데이터 분석을 위한 파이썬 11장.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6bd2a91f",
   "metadata": {},
   "source": [
    "# 11장 Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e808705f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#time series is formed by some records that are recorded repeatedly at many points in time\n",
    "\n",
    "#time series that have fixed frequency: data points occur at regular intervals according to some rule, ex= every 5 minutes\n",
    "\n",
    "#irregular time series: without fixed unit of time or offset between units\n",
    "\n",
    "how we mark and refer to time series data depnds on:\n",
    "- timestamps: specific instants in time    \n",
    "- fixed periods\n",
    "- intervals of time\n",
    "- Experiment or elapsed time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e7bfc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d70d3b",
   "metadata": {},
   "source": [
    "## 11.1 Date and Time Data Types and Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49e9f4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 1, 21, 22, 39, 20, 469339)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "now=datetime.now()\n",
    "\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dededcab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2023, 1, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.year, now.month, now.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e665a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#datetime.timedelta => timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8faf58be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=926, seconds=56700)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta= datetime (2011, 1, 7) -datetime(2008, 6, 24, 8, 15) #year, month, day, hour, minute, second,...\n",
    "\n",
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6621689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "926"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bfe551ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56700"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd4e87c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 19, 0, 0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "start= datetime(2011,1,7)\n",
    "\n",
    "start + timedelta(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6793ffdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 12, 14, 0, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start - 2 * timedelta(12) #difference between two datetime values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9c37a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=12)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timedelta(12) => 0:00:12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e84ded8",
   "metadata": {},
   "source": [
    "###### 11.1.1 Converting Between String and Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "824f1fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = datetime(2011, 1, 3)\n",
    "\n",
    "str(stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04569f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp.strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70c3c555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = \"2011-01-03\"\n",
    "\n",
    "datetime.strptime(value, \"%Y-%m-%d\") #can use many of the same format codes to convert strings and ates using strptime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f67a6ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datestrs = [\"7/6/2011\", \"8/6/2011\"]\n",
    "\n",
    "[datetime.strptime(x, \"%m/%d/%Y\") for x in datestrs] #.strptime is one way to parse a date with a known format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ab1ad47",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pandas.to_datetime method parses many different kinds of date representations.\n",
    "\n",
    "datestrs = [\"2011-07-06 12:00:00\", \"2011-08-06 00:00:00\"]\n",
    "\n",
    "pd.to_datetime(datestrs)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "681c22ab",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#also handles values that should be considered missing\n",
    "\n",
    "idx = pd.to_datetime(datestrs + [None])\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "81b5a622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaT"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx[2] #Nat (Not a Time) is pandas's null value for time-stamp data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "14f0d777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa04d13a",
   "metadata": {},
   "source": [
    "## 11.2 Time Series Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fd65c989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.725710\n",
       "2011-01-05    0.740336\n",
       "2011-01-07    1.204665\n",
       "2011-01-08    0.092522\n",
       "2011-01-10    0.814767\n",
       "2011-01-12    2.624051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = [datetime(2011, 1, 2), datetime(2011, 1, 5),\n",
    "         datetime(2011, 1, 7), datetime(2011, 1, 8),\n",
    "         datetime(2011, 1, 10), datetime(2011, 1, 12)]\n",
    "\n",
    "ts = pd.Series(np.random.standard_normal(6), index=dates)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e7d87479",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index #datetime objects have been put in a datetimeindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f0c05605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    1.451420\n",
       "2011-01-05         NaN\n",
       "2011-01-07    2.409330\n",
       "2011-01-08         NaN\n",
       "2011-01-10    1.629535\n",
       "2011-01-12         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts + ts[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "906120cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.725710\n",
       "2011-01-07    1.204665\n",
       "2011-01-10    0.814767\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[::2] #selects every second element in ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "037345cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a19b84dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-02 00:00:00')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp=ts.index[0]\n",
    "\n",
    "stamp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef6e522",
   "metadata": {},
   "source": [
    "###### 11.2.1 Indexing, Selection, Subsetting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8f599f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-07 00:00:00')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp =ts.index[2]\n",
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c24d4f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2046651097072008"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[stamp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b46056b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8147674223720353"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[\"2011-01-10\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d5ad60b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.689183\n",
       "2000-01-02    1.647350\n",
       "2000-01-03    0.505722\n",
       "2000-01-04    0.136209\n",
       "2000-01-05   -0.685116\n",
       "                ...   \n",
       "2002-09-22   -0.177609\n",
       "2002-09-23   -0.869477\n",
       "2002-09-24   -0.891454\n",
       "2002-09-25   -0.562129\n",
       "2002-09-26    1.195165\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts = pd.Series(np.random.standard_normal(1000),\n",
    "                      index=pd.date_range(\"2000-01-01\", periods=1000))\n",
    "\n",
    "longer_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b5d71b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-01   -1.727242\n",
       "2001-01-02   -1.325260\n",
       "2001-01-03    1.037234\n",
       "2001-01-04    0.338015\n",
       "2001-01-05    0.882145\n",
       "                ...   \n",
       "2001-12-27    1.243932\n",
       "2001-12-28    0.200911\n",
       "2001-12-29   -1.652506\n",
       "2001-12-30    1.121157\n",
       "2001-12-31    1.442087\n",
       "Freq: D, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts['2001'] #2001 is interpreted as a year and selects that time period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8bbc85b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-05-01   -1.083542\n",
       "2001-05-02    0.231546\n",
       "2001-05-03    0.350813\n",
       "2001-05-04   -0.029709\n",
       "2001-05-05   -0.202082\n",
       "2001-05-06   -0.546310\n",
       "2001-05-07    1.451088\n",
       "2001-05-08    0.455409\n",
       "2001-05-09   -0.193276\n",
       "2001-05-10   -0.799656\n",
       "2001-05-11    0.683217\n",
       "2001-05-12   -0.528196\n",
       "2001-05-13   -0.040097\n",
       "2001-05-14    0.789138\n",
       "2001-05-15    0.758517\n",
       "2001-05-16    0.705675\n",
       "2001-05-17    1.816233\n",
       "2001-05-18   -0.317611\n",
       "2001-05-19   -0.571357\n",
       "2001-05-20   -1.147947\n",
       "2001-05-21   -0.407079\n",
       "2001-05-22   -0.060952\n",
       "2001-05-23    0.517504\n",
       "2001-05-24   -0.426166\n",
       "2001-05-25   -0.966852\n",
       "2001-05-26    1.491424\n",
       "2001-05-27    0.431271\n",
       "2001-05-28   -0.683146\n",
       "2001-05-29    1.545995\n",
       "2001-05-30   -0.315530\n",
       "2001-05-31   -0.114307\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts[\"2001-05\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86cda069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07    1.204665\n",
       "2011-01-08    0.092522\n",
       "2011-01-10    0.814767\n",
       "2011-01-12    2.624051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#slicing with datetime\n",
    "\n",
    "ts[datetime(2011, 1, 7):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e695133e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-05-01   -1.083542\n",
       "2001-05-02    0.231546\n",
       "2001-05-03    0.350813\n",
       "2001-05-04   -0.029709\n",
       "2001-05-05   -0.202082\n",
       "                ...   \n",
       "2002-09-22   -0.177609\n",
       "2002-09-23   -0.869477\n",
       "2002-09-24   -0.891454\n",
       "2002-09-25   -0.562129\n",
       "2002-09-26    1.195165\n",
       "Freq: D, Length: 514, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts[\"2001-05-01\":]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eaaf9f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07    1.204665\n",
       "2011-01-08    0.092522\n",
       "2011-01-10    0.814767\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[datetime(2011, 1, 7):datetime(2011, 1, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1fe3faf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.725710\n",
       "2011-01-05    0.740336\n",
       "2011-01-07    1.204665\n",
       "2011-01-08    0.092522\n",
       "2011-01-10    0.814767\n",
       "2011-01-12    2.624051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#most time series data is ordered chronologically => can slice with timestamps not contained in a time series to perform a range query\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ed9f1642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07    1.204665\n",
       "2011-01-08    0.092522\n",
       "2011-01-10    0.814767\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[\"2011-01-06\":\"2011-01-11\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1de981ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.725710\n",
       "2011-01-05    0.740336\n",
       "2011-01-07    1.204665\n",
       "2011-01-08    0.092522\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#truncate => slices a series between two dates\n",
    "\n",
    "ts.truncate(after=\"2011-01-09\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "11b56e8d",
   "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>-0.026042</td>\n",
       "      <td>-1.532715</td>\n",
       "      <td>1.146967</td>\n",
       "      <td>0.252596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>0.583958</td>\n",
       "      <td>0.973932</td>\n",
       "      <td>-0.010670</td>\n",
       "      <td>-0.667157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16</th>\n",
       "      <td>3.064016</td>\n",
       "      <td>2.365957</td>\n",
       "      <td>1.073266</td>\n",
       "      <td>1.289617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-23</th>\n",
       "      <td>1.093919</td>\n",
       "      <td>0.086299</td>\n",
       "      <td>-1.769262</td>\n",
       "      <td>1.183625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-30</th>\n",
       "      <td>-0.758976</td>\n",
       "      <td>0.442969</td>\n",
       "      <td>0.261708</td>\n",
       "      <td>0.728587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2001-05-02 -0.026042 -1.532715  1.146967  0.252596\n",
       "2001-05-09  0.583958  0.973932 -0.010670 -0.667157\n",
       "2001-05-16  3.064016  2.365957  1.073266  1.289617\n",
       "2001-05-23  1.093919  0.086299 -1.769262  1.183625\n",
       "2001-05-30 -0.758976  0.442969  0.261708  0.728587"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"2000-01-01\", periods=100, freq=\"W-WED\")\n",
    "\n",
    "long_df = pd.DataFrame(np.random.standard_normal((100, 4)),\n",
    "                       index=dates,\n",
    "                       columns=[\"Colorado\", \"Texas\",\n",
    "                                \"New York\", \"Ohio\"])\n",
    "\n",
    "long_df.loc[\"2001-05\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf6592c",
   "metadata": {},
   "source": [
    "###### 11.2.2 Time Series with Duplicated Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f5bed779",
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.DatetimeIndex([\"2000-01-01\", \"2000-01-02\", \"2000-01-02\",\n",
    "                          \"2000-01-02\", \"2000-01-03\"])\n",
