Python for Data Analysis Workbook VI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Data Analysis Workbook 6 (Time Series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note on Time-Series\n",
    "\n",
    "Time-series data may either be *fixed frequency* or *irregular* <br>\n",
    "\n",
    "How you mark and refer to the time series data depends on the application and you may have one of the following: <br>\n",
    "\n",
    "1) ***Timestamps*** or specific instants in time <br>\n",
    "2) ***Periods*** that are already delineated like month of January, year 2010, etc. <br>\n",
    "3) ***Intervals*** of time that are indicated by a start/end timestamp <br>\n",
    "4) ***Elapsed Time*** where each timestamp is a measure of time relative to a particular start time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date and Time Data Types and Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 22:55:14.420593\n",
      "[2020, 7, 2]\n",
      "\n",
      "\n",
      "926 days, 15:45:00\n",
      "\n",
      "\n",
      "[926, 56700]\n"
     ]
    }
   ],
   "source": [
    "# Python standard library includes data types for date and time data as well as calendar-related functionality\n",
    "\n",
    "print(datetime.now())\n",
    "now = datetime.now()\n",
    "\n",
    "print([now.year, now.month, now.day])\n",
    "print('\\n')\n",
    "# Can also calculate temporal difference using timedelta\n",
    "\n",
    "delta = datetime(2011, 1, 7) - datetime(2008, 6, 24, 8, 15)\n",
    "print(delta)\n",
    "print('\\n')\n",
    "print([delta.days, delta.seconds])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 19, 0, 0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can use timedelta to shift a series \n",
    "\n",
    "from datetime import timedelta\n",
    "\n",
    "start = datetime(2011, 1, 7)\n",
    "\n",
    "start + timedelta(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting between Strings and Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# String --> DateTime\n",
    "\n",
    "stamp = datetime(2011, 1, 3)\n",
    "str(stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datetime --> String\n",
    "\n",
    "stamp.strftime('%Y-%m-%d') # Need to provide a format based on codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Codes**\n",
    "\n",
    "%Y - four-digit year <br>\n",
    "%y - two-digit year <br>\n",
    "%m - two-digit month <br>\n",
    "%d - two-digit day <br>\n",
    "%H - Hour, military time <br>\n",
    "%I - Hour, regular time <br>\n",
    "%M - two-digit minute <br>\n",
    "%s - second (60 & 61 account for leap seconds) <br>\n",
    "%w - weekday as integer (starting with Sunday = 0 <br>\n",
    "%W - week number <br>\n",
    "%z - UTC time <br>\n",
    "%a - abbreviated weekday name <br>\n",
    "%A - full weekday name <br>\n",
    "%b - abbreviated month name <br>\n",
    "%B - full month name <br>\n",
    "%c - full date and time <br>\n",
    "%p - local equivalent of am/pm <br>\n",
    "%x - locale-appropriate formatted date <br>\n",
    "%X - locale-appropriate formatted time <br>\n",
    "%F - shortcut for %Y-%m-%d <br>\n",
    "%D - shortcut for %m/%d/%y <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-01-03 00:00:00\n",
      "\n",
      "\n",
      "2011-01-03 00:00:00\n",
      "\n",
      "\n",
      "2011-12-06 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# String --> Datetime (using strptime())\n",
    "\n",
    "value = '2011-01-03'\n",
    "print(datetime.strptime(value, '%Y-%m-%d'))\n",
    "print('\\n')\n",
    "\n",
    "# Can also just use the parse method from dateutil library, which can parse most human-readable date represenations\n",
    "\n",
    "from dateutil.parser import parse\n",
    "print(parse(value))\n",
    "print('\\n')\n",
    "print(parse('6/12/2011', dayfirst = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to convert to datetime, use pandas to_datetime method\n",
    "\n",
    "datestrs = ['2011-07-06 12:00:00', '2011-08-06 00:00:00']\n",
    "\n",
    "pd.to_datetime(datestrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-01-02    0.145210\n",
      "2011-01-05   -0.323280\n",
      "2011-01-07    2.043052\n",
      "2011-01-08    1.159492\n",
      "2011-01-10    0.803376\n",
      "2011-01-12   -0.525725\n",
      "dtype: float64\n"
     ]
    },
    {
     "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)]\n",
    "\n",
    "ts = pd.Series(np.random.randn(6), index = dates)\n",
    "print(ts)\n",
    "\n",
    "ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.290419\n",
       "2011-01-05         NaN\n",
       "2011-01-07    4.086105\n",
       "2011-01-08         NaN\n",
       "2011-01-10    1.606752\n",
