pandas_ii/nyc_open_data_pandas_ii.ipynb

{
 "metadata": {
  "name": "Pandas"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"Before Pandas\", len(dir())\nimport pandas\nfrom pandas import *\nprint \"After Pandas\", len(dir())\nfrom numpy import *\nprint \"After NumPy\", len(dir())",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Before Pandas 21\nAfter Pandas"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 165\nAfter NumPy 706\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "**Reading Data**"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import urlparse\nimport httplib\nimport pandas\n\ncsv_data = \"http://priede.bf.lu.lv/ftp/grozs/Datorlietas/Geog5028/TIS_PRG/HomePlanet/ASTEROID.CSV\"\nfwf_data = \"http://ssd.jpl.nasa.gov/dat/ELEMENTS.NUMBR\"\ntax_data = \"http://sbn.psi.edu/ferret/reformatTable.action?productId=TAXONOMY10_TAB&dataSetId=EAR-A-5-DDR-TAXONOMY-V6.0\"\n\ndef save_file(location):\n    \"\"\"Read data at url\"\"\"\n    url = urlparse.urlparse(location)\n    connection = httplib.HTTPConnection(url.netloc)\n    connection.connect()\n    connection.request('GET', url.path)\n    response = connection.getresponse()\n    data = response.read()\n    filename = '/tmp/' + url.path.split('/')[-1]\n    print filename\n    with open(filename, 'rw+') as tmp:\n        tmp.write(data)\n    return filename\n\n\n# Read NASA asteroid data\n#filename = save_file(fwf_data)\nfilename = \"data/ELEMENTS.NUMBR.txt\"\nwith open(filename, 'r') as f:\n    widths = map(lambda line: len(line) + 1, f.read().splitlines()[1].split(' '))\nasteroids_nasa = pandas.read_fwf(filename, widths=widths, skiprows=[1])\n\nwidths = [11, 18, 11, 13, 13, 14, 14, 14, 14, 13, 13, 12, 12, 10, 10, 15, 15, 16, 15, 22]\nasteroids_taxonomy = pandas.read_fwf(tax_data, widths=widths, skiprows=[1])\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print asteroids_nasa.xs(0)\nprint '\\n'\nprint asteroids_taxonomy.xs(0)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Num                        1\nAST_NAME               Ceres\nEpoch                  56600\nSemimajor Axis      2.766807\nEccentricity      0.07579726\ni                   10.59398\nw                   72.29215\nNode                80.32764\nM                   10.55758\nH                       3.34\nG                       0.12\nRef\\n               JPL 32\\n\nName: 0, dtype: object\n\n\nAST_NUMBER                                       1\nAST_NAME                                     Ceres\nPROV_ID                                          -\nTHOLEN_CLASS                                     G\nTHOLEN_PARAM                                    7G\nBARUCCI_CLASS                                   G0\nBARUCCI_PARAM                                   7I\nTEDESCO_CLASS                                   G?\nTEDESCO_PARAM                                   2I\nHOWELL_CLASS                                   CvB\nHOWELL_PARAM                                    65\nSMASS_CLASS                                      -\nSMASS_PARAM                                      -\nBUS_CLASS                                        C\nBUS_PARAM                                        s\nS3OS2_CLASS_TH                                   C\nS3OS2_CLASS_BB                                   C\nBUS_DEMEO_CLASS                                  C\nDEMEO_REF_CODE                                   a\nCOMMENT              \\n    -                    \\n\nName: 0, dtype: object\n"
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "**Descriptions**"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa.ix[:, 0:2].head(10)",
     "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>Num</th>\n      <th>Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>  1</td>\n      <td>   Ceres</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>  2</td>\n      <td>  Pallas</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>  3</td>\n      <td>    Juno</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>  4</td>\n      <td>   Vesta</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  5</td>\n      <td> Astraea</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>  6</td>\n      <td>    Hebe</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  7</td>\n      <td>    Iris</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>  8</td>\n      <td>   Flora</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>  9</td>\n      <td>   Metis</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td> 10</td>\n      <td>  Hygiea</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 26,