    "dup_ts = pd.Series(np.arange(5), index=dates)\n",
    "dup_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "99afc5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if index is unique\n",
    "\n",
    "dup_ts.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c9c21405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts[\"2000-01-03\"]  # not duplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "330e0917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts[\"2000-01-02\"]  # duplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f88c443d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    2\n",
       "2000-01-03    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#aggregate data having nonunique timestamps\n",
    "\n",
    "grouped = dup_ts.groupby(level=0)\n",
    "\n",
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1e9f3cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    3\n",
       "2000-01-03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56092a8",
   "metadata": {},
   "source": [
    "## 11.3 Date Ranges, Frequencies, and Shifting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f705a9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.725710\n",
       "2011-01-05    0.740336\n",
       "2011-01-07    1.204665\n",
       "2011-01-08    0.092522\n",
       "2011-01-10    0.814767\n",
       "2011-01-12    2.624051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e3753c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x121485e20>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampler=ts.resample(\"D\") #\"D\" is interpreted as daily frequency\n",
    "\n",
    "resampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e8570f",
   "metadata": {},
   "source": [
    "###### 11.3.1 Generating Date Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9eb7b519",
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.date_range(\"2012-04-01\", \"2012-06-01\")\n",
    "\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "32e316e2",
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pandas.date_range generates daily timestamps\n",
    "\n",
    "pd.date_range(start=\"2012-04-01\", periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "94ec13c0",
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(end=\"2012-06-01\", periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0905aa37",
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2000-01-01\", \"2000-12-01\", freq=\"BM\") #business end of month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "df4ef131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02 12:56:31', '2012-05-03 12:56:31',\n",
       "               '2012-05-04 12:56:31', '2012-05-05 12:56:31',\n",
       "               '2012-05-06 12:56:31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pandas.date_range by default preserves the time (if any) of the start or end timestamp\n",
    "\n",
    "pd.date_range(\"2012-05-02 12:56:31\", periods=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b6ac85",
   "metadata": {},
   "source": [
    "###### 11.3.2 Frequencies and Date Offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "317ed091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Hour>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import Hour, Minute\n",
    "\n",
    "hour = Hour() #hourly frequency represented with Hour class\n",
    "\n",
    "hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e2d59e1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4 * Hours>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "four_hours = Hour(4)\n",
    "\n",
    "four_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "acb21d9b",
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2000-01-01\", \"2000-01-03 23:59\", freq=\"4H\") #freq에 4H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1b460c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<150 * Minutes>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hour(2) + Minute(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4312f583",
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2000-01-01\", periods=10, freq=\"1h30min\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec3fcf9",
   "metadata": {},
   "source": [
    "###### Week of month dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "29f1b754",
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#frequency class WOM that enables to get dates like 매달 세번째 금요일\n",
    "\n",
    "monthly_dates = pd.date_range(\"2012-01-01\", \"2012-09-01\", freq=\"WOM-3FRI\")\n",
    "\n",
    "list(monthly_dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d52ff4",
   "metadata": {},
   "source": [
    "###### 11.3.3 Shifting (Leading and Lagging) Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1825573d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    1.226911\n",
       "2000-02-29    1.209476\n",
       "2000-03-31    0.369512\n",
       "2000-04-30    0.495395\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.standard_normal(4),\n",
    "               index=pd.date_range(\"2000-01-01\", periods=4, freq=\"M\"))\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cf1b7308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29         NaN\n",
       "2000-03-31    1.226911\n",
       "2000-04-30    1.209476\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2) #shift method doing shift foward or back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0678a98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.369512\n",
       "2000-02-29    0.495395\n",
       "2000-03-31         NaN\n",
       "2000-04-30         NaN\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "132e905e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29   -0.014210\n",
       "2000-03-31   -0.694486\n",
       "2000-04-30    0.340672\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts / ts.shift(1) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ba0f1b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-31    1.226911\n",
       "2000-04-30    1.209476\n",
       "2000-05-31    0.369512\n",
       "2000-06-30    0.495395\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2, freq=\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1dadd4de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-02-03    1.226911\n",
       "2000-03-03    1.209476\n",
       "2000-04-03    0.369512\n",
       "2000-05-03    0.495395\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(3, freq=\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "031f8f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31 01:30:00    1.226911\n",
       "2000-02-29 01:30:00    1.209476\n",
       "2000-03-31 01:30:00    0.369512\n",
       "2000-04-30 01:30:00    0.495395\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(1, freq=\"90T\") #T stands for minute"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66a1384",
   "metadata": {},
   "source": [
    "###### Shifting dates with offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ed95428e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-20 00:00:00')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import Day, MonthEnd\n",
    "\n",
    "now = datetime(2011, 11, 17)\n",
    "\n",
    "now + 3 * Day()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c474b937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8002044c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-01-31 00:00:00')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + MonthEnd(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "83a14d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "173309ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " offset.rollforward(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "60a2ebcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-10-31 00:00:00')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollback(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6bbbc433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-15   -0.765998\n",
       "2000-01-19    0.018349\n",
       "2000-01-23    0.222134\n",
       "2000-01-27   -0.720153\n",
       "2000-01-31    0.609508\n",
       "2000-02-04   -0.137666\n",
       "2000-02-08   -1.557173\n",
       "2000-02-12    0.016468\n",
       "2000-02-16    0.192989\n",
       "2000-02-20   -0.364213\n",
       "2000-02-24   -0.432887\n",
       "2000-02-28    2.012606\n",
       "2000-03-03   -0.434549\n",
       "2000-03-07   -1.400506\n",
       "2000-03-11    0.972851\n",
       "2000-03-15    0.292786\n",
       "2000-03-19    1.601278\n",
       "2000-03-23    0.606969\n",
       "2000-03-27    0.434783\n",
       "2000-03-31    1.231878\n",
       "Freq: 4D, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.standard_normal(20),\n",
    "               index=pd.date_range(\"2000-01-15\", periods=20, freq=\"4D\"))\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e7c680fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.127232\n",
       "2000-02-29   -0.038554\n",
       "2000-03-31    0.413186\n",
       "dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(MonthEnd().rollforward).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0586f412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.127232\n",
       "2000-02-29   -0.038554\n",
       "2000-03-31    0.413186\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"M\").mean() #using resample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0acc2faf",
   "metadata": {},
   "source": [
    "## 11.4 Time Zone Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "804c591e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytz\n",
    "\n",
    "pytz.common_timezones[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "636bbfee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz = pytz.timezone(\"America/New_York\")\n",
    "\n",
    "tz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92bad9c5",
   "metadata": {},
   "source": [
    "###### 11.4.1 Time Zone Localization and conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0c85d205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.168531\n",
       "2012-03-10 09:30:00   -0.713659\n",
       "2012-03-11 09:30:00    1.015122\n",
       "2012-03-12 09:30:00    0.166584\n",
       "2012-03-13 09:30:00    1.004065\n",
       "2012-03-14 09:30:00   -0.789301\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"2012-03-09 09:30\", periods=6)\n",
    "\n",
    "ts = pd.Series(np.random.standard_normal(len(dates)), index=dates)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1d2c6458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ts.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "954d4428",
   "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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2012-03-09 09:30\", periods=10, tz=\"UTC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "15e41948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.168531\n",
       "2012-03-10 09:30:00   -0.713659\n",
       "2012-03-11 09:30:00    1.015122\n",
       "2012-03-12 09:30:00    0.166584\n",
       "2012-03-13 09:30:00    1.004065\n",
       "2012-03-14 09:30:00   -0.789301\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "93af2567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00+00:00   -0.168531\n",
       "2012-03-10 09:30:00+00:00   -0.713659\n",
       "2012-03-11 09:30:00+00:00    1.015122\n",
       "2012-03-12 09:30:00+00:00    0.166584\n",
       "2012-03-13 09:30:00+00:00    1.004065\n",
       "2012-03-14 09:30:00+00:00   -0.789301\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize(\"UTC\") #conversion from naive to localized, 현재의 시간을 시간대로 현지화 하기\n",
    "\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0a46f474",
   "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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c4462821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 04:30:00-05:00   -0.168531\n",
       "2012-03-10 04:30:00-05:00   -0.713659\n",
       "2012-03-11 05:30:00-04:00    1.015122\n",
       "2012-03-12 05:30:00-04:00    0.166584\n",
       "2012-03-13 05:30:00-04:00    1.004065\n",
       "2012-03-14 05:30:00-04:00   -0.789301\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert(\"America/New_York\") #converted to another time zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "81dc647b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 14:30:00+00:00   -0.168531\n",
       "2012-03-10 14:30:00+00:00   -0.713659\n",
       "2012-03-11 13:30:00+00:00    1.015122\n",
       "2012-03-12 13:30:00+00:00    0.166584\n",
       "2012-03-13 13:30:00+00:00    1.004065\n",
       "2012-03-14 13:30:00+00:00   -0.789301\n",
       "dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern = ts.tz_localize(\"America/New_York\")\n",
    "\n",
    "ts_eastern.tz_convert(\"UTC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "30efb514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 15:30:00+01:00   -0.168531\n",
       "2012-03-10 15:30:00+01:00   -0.713659\n",
       "2012-03-11 14:30:00+01:00    1.015122\n",
       "2012-03-12 14:30:00+01:00    0.166584\n",
       "2012-03-13 14:30:00+01:00    1.004065\n",
       "2012-03-14 14:30:00+01:00   -0.789301\n",
       "dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert(\"Europe/Berlin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cab370de",
   "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=None)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tz_localize and tz_convert are also instance methods on DatetimeIndex\n",
    "\n",
    "ts.index.tz_localize(\"Asia/Shanghai\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19aace69",
   "metadata": {},
   "source": [
    "###### 11.4.2 Operations with Time Zone-Aware Timestamp Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "05ba610e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-11 23:00:00-0500', tz='America/New_York')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = pd.Timestamp(\"2011-03-12 04:00\")\n",
    "\n",
    "stamp_utc = stamp.tz_localize(\"utc\")\n",
