Chapter11_12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.parser import parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import Hour, Minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import Day, MonthEnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2019, 3, 15, 20, 47, 20, 47414)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2019, 3, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.year, now.month, now.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta = datetime(2011, 1, 7) - datetime(2008, 6, 24, 8, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=926, seconds=56700)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "926"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56700"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime(2011, 1, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 19, 0, 0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start + timedelta(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 12, 14, 0, 0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start - 2*timedelta(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = datetime(2011, 1, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "value = '2001-01-03'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2001, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime(value, '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "datestrs = ['7/6/2011', '8/6/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[datetime.strptime(x, '%m/%d/%Y') for x in datestrs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse('2011-01-03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1997, 1, 31, 22, 45)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse('Jan 31, 1997 10:45 PM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 12, 6, 0, 0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse('6/12/2011', dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "datestrs = ['2011-07-06 12:00:00','2011-08-06 00:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06 12:00:00', '2011-08-06 00:00:00'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(datestrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = pd.to_datetime(datestrs + [ None ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06 12:00:00', '2011-08-06 00:00:00', 'NaT'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaT"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = [datetime(2011,1,2),datetime(2011,1,5),datetime(2011,1,7),\n",
    "        datetime(2011,1,8),datetime(2011,1,10),datetime(2011,1,12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Series(np.random.randn(6), index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.159922\n",
       "2011-01-05    0.730815\n",
       "2011-01-07   -0.315554\n",
       "2011-01-08   -1.278903\n",
       "2011-01-10    0.611757\n",
       "2011-01-12    0.699297\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-02', '2011-01-05', '2011-01-07', '2011-01-08',\n",
       "               '2011-01-10', '2011-01-12'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.319843\n",
       "2011-01-05         NaN\n",
       "2011-01-07   -0.631107\n",
       "2011-01-08         NaN\n",
       "2011-01-10    1.223514\n",
       "2011-01-12         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts + ts[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = ts.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-02 00:00:00')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = ts.index[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3155536576433482"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[stamp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6117568876483102"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[ '1/10/2011' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6117568876483102"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[ '20110110' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "longer_ts = Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.652150\n",
       "2000-01-02    0.284531\n",
       "2000-01-03    0.018196\n",
       "2000-01-04   -0.148560\n",
       "2000-01-05    0.337829\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002-09-22    0.828324\n",
       "2002-09-23   -0.549581\n",
       "2002-09-24   -0.162035\n",
       "2002-09-25    0.109025\n",
       "2002-09-26    0.802719\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-01   -0.326189\n",
       "2001-01-02   -1.726268\n",
       "2001-01-03   -1.108551\n",
       "2001-01-04   -0.761314\n",
       "2001-01-05   -0.177715\n",
       "2001-01-06   -1.318039\n",
       "2001-01-07    0.950960\n",
       "2001-01-08    0.903224\n",
       "2001-01-09   -0.803048\n",
       "2001-01-10    1.057701\n",
       "2001-01-11    0.370880\n",
       "2001-01-12    0.219969\n",
       "2001-01-13   -2.067432\n",
       "2001-01-14    0.812192\n",
       "2001-01-15    0.614266\n",
       "2001-01-16    1.106417\n",
       "2001-01-17   -1.055612\n",
       "2001-01-18    1.593061\n",
       "2001-01-19    1.117462\n",
       "2001-01-20   -1.529112\n",
       "2001-01-21    0.874717\n",
       "2001-01-22   -2.310922\n",
       "2001-01-23   -0.969617\n",
       "2001-01-24    0.542932\n",
       "2001-01-25    0.072472\n",
       "2001-01-26   -0.527031\n",
       "2001-01-27    1.107043\n",
       "2001-01-28   -0.125417\n",
       "2001-01-29    1.139929\n",
       "2001-01-30    0.029955\n",
       "                ...   \n",
       "2001-12-02    0.900393\n",
       "2001-12-03   -0.310121\n",
       "2001-12-04   -0.299248\n",
       "2001-12-05   -0.972230\n",
       "2001-12-06    0.341638\n",
       "2001-12-07   -0.863106\n",
       "2001-12-08    2.055043\n",
       "2001-12-09   -1.117437\n",
       "2001-12-10    1.901754\n",
       "2001-12-11   -0.434311\n",
       "2001-12-12    0.684716\n",
       "2001-12-13   -1.200158\n",
