{
 "metadata": {
  "name": "pandas_timeseries"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Timeseries with pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Working with time-series data is an important part of data analysis.\n",
      "\n",
      "Starting with v0.8, the _pandas_ library has included a rich API for time-series manipulations.\n",
      "\n",
      "The _pandas_ time-series API includes:\n",
      "\n",
      "- Creating date ranges\n",
      "  - From files\n",
      "  - From scratch\n",
      "- Manipulations: Shift, resample, filter\n",
      "- Field accessors (e.g., hour of day)\n",
      "- Plotting\n",
      "- Time zones (localization and conversion)\n",
      "- Dual representations (point-in-time vs interval)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime, date, time\n",
      "import sys\n",
      "sys.version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'2.7.3 |EPD 7.3-2 (64-bit)| (default, Apr 12 2012, 11:14:05) \\n[GCC 4.0.1 (Apple Inc. build 5493)]'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from pandas import Series, DataFrame, Panel\n",
      "pd.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "'0.9.1.dev-88c9ef5'"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "'1.6.1'"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "mpl.rc('figure', figsize=(10, 8))\n",
      "mpl.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "'1.1.0'"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Example using tick data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sample trade ticks from 2011-11-01 to 2011-11-03 for a single security"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('data.csv', 'r') as fh:\n",
      "    print fh.readline() # headers\n",
      "    print fh.readline() # first row"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Date,Time,Price,Volume,Exchange Code,Sales Condition,Correction Indicator,Sequence Number,Trade Stop Indicator,Source of Trade,MDS 127 / TRF (Trade Reporting Facility) (*),Exclude Record Flag,Filtered Price\r\n",
        "\n",
        "11/01/2011,08:04:56.364,104.55,100,P,T,00,196,N,C,,,\r\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`parse_dates`: use a list or dict for flexible (possibly multi-column) date parsing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_csv('data.csv', \n",
      "                    parse_dates={'Timestamp': ['Date', 'Time']},\n",
      "                    index_col='Timestamp')\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 14632 entries, 2011-11-01 08:04:56.364000 to 2011-11-03 16:09:47.834000\n",
        "Data columns:\n",
        "Price                                           14632  non-null values\n",
        "Volume                                          14632  non-null values\n",
        "Exchange Code                                   14632  non-null values\n",
        "Sales Condition                                 14632  non-null values\n",
        "Correction Indicator                            14632  non-null values\n",
        "Sequence Number                                 14632  non-null values\n",
        "Trade Stop Indicator                            14632  non-null values\n",
        "Source of Trade                                 14632  non-null values\n",
        "MDS 127 / TRF (Trade Reporting Facility) (*)    2421  non-null values\n",
        "Exclude Record Flag                             28  non-null values\n",
        "Filtered Price                                  0  non-null values\n",
        "dtypes: float64(2), int64(3), object(6)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticks = data.ix[:, ['Price', 'Volume']]\n",
      "ticks.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>Price</th>\n",
        "      <th>Volume</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Timestamp</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:04:56.364000</strong></td>\n",
        "      <td> 104.55</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:53:07.459000</strong></td>\n",
        "      <td> 104.43</td>\n",
        "      <td>  150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:53:09.284000</strong></td>\n",
        "      <td> 104.43</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:54:03.921000</strong></td>\n",
        "      <td> 104.50</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:58:06.162000</strong></td>\n",
        "      <td> 104.46</td>\n",
        "      <td> 1000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                             Price  Volume\n",
        "Timestamp                                 \n",
        "2011-11-01 08:04:56.364000  104.55     100\n",
        "2011-11-01 08:53:07.459000  104.43     150\n",
        "2011-11-01 08:53:09.284000  104.43     100\n",
        "2011-11-01 08:54:03.921000  104.50     100\n",
        "2011-11-01 08:58:06.162000  104.46    1000"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####`resample`: regularization and frequency conversion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bars = ticks.Price.resample('1min', how='ohlc')\n",
      "bars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 3366 entries, 2011-11-01 08:05:00 to 2011-11-03 16:10:00\n",
        "Freq: T\n",
        "Data columns:\n",
        "open     1104  non-null values\n",
        "high     1104  non-null values\n",
        "low      1104  non-null values\n",
        "close    1104  non-null values\n",
        "dtypes: float64(4)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minute_range = bars.high - bars.low\n",
      "minute_range.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "count    1104.000000\n",
        "mean        0.017898\n",
        "std         0.018568\n",
        "min         0.000000\n",
        "25%         0.000875\n",
        "50%         0.013900\n",
        "75%         0.029000\n",
        "max         0.150000"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minute_return = bars.close / bars.open - 1\n",
      "minute_return.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "count    1104.000000\n",
        "mean        0.000003\n",
        "std         0.000191\n",
        "min        -0.001442\n",
        "25%        -0.000096\n",
        "50%         0.000000\n",
        "75%         0.000096\n",
        "max         0.000962"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute a VWAP using resample"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "volume = ticks.Volume.resample('1min', how='sum')\n",
      "value = ticks.prod(axis=1).resample('1min', how='sum')\n",
      "vwap = value / volume"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Convenient indexing for time series data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vwap.ix['2011-11-01 09:27':'2011-11-01 09:32']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Timestamp\n",
        "2011-11-01 09:27:00           NaN\n",
        "2011-11-01 09:28:00           NaN\n",
        "2011-11-01 09:29:00    104.170000\n",
        "2011-11-01 09:30:00    103.300800\n",
        "2011-11-01 09:31:00    104.231368\n",
        "2011-11-01 09:32:00    104.236679\n",
        "Freq: T"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####`at_time`: same (b)at_time (same bat channel)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bars.open.at_time('9:30')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "Timestamp\n",
        "2011-11-01 09:30:00    103.3008\n",
        "2011-11-02 09:30:00    103.8600\n",
        "2011-11-03 09:30:00         NaN\n",
        "Name: open"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bars.close.at_time('16:00')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "Timestamp\n",
        "2011-11-01 16:00:00    104.56\n",
        "2011-11-02 16:00:00    104.32\n",
        "2011-11-03 16:00:00    103.82\n",
        "Name: close"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####`between_time`: intraday time range"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered = vwap.between_time('10:00', '16:00')\n",
      "filtered.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "Timestamp\n",
        "2011-11-01 10:00:00    104.145000\n",
        "2011-11-01 10:01:00    104.245387\n",
