visulaization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "895dc0f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f146c276",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_url = ('https://raw.githubusercontent.com/m-mehdi/pandas_tutorials/main/weekly_stocks.csv')\n",
    "df = pd.read_csv(dataset_url, parse_dates=['Date'], index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2854c93",
   "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>MSFT</th>\n",
       "      <th>FB</th>\n",
       "      <th>AAPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-05-24</th>\n",
       "      <td>249.679993</td>\n",
       "      <td>328.730011</td>\n",
       "      <td>124.610001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>250.789993</td>\n",
       "      <td>330.350006</td>\n",
       "      <td>125.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-07</th>\n",
       "      <td>257.890015</td>\n",
       "      <td>331.260010</td>\n",
       "      <td>127.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14</th>\n",
       "      <td>259.429993</td>\n",
       "      <td>329.660004</td>\n",
       "      <td>130.460007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>265.019989</td>\n",
       "      <td>341.369995</td>\n",
       "      <td>133.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-28</th>\n",
       "      <td>277.649994</td>\n",
       "      <td>354.700012</td>\n",
       "      <td>139.960007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-05</th>\n",
       "      <td>277.940002</td>\n",
       "      <td>350.420013</td>\n",
       "      <td>145.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-12</th>\n",
       "      <td>280.750000</td>\n",
       "      <td>341.160004</td>\n",
       "      <td>146.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19</th>\n",
       "      <td>289.670013</td>\n",
       "      <td>369.790009</td>\n",
       "      <td>148.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-26</th>\n",
       "      <td>284.910004</td>\n",
       "      <td>356.299988</td>\n",
       "      <td>145.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-02</th>\n",
       "      <td>289.459991</td>\n",
       "      <td>363.510010</td>\n",
       "      <td>146.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-09</th>\n",
       "      <td>292.850006</td>\n",
       "      <td>363.179993</td>\n",
       "      <td>149.100006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16</th>\n",
       "      <td>304.359985</td>\n",
       "      <td>359.369995</td>\n",
       "      <td>148.190002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-23</th>\n",
       "      <td>299.720001</td>\n",
       "      <td>372.630005</td>\n",
       "      <td>148.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-30</th>\n",
       "      <td>301.140015</td>\n",
       "      <td>376.260010</td>\n",
       "      <td>154.300003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>295.709991</td>\n",
       "      <td>378.690002</td>\n",
       "      <td>148.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-13</th>\n",
       "      <td>299.869995</td>\n",
       "      <td>364.720001</td>\n",
       "      <td>146.059998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>299.350006</td>\n",
       "      <td>352.959991</td>\n",
       "      <td>146.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-27</th>\n",
       "      <td>289.100006</td>\n",
       "      <td>343.010010</td>\n",
       "      <td>142.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-04</th>\n",
       "      <td>294.850006</td>\n",
       "      <td>330.049988</td>\n",
       "      <td>142.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>304.209991</td>\n",
       "      <td>324.760010</td>\n",
       "      <td>144.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>309.160004</td>\n",
       "      <td>324.609985</td>\n",
       "      <td>148.690002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-25</th>\n",
       "      <td>331.619995</td>\n",
       "      <td>323.570007</td>\n",
       "      <td>149.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>336.059998</td>\n",
       "      <td>341.130005</td>\n",
       "      <td>151.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-08</th>\n",
       "      <td>336.720001</td>\n",
       "      <td>340.890015</td>\n",
       "      <td>149.990005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>343.109985</td>\n",
       "      <td>345.299988</td>\n",