       "2001-12-14   -0.544994\n",
       "2001-12-15   -0.864945\n",
       "2001-12-16    2.027660\n",
       "2001-12-17   -1.929022\n",
       "2001-12-18   -0.895989\n",
       "2001-12-19   -0.526521\n",
       "2001-12-20    0.412177\n",
       "2001-12-21    0.642396\n",
       "2001-12-22   -1.364559\n",
       "2001-12-23    0.861793\n",
       "2001-12-24    0.076816\n",
       "2001-12-25   -0.696647\n",
       "2001-12-26    1.273962\n",
       "2001-12-27   -1.319156\n",
       "2001-12-28   -0.977983\n",
       "2001-12-29   -0.156131\n",
       "2001-12-30    0.387039\n",
       "2001-12-31   -0.331582\n",
       "Freq: D, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts[ '2001' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-05-01    2.126699\n",
       "2001-05-02    0.411139\n",
       "2001-05-03    0.059369\n",
       "2001-05-04   -0.627850\n",
       "2001-05-05    1.004102\n",
       "2001-05-06    1.241345\n",
       "2001-05-07   -0.276506\n",
       "2001-05-08    0.592605\n",
       "2001-05-09    1.478300\n",
       "2001-05-10    0.954844\n",
       "2001-05-11   -0.398063\n",
       "2001-05-12    0.019320\n",
       "2001-05-13   -0.526927\n",
       "2001-05-14   -1.084186\n",
       "2001-05-15   -0.006387\n",
       "2001-05-16   -1.755455\n",
       "2001-05-17    0.840864\n",
       "2001-05-18    0.908336\n",
       "2001-05-19   -0.735310\n",
       "2001-05-20    0.509201\n",
       "2001-05-21    0.275688\n",
       "2001-05-22    0.566180\n",
       "2001-05-23   -0.206879\n",
       "2001-05-24   -0.660691\n",
       "2001-05-25   -0.463866\n",
       "2001-05-26    2.627613\n",
       "2001-05-27   -1.816174\n",
       "2001-05-28    0.145187\n",
       "2001-05-29   -1.685749\n",
       "2001-05-30    0.239355\n",
       "2001-05-31   -0.581003\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts[ '2001-05' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07   -0.315554\n",
       "2011-01-08   -1.278903\n",
       "2011-01-10    0.611757\n",
       "2011-01-12    0.699297\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[ datetime(2011, 1, 7): ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.159922\n",
       "2011-01-05    0.730815\n",
       "2011-01-07   -0.315554\n",
       "2011-01-08   -1.278903\n",
       "2011-01-10    0.611757\n",
       "2011-01-12    0.699297\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07   -0.315554\n",
       "2011-01-08   -1.278903\n",
       "2011-01-10    0.611757\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['1/6/2011':'1/11/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.159922\n",
       "2011-01-05    0.730815\n",
       "2011-01-07   -0.315554\n",
       "2011-01-08   -1.278903\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.truncate(after='1/9/2011')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('1/1/2000', periods=100, freq='W-WED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_df =pd.DataFrame(np.random.randn(100,4),index=dates,columns = ['colorado','texas','new york','ohio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>colorado</th>\n",
       "      <th>texas</th>\n",
       "      <th>new york</th>\n",
       "      <th>ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-05-02</th>\n",
       "      <td>1.023915</td>\n",
       "      <td>-2.373437</td>\n",
       "      <td>-1.247358</td>\n",
       "      <td>0.392012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>1.278943</td>\n",
       "      <td>-0.088795</td>\n",
       "      <td>-0.947360</td>\n",
       "      <td>0.596230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16</th>\n",
       "      <td>-0.752097</td>\n",
       "      <td>-0.504396</td>\n",
       "      <td>-0.351417</td>\n",
       "      <td>-1.695370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-23</th>\n",
       "      <td>-0.790025</td>\n",
       "      <td>1.947463</td>\n",
       "      <td>0.580501</td>\n",
       "      <td>0.263757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-30</th>\n",
       "      <td>1.260969</td>\n",
       "      <td>0.649443</td>\n",
       "      <td>0.561933</td>\n",
       "      <td>0.101202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            colorado     texas  new york      ohio\n",
       "2001-05-02  1.023915 -2.373437 -1.247358  0.392012\n",
       "2001-05-09  1.278943 -0.088795 -0.947360  0.596230\n",
       "2001-05-16 -0.752097 -0.504396 -0.351417 -1.695370\n",
       "2001-05-23 -0.790025  1.947463  0.580501  0.263757\n",
       "2001-05-30  1.260969  0.649443  0.561933  0.101202"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_df.loc['5-2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.DatetimeIndex([ '1/1/2000', '1/2/2000', '1/2/2000', '1/2/2000', '1/3/2000' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_ts = Series(np.arange(5), index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "2000-01-03    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts[ '1/3/2000' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts[ '1/2/2000' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = dup_ts.groupby(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    2\n",
       "2000-01-03    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    3\n",
       "2000-01-03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.159922\n",
       "2011-01-05    0.730815\n",
       "2011-01-07   -0.315554\n",
       "2011-01-08   -1.278903\n",
       "2011-01-10    0.611757\n",
       "2011-01-12    0.699297\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "resampler = ts.resample('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.date_range('2012-04-01', '2012-06-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20',\n",
       "               '2012-04-21', '2012-04-22', '2012-04-23', '2012-04-24',\n",
