02. Time Resampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 읽기; 한번에 인덱스화 해서 읽는 방법은 나중에 소개됩니다.\n",
    "df = pd.read_csv('data/apple_stock.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>30.478571</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>30.447144</td>\n",
       "      <td>30.104286</td>\n",
       "      <td>88102700.0</td>\n",
       "      <td>20.159719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>30.642857</td>\n",
       "      <td>30.340000</td>\n",
       "      <td>30.490000</td>\n",
       "      <td>30.572857</td>\n",
       "      <td>123432400.0</td>\n",
       "      <td>20.473503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>30.798571</td>\n",
       "      <td>30.464285</td>\n",
       "      <td>30.657143</td>\n",
       "      <td>30.625713</td>\n",
       "      <td>150476200.0</td>\n",
       "      <td>20.508902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>30.747143</td>\n",
       "      <td>30.107143</td>\n",
       "      <td>30.625713</td>\n",
       "      <td>30.138571</td>\n",
       "      <td>138040000.0</td>\n",
       "      <td>20.182680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>30.285715</td>\n",
       "      <td>29.864286</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>30.082857</td>\n",
       "      <td>119282800.0</td>\n",
       "      <td>20.145369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       High        Low       Open      Close       Volume  \\\n",
       "0  2009-12-31  30.478571  30.080000  30.447144  30.104286   88102700.0   \n",
       "1  2010-01-04  30.642857  30.340000  30.490000  30.572857  123432400.0   \n",
       "2  2010-01-05  30.798571  30.464285  30.657143  30.625713  150476200.0   \n",
       "3  2010-01-06  30.747143  30.107143  30.625713  30.138571  138040000.0   \n",
       "4  2010-01-07  30.285715  29.864286  30.250000  30.082857  119282800.0   \n",
       "\n",
       "   Adj Close  \n",
       "0  20.159719  \n",
       "1  20.473503  \n",
       "2  20.508902  \n",
       "3  20.182680  \n",
       "4  20.145369  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2265 entries, 0 to 2264\n",
      "Data columns (total 7 columns):\n",
      "Date         2265 non-null object\n",
      "High         2265 non-null float64\n",
      "Low          2265 non-null float64\n",
      "Open         2265 non-null float64\n",
      "Close        2265 non-null float64\n",
      "Volume       2265 non-null float64\n",
      "Adj Close    2265 non-null float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 123.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DatetimeIndex 아닌 RangeIndex 를 갖고 있으므로, Date 칼럼으로부터 DateTime 인덱스 생성."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = df['Date'].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2265 entries, 0 to 2264\n",
      "Data columns (total 7 columns):\n",
      "Date         2265 non-null datetime64[ns]\n",
      "High         2265 non-null float64\n",
      "Low          2265 non-null float64\n",
      "Open         2265 non-null float64\n",
      "Close        2265 non-null float64\n",
      "Volume       2265 non-null float64\n",
      "Adj Close    2265 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(6)\n",
      "memory usage: 123.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date 칼럼이 object 에서 datetime 으로 형변환됨."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>30.478571</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>30.447144</td>\n",
       "      <td>30.104286</td>\n",
       "      <td>88102700.0</td>\n",
       "      <td>20.159719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.642857</td>\n",
       "      <td>30.340000</td>\n",
       "      <td>30.490000</td>\n",
       "      <td>30.572857</td>\n",
       "      <td>123432400.0</td>\n",
       "      <td>20.473503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.798571</td>\n",
       "      <td>30.464285</td>\n",
       "      <td>30.657143</td>\n",
       "      <td>30.625713</td>\n",
       "      <td>150476200.0</td>\n",
       "      <td>20.508902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.747143</td>\n",
       "      <td>30.107143</td>\n",
       "      <td>30.625713</td>\n",
       "      <td>30.138571</td>\n",
       "      <td>138040000.0</td>\n",
       "      <td>20.182680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.285715</td>\n",
       "      <td>29.864286</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>30.082857</td>\n",
       "      <td>119282800.0</td>\n",
       "      <td>20.145369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close       Volume  Adj Close\n",
       "Date                                                                          \n",
       "2009-12-31  30.478571  30.080000  30.447144  30.104286   88102700.0  20.159719\n",
       "2010-01-04  30.642857  30.340000  30.490000  30.572857  123432400.0  20.473503\n",
       "2010-01-05  30.798571  30.464285  30.657143  30.625713  150476200.0  20.508902\n",
       "2010-01-06  30.747143  30.107143  30.625713  30.138571  138040000.0  20.182680\n",
       "2010-01-07  30.285715  29.864286  30.250000  30.082857  119282800.0  20.145369"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datetime 타입의 인덱스 생성됨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "monthly 혹은 yearly groupby 후 aggregation 할 때의 문제점."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'] = df.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16141.351418</td>\n",
       "      <td>15840.451458</td>\n",
       "      <td>16007.617104</td>\n",
       "      <td>15989.401403</td>\n",
       "      <td>1.748822e+10</td>\n",
       "      <td>14038.491524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15739.942860</td>\n",
       "      <td>15445.045748</td>\n",
       "      <td>15572.844261</td>\n",
       "      <td>15607.478556</td>\n",
       "      <td>1.511651e+10</td>\n",
       "      <td>13799.143261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18676.318544</td>\n",
       "      <td>18358.607164</td>\n",
       "      <td>18522.812897</td>\n",
       "      <td>18516.759998</td>\n",
       "      <td>1.646522e+10</td>\n",
       "      <td>16387.278601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17763.422855</td>\n",
       "      <td>17444.350006</td>\n",
       "      <td>17612.804337</td>\n",
       "      <td>17595.992851</td>\n",
       "      <td>1.656252e+10</td>\n",
       "      <td>15549.686649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18874.642849</td>\n",
       "      <td>18552.902849</td>\n",
       "      <td>18711.531479</td>\n",
       "      <td>18731.747120</td>\n",
       "      <td>1.595049e+10</td>\n",
       "      <td>16741.255772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19040.630035</td>\n",
       "      <td>18743.354290</td>\n",
       "      <td>18909.838608</td>\n",
       "      <td>18881.168510</td>\n",
       "      <td>1.427367e+10</td>\n",
       "      <td>16927.685444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18908.574245</td>\n",
       "      <td>18616.531422</td>\n",
       "      <td>18760.998562</td>\n",
       "      <td>18775.181419</td>\n",
       "      <td>1.403693e+10</td>\n",
       "      <td>16779.291849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21322.417149</td>\n",
       "      <td>20943.891430</td>\n",
       "      <td>21113.692909</td>\n",
       "      <td>21152.527245</td>\n",
       "      <td>1.505109e+10</td>\n",
       "      <td>19060.226547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19596.267178</td>\n",
       "      <td>19222.517181</td>\n",
       "      <td>19423.325752</td>\n",
       "      <td>19400.800022</td>\n",
       "      <td>1.547466e+10</td>\n",
       "      <td>17478.810833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21670.588585</td>\n",
       "      <td>21245.169964</td>\n",
       "      <td>21466.111412</td>\n",
       "      <td>21471.372849</td>\n",
       "      <td>1.579270e+10</td>\n",
       "      <td>19412.855421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19932.064278</td>\n",
       "      <td>19547.734276</td>\n",
       "      <td>19761.048534</td>\n",
       "      <td>19746.541416</td>\n",
       "      <td>1.301104e+10</td>\n",
       "      <td>17929.619272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19514.165709</td>\n",
       "      <td>19137.724298</td>\n",
       "      <td>19344.287140</td>\n",
       "      <td>19309.390026</td>\n",
       "      <td>1.223923e+10</td>\n",
       "      <td>17473.007708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               High           Low          Open         Close        Volume  \\\n",
       "month                                                                         \n",
       "1      16141.351418  15840.451458  16007.617104  15989.401403  1.748822e+10   \n",
       "2      15739.942860  15445.045748  15572.844261  15607.478556  1.511651e+10   \n",
       "3      18676.318544  18358.607164  18522.812897  18516.759998  1.646522e+10   \n",
       "4      17763.422855  17444.350006  17612.804337  17595.992851  1.656252e+10   \n",
       "5      18874.642849  18552.902849  18711.531479  18731.747120  1.595049e+10   \n",
       "6      19040.630035  18743.354290  18909.838608  18881.168510  1.427367e+10   \n",
       "7      18908.574245  18616.531422  18760.998562  18775.181419  1.403693e+10   \n",
       "8      21322.417149  20943.891430  21113.692909  21152.527245  1.505109e+10   \n",
       "9      19596.267178  19222.517181  19423.325752  19400.800022  1.547466e+10   \n",
       "10     21670.588585  21245.169964  21466.111412  21471.372849  1.579270e+10   \n",