    "\n",
    "stamp_utc.tz_convert(\"America/New_York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "42f0a59f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_moscow = pd.Timestamp(\"2011-03-12 04:00\", tz=\"Europe/Moscow\")\n",
    "\n",
    "stamp_moscow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "57fa4028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.value #changing timezone does not alter the internal UTC value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "4605bc79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert(\"America/New_York\").value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af326e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Daylight Saving Time (DST) => 가을에는 한시간 뒤로 미루기, 봄에는 한시간 추가해서 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "31d8a734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-11 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = pd.Timestamp(\"2012-03-11 01:30\", tz=\"US/Eastern\")\n",
    "\n",
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "ea501f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-11 03:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp+Hour() #봄에는 한시간 추가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "53080c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 00:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = pd.Timestamp(\"2012-11-04 00:30\", tz=\"US/Eastern\")\n",
    "\n",
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "20a0da84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp+2*Hour() #가을에는 한시간 뒤로"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d7af67",
   "metadata": {},
   "source": [
    "###### 11.4.3 Operations Between Different Time Zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "59d920be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00   -0.285666\n",
       "2012-03-08 09:30:00   -1.120525\n",
       "2012-03-09 09:30:00    1.371808\n",
       "2012-03-12 09:30:00    0.798324\n",
       "2012-03-13 09:30:00   -0.539674\n",
       "2012-03-14 09:30:00    0.392996\n",
       "2012-03-15 09:30:00    0.237782\n",
       "2012-03-16 09:30:00   -0.488589\n",
       "2012-03-19 09:30:00    0.731637\n",
       "2012-03-20 09:30:00   -0.803211\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"2012-03-07 09:30\", periods=10, freq=\"B\")\n",
    "\n",
    "ts = pd.Series(np.random.standard_normal(len(dates)), index=dates)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e0aeaae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00+00:00   -0.285666\n",
       "2012-03-08 09:30:00+00:00   -1.120525\n",
       "2012-03-09 09:30:00+00:00    1.371808\n",
       "2012-03-12 09:30:00+00:00    0.798324\n",
       "2012-03-13 09:30:00+00:00   -0.539674\n",
       "2012-03-14 09:30:00+00:00    0.392996\n",
       "2012-03-15 09:30:00+00:00    0.237782\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1 = ts[:7].tz_localize(\"Europe/London\")\n",
    "\n",
    "ts1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "08a9c9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 13:30:00+04:00    1.371808\n",
       "2012-03-12 13:30:00+04:00    0.798324\n",
       "2012-03-13 13:30:00+04:00   -0.539674\n",
       "2012-03-14 13:30:00+04:00    0.392996\n",
       "2012-03-15 13:30:00+04:00    0.237782\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2 = ts1[2:].tz_convert(\"Europe/Moscow\")\n",
    "\n",
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "bc6ee95f",
   "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=None)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = ts1 + ts2\n",
    "\n",
    "result.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f315a78c",
   "metadata": {},
   "source": [
    "## 11.5 Periods and Period Arithmetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8eea7aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011', 'A-DEC')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period(\"2011\", freq=\"A-DEC\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c0c8943d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016', 'A-DEC')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "aab5676b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2009', 'A-DEC')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c2585592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3 * YearEnds: month=12>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period(\"2014\", freq=\"A-DEC\") - p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e3818f64",
   "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": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periods = pd.period_range(\"2000-01-01\", \"2000-06-30\", freq=\"M\")\n",
    "\n",
    "periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "71269717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -0.171075\n",
       "2000-02    0.562138\n",
       "2000-03    0.025020\n",
       "2000-04    0.192304\n",
       "2000-05   -0.488207\n",
       "2000-06   -0.523321\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.random.standard_normal(6), index=periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5890b51f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = [\"2001Q3\", \"2002Q2\", \"2003Q1\"]\n",
    "\n",
    "index = pd.PeriodIndex(values, freq=\"Q-DEC\")\n",
    "\n",
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f664bca4",
   "metadata": {},
   "source": [
    "###### Period Frequency Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9a3678eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011', 'A-DEC')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period(\"2011\", freq=\"A-DEC\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c200da86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-01', 'M')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"M\", how=\"start\") #asfreq converts to another frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c23bf657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-12', 'M')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"M\", how=\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1f290f61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-12', 'M')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "44973d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011', 'A-JUN')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period(\"2011\", freq=\"A-JUN\") #A 다음 달은 해당 연도의 달을 끝으로 가는 12달 을 보여준다 ,,, jun 이 끝이니까 시작은 jul부터\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a61aa516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2010-07', 'M')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"M\", how=\"start\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "847f4ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-06', 'M')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"M\", how=\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "95816619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012', 'A-JUN')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period(\"Aug-2011\", \"M\")\n",
    "\n",
    "p.asfreq(\"A-JUN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "4db016c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    0.145071\n",
       "2007    0.899672\n",
       "2008    0.455905\n",
       "2009   -1.311002\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PeriodIndex objects or time series can be similarly converted with the same semantics\n",
    "periods = pd.period_range(\"2006\", \"2009\", freq=\"A-DEC\")\n",
    "\n",
    "ts = pd.Series(np.random.standard_normal(len(periods)), index=periods)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5de205a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01    0.145071\n",
       "2007-01    0.899672\n",
       "2008-01    0.455905\n",
       "2009-01   -1.311002\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq(\"M\", how=\"start\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fcdbaab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-12-29    0.145071\n",
       "2007-12-31    0.899672\n",
       "2008-12-31    0.455905\n",
       "2009-12-31   -1.311002\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq(\"B\", how=\"end\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b0f4f0",
   "metadata": {},
   "source": [
    "###### 11.5.2 Quarterly Period Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5c4665c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JAN')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period(\"2012Q4\", freq=\"Q-JAN\")\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "f96c5895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-11-01', 'D')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"D\", how=\"start\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "0fab1cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-31', 'D')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"D\", how=\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "638d58eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-30 16:00', 'T')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm = (p.asfreq(\"B\", how=\"end\") - 1).asfreq(\"T\", how=\"start\") + 16 * 60\n",
    "\n",
    "p4pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a7b8ca93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-01-30 16:00:00')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm.to_timestamp() #to_timestamp method returns the Timestamp at the start of the period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "2896c29d",
   "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": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periods = pd.period_range(\"2011Q3\", \"2012Q4\", freq=\"Q-JAN\") #2011Q3 가 시작, 2012Q4가 마지막, 2012 Jan이 마지막이어야 된다\n",
    "\n",
    "ts = pd.Series(np.arange(len(periods)), index=periods)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5cfb3990",
   "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": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_periods = (periods.asfreq(\"B\", \"end\") - 1).asfreq(\"H\", \"start\") + 16\n",
    "\n",
    "ts.index = new_periods.to_timestamp()\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7659fcbe",
   "metadata": {},
   "source": [
    "###### 11.5.3 Converting Timestamps to Periods (back and forth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "caedfa12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.088900\n",
       "2000-02-29   -0.659351\n",
       "2000-03-31   -1.154503\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"2000-01-01\", periods=3, freq=\"M\")\n",
    "\n",
    "ts = pd.Series(np.random.standard_normal(3), index=dates)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b500ceb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -0.088900\n",
       "2000-02   -0.659351\n",
       "2000-03   -1.154503\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = ts.to_period() #converted to periods\n",
    "\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "0bd4080c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29    0.175516\n",
       "2000-01-30   -1.834492\n",
       "2000-01-31    0.802625\n",
       "2000-02-01   -1.225809\n",
       "2000-02-02    0.914794\n",
       "2000-02-03   -2.506614\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#periods refer to nonoverlapping time spans, a timestamp can only belong to a single period for a given frequency\n",
    "dates = pd.date_range(\"2000-01-29\", periods=6)\n",
    "\n",
    "ts2 = pd.Series(np.random.standard_normal(6), index=dates)\n",
    "\n",
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "09d5e458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.175516\n",
       "2000-01   -1.834492\n",
       "2000-01    0.802625\n",
       "2000-02   -1.225809\n",
       "2000-02    0.914794\n",
       "2000-02   -2.506614\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2.to_period(\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "3cc13a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29    0.175516\n",
       "2000-01-30   -1.834492\n",
       "2000-01-31    0.802625\n",
       "2000-02-01   -1.225809\n",
       "2000-02-02    0.914794\n",
       "2000-02-03   -2.506614\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = ts2.to_period()\n",
    "\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "5902b24e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29 23:59:59.999999999    0.175516\n",
       "2000-01-30 23:59:59.999999999   -1.834492\n",
       "2000-01-31 23:59:59.999999999    0.802625\n",
       "2000-02-01 23:59:59.999999999   -1.225809\n",
       "2000-02-02 23:59:59.999999999    0.914794\n",
       "2000-02-03 23:59:59.999999999   -2.506614\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts.to_timestamp(how=\"end\") #converting back to timestamps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96741b4",
   "metadata": {},
   "source": [
    "###### 11.5.4 Creating a PeriodIndex from Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "fe9a71eb",
   "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</td>\n",
       "      <td>1</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</td>\n",
       "      <td>2</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</td>\n",
       "      <td>3</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</td>\n",
       "      <td>4</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</td>\n",
       "      <td>1</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        1  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959        2  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959        3  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959        4  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960        1  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": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"examples/macrodata.csv\")\n",
    "\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "4ff552ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1959\n",
       "1      1959\n",
       "2      1959\n",
