pandas_demos.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import DataFrame\n",
      "import numpy as np\n",
      "from datetime import datetime, timedelta\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.ones((20, 5))\n",
      "dates = [ datetime.now() + i * timedelta(days = 1) for i in range(20) ]\n",
      "colnames = map(str, range(5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.random.randn(len(dates), 5)\n",
      "df = DataFrame(data=data, index=dates)\n",
      "df[\"day\"] = df.index.map(lambda d: d.day)\n",
      "\n",
      "\n",
      "df1 = df.ix[df.day.isin([15,16,18]),:]\n",
      "print df1\n",
      "print \"droped:\"\n",
      "print df1.drop([\"day\"], axis = 1)\n",
      " \n",
      "print df.values\n",
      "\n",
      "res = df.ix[df.day.isin([15,16,18]),:].mean(axis = 0)\n",
      "print res.values\n",
      "\n",
      "\n",
      "print res"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Empty DataFrame\n",
        "Columns: [0, 1, 2, 3, 4, day]\n",
        "Index: []\n",
        "droped:\n",
        "Empty DataFrame\n",
        "Columns: [0, 1, 2, 3, 4]\n",
        "Index: []\n",
        "[[ -7.44831531e-01  -1.36531116e+00   1.28328351e-01   2.72524519e-02\n",
        "    1.37960795e-01   1.90000000e+01]\n",
        " [  3.42841288e-01  -7.97931535e-01   1.45823140e-02  -2.30170644e+00\n",
        "   -6.51910831e-01   2.00000000e+01]\n",
        " [ -4.17918494e-01  -2.51063035e-01   1.06589498e+00  -7.04373573e-01\n",
        "    1.35170753e-02   2.10000000e+01]\n",
        " [ -1.01906632e+00   3.16830294e-01  -3.00903684e-01  -4.73693362e-01\n",
        "   -7.46221439e-01   2.20000000e+01]\n",
        " [ -2.76801052e-01  -8.29202293e-01   1.98744312e+00   9.59348115e-01\n",
        "    1.97567067e+00   2.30000000e+01]\n",
        " [ -3.94247172e-02  -2.79199606e+00   1.09363541e+00  -7.61565252e-01\n",
        "    1.27244577e+00   2.40000000e+01]\n",
        " [ -8.50999281e-01  -6.05442503e-01  -5.66872290e-01  -8.77013002e-01\n",
        "    6.85959266e-01   2.50000000e+01]\n",
        " [  1.24646628e+00  -2.01967170e-01   7.28612647e-01   1.95553098e-01\n",
        "    3.52824363e-01   2.60000000e+01]\n",
        " [  4.36861607e-01   5.87942110e-01   1.41376042e+00   8.23691190e-02\n",
        "   -2.07049489e+00   2.70000000e+01]\n",
        " [ -4.95451473e-01  -3.08211176e-01   1.63313574e+00  -9.69930991e-01\n",
        "   -5.13323955e-01   2.80000000e+01]\n",
        " [ -2.82395355e-01  -6.89822165e-01   1.73839184e+00   2.06986626e-01\n",
        "    1.08340268e+00   2.90000000e+01]\n",
        " [  8.44687971e-01  -5.01199895e-01  -4.24066950e-01   4.71271807e-01\n",
        "   -5.80304597e-01   3.00000000e+01]\n",
        " [ -2.62193483e+00   2.11219816e-01   4.13580039e-01   1.36456077e+00\n",
        "   -1.88870241e-01   3.10000000e+01]\n",
        " [ -5.34029014e-01   1.39211272e+00   3.72745996e-01   6.47383479e-01\n",
        "   -1.66215818e+00   1.00000000e+00]\n",
        " [ -1.03891043e-02   5.87896090e-01  -4.38817109e-01  -3.78432562e-01\n",
        "   -9.64351359e-01   2.00000000e+00]\n",
        " [  1.58173046e-01  -6.22924443e-01  -1.57671414e+00  -2.75170219e+00\n",
        "   -9.36724478e-01   3.00000000e+00]\n",
        " [  1.21320663e+00   9.61226406e-01  -1.51097885e-01   2.38049436e-02\n",
        "   -1.27102314e+00   4.00000000e+00]\n",
        " [  2.88270212e-01  -4.80219037e-01  -1.89332205e+00   1.45570193e+00\n",
        "   -2.62494926e-02   5.00000000e+00]\n",
        " [ -1.60462649e+00   5.97960532e-01   7.68121794e-01  -2.12248712e+00\n",
        "   -1.31910639e+00   6.00000000e+00]\n",
        " [ -8.14038126e-01  -9.61299138e-02  -8.86871667e-02   1.23979662e+00\n",
        "    2.25528524e+00   7.00000000e+00]]\n",
        "[ nan  nan  nan  nan  nan  nan]\n",
        "0     NaN\n",
        "1     NaN\n",
        "2     NaN\n",
        "3     NaN\n",
        "4     NaN\n",
        "day   NaN\n",
        "dtype: float64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stamp_year = 2012\n",
      "d0 = datetime(stamp_year,  1,  1)\n",
      "d1 = datetime(stamp_year, 12, 31)\n",
      "\n",
      "\n",
      "dr = pd.DatetimeIndex(start = d0, end = d1, freq = pd.datetools.offsets.Day())\n",
      "\n",
      "df = pd.DataFrame(index=dr, data=range(366))\n",
      "print len(dr), dr[0], dr[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "366 2012-01-01 00:00:00 2012-12-31 00:00:00\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = df.select(lambda d: d > datetime(2012,10,5))\n",
      "len(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "87"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "plt.plot(x.index, x.ix[x.index, :])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1065f9d90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SQ5kFo2HDhli4cCFatmwJABg8eDCcnZ1x4sQJxMfHw8LCAgMGDAAADBo0CObm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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1065dda90>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.resample('5D')[:-1].tail(5)\n"
     ],
     "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>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-12-06</th>\n",
        "      <td> 342</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-11</th>\n",
        "      <td> 347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-16</th>\n",
        "      <td> 352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-21</th>\n",
        "      <td> 357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-26</th>\n",