       "11     19932.064278  19547.734276  19761.048534  19746.541416  1.301104e+10   \n",
       "12     19514.165709  19137.724298  19344.287140  19309.390026  1.223923e+10   \n",
       "\n",
       "          Adj Close  \n",
       "month                \n",
       "1      14038.491524  \n",
       "2      13799.143261  \n",
       "3      16387.278601  \n",
       "4      15549.686649  \n",
       "5      16741.255772  \n",
       "6      16927.685444  \n",
       "7      16779.291849  \n",
       "8      19060.226547  \n",
       "9      17478.810833  \n",
       "10     19412.855421  \n",
       "11     17929.619272  \n",
       "12     17473.007708  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('month').agg(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>High</th>\n",
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       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>30.478571</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>30.447144</td>\n",
       "      <td>30.104286</td>\n",
       "      <td>8.810270e+07</td>\n",
       "      <td>20.159719</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>9445.277117</td>\n",
       "      <td>9246.514246</td>\n",
       "      <td>9358.474289</td>\n",
       "      <td>9354.328560</td>\n",
       "      <td>3.775623e+10</td>\n",
       "      <td>6264.245874</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>13227.248539</td>\n",
       "      <td>12970.717155</td>\n",
       "      <td>13106.211399</td>\n",
       "      <td>13104.155712</td>\n",
       "      <td>3.101483e+10</td>\n",
       "      <td>8775.365667</td>\n",
       "      <td>1650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>20779.478600</td>\n",
       "      <td>20354.327164</td>\n",
       "      <td>20594.740028</td>\n",
       "      <td>20573.204269</td>\n",
       "      <td>3.299105e+10</td>\n",
       "      <td>14019.096821</td>\n",
       "      <td>1625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>17195.001381</td>\n",
       "      <td>16856.895695</td>\n",
       "      <td>17032.612881</td>\n",
       "      <td>17014.855690</td>\n",
       "      <td>2.560539e+10</td>\n",
       "      <td>13377.068882</td>\n",
       "      <td>1649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>23439.191429</td>\n",
       "      <td>23051.835686</td>\n",
       "      <td>23239.397186</td>\n",
       "      <td>23250.692886</td>\n",
       "      <td>1.591449e+10</td>\n",
       "      <td>21242.415085</td>\n",
       "      <td>1651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>30553.620010</td>\n",
       "      <td>29953.500031</td>\n",
       "      <td>30284.289993</td>\n",
       "      <td>30250.079948</td>\n",
       "      <td>1.306315e+10</td>\n",
       "      <td>28458.719048</td>\n",
       "      <td>1655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>26567.650009</td>\n",
       "      <td>26129.949989</td>\n",
       "      <td>26335.959984</td>\n",
       "      <td>26360.209991</td>\n",
       "      <td>9.680622e+09</td>\n",
       "      <td>25313.373260</td>\n",
       "      <td>1652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>38002.920044</td>\n",
       "      <td>37521.400078</td>\n",
       "      <td>37761.670013</td>\n",
       "      <td>37788.319946</td>\n",
       "      <td>6.810426e+09</td>\n",
       "      <td>36972.054916</td>\n",
       "      <td>1640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>47939.520004</td>\n",
       "      <td>46983.060043</td>\n",
       "      <td>47463.110077</td>\n",
       "      <td>47452.410126</td>\n",
       "      <td>8.537986e+09</td>\n",
       "      <td>47134.853607</td>\n",
       "      <td>1633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              High           Low          Open         Close        Volume  \\\n",
       "Date                                                                         \n",
       "2009     30.478571     30.080000     30.447144     30.104286  8.810270e+07   \n",
       "2010   9445.277117   9246.514246   9358.474289   9354.328560  3.775623e+10   \n",
       "2011  13227.248539  12970.717155  13106.211399  13104.155712  3.101483e+10   \n",
       "2012  20779.478600  20354.327164  20594.740028  20573.204269  3.299105e+10   \n",
       "2013  17195.001381  16856.895695  17032.612881  17014.855690  2.560539e+10   \n",
       "2014  23439.191429  23051.835686  23239.397186  23250.692886  1.591449e+10   \n",
       "2015  30553.620010  29953.500031  30284.289993  30250.079948  1.306315e+10   \n",
       "2016  26567.650009  26129.949989  26335.959984  26360.209991  9.680622e+09   \n",
       "2017  38002.920044  37521.400078  37761.670013  37788.319946  6.810426e+09   \n",
       "2018  47939.520004  46983.060043  47463.110077  47452.410126  8.537986e+09   \n",
       "\n",
       "         Adj Close  month  \n",
       "Date                       \n",
       "2009     20.159719     12  \n",
       "2010   6264.245874   1659  \n",
       "2011   8775.365667   1650  \n",
       "2012  14019.096821   1625  \n",
       "2013  13377.068882   1649  \n",
       "2014  21242.415085   1651  \n",
       "2015  28458.719048   1655  \n",
       "2016  25313.373260   1652  \n",
       "2017  36972.054916   1640  \n",
       "2018  47134.853607   1633  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.index.year).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <th>12</th>\n",
       "      <td>30.478571</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>30.447144</td>\n",
       "      <td>30.104286</td>\n",
       "      <td>8.810270e+07</td>\n",
       "      <td>20.159719</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2010</th>\n",
       "      <th>1</th>\n",
       "      <td>573.331425</td>\n",
       "      <td>557.244289</td>\n",
       "      <td>568.397141</td>\n",
       "      <td>563.798571</td>\n",
       "      <td>3.792249e+09</td>\n",
       "      <td>377.554930</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>544.092857</td>\n",
       "      <td>532.875715</td>\n",
       "      <td>537.678570</td>\n",
       "      <td>539.588568</td>\n",
       "      <td>2.694020e+09</td>\n",
       "      <td>361.342400</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>738.235716</td>\n",
       "      <td>727.080000</td>\n",
       "      <td>732.047146</td>\n",
       "      <td>734.064285</td>\n",
       "      <td>3.038543e+09</td>\n",
       "      <td>491.575537</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>759.410004</td>\n",
       "      <td>744.359997</td>\n",
       "      <td>753.004292</td>\n",
       "      <td>753.445721</td>\n",
       "      <td>3.091782e+09</td>\n",
       "      <td>504.554579</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>730.337135</td>\n",
       "      <td>699.494276</td>\n",
       "      <td>719.941429</td>\n",
       "      <td>718.461422</td>\n",
       "      <td>4.520664e+09</td>\n",
       "      <td>481.126873</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>832.535709</td>\n",
       "      <td>812.074276</td>\n",
       "      <td>824.641434</td>\n",
       "      <td>820.742855</td>\n",
       "      <td>4.162813e+09</td>\n",
       "      <td>549.620947</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>775.377140</td>\n",
       "      <td>753.262863</td>\n",
       "      <td>766.438568</td>\n",
       "      <td>764.849998</td>\n",
       "      <td>3.917426e+09</td>\n",
       "      <td>512.191607</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>797.491417</td>\n",
       "      <td>783.245712</td>\n",
       "      <td>790.775719</td>\n",
       "      <td>789.872864</td>\n",
       "      <td>2.397280e+09</td>\n",
       "      <td>528.948475</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>827.359993</td>\n",
       "      <td>811.739990</td>\n",
       "      <td>818.522850</td>\n",
       "      <td>821.552849</td>\n",
       "      <td>2.962480e+09</td>\n",
       "      <td>550.163418</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>909.855713</td>\n",
       "      <td>893.007137</td>\n",
       "      <td>901.279999</td>\n",
       "      <td>902.802849</td>\n",
       "      <td>3.058644e+09</td>\n",
       "      <td>604.573469</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>941.915722</td>\n",
       "      <td>926.407131</td>\n",
       "      <td>934.340004</td>\n",
       "      <td>934.764286</td>\n",
       "      <td>2.377022e+09</td>\n",
       "      <td>625.976896</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1015.334286</td>\n",
       "      <td>1005.722858</td>\n",
       "      <td>1011.407139</td>\n",
       "      <td>1010.384293</td>\n",
       "      <td>1.743309e+09</td>\n",
       "      <td>676.616745</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2011</th>\n",
       "      <th>1</th>\n",
       "      <td>973.799992</td>\n",
       "      <td>955.328583</td>\n",
       "      <td>964.618561</td>\n",
       "      <td>966.800003</td>\n",
       "      <td>2.710384e+09</td>\n",
       "      <td>647.430040</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>958.600002</td>\n",
       "      <td>944.887138</td>\n",
       "      <td>950.895706</td>\n",
       "      <td>953.210007</td>\n",
       "      <td>2.323987e+09</td>\n",
       "      <td>638.329285</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1151.114277</td>\n",
       "      <td>1131.195709</td>\n",
       "      <td>1143.562855</td>\n",
       "      <td>1141.755714</td>\n",
       "      <td>2.826615e+09</td>\n",
       "      <td>764.591324</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>981.997139</td>\n",
       "      <td>965.321423</td>\n",
       "      <td>975.171432</td>\n",
       "      <td>972.615704</td>\n",