       "      <td>160.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22</th>\n",
       "      <td>329.679993</td>\n",
       "      <td>333.119995</td>\n",
       "      <td>156.809998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-29</th>\n",
       "      <td>323.010010</td>\n",
       "      <td>306.839996</td>\n",
       "      <td>161.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <td>342.540009</td>\n",
       "      <td>329.750000</td>\n",
       "      <td>179.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-13</th>\n",
       "      <td>323.799988</td>\n",
       "      <td>333.790009</td>\n",
       "      <td>171.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-20</th>\n",
       "      <td>334.690002</td>\n",
       "      <td>335.239990</td>\n",
       "      <td>176.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>336.320007</td>\n",
       "      <td>336.350006</td>\n",
       "      <td>177.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>314.040009</td>\n",
       "      <td>331.790009</td>\n",
       "      <td>172.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-10</th>\n",
       "      <td>310.200012</td>\n",
       "      <td>331.899994</td>\n",
       "      <td>173.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-17</th>\n",
       "      <td>296.029999</td>\n",
       "      <td>303.170013</td>\n",
       "      <td>162.410004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-24</th>\n",
       "      <td>308.260010</td>\n",
       "      <td>301.709991</td>\n",
       "      <td>170.330002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-31</th>\n",
       "      <td>305.940002</td>\n",
       "      <td>237.089996</td>\n",
       "      <td>172.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-07</th>\n",
       "      <td>295.040009</td>\n",
       "      <td>219.550003</td>\n",
       "      <td>168.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-14</th>\n",
       "      <td>287.929993</td>\n",
       "      <td>206.160004</td>\n",
       "      <td>167.300003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-21</th>\n",
       "      <td>297.309998</td>\n",
       "      <td>210.479996</td>\n",
       "      <td>164.850006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>289.859985</td>\n",
       "      <td>200.059998</td>\n",
       "      <td>163.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-07</th>\n",
       "      <td>280.070007</td>\n",
       "      <td>187.610001</td>\n",
       "      <td>154.729996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-14</th>\n",
       "      <td>300.429993</td>\n",
       "      <td>216.490005</td>\n",
       "      <td>163.979996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-21</th>\n",
       "      <td>303.679993</td>\n",
       "      <td>221.820007</td>\n",
       "      <td>174.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-28</th>\n",
       "      <td>309.420013</td>\n",
       "      <td>224.850006</td>\n",
       "      <td>174.309998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-04</th>\n",
       "      <td>296.970001</td>\n",
       "      <td>222.330002</td>\n",
       "      <td>170.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-11</th>\n",
       "      <td>279.829987</td>\n",
       "      <td>210.179993</td>\n",
       "      <td>165.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-18</th>\n",
       "      <td>274.029999</td>\n",
       "      <td>184.110001</td>\n",
       "      <td>161.789993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-25</th>\n",
       "      <td>277.519989</td>\n",
       "      <td>200.470001</td>\n",
       "      <td>157.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-02</th>\n",
       "      <td>274.730011</td>\n",
       "      <td>203.770004</td>\n",
       "      <td>157.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-09</th>\n",
       "      <td>261.119995</td>\n",
       "      <td>198.619995</td>\n",
       "      <td>147.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-16</th>\n",
       "      <td>252.559998</td>\n",
       "      <td>193.539993</td>\n",
       "      <td>137.589996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  MSFT          FB        AAPL\n",
       "Date                                          \n",
       "2021-05-24  249.679993  328.730011  124.610001\n",
       "2021-05-31  250.789993  330.350006  125.889999\n",
       "2021-06-07  257.890015  331.260010  127.349998\n",
       "2021-06-14  259.429993  329.660004  130.460007\n",
       "2021-06-21  265.019989  341.369995  133.110001\n",