       "3      1959\n",
       "4      1960\n",
       "       ... \n",
       "198    2008\n",
       "199    2008\n",
       "200    2009\n",
       "201    2009\n",
       "202    2009\n",
       "Name: year, Length: 203, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "620e6b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      2\n",
       "2      3\n",
       "3      4\n",
       "4      1\n",
       "      ..\n",
       "198    3\n",
       "199    4\n",
       "200    1\n",
       "201    2\n",
       "202    3\n",
       "Name: quarter, Length: 203, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"quarter\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "5095dc0f",
   "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": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.PeriodIndex(year=data[\"year\"], quarter=data[\"quarter\"],\n",
    "                       freq=\"Q-DEC\")\n",
    "\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "8b8fe045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959Q1    0.00\n",
       "1959Q2    2.34\n",
       "1959Q3    2.74\n",
       "1959Q4    0.27\n",
       "1960Q1    2.31\n",
       "          ... \n",
       "2008Q3   -3.16\n",
       "2008Q4   -8.79\n",
       "2009Q1    0.94\n",
       "2009Q2    3.37\n",
       "2009Q3    3.56\n",
       "Freq: Q-DEC, Name: infl, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = index\n",
    "\n",
    "data[\"infl\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de63e7f9",
   "metadata": {},
   "source": [
    "## 11.6 Resampling and Frequency Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "00d79f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.315877\n",
       "2000-01-02    0.967992\n",
       "2000-01-03   -0.918616\n",
       "2000-01-04    0.285188\n",
       "2000-01-05   -0.232527\n",
       "                ...   \n",
       "2000-04-05    0.194401\n",
       "2000-04-06    0.700788\n",
       "2000-04-07   -0.100054\n",
       "2000-04-08    0.094939\n",
       "2000-04-09   -0.594576\n",
       "Freq: D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"2000-01-01\", periods=100)\n",
    "\n",
    "ts = pd.Series(np.random.standard_normal(len(dates)), index=dates)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "81bc617a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.274213\n",
       "2000-02-29    0.207208\n",
       "2000-03-31   -0.242707\n",
       "2000-04-30   -0.028358\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"M\").mean() #similar api to groupby, can resample to group the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "26c24bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.274213\n",
       "2000-02    0.207208\n",
       "2000-03   -0.242707\n",
       "2000-04   -0.028358\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"M\", kind=\"period\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "335a4a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#resample is a flexible method that can be used to process 큰 time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c14fbe2",
   "metadata": {},
   "source": [
    "###### 11.6.1 Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "e53fca70",
   "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": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#downsampling- sample의 개수를 줄이는 처리과정\n",
    "dates = pd.date_range(\"2000-01-01\", periods=12, freq=\"T\")\n",
    "\n",
    "ts = pd.Series(np.arange(len(dates)), index=dates)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "ca654ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00    10\n",
       "2000-01-01 00:05:00    35\n",
       "2000-01-01 00:10:00    21\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"5min\").sum() #defines bin edges in five minute increments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "f0141be4",
   "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": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"5min\", closed=\"right\").sum() #오른쪽에 있는거는 포함하고 왼쪽 첫번째는 무시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "297dfa6f",
   "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": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"5min\", closed=\"right\", label=\"right\").sum() #똑같은데 라벨만 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "112aedc3",
   "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": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.frequencies import to_offset\n",
    "\n",
    "result = ts.resample(\"5min\", closed=\"right\", label=\"right\").sum()\n",
    "\n",
    "result.index = result.index + to_offset(\"-1s\")\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d32d20e",
   "metadata": {},
   "source": [
    "###### Open-high-low-close (OHLC) resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f6e4c898",
   "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>2000-01-01 00:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:05:00</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:10:00</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     open  high  low  close\n",
       "2000-01-01 00:00:00     8     8    0      0\n",
       "2000-01-01 00:05:00     5    11    1     11\n",
       "2000-01-01 00:10:00     4     4    2      2"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.permutation(np.arange(len(dates))), index=dates)\n",
    "\n",
    "ts.resample(\"5min\").ohlc()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e14f252",
   "metadata": {},
   "source": [
    "###### 11.6.2 Unsampling and Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "f8b3c3a0",
   "metadata": {},
   "outputs": [
    {
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       "  <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>0.694955</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.809058</td>\n",
       "      <td>0.871991</td>\n",
       "      <td>1.110635</td>\n",
       "      <td>0.772717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-12 -0.809058  0.871991  1.110635  0.772717"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Upsampling is converting from a lower frequency to a higher frequency\n",
    "\n",
    "frame = pd.DataFrame(np.random.standard_normal((2, 4)),\n",
    "                     index=pd.date_range(\"2000-01-01\", periods=2,\n",
    "                                         freq=\"W-WED\"),\n",
    "                     columns=[\"Colorado\", \"Texas\", \"New York\", \"Ohio\"])\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "49ae8b81",
   "metadata": {},
   "outputs": [
    {
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       "      <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>0.694955</td>\n",
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       "      <td>0.993572</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-07</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>2000-01-08</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>-0.809058</td>\n",
       "      <td>0.871991</td>\n",
       "      <td>1.110635</td>\n",
       "      <td>0.772717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.694955  0.674263  1.376865  0.993572\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 -0.809058  0.871991  1.110635  0.772717"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily = frame.resample(\"D\").asfreq()\n",
    "\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "79565e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.694955</td>\n",
       "      <td>0.674263</td>\n",
       "      <td>1.376865</td>\n",
       "      <td>0.993572</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>0.694955</td>\n",
       "      <td>0.674263</td>\n",
       "      <td>1.376865</td>\n",
       "      <td>0.993572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>0.694955</td>\n",
       "      <td>0.674263</td>\n",
       "      <td>1.376865</td>\n",
       "      <td>0.993572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.809058</td>\n",
       "      <td>0.871991</td>\n",
       "      <td>1.110635</td>\n",
       "      <td>0.772717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-06  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-07  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-08  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-09  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-10  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-11  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-12 -0.809058  0.871991  1.110635  0.772717"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample(\"D\").ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "c957cbac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2000-01-05</th>\n",
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       "      <th>2000-01-07</th>\n",
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       "      <th>2000-01-08</th>\n",
       "      <td>NaN</td>\n",
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       "      <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>-0.809058</td>\n",
       "      <td>0.871991</td>\n",
       "      <td>1.110635</td>\n",
       "      <td>0.772717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-06  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-07  0.694955  0.674263  1.376865  0.993572\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 -0.809058  0.871991  1.110635  0.772717"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filling certain number of periods foward to limit how far to continue using an observed value\n",
    "\n",
    "frame.resample(\"D\").ffill(limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "741021bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>2000-01-06</th>\n",
       "      <td>0.694955</td>\n",
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       "      <td>0.993572</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
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       "      <td>0.871991</td>\n",
       "      <td>1.110635</td>\n",
       "      <td>0.772717</td>\n",
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      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-06  0.694955  0.674263  1.376865  0.993572\n",
       "2000-01-13 -0.809058  0.871991  1.110635  0.772717"
      ]
     },
     "execution_count": 170,
     "metadata": {},
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    }
   ],
   "source": [
    "frame.resample(\"W-THU\").ffill() #new date index need not coincide with old one"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206f3d48",
   "metadata": {},
   "source": [
    "###### 11.6.3 Resampling with Periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "5c024c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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</th>\n",
       "      <td>0.220385</td>\n",
       "      <td>-1.327755</td>\n",
       "      <td>-1.411048</td>\n",
       "      <td>0.390866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02</th>\n",
       "      <td>-0.858477</td>\n",
       "      <td>-0.275175</td>\n",
       "      <td>1.158981</td>\n",
       "      <td>1.375088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03</th>\n",
       "      <td>0.268658</td>\n",
       "      <td>-1.059673</td>\n",
       "      <td>0.366389</td>\n",
       "      <td>0.174920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04</th>\n",
       "      <td>0.621830</td>\n",
       "      <td>0.184389</td>\n",
       "      <td>-0.558022</td>\n",
       "      <td>-2.101077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05</th>\n",
       "      <td>-0.740302</td>\n",
       "      <td>0.346609</td>\n",
       "      <td>-0.083270</td>\n",
       "      <td>0.768350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Colorado     Texas  New York      Ohio\n",
       "2000-01  0.220385 -1.327755 -1.411048  0.390866\n",
       "2000-02 -0.858477 -0.275175  1.158981  1.375088\n",
       "2000-03  0.268658 -1.059673  0.366389  0.174920\n",
       "2000-04  0.621830  0.184389 -0.558022 -2.101077\n",
       "2000-05 -0.740302  0.346609 -0.083270  0.768350"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.random.standard_normal((24, 4)),\n",
    "                     index=pd.period_range(\"1-2000\", \"12-2001\",\n",
    "                                           freq=\"M\"),\n",
    "                     columns=[\"Colorado\", \"Texas\", \"New York\", \"Ohio\"])\n",
    "\n",
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "d012a49d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      Colorado     Texas  New York      Ohio\n",
       "2000  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001 -0.030587  0.064420  0.712082  0.149596"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame = frame.resample(\"A-DEC\").mean() #A-DEC 해당년도에 12월을 끝으로, 매해 12년 (안에는 12개달이 묶임)\n",
    "\n",
    "annual_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "371b572f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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>2000Q1</th>\n",
       "      <td>0.218754</td>\n",
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       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
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       "      <th>2000Q2</th>\n",
       "      <td>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q3</th>\n",
       "      <td>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q1  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2000Q2  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2000Q3  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2000Q4  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q1 -0.030587  0.064420  0.712082  0.149596\n",
       "2001Q2 -0.030587  0.064420  0.712082  0.149596\n",
       "2001Q3 -0.030587  0.064420  0.712082  0.149596\n",
       "2001Q4 -0.030587  0.064420  0.712082  0.149596"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q-DEC: Quarterly, year ending in December\n",