       "      <td>2.313457e+09</td>\n",
       "      <td>651.324560</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1032.851433</td>\n",
       "      <td>1018.240005</td>\n",
       "      <td>1025.958569</td>\n",
       "      <td>1025.354282</td>\n",
       "      <td>1.728015e+09</td>\n",
       "      <td>686.641624</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1051.567142</td>\n",
       "      <td>1031.604290</td>\n",
       "      <td>1043.190010</td>\n",
       "      <td>1040.541424</td>\n",
       "      <td>2.315963e+09</td>\n",
       "      <td>696.811869</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1071.921425</td>\n",
       "      <td>1050.395718</td>\n",
       "      <td>1058.758564</td>\n",
       "      <td>1063.537140</td>\n",
       "      <td>2.663487e+09</td>\n",
       "      <td>712.211285</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1254.649998</td>\n",
       "      <td>1219.489994</td>\n",
       "      <td>1239.569992</td>\n",
       "      <td>1237.934288</td>\n",
       "      <td>4.035650e+09</td>\n",
       "      <td>828.998539</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1191.664288</td>\n",
       "      <td>1164.200008</td>\n",
       "      <td>1177.138565</td>\n",
       "      <td>1177.480000</td>\n",
       "      <td>2.994362e+09</td>\n",
       "      <td>788.514557</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1205.449989</td>\n",
       "      <td>1174.577141</td>\n",
       "      <td>1190.231430</td>\n",
       "      <td>1191.690002</td>\n",
       "      <td>3.285357e+09</td>\n",
       "      <td>798.030468</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1166.638569</td>\n",
       "      <td>1145.058582</td>\n",
       "      <td>1158.804287</td>\n",
       "      <td>1154.445717</td>\n",
       "      <td>2.240925e+09</td>\n",
       "      <td>773.089329</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1186.994286</td>\n",
       "      <td>1170.418564</td>\n",
       "      <td>1178.311428</td>\n",
       "      <td>1178.791431</td>\n",
       "      <td>1.576633e+09</td>\n",
       "      <td>789.392788</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2012</th>\n",
       "      <th>1</th>\n",
       "      <td>1231.452858</td>\n",
       "      <td>1216.601433</td>\n",
       "      <td>1225.028564</td>\n",
       "      <td>1224.508568</td>\n",
       "      <td>1.714964e+09</td>\n",
       "      <td>820.007877</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1430.997147</td>\n",
       "      <td>1404.107147</td>\n",
       "      <td>1413.722862</td>\n",
       "      <td>1421.631432</td>\n",
       "      <td>2.842139e+09</td>\n",
       "      <td>952.013706</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1829.345703</td>\n",
       "      <td>1792.201431</td>\n",
       "      <td>1812.982857</td>\n",
       "      <td>1815.024284</td>\n",
       "      <td>3.896084e+09</td>\n",
       "      <td>1215.454231</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1758.535728</td>\n",
       "      <td>1710.428566</td>\n",
       "      <td>1741.735703</td>\n",
       "      <td>1731.437141</td>\n",
       "      <td>3.899748e+09</td>\n",
       "      <td>1159.479080</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1798.158585</td>\n",
       "      <td>1754.962852</td>\n",
       "      <td>1778.181435</td>\n",
       "      <td>1774.687134</td>\n",
       "      <td>2.776586e+09</td>\n",
       "      <td>1188.442024</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2016</th>\n",
       "      <th>7</th>\n",
       "      <td>1983.279999</td>\n",
       "      <td>1960.049995</td>\n",
       "      <td>1969.019989</td>\n",
       "      <td>1971.129997</td>\n",
       "      <td>6.857796e+08</td>\n",
       "      <td>1891.232445</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2487.230011</td>\n",
       "      <td>2460.689995</td>\n",
       "      <td>2471.650002</td>\n",
       "      <td>2476.300018</td>\n",
       "      <td>6.301285e+08</td>\n",
       "      <td>2387.152748</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2346.650017</td>\n",
       "      <td>2305.960007</td>\n",
       "      <td>2324.140015</td>\n",
       "      <td>2327.999992</td>\n",
       "      <td>9.680156e+08</td>\n",
       "      <td>2245.737114</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2444.150002</td>\n",
       "      <td>2413.769981</td>\n",
       "      <td>2430.619980</td>\n",
       "      <td>2429.850006</td>\n",
       "      <td>6.869143e+08</td>\n",
       "      <td>2343.988197</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2332.439995</td>\n",
       "      <td>2288.760017</td>\n",
       "      <td>2311.729988</td>\n",
       "      <td>2313.239990</td>\n",
       "      <td>7.213055e+08</td>\n",
       "      <td>2241.850868</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2415.529991</td>\n",
       "      <td>2384.449997</td>\n",
       "      <td>2393.909988</td>\n",
       "      <td>2401.050003</td>\n",
       "      <td>6.087717e+08</td>\n",
       "      <td>2328.097778</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2017</th>\n",
       "      <th>1</th>\n",
       "      <td>2397.619995</td>\n",
       "      <td>2375.710007</td>\n",
       "      <td>2381.869987</td>\n",
       "      <td>2391.399994</td>\n",
       "      <td>5.631220e+08</td>\n",
       "      <td>2318.740952</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2549.070007</td>\n",
       "      <td>2523.899986</td>\n",
       "      <td>2531.460014</td>\n",
       "      <td>2540.569977</td>\n",
       "      <td>5.749686e+08</td>\n",
       "      <td>2470.778885</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3247.550003</td>\n",
       "      <td>3213.309982</td>\n",
       "      <td>3228.330002</td>\n",
       "      <td>3234.210007</td>\n",
       "      <td>5.616284e+08</td>\n",
       "      <td>3149.539642</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2728.360001</td>\n",
       "      <td>2704.219986</td>\n",
       "      <td>2717.570023</td>\n",
       "      <td>2714.849991</td>\n",
       "      <td>3.733041e+08</td>\n",
       "      <td>2643.776230</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3367.349991</td>\n",
       "      <td>3324.390015</td>\n",
       "      <td>3343.249985</td>\n",
       "      <td>3349.009979</td>\n",
       "      <td>6.539818e+08</td>\n",
       "      <td>3269.984604</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3279.949997</td>\n",
       "      <td>3224.819992</td>\n",
       "      <td>3260.730026</td>\n",
       "      <td>3252.289963</td>\n",
       "      <td>6.841781e+08</td>\n",
       "      <td>3180.219025</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2983.529999</td>\n",
       "      <td>2942.110031</td>\n",
       "      <td>2961.930008</td>\n",
       "      <td>2965.989990</td>\n",
       "      <td>4.220119e+08</td>\n",
       "      <td>2900.263428</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3683.490021</td>\n",
       "      <td>3629.480026</td>\n",
       "      <td>3655.780029</td>\n",
       "      <td>3657.490036</td>\n",
       "      <td>6.610690e+08</td>\n",
       "      <td>3586.263855</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3177.390030</td>\n",
       "      <td>3128.930008</td>\n",
       "      <td>3162.180008</td>\n",
       "      <td>3152.169998</td>\n",
       "      <td>6.803741e+08</td>\n",
       "      <td>3094.421860</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3488.910004</td>\n",
       "      <td>3446.750031</td>\n",
       "      <td>3461.829956</td>\n",
       "      <td>3471.979996</td>\n",
       "      <td>5.042913e+08</td>\n",
       "      <td>3408.372849</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3641.990005</td>\n",
       "      <td>3590.809998</td>\n",
       "      <td>3622.299988</td>\n",
       "      <td>3620.530014</td>\n",
       "      <td>6.006634e+08</td>\n",
       "      <td>3562.712875</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3457.709991</td>\n",
       "      <td>3416.970016</td>\n",
       "      <td>3434.439987</td>\n",
       "      <td>3437.830002</td>\n",
       "      <td>5.308335e+08</td>\n",
       "      <td>3386.980713</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2018</th>\n",
       "      <th>1</th>\n",
       "      <td>3679.270020</td>\n",
       "      <td>3629.180023</td>\n",
       "      <td>3654.449982</td>\n",
       "      <td>3654.109985</td>\n",
       "      <td>6.594873e+08</td>\n",
       "      <td>3600.061661</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3227.869965</td>\n",
       "      <td>3144.110016</td>\n",
       "      <td>3178.250000</td>\n",
       "      <td>3185.139999</td>\n",
       "      <td>9.278943e+08</td>\n",
       "      <td>3146.956253</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3712.790009</td>\n",
       "      <td>3634.290039</td>\n",
       "      <td>3675.990005</td>\n",
       "      <td>3664.420029</td>\n",
       "      <td>7.130109e+08</td>\n",
       "      <td>3624.938492</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3604.809982</td>\n",
       "      <td>3534.140015</td>\n",
       "      <td>3565.070007</td>\n",
       "      <td>3566.520020</td>\n",
       "      <td>6.660579e+08</td>\n",
       "      <td>3528.093307</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4102.319992</td>\n",
       "      <td>4044.970001</td>\n",
       "      <td>4069.040039</td>\n",
       "      <td>4081.809998</td>\n",
       "      <td>6.209763e+08</td>\n",
       "      <td>4047.851425</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3986.470016</td>\n",