       "text": "   Num     Name\n0    1    Ceres\n1    2   Pallas\n2    3     Juno\n3    4    Vesta\n4    5  Astraea\n5    6     Hebe\n6    7     Iris\n7    8    Flora\n8    9    Metis\n9   10   Hygiea"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nIndex: 8 entries, count to max\nData columns (total 10 columns):\nNum      8  non-null values\nEpoch    8  non-null values\na        8  non-null values\ne        8  non-null values\ni        8  non-null values\nw        8  non-null values\nNode     8  non-null values\nM        8  non-null values\nH        8  non-null values\nG        8  non-null values\ndtypes: float64(10)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "                 Num          Epoch              a              e  \\\ncount  369956.000000  369956.000000  369956.000000  369956.000000   \nmean   184978.500000   56599.954484       2.714804       0.141123   \nstd    106797.242431      12.140078       2.343523       0.071773   \nmin         1.000000   52045.000000       0.617616       0.000093   \n25%     92489.750000   56600.000000       2.381873       0.088329   \n50%    184978.500000   56600.000000       2.625409       0.135797   \n75%    277467.250000   56600.000000       2.949065       0.185448   \nmax    369956.000000   56600.000000     780.919318       0.969913   \n\n                   i              w           Node              M  \\\ncount  369956.000000  369956.000000  369956.000000  369956.000000   \nmean        7.861723     180.868751     168.365091     178.125203   \nstd         5.744538     103.414791     102.345112     103.393876   \nmin         0.004510       0.000010       0.001770       0.000071   \n25%         3.576205      91.075698      81.163162      89.246209   \n50%         6.322225     181.707315     160.348955     175.939274   \n75%        10.870740     270.507750     250.499795     267.171522   \nmax       170.352670     359.999930     359.999390     359.999558   \n\n                   H              G  \ncount  369956.000000  369956.000000  \nmean       15.733793       0.150010  \nstd         1.290917       0.002427  \nmin        -1.190000      -0.250000  \n25%        15.020000       0.150000  \n50%        15.880000       0.150000  \n75%        16.610000       0.150000  \nmax        24.210000       0.600000  "
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa.ix[:, 3].describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": "count    369956.000000\nmean          2.714804\nstd           2.343523\nmin           0.617616\n25%           2.381873\n50%           2.625409\n75%           2.949065\nmax         780.919318\ndtype: float64"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa.ix[:, 'a'].describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": "count    369956.000000\nmean          2.714804\nstd           2.343523\nmin           0.617616\n25%           2.381873\n50%           2.625409\n75%           2.949065\nmax         780.919318\ndtype: float64"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa.ix[:, 3:7].corr()",
     "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>a</th>\n      <th>e</th>\n      <th>i</th>\n      <th>w</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td> 1.000000</td>\n      <td> 0.028508</td>\n      <td> 0.057180</td>\n      <td> 0.001323</td>\n    </tr>\n    <tr>\n      <th>e</th>\n      <td> 0.028508</td>\n      <td> 1.000000</td>\n      <td> 0.102916</td>\n      <td> 0.014825</td>\n    </tr>\n    <tr>\n      <th>i</th>\n      <td> 0.057180</td>\n      <td> 0.102916</td>\n      <td> 1.000000</td>\n      <td>-0.000701</td>\n    </tr>\n    <tr>\n      <th>w</th>\n      <td> 0.001323</td>\n      <td> 0.014825</td>\n      <td>-0.000701</td>\n      <td> 1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 30,