    "annual_frame.resample(\"Q-DEC\").ffill() #디폴트 convention은 start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "517762d2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>2000Q4</th>\n",
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       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.218754</td>\n",
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       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q1  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q2  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q3  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q4 -0.030587  0.064420  0.712082  0.149596"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample(\"Q-DEC\", convention=\"end\").ffill() #convention은 구간의 끝을 정해줌\n",
    "\n",
    "#convention= end 이면 end인 Q4가 시작에서 부터 다음 Q4전까지 한 묶음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "69fcde2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q1       NaN       NaN       NaN       NaN\n",
       "2001Q2       NaN       NaN       NaN       NaN\n",
       "2001Q3       NaN       NaN       NaN       NaN\n",
       "2001Q4 -0.030587  0.064420  0.712082  0.149596"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample(\"Q-DEC\", convention=\"end\").asfreq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "6e48bcc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
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       "      <th>2001Q2</th>\n",
       "      <td>0.218754</td>\n",
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       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
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       "      <th>2001Q3</th>\n",
       "      <td>0.218754</td>\n",
       "      <td>-0.370421</td>\n",
       "      <td>-0.363468</td>\n",
       "      <td>-0.229762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q1</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q2</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q3</th>\n",
       "      <td>-0.030587</td>\n",
       "      <td>0.064420</td>\n",
       "      <td>0.712082</td>\n",
       "      <td>0.149596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q1  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q2  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q3  0.218754 -0.370421 -0.363468 -0.229762\n",
       "2001Q4 -0.030587  0.064420  0.712082  0.149596\n",
       "2002Q1 -0.030587  0.064420  0.712082  0.149596\n",
       "2002Q2 -0.030587  0.064420  0.712082  0.149596\n",
       "2002Q3 -0.030587  0.064420  0.712082  0.149596"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample(\"Q-MAR\").ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e606ce",
   "metadata": {},
   "source": [
    "###### 11.6.4 Grouped Time Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "0a7889ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-20 00:01:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-20 00:02:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-20 00:03:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-20 00:04:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-05-20 00:06:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-20 00:07:00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-05-20 00:08:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-05-20 00:09:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-05-20 00:11:00</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-05-20 00:12:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For time series data, the resample method is semantically a group operation based on a time intervalization\n",
    "\n",
    "\n",
    "N = 15\n",
    "times = pd.date_range(\"2017-05-20 00:00\", freq=\"1min\", periods=N)\n",
    "\n",
    "df = pd.DataFrame({\"time\": times,\n",
    "                   \"value\": np.arange(N)})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "6fc961cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-05-20 00:00:00</th>\n",
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       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>5</td>\n",
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       "      <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": 177,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.set_index(\"time\").resample(\"5min\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "20f852a4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>b</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>c</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-20 00:01:00</td>\n",
       "      <td>a</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-20 00:01:00</td>\n",
       "      <td>b</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-05-20 00:01:00</td>\n",
       "      <td>c</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-05-20 00:02:00</td>\n",
       "      <td>a</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time key  value\n",
       "0 2017-05-20 00:00:00   a    0.0\n",
       "1 2017-05-20 00:00:00   b    1.0\n",
       "2 2017-05-20 00:00:00   c    2.0\n",
       "3 2017-05-20 00:01:00   a    3.0\n",
       "4 2017-05-20 00:01:00   b    4.0\n",
       "5 2017-05-20 00:01:00   c    5.0\n",
       "6 2017-05-20 00:02:00   a    6.0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({\"time\": times.repeat(3),\n",
    "                    \"key\": np.tile([\"a\", \"b\", \"c\"], N),\n",
    "                    \"value\": np.arange(N * 3.)})\n",
    "df2.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "abe35759",
   "metadata": {},
   "outputs": [],
   "source": [
    "#doing it for each value of key\n",
    "\n",
    "time_key = pd.Grouper(freq=\"5min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "b0463b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled = (df2.set_index(\"time\")\n",
    "             .groupby([\"key\", time_key])\n",
    "             .sum())\n",
    "resampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "4e8429a3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>key</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c</td>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c</td>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key                time  value\n",
       "0   a 2017-05-20 00:00:00   30.0\n",
       "1   a 2017-05-20 00:05:00  105.0\n",
       "2   a 2017-05-20 00:10:00  180.0\n",
       "3   b 2017-05-20 00:00:00   35.0\n",
       "4   b 2017-05-20 00:05:00  110.0\n",
       "5   b 2017-05-20 00:10:00  185.0\n",
       "6   c 2017-05-20 00:00:00   40.0\n",
       "7   c 2017-05-20 00:05:00  115.0\n",
       "8   c 2017-05-20 00:10:00  190.0"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d44585",
   "metadata": {},
   "source": [
    "## 11.7 Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "e75984bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 185,
     "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": [
    "close_px_all = pd.read_csv(\"examples/stock_px.csv\",\n",
    "                           parse_dates=True, index_col=0)\n",
    "\n",
    "close_px = close_px_all[[\"AAPL\", \"MSFT\", \"XOM\"]]\n",
    "\n",
    "close_px = close_px.resample(\"B\").ffill()\n",
    "close_px[\"AAPL\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "863d072d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "a0488b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x126a250a0>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vYUs/2XL8tac/PhzB40dHJ2PX27aPm4whdaC8vDzef//94LHN1vourRBChGPW7sP91R1gNLF+nF/R7R0vmgFmUireq8JPUGiJPfN0/LveQtXuBWB+Zf2mfRfmuhif3bqJDJa223ylaDW73R7cjE8IIY4k/57/NJ2MgNTFfvTAT3UvmAZxCU3WbY5mj+f7no/y2uolbPD25DtfPwB6xdt4amxKm9o85KhJSDWLz2u50hEUP/GjVl+zc+dOhgwZgsPhYNSoUcyaNYs+ffoc+eCEEMcMZfgIHPiUwJ73mqyTuMKP88BPD8EmpuCfcFGbX++j3W7+a6lBbeCMYJldg/8en0ZaK545CkfGkDrIqFGjePrpp3n77bd56qmnKCws5JxzzqG09DA3zBJCHNP8BW/g2/xUk+f1WoVrR/3iqb6f3wGuyJbyaey1/Bqu/qyU2oB17bs5p6ZwcmbLa9W15KjpIUW7s88+23I8atQoRowYweuvv84dd9zRSVEJIbo6/85/NXs+/odA8Fbd3omXkXzapDa9zitbavjVl+U0Xob196OSmD64bbPqGpOE1EkSEhIYPHgw27dv7+xQhBBdlPJXQUiKsHIU1t2q8596FkVjzqP1j6vC6/mhyUjXYM6pydwwuG1jUeEcNQmpLWM6ncnj8ZCfn88ZZ5zRcmUhhAjDbGafo0PslXVpxHfpL6DK0+rXWLrPwy8bJSO7Bi+OT2NKn9hWt9ecFseQnn/+ecaMGUNOTg45OTmcffbZfPzxx8HzSilmz57N4MGD6d69O5MnT2bTpk2WNrxeLzNmzKBfv35kZ2czbdo09u7de0TfSLT77W9/y/Lly9m5cydff/01119/PbW1tVx11VWdHZoQootSvvIW62gGBEaNQ3XPaXX7m8v9XLekFKNBNrJr8M8JRz4ZQQQJKTs7m9///vd8/vnnLFmyhHHjxvHzn/+c9evXA/Dkk08yb948Hn/8cRYvXkxGRgaXXnopVVX1ayfNnDmTRYsW8eKLL/LBBx9QVVXF1KlTMYzD3KWwC9m3bx833ngjJ598Mtdeey1Op5NPPvmE3Nzczg5NCNFFtZSQXNvqvmN9U65rddslHoOpn5ZQ6avPRhp1PaMLex/5ZAQR3LKbPHmy5fh3v/sdL774Il999RXDhg1j/vz53HXXXUyZMgWA+fPnk5eXx4IFC5g+fToVFRW88sorzJs3jwkTJgDw7LPPMnz4cJYuXcpZZ53VDm8r+rz00kudHYIQ4mjjr2j6nKGI/yFA4KSxmLkDWtesqbh+SSk7q6ydht+PSmqXntEhrZr2bRgG77zzDjU1NYwePZqCggIKCwuZOHFisE5sbCxjxoxh9erVAKxduxa/32+p06tXLwYNGhSsI4QQovUa95AcmeeT8bqPlE99ZCzwYq9Q+M+5vFVteg3FzZ+XsfyAz1J+bV4cvzzuyE1gCCeiSQ0bNmzgnHPOwePxEB8fz6uvvsqwYcOCCSUjI8NSPyMjg/376zaEKioqwmazkZ6eHlKnqKio2dfNz88PKXO5XMTExEQSdofxeFo/UNiRKisrg591uM80WnWVWLtKnCCxtpfOijWlpICGTxS5t+wnzW8S89MQvbtbD360xUOD+JqL1W/C3ZtiWFlmfcD1pCSDWzOK2bq1+LDizctrfqv0iBJSXl4ey5Yto6Kigvfee49bb73VsiZb4+1plVItblkbSZ1wwVdUVOBytX2tpCPN4/FEVTzhJCUlkZOTQ35+fov/IKJFV4m1q8QJEmt76cxY3dU+zAarBaXtte4Mq581hbwGizg3F6tSituWl7OyzLr8UK94G29N7k63w1yFIRIR3bJzOp3069ePE088kYceeojhw4fz9NNPB9dla9zTKS4uDvaaMjMzMQyDkpKSJusIIYRoPeW2Tvt2bN1lOQ6cMiHitl7Jr+VfW63JKC/Zzvvnd+uQZARtXDrINE18Ph+9e/cmKyuLJUuWBM95PB5WrlzJKaecAsCIESNwOByWOnv37mXz5s3BOkIIIVrHqMpHeRr1iKrrZ8QZA4ej0iNbzHlDqZ97V5Vbyvon2fjf87vRJ7HjHldt8ZUefvhhzjnnHHr27El1dTULFixg+fLlvPXWW2iaxq233srcuXPJy8tjwIABPPHEE8THx3P55XUDacnJyVx77bXMmjWLjIwMUlNTefDBBxk2bBjjx49vU9CR3O4TdZRq/iluIUTXY5R+i2ftA5YyzXCgB+rHswPHnxpRW15DcdMXpXgaTKiLt2u8flY6mbEdu0VOiwmpsLCQm2++maKiIpKSkhg2bBgLFiwITte+8847cbvdzJgxg/LyckaOHMnChQtJTEwMtvHoo49is9mYPn06Ho+HcePG8cwzz7RpP6D4+HjKy8tJSUmRpBSB2traqB/jEkK0jq9gQUiZrdr6x6fZf0hEbf15bSUbywKWsr+MSWFQiqOJK9pPiwlp/vz5zZ7XNI2ZM2cyc+bMJuu4XC7mzJnDnDlzmqwTKbvdTmJiIpWVlYfd1pFQWVlJUlJSZ4fRJLvdHnWzEoUQh8cs+zakzFbmDf6uNA2j76AW29lc7uep9dWWsiv7xzK1f9tWAz9cXXItO7vdTnJyW5YIPPKKiorIyWn9khxCCNEWyvSHLW/YQzKze0Ns8ytwK6W4e2U5frO+rEeczp9PSTkSYbaJ7IckhBBdiPKWhC13FNVnFrP/0Bbb+c9Od8jDr7NHp5AS03lpQRKSEEJ0Icob+nCqo8xFzO76hGQMPanZNryG4vffWIc9zuoZw5Q+nTveLAlJCCG6kHAJKeX9ChpO8WopIb30Y41lnTqbBo+dktzpE8UkIQkhRBfSOCE5HCPRzQbPH+X0RyWnNXl9pc/kie+rLGXTB8WTl9zxs+oa65KTGoQQ4mijlMKs2AimDz3leDQ9/GMxZqMxJPtB6603Y9jIZl9n3oZqSrz1t/cS7Br3jUhs5oqOIwlJCCGigH/HK/h3vg6ALWsirmH3hq3XuIdkL7Cu1mAMbTohlfpgXqNp3rcfl0BGBz8A2xS5ZSeEEJ1MGR78BW8Hj43CxRhV28LX9TRKSAdK68/Z7BiDhjf5Oi/tdlAdqL+9lx6jc/uw9t1SojUkIQkhRAdS/ioCB1dg1u6rL3PvB2V9vsgs+y789Y1u2em1DZ4/GjAMXOEfat1ZFeCdA9abYnefkEiSM3rSgNyyE0KIDqJ8Zbi/+U1dAtJ0XCMex5Y6PGRcCMCs2R16vTJRPmtdW4OEFGhm/OiP31YSUPWz6HISbNwwuPmHZzta9KRGIYQ4yvn3vFeXjACUiX/3Oz+dCF0KrfFusHVlpaDqp2trXoXWYBm6wKlnhX3dz/Z6WLDdbSl74MQkYmzRtR6o9JCEEKKDBAqXWo6N4lUo04/pKQypq/zlmDW78Hw/CzQde/ez0JMGWurYKhv0joaehMrqGdJOld/kzi/LLWXDUu1c2S+27W+knUhCEkKIDqLZE1FYZ8V5vrkbs2pLSF2zcjPu1TcHj/07XkGLz7XUcZTVT9/2T7g47Gs+8nUle2rqe1W6Bk+NTcWmR1fvCOSWnRBCdBgVqA4pC5eMmry+xrojrL2srodk5A7AGDUupP6KA16e/7HGUnbb0ARGZjgjfs2OJAlJCCE6iAozVnQ47CV1PSTfxdeAbv06dwcUv/yyzFKW4zJ54KToeAg2HElIQgjRAZQyIFDTcsVWsJcrzO45GCPPCDn32HeVbKs0LGUP5vmIs0fv1370RiaEEEeTQA2gWqwWKc2n0P3gu+gaaLTM0HfFPv6+wXp78L8GxTMy2SSaSUISQogOoHwVR7Q9vVYRGHkGgbHnWMp9huL25WU0WG+VnnE2Hh4VvTtbHyIJSQghOoDylbVcqRV004Xnhnuh0ZYRf/2hio1lAWvZmJSoWpGhKdEfoRBCHAWa2um1rcyBp0K8dYLC2mIfc9Zat5a4sn8s5+R07sZ7kZKEJIQQHcCs3dOm62yZodO5AfS47pZjd0Dx/5aV0WDtVDJcOo+NTm7T63YGSUhCCNEBjJI1LdZxDvwltrS69ej0hP7EnvoSruMewN7jvJC6Wox1E75Hvqngx3Lrrbq/jEkhzRUdW0tEQlZqEEKIdqYMD2a1dTuJ2M0B3IMafgXbcPSajKPXZJTpB80e3FJcT+gd0qYWV79M0JK9HuZvtE4pv3pAHBf1jr7lgZojPSQhhGhnxsEVoOqnXOtuB/E/GDS8v2bveUHwd013BJMRgJ6YF9KmHle3jNC+GoObvmj0AGyCjcdO6Tq36g6RhCSEEO0sULTccuza5sZWo0j+wo/jgIk95TScfa9u8no9aTCaq37MSE8cgObKxG8qbvi8lGJPfbLTgPlnpHaJWXWNyS07IYRoR8rwYJR+bSlz7ahbQcG128QRk4f76oeabUPT7cQMfxD/9lfBFoNzwE1omsYfv65gZaHPUvf+ExM5vXvMkX0THUQSkhBCtCOj5Gsw65OG7rVjL/UEj/2nnxtRO7bEPGwn/D54/OEuN0+ut67GMDE7hhknRO9adS3pen06IYToQoySryzHMTt8NHyU1Tju5Fa3WVAV4NZl1nGj7Did585MRdeib1uJSElCEkKIdtR4e4mY3fVTs81uWaiMHq1qzx1Q/GJpKeW++gkRNg1eGp9Gty40xTscSUhCCNFOlOHFrCmwlDmK6ycgGINHtKo9Uylu/qKU74r9lvKHRyVxalbXHDdqSBKSEEK0k8De963TvT0O9AZzEFqTkPym4rZlZSwq8FjKL8h1ccewhMMNNSrIpAYhhGgHgZKv8G193lLmKPRajiNNSLUBk+lLy/h4tzUZDU2xM/+MVMszS12ZJCQhhGgH/h2vhZQ5D9RvmGd26x7R+JHXUPz8s1KW7LMms+6xOm+enU5yF3zeqClHzzsRQogooZSJWbk5pNy1vT4hGUNObLGdgKm4YWloMuqdYOODCzLISTi6+hRH17sRQogoULf3kXV32KRvk9B99bfcjGEjW2xn1tcVvL+r0W26VDsLz+lG97iuPaMuHOkhCSHEEaY8hZZjPaYXsT8U1Z+32Qgcf0qzbbyaX8PTG6wLpg5MtvPeeUdnMgJJSEIIccQpT5HlWK+1TjowBo8I2VyvodWFXn6zotxS1jPOxr/P7dblnzVqjtyyE0KINlD+Kryb/kr3km/xmpNwDrgZzeYEwGyUkBx7ii3HxkmnN9nuzqoAP19ciq9+tjixNo3XzkqjZ/zRm4xAekhCCNEm3i1PYxSvQFceAnvfx/PNr1Gqbtyo8S07W1Fl8HdlsxM4+cywbdYGTKZ9WmJZvRtg3ukpjOjmPMLvIPpIQhJCiFYyqrZjFC6xlJnV2/B+/zsg9Jadrbp+gkNg5BmoZOtur4f88dvKkF1f7zkhkZ/1izsSYUc9SUhCCBEB1WDFBePgl2HrGKVfY9bsxmzcQ2qQkPznXRH22tWFXuY3msTws76xPHhi1129u7VaTEh/+ctfmDBhAjk5OfTv35+pU6eyceNGSx2lFLNnz2bw4MF0796dyZMns2nTJksdr9fLjBkz6NevH9nZ2UybNo29e/ce2XcjhBBHmFIK7+Z51C6ZTM3i8zBKvw1Zn64ho2wtqmafpcxWU5eQ/KdNwuw/NOQad0Bx+/Jyy0Tx3AQbT41NOWpWYYhEiwlp+fLl3HDDDXz88ce899572O12LrnkEsrK6pc+f/LJJ5k3bx6PP/44ixcvJiMjg0svvZSqqqpgnZkzZ7Jo0SJefPFFPvjgA6qqqpg6dSqGYYR7WSGE6FSBouXUrr6F2iXnE9i7iEPPFXnWPoBxcHnT130/D6i/7aa7FboXzIxsfFfdFvaa2d9VsrXSeqvu72NTSHAcWzexWpxlt3DhQsvxs88+S25uLqtWreL8889HKcX8+fO56667mDJlCgDz588nLy+PBQsWMH36dCoqKnjllVeYN28eEyZMCLYzfPhwli5dyllnndUOb00IIdpGBWrxbnoCDE/LlRsxY63HtnITI6c/nnv+HHbs6OuDPv6xwbrR3i8GxnFmtqvVr93VtTr9VldXY5omKSkpABQUFFBYWMjEiRODdWJjYxkzZgyrV68GYO3atfj9fkudXr16MWjQoGAdIUT7MMq+x7PuEZLL3sL0Frd8gcCs2dW6ZGSoJk/p9gzcM/+GSkkPOecJKO5YXobZ4PJe8TYeOTm5NeEeNVr9HNL999/P8OHDGT16NACFhXWDdxkZGZZ6GRkZ7N+/H4CioiJsNhvp6ekhdYqKrLNRhBBHjvJX4Vn3MBhu4gHP2gJiR89H047u51kOl2pF4tZrFbYahT8j/FiPOv3nTT4E+9ja0Fl1T45NIekoWjC1NVqVkB544AFWrVrFRx99hM1m/QfdeOBNKdXiYFxLdfLz81sTXqfpKnGCxNoeojnOGPd60g138FjV7GLX+g/wuQZ3YlSR6czPNb5qE5H2UfRahV5pg4zQXpKpx1FQ1RMV5r2sKtN5ckMMNNjQ/OKsALm1u2nPt96Zn2teXl6z5yNOSDNnzmThwoUsWrSIPn36BMuzsrKAul5Qr169guXFxcXBXlNmZiaGYVBSUkK3bt0sdcaMGdPm4KNBfn5+l4gTJNb2EO1x+vdswtfoj/3sZB+O3OiNGTr/c/Vt/Rx/eaSVXTD