       "      <td>3935.799988</td>\n",
       "      <td>3963.130020</td>\n",
       "      <td>3961.050003</td>\n",
       "      <td>5.276245e+08</td>\n",
       "      <td>3933.482071</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4022.339996</td>\n",
       "      <td>3969.309967</td>\n",
       "      <td>3994.119995</td>\n",
       "      <td>3996.540009</td>\n",
       "      <td>3.936914e+08</td>\n",
       "      <td>3968.725082</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4934.909988</td>\n",
       "      <td>4860.029999</td>\n",
       "      <td>4883.340012</td>\n",
       "      <td>4906.960037</td>\n",
       "      <td>7.002737e+08</td>\n",
       "      <td>4884.851288</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4266.080002</td>\n",
       "      <td>4181.480011</td>\n",
       "      <td>4224.290009</td>\n",
       "      <td>4219.400024</td>\n",
       "      <td>6.789720e+08</td>\n",
       "      <td>4204.729004</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5150.640030</td>\n",
       "      <td>5011.109985</td>\n",
       "      <td>5087.600021</td>\n",
       "      <td>5079.449997</td>\n",
       "      <td>7.897485e+08</td>\n",
       "      <td>5061.788559</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4072.939987</td>\n",
       "      <td>3962.709976</td>\n",
       "      <td>4028.209991</td>\n",
       "      <td>4015.949997</td>\n",
       "      <td>9.613264e+08</td>\n",
       "      <td>4012.316437</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3179.080017</td>\n",
       "      <td>3075.930023</td>\n",
       "      <td>3139.619995</td>\n",
       "      <td>3121.060028</td>\n",
       "      <td>8.989225e+08</td>\n",
       "      <td>3121.060028</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High          Low         Open        Close        Volume  \\\n",
       "Date Date                                                                     \n",
       "2009 12      30.478571    30.080000    30.447144    30.104286  8.810270e+07   \n",
       "2010 1      573.331425   557.244289   568.397141   563.798571  3.792249e+09   \n",
       "     2      544.092857   532.875715   537.678570   539.588568  2.694020e+09   \n",
       "     3      738.235716   727.080000   732.047146   734.064285  3.038543e+09   \n",
       "     4      759.410004   744.359997   753.004292   753.445721  3.091782e+09   \n",
       "     5      730.337135   699.494276   719.941429   718.461422  4.520664e+09   \n",
       "     6      832.535709   812.074276   824.641434   820.742855  4.162813e+09   \n",
       "     7      775.377140   753.262863   766.438568   764.849998  3.917426e+09   \n",
       "     8      797.491417   783.245712   790.775719   789.872864  2.397280e+09   \n",
       "     9      827.359993   811.739990   818.522850   821.552849  2.962480e+09   \n",
       "     10     909.855713   893.007137   901.279999   902.802849  3.058644e+09   \n",
       "     11     941.915722   926.407131   934.340004   934.764286  2.377022e+09   \n",
       "     12    1015.334286  1005.722858  1011.407139  1010.384293  1.743309e+09   \n",
       "2011 1      973.799992   955.328583   964.618561   966.800003  2.710384e+09   \n",
       "     2      958.600002   944.887138   950.895706   953.210007  2.323987e+09   \n",
       "     3     1151.114277  1131.195709  1143.562855  1141.755714  2.826615e+09   \n",
       "     4      981.997139   965.321423   975.171432   972.615704  2.313457e+09   \n",
       "     5     1032.851433  1018.240005  1025.958569  1025.354282  1.728015e+09   \n",
       "     6     1051.567142  1031.604290  1043.190010  1040.541424  2.315963e+09   \n",
       "     7     1071.921425  1050.395718  1058.758564  1063.537140  2.663487e+09   \n",
       "     8     1254.649998  1219.489994  1239.569992  1237.934288  4.035650e+09   \n",
       "     9     1191.664288  1164.200008  1177.138565  1177.480000  2.994362e+09   \n",
       "     10    1205.449989  1174.577141  1190.231430  1191.690002  3.285357e+09   \n",
       "     11    1166.638569  1145.058582  1158.804287  1154.445717  2.240925e+09   \n",
       "     12    1186.994286  1170.418564  1178.311428  1178.791431  1.576633e+09   \n",
       "2012 1     1231.452858  1216.601433  1225.028564  1224.508568  1.714964e+09   \n",
       "     2     1430.997147  1404.107147  1413.722862  1421.631432  2.842139e+09   \n",
       "     3     1829.345703  1792.201431  1812.982857  1815.024284  3.896084e+09   \n",
       "     4     1758.535728  1710.428566  1741.735703  1731.437141  3.899748e+09   \n",
       "     5     1798.158585  1754.962852  1778.181435  1774.687134  2.776586e+09   \n",
       "...                ...          ...          ...          ...           ...   \n",
       "2016 7     1983.279999  1960.049995  1969.019989  1971.129997  6.857796e+08   \n",
       "     8     2487.230011  2460.689995  2471.650002  2476.300018  6.301285e+08   \n",
       "     9     2346.650017  2305.960007  2324.140015  2327.999992  9.680156e+08   \n",
       "     10    2444.150002  2413.769981  2430.619980  2429.850006  6.869143e+08   \n",
       "     11    2332.439995  2288.760017  2311.729988  2313.239990  7.213055e+08   \n",
       "     12    2415.529991  2384.449997  2393.909988  2401.050003  6.087717e+08   \n",
       "2017 1     2397.619995  2375.710007  2381.869987  2391.399994  5.631220e+08   \n",
       "     2     2549.070007  2523.899986  2531.460014  2540.569977  5.749686e+08   \n",
       "     3     3247.550003  3213.309982  3228.330002  3234.210007  5.616284e+08   \n",
       "     4     2728.360001  2704.219986  2717.570023  2714.849991  3.733041e+08   \n",
       "     5     3367.349991  3324.390015  3343.249985  3349.009979  6.539818e+08   \n",
       "     6     3279.949997  3224.819992  3260.730026  3252.289963  6.841781e+08   \n",
       "     7     2983.529999  2942.110031  2961.930008  2965.989990  4.220119e+08   \n",
       "     8     3683.490021  3629.480026  3655.780029  3657.490036  6.610690e+08   \n",
       "     9     3177.390030  3128.930008  3162.180008  3152.169998  6.803741e+08   \n",
       "     10    3488.910004  3446.750031  3461.829956  3471.979996  5.042913e+08   \n",
       "     11    3641.990005  3590.809998  3622.299988  3620.530014  6.006634e+08   \n",
       "     12    3457.709991  3416.970016  3434.439987  3437.830002  5.308335e+08   \n",
       "2018 1     3679.270020  3629.180023  3654.449982  3654.109985  6.594873e+08   \n",
       "     2     3227.869965  3144.110016  3178.250000  3185.139999  9.278943e+08   \n",
       "     3     3712.790009  3634.290039  3675.990005  3664.420029  7.130109e+08   \n",
       "     4     3604.809982  3534.140015  3565.070007  3566.520020  6.660579e+08   \n",
       "     5     4102.319992  4044.970001  4069.040039  4081.809998  6.209763e+08   \n",
       "     6     3986.470016  3935.799988  3963.130020  3961.050003  5.276245e+08   \n",
       "     7     4022.339996  3969.309967  3994.119995  3996.540009  3.936914e+08   \n",
       "     8     4934.909988  4860.029999  4883.340012  4906.960037  7.002737e+08   \n",
       "     9     4266.080002  4181.480011  4224.290009  4219.400024  6.789720e+08   \n",
       "     10    5150.640030  5011.109985  5087.600021  5079.449997  7.897485e+08   \n",
       "     11    4072.939987  3962.709976  4028.209991  4015.949997  9.613264e+08   \n",
       "     12    3179.080017  3075.930023  3139.619995  3121.060028  8.989225e+08   \n",
       "\n",
       "             Adj Close  month  \n",
       "Date Date                      \n",
       "2009 12      20.159719     12  \n",
       "2010 1      377.554930     19  \n",
       "     2      361.342400     38  \n",
       "     3      491.575537     69  \n",
       "     4      504.554579     84  \n",
       "     5      481.126873    100  \n",
       "     6      549.620947    132  \n",
       "     7      512.191607    147  \n",
       "     8      528.948475    176  \n",
       "     9      550.163418    189  \n",
       "     10     604.573469    210  \n",
       "     11     625.976896    231  \n",
       "     12     676.616745    264  \n",
       "2011 1      647.430040     20  \n",
       "     2      638.329285     38  \n",
       "     3      764.591324     69  \n",
       "     4      651.324560     80  \n",
       "     5      686.641624    105  \n",
       "     6      696.811869    132  \n",
       "     7      712.211285    140  \n",
       "     8      828.998539    184  \n",
       "     9      788.514557    189  \n",
       "     10     798.030468    210  \n",
       "     11     773.089329    231  \n",
       "     12     789.392788    252  \n",
       "2012 1      820.007877     20  \n",
       "     2      952.013706     40  \n",
       "     3     1215.454231     66  \n",
       "     4     1159.479080     80  \n",
       "     5     1188.442024    110  \n",
       "...                ...    ...  \n",
       "2016 7     1891.232445    140  \n",
       "     8     2387.152748    184  \n",
       "     9     2245.737114    189  \n",
       "     10    2343.988197    210  \n",
       "     11    2241.850868    231  \n",
       "     12    2328.097778    252  \n",
       "2017 1     2318.740952     20  \n",
       "     2     2470.778885     38  \n",
       "     3     3149.539642     69  \n",
       "     4     2643.776230     76  \n",
       "     5     3269.984604    110  \n",
       "     6     3180.219025    132  \n",
       "     7     2900.263428    140  \n",
       "     8     3586.263855    184  \n",
       "     9     3094.421860    180  \n",
       "     10    3408.372849    220  \n",
       "     11    3562.712875    231  \n",
       "     12    3386.980713    240  \n",
       "2018 1     3600.061661     21  \n",
       "     2     3146.956253     38  \n",