        "      <td> 362</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "              0\n",
        "2012-12-06  342\n",
        "2012-12-11  347\n",
        "2012-12-16  352\n",
        "2012-12-21  357\n",
        "2012-12-26  362"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.resample('5D', how=np.mean, closed=\"left\").head(5)"
     ],
     "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>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-01</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-06</th>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-11</th>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-16</th>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-21</th>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "             0\n",
        "2012-01-01   2\n",
        "2012-01-06   7\n",
        "2012-01-11  12\n",
        "2012-01-16  17\n",
        "2012-01-21  22"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = pd.rolling_mean(df, 1, freq='5D')\n",
      "\n",
      "print df1.head(5)\n",
      "print df1.tail(5)\n",
      "print df1.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "             0\n",
        "2012-01-01   2\n",
        "2012-01-06   7\n",
        "2012-01-11  12\n",
        "2012-01-16  17\n",
        "2012-01-21  22\n",
        "              0\n",
        "2012-12-11  347\n",
        "2012-12-16  352\n",
        "2012-12-21  357\n",
        "2012-12-26  362\n",
        "2012-12-31  365\n",
        "                0\n",
        "count   74.000000\n",
        "mean   184.472973\n",
        "std    107.482808\n",
        "min      2.000000\n",
        "25%     93.250000\n",
        "50%    184.500000\n",
        "75%    275.750000\n",
        "max    365.000000\n"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail(5)"
     ],
     "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>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-12-27</th>\n",
        "      <td> 361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-28</th>\n",
        "      <td> 362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-29</th>\n",
        "      <td> 363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-30</th>\n",
        "      <td> 364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-31</th>\n",
        "      <td> 365</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "              0\n",
        "2012-12-27  361\n",
        "2012-12-28  362\n",
        "2012-12-29  363\n",
        "2012-12-30  364\n",
        "2012-12-31  365"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "'0.12.0'"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates = pd.DatetimeIndex(start = datetime(2001, 1, 1), end = datetime(2001, 12, 31), freq = \"1D\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(dates)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "365"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = pd.DataFrame(data = np.random.randn(len(dates)), index=dates)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1.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>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2001-01-01</th>\n",
        "      <td> 0.256407</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001-01-02</th>\n",
        "      <td>-0.213084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001-01-03</th>\n",
        "      <td>-0.267219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001-01-04</th>\n",
        "      <td> 0.139862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001-01-05</th>\n",
        "      <td> 0.360617</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                   0\n",
        "2001-01-01  0.256407\n",
        "2001-01-02 -0.213084\n",
        "2001-01-03 -0.267219\n",
        "2001-01-04  0.139862\n",
        "2001-01-05  0.360617"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfr = df1.resample(\"5D\")\n",
      "df1.plot()\n",
      "dfr.plot()\n",
      "dfr.tail()"
     ],
     "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>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2001-12-12</th>\n",
        "      <td>  2.196829e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001-12-17</th>\n",
        "      <td> -3.191230e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001-12-22</th>\n",
        "      <td>  1.587642e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001-12-27</th>\n",
        "      <td>  8.215294e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002-01-01</th>\n",
        "      <td> 5.840823e-308</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "                        0\n",
        "2001-12-12   2.196829e-01\n",
        "2001-12-17  -3.191230e-01\n",
        "2001-12-22   1.587642e-01\n",
        "2001-12-27   8.215294e-01\n",
        "2002-01-01  5.840823e-308"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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1hJX+IhNDmA4fRoF/6UsUainBnq1fo9282f2Z60u3UcJ64OefTwspAdWKkTtn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+AAAE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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107e8e310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Dv2SO0k9FRdKth4qLG77YxeipUfz8M6tsvv0W+O9/le3D6xM8vXqxCIp37F98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\nCO+GHAVBEAThEMMdRU1NDdLS0hAfH4/Q0FD06dMHO3bssK3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       "text": [
        "<matplotlib.figure.Figure at 0x1068888d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
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 ]
}