        "2011-11-01 10:02:00    104.170771\n",
        "2011-11-01 10:03:00    104.170000\n",
        "2011-11-01 10:04:00    104.160952\n",
        "2011-11-01 10:05:00    104.110870\n",
        "2011-11-01 10:06:00    104.093576\n",
        "2011-11-01 10:07:00    104.145132\n",
        "2011-11-01 10:08:00    104.157924\n",
        "2011-11-01 10:09:00    104.154646\n",
        "2011-11-01 10:10:00    104.150756\n",
        "2011-11-01 10:11:00    104.182591\n",
        "2011-11-01 10:12:00    104.116803\n",
        "2011-11-01 10:13:00    104.092174\n",
        "2011-11-01 10:14:00    104.067768\n",
        "2011-11-01 10:15:00    104.070000\n",
        "2011-11-01 10:16:00    104.073333\n",
        "2011-11-01 10:17:00    104.080906\n",
        "2011-11-01 10:18:00    104.082566\n",
        "2011-11-01 10:19:00    104.085140"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vol = volume.between_time('10:00', '16:00')\n",
      "vol.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "Timestamp\n",
        "2011-11-01 10:00:00      200\n",
        "2011-11-01 10:01:00    18767\n",
        "2011-11-01 10:02:00    67287\n",
        "2011-11-01 10:03:00     2000\n",
        "2011-11-01 10:04:00     2100\n",
        "2011-11-01 10:05:00     2300\n",
        "2011-11-01 10:06:00    11509\n",
        "2011-11-01 10:07:00     3526\n",
        "2011-11-01 10:08:00      734\n",
        "2011-11-01 10:09:00     1106\n",
        "2011-11-01 10:10:00     1800\n",
        "2011-11-01 10:11:00     1760\n",
        "2011-11-01 10:12:00     2961\n",
        "2011-11-01 10:13:00     2300\n",
        "2011-11-01 10:14:00     5818\n",
        "2011-11-01 10:15:00      200\n",
        "2011-11-01 10:16:00      600\n",
        "2011-11-01 10:17:00     1040\n",
        "2011-11-01 10:18:00     1576\n",
        "2011-11-01 10:19:00     2813"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####`fillna`: handling missing data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered.ix['2011-11-03':'2011-11-04'].head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "Timestamp\n",
        "2011-11-03 10:00:00           NaN\n",
        "2011-11-03 10:01:00    103.968667\n",
        "2011-11-03 10:02:00           NaN\n",
        "2011-11-03 10:03:00    103.997500\n",
        "2011-11-03 10:04:00    104.044295\n",
        "2011-11-03 10:05:00           NaN\n",
        "2011-11-03 10:06:00    104.135118\n",
        "2011-11-03 10:07:00    104.130000\n",
        "2011-11-03 10:08:00    104.120000\n",
        "2011-11-03 10:09:00    104.101250\n",
        "2011-11-03 10:10:00           NaN\n",
        "2011-11-03 10:11:00           NaN\n",
        "2011-11-03 10:12:00           NaN\n",
        "2011-11-03 10:13:00           NaN\n",
        "2011-11-03 10:14:00    104.086667\n",
        "2011-11-03 10:15:00    104.049200\n",
        "2011-11-03 10:16:00    104.028333\n",
        "2011-11-03 10:17:00    103.976438\n",
        "2011-11-03 10:18:00    103.912492\n",
        "2011-11-03 10:19:00           NaN"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filled = filtered.fillna(method='pad', limit=1)\n",
      "filled.ix['2011-11-03':'2011-11-04'].head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "Timestamp\n",
        "2011-11-03 10:00:00    104.305099\n",
        "2011-11-03 10:01:00    103.968667\n",
        "2011-11-03 10:02:00    103.968667\n",
        "2011-11-03 10:03:00    103.997500\n",
        "2011-11-03 10:04:00    104.044295\n",
        "2011-11-03 10:05:00    104.044295\n",
        "2011-11-03 10:06:00    104.135118\n",
        "2011-11-03 10:07:00    104.130000\n",
        "2011-11-03 10:08:00    104.120000\n",
        "2011-11-03 10:09:00    104.101250\n",
        "2011-11-03 10:10:00    104.101250\n",
        "2011-11-03 10:11:00           NaN\n",
        "2011-11-03 10:12:00           NaN\n",
        "2011-11-03 10:13:00           NaN\n",
        "2011-11-03 10:14:00    104.086667\n",
        "2011-11-03 10:15:00    104.049200\n",
        "2011-11-03 10:16:00    104.028333\n",
        "2011-11-03 10:17:00    103.976438\n",
        "2011-11-03 10:18:00    103.912492\n",
        "2011-11-03 10:19:00    103.912492"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vol = vol.fillna(0.)\n",
      "vol.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "Timestamp\n",
        "2011-11-01 10:00:00      200\n",
        "2011-11-01 10:01:00    18767\n",
        "2011-11-01 10:02:00    67287\n",
        "2011-11-01 10:03:00     2000\n",
        "2011-11-01 10:04:00     2100\n",
        "2011-11-01 10:05:00     2300\n",
        "2011-11-01 10:06:00    11509\n",
        "2011-11-01 10:07:00     3526\n",
        "2011-11-01 10:08:00      734\n",
        "2011-11-01 10:09:00     1106\n",
        "2011-11-01 10:10:00     1800\n",
        "2011-11-01 10:11:00     1760\n",
        "2011-11-01 10:12:00     2961\n",
        "2011-11-01 10:13:00     2300\n",
        "2011-11-01 10:14:00     5818\n",
        "2011-11-01 10:15:00      200\n",
        "2011-11-01 10:16:00      600\n",
        "2011-11-01 10:17:00     1040\n",
        "2011-11-01 10:18:00     1576\n",
        "2011-11-01 10:19:00     2813"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Simple plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filled.ix['2011-11-03':'2011-11-04'].plot()\n",
      "plt.ylim(103.5, 104.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(103.5, 104.5)"
       ]
      },
      {
       "output_type": "display_data",
       "svg": [
        "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
        "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
        "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
        "<!-- Created with matplotlib (http://matplotlib.sourceforge.net/) -->\n",
        "<svg height=\"487pt\" version=\"1.1\" viewBox=\"0 0 616 487\" width=\"616pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
        " <defs>\n",
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     "metadata": {},
     "source": [
      "####Lead/lag"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticks.head()"
     ],
     "language": "python",
     "metadata": {},
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        "      <th></th>\n",
        "      <th></th>\n",
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        "      <td><strong>2011-11-01 08:04:56.364000</strong></td>\n",
        "      <td> 104.55</td>\n",
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        "      <td><strong>2011-11-01 08:53:07.459000</strong></td>\n",
        "      <td> 104.43</td>\n",
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        "      <td> 104.43</td>\n",
        "      <td>  100</td>\n",
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        "      <td><strong>2011-11-01 08:54:03.921000</strong></td>\n",
        "      <td> 104.50</td>\n",
        "      <td>  100</td>\n",
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        "      <td><strong>2011-11-01 08:58:06.162000</strong></td>\n",
        "      <td> 104.46</td>\n",
        "      <td> 1000</td>\n",
        "    </tr>\n",
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        "                             Price  Volume\n",
        "Timestamp                                 \n",
        "2011-11-01 08:04:56.364000  104.55     100\n",
        "2011-11-01 08:53:07.459000  104.43     150\n",
        "2011-11-01 08:53:09.284000  104.43     100\n",
        "2011-11-01 08:54:03.921000  104.50     100\n",
        "2011-11-01 08:58:06.162000  104.46    1000"
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     "source": [
      "**`shift`** realigns values"
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        "      <td><strong>2011-11-01 08:04:56.364000</strong></td>\n",
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        "      <td> 104.43</td>\n",
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        "      <td><strong>2011-11-01 08:58:06.162000</strong></td>\n",
        "      <td> 104.50</td>\n",
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       "prompt_number": 23,
       "text": [
        "                             Price  Volume\n",
        "Timestamp                                 \n",