       "     3     3624.938492     63  \n",
       "     4     3528.093307     84  \n",
       "     5     4047.851425    110  \n",
       "     6     3933.482071    126  \n",
       "     7     3968.725082    147  \n",
       "     8     4884.851288    184  \n",
       "     9     4204.729004    171  \n",
       "     10    5061.788559    230  \n",
       "     11    4012.316437    231  \n",
       "     12    3121.060028    228  \n",
       "\n",
       "[109 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([df.index.year, df.index.month]).agg(sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## resample()\n",
    "시계열 데이터를 처리할 때 자주 하는 것 중 하나가 시간 인덱스를 기준으로 resampling 하는 것입니다. 그러기 위해 **resample** 메서드를 사용합니다.\n",
    "\n",
    "#### timeseries [offset alias](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) (중요)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table border=\"1\" class=\"docutils\">\n",
    "<colgroup>\n",
    "<col width=\"13%\" />\n",
    "<col width=\"87%\" />\n",
    "</colgroup>\n",
    "<thead valign=\"bottom\">\n",
    "<tr class=\"row-odd\"><th class=\"head\">Alias</th>\n",
    "<th class=\"head\">Description</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody valign=\"top\">\n",
    "<tr class=\"row-even\"><td>B</td>\n",
    "<td>business day frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>C</td>\n",
    "<td>custom business day frequency (experimental)</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>D</td>\n",
    "<td>calendar day frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>W</td>\n",
    "<td>weekly frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>M</td>\n",
    "<td>month end frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>SM</td>\n",
    "<td>semi-month end frequency (15th and end of month)</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>BM</td>\n",
    "<td>business month end frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>CBM</td>\n",
    "<td>custom business month end frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>MS</td>\n",
    "<td>month start frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>SMS</td>\n",
    "<td>semi-month start frequency (1st and 15th)</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>BMS</td>\n",
    "<td>business month start frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>CBMS</td>\n",
    "<td>custom business month start frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>Q</td>\n",
    "<td>quarter end frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>BQ</td>\n",
    "<td>business quarter endfrequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>QS</td>\n",
    "<td>quarter start frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>BQS</td>\n",
    "<td>business quarter start frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>A</td>\n",
    "<td>year end frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>BA</td>\n",
    "<td>business year end frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>AS</td>\n",
    "<td>year start frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>BAS</td>\n",
    "<td>business year start frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>BH</td>\n",
    "<td>business hour frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>H</td>\n",
    "<td>hourly frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>T, min</td>\n",
    "<td>minutely frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>S</td>\n",
    "<td>secondly frequency</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>L, ms</td>\n",
    "<td>milliseconds</td>\n",
    "</tr>\n",
    "<tr class=\"row-odd\"><td>U, us</td>\n",
    "<td>microseconds</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>N</td>\n",
    "<td>nanoseconds</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**resample** 호출 시 rule 파라메터를 입력해야 하는데, 입력한 rule 단위로 resampling 하게 됩니다. resampling 되어 리턴된 객체의 row 를 재조합하기 위해 aggregation 함수 (mean, sum, count, etc) 따위를 실행해서 결과를 도출합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
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       "    }\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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>30.478571</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>30.447144</td>\n",
       "      <td>30.104286</td>\n",
       "      <td>8.810270e+07</td>\n",
       "      <td>20.159719</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>37.481258</td>\n",
       "      <td>36.692517</td>\n",
       "      <td>37.136803</td>\n",
       "      <td>37.120351</td>\n",
       "      <td>1.498263e+08</td>\n",
       "      <td>24.858119</td>\n",
       "      <td>6.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>52.489082</td>\n",
       "      <td>51.471100</td>\n",
       "      <td>52.008775</td>\n",
       "      <td>52.000618</td>\n",
       "      <td>1.230747e+08</td>\n",
       "      <td>34.822880</td>\n",
       "      <td>6.547619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>83.117914</td>\n",
       "      <td>81.417309</td>\n",
       "      <td>82.378960</td>\n",
       "      <td>82.292817</td>\n",
       "      <td>1.319642e+08</td>\n",
       "      <td>56.076387</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>68.234132</td>\n",
       "      <td>66.892443</td>\n",
       "      <td>67.589734</td>\n",
       "      <td>67.519269</td>\n",
       "      <td>1.016087e+08</td>\n",
       "      <td>53.083607</td>\n",
       "      <td>6.543651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>93.012664</td>\n",
       "      <td>91.475538</td>\n",
       "      <td>92.219830</td>\n",
       "      <td>92.264654</td>\n",
       "      <td>6.315273e+07</td>\n",
       "      <td>84.295298</td>\n",
       "      <td>6.551587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>121.244524</td>\n",
       "      <td>118.863095</td>\n",
       "      <td>120.175754</td>\n",
       "      <td>120.040000</td>\n",
       "      <td>5.183789e+07</td>\n",
       "      <td>112.931425</td>\n",
       "      <td>6.567460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>105.427183</td>\n",
       "      <td>103.690278</td>\n",
       "      <td>104.507778</td>\n",
       "      <td>104.604008</td>\n",
       "      <td>3.841516e+07</td>\n",
       "      <td>100.449894</td>\n",
       "      <td>6.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>151.406056</td>\n",
       "      <td>149.487650</td>\n",
       "      <td>150.444900</td>\n",
       "      <td>150.551075</td>\n",
       "      <td>2.713317e+07</td>\n",
       "      <td>147.299024</td>\n",
       "      <td>6.533865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>190.994104</td>\n",
       "      <td>187.183506</td>\n",
       "      <td>189.096056</td>\n",
       "      <td>189.053427</td>\n",
       "      <td>3.401588e+07</td>\n",
       "      <td>187.788261</td>\n",
       "      <td>6.505976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close        Volume  \\\n",
       "Date                                                                       \n",
       "2009-12-31   30.478571   30.080000   30.447144   30.104286  8.810270e+07   \n",
       "2010-12-31   37.481258   36.692517   37.136803   37.120351  1.498263e+08   \n",
       "2011-12-31   52.489082   51.471100   52.008775   52.000618  1.230747e+08   \n",
       "2012-12-31   83.117914   81.417309   82.378960   82.292817  1.319642e+08   \n",
       "2013-12-31   68.234132   66.892443   67.589734   67.519269  1.016087e+08   \n",
       "2014-12-31   93.012664   91.475538   92.219830   92.264654  6.315273e+07   \n",
       "2015-12-31  121.244524  118.863095  120.175754  120.040000  5.183789e+07   \n",
       "2016-12-31  105.427183  103.690278  104.507778  104.604008  3.841516e+07   \n",
       "2017-12-31  151.406056  149.487650  150.444900  150.551075  2.713317e+07   \n",
       "2018-12-31  190.994104  187.183506  189.096056  189.053427  3.401588e+07   \n",
       "\n",
       "             Adj Close      month  \n",
       "Date                               \n",
       "2009-12-31   20.159719  12.000000  \n",
       "2010-12-31   24.858119   6.583333  \n",
       "2011-12-31   34.822880   6.547619  \n",
       "2012-12-31   56.076387   6.500000  \n",
       "2013-12-31   53.083607   6.543651  \n",
       "2014-12-31   84.295298   6.551587  \n",
       "2015-12-31  112.931425   6.567460  \n",
       "2016-12-31  100.449894   6.555556  \n",
       "2017-12-31  147.299024   6.533865  \n",
       "2018-12-31  187.788261   6.505976  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yearly Means\n",
    "df.resample(rule='A').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Resampling\n",
    "\n",
    "resampling 이후 호출할 함수를 임의로 만들 수도 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def first_day(sample):\n",
    "    \"\"\"\n",
    "    샘플링 기간에 상관 없이 기간 내 첫번째 데이터를 리턴\n",
    "    \"\"\"\n",
    "    return sample[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>30.478571</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>30.447144</td>\n",
       "      <td>30.104286</td>\n",
       "      <td>88102700.0</td>\n",
       "      <td>20.159719</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>30.642857</td>\n",
       "      <td>30.340000</td>\n",
       "      <td>30.490000</td>\n",
       "      <td>30.572857</td>\n",
       "      <td>123432400.0</td>\n",
       "      <td>20.473503</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>47.180000</td>\n",
       "      <td>46.405716</td>\n",
       "      <td>46.520000</td>\n",
       "      <td>47.081429</td>\n",
       "      <td>111284600.0</td>\n",
       "      <td>31.528688</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>58.928570</td>\n",