       "2021-06-28  277.649994  354.700012  139.960007\n",
       "2021-07-05  277.940002  350.420013  145.110001\n",
       "2021-07-12  280.750000  341.160004  146.389999\n",
       "2021-07-19  289.670013  369.790009  148.559998\n",
       "2021-07-26  284.910004  356.299988  145.860001\n",
       "2021-08-02  289.459991  363.510010  146.139999\n",
       "2021-08-09  292.850006  363.179993  149.100006\n",
       "2021-08-16  304.359985  359.369995  148.190002\n",
       "2021-08-23  299.720001  372.630005  148.600006\n",
       "2021-08-30  301.140015  376.260010  154.300003\n",
       "2021-09-06  295.709991  378.690002  148.970001\n",
       "2021-09-13  299.869995  364.720001  146.059998\n",
       "2021-09-20  299.350006  352.959991  146.919998\n",
       "2021-09-27  289.100006  343.010010  142.649994\n",
       "2021-10-04  294.850006  330.049988  142.899994\n",
       "2021-10-11  304.209991  324.760010  144.839996\n",
       "2021-10-18  309.160004  324.609985  148.690002\n",
       "2021-10-25  331.619995  323.570007  149.800003\n",
       "2021-11-01  336.059998  341.130005  151.279999\n",
       "2021-11-08  336.720001  340.890015  149.990005\n",
       "2021-11-15  343.109985  345.299988  160.550003\n",
       "2021-11-22  329.679993  333.119995  156.809998\n",
       "2021-11-29  323.010010  306.839996  161.839996\n",
       "2021-12-06  342.540009  329.750000  179.449997\n",
       "2021-12-13  323.799988  333.790009  171.139999\n",
       "2021-12-20  334.690002  335.239990  176.279999\n",
       "2021-12-27  336.320007  336.350006  177.570007\n",
       "2022-01-03  314.040009  331.790009  172.169998\n",
       "2022-01-10  310.200012  331.899994  173.070007\n",
       "2022-01-17  296.029999  303.170013  162.410004\n",
       "2022-01-24  308.260010  301.709991  170.330002\n",
       "2022-01-31  305.940002  237.089996  172.389999\n",
       "2022-02-07  295.040009  219.550003  168.639999\n",
       "2022-02-14  287.929993  206.160004  167.300003\n",
       "2022-02-21  297.309998  210.479996  164.850006\n",
       "2022-02-28  289.859985  200.059998  163.169998\n",
       "2022-03-07  280.070007  187.610001  154.729996\n",
       "2022-03-14  300.429993  216.490005  163.979996\n",
       "2022-03-21  303.679993  221.820007  174.720001\n",
       "2022-03-28  309.420013  224.850006  174.309998\n",
       "2022-04-04  296.970001  222.330002  170.089996\n",
       "2022-04-11  279.829987  210.179993  165.289993\n",
       "2022-04-18  274.029999  184.110001  161.789993\n",
       "2022-04-25  277.519989  200.470001  157.649994\n",
       "2022-05-02  274.730011  203.770004  157.279999\n",
       "2022-05-09  261.119995  198.619995  147.110001\n",
       "2022-05-16  252.559998  193.539993  137.589996"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c8a3df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y='MSFT', figsize=(9,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca5fb4ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(y=['FB', 'AAPL', 'MSFT'], figsize=(10,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c139299c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Facebook Stock'}, xlabel='Date', ylabel='USD'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y='FB', figsize=(10,6), title='Facebook Stock', ylabel='USD')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96404e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  MSFT          FB        AAPL\n",
      "Date                                          \n",
      "2022-03-31  298.400002  212.692505  166.934998\n",
      "2022-04-30  282.087494  204.272499  163.704994\n",
      "2022-05-31  262.803335  198.643331  147.326665\n"
     ]
    }
   ],
   "source": [
    "df_3Months = df.resample(rule='M').mean()[-3:]\n",
    "print(df_3Months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb238640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method resample in module pandas.core.frame:\n",
      "\n",
      "resample(rule, axis=0, closed: 'str | None' = None, label: 'str | None' = None, convention: 'str' = 'start', kind: 'str | None' = None, loffset=None, base: 'int | None' = None, on=None, level=None, origin: 'str | TimestampConvertibleTypes' = 'start_day', offset: 'TimedeltaConvertibleTypes | None' = None) -> 'Resampler' method of pandas.core.frame.DataFrame instance\n",
      "    Resample time-series data.\n",
      "    \n",
      "    Convenience method for frequency conversion and resampling of time series.\n",
      "    The object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`,\n",
