",
      "'''\n",
      "curs.execute(t_sql)\n",
      "#print curs.fetchall()\n",
      "t_metros = []\n",
      "t_states = []\n",
      "t_counts = []\n",
      "for elem in curs.fetchall():\n",
      "    t_metros.append(elem[0])\n",
      "    t_states.append(elem[1])\n",
      "    t_counts.append(elem[2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'How many metro areas are represented ? ',len(t_states)\n",
      "\n",
      "##plot a few at a time\n",
      "p0 = t_metros[0:14]\n",
      "p1 = t_states[0:14]\n",
      "p2 = t_counts[0:14]\n",
      "\n",
      "\n",
      "y_pos = np.arange(len(p1))\n",
      "\n",
      "plt.barh( y_pos, p2,  align='center', alpha=0.4, height=1.1)\n",
      "plt.yticks(y_pos, t_metros, rotation=6, size='small')\n",
      "plt.xlabel('Count')\n",
      "plt.title('Bernt Neighborhoods Count (highest), by Metro Area')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "How many metro areas are represented ?  118\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V6eK4MTJa/z7xesyrkbfra8gyLyIS0cpwn0yrLkLLWJ5C9StHAsOA76J1oXPx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\nnwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe150052c10>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url=\"http://ct.light42.com/www_shared/bernt_misc/bay_area_coverage_submetro.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://ct.light42.com/www_shared/bernt_misc/bay_area_coverage_submetro.png\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "<IPython.core.display.Image at 0x7fe1427f1ed0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}