       "               '2012-04-25', '2012-04-26', '2012-04-27', '2012-04-28',\n",
       "               '2012-04-29', '2012-04-30', '2012-05-01', '2012-05-02',\n",
       "               '2012-05-03', '2012-05-04', '2012-05-05', '2012-05-06',\n",
       "               '2012-05-07', '2012-05-08', '2012-05-09', '2012-05-10',\n",
       "               '2012-05-11', '2012-05-12', '2012-05-13', '2012-05-14',\n",
       "               '2012-05-15', '2012-05-16', '2012-05-17', '2012-05-18',\n",
       "               '2012-05-19', '2012-05-20', '2012-05-21', '2012-05-22',\n",
       "               '2012-05-23', '2012-05-24', '2012-05-25', '2012-05-26',\n",
       "               '2012-05-27', '2012-05-28', '2012-05-29', '2012-05-30',\n",
       "               '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2012-04-01', periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-13', '2012-05-14', '2012-05-15', '2012-05-16',\n",
       "               '2012-05-17', '2012-05-18', '2012-05-19', '2012-05-20',\n",
       "               '2012-05-21', '2012-05-22', '2012-05-23', '2012-05-24',\n",
       "               '2012-05-25', '2012-05-26', '2012-05-27', '2012-05-28',\n",
       "               '2012-05-29', '2012-05-30', '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(end='2012-06-01', periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-28',\n",
       "               '2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',\n",
       "               '2000-09-29', '2000-10-31', '2000-11-30'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01', '2000-12-01', freq='BM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02', '2012-05-03', '2012-05-04', '2012-05-05',\n",
       "               '2012-05-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2012-05-02 12:56:31', periods=5, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "hour = Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Hour>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "four_hours = Hour(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4 * Hours>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "four_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 04:00:00',\n",
       "               '2000-01-01 08:00:00', '2000-01-01 12:00:00',\n",
       "               '2000-01-01 16:00:00', '2000-01-01 20:00:00',\n",
       "               '2000-01-02 00:00:00', '2000-01-02 04:00:00',\n",
       "               '2000-01-02 08:00:00', '2000-01-02 12:00:00',\n",
       "               '2000-01-02 16:00:00', '2000-01-02 20:00:00',\n",
       "               '2000-01-03 00:00:00', '2000-01-03 04:00:00',\n",
       "               '2000-01-03 08:00:00', '2000-01-03 12:00:00',\n",
       "               '2000-01-03 16:00:00', '2000-01-03 20:00:00'],\n",
       "              dtype='datetime64[ns]', freq='4H')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01', '2000-01-03 23:59', freq='4h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<150 * Minutes>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hour(2) + Minute(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:30:00',\n",
       "               '2000-01-01 03:00:00', '2000-01-01 04:30:00',\n",
       "               '2000-01-01 06:00:00', '2000-01-01 07:30:00',\n",
       "               '2000-01-01 09:00:00', '2000-01-01 10:30:00',\n",
       "               '2000-01-01 12:00:00', '2000-01-01 13:30:00'],\n",
       "              dtype='datetime64[ns]', freq='90T')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-1-1', periods=10, freq='1h30min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range('2012-1-1', '2012-9-1', freq='WOM-3FRI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2012-01-20 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-02-17 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-03-16 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-04-20 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-05-18 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-06-15 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-07-20 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-08-17 00:00:00', freq='WOM-3FRI')]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Series(np.random.randn(4), index=pd.date_range('1/1/2000', periods=4, freq='M'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.174694\n",
       "2000-02-29    1.303243\n",
       "2000-03-31   -1.420485\n",
       "2000-04-30    0.011362\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29         NaN\n",
       "2000-03-31    0.174694\n",
       "2000-04-30    1.303243\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -1.420485\n",
       "2000-02-29    0.011362\n",
       "2000-03-31         NaN\n",
       "2000-04-30         NaN\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29    6.460126\n",
       "2000-03-31   -2.089962\n",
       "2000-04-30   -1.007999\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts / ts.shift(1) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-31    0.174694\n",
       "2000-04-30    1.303243\n",
       "2000-05-31   -1.420485\n",
       "2000-06-30    0.011362\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2, freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31 04:30:00    0.174694\n",
       "2000-02-29 04:30:00    1.303243\n",
       "2000-03-31 04:30:00   -1.420485\n",
       "2000-04-30 04:30:00    0.011362\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(3, freq='90T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime(2011, 11, 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-20 00:00:00')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + 3 * Day()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-31 00:00:00')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + MonthEnd(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollforward(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-10-31 00:00:00')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollback(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Series(np.random.randn(20), index=pd.date_range('1/15/2000', periods=20, freq='4d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-15   -0.862678\n",
