Time Resampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90a4a85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "822408a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>38.0061</td>\n",
       "      <td>6906098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>37.2781</td>\n",
       "      <td>11623796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>36.9748</td>\n",
       "      <td>7664340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>37.8848</td>\n",
       "      <td>9732554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>38.4961</td>\n",
       "      <td>13170548</td>\n",
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      ],
      "text/plain": [
       "              Close    Volume\n",
       "Date                         \n",
       "2015-01-02  38.0061   6906098\n",
       "2015-01-05  37.2781  11623796\n",
       "2015-01-06  36.9748   7664340\n",
       "2015-01-07  37.8848   9732554\n",
       "2015-01-08  38.4961  13170548"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/starbucks.csv\",index_col='Date',parse_dates=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c49a2539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1006, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "453e64a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1006 entries, 2015-01-02 to 2018-12-31\n",
      "Data columns (total 2 columns):\n",
      "Close     1006 non-null float64\n",
      "Volume    1006 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 23.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b262aafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-02', '2015-01-05', '2015-01-06', '2015-01-07',\n",
       "               '2015-01-08', '2015-01-09', '2015-01-12', '2015-01-13',\n",
       "               '2015-01-14', '2015-01-15',\n",
       "               ...\n",
       "               '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20',\n",
       "               '2018-12-21', '2018-12-24', '2018-12-26', '2018-12-27',\n",
       "               '2018-12-28', '2018-12-31'],\n",
       "              dtype='datetime64[ns]', name='Date', length=1006, freq=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1749fe2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>38.729470</td>\n",
       "      <td>1.336800e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>42.862432</td>\n",
       "      <td>7.908719e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>44.321836</td>\n",
       "      <td>8.905969e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>45.508914</td>\n",
       "      <td>8.540996e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>47.488650</td>\n",
       "      <td>6.723716e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>49.761627</td>\n",
       "      <td>6.856079e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>52.437959</td>\n",
       "      <td>8.087759e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>52.797976</td>\n",
       "      <td>1.064881e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>53.006186</td>\n",
       "      <td>8.935548e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>57.051373</td>\n",
       "      <td>8.893461e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>58.296355</td>\n",
       "      <td>7.077431e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>57.219514</td>\n",
       "      <td>7.952858e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>55.138963</td>\n",
       "      <td>1.383605e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>54.446810</td>\n",
       "      <td>1.119357e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>55.858968</td>\n",
       "      <td>8.203693e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>56.531200</td>\n",
       "      <td>9.407233e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>53.016571</td>\n",
       "      <td>7.849410e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>52.669300</td>\n",
       "      <td>8.495649e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>54.551905</td>\n",