       "2011-01-12         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts + ts[::2] # Selects every second element in the Timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-02 00:00:00')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a Timestamp object, which can be substituted anywhere for a datetime but also stores frequency info\n",
    "\n",
    "ts.index[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1452095521005706"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indexing works like any other pandas.Series and also can parse most date-like strings\n",
    "\n",
    "ts['1/2/11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-01-01   -1.606889\n",
      "2000-01-02   -0.706294\n",
      "2000-01-03   -0.022372\n",
      "2000-01-04   -0.078844\n",
      "2000-01-05   -1.884789\n",
      "                ...   \n",
      "2002-09-22    1.117063\n",
      "2002-09-23   -0.721747\n",
      "2002-09-24   -0.956305\n",
      "2002-09-25    0.229228\n",
      "2002-09-26    0.365482\n",
      "Freq: D, Length: 1000, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2001-01-01   -0.466466\n",
       "2001-01-02   -0.255349\n",
       "2001-01-03    0.147402\n",
       "2001-01-04   -1.517411\n",
       "2001-01-05   -0.231846\n",
       "                ...   \n",
       "2001-12-27   -1.300224\n",
       "2001-12-28    1.610084\n",
       "2001-12-29   -0.691538\n",
       "2001-12-30    1.291405\n",
       "2001-12-31    0.193227\n",
       "Freq: D, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts = pd.Series(np.random.randn(1000), index = pd.date_range('1/1/2000', periods = 1000))\n",
    "print(longer_ts)\n",
    "\n",
    "longer_ts['2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-12-27   -1.300224\n",
       "2001-12-28    1.610084\n",
       "2001-12-29   -0.691538\n",
       "2001-12-30    1.291405\n",
       "2001-12-31    0.193227\n",
       "                ...   \n",
       "2002-09-22    1.117063\n",
       "2002-09-23   -0.721747\n",
       "2002-09-24   -0.956305\n",
       "2002-09-25    0.229228\n",
       "2002-09-26    0.365482\n",
       "Freq: D, Length: 274, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also slice\n",
    "\n",
    "longer_ts['2001-12-27':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-05   -0.323280\n",
       "2011-01-07    2.043052\n",
       "2011-01-08    1.159492\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to select a date range is to use the truncate method\n",
    "\n",
    "ts.truncate(before = '1/3/2011', after = '1/9/2011')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time Series with Duplicate Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts = pd.Series(np.arange(5), \n",
    "                   index = pd.DatetimeIndex(['1/1/2000', '1/2/2000', '1/2/2000', '1/2/2000', '1/3/2000']))\n",
    "\n",
    "print(dup_ts)\n",
    "dup_ts.is_unique\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    3\n",
       "2000-01-03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can aggregate down using groupby\n",
    "\n",
    "dup_ts.groupby(level = 0).count() # groupby index "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date Ranges, Shifting, and Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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')\n"
     ]
    },
    {
     "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating a date range\n",
    "\n",
    "index = pd.date_range('2012-04-01', '2012-06-01')\n",
    "print(pd.date_range(start = '2012-04-01', periods = 62)) # equivalent\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can pass in custom frequencies\n",
    "\n",
    "pd.date_range('2000-01-01', '2000-12-01', freq = 'BM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also normalize time data by setting the normalize argument to True\n",
    "\n",
    "pd.date_range('2012-05-02 12:56:31', periods = 5, normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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'],\n",
      "              dtype='datetime64[ns]', freq='4H')\n"
     ]
    },
    {
     "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",
       "               '2000-01-01 15:00:00', '2000-01-01 16:30:00',\n",
       "               '2000-01-01 18:00:00', '2000-01-01 19:30:00',\n",
       "               '2000-01-01 21:00:00', '2000-01-01 22:30:00',\n",
       "               '2000-01-02 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq='90T')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also create custom frequencies by putting an integer beofre the base frequency\n",
    "\n",
    "print(pd.date_range('2000-01-01', '2000-01-02', freq = '4h'))\n",
    "\n",
    "pd.date_range('2000-01-01', '2000-01-02', freq = '1h30min')"
   ]
  },
  {
   "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": [
    "# One useful frequency class is week of month\n",