       "2000-01-19    1.078716\n",
       "2000-01-23   -0.885833\n",
       "2000-01-27   -0.550450\n",
       "2000-01-31   -1.268958\n",
       "2000-02-04    1.072412\n",
       "2000-02-08   -0.916231\n",
       "2000-02-12    0.623341\n",
       "2000-02-16    0.069354\n",
       "2000-02-20    0.677566\n",
       "2000-02-24    0.306864\n",
       "2000-02-28   -0.980866\n",
       "2000-03-03   -2.012966\n",
       "2000-03-07    0.039568\n",
       "2000-03-11    0.530197\n",
       "2000-03-15    0.577220\n",
       "2000-03-19   -1.399027\n",
       "2000-03-23    2.694867\n",
       "2000-03-27    1.181804\n",
       "2000-03-31   -0.422483\n",
       "Freq: 4D, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.497840\n",
       "2000-02-29    0.121777\n",
       "2000-03-31    0.148647\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(offset.rollforward).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.497840\n",
       "2000-02-29    0.121777\n",
       "2000-03-31    0.148647\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pytz.common_timezones[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "tz = pytz.timezone('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range('3/9/2012 9:30', periods=6, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Series(np.random.randn(len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.277661\n",
       "2012-03-10 09:30:00    2.463743\n",
       "2012-03-11 09:30:00    1.491956\n",
       "2012-03-12 09:30:00   -0.948347\n",
       "2012-03-13 09:30:00   -1.184948\n",
       "2012-03-14 09:30:00   -1.598220\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ts.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n",
       "               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n",
       "               '2012-03-15 09:30:00+00:00', '2012-03-16 09:30:00+00:00',\n",
       "               '2012-03-17 09:30:00+00:00', '2012-03-18 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('3/9/2012 9:30', periods=10, freq='D', tz='UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.277661\n",
       "2012-03-10 09:30:00    2.463743\n",
       "2012-03-11 09:30:00    1.491956\n",
       "2012-03-12 09:30:00   -0.948347\n",
       "2012-03-13 09:30:00   -1.184948\n",
       "2012-03-14 09:30:00   -1.598220\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_utc = ts.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00+00:00   -0.277661\n",
       "2012-03-10 09:30:00+00:00    2.463743\n",
       "2012-03-11 09:30:00+00:00    1.491956\n",
       "2012-03-12 09:30:00+00:00   -0.948347\n",
       "2012-03-13 09:30:00+00:00   -1.184948\n",
       "2012-03-14 09:30:00+00:00   -1.598220\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n",
       "               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 04:30:00-05:00   -0.277661\n",
       "2012-03-10 04:30:00-05:00    2.463743\n",
       "2012-03-11 05:30:00-04:00    1.491956\n",
       "2012-03-12 05:30:00-04:00   -0.948347\n",
       "2012-03-13 05:30:00-04:00   -1.184948\n",
       "2012-03-14 05:30:00-04:00   -1.598220\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_eastern = ts.tz_localize('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 14:30:00+00:00   -0.277661\n",
       "2012-03-10 14:30:00+00:00    2.463743\n",
       "2012-03-11 13:30:00+00:00    1.491956\n",
       "2012-03-12 13:30:00+00:00   -0.948347\n",
       "2012-03-13 13:30:00+00:00   -1.184948\n",
       "2012-03-14 13:30:00+00:00   -1.598220\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 15:30:00+01:00   -0.277661\n",
       "2012-03-10 15:30:00+01:00    2.463743\n",
       "2012-03-11 14:30:00+01:00    1.491956\n",
       "2012-03-12 14:30:00+01:00   -0.948347\n",
       "2012-03-13 14:30:00+01:00   -1.184948\n",
       "2012-03-14 14:30:00+01:00   -1.598220\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert('Europe/Berlin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+08:00', '2012-03-10 09:30:00+08:00',\n",
       "               '2012-03-11 09:30:00+08:00', '2012-03-12 09:30:00+08:00',\n",
       "               '2012-03-13 09:30:00+08:00', '2012-03-14 09:30:00+08:00'],\n",
       "              dtype='datetime64[ns, Asia/Shanghai]', freq='D')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.tz_localize('Asia/Shanghai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = pd.Timestamp('2011-3-12 4:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp_utc = stamp.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-11 23:00:00-0500', tz='America/New_York')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp_moscow = pd.Timestamp('2011-3-12 4:00', tz='Europe/Moscow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_moscow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert('America/New_York').value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = pd.Timestamp('2012-3-12 1:30', tz='US/Eastern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 01:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 02:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp + Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = pd.Timestamp('2012-11-4 0:30', tz='US/Eastern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 00:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp + 2*Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range('3/7/2012 9:30', periods=10, freq='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Series(np.random.randn(len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00   -0.321833\n",
       "2012-03-08 09:30:00    0.420533\n",
       "2012-03-09 09:30:00   -1.675479\n",