        "2011-11-01 08:04:56.364000     NaN     NaN\n",
        "2011-11-01 08:53:07.459000  104.55     100\n",
        "2011-11-01 08:53:09.284000  104.43     150\n",
        "2011-11-01 08:54:03.921000  104.43     100\n",
        "2011-11-01 08:58:06.162000  104.50     100"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticks.shift(-1).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>Price</th>\n",
        "      <th>Volume</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Timestamp</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:04:56.364000</strong></td>\n",
        "      <td> 104.43</td>\n",
        "      <td>  150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:53:07.459000</strong></td>\n",
        "      <td> 104.43</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:53:09.284000</strong></td>\n",
        "      <td> 104.50</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:54:03.921000</strong></td>\n",
        "      <td> 104.46</td>\n",
        "      <td> 1000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:58:06.162000</strong></td>\n",
        "      <td> 104.46</td>\n",
        "      <td> 1000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "                             Price  Volume\n",
        "Timestamp                                 \n",
        "2011-11-01 08:04:56.364000  104.43     150\n",
        "2011-11-01 08:53:07.459000  104.43     100\n",
        "2011-11-01 08:53:09.284000  104.50     100\n",
        "2011-11-01 08:54:03.921000  104.46    1000\n",
        "2011-11-01 08:58:06.162000  104.46    1000"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**`tshift`** manipulates index values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticks.tshift(1, 'min').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>Price</th>\n",
        "      <th>Volume</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:05:56.364000</strong></td>\n",
        "      <td> 104.55</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:54:07.459000</strong></td>\n",
        "      <td> 104.43</td>\n",
        "      <td>  150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:54:09.284000</strong></td>\n",
        "      <td> 104.43</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:55:03.921000</strong></td>\n",
        "      <td> 104.50</td>\n",
        "      <td>  100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-11-01 08:59:06.162000</strong></td>\n",
        "      <td> 104.46</td>\n",
        "      <td> 1000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                             Price  Volume\n",
        "2011-11-01 08:05:56.364000  104.55     100\n",
        "2011-11-01 08:54:07.459000  104.43     150\n",
        "2011-11-01 08:54:09.284000  104.43     100\n",
        "2011-11-01 08:55:03.921000  104.50     100\n",
        "2011-11-01 08:59:06.162000  104.46    1000"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####SSS: stupidly simple strategy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minute_return.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "Timestamp\n",
        "2011-11-01 08:05:00     0\n",
        "2011-11-01 08:06:00   NaN\n",
        "2011-11-01 08:07:00   NaN\n",
        "2011-11-01 08:08:00   NaN\n",
        "2011-11-01 08:09:00   NaN\n",
        "Freq: T"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mr = minute_return.between_time('9:30', '16:00')\n",
      "mr.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "Timestamp\n",
        "2011-11-01 09:30:00    0.000000\n",
        "2011-11-01 09:31:00    0.000192\n",
        "2011-11-01 09:32:00   -0.000192\n",
        "2011-11-01 09:33:00    0.000000\n",
        "2011-11-01 09:34:00   -0.000096"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lagged = mr.shift(1)\n",
      "lagged.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "Timestamp\n",
        "2011-11-01 09:30:00         NaN\n",
        "2011-11-01 09:31:00    0.000000\n",
        "2011-11-01 09:32:00    0.000192\n",
        "2011-11-01 09:33:00   -0.000192\n",
        "2011-11-01 09:34:00    0.000000"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We shouldn't use shift here because:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lagged.at_time('9:30')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "Timestamp\n",
        "2011-11-01 09:30:00         NaN\n",
        "2011-11-02 09:30:00    0.000191\n",
        "2011-11-03 09:30:00    0.000192"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mr.at_time('16:00')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "Timestamp\n",
        "2011-11-01 16:00:00    0.000191\n",
        "2011-11-02 16:00:00    0.000192\n",
        "2011-11-03 16:00:00   -0.000193"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lagged = minute_return.tshift(1, 'min').between_time('9:30', '16:00')\n",
      "lagged.at_time('9:30')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "Timestamp\n",
        "2011-11-01 09:30:00     0\n",
        "2011-11-02 09:30:00     0\n",
        "2011-11-03 09:30:00   NaN"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's play"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.ols(y=mr, x=lagged)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "\n",
        "-------------------------Summary of Regression Analysis-------------------------\n",
        "\n",
        "Formula: Y ~ <x> + <intercept>\n",
        "\n",
        "Number of Observations:         1009\n",
        "Number of Degrees of Freedom:   2\n",
        "\n",
        "R-squared:         0.0011\n",
        "Adj R-squared:     0.0001\n",
        "\n",
        "Rmse:              0.0002\n",
        "\n",
        "F-stat (1, 1007):     1.1007, p-value:     0.2944\n",
        "\n",
        "Degrees of Freedom: model 1, resid 1007\n",
        "\n",
        "-----------------------Summary of Estimated Coefficients------------------------\n",
        "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
        "--------------------------------------------------------------------------------\n",
        "             x     0.0338     0.0322       1.05     0.2944    -0.0293     0.0969\n",
        "     intercept     0.0000     0.0000       0.50     0.6148    -0.0000     0.0000\n",
        "---------------------------------End of Summary---------------------------------\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mr = vwap / bars.open - 1\n",
      "mr = mr.between_time('9:30', '16:00')\n",
      "lagged = mr.tshift(1, 'min').between_time('9:30', '16:00')\n",
      "pd.ols(y=mr, x=lagged)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "\n",
        "-------------------------Summary of Regression Analysis-------------------------\n",
        "\n",
        "Formula: Y ~ <x> + <intercept>\n",
        "\n",
        "Number of Observations:         1007\n",
        "Number of Degrees of Freedom:   2\n",
        "\n",
        "R-squared:         0.0024\n",
        "Adj R-squared:     0.0014\n",
        "\n",
        "Rmse:              0.0001\n",
        "\n",
        "F-stat (1, 1005):     2.4262, p-value:     0.1196\n",
        "\n",
        "Degrees of Freedom: model 1, resid 1005\n",
        "\n",
        "-----------------------Summary of Estimated Coefficients------------------------\n",
        "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
        "--------------------------------------------------------------------------------\n",
        "             x     0.0503     0.0323       1.56     0.1196    -0.0130     0.1136\n",
        "     intercept     0.0000     0.0000       0.66     0.5120    -0.0000     0.0000\n",
        "---------------------------------End of Summary---------------------------------\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inter = mr * vol\n",
      "inter = inter.between_time('9:30', '16:00')\n",
      "lagged_inter = inter.tshift(1, 'min').between_time('9:30', '16:00')\n",
      "pd.ols(y=mr, x=lagged_inter)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "\n",
        "-------------------------Summary of Regression Analysis-------------------------\n",
        "\n",
        "Formula: Y ~ <x> + <intercept>\n",
        "\n",
        "Number of Observations:         919\n",