2Sjjwasip+NOeZ0BM6K26Mq/JH78pRFE/lTw7Tueps3JIiWm/3lFnf64tieid33fffSxYsID33nuPgQMHWs717t2brKwsliypf0jM4/GwcuVKTjmlbvHAESNG4HA4LHX27t3L5s2bg3WEEEeWf/8n+LY8HVKufBWdEE3XYtbsirhu6cAL0HqFfo/Zc36GHiYZATywpoJCd4OdZDV47sy0dk1GXUGLPaR77rmHN998k1dffZWUlJTgmFF8fDwJCQlomsatt97K3LlzycvLY8CAATzxxBPEx8dz+eWXA5CcnMy1117LrFmzyMjIIDU1lQcffJBhw4Yxfvz4dn2DQhwrlOHBrNyCntgfzAC+zX8PXy/iP/0bXGMGwHCjOY7+hzSNik0YJWsirl+TfDp64gCwJ0Kg/lEXR88LwtZfWejlX1trLWW/OT6R07vHtC3go0iLCemFF14ACE7pPuS+++5j5syZANx555243W5mzJhBeXk5I0eOZOHChSQm1v/jffTRR7HZbEyfPh2Px8O4ceN45plnQsaihBCtZ3qLca+5A/zlaM5UHH2uBtMXtm7dXj2RM8rX4/l+Fhi1oNmwdTsF54Cb0WO7H4nQo07jbccb0jwK5aof84k7/Q1UwUE0TSPu9Nfwb38Zs3Yfzn7Xocf1CrneMBX3rrL2UIek2Ln3hKM/0UeixYRUXl7eYiOapjFz5sxgggrH5XIxZ84c5syZ06oAhRAt8+U/y6FBD+Urw7dlXpN1la884naVUnh//GtdMgJQBsbBFbgPriD2lOfR43MOI+roY9bswqzYGPac5lF0W+il9rLJBE4chS3rTDRNBw7WndedOAfc1Gz7/725hh9K/ZayJ05LwWk7dlZjaM6xfcNSiKOA6T6AUbQs4vqtGUMyy9ehasMv8eXb9lLE7XQF/v3/h3v1zSHleq3CuccgZYkPzZkE42/H3n3CT8kocoW1Bo98W2kpu7xfLGPlVl2Q7IckRBfX1EKfTVG1RaiqCrTElic2+3f/u+nXLV6J8lcdNeNK/u3/E1Lm2mKQvLK+R+O97AqIbdvK2w+sqaDSVz81PN6u8ftRx+YDsE2RHpIQXZzp3tdypYZsEDfzKqgqb7GqUbGp2fPOR6cTf9tFuP4+C62itHVxRBHlqwj7MGzs1vqHVs2UdPyTLm1T+4v3enhnh9tS9sBJSUf9hnutJQlJiC5OuQtbrKP5rQ9tKtzEvP1C8+36K8Hf/O296iGVaDVV2L/+gtg/3I5WXoJRtR2zpiC4WV1XYNbuDimL2WngPFj/Hrw3zYS4hFa3XeEz+dWX5Zay4WkObhkS3+q2jnaSkITo4hpvl92Ya7uBrdyaHEwX2L/8GK28pOl2a0K/pBsLdNOpPNWOP01DP7ifwCd34/nqNtyrb8G/87XI3kAUaPxeHZUppHxef6suMOREjONGtantmasr2FNTv6uBBvxtTAp2XSYyNCYJSYguTCkVsl12Y/YyE91jTUi1Q+1oAT/2Lz5o8rpwvYZw3IPslF4UQ+UpdjzpB4Ll/h2v4tv2z4ja6GxmrfVBWOd26+27pjbVa8n/Frh5vdEzR7cPS2BkxtG/HXlbSEISoisLVIHpbbaKrUqh+6xjFWZc3V/njs/fBzP8nmQhvYY+V5P2Vd8mX8c9OHSOlL/gjRZ7cNFANVqZwV7RYHfYPgMxTjit1W0edBvcuaLcUjYkxc5vT0pqU4zHAklIQnRhkXzZ26oUeuYIS5n6KXfoxYXoO7aEvU41miyhm0k4Nm4i4St/2PpNMUq/bVX9jqb8VRhl31vKbBX1PUrf5TdBK3dtVUrx6xXlFHvqE5tdg/lnpOKyy626pkhCEqILM6t3tFhH9zhgzI2WMiO2/kvR9uN3Ya9rfCvQsb3ueaT4jQa6uxUTFrZ+HnndDmZUbqF22RWg6mfTaV6F/aeEFDhxLMbwk1vd7pvb3Ly/y7qf0n0jEhnRTW7VNUcSkhBdWGDfh82e12sUxpjJkN7HUq5coH7KSbZNoQlJKYXpPmApc377Q/B3sxXfq2rfNzg+WdhyxQ6mArV4vr4rpDx2i4FmgnI48Dax5Xhz9lQHuHdVuaVsZDcHvz7+6Hheqz1JQhKii1KBmiaXuTnEUaTwXzANTbdj6A2mLGtacKtt25YfIGDdJA5/Zf1yQQCaA/umH4OHaR+GXycvHH+ahvbmM1QUt24NvfaiTAPP+kep/eJn0GD7h0NidteNqfnHX4TK6tmqtk2luH15OZUNptm7bPDMuFSZVRcBSUhCdFGBgytbrGNLHIJKr9uzzLRZB9PNnxYJ1bwe9B0/Ws4ZldZj3Uyg4depnjwY14mPY+9+dsuB2jW0eD/PPbeAd3e6W67fzgJ738co+iLsOd2tcJTUJRP/hItb3fbzm2r4fL91ksnDo5LJS3a0PtBjkCQkIbqolnpHANrpvwz+buiNElLDcaSN1okHIeNHB60z8QInn4kt9QRiht6Nim965l3wtZM1pu1ewi8Wl3R6Ugoc+LTJc0nL/WgmGAOHo3r2aVW7m8r8PPy1da26cT1iuFkegI2YJCQhuqjGzwklrLMu0umoSYPsAfX1bdYxDCOu4cSGtZZzqtHsPfte67JAgZPPZGdVgJmry1l4sOXbWkaCxpDafQyr2c2Nn5fy2V5Pi9e0B9Nbglm1NaTcVq6R+YqHmH11t/D84y9qVbvVfpNfLCnFbdTfqktyaMw7PQW9lTP0jmWSkIToohrvahqztZKUT3zE7DCI/y6Ac+SfrPVDbtnV/27LXw+B+uncpuegpa6tpn6sxZs7kBs3xnDSO4XM31jD1+4+LcZqJNR9KV9+cA1+E675rJQVB5p/fqo9GMWrAOsMwZjAMNL+143201tU8UkETj4z4jaVgrtXlrO5wjoO9/ipKeQkyPrVrSEJSYguyPQWW9eZUzq2akXMPpOUL/zExI6DbOutNEO39pDMlPpVqzW/D72gvufQ+Jadrbr+96ddx/P2djfmT9/r3/v6tBjvoYR02cHVoBRuQ3HlJyV8uqdje0pG2VrLsSP35yS9tw29QS7xT7wYnJFvCbGoyMab26y3Ia8aEMe0/rGHE+oxSRKSEF2QUbzacmyv0NAa/OEfOGVCyDWNe0hGujVB2fLX15VX5mM2ntRQU9/4u3FDLOfW+XqHvJY927p996GENKR2H0N+2l+pOqCY+mkJr+bXhFzfHpRSGGXrLGUxW8rQG6x6rlyx+M69POI299ca/GW7dQ784BQ7T5yajCa36lpNEpIQXZBRan12yLW9vqehbPawC4GaNutK1WaitRdg27oeFajBs/Z+64VKYautS0hu3cGapP7BU0NS7Mw7PQ192EPBMj1pMI6+11jjTdCCN8rGl9dPxjAU/OrLcj7f1wG37/wV1l6l7iT2g0+sVc6+DBJTIm7y3lXl1BgNtjS3a/xzQhrxDvlqbQu5wSlEF2RWbrYcO/fVj/GYA4ZBTOjtIlOzbixnuqzr2+n56wns/wQC1h6L5ic4vrIyaSA+vW4K801D4nl0dDIOXQNOQ6X/G9O9Bz2hH6CDLa7+WSa7hukCmwfu0LfyDOcEE5SpYPrSUpZclEHvxPb7SjJr91jfrz8evWp/8Fi54vCdd2XE7b1f4GZRgfWW46yRSQxOkSnebSVpXIguRhk+lLfBpAMF9rL6W2qBISeGvc7UrQlJaX5Ug7ESvbwEsyjMMkINnh1dmjIEpw5/H5vCnFNTfkpGdTR7LLbEPDTNhqZpaK5u1mZ+mtU3cN96/jk+1fJcU6nX5OrPSqj2hz6oeqSYjbZid+yxPqjrP/cKSIhs4dMKn8mMRqsxjMpwcNNgmeJ9OCQhCdHFNJ5woLu1YA8GwBh6UtjrQhKSUY3Zb7C1rLIg5LrY7fXPIP2QNYz/PT+Dawe2/MWrOdOsr//Tc09aVQWXOgp5sNGq1xvKAty2rAyznTb2U416SPby+pkMKi4h4rEjpRS/WVHO/lrrwqlPjknFJqsxHBZJSEJ0MY23LLdV1icM5YwJSTLBc7rLWhCoJTBgmKXIMEK38Y77oe6L22Nz8udrTuPkzMgWsmsqIUHdc0+/OT6Bi3tbY3qvwMOc76siar8lKlBLoOTr4IrojXtItsoGU9mn3Qrxka0198Y2d8h25HcOT2BYmtyqO1wyhiRElFKGB7NqK3p8bzRH/Zelcu+31LNVNdi7Z9Dx4GgiYWg267gOCqN//YOzgUQNy/xnIPNlT/DWmj5oOD2SGiW1ZugxaTRc38Fo0EGz/bgWfdKlzD8jle1VxawvrX8GavZ3VaQMtpGXF/FLhVC+Ctxf/yrYm9RTT8Iss65GYa/8aUXvE04lMO6CkDbC2VEZYMbKcktZXrzJvSNkj6MjQXpIQkShwIHF1H5+CZ5v76F22RUYZQ1W2m7UQ7JXNRg/OnFss+1qdutMu6rs7ODv/kzr7abysnjLOI8aMiLC6H96rRZ6SChFvEPn9bPSSI+xfhU9vMXJ9yWRL+DamH/fB5Zbm42TEYDtp4TknXZrRPsd+U3FjZ+XUh2o/7xjbRp/HOQlxia36o4ESUhCRBmlTLwb/2wp826ag+kuxP3VLwnsec9yzvZTQlLOGAKnntVs25rDOvZz2edlbIqrS0r+DOvXQfpe69bbxuAREb8HAC2mUUJqsGqBVlWBtq9uvCo3wc7/TEyj4b51blPjgg+Kmf1dJRW+1k90MMt/aPa85lboPggMH43KDn2OKpzHvqvkm2Lr5oR/Gp1Mv7j2GfM6FklCEiLKmNXbQ8qUpwjPt3djVuWHnLOX/nTrafT4FsdBVKNnkexmNSuSBtZdn2b9OkhssLSPSkjC7Gd9ILYljXtIRop1UoVt26bg72O7x/DnU1Ms52sCisfXVnHC2wf4y7oqij3ht1pvTCmFURn6OTV06HadP8KJDMsPePnLumpL2QW5LqYPimviCtEWkpCEiDJmo+20D1He0AkH9hITe6VCaRr+c5t/hqbEBytKrONLyXotK5PrBmsCidbbTvby+p5J4KTTwd66IeeQHlKj727b9k2W4/8aHM8dw6wJE6Dcp3jkm0oGv3GAqZ+W8OEuN6qZmXjKVwqB5idGOIpNjOw+GMe1vBtsmdfkls/LLCvg9YjT+fvYFFmN4QiThCRElGm8vE1zYgrqeg2Bsedi5vZvsl6Fz+RXG1wUeKyTEsa5NrIhYzCmE5SrwZerodAbPB8bGD0+4pgO0WIaPYek1wZ3qQXQt/9IY384OYknx6SQbA9NOAEFH+/2cNVnpVzwYTEbSv0hdQBUo51uQ5iKmAID/3lXtDh2pJTizi/L2Ftb3zvTgGfOSCW90YPF4vBJQhIiiijDi1GyuuWKAIYidqtBYNgovNf/uslqnoDi6s9K2FKjU9mom3JN4ucsvGYI/hxr8rBVqeCEBrNHDsaw0KWIWqLZXGjOVEuZN7f+K0ffsw181iWDNE3j+kHx/GeUmwdPTCTJGT5hrCz0Me69ImZ9VRF8mNYo+57a1bfg+fbuJmNy7jNIXubHljyUwBnntfgeXsmv5b1GqzH86rgEzsyOfLahiJwkJCGiSMTJCEheEsA44zI8d/2p2dWpH1hTwZcH6maslRqhY0xxnq34RltXdzg0xgLgO/cK0Nv2VaHFWvdK8varf33NMNALwo/1JNhhxogk1l3enYdHJjE4JfR2oaHgqfXVnLywkIX5JXh++AOqJvTBXlulSeoHXjJf9pD6iR97wkjcv54NevM9nPwKP/evrrCUjUh3hDzQK44cSUhCRBGjvOVdYAGSVrowr/sLvp//stlk9O8dtby0uf7e2we1oas4eL67F18Pa0/EVlGXkIy+gwmccX5EMYWjJw2yHBtpTU9sCCclRueu4xNZeUkmSy7K4Oyeoe91f63Ja9+shkB1mBYgboOB82Bdjy9w0ul47n6sxSWC3AHFjZ+XUdtginecXeOFM1NxyhTvdiMJSYgoYlZtsRzbS8JPeQ7c8d+Yg09otq3tlQF+9WW5tf3YXpjpY0LbK/nCcuw8YKISkvD88hGwt30FAnum9bkoM67Rgq7bm09Ih2iaxondnLx1djqvTkyjZ6N2To4J3QX2kEPT4o3eA/H8v9+22DNSSnHH8jK+L7GOUT1+SjIDkmU1hvYkCUmITmZUbcfz/SxqFp+HWWHtIbm2h0511uL7oCWnhpQ35DUU05eWUuWv/wvfoSn+OSGN2F7Nr0qg+cDm7If7nj+j0jNb8U7CtOVIsRybDuv7sYWZ2NBse5rGhb1jWf2zTH49PAGnDk78XBy3pslr7OUmKj4Rz11/hJiWx34eX1sVsjTQJX1iuSZPpni3N1k6SIhOpJSJd8OfUI3WWQPQfHU7wDa+EaW7slpsd9ZXFSF/4d/V188J6U6Ub0ATV/3UfvZpeB6eFdHqBS3RHNZbY0q5UTYbmlGXmPSD+6CyHJJSWtVugkPnoVHJXDUgjvdXvEZvR+iUeKj7DHU3+H52BSqt5eT69rZaHltrnTI+KNnO38bIFO+OID0kITqRWbExbDKCui0lbJVhnrfRmv/P9oVN1Ty7ybqn0UW9XVzRo26dOs2Zgr3nhU1eb0s78YgkIwDs8XVr6B1iejByrVur23a0rpfUUF68m1tcbzR5Pm6TgTsmnu2nXdxiW0v2erhtuXVLirQYnTcmpZMSI1+VHUE+ZSE6UeOdXxuyl5poJjgqrA+L2tJHN3nNm9tquWeVdWZYboKNv49NteQYZ94t6MlDQ67XXFnYuze//FBraJoW0ksy+vezHLc0saE5vvznmjwXU2AQvzbAn3ucxwkf1HD9khI+2OWm3Bs6Lrd0n4drFpfScDsmhw6vTkyjb5LcSOoo8kkL0YnMyvBfxppPEb+x7raWY+CN+IvmgfKjJ+Zh7z4h7DUf7XZz2zLrX/ixNo3/Hp9GSoxOgy390HQHMcf9Fvfqm+tnp2l2Yk/+O5r9CG8yZ48DX31cgd69aThXLtKJDY2ZNQUEDnxqKVPuvmS+uwlM0P1Qao/nqV7nYSp4d6eHd3fWPVOU4tTok2gnO97G/lqD74pDH7J9+vRUxnRvegajOPIkIQnRSZRSGBWhX8aJq/y4thnoAQgMG4U27Dzi8k7DrN2NnpiHZgv9ktxY5ufGpWUYDe7wOXR4ZWIaIzPCb0ehx6QRO+opAgeXo8ekY8s8E00/8l8Jmj3BsuyOkd3Dct62/UdQqtW3CY3y9SFl3T7bj97gWdu/5Eym0h46GaHcp1hb4mdtSfjVHh4dncwV/WUSQ0eThCREJzHLvgPDOpsr8388aA2+vX2XXA+A5kzG5kwO206px+CqT0ss2yLoGjw/Lo1JvZqfVabHZePs3fwaeIfNZu1xmckJKFccmqduNXGtphKtaC8qq1ermjWrd1iOY+xjsZd8Vn/eFUefy65g6DaTjWWBxpc36YETE7ktzJp6ov3JGJIQncRX8Jbl2FERZ0lGgWEjMQcOb7YNr6G4ZnEpBdXW6dSPn5LMJX1jj1ish0Nr3EMxazEa7Wrb2unfELoqesw663HgjPOYNjyDL6dk8u653bhlSDxDU+w4m/jWG58dw4cXdJPN9jqR9JCE6ARG2Q+YZWstZbHrrZMRDvWOmqKU4q4V5awotG5kN31QHDcOPsLjQIeh8aaAZvUOKk4ogf5O4tYHiN1hom/bCKdNirhNZfpDntlybrD2mPwTp9S9vqZxZnYMZ2bX3eo0leJArcmOqgAlHhOHDsenO+kZL4uldjZJSEJ0At+2FyzHms+Ga2v9Ip6BoSdhDjy+2TbmrqvmX1utm+iN7e7k8VOi7JkZu7Wn5i94E5xAmk7lGQ4cpT5sG0J3dG2O5/uHLMea6UCvtX5+TW28p2sa2fE2siUBRZ2Ibtl9+eWXTJs2jSFDhpCSksJrr71mOa+UYvbs2QwePJju3bszefJkNm2yDtZ6vV5mzJhBv379yM7OZtq0aezdG/75CyGOZqZ7P2blZktZ4lcey3bhvkt+0Wwb8zdU88dvKy1l/ZNsvDIhLerWWtNszUwO0DS8OTq2fTvRSosiak8ZnpAdYe3FAcvnFxg3uQ2Ris4WUUKqqalh6NChPPbYY8TGht6XfvLJJ5k3bx6PP/44ixcvJiMjg0svvZSqqvonnmfOnMmiRYt48cUX+eCDD6iqqmLq1KkYRmS7QApxtDBK14aUubbW/3cQGHEa5qDwvSNTKWZ/V8nMNdbbeylOjTcnpZMWhXv0aPbmx7KM2LpUYlv/dUTtmRU/grLOjnNtrb9tqeISCIw8vZVRimgQUUI655xzmDVrFlOmTEFvtAy9Uor58+dz1113MWXKFIYOHcr8+fOprq5mwYIFAFRUVPDKK6/wyCOPMGHCBEaMGMGzzz7Lhg0bWLp06RF/U0JEM6N4leU4/lt/8K97ZbPhnfr/wl5X6TP5+WelPN5oaZtYm8ZrZ6VH7cKfWgtLHZkJde/evq7prTeUvwoVqFt9wgizjXtsfn1C9110TbMroIvoddiz7AoKCigsLGTixInBstjYWMaMGcPq1XX/wNauXYvf77fU6dWrF4MGDQrWEeJYYHpLQ/Y8itlfvzyAf9LPwo59bCn3M+n9g3y427pZXIwN/jUpjbFR/ACn5mp+DblAkkYgWcPNSvzb3kaZ1rsm/j2LqF1+FbVfXIFvx2uoml2W84lr/A02E8zFf85lRzJ80YEOe1JDYWEhABkZGZbyjIwM9u/fD0BRURE2m4309PSQOkVFTd83zs8Pv3lXtOkqcYLE2h52rv8UTfnwxQxo8eHOxIoPaLhFnu5W2Evq5np7k9P58YRxmI3e9wdFNmZvdeIxrW2nOhSPDfbSs2Y3kX5UnfGZ2gIlNNdHMlJ0ys5xYsZpUPAiFbt/hJ7TyM/PRzcqyNr3LBp1zxH5d7wS2n55/Vz57RMuo2rHziP8DlrWVf6tQufGmpeX1+z5IzbLrvGsHqVUizN9WqrTUvDRID8/v0vECRJre9j/zTySKhYBYMsaj2vY/ZbzyjQI7HmXwMHlYPowq6z79ri2GsFnj8yb7qf/sPrnjpRSzPm+ike3WG/RQd3Opa9MTCMnIfL/hDvrM1X+7tTub76OGVf/PRBXsxzW5dLtsuvx7/s/fPuaeajVVDiK63qYgVHj6H7uFLofiaBboav8W4Xoj/Wwb9llZdX97dO4p1NcXBzsNWVmZmIYBiUlJU3WEaIrUKYfpepuKSllkFBZv5aaUbgUo2qbpb6/4A18W5/DrNgYkowAYrfUteU//VyM4ScHywOm4s4V5Tz6XWgymto/lg8vyGhVMupUrVwbT8VqpJa8gnr1ZszCL5tvulyh+0E5Y/BeffvhRCmiwGEnpN69e5OVlcWSJUuCZR6Ph5UrV3LKKacAMGLECBwOh6XO3r172bx5c7COENFMKRPvxieoXXoRtUsvxr/7XVTNLnRlXfqn4XRkZQbw73mvyTY1j8JWrTCTUvFeVf9lWu03ufqzEv5ni/UZoxgbPDU2hWfOSCXWHl1Tu5ujtbBdRjieAXZqs3cRKGt+jPnQ9hy+i69Fpbe8T5SIbhH9iVVdXc327XXLcpimyZ49e1i3bh2pqank5ORw6623MnfuXPLy8hgwYABPPPEE8fHxXH755QAkJydz7bXXMmvWLDIyMkhNTeXBBx9k2LBhjB8/vt3enBBHSmDPu/UrSysDX/587L1C99gxD36H6nE2gYNfYpR8A/6KkDqHOEpMNMDzi99AQt1yNYW1BlM/LQlZ9DPFqfGvSemclhW9kxeaoycPDVlZ4UiwVSrM7N74z596xNsWHS+ihPTdd99x0UUXBY9nz57N7Nmzueqqq5g/fz533nknbrebGTNmUF5ezsiRI1m4cCGJifXDt48++ig2m43p06fj8XgYN24czzzzDDZb9D03IURDyjTw7QzdBC4QpvejClbi23M1gQRvyLnGYnabeK67C2PkGUDdit1XflLCnhrrLLOcBBvvnJ3OwJTonNYdCUfuFXjX/xGUgRaTScxx9+P55jeH3a6tWsNz8wNg77qfjainlZeXh9mSUkQq2gcJG+pKsRb88D49EiqwZ5yGntCv5QvakVGx8fC/PE2Fo0jh7153+8pxwCQu5XoCF/wcqNsg7rrFpVT6rf85npDu4K1J6WTFHf4fbp39/79Zuw+zdhe25GFgT6B2yfmH3WZ8+m1wQsu7wbanzv5cWyPaY+0io6LiWGKUfku3g//AfxD8O1/DNerv2BL7d1o8/t3/Oew2Er8KEPejgS9LJ9B/EGrcDQSGjALgf7bU8JsV5QQa/Wl4Tq8YXhyfRqLj6FiUX4/LRo/LbtvFpg2bSsSwlQeLNEcK6rjz6DqjaaIlR8e/dHFUCRxYXH+gTPw7Xmu6cnvGcXAltV9eg1H0xWG1Yy/TiSnJxD/2XAK3PYN5y3zUkFG4A4rfrCjnV1+GJqMbBsfz+lnpR00yCsfe8yJrgSP8fk8AWmIOrrPfwN7zwrpjZxrOIXeh2cJvPii6JukhiagTKPnKcmwUr+jwGIzKzXh/+H1Iub3UxLXNoPrkcGMWGmDNLLqrF44LH8d9Wf1D4aZSfLrHy32ry9lRFbqW4x9OTuKOYQnRtWJ3O3D0vgKz8kfMmgLsPc7B2X86nh/+gFmxAUzrpA5bat3afjGD7sA54CbQbO2yu63oXPL/qIgqyvSFnZmmAu4WF+k8Iq+vTJSniMC+j8Kej18XwHnApPokOzRYVTvu9DfqlgUq/AylTGwpw7GljQTdiaZpKKX4vsTPOzvc/HuHO2TiAoDLBs+OS2NKn+jYWK+96a5MXKOeAhVA0+sSfOyJjwFwcNVDxNXWT/m2dzs1+Hu4LdzF0UESkogKSpmAhtlonbKgHz+DIeeBrf3+ySpfBZ4fft/k9GTNq4jZY6IZEH+gLzU5BzAVxA6fieZMweZMwZZonYCxqawuCS3cXsv2ML2hQ/ok2nhlYjrD046t2WKapoEW+p4rU6aQEOPFrNyCPfs89NQTOyE60dEkIYlOZ9bsxrvhUczqHU3W8Wx7ivRnnsPsMwhj6Ej8Ey6C2CO3K6rp3o975fRm6yR8G0AzwH/aJLj6fuIw2bptB3ndBgXrGKZiY3mAj3d7WLi9lo3lzSx7Q91NvhuHxPPbk5JIbmpv7WOQaUsM9pbEsUMSkuhUygzg+eERVO3uZuuZiTruXjXE7Pwa5w9f4fjwTXxTbyFw+nnBOkbZ9xiVW7AlD8WWMiziGEz3Adxf39lMBUX6ez7sFQr/uAvwTr8bpekUuTU2VNvZuNPNDyV+vi728e1BX8jU7XBibRrn57r45XEJnNhNBuaFAElIopMZZWtbTEaHVI92UD0aXNsNkr4sw/X8Y7i9lfjHnIlRvBJf/jMA+IGYYTOxZ50ZUbu+bS+Bv7LJ88lf+LFXKDaefBHPHfdfrP24lHWlfip9CnABpRG9jlOHSb1cXNY3lnNzXCQcxTPohGgLSUiiU/m3vdjqazz9bNhLTJxFJlXeF1ErXwqp49vxP9gyx4WdqWZUbMS/81+g6WiO5Candes1iriNAZwFit/0v4an4s6DjbVh6zbFpsH47Bh+1jeWybmxpMRIEhKiKZKQRKcxKn5sctzItTVA4tcBDk5zhT1ffbIDe6mJcoWfGq1q92J/+bfYHD0wThqLMaRuUFwF3HjWPQL+8rDX6dWKbu94gw9bejU71wy5nbeyTov4faU4NU7NiuHsXjFM6RNLtyjcVlyIaCQJSXQaf8Fb1gJDI+0jD/ZiFUwINr03hlkQ9vpAWvO9Da+xhpQlfvi/BfhPHo/v2l8R8KxrMhkBxP0YCL72a5lj+VOfS9jSxOoCiQ6NLIdBv7RYeifaGdnNyagMB/2T7Ef9M0RCtAdJSKJTmDW7MYpXWsqSVvlwFNdPCPCdcxm2ftkYW59v02t4+9pw7zOJ3Wrg+GopnnVfs/78HPKaWBBAcytit9ZNzf5D70v5fd/L62NzapzUzclJ3RyckO7khHQHvRNsbN26lby8nDbFJ4SwkoQkOpzyV+NecxsNVzWwl5i4ttY/p1PZ/zj0q27DjolZU0Dg4JcQqGn1a1WeasdebOIoVyR4qxnk2I4ZZvUzV36AhO8D6F5YnDKMN0+4kuk9XIzOjAn2enTp9QjRriQhiQ7n/fGvoKxLw8RtqL9VZnbLYueUG+in29CwETPkN8QMqVtt27fzDfzb/xnSpm7vxcaaS6nyruHkpAabutk0SqfE4CgysZealq2yD4n/PkDC2rrnhcrTsul978Os6iE7GQvR0SQhiQ5lVu/EOGjdllqvVbgKTACUw4HnjkcwAuHHh5x9pqH8lQR2LwyW+Ynhgv2/YWNtMhrH85zdzwVx31qu82fq+DND20x734ujpK6nZvQZiOPOP5GVJslIiM4gc1BFh1H+Stzf3RdSnrTMj1aXj/Be92vMvoNC6gTbUIodiZPxanHBsodKLmdjbd3AkELnpoO3saC65VlxsZsCOEoUyuHAe8VNuGc9jZJkJESnkR6SaFcqUIt/5+v4d/8bVOhabnEbAsQcqMtGvvOuJDDugtA2lGJtiZ93trt5r8DNrmrob3+Qc+K+Y70vl2WexqsyaMz338hIey19A983GZtNy8J71RQCp0xEpXY7rPcphDh8kpBEuzG9Jbi//HmzdeI21o3dBE44Fd/UWyzn9tYYvLylhne217Kt0prMtgW6M7/SuuNokkNj2oA4rh8Yz9BUO6hHCRQuxpf/LASqQ17buP4xVLzMkBMiWkhCEu1C+at+mknXtPR3vdhqITB8NJ7bHwK97gHSEo/B33Y4WLDyAN6mF8gOOrGbg+mD4rmsbyzxDZfj0Ww4epyNo8fZ+Lb9E3/BG/XnbHFocT3b8taEEO1EEpI4opThxSj9Bv/ud8Pua3RIwho/9nKF/5QJeG9+AOwOKnwmT2+o5ukN1VT5m96GwWWD8dkuzstxcXYvFz3jW14JwZFzCf59HwUfirV3n4imyRCqENFEEtIxTimFWb0dzZGE7jq8AX2lDDzrHsIsW9tsvZhdBnE/GvjOuxLf1P9HjQHPraviyR+qKPeFXynbocPZvVxc3jeWc9qwMKnmTCH2pMcJ7P8ULS4be/dJrbpeCNH+JCEdo5RS+Le9gH/XO8EyR+6VaK4MNEciKBM9MQ89gjGWQNFy/Dv/hVm9rck66Qu9KAcop4Zu74175q/ZmzOMV9ZV8/ymGg56zLDX9Yyz8evjE7i8X9xhL0yqx/fGOeCGw2pDCNF+JCEdo4yizy3JCMC/q9HacgqSdg5GjZ2OOeh40DSU4UGz1S94alTm413/KBA+oUDdg6f2KoWKceE99wY+HnoBL+Z7+HDlAYwmtg5KsSvuPjGZGwcnEGuXFRKEOBZIQjoGKdNfN/OsJRq44zaSNvtOvEN6Uznag0kFemIeMcc/hB7TDf+ut2kqGWk+Rfy6AHEbDdy5A5k/aQbzihIp+Ky8yZdMcmr86rhEJjkPMGJIr7a9QSFElyQJ6RijlIn3hz+gfGUR1fdnaPiydCpOPMChNbjNqnzcX17T7HWJq/3E/Vg3Re7LXqO5oPct1Gx3AeGnzcXbNW4dmsAdxyWQEqOTn38g8jclhDgqSEI6liiFf+cbGCVrIr9G1yg7L/ItthO+8ePcZ+IoVZTZ4/ld3yt4JnsSNLEw6ZAUO9MHxXNl/8MfIxJCdG2SkI5SpucgZvV2bMlDwR4PyiSj8DH8/n2NKiqSVgSoOc6GGafh2mrg65eK4apq9Wse6hXV6DE83XMCf+xzKWWOhJB6MTaY0juW6YPjOTXTKXsHCSEASUhHpcCBxXg3zqHh9g4A4Z7sSfvAh6NEEbvNwMg7Du8vfoPp2IOx4dFWvaaj0GRx8fE8M3wSS1KG4rWF9qpGpDu4Ji/uiMyYE0IcfSQhHWVUwI138z9onIzCSfja/9Piok58V96Cf9KloOvYVB9sxeMxCpeGXOPX4qgMJJBECQ6bwbbKLD6vOo6/uSdTclzoznfdXDpX9Ivl6rx4hqc1/bCrEEJIQjqKKDOAb9sLYNS2WDf5cx+unSaBoSfh+/kdmL36Bc9pmo5r2P2YebdTUFrEur0F2EtX8nVVOs9UnoNbxYQ22CDXOHU4P9fFVQPiOKunC4cut+SEEC2ThHSUMCo3182e8xY3W0+vVaQs8aGnDsN9/w0YQ060nD+0svbb22v5aJeH7VUuYNBPP02zaXBqlpPL+sZxad9YUuWWnBCilSQhHQVM9348X98Z