       "text": "          a         e         i         w\na  1.000000  0.028508  0.057180  0.001323\ne  0.028508  1.000000  0.102916  0.014825\ni  0.057180  0.102916  1.000000 -0.000701\nw  0.001323  0.014825 -0.000701  1.000000"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa.rename(columns={'Name': 'AST_NAME', 'a': 'Semimajor Axis', 'e': 'Eccentricity'})",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nInt64Index: 369956 entries, 0 to 369955\nData columns (total 12 columns):\nNum               369956  non-null values\nAST_NAME          369956  non-null values\nEpoch             369956  non-null values\nSemimajor Axis    369956  non-null values\nEccentricity      369956  non-null values\ni                 369956  non-null values\nw                 369956  non-null values\nNode              369956  non-null values\nM                 369956  non-null values\nH                 369956  non-null values\nG                 369956  non-null values\nRef\n              369956  non-null values\ndtypes: float64(8), int64(2), object(2)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 31,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 369956 entries, 0 to 369955\nData columns (total 12 columns):\nNum               369956  non-null values\nAST_NAME          369956  non-null values\nEpoch             369956  non-null values\nSemimajor Axis    369956  non-null values\nEccentricity      369956  non-null values\ni                 369956  non-null values\nw                 369956  non-null values\nNode              369956  non-null values\nM                 369956  non-null values\nH                 369956  non-null values\nG                 369956  non-null values\nRef\n              369956  non-null values\ndtypes: float64(8), int64(2), object(2)"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nInt64Index: 369956 entries, 0 to 369955\nData columns (total 12 columns):\nNum      369956  non-null values\nName     369956  non-null values\nEpoch    369956  non-null values\na        369956  non-null values\ne        369956  non-null values\ni        369956  non-null values\nw        369956  non-null values\nNode     369956  non-null values\nM        369956  non-null values\nH        369956  non-null values\nG        369956  non-null values\nRef\n     369956  non-null values\ndtypes: float64(8), int64(2), object(2)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 32,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 369956 entries, 0 to 369955\nData columns (total 12 columns):\nNum      369956  non-null values\nName     369956  non-null values\nEpoch    369956  non-null values\na        369956  non-null values\ne        369956  non-null values\ni        369956  non-null values\nw        369956  non-null values\nNode     369956  non-null values\nM        369956  non-null values\nH        369956  non-null values\nG        369956  non-null values\nRef\n     369956  non-null values\ndtypes: float64(8), int64(2), object(2)"
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa.rename(columns={'Name': 'AST_NAME', 'a': 'Semimajor Axis', 'e': 'Eccentricity'}, inplace=True)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_nasa",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nInt64Index: 369956 entries, 0 to 369955\nData columns (total 12 columns):\nNum               369956  non-null values\nAST_NAME          369956  non-null values\nEpoch             369956  non-null values\nSemimajor Axis    369956  non-null values\nEccentricity      369956  non-null values\ni                 369956  non-null values\nw                 369956  non-null values\nNode              369956  non-null values\nM                 369956  non-null values\nH                 369956  non-null values\nG                 369956  non-null values\nRef\n              369956  non-null values\ndtypes: float64(8), int64(2), object(2)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 34,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 369956 entries, 0 to 369955\nData columns (total 12 columns):\nNum               369956  non-null values\nAST_NAME          369956  non-null values\nEpoch             369956  non-null values\nSemimajor Axis    369956  non-null values\nEccentricity      369956  non-null values\ni                 369956  non-null values\nw                 369956  non-null values\nNode              369956  non-null values\nM                 369956  non-null values\nH                 369956  non-null values\nG                 369956  non-null values\nRef\n              369956  non-null values\ndtypes: float64(8), int64(2), object(2)"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "asteroids_taxonomy",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nInt64Index: 2615 entries, 0 to 2614\nData columns (total 20 columns):\nAST_NUMBER                2615  non-null values\nAST_NAME                  2615  non-null values\nPROV_ID                   2615  non-null values\nTHOLEN_CLASS              2615  non-null values\nTHOLEN_PARAM              2615  non-null values\nBARUCCI_CLASS             2615  non-null values\nBARUCCI_PARAM             2615  non-null