        "Number of Degrees of Freedom:   2\n",
        "\n",
        "R-squared:         0.0055\n",
        "Adj R-squared:     0.0044\n",
        "\n",
        "Rmse:              0.0001\n",
        "\n",
        "F-stat (1, 917):     5.0453, p-value:     0.0249\n",
        "\n",
        "Degrees of Freedom: model 1, resid 917\n",
        "\n",
        "-----------------------Summary of Estimated Coefficients------------------------\n",
        "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
        "--------------------------------------------------------------------------------\n",
        "             x    -0.0000     0.0000      -2.25     0.0249    -0.0000    -0.0000\n",
        "     intercept     0.0000     0.0000       0.98     0.3251    -0.0000     0.0000\n",
        "---------------------------------End of Summary---------------------------------\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Convert to percentage volume"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vol = vol.groupby(vol.index.day).transform(lambda x: x/x.sum())\n",
      "vol.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "Timestamp\n",
        "2011-11-01 10:00:00    0.000115\n",
        "2011-11-01 10:01:00    0.010772\n",
        "2011-11-01 10:02:00    0.038624\n",
        "2011-11-01 10:03:00    0.001148\n",
        "2011-11-01 10:04:00    0.001205"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Verify"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vol.resample('D', how='sum')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "Timestamp\n",
        "2011-11-02    1\n",
        "2011-11-03    1\n",
        "2011-11-04    1\n",
        "Freq: D"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inter = mr * vol\n",
      "inter = inter.between_time('9:30', '16:00')\n",
      "lagged_inter = inter.tshift(1, 'min').between_time('9:30', '16:00')\n",
      "pd.ols(y=mr, x=lagged_inter)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "\n",
        "-------------------------Summary of Regression Analysis-------------------------\n",
        "\n",
        "Formula: Y ~ <x> + <intercept>\n",
        "\n",
        "Number of Observations:         919\n",
        "Number of Degrees of Freedom:   2\n",
        "\n",
        "R-squared:         0.0050\n",
        "Adj R-squared:     0.0039\n",
        "\n",
        "Rmse:              0.0001\n",
        "\n",
        "F-stat (1, 917):     4.6031, p-value:     0.0322\n",
        "\n",
        "Degrees of Freedom: model 1, resid 917\n",
        "\n",
        "-----------------------Summary of Estimated Coefficients------------------------\n",
        "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
        "--------------------------------------------------------------------------------\n",
        "             x    -4.1357     1.9276      -2.15     0.0322    -7.9139    -0.3576\n",
        "     intercept     0.0000     0.0000       0.98     0.3283    -0.0000     0.0000\n",
        "---------------------------------End of Summary---------------------------------\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vivaldi FTW"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hour = vol.index.hour\n",
      "hourly_volume = vol.groupby(hour).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "hourly_volume.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
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     "source": [
      "Expanding window of hourly means for volume"
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hourly = vol.resample('H')\n",
      "\n",
      "def calc_mean(hr):\n",
      "    hr = time(hour=hr)\n",
      "    data = hourly.at_time(hr)\n",
      "    return pd.expanding_mean(data)\n",
      "\n",
      "df = pd.concat([calc_mean(hr) for hr in range(10, 16)])\n",
      "df = df.sort_index()\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "prompt_number": 40,
       "text": [
        "Timestamp\n",
        "2011-11-01 10:00:00    0.000115\n",
        "2011-11-01 11:00:00    0.002510\n",
        "2011-11-01 12:00:00    0.001253\n",
        "2011-11-01 13:00:00    0.000628\n",
        "2011-11-01 14:00:00    0.003013\n",
        "2011-11-01 15:00:00    0.004419\n",
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        "2011-11-02 14:00:00    0.002224\n",
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     "source": [
      "Compute deviations from the hourly means"
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     "collapsed": false,
     "input": [
      "clean_vol = vol.between_time('10:00', '15:59')\n",
      "dev = clean_vol - df.reindex(clean_vol.index, method='pad') # be careful over day boundaries\n",
      "dev"
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     "language": "python",
     "metadata": {},
     "outputs": [
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       "prompt_number": 41,
       "text": [
        "Timestamp\n",
        "2011-11-01 10:00:00    0.000000\n",
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        "2011-11-01 10:08:00    0.000307\n",
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        "2011-11-01 10:13:00    0.001205\n",
        "2011-11-01 10:14:00    0.003225\n",
        "...\n",
        "2011-11-03 15:45:00   -0.002881\n",
        "2011-11-03 15:46:00   -0.002411\n",
        "2011-11-03 15:47:00   -0.002708\n",
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        "2011-11-03 15:49:00   -0.001083\n",
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        "2011-11-03 15:58:00   -0.002521\n",
        "2011-11-03 15:59:00   -0.000219\n",
        "Length: 1080"
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     "prompt_number": 41
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     "input": [
      "inter = mr * dev\n",
      "inter = inter.between_time('10:00', '15:59')\n",
      "pd.ols(y=mr, x=inter.tshift(1, 'min'))"
     ],
     "language": "python",
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       "text": [
        "\n",
        "-------------------------Summary of Regression Analysis-------------------------\n",
        "\n",
        "Formula: Y ~ <x> + <intercept>\n",
        "\n",
        "Number of Observations:         919\n",
        "Number of Degrees of Freedom:   2\n",
        "\n",
        "R-squared:         0.0059\n",
        "Adj R-squared:     0.0048\n",
        "\n",
        "Rmse:              0.0001\n",
        "\n",
        "F-stat (1, 917):     5.3977, p-value:     0.0204\n",
        "\n",
        "Degrees of Freedom: model 1, resid 917\n",
        "\n",
        "-----------------------Summary of Estimated Coefficients------------------------\n",
        "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
        "--------------------------------------------------------------------------------\n",
        "             x    -4.6532     2.0029      -2.32     0.0204    -8.5789    -0.7276\n",
        "     intercept     0.0000     0.0000       0.97     0.3299    -0.0000     0.0000\n",
        "---------------------------------End of Summary---------------------------------\n"
       ]
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     "prompt_number": 42
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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Date range creation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`pd.date_range`"
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng = pd.date_range('2005', '2012', freq='M')\n",
      "rng"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "prompt_number": 43,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-10-31 00:00:00, ..., 2012-09-30 00:00:00]\n",
        "Length: 84, Freq: M, Timezone: None"
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     "prompt_number": 43
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.date_range('2005', periods=7*12, freq='M')"
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     "language": "python",
     "metadata": {},
     "outputs": [
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       "prompt_number": 44,
       "text": [
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        "[2005-10-31 00:00:00, ..., 2012-09-30 00:00:00]\n",