9lzslgAxu0yUHXQvaOnHE/ivazCGjrTMfNtbY/D2tlre2FbLj+WBiF43xalxTi8X5+a4OKunS8aFhBCHRRJSF2fW7Ma95taw51xbAiStrEsuZo9ctp4zleyzJgfP1/hN3t/l4V9ba/l8nzeCUScYnGLn3J+S0OhMJ3a5HSeEOEIkIXVhSik86/8AytqjsVWYxG4xgg+mBoaNwnP7Q9TsO0CR2+Cj3R7+d5eHz/d58LSwvYNTh9O7x3BuTl0S6pMo/2SEEO1Dvl26GBVwY1RsxCj9GqNwaciKC44ik9SPfGg/dXd8E6fww4W38sE2Pwu3xLB++YEWe0J2rW5l7an94zirVwyJrVxZWwgh2kISUpRQysQoWYNZsRHNkYQtYyzKUwSaHS2go3tteHf/k0DtWppbyDT1k7pkFHDG8vbpN/Jwwli2vVfy09nm9w06Id3BVQPiuKxvLBmxLe8xJIQQR5IkpA6iDC/KX4HyFqPZ49HictE0DeWrwihajX/XvzA9e+sv2PpCq18jaZkPLQCrkgdy7eBb2WFmQmXz9+R6xdv4Wd9Ypg2IY2iqPCckhOg8kpCaYHpL0DQdzZnaYl1l+tF065e5WbMb75anMSs3geEJvSigoRmKcI/0tNahMSP3rhjuzpvGC9kTMZvZDfWEdAcX5Lq4IDeW41LtsnSPECIqHFMJSfmrMD2FKM9BNEciGi7M4o0Eir9AeUvQTBuaoWOqEkxbTd1FpobuteMsj8NRlQQ2B9jtoMdgxPrIjt9D7e4adCMOzXRg4kHZvNDSsItdodr46WtuhbPIxHnAJGanwT6VxuvdTuax0VMocoauluDQ4YzuMYx0VXH9yBx6JRxT/7cLIbqIo+qbyajYRGDvB6hANdhi0Z3pqIrdmNX5qEANSg/TU2mscSLRFWasH09sBZ4eFU1eZtpqWxqiiYitysR0aShHg15LQIFdw/RpFO9P5rtt/dmnpbM5rgerjx/AltgeIatpZ8XqTMiO4ZxeLs7q5SLZqZOfXybJSAgRtaL628m7+R9g+lCGG9B/+tLV0PfvQqssr5tJpkD3OlCagaf7Qcv1ltGTKJgopnkUmgmmC2jw/I5WrVBFNop2p7CxJpddCWmoTMU6rQ9LtWEUOpNwaiZe7IAG/euuc+iQ6bJxYpxOpkunb5Kdkd2cjMxw0jfRJrfihBBdSlQnpMDe98OWGzqQ0qGhtInmUbi2GFTVxrG/NpVNvhy2x2ayNyEVd7KDDbYcduoZVBIPNnD2g7QYnbQYnZQYndQYnUkxOpmxOhkuG5mxOpmxdf+bFWsj2alJ0hFCHDU6PCG98MILPPXUUxQWFjJ48GBmz57NmDFjOubFTYWtSmGrVZgODTNeQ/fq2CvjsPvSwBWHP9mLcmrYbb2xxx6HGasTsJcT8GxE+YtBaaBMytyl1PjtbC3pwfaDPYjDi25TFNsT2G/LQk+MYbtzMMagbiS7HKS5dFKdOskxGic66xLO1c66pFP3oxFrkwQjhDh2dWhCWrhwIffffz9z587l1FNP5YUXXuCKK65g1apV5OTkHJkXCSjiNhvoboURq4PLhU11x5aYh8rsBwNyUakZmMmpEJ8Eet29vEMPizb8QA7d8rMROjy0p8iHx1B49+/h7P65JDh0Ehwa8XYNXZKKEEK0WocmpHnz5nH11Vdz/fXXAzBnzhw+++wzXnrpJR566KGQ+s68WwAdzZkCKFAKMNEK90J1BWiAFsAwDqAwsdkzccSNQOvfC5XaDT05FWx2FBDZcqGROzmzbgO6/GqTvGR5fkcIIQ5XhyUkn8/H2rVr+eUvf2kpnzhxIqtXrw57jSPn0vCNdbceNuy9/DTPQQghRBfTYQmppKQEwzDIyMiwlGdkZFBUVBT2mvz8/I4I7bB1lThBYm0PXSVOkFjbi8Qamby8vGbPd/ikhsaD9kqpJgfyWwo+GuTn53eJOEFibQ9dJU6QWNuLxHrkdNjTOenp6dhstpDeUHFxcUivSQghxLGnwxKS0+lkxIgRLFmyxFK+ZMkSTjnllI4KQwghRJTq0Ft2t99+O7fccgsjR47klFNO4aWXXuLAgQNMnz69I8MQQggRhTo0If3sZz+jtLSUOXPmUFhYyJAhQ3jrrbfIzc3tyDCEEEJEIa28vFxmSQshhOh0UbDkqBBCCCEJSQghRJSQhCSEECIqSEISQggRFSQhCSGEiArHdEL6y1/+woQJE8jJyaF///5MnTqVjRs3WuoopZg9ezaDBw+me/fuTJ48mU2bNlnqeL1eZsyYQb9+/cjOzmbatGns3bs37Gt6PB7Gjh1LSkoK3333XVTGOnz4cFJSUiw/Dz/8cFTGCvDZZ59x9tln06NHD3Jzc7n44oujLtZly5aFfKaHfv7zn/9ETZwAW7du5eqrr6Zfv3706tWLSZMm8emnn0bycXZ4rGvXruWSSy4hNzeXvn37cuedd1JdXd3hsf7zn//kwgsvJDc3l5SUFAoKCkJeq7y8nJtvvpnc3Fxyc3O5+eabKS8vj8pYn3jiCc4991yys7NJSUmJOMbDdUwnpOXLl3PDDTfw8ccf895772G327nkkksoKysL1nnyySeZN28ejz/+OIsXLyYjI4NLL72UqqqqYJ2ZM2eyaNEiXnzxRT744AOqqqqYOnUqhmGEvObvfvc7evbsGfWx3nvvvWzevDn4c88990RlrO+//z7/9V//xdSpU/niiy/45JNPuOaaa6Iu1lNOOcXyeW7evJnf/OY3JCQkMGnSpKiJE2Dq1Kl4vV7effddvvjiC0499VSuvvpqduzYEVWf6f79+7nkkkvo06cPn332Ge+88w4//vgjt912W0RxHslYa2trmThxIvfff3+Tr3XjjTeybt063n77bRYsWMC6deu45ZZbojJWr9fLhRdeyK233hpxfEdEeXm5kp+6nz179ihd19W//vUvVV5ersrKylRWVpb67W9/G6yzf/9+lZCQoP7617+q8vJyVVBQoBwOh3ruueeCddavX680TVPvvPOOpf3XXntNDR48WK1evVoBasmSJVEZa05OjvrDH/4Q9Z9rSUmJ6tWrl3ryySejPtZwPwMGDFDXX399VMW5bds2Baj33nsvWKe4uFjpuq5efvnlqIr1b3/7m0pLS1MlJSXBOl9++aUC1LffftthsTb8WbJkiQLU999/byk/9N/8Rx99FCz78MMPFaC++uqrqIq14c/LL7+sgCP231dLP8d0D6mx6upqTNMMdlELCgooLCxk4sSJwTqxsbGMGTMmuIfT2rVr8fv9ljq9evVi0KBBln2e9u7dy913381zzz2Hy+WK6lgB/v73v9O3b19OP/10nnjiCXw+X9TFunbtWvbs2YPT6WTcuHEMHDiQSy+9lO+//z7qYm1s2bJlbN26lV/84hdRFWdaWhqDBg3izTffpLq6GsMw+Oc//0lCQkKb15xsr1i9Xi8OhwObzWZpB2DlypUdFmsk1qxZE/IZnnrqqcTHx7eqnY6ItTNJQmrg/vvvZ/jw4YwePRqAwsJCgGb3cCoqKsJms5Gent5kHcMwuOmmm7j99ts5/vjjozpWgFtuuYUXXniBRYsWcfPNN/P0009z9913R12sO3fuBOBPf/oTd999N2+99RbZ2dlceOGF7N+/P6pibezll1/muOOO48QTT4yqODVN49///jebNm0iJyeHzMxMHnvsMRYsWED37o12xuzkWMeNG0dJSQl//etf8fl8lJeXB8c6D71GR8QaiaKiItLT0y1b7WiaRrdu3VrVTkfE2pkkIf3kgQceYNWqVbzyyiuWv7igdXs4haszd+5cHA4Hd9xxR9THCnDHHXcwbtw4jjvuOK677jrmzp3LK6+8QmlpaVTFapomAPfccw9TpkxhxIgRPPnkkyQnJ/Pmm29GVawNlZWVsWjRojb3jtozTqUUd999N2lpaXz44Yd89tlnTJkyheuuu459+/ZFVaxDhgxh/vz5zJ8/nx49ejBw4EB69+5NZmZmyGt1RqyNhavflnY6ItbOIgmJusHTd955h/fee48+ffoEy7OysgCa3cMpMzMTwzAoKSlpss7nn3/OsmXL6NatG+np6Zx00kkATJo0iZtuuimqYg1n5MiRAGzfvj2qYj3UzqBBg4Ln7XY7/fr1Y8+ePVEVa0Ovv/46uq5zxRVXtCrGjojziy++4KOPPuKFF17g1FNPZcSIEcydO5e4uDhee+21qIoV4IorrmDLli1s2rSJ7du3c//991NcXEzv3r07LNZIZGZmUlxcjFL1S4cqpSgpKWn1fnDtHWtnOuYT0n333ceCBQt47733GDhwoOVc7969ycrKsuzh5PF4WLlyZfBe8IgRI3A4HJY6e/fuZfPmzcE68+bNY/ny5Sxbtoxly5bx9ttvA/D888+3ajp1R8Qazg8//ADU/4OPllhHjBhBTEyMZUtm0zTZsWMHOTk5URVrQ6+88gqXXHIJycnJEcfYUXHW1tYCoOvWrwZd14M90miJtaHMzEwSEhJYuHAhLpeL8ePHd1iskRg9ejTV1dWsWbMmWLZmzRpqampa1U5HxNqZOnwL82hyzz338Oabb/Lqq6+SkpISvAcbHx9PQkICmqZx6623MnfuXPLy8hgwYABPPPEE8fHxXH755QAkJydz7bXXMmvWLDIyMkhNTeXBBx9k2LBhwf8oGv4Vc6h9gL59+0Y8BbyjYl2zZg1fffUVZ5xxBklJSXz33Xc88MADnH/++RF/yXdUrElJSUyfPp3HHnuMnj17kpuby3PPPUdFRQVXXnllVMV6yMqVK/nxxx/529/+FlF8HR3n6NGjSU1N5fbbb+fee+8lNjaWl19+mZ07d3LuuedGVawAzz33HKNHjyYhIYElS5Ywa9YsHnrooYifnTkSsULd+E1hYSFbt24FYPPmzVRUVJCTk0NqaiqDBg1i0qRJ/PrXv+bJJ59EKcWvf/1rzj333Ii3FO+oWAF2795NWVkZu3btAmDdunUA9OvXj4SEhIjibZOOms4XjT9A2J/77rsvWKesrEzdd999KisrS8XExKgxY8aoFStWWNo5cOCAuummm1RqaqqKjY1V5557rlq/fn2Tr/v999+3etp3R8W6dOlSNWrUKJWUlKRcLpfKy8tT9913n9q3b1/UxVpeXq4OHjyofvWrX6nMzEyVmJioxo4dq5YuXRqVsZaXl6tp06apQYMGRfW/1SVLlqiJEyeq1NRUlZiYqEaOHKnefPPNqIx16tSpKjU1VTmdTjVs2DD1zDPPdMrnet9994VtZ968ecE6O3bsUFdeeaVKTExUiYmJ6sorr1Q7d+6MylivuuqqsHUWLVrU6n+7rfmR/ZCEEEJEhWN+DEkIIUR0kIQkhBAiKkhCEkIIERUkIQkhhIgKkpCEEEJEBUlIQgghooIkJCGEEFFBEpIQQoioIAlJCCFEVPj/3fCvvlbAzmwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px[\"AAPL\"].rolling(250).mean().plot(label=\"250\")#250개씩 묶어서 그거의 평균을 낸 값\n",
    "\n",
    "close_px[\"AAPL\"].rolling(50).mean().plot(label=\"50\")\n",
    "\n",
    "close_px[\"AAPL\"].rolling(5).mean().plot(label=\"5\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "05c267a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003-01-09         NaN\n",
       "2003-01-10         NaN\n",
       "2003-01-13         NaN\n",
       "2003-01-14         NaN\n",
       "2003-01-15         NaN\n",
       "2003-01-16    0.009628\n",
       "2003-01-17    0.013818\n",
       "Freq: B, Name: AAPL, dtype: float64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std250 = close_px[\"AAPL\"].pct_change().rolling(250, min_periods=10).std()\n",
    "\n",
    "std250[5:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "81a07b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 189,
     "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": [
    "std250.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "0db4ac06",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "227b03fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The expanding mean starts the time window from the same point as the rolling window and increases the size of the window until it encompasses the whole series\n",
    "expanding_mean = std250.expanding().mean()\n",
    "\n",
    "plt.style.use('grayscale')\n",
    "close_px.rolling(60).mean().plot(logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "8017addc",
   "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>7.400000</td>\n",
       "      <td>21.110000</td>\n",
       "      <td>29.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.425000</td>\n",
       "      <td>21.125000</td>\n",
       "      <td>29.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.433333</td>\n",
       "      <td>21.256667</td>\n",
       "      <td>29.473333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.432500</td>\n",
       "      <td>21.425000</td>\n",
       "      <td>29.342500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.402000</td>\n",
       "      <td>21.402000</td>\n",
       "      <td>29.240000</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>389.351429</td>\n",
       "      <td>25.602143</td>\n",
       "      <td>72.527857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>388.505000</td>\n",
       "      <td>25.674286</td>\n",
       "      <td>72.835000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>388.531429</td>\n",
       "      <td>25.810000</td>\n",
       "      <td>73.400714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>388.826429</td>\n",
       "      <td>25.961429</td>\n",
       "      <td>73.905000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>391.038000</td>\n",
       "      <td>26.048667</td>\n",
       "      <td>74.185333</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    7.400000  21.110000  29.220000\n",
       "2003-01-03    7.425000  21.125000  29.230000\n",
       "2003-01-06    7.433333  21.256667  29.473333\n",
       "2003-01-07    7.432500  21.425000  29.342500\n",
       "2003-01-08    7.402000  21.402000  29.240000\n",
       "...                ...        ...        ...\n",
       "2011-10-10  389.351429  25.602143  72.527857\n",
       "2011-10-11  388.505000  25.674286  72.835000\n",
       "2011-10-12  388.531429  25.810000  73.400714\n",
       "2011-10-13  388.826429  25.961429  73.905000\n",
       "2011-10-14  391.038000  26.048667  74.185333\n",
       "\n",
       "[2292 rows x 3 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rolling function also accepts a string indicating a fixed-size time offset rolling() in moving window functions rather than a set number of periods\n",
    "close_px.rolling(\"20D\").mean() \n",
    "#각 날짜를 구간의 끝으로하는 평균,, 1월2일부터 전에 20일인데 데이터가 없으니까 그냥 1월 2일 값. 1월 3일은 그전에 20일이니까 있는 데이터가 1월 2일+1월 3일 밖에 없어서 그 둘의 평균"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1861d4",
   "metadata": {},
   "source": [
    "###### 11.7.1 Exponentially Weighted Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "ce5dd01c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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49j179ug1sVckSe5CiGrv7t27QPFXWqrMJLkLIao9bcvd3t6+giPRH0nuQohq7+7du5ibm1fIqJayIsldCFHtRUVFGVSXDEhyF0IIwsPDS7QYR2UmyV0IUe3duXOH5s2bV3QYeiXJXQhRKa1du5Zvv/22zK+TmZnJ3bt3adasWZlfqzxJchdCVEo//PADn3zySZlfJzw8HI1GI8ldCCHKQ3JyMpGRkURGRpbqPFFRUYSFhRX4+Z07dwAMLrlL+QEhRKURGhrK5MmTGTJkCMnJyUBOQa9GjRqV+JzasesFrfFsqMldWu5CiErj4MGDHDp0iPnz5yvJ/eTJk2V6zTt37qBSqWjSpEmZXqe8FZrcvb29GTlyJJ6ennk++/nnn3FxcSEpKUnZtmnTJsaNG8f48eM5deqUfqMVQhi0f/75B8ipf65N7mWdR0JDQ7G3tzeYmjJahSb3wYMH4+3tnWd7TEwMZ86c0VlfMDQ0lMDAQNatW4e3tzerVq0iKytLvxELIQyWNrknJiaSnZ2NWq3m9OnTJc4jubtisrOz8/38zp07BtclA0VI7p07d8bS0jLP9m+++YapU6fqbAsODsbV1RVTU1Ps7Oywt7fn+vXr+otWCGHQtMldq02bNqSkpHD//v0SnS93r4L2LwGtixcvYmFhweHDh6lfv36Jzl+ZlajPPTg4mPr169OqVSud7XFxcTRo0EB5b2NjQ1xcXOkiFEJUG7dv39Z5r32QmpKSUqLzxcTEKK+f/AVx6NAhHjx4AECXLl1KdP7KrNijZR4+fMjGjRv59NNP83xW0NPo/Pj7+7N3717lnEKI6is7Oxs/Pz9iYmLo3r07Z86cAf430qWkyT02NlZ5nZiYqMxCzcjIYP/+/djZ2XHkyJFSjcaprIrdcr979y7R0dFMnjyZMWPGEBsby5QpU0hISMDGxkbnN2VsbGyBf+64ubnh6+uLr68vNjY2Jf8GQogq77PPPuP1118HdFcuKk3LXaPR4OHhobxPTExUXn/44Yf8/vvvtGzZktatW2NmZlbCyCuvYif3li1bsmvXLrZs2cKWLVuwsbHh+++/x8rKCmdnZwIDA8nMzCQqKorIyEjatGlTFnELIQzE3r17mTt3LgCWlpb06NFD+aw0yf327duEhoYq77XJXaPRsG7dOiAnyRuqQrtlli5dyvnz50lKSsLd3Z2JEycydOjQfPdt0aIFLi4ueHp6olarmTVrFmq1Wu9BCyEMx65du6hRowZhYWEYGxtz69Yt5TPt2PP79++TlJREnTp1inze48ePAzm/PIYNG0ZiYiLbtm3j4sWLJCUlsX79eoYNG6bfL1OJFJrc58+f/9TPt2zZovPew8ND508hIYR4mtu3b9OlSxesra0BdEbntWvXDoAxY8YAkJWVhZFR0TocTpw4gYWFBf369cPExIQ9e/bg7+8PwPDhwxk7dqw+v0alIzNUhRAV6vbt27Rs2VJ5nzu5PzkMe/HixTot+ydlZmayfPly0tLSOH78OE5OTpibm/PCCy8oiR1yGq0mJiZ6/BaVjyR3IUSFyczMJDw8XCe5a7teOnTokGfZuyVLluDg4JBnzLrW5s2bmTt3LnPnzuXChQs8//zzAAwcOFDZZ+zYsXTr1k3fX6XSkeQuhKgwt2/fRqPR0KJFC2VbrVq12L9/P4cPHy6wJMAPP/yQ7/b09HQAdu/eTVZWFj179gSgU6dOyj7fffcdKpVKX1+h0pLkLoQoV9nZ2URERAAQGBgIoLSwtQYPHoy1tXW+Sbhhw4ZcvXo133Nrh2JrKz1qf2k4ODgo+xjSIthPI8ldCFGufv75Z5o0acKUKVN4++23eeaZZ3SSb2Hq1auXbzmCvXv3snDhwjz7AjpL6FWHVjtIPXchRDm7efMmAKtXrwbgo48+KlbCrVu3br7JfebMmXm2aZO7sXH1S3XV7xsLISpUfHy88rpDhw5MmjTpqfvv2bNHGTVjb2/PzJkz861Z1bZt2zyFx2rVqqW83r9/PzVr1ixN6FWKJHchRLnKXaIk94POgri5uem8r1evHiEhIXn2085i9fLy4rPPPsvz+eDBg4sbapUmfe5CiHIVExNDr1692LBhA998802xjy+oWyY6OpoxY8bg6uqqhyirPknuQohyde/ePRo2bMjrr79O3bp1i3183bp1SUxMzFOFNjo6GltbW50FhKozSe5CiHIVExOjs+5DcdWtW5esrCyWLl2qLAaUlpZGSkqKJPdcJLkLIcqNj49PnkV9iks7AmbhwoW89NJLQM5fAwC2tralOrchkeQuhCg327dvB2D06NElPkfurhztqJmoqCggJ7lra8aU5hqGQEbLCCH0TqPR5Dt2PTY2luHDh9OxY8cSnzv3sebm5kBOfzvkJHeABw8eFFi6oLqQlrsQolSSkpIICwtT3g8ZMiRPOQGt2NjYUq+8lnsBoJSUFDQaTZ7kXqtWrWq/loQkdyFEiV27do3OnTszYMAAZdtvv/3GyZMn2bVrFxcvXlS2azQa4uLi9LKs5okTJxgwYABpaWnExMQQHR2NkZFRgct6VkfSLSOEKJG0tDRefPFFoqKiUKvVeRbSGDVqFAAPHz6kRo0aJCUl8ejRI70k9x49evDOO+/w+++/8/fffxMdHU3Dhg2rfWs9N2m5CyFKZPXq1URFRTFu3DiysrKIjo7WWYRaa8mSJUBOlwygl+QO8MwzzwDw999/c/PmTaVLRuQoNLl7e3szcuRIPD09lW0+Pj6MHz+eSZMmMX/+fFJTU5XPNm3axLhx4xg/fjynTp0qm6iFEBVKo9GwZs0aevbsyWuvvQZAeHg4kZGROvu1bNmSTz75BBcXF6Xyo76Se/PmzVGpVKxYsYI//viDxo0b6+W8hqLQ5D548GC8vb11tnXr1o1169axdu1aGjduzKZNmwAIDQ0lMDCQdevW4e3tzapVq8jKyiqbyIUQFWLv3r0888wzXLlyhddff11ZxDp3cu/Xrx8A8+bNA+Dw4cPK8foah16jRg0aN27M5cuXAVizZo1ezmsoCk3unTt3zrOO4XPPPaf0bbVr1075cys4OBhXV1dMTU2xs7PD3t5emUEmhDAMbm5uSvXFPn36KMn9lVde4cqVKwCsXbuWtLQ0evTooXPsypUr6dKli95i0XbN2Nvby+SlJ5T6ger+/ftxcXEBciYUaFcrh5w/v/IrzQng7+/P3r17gZwHLkKIqsfBwQETExPq1KlDUlISK1aswMbGhubNm2NkZISjo6Oy7x9//EHv3r31ev1nnnmGw4cPY2dnp9fzGoJSJfeNGzeiVqvp378/QJ5CPk/j5uamlPL08vIqTRhCiHKS+/kaoEwUunHjBra2tsTExDBu3Dhl1IyxsTE+Pj506NCBXr166T0ebctdknteJU7uv/32G8ePH+fzzz9XZqLZ2Njo1GqOjY2VcadCGJDcddRzF+jK3SXyZP31qVOnllk8LVu2BJCRMvko0VDIU6dOsWXLFpYtW6azsomzszOBgYFkZmYSFRVFZGSkzmwyIUTVpl3YeuvWrcqDTNBdl3TEiBHlFo+03AtWaMt96dKlnD9/nqSkJNzd3Zk4cSJ+fn48evRI6U5p164ds2fPpkWLFri4uODp6YlarWbWrFkyqUAIAxIeHg5Ar1698vxVfu7cOZKTk8t1KTtHR0fq1avHs88+W27XrCoKTe7z58/Ps23o0KEF7u/h4YGHh0fpohJCVEoRERGo1ep8a6ZXRIKtXbs2cXFxxVpgu7qQ8gNCiCKLiIjA3t6+Uv1Fnrvkgfgf+akIIYosJCREZoJWEZLchRAF0mg0TJs2jb179+Lk5MSxY8cYNmxYRYclikC6ZYQQBbpw4QI+Pj74+PgA4OTkxPvvv1/BUYmikJa7EKJAv/zyi8771atXK8vYicpNkrsQokAHDhygc+fOyvvc5QRE5SbJXQiRr7S0NE6fPs2QIUOUbTVq1KjAiERxSJ+7EEJHXFwctWrV4qeffuLx48f069ePkSNHSoG/KkaSuxBCERYWRrNmzRg9ejQBAQG4uLjw4osvYmwsqaKqkf9jQgjFsmXLANi+fTsAU6ZMkcReRUmfuxACgPT0dCWpa5VFmV5RPuRXshACgEmTJpGYmMgvv/zCV199hZGRkbLKkqh6JLkLIUhPT2fnzp1Mnz6dESNGlGvZXlE2pFtGCEFwcDAZGRm89NJLFR2K0BNJ7kIINm/ejJmZmd7XOBUVR5K7ENVcamoqmzZt4vXXX8fCwqKiwxF6IsldiGru2rVrZGRkMHjw4IoORehRoQ9Uvb29OXHiBHXr1mXdunUAJCcns2TJEqKjo7G1tWXhwoXUrl0bgE2bNrFv3z7UajUzZszAycmpbL+BEKJUrl+/DiDrHRuYQlvugwcPxtvbW2ebn58fXbt2ZePGjXTt2hU/Pz8AQkNDCQwMZN26dXh7e7Nq1SqysrLKJnIhhF5cv34dY2