    "\n",
    "rng = pd.date_range('2012-01-01', '2012-09-01', freq = 'WOM-3FRI')\n",
    "list(rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-01-31    0.220500\n",
      "2000-02-29    0.646986\n",
      "2000-03-31    0.085404\n",
      "2000-04-30    0.508564\n",
      "Freq: M, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29         NaN\n",
       "2000-03-31    0.220500\n",
       "2000-04-30    0.646986\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can easily shift data within a date range forward or backwards using the shift() method\n",
    "\n",
    "ts = pd.Series(np.abs(np.random.randn(4)), index = pd.date_range('1/1/2000', periods = 4, freq = 'M'))\n",
    "print(ts)\n",
    "\n",
    "ts.shift(2)\n",
    "# ts.shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29    1.934173\n",
       "2000-03-31   -0.867997\n",
       "2000-04-30    4.954780\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Common use of shift is to calculate percent changes\n",
    "\n",
    "ts / ts.shift(1) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-31    0.220500\n",
       "2000-04-30    0.646986\n",
       "2000-05-31    0.085404\n",
       "2000-06-30    0.508564\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive shift only does the data, but passing in a frequency enables the index to shift as well\n",
    "\n",
    "ts.shift(2, freq = 'M')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-zone handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytz # special library for handling time zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pytz.common_timezones[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get timezone objet using .timezone\n",
    "pytz.timezone('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Date ranges can be generated with an explicit timezone set (default is none)\n",
    "pd.date_range('3/9/2012 9:30', periods = 10, freq = 'D', tz = 'UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31 00:00:00+00:00    0.220500\n",
       "2000-02-29 00:00:00+00:00    0.646986\n",
       "2000-03-31 00:00:00+00:00    0.085404\n",
       "2000-04-30 00:00:00+00:00    0.508564\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can go from naive to localize using the localize() method\n",
    "\n",
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-30 19:00:00-05:00    0.220500\n",
       "2000-02-28 19:00:00-05:00    0.646986\n",
       "2000-03-30 19:00:00-05:00    0.085404\n",
       "2000-04-29 20:00:00-04:00    0.508564\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can be converted with the tz_convert() method\n",
    "\n",
    "ts_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-03-12 04:00:00+00:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In addition to date ranges, individual timestamps can be made time-zone aware\n",
    "\n",
    "stamp = pd.Timestamp('2011-03-12 04:00')\n",
    "stamp_utc = stamp.tz_localize('utc')\n",
    "print(stamp_utc)\n",
    "\n",
    "# Or pass a time zone directly into the Timestamp on creation\n",
    "\n",
    "pd.Timestamp('2011-03-12 04:00', tz = 'Europe/Moscow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all timestamp objects store the Unix timestamp value \n",
    "\n",
    "stamp_utc.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note, joining two different time zone series results in all being converted to UTC*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Periods and Period Arithmetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Periods represent timespans like days, months, or quarters. Period class requires an integer and frequency\n",
    "\n",
    "p = pd.Period(2007, freq = 'A-DEC') # annual from first of month to last day in December (inclusive)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012', 'A-DEC')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can easily shift periods using simple integer arithmetic\n",
    "\n",
    "p + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<7 * YearEnds: month=12>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference of two periods is number of units between them\n",
    "\n",
    "a = pd.Period('2014', freq = 'A-DEC')\n",
    "a - p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]', freq='M')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01    0.520244\n",
       "2000-02    0.401450\n",
       "2000-03    0.479894\n",
       "2000-04    0.040798\n",
       "2000-05   -1.399633\n",
       "2000-06   -1.075704\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can construct ranges of periods using the period_range() function\n",
    "\n",
    "rng = pd.period_range('2000-01-01', '2000-06-30', freq = 'M')\n",
    "print(rng)\n",
    "\n",
    "# The PeriodIndex class stores a sequence of periods and can serve as an axis index for any pandas data structure\n",