        "Length: 84, Freq: M, Timezone: None"
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      "pd.date_range(end='2012', periods=7*12, freq='M')"
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     "language": "python",
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        "[2005-10-31 00:00:00, ..., 2012-09-30 00:00:00]\n",
        "Length: 84, Freq: M, Timezone: None"
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     "source": [
      "#### Frequency constants\n",
      "\n",
      "<table>\n",
      "    <tr><td>Name</td><td>Description</td></tr>\n",
      "    <tr><td>D</td><td>Calendar day</td></tr>\n",
      "    <tr><td>B</td><td>Business day</td></tr>\n",
      "    <tr><td>M</td><td>Calendar end of month</td></tr>\n",
      "    <tr><td>MS</td><td>Calendar start of month</td></tr>\n",
      "    <tr><td>BM</td><td>Business end of month</td></tr>\n",
      "    <tr><td>BMS</td><td>Business start of month</td></tr>\n",
      "    <tr><td>W-{MON, TUE,...}</td><td>Week ending on Monday, Tuesday, ...</td></tr>\n",
      "    <tr><td>Q-{JAN, FEB,...}</td><td>Quarter end with year ending January, February...</td></tr>\n",
      "    <tr><td>QS-{JAN, FEB,...}</td><td>Quarter start with year ending January, February...</td></tr>\n",
      "    <tr><td>BQ-{JAN, FEB,...}</td><td>Business quarter end with year ending January, February...</td></tr>\n",
      "    <tr><td>BQS-{JAN, FEB,...}</td><td>Business quarter start with year ending January, February...</td></tr>\n",
      "    <tr><td>A-{JAN, FEB, ...}</td><td>Year end (December)</td></tr>\n",
      "    <tr><td>AS-{JAN, FEB, ...}</td><td>Year start (December)</td></tr>    \n",
      "    <tr><td>BA-{JAN, FEB, ...}</td><td>Business year end (December)</td></tr>  \n",
      "    <tr><td>BAS-{JAN, FEB, ...}</td><td>Business year start (December)</td></tr>  \n",
      "    <tr><td>H</td><td>Hour</td></tr>\n",
      "    <tr><td>T</td><td>Minute</td></tr>\n",
      "    <tr><td>s</td><td>Second</td></tr>\n",
      "    <tr><td>L, ms</td><td>Millisecond</td></tr>\n",
      "    <tr><td>U</td><td>Microsecond</td></tr>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Anchored offsets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.date_range('2005', periods=4, freq='Q')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-12-31 00:00:00, ..., 2006-09-30 00:00:00]\n",
        "Length: 4, Freq: Q-DEC, Timezone: None"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.date_range('2005', periods=4, freq='Q-NOV')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-11-30 00:00:00, ..., 2006-08-31 00:00:00]\n",
        "Length: 4, Freq: Q-NOV, Timezone: None"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Week anchor indicates end of week"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wkrng = pd.date_range('2012-10-25', periods=3, freq='W')\n",
      "wkrng"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2012-10-28 00:00:00, ..., 2012-11-11 00:00:00]\n",
        "Length: 3, Freq: W-SUN, Timezone: None"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wkrng[0].dayofweek"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "6"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Year anchor indicates year ending month"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.date_range('2005', periods=3, freq='A-JUN')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2006-06-30 00:00:00, ..., 2008-06-30 00:00:00]\n",
        "Length: 3, Freq: A-JUN, Timezone: None"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`DatetimeIndex` is a subclass of Index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "isinstance(rng, pd.Index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng[2:4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-12-31 00:00:00, 2006-01-31 00:00:00]\n",
        "Length: 2, Freq: M, Timezone: None"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use it for `Series/DataFrame` labelling"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = Series(randn(len(rng)), rng)\n",
      "s.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "2005-10-31    1.026565\n",
        "2005-11-30   -0.306949\n",
        "2005-12-31   -1.838600\n",
        "2006-01-31   -1.325459\n",
        "2006-02-28    1.642384\n",
        "Freq: M"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(randn(len(rng), 3), rng, ['X', 'Y', 'Z'])\n",
      "df.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>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2005-10-31</strong></td>\n",
        "      <td>-0.256664</td>\n",
        "      <td> 0.255390</td>\n",
        "      <td>-0.765260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2005-11-30</strong></td>\n",
        "      <td> 2.035113</td>\n",
        "      <td> 1.694331</td>\n",
        "      <td> 1.436986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2005-12-31</strong></td>\n",
        "      <td> 1.094142</td>\n",
        "      <td>-1.098397</td>\n",
        "      <td> 0.643988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-01-31</strong></td>\n",
        "      <td> 0.714353</td>\n",
        "      <td> 0.429468</td>\n",
        "      <td> 0.702067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-02-28</strong></td>\n",
        "      <td> 0.318068</td>\n",
        "      <td> 0.656602</td>\n",
        "      <td> 1.701144</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "                   X         Y         Z\n",
        "2005-10-31 -0.256664  0.255390 -0.765260\n",
        "2005-11-30  2.035113  1.694331  1.436986\n",
        "2005-12-31  1.094142 -1.098397  0.643988\n",
        "2006-01-31  0.714353  0.429468  0.702067\n",
        "2006-02-28  0.318068  0.656602  1.701144"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Label indexing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[datetime(2005, 1, 31) : datetime(2006, 12, 31)] #slice end inclusive"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "2005-10-31    1.026565\n",
        "2005-11-30   -0.306949\n",
        "2005-12-31   -1.838600\n",
        "2006-01-31   -1.325459\n",
        "2006-02-28    1.642384\n",
        "2006-03-31   -0.274467\n",
        "2006-04-30   -0.433675\n",
        "2006-05-31   -0.963449\n",
        "2006-06-30    1.112873\n",
        "2006-07-31   -0.089778\n",
        "2006-08-31    0.046948\n",
        "2006-09-30    0.035094\n",
        "2006-10-31    1.113169\n",
        "2006-11-30   -0.401821\n",
        "2006-12-31    0.723640\n",
        "Freq: M"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['2005-1-31':'2006-12-31']"
     ],
     "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>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2005-10-31</strong></td>\n",
        "      <td>-0.256664</td>\n",
        "      <td> 0.255390</td>\n",
        "      <td>-0.765260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2005-11-30</strong></td>\n",
        "      <td> 2.035113</td>\n",
        "      <td> 1.694331</td>\n",
        "      <td> 1.436986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2005-12-31</strong></td>\n",
        "      <td> 1.094142</td>\n",
        "      <td>-1.098397</td>\n",
        "      <td> 0.643988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-01-31</strong></td>\n",
        "      <td> 0.714353</td>\n",
        "      <td> 0.429468</td>\n",
        "      <td> 0.702067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-02-28</strong></td>\n",
        "      <td> 0.318068</td>\n",
        "      <td> 0.656602</td>\n",
        "      <td> 1.701144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-03-31</strong></td>\n",
        "      <td> 0.192433</td>\n",
        "      <td> 1.127046</td>\n",
        "      <td>-2.496724</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-04-30</strong></td>\n",
        "      <td> 1.417775</td>\n",
        "      <td> 0.845626</td>\n",
        "      <td>-0.713990</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-05-31</strong></td>\n",
        "      <td> 0.057333</td>\n",
        "      <td> 0.016464</td>\n",
        "      <td>-0.311196</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-06-30</strong></td>\n",