       "      <td>1.018464e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>53.518291</td>\n",
       "      <td>7.797496e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>52.022771</td>\n",
       "      <td>9.178403e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>50.945319</td>\n",
       "      <td>7.073672e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>52.845452</td>\n",
       "      <td>1.128694e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>55.331981</td>\n",
       "      <td>7.898740e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>55.057330</td>\n",
       "      <td>9.438392e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>54.157374</td>\n",
       "      <td>1.032388e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>54.155570</td>\n",
       "      <td>9.806376e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>56.968300</td>\n",
       "      <td>9.546442e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
       "      <td>59.081968</td>\n",
       "      <td>7.295628e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>59.031950</td>\n",
       "      <td>7.745303e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>55.933905</td>\n",
       "      <td>1.105518e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>52.567952</td>\n",
       "      <td>1.024694e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>52.995195</td>\n",
       "      <td>9.225082e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>53.310614</td>\n",
       "      <td>8.129790e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>55.359462</td>\n",
       "      <td>1.025010e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>57.006955</td>\n",
       "      <td>8.758599e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>58.205433</td>\n",
       "      <td>1.155463e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>54.729784</td>\n",
       "      <td>1.255456e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>56.913367</td>\n",
       "      <td>8.852274e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>57.635371</td>\n",
       "      <td>8.503481e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31</th>\n",
       "      <td>56.482395</td>\n",
       "      <td>6.841657e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>53.663433</td>\n",
       "      <td>1.612197e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>50.105995</td>\n",
       "      <td>1.165892e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>52.279430</td>\n",
       "      <td>8.617108e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>55.234000</td>\n",
       "      <td>9.342414e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>57.035104</td>\n",
       "      <td>1.243318e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>66.363652</td>\n",
       "      <td>1.557092e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>64.609474</td>\n",
       "      <td>1.316915e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                Close        Volume\n",
       "Date                               \n",
       "2015-01-31  38.729470  1.336800e+07\n",
       "2015-02-28  42.862432  7.908719e+06\n",
       "2015-03-31  44.321836  8.905969e+06\n",
       "2015-04-30  45.508914  8.540996e+06\n",
       "2015-05-31  47.488650  6.723716e+06\n",
       "2015-06-30  49.761627  6.856079e+06\n",
       "2015-07-31  52.437959  8.087759e+06\n",
       "2015-08-31  52.797976  1.064881e+07\n",
       "2015-09-30  53.006186  8.935548e+06\n",
       "2015-10-31  57.051373  8.893461e+06\n",
       "2015-11-30  58.296355  7.077431e+06\n",
       "2015-12-31  57.219514  7.952858e+06\n",
       "2016-01-31  55.138963  1.383605e+07\n",
       "2016-02-29  54.446810  1.119357e+07\n",
       "2016-03-31  55.858968  8.203693e+06\n",
       "2016-04-30  56.531200  9.407233e+06\n",
       "2016-05-31  53.016571  7.849410e+06\n",
       "2016-06-30  52.669300  8.495649e+06\n",
       "2016-07-31  54.551905  1.018464e+07\n",
       "2016-08-31  53.518291  7.797496e+06\n",