       "2012-03-12 09:30:00    1.044238\n",
       "2012-03-13 09:30:00   -0.667653\n",
       "2012-03-14 09:30:00   -0.700270\n",
       "2012-03-15 09:30:00    2.130555\n",
       "2012-03-16 09:30:00    0.364765\n",
       "2012-03-19 09:30:00    0.634206\n",
       "2012-03-20 09:30:00    0.799845\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts1 = ts[:7].tz_localize('Europe/London')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts2 = ts1[2:].tz_convert('Europe/Moscow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ts1 + ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-07 09:30:00+00:00', '2012-03-08 09:30:00+00:00',\n",
       "               '2012-03-09 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n",
       "               '2012-03-15 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='B')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period(2007, freq='A-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012', 'A-DEC')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2005', 'A-DEC')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period('2014', freq='A-DEC') - p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.period_range('2000-01-01','2000-06-30',freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -1.932523\n",
       "2000-02    0.944513\n",
       "2000-03   -1.077019\n",
       "2000-04   -0.907038\n",
       "2000-05    1.060519\n",
       "2000-06    1.114575\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Series(np.random.randn(6), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = ['2001Q3', '2002Q2', '2003Q1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.PeriodIndex(values, freq='Q-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('2007', freq='A-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-01', 'M')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-12', 'M')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('2007', freq='A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-JUN')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2006-07', 'M')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', 'start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-06', 'M')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', 'end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('Aug-2007', 'M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2008', 'A-JUN')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.period_range('2006', '2009', freq='A-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Series(np.random.randn(len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006   -2.560921\n",
       "2007   -0.414002\n",
       "2008   -0.473147\n",
       "2009   -0.770864\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01   -2.560921\n",
       "2007-01   -0.414002\n",
       "2008-01   -0.473147\n",
       "2009-01   -0.770864\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('M', how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-12-29   -2.560921\n",
       "2007-12-31   -0.414002\n",
       "2008-12-31   -0.473147\n",
       "2009-12-31   -0.770864\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('B', how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('2012Q4', freq='Q-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JUN')"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-04-01', 'D')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D', 'start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-06-30', 'D')"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D', 'end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('Aug-2007','M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2008', 'A-JUN')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.period_range('2006','2009',freq='A-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    1.232113\n",
       "2007   -1.035838\n",
       "2008   -0.940899\n",
       "2009    0.574347\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01    1.232113\n",
       "2007-01   -1.035838\n",
       "2008-01   -0.940899\n",
       "2009-01    0.574347\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq(\"M\",how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-12-29    1.232113\n",
       "2007-12-31   -1.035838\n",
       "2008-12-31   -0.940899\n",
       "2009-12-31    0.574347\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('B',how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('2012Q4',freq='Q-JAN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JAN')"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-11-01', 'D')"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D','start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-31', 'D')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D','end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range('2001-01-01',periods=100,freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-01    0.371977\n",
       "2001-01-02    0.568672\n",
       "2001-01-03    1.059694\n",
       "2001-01-04    0.574675\n",
       "2001-01-05    0.965309\n",
       "2001-01-06   -0.130830\n",
       "2001-01-07    2.760269\n",
       "2001-01-08    1.506110\n",
       "2001-01-09    0.155478\n",
       "2001-01-10   -0.615050\n",
       "2001-01-11    1.167855\n",
       "2001-01-12    0.733682\n",
       "2001-01-13   -0.520651\n",
       "2001-01-14   -1.355114\n",
       "2001-01-15   -0.179697\n",
       "2001-01-16   -2.239893\n",
       "2001-01-17    1.533726\n",
       "2001-01-18   -0.015309\n",
       "2001-01-19    1.735755\n",
       "2001-01-20   -0.392549\n",
       "2001-01-21   -0.075892\n",
       "2001-01-22    1.049052\n",
       "2001-01-23   -2.016343\n",
       "2001-01-24    1.409391\n",
       "2001-01-25    0.885606\n",
       "2001-01-26    0.029390\n",
       "2001-01-27    0.130297\n",
       "2001-01-28    0.182258\n",
       "2001-01-29   -0.576462\n",