        "      <td>-0.535289</td>\n",
        "      <td> 0.131742</td>\n",
        "      <td> 0.459589</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-07-31</strong></td>\n",
        "      <td>-0.787894</td>\n",
        "      <td>-1.482390</td>\n",
        "      <td>-0.341181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-08-31</strong></td>\n",
        "      <td> 0.131652</td>\n",
        "      <td> 2.037986</td>\n",
        "      <td>-0.547023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-09-30</strong></td>\n",
        "      <td> 0.883980</td>\n",
        "      <td> 1.178719</td>\n",
        "      <td>-1.960677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-10-31</strong></td>\n",
        "      <td> 0.096330</td>\n",
        "      <td>-0.034388</td>\n",
        "      <td> 0.944521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-11-30</strong></td>\n",
        "      <td>-0.559477</td>\n",
        "      <td> 0.178275</td>\n",
        "      <td>-0.637401</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2006-12-31</strong></td>\n",
        "      <td>-0.730724</td>\n",
        "      <td> 0.363436</td>\n",
        "      <td>-1.048955</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "                   X         Y         Z\n",
        "2005-10-31 -0.256664  0.255390 -0.765260\n",
        "2005-11-30  2.035113  1.694331  1.436986\n",
        "2005-12-31  1.094142 -1.098397  0.643988\n",
        "2006-01-31  0.714353  0.429468  0.702067\n",
        "2006-02-28  0.318068  0.656602  1.701144\n",
        "2006-03-31  0.192433  1.127046 -2.496724\n",
        "2006-04-30  1.417775  0.845626 -0.713990\n",
        "2006-05-31  0.057333  0.016464 -0.311196\n",
        "2006-06-30 -0.535289  0.131742  0.459589\n",
        "2006-07-31 -0.787894 -1.482390 -0.341181\n",
        "2006-08-31  0.131652  2.037986 -0.547023\n",
        "2006-09-30  0.883980  1.178719 -1.960677\n",
        "2006-10-31  0.096330 -0.034388  0.944521\n",
        "2006-11-30 -0.559477  0.178275 -0.637401\n",
        "2006-12-31 -0.730724  0.363436 -1.048955"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Partial indexing**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['2005':'2006']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "2005-10-31    1.026565\n",
        "2005-11-30   -0.306949\n",
        "2005-12-31   -1.838600\n",
        "2006-01-31   -1.325459\n",
        "2006-02-28    1.642384\n",
        "2006-03-31   -0.274467\n",
        "2006-04-30   -0.433675\n",
        "2006-05-31   -0.963449\n",
        "2006-06-30    1.112873\n",
        "2006-07-31   -0.089778\n",
        "2006-08-31    0.046948\n",
        "2006-09-30    0.035094\n",
        "2006-10-31    1.113169\n",
        "2006-11-30   -0.401821\n",
        "2006-12-31    0.723640\n",
        "Freq: M"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "positional indexing still works"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[:2] # slice end exclusive"
     ],
     "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>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2005-10-31</strong></td>\n",
        "      <td>-0.256664</td>\n",
        "      <td> 0.255390</td>\n",
        "      <td>-0.765260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2005-11-30</strong></td>\n",
        "      <td> 2.035113</td>\n",
        "      <td> 1.694331</td>\n",
        "      <td> 1.436986</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "                   X         Y         Z\n",
        "2005-10-31 -0.256664  0.255390 -0.765260\n",
        "2005-11-30  2.035113  1.694331  1.436986"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Elements of DatetimeIndex"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Elements boxed as `Timestamp` (subclass of `datetime.datetime`)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elm = rng[0]\n",
      "elm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<Timestamp: 2005-10-31 00:00:00>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "isinstance(elm, datetime)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Why do we need this subclass?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elm.nanosecond"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Implemented internally using numpy.datetime64 (dtype='M8[ns]')"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "val = rng.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(val)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "val.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "dtype('datetime64[ns]')"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Upgrade Numpy to 1.7b to fix repr issue"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "val[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "1970-01-14 40:00:00"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or use DatetimeIndex.asobject for workaround"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.asobject.values[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "<Timestamp: 2005-10-31 00:00:00>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Other views"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.asobject"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "Index([2005-10-31 00:00:00, 2005-11-30 00:00:00, 2005-12-31 00:00:00, 2006-01-31 00:00:00, 2006-02-28 00:00:00, 2006-03-31 00:00:00, 2006-04-30 00:00:00, 2006-05-31 00:00:00, 2006-06-30 00:00:00, 2006-07-31 00:00:00, 2006-08-31 00:00:00, 2006-09-30 00:00:00, 2006-10-31 00:00:00, 2006-11-30 00:00:00, 2006-12-31 00:00:00, 2007-01-31 00:00:00, 2007-02-28 00:00:00, 2007-03-31 00:00:00, 2007-04-30 00:00:00, 2007-05-31 00:00:00, 2007-06-30 00:00:00, 2007-07-31 00:00:00, 2007-08-31 00:00:00, 2007-09-30 00:00:00, 2007-10-31 00:00:00, 2007-11-30 00:00:00, 2007-12-31 00:00:00, 2008-01-31 00:00:00, 2008-02-29 00:00:00, 2008-03-31 00:00:00, 2008-04-30 00:00:00, 2008-05-31 00:00:00, 2008-06-30 00:00:00, 2008-07-31 00:00:00, 2008-08-31 00:00:00, 2008-09-30 00:00:00, 2008-10-31 00:00:00, 2008-11-30 00:00:00, 2008-12-31 00:00:00, 2009-01-31 00:00:00, 2009-02-28 00:00:00, 2009-03-31 00:00:00, 2009-04-30 00:00:00, 2009-05-31 00:00:00, 2009-06-30 00:00:00, 2009-07-31 00:00:00, 2009-08-31 00:00:00, 2009-09-30 00:00:00, 2009-10-31 00:00:00, 2009-11-30 00:00:00, 2009-12-31 00:00:00, 2010-01-31 00:00:00, 2010-02-28 00:00:00, 2010-03-31 00:00:00, 2010-04-30 00:00:00, 2010-05-31 00:00:00, 2010-06-30 00:00:00, 2010-07-31 00:00:00, 2010-08-31 00:00:00, 2010-09-30 00:00:00, 2010-10-31 00:00:00, 2010-11-30 00:00:00, 2010-12-31 00:00:00, 2011-01-31 00:00:00, 2011-02-28 00:00:00, 2011-03-31 00:00:00, 2011-04-30 00:00:00, 2011-05-31 00:00:00, 2011-06-30 00:00:00, 2011-07-31 00:00:00, 2011-08-31 00:00:00, 2011-09-30 00:00:00, 2011-10-31 00:00:00, 2011-11-30 00:00:00, 2011-12-31 00:00:00, 2012-01-31 00:00:00, 2012-02-29 00:00:00, 2012-03-31 00:00:00, 2012-04-30 00:00:00, 2012-05-31 00:00:00, 2012-06-30 00:00:00, 2012-07-31 00:00:00, 2012-08-31 00:00:00, 2012-09-30 00:00:00], dtype=object)"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.to_pydatetime()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "array([2005-10-31 00:00:00, 2005-11-30 00:00:00, 2005-12-31 00:00:00,\n",
        "       2006-01-31 00:00:00, 2006-02-28 00:00:00, 2006-03-31 00:00:00,\n",
        "       2006-04-30 00:00:00, 2006-05-31 00:00:00, 2006-06-30 00:00:00,\n",
        "       2006-07-31 00:00:00, 2006-08-31 00:00:00, 2006-09-30 00:00:00,\n",
        "       2006-10-31 00:00:00, 2006-11-30 00:00:00, 2006-12-31 00:00:00,\n",
        "       2007-01-31 00:00:00, 2007-02-28 00:00:00, 2007-03-31 00:00:00,\n",
        "       2007-04-30 00:00:00, 2007-05-31 00:00:00, 2007-06-30 00:00:00,\n",
        "       2007-07-31 00:00:00, 2007-08-31 00:00:00, 2007-09-30 00:00:00,\n",
        "       2007-10-31 00:00:00, 2007-11-30 00:00:00, 2007-12-31 00:00:00,\n",
        "       2008-01-31 00:00:00, 2008-02-29 00:00:00, 2008-03-31 00:00:00,\n",
        "       2008-04-30 00:00:00, 2008-05-31 00:00:00, 2008-06-30 00:00:00,\n",
        "       2008-07-31 00:00:00, 2008-08-31 00:00:00, 2008-09-30 00:00:00,\n",
        "       2008-10-31 00:00:00, 2008-11-30 00:00:00, 2008-12-31 00:00:00,\n",