values\nTEDESCO_CLASS             2615  non-null values\nTEDESCO_PARAM             2615  non-null values\nHOWELL_CLASS              2615  non-null values\nHOWELL_PARAM              2615  non-null values\nSMASS_CLASS               2615  non-null values\nSMASS_PARAM               2615  non-null values\nBUS_CLASS                 2615  non-null values\nBUS_PARAM                 2615  non-null values\nS3OS2_CLASS_TH            2615  non-null values\nS3OS2_CLASS_BB            2615  non-null values\nBUS_DEMEO_CLASS           2615  non-null values\nDEMEO_REF_CODE            2615  non-null values\nCOMMENT              \n    2615  non-null values\ndtypes: int64(1), object(19)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 55,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2615 entries, 0 to 2614\nData columns (total 20 columns):\nAST_NUMBER                2615  non-null values\nAST_NAME                  2615  non-null values\nPROV_ID                   2615  non-null values\nTHOLEN_CLASS              2615  non-null values\nTHOLEN_PARAM              2615  non-null values\nBARUCCI_CLASS             2615  non-null values\nBARUCCI_PARAM             2615  non-null values\nTEDESCO_CLASS             2615  non-null values\nTEDESCO_PARAM             2615  non-null values\nHOWELL_CLASS              2615  non-null values\nHOWELL_PARAM              2615  non-null values\nSMASS_CLASS               2615  non-null values\nSMASS_PARAM               2615  non-null values\nBUS_CLASS                 2615  non-null values\nBUS_PARAM                 2615  non-null values\nS3OS2_CLASS_TH            2615  non-null values\nS3OS2_CLASS_BB            2615  non-null values\nBUS_DEMEO_CLASS           2615  non-null values\nDEMEO_REF_CODE            2615  non-null values\nCOMMENT              \n    2615  non-null values\ndtypes: int64(1), object(19)"
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "merged = asteroids_nasa.merge(asteroids_taxonomy, on='AST_NAME')\nprint merged",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2415 entries, 0 to 2414\nData columns (total 31 columns):\nNum                       2415  non-null values\nAST_NAME                  2415  non-null values\nEpoch                     2415  non-null values\nSemimajor Axis            2415  non-null values\nEccentricity              2415  non-null values\ni                         2415  non-null values\nw                         2415  non-null values\nNode                      2415  non-null values\nM                         2415  non-null values\nH                         2415  non-null values\nG                         2415  non-null values\nRef\n                      2415  non-null values\nAST_NUMBER                2415  non-null values\nPROV_ID                   2415  non-null values\nTHOLEN_CLASS              2415  non-null values\nTHOLEN_PARAM              2415  non-null values\nBARUCCI_CLASS             2415  non-null values\nBARUCCI_PARAM             2415  non-null values\nTEDESCO_CLASS             2415  non-null values\nTEDESCO_PARAM             2415  non-null values\nHOWELL_CLASS              2415  non-null values\nHOWELL_PARAM              2415  non-null values\nSMASS_CLASS               2415  non-null values\nSMASS_PARAM               2415  non-null values\nBUS_CLASS                 2415  non-null values\nBUS_PARAM                 2415  non-null values\nS3OS2_CLASS_TH            2415  non-null values\nS3OS2_CLASS_BB            2415  non-null values\nBUS_DEMEO_CLASS           2415  non-null values\nDEMEO_REF_CODE            2415  non-null values\nCOMMENT              \n    2415  non-null values\ndtypes: float64(8), int64(3), object(20)\n"
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "merged.pop('Ref\\n')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 64,
       "text": "0      JPL 32\\n\n1      JPL 26\\n\n2     JPL 102\\n\n3      JPL 33\\n\n4      JPL 83\\n\n5      JPL 82\\n\n6     JPL 105\\n\n7      JPL 90\\n\n8      JPL 90\\n\n9      JPL 85\\n\n10     JPL 70\\n\n11     JPL 87\\n\n12     JPL 60\\n\n13     JPL 57\\n\n14     JPL 70\\n\n...\n2400     JPL 21\\n\n2401      JPL 2\\n\n2402      JPL 3\\n\n2403     JPL 50\\n\n2404    JPL 120\\n\n2405      JPL 1\\n\n2406     JPL 31\\n\n2407      JPL 1\\n\n2408      JPL 3\\n\n2409      JPL 1\\n\n2410      JPL 3\\n\n2411      JPL 1\\n\n2412      JPL 1\\n\n2413      JPL 1\\n\n2414    JPL 127\\n\nName: Ref\n, Length: 2415, dtype: object"
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "merged.pop('COMMENT              \\n')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 65,