       "2016-09-30  52.022771  9.178403e+06\n",
       "2016-10-31  50.945319  7.073672e+06\n",
       "2016-11-30  52.845452  1.128694e+07\n",
       "2016-12-31  55.331981  7.898740e+06\n",
       "2017-01-31  55.057330  9.438392e+06\n",
       "2017-02-28  54.157374  1.032388e+07\n",
       "2017-03-31  54.155570  9.806376e+06\n",
       "2017-04-30  56.968300  9.546442e+06\n",
       "2017-05-31  59.081968  7.295628e+06\n",
       "2017-06-30  59.031950  7.745303e+06\n",
       "2017-07-31  55.933905  1.105518e+07\n",
       "2017-08-31  52.567952  1.024694e+07\n",
       "2017-09-30  52.995195  9.225082e+06\n",
       "2017-10-31  53.310614  8.129790e+06\n",
       "2017-11-30  55.359462  1.025010e+07\n",
       "2017-12-31  57.006955  8.758599e+06\n",
       "2018-01-31  58.205433  1.155463e+07\n",
       "2018-02-28  54.729784  1.255456e+07\n",
       "2018-03-31  56.913367  8.852274e+06\n",
       "2018-04-30  57.635371  8.503481e+06\n",
       "2018-05-31  56.482395  6.841657e+06\n",
       "2018-06-30  53.663433  1.612197e+07\n",
       "2018-07-31  50.105995  1.165892e+07\n",
       "2018-08-31  52.279430  8.617108e+06\n",
       "2018-09-30  55.234000  9.342414e+06\n",
       "2018-10-31  57.035104  1.243318e+07\n",
       "2018-11-30  66.363652  1.557092e+07\n",
       "2018-12-31  64.609474  1.316915e+07"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8be05db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "      <th>2015-12-31</th>\n",
       "      <td>50.078100</td>\n",
       "      <td>8.649190e+06</td>\n",
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       "      <th>2016-12-31</th>\n",
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       "      <th>2018-12-31</th>\n",
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       "                Close        Volume\n",
       "Date                               \n",
       "2015-12-31  50.078100  8.649190e+06\n",
       "2016-12-31  53.891732  9.300633e+06\n",
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "644c802b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>59.8475</td>\n",
       "      <td>38107194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>58.0465</td>\n",
       "      <td>32820193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>62.4924</td>\n",
       "      <td>53454789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>68.3530</td>\n",
       "      <td>62091071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close    Volume\n",
       "Date                         \n",
       "2015-12-31  59.8475  38107194\n",
       "2016-12-31  58.0465  32820193\n",
       "2017-12-31  62.4924  53454789\n",
       "2018-12-31  68.3530  62091071"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5597fa9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>36.9748</td>\n",
       "      <td>2215418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>49.4458</td>\n",
       "      <td>3228848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>51.2345</td>\n",
       "      <td>3479177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>47.9470</td>\n",
       "      <td>4843941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close   Volume\n",
       "Date                        \n",
       "2015-12-31  36.9748  2215418\n",
       "2016-12-31  49.4458  3228848\n",
       "2017-12-31  51.2345  3479177\n",
       "2018-12-31  47.9470  4843941"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7eb5dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>12619.6811</td>\n",
       "      <td>2179595896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>13580.7164</td>\n",
       "      <td>2343759515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>13919.7849</td>\n",
       "      <td>2333315537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>14274.3712</td>\n",
       "      <td>2818435669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close      Volume\n",
       "Date                              \n",
       "2015-12-31  12619.6811  2179595896\n",