NjZbFpYRgKTe6dO3fG0tJSZ9uxY8cYNGgQAIMGDSI4OBjIeeLu6uqKqakpdnZ22NvbK60CIUTlc/HiRfbt20fLli2llK+BKdE494SEBKytrQGwtrYmMTERyCk41K5dO2U/Gxsb4uLi9BCmEEKf5s+fz19//cWvv/4K5NRpF4ZFr5OYNBpNkff19/dn7969AFJtTohydPz4cT7++GPl/cWLF+nYsWMFRiTKQomSu5WVFfHx8VhbWxMfH0+9evWAnJZ6TEyMsl9sbCz169fP9xxubm64ubkB4OXlVZIwhBAl8NNPP1G7dm2OHDmCkZGRJHYDVaKhkM7OzgQEBAAQEBCAs7Ozsj0wMJDMzEyioqKIjIyUJ/BCVDI3btygffv2PPvsszqrLAnDUmhyX7p0KdOnTyc8PBx3d3d+/fVXXnvtNc6cOYOHhwdnzpxh7NixALRo0QIXFxc8PT2ZM2cOs2bNQq1Wl/mXEKK8BAQEMG7cOLKzs3FycuKrr76q6JCKLSQkhFatWlV0GKKMFdotM3/+/Hy3r1y5Mt/tHh4eeHh4lC4qISqp3bt34+fnR//+/Tl9+jSnT59m5syZ5R5HVlYWly9fLnbLOyMjg/DwcBn2WA3IDFUhiiE8PByAN998U9n2+PHjco9j7dq1dOnSpcDGV0H++ecfNBqNtNyrAUnuQhRDREQEgM7kvGvXrpV7HEeOHAFg/fr1ANy8eRMvLy8GDRrE7NmziYqKyve48+fPA+gMWRaGSeq5C1EMERERDB06lPj4eJKSkrh27Rp3794ttxEnERERTJs2TRlGHBERwccff8zWrVu5dOkSAAcOHOCLL77gwoULdOrUSTn20aNH7Nq1C3Nzc53twjBJy12IInr48CFxcXH07NmT48ePs2vXLiBnyG952bFjh5LYR48eDeQ8F7t06RJLly7lzp07mJmZASjxaa1YsYKtW7fSuXNnWRe1GpDkLkQRRUZGAtCoUSMgZ14HlG9yP3PmDHZ2dty6dYuNGzcye/ZszM3NGT16NPPmzaNp06YkJibSs2dP1q9fz927d5VjtV05n3zySbnFKyqOJHdhsNasWcOIESOoW7cu3bp1Y9euXaXqH9eW0mjYsCEAdevWxdjYuNySe1ZWFn/88Qfdu3enVatW1KhRg88//5z79++zdetWVCoVAKampqxYsYK7d+8yfvx4zp49i0aj4cKFC0ycOJG+ffuWS7yiYsnfZsIgpaWl6YxoSUlJYdSoUQAcPHiQF198sdjnTEhIAFBmZBsZGVG/fv1yS+4fffQRYWFhzJs3T2d7fl0svXv3pkePHhw6dIju3btz+vRpYmJiePbZZ8slVlHxpOUuDM6DBw+ws7NT3js7O3Px4kVlZMmmTZtKdF5tgTwrKytlm42NTZkm98uXLxMYGMjq1avZsGEDI0aMYMqUKUU6tmXLlsrr5557jlq1ajF8+PCyClVUMtJyFwbn8OHDpKSk0KhRIyIjI2nevDk1a9ZkwoQJHDhwgH379gGQmZnJ1atX6dKlS5HO+2TLHfLWUyqOrKwsZsyYwdWrV/Hz81P68rU0Gk2eUTju7u5FPv+yZcto1qwZjRo14u7du/Ts2ZPmzZuXKFZR9UhyFwbnt99+o1atWty6dYu1a9fy2muvKZ91794dPz8/3N3d2bVrF1lZWXz++efMnj270PNqW+65k3uLFi3w8/Pj0qVLxR4O+eeff+Lj4wPAwIEDOX36tDLSJS0tTXmAq/Xpp58WK7k3atSIxYsXFysmYTikW0YYnMOHD9O7d29q1arFjBkzlLUHACUBb9++XZmINHfuXEJDQws9b0JCAhYWFjqLWixduhQTExNWrVpV7Dhv3LgB5CTtq1evKove+Pn5YWFhgaOjo7LvoUOH8PLywtTUtNjXEdWTJHdhUO7fv8/ly5fp1atXvp936NAhz7aMjAw2b95c6LkTExN1+tsB7OzsGDhwIL/99hsJCQnFWtPg1q1b1KhRgxEjRgBw7tw5PvjgAzZs2ADk9JMvXLiQ7OxsXF1di3xeIUC6ZYSBOXDgABqNpsDk3rBhQ9zd3WnRogUrVqzAysoKa2tr5s6dS/v27Z/6wDG/5A7w0ksvsX37dqytrRk+fDirV6/GxsZGGZp4/fp1du/ezdixY2nSpIly3M2bN2ndujWNGzcG4MMPP1Q+mzhxorIgvRAlUWlb7rt27WL48OGcOHGiokMRVcSOHTvw9PSkffv2vPDCC/nuo1Kp2Lp1K4sWLQJg0aJF2NraAigt6Cc9fPiQkSNHsmfPHp3+dq1XXnlFeb1nzx4aNmxIvXr1WLFiBQDvvvsuH374IV9++SUZGRkcPnyY+Ph4/vrrLxwcHKhVq1aeXxojR44s9vcXIrdK2XLXaDR88MEHhISEcO3aNa5evSqL94oCPXjwgFGjRhEQEEDz5s3Zt28fNWrUeOoxtWrVUrpQmjZtytGjR4GcImBt27YFcvrl16xZw19//aWMiHnuuefynMvc3JwdO3bw4MED1q5dy+HDh0lKSmLOnDnUqFGDP//8E8gpk/3NN9+QkZGhHKstnd2oUSNlNE7fvn2VVcqEKKlK2XL38fEhJCSEl156iZCQEI4fP17RIYlKbOnSpcrKYP/+979p2rRpsY4fMWIEYWFhAOzfv1/Z7uPjw9GjR5XE/tVXXxU4dX/UqFF4eHhw6NAhsrOzuXnzJmZmZrzzzjukpqYqi9ZkZGTw5ptvMmDAAEaPHq200KOjowHw9vbm8OHDSpeOECVV6ZJ7dnY2Xl5e9OnTB19fXwBOnjxZwVGJymrhwoX85z//oVOnTgwYMIDx48eX6DxNmjShdevWBAYGKttCQ0MZNmwYTZo0oUuXLrz99tuFrixmZGSESqWidevWxMfH4+TkxIABAxg2bBgA//3vf/n+++85cOAA27ZtU843e/ZsOnXqxNtvv12i+IV4UqXrlgkLC+PBgweMGzeOxo0b88wzz0i/uyA8PFznYSTAiRMnWLJkCQBff/01ffr0KdU1BgwYwA8//EBQUBB9+/YlPDycf/3rX8rM1uKqWbOm0jC5fPkyKSkpjBs3Lt99P/zwQ50HqkKUVqla7tu2bWPixIl4enqydOlSMjMzSU5OxsvLCw8PD7y8vEhJSSnWObWFnbT9nt26dVMWGBDVT1hYGB9//DFNmzbl9OnTOp/9+OOPWFhYkJycXOrEDuDl5YWxsTGurq6MGTOGzMxMmjVrRq1atahVq1apzt2hQwcOHTpE3bp1Sx2nEEVR4uQeGxvLzp078fX1Zd26dWRlZREYGIifnx9du3Zl48aNdO3aFT8/v2Kd98nk7ujoSGhoqM5DKFE9BAcH06xZM2UpOW9vbyZOnEh6ejoAQUFB9OnTh9q1a+vlei1atODy5cs4OTmxbds2AJo1a6aXcwtR3krVcs/KyiIjI0P5r7W1NceOHWPQoEEADBo0SJl1V1QhISHUq1eP+vXrAznJPTs7m5CQkNKEKqog7cSi0aNH8+yzz7Jjxw5+/PFHgoKCuHXrFjdu3KBfv356vWazZs0IDg5m4cKF1KpVq9gLUAtRWZS4z93GxoZXXnmFV199lRo1atC9e3eee+45EhISlOne1tbWSj2OJ/n7+ysryjx8+FDZfvfuXWVSB6BMwb5x4wbt27cvabiiiklLS2Pr1q2MGjWKbdu2ERAQwNq1a9m2bRsHDhxg7dq1WFhYFNiHXRrGxsYsWrSIBQsWYGRU6cYcCFEkJU7uKSkpHDt2jM2bN2NhYcGiRYv4/fffi3y8m5ubMpbXy8tL2X737l3s7e2V9w4ODsD/6nCI6uHHH38kNjZWKeg1aNAgBg0axIABA5Q6Lu+//77OvaJvkthFVVbiu/fs2bPY2toqq9H07t2by5cvY2VlRXx8PADx8fH5zuh7mieTu6WlJXZ2dpw6dYrk5OSShisqAY1GQ2pqapH2vXTpElZWVnnKCPz444/KSki5qz0KIXSVOLk3aNCAq1ev8vDhQzQaDefOnaNZs2Y4OzsrE0oCAgJwdnYu8jmzsrKIjo7O0xpzdHTkl19+kW6ZKu6LL76gdu3abN26lQsXLjx134iIiDxDHwHs7e25fPkyP//8s6wqJMRTlLhbpl27dvTt25cpU6agVqtp3bo1w4YNIz09ncWLF7Nv3z4aNGig1PAoioMHD5KVlZUnuVtYWAA5/+CTk5OxtLQsadiiAv33v/8F4NVXX8XU1JT79+8XOMQwIiJC59lLbvXr19ep5yKEyKtUk5g8PT3x9PTU2WZqaqrUyygOjUbD4MGDAfK02Fq1aqW8DgoKYsSIEcqkkJ49e8pU7UpOo9Hw+PFjZRq/paUlycnJnDhxgueee0755Q05f73Fx8cTERFBjx49KipkIaq8SvHE6OHDh0piHzJkCC+99JLO58uWLePQoUNYWFjw22+/cfDgQTp27Iizs7OUJqjkHjx4QMeOHRk/fjxpaWn4+fnx999/A+Dq6kqzZs24dOkSycnJTJs2DWNjYxo2bEhcXFyBLXchROEqRfmBf/75hytXrgDwzTff5KnfYWZmhqurKy+++CL79+8nLS1N+ezy5cv07NmzXOMVT5eSksKCBQswMzNDo9Fw5coV5f9vp06dqF+/Pm3atOH69eskJCTQqVMnmjZtqhTv0pIJREKUXKVI7unp6fTp04f+/fs/dQHfgQMHsnv3bvz9/XF1deXPP//k1q1b5ReoKJLNmzfz5ZdfolarlaXstLRDW7///nuGDh3K0qVL8fLyUhL7+vXruXfvHnfv3pV+dSFKoVIkd41GwxtvvMGECROeut+LL74I5Cyl1rFjR6Kiorh582Z5hCiKYOXKldy9e5fdu3fTokULrl27xsGDB7GyslJGTWnr8vfu3ZukpCRUKhWzZs3irbfe4rvvvsPFxaXYJXuFEHlViuQOFGmYo4ODA6ampmRmZtKuXTtCQ0MJCAggLCzsqQlBW6OkIvpwExISqF27Nl999RW1a9fmzTffNMgHwBqNhvfee095v3LlSmrUqMHQoUOBnPVBs7OzdY7J/XP45ptvWLJkiVJ2QghROpXigapKpVIKhRW235EjRxgzZgxubm7MnDmT9PT0p648v379el544QVatWrFgQMHSh3rb7/9xtdff12kfRMSErCxscHU1BQvLy+mTp3K7t27Sx1DZZOUlKTM5ly4cCGxsbG8++67Ovs8++yzdOvWrcBzqFQqSexC6FGlaLl37doVc3PzIu3bs2dP5QGqnZ0dDg4OhIeH6+yTmJjIxIkT6datG2vXrqV27drY2toyffr0UvfRDxkyBIBx48blu1iyVlxcHMOHD1daq5MmTeLkyZN4eHhw9uxZpWaOIchdkrlfv36SpIWoBCpFy700GjduTEREhM62n3/+mT179rBw4ULCwsIYP348np6ehISEsH37dp3Vdkpq586dT/183rx5yvKA6enprFmzht27d5OWlsa+fftKff2ysGfPHq5fv17s43I/9+jYsaM+QxJClFCVT+6NGjUiMjKSXbt2ERsbC+QsIuLg4MDrr78O5Mym7dq1KwDu7u5MmTKlRNfSaDTKhBsfHx9lgeUnpaam8vPPPwPg4eFBzZo1AWjZsiX29vacPXu2RNcvSwkJCYwYMYJOnToVum9ERITOd9D+Qjh48KBSEVQIUbEMIrmHhYUxatQoZs+eTUxMDIcPH8bd3Z1Vq1Yxa9Ysxo0bpyR3ExMT/v777zxjqosiMTGR1NRUWrduzdmzZ9myZUu++23YsIGkpCSOHz/OTz/9pPNZt27dOHHiRIGlkCvCtWvX+OabbwB49OiRsmCK1rFjx7h69SqJiYloNBqaNGlC9+7defz4MVu2bGHlypV07txZGc0khKh4VT655x4Bc+TIERYuXEh2djbu7u7Uq1ePL7/8kjp16mBjY8Pu3buVGvKHDx9Wjrtx44ay/Wnu3LkDwJIlS3BycmLOnDnKOO67d+/SpUsXgoKC2L59Ox07dsx3ctXLL7/M33//jZWVFRcvXizNV9ebdu3asWDBAurUqYOJiQk+Pj7KZ+fOnaNXr160b98eKysrTE1Nlc/8/PwYP348bdq04eOPP66I0IUQBajyyb1nz55KWeHw8HB8fHyoW7duvt0Lw4cPp3///lhaWhIYGMjjx48BcHFxwc3NLc+D2Sdp+/ZbtGjBe++9R3h4OB9++CEhISHs3buXCxcu4OrqypEjRwpsxb7xxht8+eWXAEqffEWKjIxUXvv6+vLyyy+zdetWpctpx44dOvs/fvyYYcOGATBhwgRMTU0JCgpStgkhKocqn9y7detGQkICL7/8srLNwcGhwLHkRkZGdOnShR9//JFevXqRlJREVFQUAKtXr37qtbR9+g0aNGDEiBH06tWLzz77jNatWzN16lRlv+zsbPr27VvgeWbOnImFhQWXL18u6tcsM6dOnQJg//79vPrqq/Tu3Zvo6Gju3r0L5Az97Nu3LyEhIdja2nLkyBH8/f1ZtmwZJiYm+Pr6YmtrW5FfQQiRjyqf3LVy92EXtoJOmzZtgJzE9vbbbyvbCxsHr03uNjY21KhRg6NHj/Lvf/9b+dzd3Z22bdsycOBAZchkflQqFa1ateK///0vQUFBT71mWTt16hTGxsbKWqTasehnzpwhMzOTS5cu0aNHD5555hmioqLo06cPAHPnziU5OblMlrkTQpSewST3V199VXndrl27p+67fPly9uzZg52dnfLAc9q0aZw8eZIePXqQmZmZ73FxcXHUrFlTGZOvUql48803AXjzzTdZt24dly5dIiAggBo1ajw1Bm0VTFdXVz7++GOys7NZt26d8gukvJw8eZLOnTsrI3q6dOmCWq0mICCAEydO8OjRowIXxdAeI4SofAwmuf/73/8mNTWVHTt2PHXGKoCVlRVubm5KgrW0tFSGR546dYrvvvsu3+NiY2OpX7++TpdP165dOXnyJN9++y3m5uZ5KloWZNmyZYwePRqA+fPns3jxYt544w1lJE55ePfddwkKCqJ79+7KNjMzM1566SW+++47pWtJO9JICFF1GExyV6lUmJubM2rUKJ3FH55G2yXTpEkTunTpgkajwcHBIc8kp6CgINauXUtQUBA2NjZ5zuPk5ISxcfEm+xoZGenU01myZAmQM5U/d42WsvLo0SPlwa72l5zW3LlzadiwITY2Nixbtkyp5CiEqDpKVX4gNTWVTz/9lH/++QeVSsUHH3xAkyZNWLJkCdHR0dja2rJw4UJq166tr3j16tlnn+XAgQM6Rceef/55du7cSXZ2NkZGRuzYsUNpYYN+a4x/8MEHtGjRgvbt2/PHH38wZswYPvnkE3788Uc+++wzWrVqpfOgWJ/OnDkD5JTeffIaPXv2JDo6ukyuK4QoH6VquX/99dc4OTmxYcMG1qxZQ7NmzfDz86Nr165s3LiRrl274ufnp69Yy8SAAQN06ry4uLiQkpLCmDFjgJxFvq2srJQJS+fOndPbtc3MzJgwYQLdu3dn9uzZ2Nvb89xzz5Gamsr777/PyJEjuXTpkt6up5WRkcH777+PqakpI0eO1Pv5hRAVr8TJPS0tjYsXLypL4pmYmGBhYcGxY8cYNGgQAIMGDSI4OFg/kZaT8ePHM3LkSHbu3El8fDy3b9+mdevWjBo1CkApaVBWnn/+eZ3327dv54cffmDBggV6OX9GRgbjxo0jODiYDRs2SJEvIQxUibtloqKiqFu3Lt7e3vz99984ODgwY8YMEhISlPoi1tbWlWqafVGoVCrmzZvHrl27GDp0KFeuXGH48OGYmJiQmppa5iNEcvdv29rasnbtWmWi0bvvvqtM2NK6desWrVq1KnKN+O3bt7Njxw5mzZqlM8JICGFYStxyz8rK4ubNmwwfPpzVq1dTs2ZNNm/eXOTj/f39mTp1KlOnTi334X+F6dq1KytXruTMmTOkpqYqS/8VZzRMafznP//B0dGRNWvW6Mwg3bt3L9u3b8fBwYH09HSOHDmCg4MDL7zwAmFhYRw8eJD4+Pinnlvbl659gCuEMEwlTu42NjbY2NgoY8r79u3LzZs3sbKyUhJMfHx8npamlpubG76+vvj6+uY7AqUiqVQq3n33XT777DMA7O3ty/X6c+bM4fr16wwdOlSnT/yjjz7C3d2dW7du8eeff+Lv7w/kFPaaMmUKAwYMYMCAASQkJBR47ri4OIyNjSvtQ24hhH6UuFvGysqKBg0aKEvcnTt3jubNm9O8eXMCAgIYO3YsAQEBytqZVdGsWbNo3bo1rq6uFRbD6tWr6dSpE0eOHNEpdrZ8+XJu3ryJk5MTZmZmBAQEAPDXX39hbW2Nj4+PTkkErfj4+Dxj9YUQhqdUo2VmzpzJsmXLmDRpEiEhIYwbN47XXnuNM2fO4OHhwZkzZxg7dqy+Yi13KpWKoUOHUqtWrQqLwdramkWLFun89dCkSROCgoJ4+PAhX375JfPmzctz3KJFi3j48GGe7XFxcVJzXYhqoFTj3Fu1aoWvr2+e7StXrizNaUU+li1bhp2dHZ988gkqlYpffvkFV1dXrK2tefToEdbW1iQlJXH06FGSk5MZNGgQu3btYsyYMTqt9Pj4eEnuQlQDBjND1dA1b96czz77DFNTU0xMTHB3d1eStImJCW+99RajR4+mZ8+e9O/fn5YtWzJt2jSMjIz4v//7P+U8cXFxMvxRiGpAkruBWLJkiTJaycjIiJ07dyrdSd7e3sp+0nIXonqQ5G6gOnfuzLVr13B1dcXExISsrCwePnyoPFAVQhg2Se4GrG7durz++uukp6dz69YtduzYwePHjyt09I8QonyU6oGqqPyef/55VCoVH330EcePH6dNmzaS3IWoBiS5GzhHR0emTZvGt99+C+QUQitspSohRNUn/8qrgdzFzjp37lyBkQghyou03KsBJycn6tatq6w2JYQwfJLcqwEjI6MqV51TCFE60i0jhBAGSJK7EEIYIEnuQghhgCS5CyGEAZLkLoQQBkiSuxBCGCBJ7kIIYYAkuQshhAGqFJOYrly5gqOjY7GPS01NxcLCosyP0UpKSqJOnTrldr2SHluSOEtzPYlTf9c09PuzPH+WpTmuqsQZFhZW4GeVIrk3bdo03+X6CvPZZ5/h5eVV5sdoTZ06tdhxluZ6JT22JHGW5noSp/6uaej3Z3n+LEtzXFWKsyBVulvG2dm5XI4pjdJcr6rEKnHq75pV5WdZ2mPL83rVNU5J7mVM/vHoX1WJsyTXrCo/y9IeW57Xq65xVorkPmzYsIoOoUgkTv2SOPWrKsRZFWIEw4hTFRQUpCnHWIQQQpSDStFyF0IIoV+S3IUQwgCVa3IfMmRIeV6uxI4ePYqLi8tTx5BWFoX9TN955x1u3LhRTtHkFRsby7x58/Dw8GDcuHF8/fXXPHr0qMD9t2/fzsOHD8sxwv+pCvdnVbo3oXLfn1Xp3iwJabnn49ChQ3Ts2JHAwMBiHZeVlVVGEVVNGo2GBQsW8MILL7Bx40Y2bNhAeno6a9euLfCY7du3k5GRUY5RVi1yb+pHdbg3y30SU3p6OvPmzSM1NZXHjx/zxhtv8MILLxAdHc2cOXPo2LEjly9fxsbGho8//pgaNWqUe3xXrlxh5cqVzJs3j4kTJ3L+/HnWrVuHpaUl4eHhdOrUiXfeeQcjIyOGDBmCu7s7p0+f5q233qJjx47lGi/A+fPn+fnnn1m+fDkAq1atwtHRkcGDB5d7LLmdO3cOU1NTpfWmVquZPn06Y8eOZcKECaxfv57Tp0+jUqkYOnQoGo2G+Ph43n33XerUqcMXX3xR7jFX5vuzKt6bUDnvz6p4bxZXuSd3U1NTli5dirm5OUlJSbz11lv06tULgIiICObPn4+XlxeLFi3ijz/+YMCAAeUa359//slzzz1HkyZNsLS05ObNmwBcu3aN9evX07BhQ+bMmcPRo0fp27cvDx8+pEWLFrzxxhvlGmdVEBoaioODg842c3NzGjRowK+//kpUVBSrV69GrVaTnJyMpaUl27Zt44svvijx1O/Sqsz3p9yb+lMV783iKvfkrtFoWLNmDRcvXkSlUhEXF6cs3mxnZ0erVq0AcHR0JDo6urzD49ChQ4wePRoAFxcXAgMD6dmzJ23btsXe3h4AV1dXLl26RN++fTEyMqJPnz7lHmdVoNHkP8pWo9Fw8eJFhg8fjlqtBsDS0rI8QytQZb4/5d7Un6p4bxZXuSf3gwcPcv/+fXx9fTE2NmbMmDFkZmYCYGJiouxnZGRU7v2ESUlJ/PXXX4SGhgKQnZ2NSqWiR48eefZVqVRATktPexNUFLVarXOzan+eFa1FixYcPXpUZ1taWhqxsbHY29srP8PKpLLen1X13oTKeX9WxXuzuMr9gWpqair16tXD2NiYv/76i3v37pV3CAU6cuQIAwcOZMuWLWzZsoWtW7dia2vLpUuXuH79OlFRUWRnZxMUFESHDh0qOlxFw4YNCQ0NJTMzk9TUVM6dO1fRIQHQtWtXHj58SEBAAJDzUO+7775j0KBBdO/enT179igJMjk5GQAzMzMePHhQYTFX1vuzqt6bUDnvz6p4bxZXubXcs7KyMDU1ZcCAAcydO5epU6fSqlUrmjZtWl4hFCowMJCxY8fqbOvTpw979uyhffv2fP/99/zzzz906tSJ3r17V1CU/6P9mTZo0IB+/foxefJkGjVqROvWrSs6NCCnBbl06VK+/PJLfvrpJzQaDT169GDy5Mmo1WrCw8OZNGkSarWaYcOGMXLkSIYNG8acOXOwtrYu14dWlf3+rGr3JlTu+7Mq3ZslVW7lB0JCQvj888/57rvvyuNyevXk0/7Koir/TCubqvqzrKz3JlTdn6mhKJeW+549e9i5cyfTp08vj8tVC/Iz1R/5Weqf/EwrnhQOE0IIA1QmLfeYmBiWL19OQkICKpWKYcOGMXr0aJKTk1myZAnR0dHY2tqycOFCateuDcCmTZvYt28farWaGTNm4OTkBMCjR49YtWoVFy5cQKVSMWnSJPr27VsWYYtqQl/354MHD5g5c6Zy3tjYWAYMGMCMGTMq6qsJoSiTlnt8fDzx8fE4ODjw4MEDpk6dytKlS/ntt9+wtLRk7Nix+Pn5kZKSwtSpUwkNDWXp0qV89913xMfH4+XlxYYNG1Cr1axbt47s7GwmTZpEdnY2KSkpVWYSgaic9Hl/5jZlyhSmT59O586dK+ibCfE/ZTIU0traWpn9ZWZmRtOmTYmLi+PYsWMMGjQIgEGDBhEcHAxAcHAwrq6umJqaYmdnh729PdevXwdg//79yigBIyMjSeyi1PR5f2pFRERw//59OnXqVL5fRogClPkD1ejoaEJCQmjbti0JCQlYW1sDOf/AtDP/4uLiaNeunXKMjY0NcXFxpKamAvDDDz9w4cIF7O3tmTlzJlZWVmUdtqgmSnN/5nbo0CFcXFwMYvKLMAxlOokpPT2dBQsWMH36dMzNzQvcr6CpwFlZWcTGxtKhQwe+//572rVrh4+PT1mFK6qZ0t6fuQUFBeHq6qrP8IQolTJL7o8fP2bBggX0799fqW9hZWVFfHw8kNPvWa9ePSCnJRQTE6McGxsbS/369bG0tKRmzZrKpIx+/fopxZKEKA193J9aISEhZGVl4ejoWI7fQIinK5PkrtFoWLFiBc2aNeOVV15Rtjs7OyvTfQMCApTVvp2dnQkMDCQzM5OoqCgiIyNp06YNKpWK559/nvPnzwM5ZTqbN29eFiGLakRf96dWYGCgtNpFpVMmo2UuXbrEzJkzadmypdIHOXnyZNq2bcvixYuJiYmhQYMGLFq0SKm4tnHjRvbv36/UVdYWRIqOjmb58uWkpqZSp04d5syZQ8OGDfUdsqhG9Hl/AowdO5b//Oc/laZUgRAgk5iEEMIgyTJ7QghhgCS5CyGEAZLkLoQQBkiSuxBCGCBJ7kIIYYAkuQshhAGS5C6EEAZIkrsQQhig/wchwi8fBW2aMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aapl_px = close_px[\"AAPL\"][\"2006\":\"2007\"]\n",
    "\n",
    "ma30 = aapl_px.rolling(30, min_periods=20).mean()\n",
    "\n",
    "ewma30 = aapl_px.ewm(span=30).mean()\n",
    "\n",
    "aapl_px.plot(style=\"k-\", label=\"Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e59ac3",
   "metadata": {},
   "source": [
    "# 생각해오기 꼭.,,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "ba92150b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ma30.plot(style=\"k--\", label=\"Simple Moving Avg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "6e1b6b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ewma30.plot(style=\"k-\", label=\"EW MA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "17e3e5da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1218fe9d0>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "390bb718",
   "metadata": {},
   "source": [
    "###### 11.7.2 Binary Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "ad2bf310",
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_px = close_px_all[\"SPX\"]\n",
    "\n",
    "spx_rets = spx_px.pct_change()\n",
    "\n",
    "returns = close_px.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "f6f18642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = returns[\"AAPL\"].rolling(125, min_periods=100).corr(spx_rets)\n",
    "\n",
    "corr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "bf3ae52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = returns.rolling(125, min_periods=100).corr(spx_rets)\n",
    "\n",
    "corr.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0967a998",
   "metadata": {},
   "source": [
    "###### 11.7.3 User-Defined Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "8ca17e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import percentileofscore\n",
    "\n",
    "def score_at_2percent(x):\n",
    "    return percentileofscore(x, 0.02)\n",
    "\n",
    "result = returns[\"AAPL\"].rolling(250).apply(score_at_2percent)\n",
    "\n",
    "result.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "a17fc2b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7302aaa",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}