       "text": "0     -                    \\n\n1     -                    \\n\n2     -                    \\n\n3     -                    \\n\n4     -                    \\n\n5     -                    \\n\n6     -                    \\n\n7     -                    \\n\n8     -                    \\n\n9     -                    \\n\n10    -                    \\n\n11    -                    \\n\n12    -                    \\n\n13    -                    \\n\n14    -                    \\n\n...\n2400    -                    \\n\n2401    -                    \\n\n2402    -                    \\n\n2403    -                    \\n\n2404    -                    \\n\n2405    -                    \\n\n2406    -                    \\n\n2407    -                    \\n\n2408    -                    \\n\n2409    -                    \\n\n2410    -                    \\n\n2411    -                    \\n\n2412    -                    \\n\n2413    -                    \\n\n2414    -                    \\n\nName: COMMENT              \n, Length: 2415, dtype: object"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "merged.columns",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 66,
       "text": "Index([Num, AST_NAME, Epoch, Semimajor Axis, Eccentricity, i, w, Node, M, H, G, AST_NUMBER, PROV_ID, THOLEN_CLASS, THOLEN_PARAM, BARUCCI_CLASS, BARUCCI_PARAM, TEDESCO_CLASS, TEDESCO_PARAM, HOWELL_CLASS, HOWELL_PARAM, SMASS_CLASS, SMASS_PARAM, BUS_CLASS, BUS_PARAM, S3OS2_CLASS_TH, S3OS2_CLASS_BB, BUS_DEMEO_CLASS, DEMEO_REF_CODE], dtype=object)"
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ref_codes = merged.pop('DEMEO_REF_CODE')\nmerged",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nInt64Index: 2415 entries, 0 to 2414\nData columns (total 28 columns):\nNum                2415  non-null values\nAST_NAME           2415  non-null values\nEpoch              2415  non-null values\nSemimajor Axis     2415  non-null values\nEccentricity       2415  non-null values\ni                  2415  non-null values\nw                  2415  non-null values\nNode               2415  non-null values\nM                  2415  non-null values\nH                  2415  non-null values\nG                  2415  non-null values\nAST_NUMBER         2415  non-null values\nPROV_ID            2415  non-null values\nTHOLEN_CLASS       2415  non-null values\nTHOLEN_PARAM       2415  non-null values\nBARUCCI_CLASS      2415  non-null values\nBARUCCI_PARAM      2415  non-null values\nTEDESCO_CLASS      2415  non-null values\nTEDESCO_PARAM      2415  non-null values\nHOWELL_CLASS       2415  non-null values\nHOWELL_PARAM       2415  non-null values\nSMASS_CLASS        2415  non-null values\nSMASS_PARAM        2415  non-null values\nBUS_CLASS          2415  non-null values\nBUS_PARAM          2415  non-null values\nS3OS2_CLASS_TH     2415  non-null values\nS3OS2_CLASS_BB     2415  non-null values\nBUS_DEMEO_CLASS    2415  non-null values\ndtypes: float64(8), int64(3), object(17)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 67,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2415 entries, 0 to 2414\nData columns (total 28 columns):\nNum                2415  non-null values\nAST_NAME           2415  non-null values\nEpoch              2415  non-null values\nSemimajor Axis     2415  non-null values\nEccentricity       2415  non-null values\ni                  2415  non-null values\nw                  2415  non-null values\nNode               2415  non-null values\nM                  2415  non-null values\nH                  2415  non-null values\nG                  2415  non-null values\nAST_NUMBER         2415  non-null values\nPROV_ID            2415  non-null values\nTHOLEN_CLASS       2415  non-null values\nTHOLEN_PARAM       2415  non-null values\nBARUCCI_CLASS      2415  non-null values\nBARUCCI_PARAM      2415  non-null values\nTEDESCO_CLASS      2415  non-null values\nTEDESCO_PARAM      2415  non-null values\nHOWELL_CLASS       2415  non-null values\nHOWELL_PARAM       2415  non-null values\nSMASS_CLASS        2415  non-null values\nSMASS_PARAM        2415  non-null values\nBUS_CLASS          2415  non-null values\nBUS_PARAM          2415  non-null values\nS3OS2_CLASS_TH     2415  non-null values\nS3OS2_CLASS_BB     2415  non-null values\nBUS_DEMEO_CLASS    2415  non-null values\ndtypes: float64(8), int64(3), object(17)"