       "2016-12-31  13580.7164  2343759515\n",
       "2017-12-31  13919.7849  2333315537\n",
       "2018-12-31  14274.3712  2818435669"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a5e39bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>251</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>251</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Volume\n",
       "Date                     \n",
       "2015-12-31    252     252\n",
       "2016-12-31    252     252\n",
       "2017-12-31    251     251\n",
       "2018-12-31    251     251"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab394518",
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom function\n",
    "def first_entry(entry):\n",
    "    if len(entry):\n",
    "        return entry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f6beb70f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def last_entry(records):\n",
    "    if len(records):\n",
    "        return records[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "15bf398f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>38.0061</td>\n",
       "      <td>6906098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>55.0780</td>\n",
       "      <td>13521544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>53.1100</td>\n",
       "      <td>7809307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>56.3243</td>\n",
       "      <td>7215978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close    Volume\n",
       "Date                         \n",
       "2015-12-31  38.0061   6906098\n",
       "2016-12-31  55.0780  13521544\n",
       "2017-12-31  53.1100   7809307\n",
       "2018-12-31  56.3243   7215978"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').apply(first_entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "db345c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>56.7513</td>\n",
       "      <td>4960875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>53.2731</td>\n",
       "      <td>8344508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>56.1288</td>\n",
       "      <td>5365646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>64.4000</td>\n",
       "      <td>7690183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close   Volume\n",
       "Date                        \n",
       "2015-12-31  56.7513  4960875\n",
       "2016-12-31  53.2731  8344508\n",
       "2017-12-31  56.1288  5365646\n",
       "2018-12-31  64.4000  7690183"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').apply(last_entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3034bb2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].resample(rule='A').mean().plot.bar(title='Starbucks Yearly Average Closing Price');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "75364b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lWdu2bdd47rcGg+222y6vv/566uvr1xsu+vTpk2OOOaYcpq699toGN3NJ3ohYJ598cl566aXcf//9GTt2bIYMGZI//elP5ZVs69O1a9cccsghufjii/OhD30offv2XeO41fHm2WefbXTsmWeeybbbbrtBz/d2Oeigg9KrV69cddVVad++fR555JHceOONTT7Phr4/v//97/O73/0ukydPzvDhw8v7//KXv6xx/EsvvZQTTzwxu+66a5577rl84QtfWOPNdt7qk5/8ZL773e/mwQcfbNbnFHbq1CmvvfZann/++QaxsCiK1NfXZ9999y3vO+CAA3LAAQfk9ddfz29+85t897vfzejRo1NdXZ3jjjuuyc/91nkkb0S3HXbYobz/tddeaxSd12W77bbLPvvss95x6/oZf+v5/v73v69zzLbbbpvKyspGN7p583EA4O1lRSEAvEetKUQl/3fpZpcuXcr7Kioq1rhSq1QqpU2bNg3iQH19/QaFmLeaPn16g1Vwy5Yty89+9rMccMAB5ZtOdO/ePYsWLWpwV9SVK1fm7rvvbnCuwYMHJ3kj+m2I4cOHZ9q0aZk0aVI+//nPr/VS56222iqDBw/ORRddlJUrV2b+/PlNeo3nnHNOhg4dusaVWKv16dMnlZWVjWLb3//+99x7772NLo3eEBu6OrC5zjjjjMycOTNjxoxJdXV1jjnmmGadZ0Pen9V/1la/ptX++7//e43jTzvttDz99NOZPn16rr/++tx+++258sor1zuXs846K1tttVVGjhy5xrs/F0VRvqnPmqz+Pr31+/jTn/40L7300hq/j1tuuWV69+6dq6++OknyyCOPrHee63PggQcmSW6++eYG+3/yk5/ktdde2+jzN9fgwYPzy1/+Mk888cRaxwwZMiR//etf06lTp+yzzz6NtjXdAR0A2LSsKASA96hPfvKT2XHHHTN06NDstttuWbVqVR599NFcfvnlad++fc4888zy2D322CPTpk3LzTffnJ133jlt27bNHnvskSFDhmT69OkZOXJkjj766CxcuDD/9V//lc6dOze6O+/6bLnllhk4cGDOPvvsrFq1KpdeemmWLl1avvtwknzmM5/J17/+9Rx33HH56le/mldeeSXf+c53GoW9Aw44ICeeeGK+8Y1v5LnnnsuQIUNSUVGR3/72t2nXrl2+8pWvNHr+o48+Ou3atcvRRx+d5cuX56abbkqbNm3yxS9+MZWVlenXr186d+6c+vr6jBs3LlVVVQ1WhW2IQYMGZdCgQescs/XWW+c///M/c+GFF+bzn/98jj/++CxevDgXX3xx2rZtm7FjxzbpOZM3Pu+xW7dumTFjRg4++OBss8022XbbbVsstHzuc5/LmDFjcv/99+drX/ta2rRp06zzbMj7s9tuu2WXXXbJBRdckKIoss022+RnP/vZGi8F/8EPfpAbb7wxkyZNyu67757dd989o0aNyvnnn59+/frlE5/4xFqfp0ePHpk2bVo+85nPZK+99sqoUaOy9957J0n+8Ic/5IYbbkhRFDnyyCPX+PiBAwfmk5/8ZM4///wsXbo0/fr1y2OPPZaxY8dm7733zoknnpgk+d73vpd77703hx12WHbaaae88sor5RV0hxxyyAa9b+uy++675/jjj8/ll1+eLbfcMgcddFDmz5+fyy+/PFVVVY1Wz75dLrnkktx555058MADc+GFF2aPPfbICy+8kLvuuitnn312dtttt4wePTo//elPc+CBB+ass87KnnvumVWrVuXpp5/OrFmzcs4556R3796bZf4A8H4lFALAe9TXvva1zJgxI1deeWWeffbZrFixIp07d84hhxySMWPGNLjJwMUXX5xnn302X/ziF7Ns2bJ069YtTz75ZE4++eQsWrQo3/ve93LDDTdk5513zgUXXJC///3vDQLfhhg1alReeeWVnHHGGVm0aFF23333zJw5M/369SuP6dGjR2bMmJELL7wwRx99dDp37pyzzz47zz//fKPnmzx5cj72sY/l+uuvz+TJk1NZWZmPfOQjufDCC9c6h0MPPTQ///nPM3To0BxxxBGZPn16DjjggEyePDm33HJLlixZkm233Tb7779/fvjDHzb6/LmWMmbMmGy//fb5zne+k5tvvjmVlZUZMGBA6urq0rNnz2ad8/rrr89Xv/rVHH744VmxYkWGDx+eyZMnt8h8KysrM3To0Nx444057bTTWuSca9O6dev87Gc/y5lnnplTTz01rVq1yiGHHJJ77rknO+20U3nc448/njPOOCPDhw9vcEORyy67LHPnzs1nPvOZ/Pa3v83WW2+91ucaMmRIHn/88Vx++eX53ve+l4ULF2aLLbZIjx498qlPfWqNwXm1UqmU2267LbW1tZk0aVK++c1vZtttt82JJ56Yurq68orIvfbaK7NmzcrYsWNTX1+f9u3bp1evXrn99tvXG0031KRJk9K5c+dcf/31ufLKK7PXXnvllltuyac+9al1vv5NaYcddsivf/3rjB07NuPHj8/ixYuz3XbbZf/9988222yT5I0VvL/61a8yfvz4XHfddVmwYEEqKyuz00475ZBDDrGiEAA2g1Kx+hZ2AACwBitXrkz37t2z//7755Zbbtnc02EDPPDAA+nXr19+9KMf5YQTTtjc0wEA3iWsKAQAYI2ef/75PPHEE5k0aVKee+65XHDBBZt7SqzB7NmzM3fu3Hz84x9PZWVlfve732X8+PHp2bNnhg0btrmnBwC8iwiFAACs0cyZM3PyySenc+fOueaaa/Kxj31sc0+JNejYsWNmzZqViRMnZtmyZdl2220zePDgjBs3Lm3btt3c0wMA3kVcegwAAAAAZPPcBg0AAAAAeEcRCgEAAAAAoRAAAAAAeAfezGTVqlV55pln0qFDh5RKpc09HQAAAAB4VymKIsuWLUuXLl2yxRYbvk7wHRcKn3nmmXTt2nVzTwMAAAAA3tUWLlyYHXfccYPHv+NCYYcOHZK88UI6duy4mWcDAAAAAO8uS5cuTdeuXcudbUO940Lh6suNO3bsKBQCAAAAQDM19WP93MwEAAAAABAKAQAAAAChEAAAAACIUAgAAAAApImhsHv37imVSo22008/PUlSFEVqa2vTpUuXVFZWZsCAAZk/f/4mmTgAAAAA0HKaFArnzZuXZ599trzNnj07SXLMMcckSSZMmJArrrgiV111VebNm5eampoMHDgwy5Yta/mZAwAAAAAtpkmhcLvttktNTU15u+OOO7LLLrukf//+KYoiEydOzEUXXZRhw4alV69emTJlSl5++eVMnTp1U80fAAAAAGgBzf6MwpUrV+bGG2/MiBEjUiqVsmDBgtTX12fQoEHlMRUVFenfv38eeOCBtZ5nxYoVWbp0aYMNAAAAAHh7NTsU3nbbbXnhhRdy0kknJUnq6+uTJNXV1Q3GVVdXl4+tybhx41JVVVXeunbt2twpAQAAAADN1OxQeP3112fw4MHp0qVLg/2lUqnB10VRNNr3ZmPGjMmLL75Y3hYuXNjcKQEAAAAAzdSqOQ966qmncs8992T69OnlfTU1NUneWFnYuXPn8v5FixY1WmX4ZhUVFamoqGjONAAAAACAFtKsFYWTJk3K9ttvn8MOO6y8r0ePHqmpqSnfCTl543MM58yZk759+278TAEAAACATabJKwpXrVqVSZMmZfjw4WnV6v8eXiqVMnr06NTV1aVnz57p2bNn6urq0q5du5xwwgktOmkAAAAAoGU1ORTec889efrppzNixIhGx84777wsX748I0eOzJIlS9K7d+/MmjUrHTp0aJHJAgAAAACbRqkoimJzT+LNli5dmqqqqrz44ovp2LHj5p4OAAAAALyrNLevNfuuxwAAAADAe4dQCAAAAAAIhQAAAACAUAgAAAAApBl3PQYAAACA95PuF8xs8mOeHH/YJpjJpmVFIQAAAAAgFAIAAAAAQiEAAAAAEKEQAAAAAIhQCAAAAADEXY8BAAAA4B1hc99d2YpCAAAAAEAoBAAAAACEQgAAAAAgQiEAAAAAEKEQAAAAAIhQCAAAAABEKAQAAAAAIhQCAAAAABEKAQAAAIAIhQAAAABAhEIAAAAAIEIhAAAAABChEAAAAACIUAgAAAAARCgEAAAAACIUAgAAAAARCgEAAACACIUAAAAAQIRCAAAAACBCIQAAAAAQoRAAAAAAiFAIAAAAAEQoBAAAAAAiFAIAAAAAEQoBAAAAgAiFAAAAAECEQgAAAAAgQiEAAAAAEKEQAAAAAIhQCAAAAABEKAQAAAAAkrTa3BMAAADeW7pfMLPJj3ly/GGbYCYAQFNYUQgAAAAACIUAAAAAgFAIAAAAAEQoBAAAAAAiFAIAAAAAcddjAAB433A3YgBgXawoBAAAAACEQgAAAABAKAQAAAAAIhQCAAAAABEKAQAAAIA0IxT+4x//yOc+97l06tQp7dq1y1577ZWHH364fLwoitTW1qZLly6prKzMgAEDMn/+/BadNAAAAADQspoUCpcsWZJ+/fqldevWufPOO/OHP/whl19+ebbeeuvymAkTJuSKK67IVVddlXnz5qWmpiYDBw7MsmXLWnzyAAAAAEDLaNWUwZdeemm6du2aSZMmlfd17969/L+LosjEiRNz0UUXZdiwYUmSKVOmpLq6OlOnTs2pp57aMrMGAACAt0n3C2Y2+TFPjj9sE8wEYNNq0orC22+/Pfvss0+OOeaYbL/99tl7773z/e9/v3x8wYIFqa+vz6BBg8r7Kioq0r9//zzwwANrPOeKFSuydOnSBhsAAAAA8PZqUij829/+lmuvvTY9e/bM3XffndNOOy1nnHFGfvjDHyZJ6uvrkyTV1dUNHlddXV0+9lbjxo1LVVVVeevatWtzXgcAAAAAsBGadOnxqlWrss8++6Suri5Jsvfee2f+/Pm59tpr8/nPf748rlQqNXhcURSN9q02ZsyYnH322eWvly5dKhbCu5RLMgAAAODdq0krCjt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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].resample(rule='M').max().plot.bar(figsize=(16,6),title='Starbucks Monthly Max Closing Price',color=['#1f77b4']);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}