       "2001-01-30   -1.343709\n",
       "                ...   \n",
       "2001-03-12   -0.295620\n",
       "2001-03-13    0.112093\n",
       "2001-03-14    0.075388\n",
       "2001-03-15    1.489003\n",
       "2001-03-16   -0.243240\n",
       "2001-03-17   -0.391602\n",
       "2001-03-18   -0.832376\n",
       "2001-03-19   -0.191578\n",
       "2001-03-20    0.303875\n",
       "2001-03-21    0.829134\n",
       "2001-03-22   -0.235913\n",
       "2001-03-23    1.387293\n",
       "2001-03-24    0.385887\n",
       "2001-03-25   -0.524836\n",
       "2001-03-26    0.546362\n",
       "2001-03-27    0.458498\n",
       "2001-03-28   -1.103917\n",
       "2001-03-29   -1.215404\n",
       "2001-03-30    0.636165\n",
       "2001-03-31    0.361010\n",
       "2001-04-01   -1.232708\n",
       "2001-04-02    0.238559\n",
       "2001-04-03   -1.121163\n",
       "2001-04-04   -0.836178\n",
       "2001-04-05    1.584138\n",
       "2001-04-06    1.869983\n",
       "2001-04-07    1.546867\n",
       "2001-04-08    0.197638\n",
       "2001-04-09    0.838041\n",
       "2001-04-10   -0.683054\n",
       "Freq: D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-31    0.230228\n",
       "2001-02-28   -0.270074\n",
       "2001-03-31   -0.023790\n",
       "2001-04-30    0.240212\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01    0.230228\n",
       "2001-02   -0.270074\n",
       "2001-03   -0.023790\n",
       "2001-04    0.240212\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M',kind='period').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range('2000-01-01',periods=12,freq='T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.arange(12),index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:01:00     1\n",
       "2000-01-01 00:02:00     2\n",
       "2000-01-01 00:03:00     3\n",
       "2000-01-01 00:04:00     4\n",
       "2000-01-01 00:05:00     5\n",
       "2000-01-01 00:06:00     6\n",
       "2000-01-01 00:07:00     7\n",
       "2000-01-01 00:08:00     8\n",
       "2000-01-01 00:09:00     9\n",
       "2000-01-01 00:10:00    10\n",
       "2000-01-01 00:11:00    11\n",
       "Freq: T, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:55:00     0\n",
       "2000-01-01 00:00:00    15\n",
       "2000-01-01 00:05:00    40\n",
       "2000-01-01 00:10:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:05:00    15\n",
       "2000-01-01 00:10:00    40\n",
       "2000-01-01 00:15:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right',label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:59:59     0\n",
       "2000-01-01 00:04:59    15\n",
       "2000-01-01 00:09:59    40\n",
       "2000-01-01 00:14:59    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right',label='right',loffset='-1s').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>2000-01-01 00:00:00</th>\n",
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       "                     open  high  low  close\n",
       "2000-01-01 00:00:00     0     4    0      4\n",
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     "metadata": {},
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    "ts.resample('5min').ohlc()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame =  pd.DataFrame(np.random.randn(2,4),\n",
    "                     index=pd.date_range('1/1/2000',periods=2,freq='W-WED'),\n",
    "                     columns=['colorado','texas','new york','ohio'])"
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  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2000-01-05</th>\n",
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       "            colorado     texas  new york      ohio\n",
       "2000-01-05  0.184648  1.012755  0.107706  0.121319\n",
       "2000-01-12 -1.241907  1.287718 -0.060627  0.154019"
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     "execution_count": 204,
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    "frame"
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  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_daily = frame.resample('D').asfreq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
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       "      <th>2000-01-09</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
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       "            colorado     texas  new york      ohio\n",
       "2000-01-05  0.184648  1.012755  0.107706  0.121319\n",
       "2000-01-06       NaN       NaN       NaN       NaN\n",
       "2000-01-07       NaN       NaN       NaN       NaN\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -1.241907  1.287718 -0.060627  0.154019"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
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       "      <td>1.287718</td>\n",
       "      <td>-0.060627</td>\n",
       "      <td>0.154019</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            colorado     texas  new york      ohio\n",
       "2000-01-05  0.184648  1.012755  0.107706  0.121319\n",
       "2000-01-06  0.184648  1.012755  0.107706  0.121319\n",
       "2000-01-07  0.184648  1.012755  0.107706  0.121319\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -1.241907  1.287718 -0.060627  0.154019"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('D').ffill(limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>colorado</th>\n",
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       "      <th>ohio</th>\n",
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       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
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       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
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      "text/plain": [
       "            colorado     texas  new york      ohio\n",
       "2000-01-06  0.184648  1.012755  0.107706  0.121319\n",