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "merged.insert(11, 'DEMEO_REF_CODE', ref_codes)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "merged",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nInt64Index: 2415 entries, 0 to 2414\nData columns (total 29 columns):\nNum                2415  non-null values\nAST_NAME           2415  non-null values\nEpoch              2415  non-null values\nSemimajor Axis     2415  non-null values\nEccentricity       2415  non-null values\ni                  2415  non-null values\nw                  2415  non-null values\nNode               2415  non-null values\nM                  2415  non-null values\nH                  2415  non-null values\nG                  2415  non-null values\nDEMEO_REF_CODE     2415  non-null values\nAST_NUMBER         2415  non-null values\nPROV_ID            2415  non-null values\nTHOLEN_CLASS       2415  non-null values\nTHOLEN_PARAM       2415  non-null values\nBARUCCI_CLASS      2415  non-null values\nBARUCCI_PARAM      2415  non-null values\nTEDESCO_CLASS      2415  non-null values\nTEDESCO_PARAM      2415  non-null values\nHOWELL_CLASS       2415  non-null values\nHOWELL_PARAM       2415  non-null values\nSMASS_CLASS        2415  non-null values\nSMASS_PARAM        2415  non-null values\nBUS_CLASS          2415  non-null values\nBUS_PARAM          2415  non-null values\nS3OS2_CLASS_TH     2415  non-null values\nS3OS2_CLASS_BB     2415  non-null values\nBUS_DEMEO_CLASS    2415  non-null values\ndtypes: float64(8), int64(3), object(18)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 69,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2415 entries, 0 to 2414\nData columns (total 29 columns):\nNum                2415  non-null values\nAST_NAME           2415  non-null values\nEpoch              2415  non-null values\nSemimajor Axis     2415  non-null values\nEccentricity       2415  non-null values\ni                  2415  non-null values\nw                  2415  non-null values\nNode               2415  non-null values\nM                  2415  non-null values\nH                  2415  non-null values\nG                  2415  non-null values\nDEMEO_REF_CODE     2415  non-null values\nAST_NUMBER         2415  non-null values\nPROV_ID            2415  non-null values\nTHOLEN_CLASS       2415  non-null values\nTHOLEN_PARAM       2415  non-null values\nBARUCCI_CLASS      2415  non-null values\nBARUCCI_PARAM      2415  non-null values\nTEDESCO_CLASS      2415  non-null values\nTEDESCO_PARAM      2415  non-null values\nHOWELL_CLASS       2415  non-null values\nHOWELL_PARAM       2415  non-null values\nSMASS_CLASS        2415  non-null values\nSMASS_PARAM        2415  non-null values\nBUS_CLASS          2415  non-null values\nBUS_PARAM          2415  non-null values\nS3OS2_CLASS_TH     2415  non-null values\nS3OS2_CLASS_BB     2415  non-null values\nBUS_DEMEO_CLASS    2415  non-null values\ndtypes: float64(8), int64(3), object(18)"
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pandas.stats.api import ols\nmodel = ols(y=asteroids_nasa.xs('Semimajor Axis', axis=1), x=asteroids_nasa.xs('i', axis=1))\nprint model",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n-------------------------Summary of Regression Analysis-------------------------\n\nFormula: Y ~ <x> + <intercept>\n\nNumber of Observations:         369956\nNumber of Degrees of Freedom:   2\n\nR-squared:         0.0033\nAdj R-squared:     0.0033\n\nRmse:              2.3397\n\nF-stat (1, 369954):  1213.5610, p-value:     0.0000\n\nDegrees of Freedom: model 1, resid 369954\n\n-----------------------Summary of Estimated Coefficients------------------------\n      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n--------------------------------------------------------------------------------\n             x     0.0233     0.0007      34.84     0.0000     0.0220     0.0246\n     intercept     2.5314     0.0065     388.25     0.0000     2.5186     2.5442\n---------------------------------End of Summary---------------------------------\n\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import matplotlib.pyplot as plt\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "No module named matplotlib.pyplot",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-6-eff513f636fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\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[0m\n\u001b[0m",
        "\u001b[0;31mImportError\u001b[0m: No module named matplotlib.pyplot"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}