        "       2009-01-31 00:00:00, 2009-02-28 00:00:00, 2009-03-31 00:00:00,\n",
        "       2009-04-30 00:00:00, 2009-05-31 00:00:00, 2009-06-30 00:00:00,\n",
        "       2009-07-31 00:00:00, 2009-08-31 00:00:00, 2009-09-30 00:00:00,\n",
        "       2009-10-31 00:00:00, 2009-11-30 00:00:00, 2009-12-31 00:00:00,\n",
        "       2010-01-31 00:00:00, 2010-02-28 00:00:00, 2010-03-31 00:00:00,\n",
        "       2010-04-30 00:00:00, 2010-05-31 00:00:00, 2010-06-30 00:00:00,\n",
        "       2010-07-31 00:00:00, 2010-08-31 00:00:00, 2010-09-30 00:00:00,\n",
        "       2010-10-31 00:00:00, 2010-11-30 00:00:00, 2010-12-31 00:00:00,\n",
        "       2011-01-31 00:00:00, 2011-02-28 00:00:00, 2011-03-31 00:00:00,\n",
        "       2011-04-30 00:00:00, 2011-05-31 00:00:00, 2011-06-30 00:00:00,\n",
        "       2011-07-31 00:00:00, 2011-08-31 00:00:00, 2011-09-30 00:00:00,\n",
        "       2011-10-31 00:00:00, 2011-11-30 00:00:00, 2011-12-31 00:00:00,\n",
        "       2012-01-31 00:00:00, 2012-02-29 00:00:00, 2012-03-31 00:00:00,\n",
        "       2012-04-30 00:00:00, 2012-05-31 00:00:00, 2012-06-30 00:00:00,\n",
        "       2012-07-31 00:00:00, 2012-08-31 00:00:00, 2012-09-30 00:00:00], dtype=object)"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.to_pydatetime()[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "datetime.datetime(2005, 10, 31, 0, 0)"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Integer representation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(rng.asi8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.asi8.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "dtype('int64')"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.asi8[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "1130716800000000000"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###More fun with resampling and asfreq"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.index.freqstr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "'M'"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('30D').head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "2005-10-31    1.026565\n",
        "2005-11-30   -0.306949\n",
        "2005-12-30         NaN\n",
        "2006-01-29         NaN\n",
        "2006-02-28    1.642384\n",
        "2006-03-30         NaN\n",
        "2006-04-29         NaN\n",
        "2006-05-29         NaN\n",
        "2006-06-28         NaN\n",
        "2006-07-28         NaN\n",
        "Freq: 30D"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('30D', fill_method='ffill').head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "2005-10-31    1.026565\n",
        "2005-11-30   -0.306949\n",
        "2005-12-30   -0.306949\n",
        "2006-01-29   -1.838600\n",
        "2006-02-28    1.642384\n",
        "2006-03-30    1.642384\n",
        "2006-04-29   -0.274467\n",
        "2006-05-29   -0.433675\n",
        "2006-06-28   -0.963449\n",
        "2006-07-28    1.112873\n",
        "Freq: 30D"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Upsampling"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.ix[:3].resample('W')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "2005-11-06   NaN\n",
        "2005-11-13   NaN\n",
        "2005-11-20   NaN\n",
        "2005-11-27   NaN\n",
        "2005-12-04   NaN\n",
        "2005-12-11   NaN\n",
        "2005-12-18   NaN\n",
        "2005-12-25   NaN\n",
        "2006-01-01   NaN\n",
        "Freq: W-SUN"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.ix[:3].resample('W', fill_method='ffill')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "2005-11-06    1.026565\n",
        "2005-11-13    1.026565\n",
        "2005-11-20    1.026565\n",
        "2005-11-27    1.026565\n",
        "2005-12-04   -0.306949\n",
        "2005-12-11   -0.306949\n",
        "2005-12-18   -0.306949\n",
        "2005-12-25   -0.306949\n",
        "2006-01-01   -1.838600\n",
        "Freq: W-SUN"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "asfreq"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.asfreq('Q').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "2005-12-31   -1.838600\n",
        "2006-03-31   -0.274467\n",
        "2006-06-30    1.112873\n",
        "2006-09-30    0.035094\n",
        "2006-12-31    0.723640\n",
        "Freq: Q-DEC"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('Q', 'last').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "2005-12-31   -1.838600\n",
        "2006-03-31   -0.274467\n",
        "2006-06-30    1.112873\n",
        "2006-09-30    0.035094\n",
        "2006-12-31    0.723640\n",
        "Freq: Q-DEC"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "closed: 'left' or 'right' bin edge is closed (default is 'right')"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('Q').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "2005-12-31   -0.372995\n",
        "2006-03-31    0.014153\n",
        "2006-06-30   -0.094750\n",
        "2006-09-30   -0.002579\n",
        "2006-12-31    0.478330\n",
        "Freq: Q-DEC"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.ix[3:6].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "0.014152740332844638"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('Q', closed='left').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "2005-12-31    0.359808\n",
        "2006-03-31   -0.507225\n",
        "2006-06-30   -0.557197\n",
        "2006-09-30    0.356681\n",
        "2006-12-31    0.248814\n",
        "Freq: Q-DEC"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.ix[2:5].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "-0.50722477768944352"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "label: label the bin with 'left' or 'right' edge (default is 'right')"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('Q').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "2005-12-31   -0.372995\n",
        "2006-03-31    0.014153\n",
        "2006-06-30   -0.094750\n",
        "2006-09-30   -0.002579\n",
        "2006-12-31    0.478330\n",
        "Freq: Q-DEC"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('Q', label='left').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "2005-09-30   -0.372995\n",
        "2005-12-31    0.014153\n",
        "2006-03-31   -0.094750\n",
        "2006-06-30   -0.002579\n",
        "2006-09-30    0.478330\n",
        "Freq: Q-DEC"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "loffset: shift the result index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.resample('Q', label='left', loffset='-1D').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "2005-09-29   -0.372995\n",
        "2005-12-30    0.014153\n",
        "2006-03-30   -0.094750\n",
        "2006-06-29   -0.002579\n",
        "2006-09-29    0.478330"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Time zones"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Localization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.tz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = rng[0]\n",
      "d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "<Timestamp: 2005-10-31 00:00:00>"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.tz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "localized = rng.tz_localize('US/Eastern')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Localization assumes naive time is local (and not UTC)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "localized[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "<Timestamp: 2005-10-31 00:00:00-0500 EST, tz=US/Eastern>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "localized.asi8[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "1130734800000000000"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.asi8[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "1130716800000000000"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d_utc = d.tz_localize('UTC')\n",
      "d_utc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "<Timestamp: 2005-10-31 00:00:00+0000 UTC, tz=UTC>"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d_utc.tz_localize('US/Eastern')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "Exception",