       "2000-01-13 -1.241907  1.287718 -0.060627  0.154019"
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     "metadata": {},
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   "source": [
    "frame.resample('W-THU').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(np.random.randn(24,4),index=pd.period_range('1-2000','12-2001',freq='M'),\n",
    "                    columns=['colorado','texas','new york','ohio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "annual_frame = frame.resample('A-DEC').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-0.225985</td>\n",
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      "text/plain": [
       "      colorado     texas  new york      ohio\n",
       "2000  0.083015 -0.486063  0.217900 -0.178501\n",
       "2001 -0.225985 -0.003467  0.733653  0.011987"
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     "metadata": {},
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    "annual_frame"
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  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q1</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q2</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q3</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>-0.225985</td>\n",
       "      <td>-0.003467</td>\n",
       "      <td>0.733653</td>\n",
       "      <td>0.011987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>-0.225985</td>\n",
       "      <td>-0.003467</td>\n",
       "      <td>0.733653</td>\n",
       "      <td>0.011987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>-0.225985</td>\n",
       "      <td>-0.003467</td>\n",
       "      <td>0.733653</td>\n",
       "      <td>0.011987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.225985</td>\n",
       "      <td>-0.003467</td>\n",
       "      <td>0.733653</td>\n",
       "      <td>0.011987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        colorado     texas  new york      ohio\n",
       "2000Q1  0.083015 -0.486063  0.217900 -0.178501\n",
       "2000Q2  0.083015 -0.486063  0.217900 -0.178501\n",
       "2000Q3  0.083015 -0.486063  0.217900 -0.178501\n",
       "2000Q4  0.083015 -0.486063  0.217900 -0.178501\n",
       "2001Q1 -0.225985 -0.003467  0.733653  0.011987\n",
       "2001Q2 -0.225985 -0.003467  0.733653  0.011987\n",
       "2001Q3 -0.225985 -0.003467  0.733653  0.011987\n",
       "2001Q4 -0.225985 -0.003467  0.733653  0.011987"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample('Q-DEC').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>colorado</th>\n",
       "      <th>texas</th>\n",
       "      <th>new york</th>\n",
       "      <th>ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.083015</td>\n",
       "      <td>-0.486063</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.178501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.225985</td>\n",
       "      <td>-0.003467</td>\n",
       "      <td>0.733653</td>\n",
       "      <td>0.011987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        colorado     texas  new york      ohio\n",
       "2000Q4  0.083015 -0.486063  0.217900 -0.178501\n",
       "2001Q1  0.083015 -0.486063  0.217900 -0.178501\n",
       "2001Q2  0.083015 -0.486063  0.217900 -0.178501\n",
       "2001Q3  0.083015 -0.486063  0.217900 -0.178501\n",
       "2001Q4 -0.225985 -0.003467  0.733653  0.011987"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample('Q-DEC', convention='end').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = pd.Series(['apple','orange','apple','apple']*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['apple', 'orange'], dtype=object)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     6\n",
       "orange    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = pd.Series([0,1,0,0]*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim = pd.Series(['apple','orange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    1\n",
       "6    0\n",
       "7    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "0     apple\n",
       "0     apple\n",
       "0     apple\n",
       "1    orange\n",
       "0     apple\n",
       "0     apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim.take(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "fruits = ['apple','orange','apple','apple']*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = len(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'fruit':fruits,\n",
    "                  'basket_id':np.arange(N),\n",
    "                  'count':np.random.randint(3,15,size=N),\n",
    "                  'weight':np.random.uniform(0,4,size=N)},\n",
    "                  columns=['basket_id','fruit','count','weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "fruit_cat = df['fruit'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "Name: fruit, dtype: category\n",
       "Categories (2, object): [apple, orange]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "c  = fruit_cat.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.arrays.categorical.Categorical"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'orange'], dtype='object')"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 1, 0, 0], dtype=int8)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fruit'] = df['fruit'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "Name: fruit, dtype: category\n",
       "Categories (2, object): [apple, orange]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fruit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_categories = pd.Categorical(['foo','bar','baz','foo','bar'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[foo, bar, baz, foo, bar]\n",