    "\n",
    "pd.Series(np.random.randn(6), index = rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2001Q3', '2002Q2', '2003Q3'], dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An array of strings can also be used to create an instance of the PeriodIndex class\n",
    "\n",
    "values = ['2001Q3', '2002Q2', '2003Q3']\n",
    "index = pd.PeriodIndex(values, freq = 'Q-DEC')\n",
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Period Frequency Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('2007', freq = 'A-DEC') # annual period\n",
    "p # read as annual period for 2007 ending on the last day of December"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-01', 'M')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the asfreq() method to convert the frequency to a monthly period\n",
    "\n",
    "p.asfreq('M', how = 'start') # read first month of 2007 (January) ending last calendar day of the month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2008', 'A-JUN')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In converting from high -> low frequency, pandas determines the super-period automatically in reference to the \n",
    "# sub-period\n",
    "\n",
    "p = pd.Period('Aug-2007', 'M')\n",
    "p.asfreq('A-JUN') # actually in 2008 because the year-end mark is in June"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006    0.315369\n",
      "2007   -0.669793\n",
      "2008    1.857846\n",
      "2009   -0.577088\n",
      "Freq: A-DEC, dtype: float64\n",
      "\n",
      "\n",
      "2006-01    0.315369\n",
      "2007-01   -0.669793\n",
      "2008-01    1.857846\n",
      "2009-01   -0.577088\n",
      "Freq: M, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2006-12-29    0.315369\n",
       "2007-12-31   -0.669793\n",
       "2008-12-31    1.857846\n",
       "2009-12-31   -0.577088\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entire PeriodIndex objects can be converted with the same semantics\n",
    "\n",
    "rng = pd.period_range('2006', '2009', freq = 'A-DEC')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index = rng)\n",
    "print(ts)\n",
    "print('\\n')\n",
    "print(ts.asfreq('M', how = 'start'))\n",
    "ts.asfreq('B', how = 'end') # last business day of the year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quarterly Period Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JAN')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quarterly data is standard in accounting but is often referred to relative to an arbitrary fiscal year\n",
    "# Pandas can handle all possible quarterly frequencies though as below\n",
    "\n",
    "p = pd.Period('2012Q4', freq='Q-JAN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-11-01\n",
      "2012-01-31\n"
     ]
    }
   ],
   "source": [
    "# See start and end\n",
    "\n",
    "print(p.asfreq('D', 's'))\n",
    "print(p.asfreq('D', 'end'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-01-30 16:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-01-30 16:00:00')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Easy to do period arithmetic as in the below example, which gets a TS at 4pm on second-to-last bus. day of the qtr\n",
    "\n",
    "p4pm = (p.asfreq('B', 'e') - 1).asfreq('T', 's') + (16 * 60)\n",
    "print(p4pm)\n",
    "p4pm.to_timestamp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011Q3    0\n",
       "2011Q4    1\n",
       "2012Q1    2\n",
       "2012Q2    3\n",
       "2012Q3    4\n",
       "2012Q4    5\n",
       "Freq: Q-JAN, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can generate quarterly ranges using period_range.\n",
    "\n",
    "rng = pd.period_range('2011Q3', '2012Q4', freq = 'Q-JAN')\n",
    "ts = pd.Series(np.arange(len(rng)), index = rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010-10-28 16:00:00    0\n",
       "2011-01-28 16:00:00    1\n",
       "2011-04-28 16:00:00    2\n",
       "2011-07-28 16:00:00    3\n",
       "2011-10-28 16:00:00    4\n",
       "2012-01-30 16:00:00    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arithmetic is identical to the example shown above\n",
    "\n",
    "new_rng = (rng.asfreq('B', 'e') - 1).asfreq('T', 's') + (16 * 60)\n",
    "ts.index = new_rng.to_timestamp()\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting Timestamps to Periods (and back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001-01-31   -0.135723\n",
      "2001-02-28   -1.042507\n",
      "2001-03-31    0.538496\n",
      "Freq: M, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2001Q4   -0.135723\n",
       "2002Q1   -1.042507\n",
       "2002Q1    0.538496\n",
       "Freq: Q-JAN, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('2001-01-01', periods = 3, freq='M')\n",