       "evalue": "Cannot localize tz-aware Timestamp, use tz_convert for conversions",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-95-5747806c09b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md_utc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz_localize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'US/Eastern'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/changshe/code/pandas/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.Timestamp.tz_localize (pandas/src/tseries.c:32388)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mException\u001b[0m: Cannot localize tz-aware Timestamp, use tz_convert for conversions"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####TZ conversions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "localized.tz_convert('UTC')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-10-31 05:00:00, ..., 2012-09-30 04:00:00]\n",
        "Length: 84, Freq: M, Timezone: UTC"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d_ny = d_utc.tz_convert('US/Eastern')\n",
      "d_ny"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "<Timestamp: 2005-10-30 19:00:00-0500 EST, tz=US/Eastern>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.tz_convert('US/Eastern')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "Exception",
       "evalue": "Cannot convert tz-naive timestamps, use tz_localize to localize",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-98-ce1650e7ae36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrng\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'US/Eastern'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/changshe/code/pandas/pandas/tseries/index.pyc\u001b[0m in \u001b[0;36mtz_convert\u001b[0;34m(self, tz)\u001b[0m\n\u001b[1;32m   1275\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1276\u001b[0m             \u001b[0;31m# tz naive, use tz_localize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1277\u001b[0;31m             raise Exception('Cannot convert tz-naive timestamps, use '\n\u001b[0m\u001b[1;32m   1278\u001b[0m                             'tz_localize to localize')\n\u001b[1;32m   1279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mException\u001b[0m: Cannot convert tz-naive timestamps, use tz_localize to localize"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Period representation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A lot of time series data is better represented as intervals of time rather than points in time.\n",
      "\n",
      "This is represented in _pandas_ as Period and PeriodIndex"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Creating periods"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = pd.Period('2005', 'A')\n",
      "p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "Period('2005', 'A-DEC')"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Period('2006Q1', 'Q-MAR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "Period('2006Q1', 'Q-MAR')"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Period('2007-1-1', 'B')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "Period('2007-01-01', 'B')"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "No xxx-start frequencies"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Period('2005', 'AS')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "'AS-JAN'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-102-fafe81610ca8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPeriod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2005'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'AS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/changshe/code/pandas/pandas/tseries/period.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, value, freq, ordinal, year, month, quarter, day, hour, minute, second)\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m         \u001b[0mbase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_gfc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmult\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Only mult == 1 supported'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/changshe/code/pandas/pandas/tseries/frequencies.pyc\u001b[0m in \u001b[0;36mget_freq_code\u001b[0;34m(freqstr)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0mbase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_base_and_stride\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreqstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m     \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_period_str_to_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/changshe/code/pandas/pandas/tseries/frequencies.pyc\u001b[0m in \u001b[0;36m_period_str_to_code\u001b[0;34m(freqstr)\u001b[0m\n\u001b[1;32m    694\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_period_code_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfreqstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m     \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m         \u001b[0malias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_period_alias_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfreqstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    697\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_period_code_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0malias\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: 'AS-JAN'"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####PeriodRange"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.period_range('2005', '2012', freq='A')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "<class 'pandas.tseries.period.PeriodIndex'>\n",
        "freq: A-DEC\n",
        "[2005, ..., 2012]\n",
        "length: 8"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prng = pd.period_range('2005', periods=7, freq='A')\n",
      "prng"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "<class 'pandas.tseries.period.PeriodIndex'>\n",
        "freq: A-DEC\n",
        "[2005, ..., 2011]\n",
        "length: 7"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Converting between representations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "Period('2005', 'A-DEC')"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p.to_timestamp()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "<Timestamp: 2005-01-01 00:00:00>"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p.to_timestamp('M', 's')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "<Timestamp: 2005-01-31 00:00:00>"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p.to_timestamp('M', 'e')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "<Timestamp: 2005-12-31 00:00:00>"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prng.to_timestamp(how='e')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-12-31 00:00:00, ..., 2011-12-31 00:00:00]\n",
        "Length: 7, Freq: A-DEC, Timezone: None"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prng.to_timestamp('M', 'e')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-12-31 00:00:00, ..., 2011-12-31 00:00:00]\n",
        "Length: 7, Freq: A-DEC, Timezone: None"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2005-10-31 00:00:00, ..., 2012-09-30 00:00:00]\n",
        "Length: 84, Freq: M, Timezone: None"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.to_period()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "<class 'pandas.tseries.period.PeriodIndex'>\n",
        "freq: M\n",
        "[2005-10, ..., 2012-09]\n",
        "length: 84"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng.to_period('D')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "<class 'pandas.tseries.period.PeriodIndex'>\n",
        "freq: D\n",
        "[2005-10-31, ..., 2012-09-30]\n",
        "length: 84"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Bugggg"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "Period('2005', 'A-DEC')"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p.end_time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "<Timestamp: 2005-12-31 00:00:00>"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datetime(2005, 12, 31, 10, 0, 0) < p.end_time # WAT?!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look for a 0.9.1 bugfix release next week"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}