       "Categories (3, object): [bar, baz, foo]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ['foo','bar','baz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "codes = [0,1,2,0,0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_cats_2 = pd.Categorical.from_codes(codes, categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[foo, bar, baz, foo, foo, bar]\n",
       "Categories (3, object): [foo, bar, baz]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cats_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_cats_2 = pd.Categorical.from_codes(codes, categories, ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[foo, bar, baz, foo, foo, bar]\n",
       "Categories (3, object): [foo < bar < baz]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cats_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Categorical.as_ordered of [foo, bar, baz, foo, foo, bar]\n",
       "Categories (3, object): [foo < bar < baz]>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cats_2.as_ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "draws = np.random.randn(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.20470766,  0.47894334, -0.51943872, -0.5557303 ,  1.96578057])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = pd.date_range('2017-05-20 00:00',freq='1min',periods=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'time':times,\n",
    "                  'value':np.arange(N)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
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       "      <th>2</th>\n",
       "      <td>2017-05-20 00:02:00</td>\n",
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       "      <th>3</th>\n",
       "      <td>2017-05-20 00:03:00</td>\n",
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       "      <th>4</th>\n",
       "      <td>2017-05-20 00:04:00</td>\n",
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       "      <td>2017-05-20 00:06:00</td>\n",
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       "      <th>7</th>\n",
       "      <td>2017-05-20 00:07:00</td>\n",
       "      <td>7</td>\n",
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       "      <th>8</th>\n",
       "      <td>2017-05-20 00:08:00</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-05-20 00:09:00</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>10</td>\n",
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       "      <th>11</th>\n",
       "      <td>2017-05-20 00:11:00</td>\n",
       "      <td>11</td>\n",
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       "      <th>12</th>\n",
       "      <td>2017-05-20 00:12:00</td>\n",
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       "      <th>13</th>\n",
       "      <td>2017-05-20 00:13:00</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-05-20 00:14:00</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time  value\n",
       "0  2017-05-20 00:00:00      0\n",
       "1  2017-05-20 00:01:00      1\n",
       "2  2017-05-20 00:02:00      2\n",
       "3  2017-05-20 00:03:00      3\n",
       "4  2017-05-20 00:04:00      4\n",
       "5  2017-05-20 00:05:00      5\n",
       "6  2017-05-20 00:06:00      6\n",
       "7  2017-05-20 00:07:00      7\n",
       "8  2017-05-20 00:08:00      8\n",
       "9  2017-05-20 00:09:00      9\n",
       "10 2017-05-20 00:10:00     10\n",
       "11 2017-05-20 00:11:00     11\n",
       "12 2017-05-20 00:12:00     12\n",
       "13 2017-05-20 00:13:00     13\n",
       "14 2017-05-20 00:14:00     14"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>value</th>\n",
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       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:00:00</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:10:00</th>\n",
       "      <td>5</td>\n",
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      "text/plain": [
       "                     value\n",
       "time                      \n",
       "2017-05-20 00:00:00      5\n",
       "2017-05-20 00:05:00      5\n",
       "2017-05-20 00:10:00      5"
      ]
     },
     "execution_count": 249,
     "metadata": {},
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   ],
   "source": [
    "df.set_index('time').resample('5min').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'time':times.repeat(3),\n",
    "                   'key':np.tile(['a','b','c'],N),\n",
    "                   'value':np.arange(N*3.)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: pd.TimeGrouper is deprecated and will be removed; Please use pd.Grouper(freq=...)\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "time_key = pd.TimeGrouper('5min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeGrouper(freq=<5 * Minutes>, axis=0, sort=True, closed='left', label='left', how='mean', convention='e', base=0)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "resampled = (df2.set_index('time').groupby(['key',time_key]).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">a</th>\n",
       "      <th>2017-05-20 00:00:00</th>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:10:00</th>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">b</th>\n",
       "      <th>2017-05-20 00:00:00</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:10:00</th>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">c</th>\n",
       "      <th>2017-05-20 00:00:00</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20 00:10:00</th>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         value\n",
       "key time                      \n",
       "a   2017-05-20 00:00:00   30.0\n",
       "    2017-05-20 00:05:00  105.0\n",
       "    2017-05-20 00:10:00  180.0\n",
       "b   2017-05-20 00:00:00   35.0\n",
       "    2017-05-20 00:05:00  110.0\n",
       "    2017-05-20 00:10:00  185.0\n",
       "c   2017-05-20 00:00:00   40.0\n",
       "    2017-05-20 00:05:00  115.0\n",
       "    2017-05-20 00:10:00  190.0"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled"
   ]
  }
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