    "ts = pd.Series(np.random.randn(3), index = rng)\n",
    "print(ts)\n",
    "ts.to_period('Q-JAN', 'e') # Same frequency is inferred but can pass in custom frequency if desired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-31 00:00:59.999999999   -0.135723\n",
       "2001-02-28 00:00:59.999999999   -1.042507\n",
       "2001-03-31 00:00:59.999999999    0.538496\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and back to timestamps\n",
    "\n",
    "ts.to_period('T', 's').to_timestamp(how = 'end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create PeriodIndex from Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sometimes fixed frequency datasets are stored with timespan info in multiple columns\n",
    "\n",
    "data = pd.read_csv('pydata-book/examples/macrodata.csv')\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',\n",
       "             '1960Q3', '1960Q4', '1961Q1', '1961Q2',\n",
       "             ...\n",
       "             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',\n",
       "             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],\n",
       "            dtype='period[Q-DEC]', length=203, freq='Q-DEC')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert multiple date columns into a single period index\n",
    "\n",
    "index = pd.PeriodIndex(year = data.year, quarter = data.quarter, freq = 'Q-DEC')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959Q1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959Q2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959Q3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959Q4</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960Q1</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  quarter   realgdp  realcons  realinv  realgovt  realdpi  \\\n",
       "1959Q1  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9   \n",
       "1959Q2  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7   \n",
       "1959Q3  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4   \n",
       "1959Q4  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3   \n",
       "1960Q1  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5   \n",
       "\n",
       "          cpi     m1  tbilrate  unemp      pop  infl  realint  \n",
       "1959Q1  28.98  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1959Q2  29.15  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "1959Q3  29.35  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "1959Q4  29.37  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "1960Q1  29.54  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = index\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling and Frequency Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-01-01   -1.345676\n",
      "2000-01-02    1.664061\n",
      "2000-01-03   -0.775463\n",
      "2000-01-04   -0.062302\n",
      "2000-01-05   -0.298242\n",
      "                ...   \n",
      "2000-04-05   -0.432318\n",
      "2000-04-06   -0.269368\n",
      "2000-04-07    2.098294\n",
      "2000-04-08   -0.426513\n",
      "2000-04-09    0.194636\n",
      "Freq: D, Length: 100, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.209822\n",
       "2000-02-29   -0.160211\n",
       "2000-03-31    0.086351\n",
       "2000-04-30    0.115538\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resampling is the process of converting a time series from one frequency to another\n",
    "# Going from high-frequency to low is called down-sampling, while from low-high is called upsampling\n",
    "# the pandas resample() method handles all of this and has a similar API to groupby\n",
    "\n",
    "rng = pd.date_range('2000-01-01', periods = 100, freq = 'D')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index = rng)\n",
    "print(ts)\n",
    "ts.resample('M').mean() # resample and aggregate data to the monthly level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.242749\n",
       "2000-02   -0.284144\n",
       "2000-03   -0.058050\n",
       "2000-04   -0.373702\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also specify the type of object you want to resample to, such as a period  or timestamp\n",
    "# Input type is kept by default though which is in this case a date range\n",
    "\n",
    "ts.resample('M', kind = 'period').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001-01-01 00:00:00     0\n",
      "2001-01-01 00:01:00     1\n",
      "2001-01-01 00:02:00     2\n",
      "2001-01-01 00:03:00     3\n",
      "2001-01-01 00:04:00     4\n",
      "2001-01-01 00:05:00     5\n",
      "2001-01-01 00:06:00     6\n",
      "2001-01-01 00:07:00     7\n",
      "2001-01-01 00:08:00     8\n",
      "2001-01-01 00:09:00     9\n",
      "2001-01-01 00:10:00    10\n",
      "2001-01-01 00:11:00    11\n",