       "      <td>58.428570</td>\n",
       "      <td>58.485714</td>\n",
       "      <td>58.747143</td>\n",
       "      <td>75555200.0</td>\n",
       "      <td>39.340778</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>79.285713</td>\n",
       "      <td>77.375717</td>\n",
       "      <td>79.117142</td>\n",
       "      <td>78.432854</td>\n",
       "      <td>140129500.0</td>\n",
       "      <td>55.923737</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>79.575714</td>\n",
       "      <td>78.860001</td>\n",
       "      <td>79.382858</td>\n",
       "      <td>79.018570</td>\n",
       "      <td>58671200.0</td>\n",
       "      <td>67.251503</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>111.440002</td>\n",
       "      <td>107.349998</td>\n",
       "      <td>111.389999</td>\n",
       "      <td>109.330002</td>\n",
       "      <td>53204600.0</td>\n",
       "      <td>101.963631</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>105.370003</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.610001</td>\n",
       "      <td>105.349998</td>\n",
       "      <td>67649400.0</td>\n",
       "      <td>99.925850</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>116.330002</td>\n",
       "      <td>114.760002</td>\n",
       "      <td>115.800003</td>\n",
       "      <td>116.150002</td>\n",
       "      <td>28781900.0</td>\n",
       "      <td>112.620964</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>172.300003</td>\n",
       "      <td>169.259995</td>\n",
       "      <td>170.160004</td>\n",
       "      <td>172.259995</td>\n",
       "      <td>25555900.0</td>\n",
       "      <td>169.712067</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close       Volume  \\\n",
       "Date                                                                      \n",
       "2009-12-31   30.478571   30.080000   30.447144   30.104286   88102700.0   \n",
       "2010-12-31   30.642857   30.340000   30.490000   30.572857  123432400.0   \n",
       "2011-12-31   47.180000   46.405716   46.520000   47.081429  111284600.0   \n",
       "2012-12-31   58.928570   58.428570   58.485714   58.747143   75555200.0   \n",
       "2013-12-31   79.285713   77.375717   79.117142   78.432854  140129500.0   \n",
       "2014-12-31   79.575714   78.860001   79.382858   79.018570   58671200.0   \n",
       "2015-12-31  111.440002  107.349998  111.389999  109.330002   53204600.0   \n",
       "2016-12-31  105.370003  102.000000  102.610001  105.349998   67649400.0   \n",
       "2017-12-31  116.330002  114.760002  115.800003  116.150002   28781900.0   \n",
       "2018-12-31  172.300003  169.259995  170.160004  172.259995   25555900.0   \n",
       "\n",
       "             Adj Close  month  \n",
       "Date                           \n",
       "2009-12-31   20.159719     12  \n",
       "2010-12-31   20.473503      1  \n",
       "2011-12-31   31.528688      1  \n",
       "2012-12-31   39.340778      1  \n",
       "2013-12-31   55.923737      1  \n",
       "2014-12-31   67.251503      1  \n",
       "2015-12-31  101.963631      1  \n",
       "2016-12-31   99.925850      1  \n",
       "2017-12-31  112.620964      1  \n",
       "2018-12-31  169.712067      1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').apply(first_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df[['Close']].resample('A').mean().plot(kind='bar')\n",
    "plt.title('Yearly Mean Close Price for Apple');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kpJNOSpLc8pa3zCmnnHLt/9/2trcdwB4AAADrRgG3jbvc5S4599xz84//+I+5wx3ukCR56UtfmjPPPDPHHXdcLrzwwtz73vfONddck/e85z059dRTc/vb3z4PfehDc8c73vGQswcAANaRAm4bxx9/fF74whfm3HPPzXHHHZerr746N7/5zfOTP/mTedaznpW3v/3tOeecc3LxxRfnCU94QpLk3HPPzXd+53fmpje9aVpredSjHpXP/uzPzoknnni9uMcff/xh7RYAADBj1Vo71ATOOuusduGFF15v29/93d/lUz7lUw4po6N78pOfnPvc5z65xz3u0TXu6PsNAAD0V1Vvba2dtZPXDvsg75Edd9xxOeEEJy8BAICDpQrZhSc96UmHnQIAAHAMcgYOAABgJoYt4A773ryDdqztLwAAsLohL6E8+eSTc9lll+W0005LVR12OvuutZbLLrssJ5988mGnAgAAs3PGOecf9TUXPfPsA8hk/w1ZwJ1++um55JJLcumllx52Kgfm5JNPzumnn37YaQAAAAMbsoA78cQTc9vb3vaw0wAAABjKkAUcAABwbDiWLn/sYdhFTAAAALg+BRwAAMBMKOAAAABmwj1wAADArhzt/jX3rvXnDBwAAMBMKOAAAABmQgEHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMKOAAAABmQgEHAAAwEwo4AACAmVDAAQAAzMQJh50AAADACM445/yjvuaiZ559AJlszxk4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMKOAAAABmQgEHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMKOAAAABmQgEHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMKOAAAABmQgEHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMnLCTF1XVLyT5cJJTk7yytfbCqrpPku9M8oEkl7TWHje9dsvtAAAA7M2OzsC11r65tfaoJF+V5FuqqpKcm+TLW2tfmeS/quq+223fr+QBAACOJateQnlSksuS3CHJ37bWrpy2vyLJvY6wHQAAgD1atYB7apIfSXJaksuXtl8+bdtu+/VU1SOr6sKquvDSSy9dMQUAAIBj044LuKr6ziR/0Vp7QxZn4U5d+vKp07bttl9Pa+281tpZrbWzbnGLW+wqcQAAgGPNjgq4qnp0kve11l40bXpbkk+rqhtNnz8gyeuPsB0AAIA9OuoqlFV1tywWJvn9qrrrtPl7sric8sVV9YEk/5bk91trrapusH1/UgcAADi2HLWAa639aZJbb/Gl9yS5YIvXX7DVdgAAAPbGg7wBAABmQgEHAAAwEwo4AACAmVDAAQAAzMRRFzEBAADWyxnnnH/U11z0zLMPIBNW5QwcAADATDgDBwAAM3K0s2fOnK03Z+AAAABmQgEHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJz4EDAIADcLTntyWe4cbROQMHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMKOAAAABmwoO8AQAAOtnvB7Y7AwcAADATCjgAAICZcAklAABra78vZ4OD5gwcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEVSgBAOjuaKs/7mTlRytIwg05AwcAADATCjgAAICZcAklAAAchcs5GYUzcAAAADOhgAMAAJgJBRwAAMBMKOAAAABmwiImAABrwrPXYP05AwcAADATCjgAAICZUMABAADMhAIOAABgJhRwAAAAM6GAAwAAmAkFHAAAwEwo4AAAAGZCAQcAADATCjgAAICZUMABAADMhAIOAABgJk447AQAAI51Z5xz/lFfc9Ezzz6ATIDROQMHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYsYgIAsAcWIAEOkjNwAAAAM+EMHABwTHLmDJgjZ+AAAABmQgEHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMKOAAAABmQgEHAAAwEwo4AACAmVDAAQAAzIQCDgAAYCZOOOwEAIB5OOOc84/6moueefYBZAJw7FLAAQAHRhEIsDcuoQQAAJgJBRwAAMBMuIQSAJgdl2ICxypn4AAAAGZCAQcAADATO7qEsqqOT/KUJGe11r5w2vYHSd629LJzWmtXVNUdkzw9yX8m+a8kj2ytXdU3bQAAgGPPTu+Bu3+S85PcZXlja+1btnjt05M8rLV2eVU9IsnDk/zCXpIEAABgh5dQttZe0Vp746bN76+qJ1XVL1bVNyRJVZ2c5OrW2uXTa16R5F790gUAADh27XoVytbaA5OkqirJT1fVvyT5xyRXLL3s8iSn7ilDAAAAknRYxKS11rK4vPKOSS5L8tFLXz41iyLueqrqkVV1YVVdeOmll+41BQAAgGNCr1UoPz/Jha21K5OcVFUbZ90ekOT1m1/cWjuvtXZWa+2sW9ziFp1SAAAAWG+rXkL5oY3/VNWPJfnIJCcneXNr7Q3Tl56Q5Jeq6v1Jrkzy2B6JAgAAHOtWKuBaa1+89P/HbfOav07ywD3mBQAAwCYe5A0AADATCjgAAICZUMABAADMhAIOAABgJhRwAAAAM6GAAwAAmIlVnwMHABygM845/6ivueiZZ+85zk5iAHD4nIEDAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZuKEw04AANbVGeecf8SvX/TMsw8oEwDWhTNwAAAAM6GAAwAAmAkFHAAAwEwo4AAAAGZCAQcAADATVqE8AEdbhSyxEhkAAHB0zsABAADMxNqegXPWCwAAWDdrW8Cto1EeCKs4BgCAw+ESSgAAgJlQwAEAAMyESyiPMS5/BACA+XIGDgAAYCYUcAAAADPhEsqjGGXlRwAAAGfgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMnHHYCsC7OOOf8o77momeefQCZAACwrpyBAwAAmAln4ABgE2fUARiVAo5DY4IEAACrUcBBjl5MKiQBABiBe+AAAABmQgEHAAAwEy6hhMG4nBMAgO0MWcBZ3AIAAOCGhizgAJgXf3gDgIOhgANgrSgmAVhnFjEBAACYCWfgmDV/aQcA4FjiDBwAAMBMOAMHcIzz6AoAmA9n4AAAAGZCAQcAADATLqEEYAgWJQKAo3MGDgAAYCYUcAAAADPhEkpgeC6tAwBYcAYOAABgJhRwAAAAM6GAAwAAmAn3wAH7yv1r+0fbAsCxxxk4AACAmVDAAQAAzIQCDgAAYCYUcAAAADOhgAMAAJgJBRwAAMBMeIwArCHLywMArCdn4AAAAGZCAQcAADATLqEEOARHu8zVJa4AwFacgQMAAJgJBRwAAMBMKOAAAABmYkcFXFUdX1U/WFW/t7TtPlV1flX9RlX92NG2AwAAsDc7PQN3/yTnZ1r0pKoqyblJvry19pVJ/quq7rvd9n3IGwAA4JizowKutfaK1toblzbdIcnfttaunD5/RZJ7HWE7AAAAe7Tbe+BOS3L50ueXT9u22349VfXIqrqwqi689NJLd5kCAADAsWW3z4G7LMmpS5+fOm3bbvv1tNbOS3Jekpx11lltlzkArORoz15LPH8NABjbbgu4tyX5tKq60XS55AOSvP4I2wHWgiIQADhMqxZwH0qS1to1VfXUJC+uqg8k+bckv99aa1tt75oxcCAUKgAA41mpgGutffHS/y9IcsEWr9lyOwAAAHvjQd4AAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYiRN2+41V9RdJ3jx9elWS/91aa1V1nyTfmeQDSS5prT1u72kCAACw6wIuyWWttW9Z3lBVleTcJF/cWruyqn6wqu7bWnvNnrIEAABgT5dQHldVT6mqX66q+0/b7pDkb1trV06fvyLJvfaUIQAAAEn2cAautfYFSVJVJyT5jar6+ySnJbl86WWXT9uup6oemeSRSXLrW996tykAAAAcU/a8iElr7eokr03yqUkuS3Lq0pdPnbZt/p7zWmtntdbOusUtbrHXFAAAAI4JvVahvGuSv0rytiSfVlU3mrY/IMnrO70HAADAMW0vq1A+L8l/J7lJkle01i6atj81yYur6gNJ/i3J73fIEwAA4Ji3l3vgvn6b7RckuWDXGQEAALAlD/IGAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATCjgAAAAZkIBBwAAMBMKOAAAgJlQwAEAAMyEAg4AAGAmFHAAAAAzoYADAACYCQUcAADATJywH0Gr6qFJvirJ1Une1Fr7kf14HwAAgGNJ9zNwVXVKkocl+bLW2pcn+fSqukPv9wEAADjW7McllHdL8prWWps+f2WSe+7D+wAAABxT6ro6q1PAqockuVFr7Vemz78gyee21p6x9JpHJnnk9OmZSf7hKGFvnuTfO6TXI84oMeQil4OOIZfxc1m3/ZHL+Lms2/7IZf9iyEUuBx1jbrncprV2ix1Faq11/UhyvyTftfT5VyR55B5jXtgptz3HGSWGXORyLO+PXI6N/ZHL+Lms2/7I5djYH7mMn8u67U/POK21fbmE8s1J7lNVNX3+ZUn+aB/eBwAA4JjSfRXK1toVVfX8JC+pqquzqDb/vvf7AAAAHGv25TECrbUXJXlRx5DnDRQXKSJMAAAgAElEQVRnlBi94shl/2L0ijNKjF5x5DJ2jF5x5LJ/MXrFGSVGrzhyGTtGrzhy2b8YveKMEqNXnJFySbIPi5gAAACwP/bjHjgAAAD2gQIOAABgJvblHrgequp2SU5LclmSt7ddXOvZI0bHXE5McmqSy1trVx1WHr302J8pzhBt2zGXPcWoqpskuctSjD9rrf3HqnlMsXr0uV5t2yVOD3vJpapumuQJSe48bfpwkj9P8iO7PU57tS5t2zPGFGekny3DtEsPxtD4uYwy5xhsHtYrlyHatpeR9mewXIaZz20Zf7R74KrqPkm+N8nfJHlvFgfyU5P8YGvtgoOK0TGXOyZ5RpKrklyR5KOTnJjkCa21vzng/fmEJD+U5GOXNr8nybmttUt2GGPP+zPFGaJtO+bSI8bXJnlYklcvxbhPkudPCwPtdH969Llebdsjlz332465/HqSn2+t/eHStnsneXhr7WEr5DLEWBysbUcazyP1/3U7zms1hgbMZYg5x2DzsF65DNG2U5wh+tyatu0w87kj6vEwuZ4fSX4nyY02bTs5ycsPMkbnXD5607ZTD2l/Xpnk9pu23SHJSw5yf0Zq25H63HR8atO245L85i72p0ef69W2e81lz/22Yy6/us32566YyxBjcbC2HW08j9L/1+04r9UYGjCXIeYcPWIMmsuht+1IfW5N23aY+dyRPka8B+4DrbUrlze01j6Y5P0HHKNXnPe31t67KcblSd53wHkkyX+11v5pU5x/TPJfK8TosT/JOG3bK5ceMa5q0yhfivHhJB9aIUbSp116tW2POD36ba9cPrKqPml5Q1XdPslHrJjLKGNxpLYdaTyP1P/X7Tiv2xgaLZdR5hwjzcN65TJK2ybj9Ll1bNuR5nPbGvEeuHdU1VOTvDzJ5VlU4F+e5F8POEavOB+oqm/MonJ/b1V99BRjlUHWa3+qqr6g3fDSlVohRo/9ScZp21659Ijxxqp6bhZ/ubk8i+umH5DkLSvESPq0S6+27RGnR7/tlcu3JXlaVX3c9P4tybuTPG7FXEYZiyO17UjjeaT+v27Hed3G0Gi5jDLnGGke1iuXUdo2GafPrWPbjjSf29Zw98AlSVV9QRbXip6axY1/r2utveagY/SIU1UnJPm6KcZHZ3EgX5fkea21HVfindrkpkken+RzsjiV++EkFyZ5VtvhjZW99meKNUTb9silY4xPSnLvpRivn/6qtkqMPbdLx37bI5c999teufQyylgcqW1HGs+D9f+1Os69jDKGBsxlpDnHEPOwjjFGatuR+txatW2vOD3mc0eMP2IBl1x7ScVpSf49u19xaM8xOuZyUqaD2Ha/sk6X/emhx/5McYZo24657ClGLVYtuutSjL2sQtmjz/Vq2y5xethLLnXdCnpnZfGXzhFW0FuLtu0ZY4oz0s+WYdqlB2No/FxGmXMMNg/rlcsQbdvLSPszWC7DzOe2jD9aAVdjrTjUezWzjRijrFrUklya3a9atKv9meIM0bYdc9mvVYvuneQFbferUO62z/Vq2x657Lnfdsxlv1bQO5SxOFjbjjSeR+r/63ac12oMDZjLEHOOweZh+7FSovlc3/0ZKZdh5nNH1DqthtLrI+OtOHToq5l13J8hVi0aqW1H6nOxCuW+9duOuazVCnqDte1o43mU/r9ux3mtxtCAuQwx5+gRY9BcDr1tR+pza9q2w8znjvRhFcr9jzPSyjqjrFqUjNO2vXIZadWiUVa+6xXHCnpbW7e2HWk8j9T/1+04r9sYGi2XUeYcI83D1nGlxFH63Dq27UjzuW1ZhXL/44y0sk7VGKsWJeO0ba9c9mPVolOTPDBWoezRb3vlsrGC3i2X3n/OK+iN1LYjjeeR+v+6Hed1G0Oj5TLKnGOkedg6rpQ4Sp9bx7YdaT63reHugUuut/rLxo1/r2u7X3Fo1zF6xKn+K+vspU2WVy3auHn8rTn8VSgPtW175NIxxsaqRaflupWPrEK5x37bK5deRhmLI7XtSON5sP6/Vse5l1HG0IC5jDTnGGIe1jHGSG07Up9bq7btFafHfO6I8Ucs4ABGVVVfn+SFrbVrlj7/1dba1YebGcyDMQSwNyPeA5eq+t1aLL+5/PkpBx2jYy7nVdVHLn3+C8sxDyqP6fu+v6pOXvr8Scuf7zDGnvdn+r4h2rZjLj1i/PmmGH8+/aVtJZ36XK+27ZHLnvttr1ySfEyuf/n5xyY5fhe5DDEWR2rbwcbzSP1/rY5z1mwMDZjLEHOOjvOWkXIZom2n7xuiz61p2w4zn9tW67QaSs+PJLfe9PltDiNGx1zudqTPD3h/virJcZs+P/6g92ektu2YS48YJx3p84Nsl45t2yOXPffbnvvU42OUsThS2w42nkfq/2t1nHt9jDKGBsxliDlHjxgD5jJE207fN0SfW9O2HWY+t93HsJdQVtXtct11o7t9aOOeY3TM5cQsbmK8vO3+4YRd9qeHHvszxRmibTvmsqcY019r7rIUYy8P8u7R53q1bZc4Pewll7ruIcR3njaN8BDitWjbnjGmOCP9bBmmXXowhsbPZZQ5x2DzsF65DNG2vYy0P4PlMsx8bsv4oxVwNdZDG3s/EPaKLG7OHOXBj0nynuz+wY+72p8pzhBt2zGX/Xrw432SPL/t/kHeu+1zvdq2Ry577rcdc9mvhxAnhzAWB2vbkcbzSP1/3Y7zWo2hAXMZYs4x2DxsPx42bT6XtW3bYeZzR9TzdF6Pj4z30MZDfyBsx/0Z4sGPI7XtSH0uHuS9b/22Yy5r9RDiwdp2tPE8Sv9ft+O8VmNowFyGmHP0iDFoLofetiP1uTVt22Hmc0f6GHERk5Ee2jjKA2HX7cGPyTht2yuXkR78OMrDg3vF8RDira1b2440nkfq/+t2nNdtDI2WyyhzjpHmYev4sOlR+tw6tu1I87lteZD3/scZ6eGEVWM8+DEZp2175bIfD348LckD4kHePfptr1w2HkL8cdP7t8z7IcQjte1I43mk/r9ux3ndxtBouYwy5xhpHraOD5sepc+tY9uONJ/b1nD3wCVJXfcAvVNz3cPvdvvQxl3H6BGn+j+ccC9tsvzgx+OyuHn8whz+g7wPtW175NIxxsaDHzdivL55kPee+22vXHoZZSyO1LYjjefB+v9aHedeRhlDA+Yy0pxjiHlYxxgjte1IfW6t2rZXnB7zuSPGH7GAS669pGLjCei7XXFozzE65nJSpoPYdr+yTpf96aHH/kxxhmjbjrnsKUYtVi2661KMvaxC2aPP9WrbLnF62Esudd0Kemdl8ZfOEVbQW4u27RljijPSz5Zh2qUHY2j8XEaZcww2D+uVyxBt28tI+zNYLsPM57aMP1oBV2OtONR7NbONGKOsWtSSXJrdr1q0q/2Z4gzRth1z2a9Vi+6d5AVt96tQ7rbP9WrbHrnsud92zGW/VtA7lLE4WNuONJ5H6v/rdpzXagwNmMsQc47B5mH7sVKi+Vzf/Rkpl2Hmc0fUOq2G0usj4604dOirmXXcnyFWLRqpbUfqc7EK5b712465rNUKeoO17WjjeZT+v27Hea3G0IC5DDHn6BFj0FwOvW1H6nNr2rbDzOeO9GEVyv2PM9LKOqOsWpSM07a9chlp1aJRVr7rFccKeltbt7YdaTyP1P/X7Tiv2xgaLZdR5hwjzcPWcaXEUfrcOrbtSPO5bVmFcv/jjLSyTtUYqxYl47Rtr1z2Y9WiU5M8MFah7NFve+WysYLeLZfef84r6I3UtiON55H6/7od53UbQ6PlMsqcY6R52DqulDhKn1vHth1pPret4e6BS663+svGjX+va7tfcWjXMXrEqf4r6+ylTZZXLdq4efytOfxVKA+1bXvk0jHGxqpFp+W6lY+sQrnHftsrl15GGYsjte1I43mw/r9Wx7mXUcbQgLmMNOcYYh7WMcZIbTtSn1urtu0Vp8d87ojxRyzgAEZVVV+f5IWttWuWPv/V1trVh5sZzIMxBLA3I94Dl6r63Vosv7n8+SkHHaNjLudV1Ucuff4LyzEPKo/p+76/qk5e+vxJy5/vMMae92f6viHatmMuPWL8+aYYfz79pW0lnfpcr7btkcue+22vXJJ8TK5/+fnHJjl+F7kMMRZHatvBxvNI/X+tjnPWbAwNmMsQc46O85aRchmibafvG6LPrWnbDjOf21brtBpKz48kt970+W0OI0bHXO52pM8PeH++Kslxmz4//qD3Z6S27ZhLjxgnHenzg2yXjm3bI5c999ue+9TjY5SxOFLbDjaeR+r/a3Wce32MMoYGzGWIOUePGAPmMkTbTt83RJ9b07YdZj633cewl1BW1e1y3XWju31o455jdMzlxCxuYry87f7hhF32p4ce+zPFGaJtO+aypxjTX2vushRjLw/y7tHnerVtlzg97CWXuu4hxHeeNo3wEOK1aNueMaY4I/1sGaZdejCGxs9llDnHYPOwXrkM0ba9jLQ/g+UyzHxuy/ijFXA11kMbez8Q9oosbs4c5cGPSfKe7P7Bj7vanynOEG3bMZf9evDjfZI8v+3+Qd677XO92rZHLnvutx1z2a+HECeHMBYHa9uRxvNI/X/djvNajaEBcxlizjHYPGw/HjZtPpe1bdth5nNH1PN0Xo+PjPfQxkN/IGzH/RniwY8jte1IfS4e5L1v/bZjLmv1EOLB2na08TxK/1+347xWY2jAXIaYc/SIMWguh962I/W5NW3bYeZzR/oYcRGTkR7aOMoDYdftwY/JOG3bK5eRHvw4ysODe8XxEOKtrVvbjjSeR+r/63ac120MjZbLKHOOkeZh6/iw6VH63Dq27UjzuW15kPf+xxnp4YRVYzz4MRmnbXvlsh8PfjwtyQPiQd49+m2vXDYeQvxx0/u3zPshxCO17UjjeaT+v27Hed3G0Gi5jDLnGGketo4Pmx6lz61j2440n9vWcPfAJUld9wC9U3Pdw+92+9DGXcfoEaf6P5xwL22y/ODH47K4efzCHP6DvA+1bXvk0jHGxoMfN2K8vnmQ9577ba9cehllLI7UtiON58H6/1od515GGUMD5jLSnGOIeVjHGCO17Uh9bq3atlecHvO5I8YfsYBLrr2kYuMJ6LtdcWjPMTrmclKmg9h2v7JOl/3pocf+THGGaNuOuewpRi1WLbrrUoy9rELZo8/1atsucXrYSy513Qp6Z2Xxl84RVtBbi7btGWOKM9LPlmHapQdjaPxcRplzDDYP65XLEG3by0j7M1guw8zntow/WgFXY6041Hs1s40Yo6xa1JJcmt2vWrSr/ZniDNG2HXPZr1WL7p3kBW33q1Duts/1atseuey533bMZb9W0DuUsThY2440nkfq/+t2nNdqDA2YyxBzjsHmYfuxUqL5XN/9GSmXYeZzR9Q6rYbS6yPjrTh06KuZddyfIVYtGqltR+pzsQrlvvXbjrms1Qp6g7XtaON5lP6/bsd5rcbQgLkMMefoEWPQXA69bUfqc2vatsPM5470YRXK/Y8z0so6o6xalIzTtr1yGWnVolFWvusVxwp6W1u3th1pPI/U/9ftOK/bGBotl1HmHCPNw9ZxpcRR+tw6tu1I87ltWYVy/+OMtLJO1RirFiXjtG2vXPZj1aJTkzwwVqHs0W975bKxgt4tl95/zivojdS2I43nkfr/uh3ndRtDo+UyypxjpHnYOq6UOEqfW8e2HWk+t63h7oFLrrf6y8aNf69ru19xaNcxesSprVfWuSCLp7HvZmWdvbTJ8qpFGzePvzV7X7Vo5f2ZYg3Rtj1y6RhjY9Wi03Ldykc9VqFcqV069tseuey53/bcpx5GGYsjte1I43mw/r9Wx7mXUcbQgLmMNOcYYh7WMcZIbTtSn1urtu0Vp8d87ojxRyzgAAAAuKER74EDAABgCwo4gBVV1YlV9bFVdeJh5wJzZAwB7N5aXkJZVZXk05P8XWvtqunGzjOSvHDzyjIHlM9Nktwl110H2/VhfrvI53ZLuRzqAyT3aqS2ndr1nku5/FHP650PWlUdn+QTs1gd7uLW2hWHnM+h99u6/rNqrsjiWv2Vn9U0mhHadspjmPHcS1XdtLX2vun/n5zk41trrz2kXA79OK/rGErGaN8pj7UZR+ZzR81niD7Xw0htO4f53LoWcM9JcnwWDzb8rSRnZvHL4jNaa48+4Fy2epjffbK4MbPPw/x2nkuXhxyOYrC2/e4kn5ZFf9tYcejLkvxla+3HDjKXHqrqfknul8VzZd6R5ObTlx7dWrvsgHMZpt9W1e8kedjycsdVdWqSX2qtPfAgc+lhsLYdZjz3UlVPymJC8g9Jfi3Jg7P4vdRaa+ccYB4jHee1GkPJcO27VuPIfG7bXIbpcz0M1razmM8N9xiBqvrZLAbrZtesMFhv1lp7WFXdLMkjW2s/MsX+xUPI5cFJvnD5ryJV9eNJfiPJjjplpzyS5DumXK79q1VVnTzlsaMBX1VPPEIuP7zTREZp2465fG5r7Ss2bXtpVf3aCnm8aos8KsmVrbUvWSFOj2P0la21b5rOwj2jtfaYqrp7kqcnedQB57Lnftsxly2fVVNVKz2rqaq+6gi5/PoOY6xb2w4znnv9nEvyKa21L66qT8jid9ETpvjP3WmANTzO6zaGkoF+t2aQOUfHecue53MdcxmibSdD9Lk1bdth5nNHMlwBl8WlFL+eZPOpymtWiHHV9O+Hknxwafuq9/x1yWXzKe3W2oerapUll3vkkWzzcMKqWuXhhPdI8sIk79pjLqO0ba9ctruPY5X7Oy5M8juttTev8D1b6XGMrkmS1to102UNaa29YZo0HXQuPfptt1yqz7Oavj3Jryb5z03bV/kZtW5tO9J47vVzbuP4vjPJ25e2X7XFa/czl5GO87qNoWSs362jzDl6zVt6zOe65TJI2ybj9Ll1bNuR5nPbGrGAe2ySr267eG7DkndW1U8lOTnJK6pq44zBqtdL98ilx8P8euSR9Hk44ddncfnLjv7CeQSjtG2vXH69qn43yWumXE7L4vkfz18hxg8kuf8ectjQ4xidXFX3yOL+lOVfCKv+EaRHLr0eztkjl2/J4lk1Pz1NPC9P8rosJpOr+JokD22t/fQeclm3th1pPPf6OXdlVT0+i3t23jJdmpwkNz7gXEY6zus2hpKxfreOMufoNW/pMZ/rlcsobZuM0+fWsW1Hms9tay3vgUuSqvr0JP80/UXii5LcOslzN//F4oBy2deH+a2Yy/LDCS9LckGHzn5oBmvbmyS521Iub27zvXH8Zkm+O8kHkvyfjXFTVbdvrf3TIeSzVv12JCO17UjjuYeqOimLe0nf0lp7d1U9JsnpWTxs971H/u7uuQxznNfRSO27huPIfG7rXIbpcz0M1rbDz+fWtoCDg1ZVN26t/ff0/+OTHNdaW+VSqS4x2D9VdWYWf1n7v0meleSZSU5Jck5r7fLDzA3mwBgCRjeH+dxwz4GrqgdU1R9X1W9W1SdU1W9X1R9W1Z1WiHHHqvr1qvqh6RKNje2r3AjcK5chYkxx7l5Vr6mqn6uqj6qqX62q36qq2x5kjF771LFdeuTyqCS/XVWvqqrPTHJ+klfXYqWoA4sxxelxnD++qp5ZVd9cVScsbf/uQ8ilV5/rEecZSb4nyb8k+c0kL0ny00lW/dlyh6r6tar6nlo8D+v/VNV5tViN78D2Z6S2HWw892qXPY+jdTvOWbMx1CtOx1yG+N3acTzveT432M+WtZrPrWnbDjOfO6LW2lAfWfxAPz6LpWL/MovrYG+S5AUrxHhZko9P8plZrGBz5rT9Fw4hlyFiTHFemuQjk9w9yZ8kuU2SWyZ53kHGGLBdeuTy4unfWyX5uyyu1z/hoGN0PM4vm77/AVlMsD5ml2NopD7XI5efX/r/a5f+/7Mr5vKyLO6L+posrvn/3CR3XKV917BtRxrPvdplu3F03jF8nNdqDA14jIb43dpxPO95PjfYz5a1ms+tadsOM5870seIi5j8e2vtmiT/UFX/3KZLKmq1JYbf01r71yT/WotnS/xULZYXXfV60R65jBIjSS5trX0gyRuq6orW2sVTnM2reO13jGSsdukR571J0lp7Z1X9VWvtg1OMVVaE6hEj6XOM3tNae8P0fa9P8pNV9f1ZfQyN1Od6xFleFvh3lv6/6tUMl7bWLkpyUVU9ok0rVVXV1SvGWKe2HWk892qX7cbRKtbtOK/bGOoVZ91+t/Yazz3mcyP9bFm3+dw6tu1I87ltjVjA3Wjp/7+w9P9VVu26dqnP1tqHqurRSZ6d5DMOIZdRYiTJSUv/X1516EabX7jPMTa//rDbpUec5dcuP+jxIw84RtLnGF37s6Etlvp+dJKfyeIvoQedS68+1yPOizf+01r78aXtq96702MSu25tO9J47tUuPcbRuh3ndRtDveKs2+/WXuO5x3xupJ8t6zafW8e2HWk+t71ep/J6fST5+G22P2yFGJ+4xbbK4ibpg85liBjT6z9zm+2PO8gYA7ZLj1xutM32+x1kjI7H+XO22HZykp87hFx69bkucZbbYzffN33vfbbZ/oxjtW0HG8+92mXP42jdjvPmttjN903fO8QYGvAYDfG7teN43vN8brCfLWs1n1vTth1mPnekj6FXoayqO7fW/uywY4yUS8f9uXVr7R2HHWOKM1K79Mjl7Nba+YcdY4oz0nFet1xe2Vr7sg65nNymyyv2EGPd2nak8TxSuwwRo2MuazWGesVZt9+tg43ndcxliD63pm07zHxus+FWodxk1XsF9itGrzijxEiSnxokRjJWu/SI86ijv+RAYiRjHed1y6XXz8+9Prg3Wb+2HWk8j9Quo8ToFWfdxlCvOOv2u3Wk8byOuYzS59axbUeaz13P6AVcDRKjV5xRYvSKI5exY/SKI5etvaJDjKTPz+F1a1u5jB2jV5x1G0O94qxbLuu2P73irFsu67Y/veL0yuV6Ri/gnjZIjF5xRomRJD8/SIxkrHZ5aocYjx0kRjLWcZ59LlV14tKnr62qL62q2+0xlx6T2Nm37SYj/dweqV1GibHrOGs+hnrFWbffrSON53XMZZQ+N1Lb9pjLJWPN565n6Hvgequqm7fW/n3F77l9kne31t5XVR+T5KTW2iV7yOFbWms/t4fv/6Qkn5rkr1a9Xrmqbtlae9f0/7sn+ewkf9Nau2CFGL2uKz45yf9K8sdtsTrbI7J4BsmPtdbeu0KcT0ty1yQfkeTiJK9u05PvV8zn3km+IIvnhlyW5PWttdesGmevqurGm/OvqjNba/9w0LlspaqOa619eMXvuXafqur4JMe11q7aQw4Pbq29ZLffP8U4JYtnSL1tlf5SVb/YWntEVd0/ydcleW2SOyd5Q2vtl1eI8zGttfesmPbmGJXk05P8XWvtqqkPn5Hkha21K1eIc1qSO2UaQ621v9xlPrdLcs8kp2Uxhv6otfaPu4m1H3bz83+/rDqOjKEt4wwzhqZYex5HxtDOrZpL77ncFPOYn8/1mstNsfY8nxtlLjflsq/zubUs4KrqoUm+Jcm/J/mR1tobp+3ntdYeuUKcJyX5xCx+mD4nySOyOGv50tbai3YY42dz3XLHleRuSd6Q5OrW2mN2GOMnW2vfXlVfkuQbsviFd9ckL2+tvWyF/dn4xfmNST4riyfD3yXJh1prT99hjL9M8v+y+IvnC1prH9rp+2+K8/wk/zeL5bTflOS/knwoyUNaaw/bYYzHJ/mPLAZqkrw/yVcl+bbW2l+vkMuPTu//yiyWsj41iwfvntRae+JO4+xVVX1XFj8Ir0ry3Nbab07bV+q3nXK5T5InJvnXJE9trf3ztP1nW2uPXiHOo5I8OIt9OjfJM7NYuvjprbU/2GGMJ+b6Y+j+SX4rizH0Iyvk8uTW2pOr6q5JfjDJW7P45fnDrbU/3mGMX2mtfUNV/VKSb96YhFfVc1trD18hl7dN739ea+21O/2+TTGek0W7tCza48ws2vkzdnqMqurrspiw3jKL59acmMXPhke06ZlAO4zz3Uk+bcpjYwx9WZK/bK392JG+t7deP/875bLncWQMbRtniDE0xdnzODKG9i+XHnO5KY753A1j7HkuN8XZ83xulLnclMu+z+eGew5cVb0q13++S7IYKFe21r5kh2G+NMnnZ/FMiKfUYnWdX8/q16Ge2Vp7aFV9VJK3JPmU1tqHq+p5SXY66G+a5FeS/NP0+Q9NH6s8bHTjkpMHJvnKtnhQ4c9MA2fHAz7XtevdWmuPmP7/e1V13gox3pjkW5M8KMnLq+pPk/zMqn9pyTRxqKobJXn4xg/RqnrgCjE+aeOXbFX9n9baD1fVS7N45sZDV4hzemvtq5c+/5ckb53ad0eq6s+z+AVzvc1Zrd9+ZmvtflO8/11Vj5t+ca/Ubzf9kll2zQqTkkdnMcn7qCTPmCZXf5TVf2bcq7V2n6q6VRa/qD4ri77/K0l2NPlMct8kP5vr2vdzMv3SXDGXj5n+/cYkX9Ja++/pbMbzkuxo8pnkhOkvjv+96pnITV6T5AlJvqWqHpfk15K8eBrbO3Wz1trDqupmSR65MRGvql9cIcbdW2uPmr7vR1trj6+qM5M8JcnDV4jzua21r9i07aVV9Ws7DdDpZ3/S4ed/pzGU9BlHxtDWRhlDSZ9xtOcxNL3/EHOokcZz+szlEvO5rfSYyyV95nN7nstN7z/MfO5IhivgklyY5Hdaa2/eQ4zL2+LU4geTPLGqvqOqviGLv7Kt4n1J0lr7j6r6s6VfNh9YIcbXZbEazkmttd+tqvet8pftTd6x6ZfTf64aoKo+Osl/VtWJ7bpLcE5ZIUSb2uElSV5Si9PVv1xVF7fWvmOFOBs/xG6a5OZL23f78OybTsm9u6pWvcTjJlV1Qmvt2h/CtbhP4yYrxHhekn9urf32iu+97NpT7a21Z1fVg6vq+7J6vz0xi1XZNl96s8rE5tK2WJb7g0m+sap+uClMsiUAAA7NSURBVKpuvItc3pskrbV3VtVfTTFTVe9fIcYDsvhr//Naa39RVV/dWnv9inkse3ebLmtorV1TVe9b4Xt/PItj/dfTJO9VSf5nkrevmENrrb0/ybOq6iey+Dnxe1V1fmvtJ3YYY2P8fiiL47RhlXublydYN5kS+4eq+o8VYiRLD9vd4fat9PjZn/T5+d9jDCV9xpExtLVRxlDSZxz1GEPJOHOokcZzj7lcYj63lR5zuc2v3+18rsdcLhlrPretEQu4H8jiL5Z7cb2/+rTWfqKqHp3F6cxVLA+uH1j6/44HyDRAn1xVD5n+Srib6vsuVfX7ueEvlY9YMc7TkvxcFqfMf6uq/iCLU+6/t0KM6+XfFpeuvLaq7rhiLn9WVb+TxaVFz6+qH8pi//7fCjHeOQ2Im2VxKc2GVX/5/ngWv/j/vyxOu5+W5H9khZtgW2s/WYvLivbiej+oWmsvqaovy+IG2FVOuT82yVe3vV33fb1JQ2vtiVX11CR3XzHO8g/l5cuAPnKnAVpr/5nkO6rqu6a/au/2B+DZ0zj66CTft7R9xz/cW2t/XlUPT/L/t3e+oZaVVRx+Vt7xX5I2KZaVJZZZISJSOPMhxqyklBihyJxRSioNjBIVEVMyzBgdtcCaVIw0MCvTzLQckYwo6Q/6QRIsETWVIjWLZrQmZ/Vh7+tc79wz3nPP2vuss87vgcOZe+ae333Wy373ft9z9n73sTSDvn1pTo+4d0iXF/tRe+C9xsy+TfNp82J50syuoLkx9I+tOdUOYJhrd7aY2VqaNpnb94Y9NnzfzG6n+VZktg8dBQzzyWfEvh9i9v8RfQhi+pH60MJk6UMQ048i+hDkGUNl6s8jj+Xav63x3PZEjOUgZjw38lgO0o3nBlLyGrhBmNkKb8+fHjHnMHe/bynvo/nK/JxRHUbBzN5Fc77+JuBeby+EXeR74y7ANHuVu/+r/fchwH7ARh9iozSz9wOb3P03AT4Hse3i8YdGPL0nDAu6qeuQf3NPd9/u0+P2k/sbhsjZxRdYDMDMjnb3O5bg9SHgOHf/9LDv3UHmXu7+7BC/vwfNNQuzF0n/fqG2epmMlUHb7CHAn939eTP7ILA/zWB4UQPQ9tPJT9D0oevnvP7qIU+jmW2XlWzrQ78dtl26JGr/P+TfHLkfqQ8NzEjRh9qMkH6kPrR4IlyWOpabfS8az83mjDyWa98bMp7LOpaD2PFcygmcBazEFJGRySW4niPZduAct8tIORH1VKQdCBzBtrZdygBp5IxqLu2n7CcCd9Cc2rac5lPy7/oQF8NHuARnjDSYrkiWbS5TRkROxT40J0f9aA4JxwoVXcY+nstUzzSRbgJnASsxRWRkcqlWTzaXagwYIL0PuG6xA6SIjKIutwCr536yaGavAH7g2y9A0LXLyAPhNmMtsHGpHhVJts2lyAh0KdWH5uSoH82h4lhBLrnrmTYyTuBuXGgnbmbXu/sJfWVkcqlWT0KXkVfLypLR5ow8QAocZFVzidrmUrRLYNuOvGpjREagS6ZtLkVGoEupPhTokmn7j8ioOFaQSwcZgS5RY6g047kdkXERk4iVmKJWc8riUq2ebC4Rq2VlyQDYMncgAeDNksnD3OMlIqOiyz1m9h1eep+Z42iWpu7bJUsGxKzaGLXyY0ROpm0uS0ZUTrU+FJWTafuPyhjm9S5z5JI7IyonagyVaTw3kIwTuIiVmKJWc8riUq2ebC4Rq2VlyYCYAVLUIKuUi7tfamYH0mxnB9Ocp3+hD3+efpZ2iWrbiFUbo1Z+jMhJs80lygjJKdiHonIybf8RGRXHCnLpJiMqJ2oMlWk8N5B0p1ACWMBKTBEZmVyq1ZPNpRpzBkiz7XL3sAOkiIyKLnOyjnH325by3iiXLBkVybTNZcmIzGmzSvShyJxKVBwryKWbjMicqcHd0z6AkzNkZHKpVo9cOq/nmAwZRV1+ksglRYZc8rskq6dUH5LLwIxSx2e5TEc92VzmP4a94XHfrE6SEZWTJSMqRy65MwBOeflf6SUjKieTy1Ju4roQWdolU9vKJXdGVE61PhSVU82l2vE5KqeaS7V6onKiXF5C9gmcEGI0IgZIUYOsai6nBWRAnnbJ1LZyyZ0RlVOtD0XlVHQRQgSS8hq4WcxsN3d/btwZmVyq1SOX7jLanDe5+6PjzqjiYmar3P1uM5sBzgYOB+4H1rn75j5dMmbIJb/LuOux5n5P33P3x0d1GNUlOkMuAzNKHZ/l0l2GXBZPum/gzOyAOT9+1MwuNbPj+87I5FKtHrl0l9HmrGqfZ4C1ZnaTmV1gZrv3mVHRBVjTPn8JeBA4AfgFcHnfLlkyirqcZWZvABhl8BqRkyUjMOdI4Dwzu8bMDh2nS6Z2qeZiZm8xs8vM7Ahgq5ldbWYbzeywvnPkkjtDLkukiwvrRnkA32yfz6D5hPsdwGeB8/vMyORSrR65dF7P1e3zhcBHgF2BVcCVfWYUdbm2fb5y3usbJrFdkrVtJpfbgSuBa4BDh/n70TlZMgJdNrTPrwfWAzcBR6tdarkAtwLvBs4EzgX2pVld8Id958gld4ZclvZI9w3cHN7q7uvc/QF33wC8cUwZmVyq1SOXbjJ2bp/3cfcb3f15d78b2NpzRkWXf7TfGjxuZq8FaL/Z2XcMLlkyKro86u6nAOcDJ7bf4h09pEdUTpaMqBwHcPcn3P1M4GTgcDO7YwwumdqlmssT7v47d18P3O/uf3P3p2mWh+87Ry65M+SyBDJO4F5pZvuxfZHDHHwjMjK5VKtHLt1lQMwkI2qiUs3lHOAk4DDgD2Z2K803CF8dg0uWjIouUZOMiJwsGVE5L9mfufuz7n4Rw9/0tlq7VHNZbmbLzGwlzb29MDMD9h4iIypHLrkz5LIEZqKCAtkCfAX497zXH+k5I5NLtXrk0l0GNJOMC4EDaCYZ9wHLgPN6zijn4s2FyGcAmNk+wGZ33zSkR4hLooyKLttNMoCLzGz9kC4ROVkyonI+v9CL7v7fMbhkapdqLhuAnwF3At8wsx/RnEL2rSEyonLkkjtDLksg9SqUQojRGHGSEZZR0SWKLO2SqW3H7WJmO7n7C6P87aicLBmRORFUa5eKLvMyZ4BdAvr0yDlyyZ0hl0VmagInhBBCCCGEEJNBxlMohRAiJWZ2L/DU/JeB/7j7sWNQEmKiUB8SQojRSTeBi9i5Rx0gsrhUq0cu3WXIpfOca4GH3f3Wxf7drlyyZMglv0umeijWh+TSXYZc8rtUqyeby45IN4EjZucecoBI5FKtHrl0lyGXDnPc/etmtmJEjxCXRBlyye+Spp6CfUgu3WXIJb9LtXqyuQzGg24oF/kAVmTIyORSrR65TEc9FV2iHlnaJVPbyiV3RmROFpdM7VLNpVo9cpmOerK5DHpoERMhhBgCMzsKeC+wnOaefb909zvHayXE5KA+JIQQo5FyAhexc486QGRxqVaPXLrLkEt3Oe19kDYDtwDPtDmrgZ3d/ew+XTJlyCW/S5Z6KvYhuXSXIZf8LtXqyeYyMD/bBC5i5x51gMjiUq0euUxHPUVdbnD34xd4/Tp3P6lnlxQZcsnvkqyeUn1ILtNRj1ymo55sLjuky/Mzl/IAbhjw+nV9ZmRyqVaPXKajnqIuPwVm5r22DLhpEtslWdvKJXFGoEupPiSX6ahHLtNRTzaXHT0yrkK5h5nNuPv/Zl8ws2XAHj1nZHKpVo9cusuQS7c5lwM/N7M/0nyq9hrgncCXx+CSJUMu+V0y1VOtD8mluwy55HepVk82l4FknMBF7NyjDhBZXKrVI5fuMuTSYY673wXcZWYHte9/GnjI3bf27ZIoQy75XdLUU7APyaW7DLnkd6lWTzaXgaS7Bm6WgJ17SEYml2r1yKW7DLl0nzMvc293n3/Tzl5csmTIJb9LpnoWyJzoPiSX7jLkkt+lWj3ZXBbMzjqBm89Sd+7RGZlcqtUjl+4y5BKTY2ZrgFOBp4CL3f2e9vWr3P0zfbpkz5BLfhf1oe4y5NJdhlzyu1SrJ5sLwCsiQiIxszVm9iszu9nMVsz5r4v6zMjkUq0euXSXIZfOcz4MvAf4OLDazD42G9+3S5YMueR3yVQPxfqQXLrLkEt+l2r1ZHPZEekmcMTs3EMOEIlcqtUjl+4y5NJtzjPe8Lw3SwG/zsw+CQx7KkOWdsnUtnLJnRGVU60PyaW7DLnkd6lWTzaXgWScwEXs3KMOEFlcqtUjl+4y5NJtzk5zf3D3rwG7Ah8Yg0uWDLnkd8lUT7U+JJfuMuSS36VaPdlcBpJxAhexc486QGRxqVaPXLrLkEuHOb7ANTruvoHmU7ZeXRJlyCW/S5p6CvYhuXSXIZf8LtXqyeYyGA+6oVzXD2BFhoxMLtXqkct01FPRJeqRpV0yta1ccmdE5mRxydQu1Vyq1SOX6agnm4u7T84qlEIIIYQQQggx7WQ8hVIIIYQQQgghxAJoAieEEEIIIYQQE8LMuAWEEEKIrjCzx4CNwFZgd2Cdu9//Mu+5zd2P6cNPCCGEGBZN4IQQQlTmT+7+KQAz2xO42czWuvuTO3jPLv2oCSGEEMOjCZwQQoipwN3/aWaXAmuAS8zsWGAVzc1VN7v7eWZ2FvA2M7sCWAfsBZwN/J1maejT3f2FsRQghBBCoGvghBBCTBePAPu3/36M5t48Dqw2s+XufgnwoLuf5u5/AS4GTnX304EHgNVjcBZCCCFeRN/ACSGEmCYOBh4xs52Bq4DV7v5XM3szzTVyz8z7/QOBL5oZwJ7Ar/tTFUIIIbZHEzghhBBTgZkdAHwBOI5mMvZYO3nbDVg551e3mtlO7amSDwMXuPtz/RsLIYQQ26MbeQshhCiLmT0O3Nn+uAm4zN0fbv9vPc0plLu3z2e5+xNmdi7wdppv6LYAZwBPAcuAz7n75n6rEEIIIbahCZwQQgghhBBCTAhaxEQIIYQQQgghJgRN4IQQQgghhBBiQtAETgghhBBCCCEmBE3ghBBCCCGEEGJC0AROCCGEEEIIISYETeCEEEIIIYQQYkLQBE4IIYQQQgghJgRN4IQQQgghhBBiQvg//818YmWHWYYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Open']]['2015':].resample('M').max().plot(kind='bar', figsize=(15,8))\n",
    "plt.title('Monthly Max Opening Price for Apple since 2015');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_daterange = \\\n",
    "pd.date_range(start=datetime(2018,9,1), \n",
    "              end=datetime(2019,1,24),\n",
    "              freq='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-09-03', '2018-09-04', '2018-09-05', '2018-09-06',\n",
       "               '2018-09-07', '2018-09-10', '2018-09-11', '2018-09-12',\n",
       "               '2018-09-13', '2018-09-14',\n",
       "               ...\n",
       "               '2019-01-11', '2019-01-14', '2019-01-15', '2019-01-16',\n",
       "               '2019-01-17', '2019-01-18', '2019-01-21', '2019-01-22',\n",
       "               '2019-01-23', '2019-01-24'],\n",
       "              dtype='datetime64[ns]', length=104, freq='B')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_daterange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_dataset = \\\n",
    "pd.DataFrame(\n",
    "    data = {'value': np.random.rand(len(daily_daterange))},\n",
    "    index=daily_daterange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-03</th>\n",
       "      <td>0.104522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-04</th>\n",
       "      <td>0.515069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-05</th>\n",
       "      <td>0.887659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-06</th>\n",
       "      <td>0.352635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-07</th>\n",
       "      <td>0.288954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               value\n",
       "2018-09-03  0.104522\n",
       "2018-09-04  0.515069\n",
       "2018-09-05  0.887659\n",
       "2018-09-06  0.352635\n",
       "2018-09-07  0.288954"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-03</th>\n",
       "      <td>0.104522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-10</th>\n",
       "      <td>0.288954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>0.196259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-24</th>\n",
       "      <td>0.116807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>0.319771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-08</th>\n",
       "      <td>0.318485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-15</th>\n",
       "      <td>0.078227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-22</th>\n",
       "      <td>0.150858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-29</th>\n",
       "      <td>0.092146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.595603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12</th>\n",
       "      <td>0.036903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-19</th>\n",
       "      <td>0.187757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-26</th>\n",
       "      <td>0.028984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-03</th>\n",
       "      <td>0.420747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-10</th>\n",
       "      <td>0.040747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-17</th>\n",
       "      <td>0.066793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-24</th>\n",
       "      <td>0.228188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>0.123885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0.203852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-14</th>\n",
       "      <td>0.185558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-21</th>\n",
       "      <td>0.057018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-28</th>\n",
       "      <td>0.144828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               value\n",
       "2018-09-03  0.104522\n",
       "2018-09-10  0.288954\n",
       "2018-09-17  0.196259\n",
       "2018-09-24  0.116807\n",
       "2018-10-01  0.319771\n",
       "2018-10-08  0.318485\n",
       "2018-10-15  0.078227\n",
       "2018-10-22  0.150858\n",
       "2018-10-29  0.092146\n",
       "2018-11-05  0.595603\n",
       "2018-11-12  0.036903\n",
       "2018-11-19  0.187757\n",
       "2018-11-26  0.028984\n",
       "2018-12-03  0.420747\n",
       "2018-12-10  0.040747\n",
       "2018-12-17  0.066793\n",
       "2018-12-24  0.228188\n",
       "2018-12-31  0.123885\n",
       "2019-01-07  0.203852\n",
       "2019-01-14  0.185558\n",
       "2019-01-21  0.057018\n",
       "2019-01-28  0.144828"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_dataset.resample('W-MON').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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     "execution_count": 24,
     "metadata": {},
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   "source": [
    "daily_dataset.resample('M').min()"
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  }
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