      "Freq: T, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-12-31 23:55:00     0\n",
       "2001-01-01 00:00:00    15\n",
       "2001-01-01 00:05:00    40\n",
       "2001-01-01 00:10:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each interval is half open and the union of all should span the entire timeframe\n",
    "# By default, the left bin edge is inclusive, but can change that by passing closed = 'right'\n",
    "\n",
    "rng = pd.date_range('2001-01-01', periods = 12, freq = 'T')\n",
    "ts = pd.Series(np.arange(12), index = rng)\n",
    "print(ts)\n",
    "\n",
    "# Aggregate into five minute chunks by sum\n",
    "\n",
    "ts.resample('5min', closed = 'right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-01 00:00:00     0\n",
       "2001-01-01 00:05:00    15\n",
       "2001-01-01 00:10:00    40\n",
       "2001-01-01 00:15:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Also note that the resulting time series is labeled from the left side of each bin; change by passing label = 'right'\n",
    "ts.resample('5min', closed = 'right', label = 'right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-12-31 23:59:59     0\n",
       "2001-01-01 00:04:59    15\n",
       "2001-01-01 00:09:59    40\n",
       "2001-01-01 00:14:59    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also add left or right offsets if desired, using additional argument or using shift() method\n",
    "\n",
    "ts.resample('5min', closed = 'right', label = 'right', loffset = '-1s').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01 00:05:00</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01 00:10:00</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     open  high  low  close\n",
       "2001-01-01 00:00:00     0     4    0      4\n",
       "2001-01-01 00:05:00     5     9    5      9\n",
       "2001-01-01 00:10:00    10    11   10     11"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas has a built-in Open-High-Low-Close aggregation function to use for resample. Great for financial data\n",
    "\n",
    "ts.resample('5min').ohlc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Upsampling and Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Colorado     Texas  New York      Ohio\n",
      "2000-01-05  0.467258  1.169215 -0.305367  0.550272\n",
      "2000-01-12 -0.658334  0.750752  0.464932  0.559029\n"
     ]
    },
    {
     "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>2000-01-05</th>\n",
       "      <td>0.467258</td>\n",
       "      <td>1.169215</td>\n",
       "      <td>-0.305367</td>\n",
       "      <td>0.550272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.658334</td>\n",
       "      <td>0.750752</td>\n",
       "      <td>0.464932</td>\n",
       "      <td>0.559029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.467258  1.169215 -0.305367  0.550272\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 -0.658334  0.750752  0.464932  0.559029"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in Upsampling, no aggregation is needed but can define how the intervals are interpolated\n",
    "\n",
    "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'])\n",
    "print(frame)\n",
    "frame.resample('D').asfreq() # convert to higher frequency with no aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>2000-01-05</th>\n",
       "      <td>0.467258</td>\n",
       "      <td>1.169215</td>\n",
       "      <td>-0.305367</td>\n",
       "      <td>0.550272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.467258</td>\n",
       "      <td>1.169215</td>\n",
       "      <td>-0.305367</td>\n",
       "      <td>0.550272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.467258</td>\n",
       "      <td>1.169215</td>\n",
       "      <td>-0.305367</td>\n",
       "      <td>0.550272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.467258</td>\n",
       "      <td>1.169215</td>\n",
       "      <td>-0.305367</td>\n",
       "      <td>0.550272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.658334</td>\n",
       "      <td>0.750752</td>\n",
       "      <td>0.464932</td>\n",
       "      <td>0.559029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.467258  1.169215 -0.305367  0.550272\n",
       "2000-01-06  0.467258  1.169215 -0.305367  0.550272\n",
       "2000-01-07  0.467258  1.169215 -0.305367  0.550272\n",
       "2000-01-08  0.467258  1.169215 -0.305367  0.550272\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 -0.658334  0.750752  0.464932  0.559029"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also define how to interpolate by usig the various fill methods\n",
    "\n",
    "frame.resample('D').ffill(limit = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Resampling by Period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Colorado     Texas  New York      Ohio\n",
      "2000-01  1.463790  0.187681 -0.032986  1.511992\n",
      "2000-02  0.011686  1.638860  1.170213 -0.373971\n",
      "2000-03  2.397814  1.104900 -0.772629  0.377619\n",
      "2000-04  0.928902  0.497185 -0.602293 -0.546680\n",
      "2000-05 -0.446211  0.529251 -1.492449 -0.555601\n"
     ]
    },
    {
     "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>2000</th>\n",
       "      <td>0.277042</td>\n",
       "      <td>0.287389</td>\n",
       "      <td>0.141913</td>\n",
       "      <td>0.219304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-0.256845</td>\n",
       "      <td>0.173546</td>\n",
       "      <td>0.124242</td>\n",
       "      <td>-0.355636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Colorado     Texas  New York      Ohio\n",
       "2000  0.277042  0.287389  0.141913  0.219304\n",
       "2001 -0.256845  0.173546  0.124242 -0.355636"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Very similar to resampling by timestamp\n",
    "\n",
    "frame = pd.DataFrame(np.random.randn(24,4), \n",
    "                     index = pd.period_range('1-2000', '12-2001', freq='M'), \n",
    "                     columns = ['Colorado', 'Texas', 'New York', 'Ohio'])\n",
    "\n",
    "print(frame[:5])\n",
    "frame.resample('A-DEC').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>7.40</td>\n",
       "      <td>21.11</td>\n",
       "      <td>29.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.14</td>\n",
       "      <td>29.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.52</td>\n",
       "      <td>29.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.43</td>\n",
       "      <td>21.93</td>\n",
       "      <td>28.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.28</td>\n",
       "      <td>21.31</td>\n",
       "      <td>28.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>388.81</td>\n",
       "      <td>26.94</td>\n",
       "      <td>76.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>400.29</td>\n",
       "      <td>27.00</td>\n",
       "      <td>76.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>402.19</td>\n",
       "      <td>26.96</td>\n",
       "      <td>77.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>408.43</td>\n",
       "      <td>27.18</td>\n",
       "      <td>76.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>422.00</td>\n",
       "      <td>27.27</td>\n",
       "      <td>78.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2214 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL   MSFT    XOM\n",
       "2003-01-02    7.40  21.11  29.22\n",
       "2003-01-03    7.45  21.14  29.24\n",
       "2003-01-06    7.45  21.52  29.96\n",
       "2003-01-07    7.43  21.93  28.95\n",
       "2003-01-08    7.28  21.31  28.83\n",
       "...            ...    ...    ...\n",
       "2011-10-10  388.81  26.94  76.28\n",
       "2011-10-11  400.29  27.00  76.27\n",
       "2011-10-12  402.19  26.96  77.16\n",
       "2011-10-13  408.43  27.18  76.37\n",
       "2011-10-14  422.00  27.27  78.11\n",
       "\n",
       "[2214 rows x 3 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Great for smoothing noisy or gappy data\n",
    "\n",
    "close_px_all = pd.read_csv('pydata-book/examples/stock_px_2.csv', parse_dates = True, index_col = 0)\n",
    "close_px = close_px_all[['AAPL', 'MSFT', 'XOM']]\n",
    "close_px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>7.40</td>\n",
       "      <td>21.11</td>\n",
       "      <td>29.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.14</td>\n",
       "      <td>29.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.52</td>\n",
       "      <td>29.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.43</td>\n",
       "      <td>21.93</td>\n",
       "      <td>28.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.28</td>\n",
       "      <td>21.31</td>\n",
       "      <td>28.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>388.81</td>\n",
       "      <td>26.94</td>\n",
       "      <td>76.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>400.29</td>\n",
       "      <td>27.00</td>\n",
       "      <td>76.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>402.19</td>\n",
       "      <td>26.96</td>\n",
       "      <td>77.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>408.43</td>\n",
       "      <td>27.18</td>\n",
       "      <td>76.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>422.00</td>\n",
       "      <td>27.27</td>\n",
       "      <td>78.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2292 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL   MSFT    XOM\n",
       "2003-01-02    7.40  21.11  29.22\n",
       "2003-01-03    7.45  21.14  29.24\n",
       "2003-01-06    7.45  21.52  29.96\n",
       "2003-01-07    7.43  21.93  28.95\n",
       "2003-01-08    7.28  21.31  28.83\n",
       "...            ...    ...    ...\n",
       "2011-10-10  388.81  26.94  76.28\n",
       "2011-10-11  400.29  27.00  76.27\n",
       "2011-10-12  402.19  26.96  77.16\n",
       "2011-10-13  408.43  27.18  76.37\n",
       "2011-10-14  422.00  27.27  78.11\n",
       "\n",
       "[2292 rows x 3 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px.resample('B').ffill() # account for holidays and to create a smooth period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121a35080>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rolling operator which is similar to resample and groupby to calculate moving window functions\n",
    "# By default all rolling window functions require all the values in the window to be non-null\n",
    "\n",
    "close_px.AAPL.plot()\n",
    "close_px.AAPL.rolling(250).mean().plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003-01-02         NaN\n",
      "2003-01-03         NaN\n",
      "2003-01-06         NaN\n",
      "2003-01-07         NaN\n",
      "2003-01-08         NaN\n",
      "2003-01-09         NaN\n",
      "2003-01-10         NaN\n",
      "2003-01-13         NaN\n",
      "2003-01-14         NaN\n",
      "2003-01-15    0.077496\n",
      "2003-01-16    0.074760\n",
      "2003-01-17    0.112368\n",
      "2003-01-21    0.138735\n",
      "2003-01-22    0.164631\n",
      "2003-01-23    0.165731\n",
      "Name: AAPL, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121b57160>"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can also set a minimum number of window periods to account for data at the beginning of the timeseries\n",
    "\n",
    "print(close_px.AAPL.rolling(250, min_periods = 10).std()[:15])\n",
    "close_px.AAPL.rolling(250, min_periods = 10).std().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121ec8b70>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can also use expanding windows.  An expanding window starts the window from the beginning\n",
    "# of the time series and increases it until it encompasses the whole series\n",
    "\n",
    "close_px.AAPL.expanding().mean().plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121e89278>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calling a moving window function to a DataFrame applies the transformation on each column\n",
    "\n",
    "close_px.rolling(60).mean().plot(logy = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1227b5a58>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can also combine the power of groupby and resample by passing in a string for a time offset into rolling\n",
    "\n",
    "close_px.rolling('14D').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exponentially Weighted Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122a6f4a8>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Places more weight on recent observations\n",
    "\n",
    "aapl_px = close_px.AAPL['2006':'2007']\n",
    "ma60 = aapl_px.rolling(60, min_periods = 20).mean()\n",
    "ewma60 = aapl_px.ewm(span = 60).mean() # result comprable to a SMA window function with window size equal to span\n",
    "\n",
    "ma60.plot(style = 'k--', label = 'Simple MA')\n",
    "ewma60.plot(style = 'b-', label = 'EWMA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Binary Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122a25c18>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlations between two variables as for stocks, etc.\n",
    "\n",
    "# First, calculate percent changes of everything\n",
    "spx_px = close_px_all['SPX']\n",
    "spx_ret = spx_px.pct_change()\n",
    "returns = close_px.pct_change()\n",
    "\n",
    "# Pairwise correlation with AAPL vs SPX\n",
    "\n",
    "returns.AAPL.rolling(125, min_periods = 100).corr(spx_ret).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122c9d278>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can also pass in a whole DF and get a corr with each column with the passed-in Series\n",
    "\n",
    "returns.rolling(125, min_periods = 100).corr(spx_ret).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### User-defined Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24d19550>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can use apply method on rolling for custom functions\n",
    "# Only requirement is that the function produces a single value from each piece of the array.\n",
    "# Example, calculate percentile rank of a particular value over the sample\n",
    "\n",
    "from scipy.stats import percentileofscore\n",
    "\n",
    "score_at_2pct = lambda x: percentileofscore(x, 0.02)\n",
    "returns.AAPL.rolling(250).apply(score_at_2pct).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}