      "    or `TimedeltaIndex`), or the caller must pass the label of a datetime-like\n",
      "    series/index to the ``on``/``level`` keyword parameter.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    rule : DateOffset, Timedelta or str\n",
      "        The offset string or object representing target conversion.\n",
      "    axis : {0 or 'index', 1 or 'columns'}, default 0\n",
      "        Which axis to use for up- or down-sampling. For `Series` this\n",
      "        will default to 0, i.e. along the rows. Must be\n",
      "        `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.\n",
      "    closed : {'right', 'left'}, default None\n",
      "        Which side of bin interval is closed. The default is 'left'\n",
      "        for all frequency offsets except for 'M', 'A', 'Q', 'BM',\n",
      "        'BA', 'BQ', and 'W' which all have a default of 'right'.\n",
      "    label : {'right', 'left'}, default None\n",
      "        Which bin edge label to label bucket with. The default is 'left'\n",
      "        for all frequency offsets except for 'M', 'A', 'Q', 'BM',\n",
      "        'BA', 'BQ', and 'W' which all have a default of 'right'.\n",
      "    convention : {'start', 'end', 's', 'e'}, default 'start'\n",
      "        For `PeriodIndex` only, controls whether to use the start or\n",
      "        end of `rule`.\n",
      "    kind : {'timestamp', 'period'}, optional, default None\n",
      "        Pass 'timestamp' to convert the resulting index to a\n",
      "        `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.\n",
      "        By default the input representation is retained.\n",
      "    loffset : timedelta, default None\n",
      "        Adjust the resampled time labels.\n",
      "    \n",
      "        .. deprecated:: 1.1.0\n",
      "            You should add the loffset to the `df.index` after the resample.\n",
      "            See below.\n",
      "    \n",
      "    base : int, default 0\n",
      "        For frequencies that evenly subdivide 1 day, the \"origin\" of the\n",
      "        aggregated intervals. For example, for '5min' frequency, base could\n",
      "        range from 0 through 4. Defaults to 0.\n",
      "    \n",
      "        .. deprecated:: 1.1.0\n",
      "            The new arguments that you should use are 'offset' or 'origin'.\n",
      "    \n",
      "    on : str, optional\n",
      "        For a DataFrame, column to use instead of index for resampling.\n",
      "        Column must be datetime-like.\n",
      "    level : str or int, optional\n",
      "        For a MultiIndex, level (name or number) to use for\n",
      "        resampling. `level` must be datetime-like.\n",
      "    origin : {'epoch', 'start', 'start_day', 'end', 'end_day'}, Timestamp\n",
      "        or str, default 'start_day'\n",
      "        The timestamp on which to adjust the grouping. The timezone of origin\n",
      "        must match the timezone of the index.\n",
      "        If a timestamp is not used, these values are also supported:\n",
      "    \n",
      "        - 'epoch': `origin` is 1970-01-01\n",
      "        - 'start': `origin` is the first value of the timeseries\n",
      "        - 'start_day': `origin` is the first day at midnight of the timeseries\n",
      "    \n",
      "        .. versionadded:: 1.1.0\n",
      "    \n",
      "        - 'end': `origin` is the last value of the timeseries\n",
      "        - 'end_day': `origin` is the ceiling midnight of the last day\n",
      "    \n",
      "        .. versionadded:: 1.3.0\n",
      "    \n",
      "    offset : Timedelta or str, default is None\n",
      "        An offset timedelta added to the origin.\n",
      "    \n",
      "        .. versionadded:: 1.1.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    pandas.core.Resampler\n",
      "        :class:`~pandas.core.Resampler` object.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    Series.resample : Resample a Series.\n",
      "    DataFrame.resample : Resample a DataFrame.\n",
      "    groupby : Group DataFrame by mapping, function, label, or list of labels.\n",
      "    asfreq : Reindex a DataFrame with the given frequency without grouping.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    See the `user guide\n",
      "    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`__\n",
      "    for more.\n",
      "    \n",
      "    To learn more about the offset strings, please see `this link\n",
      "    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Start by creating a series with 9 one minute timestamps.\n",
      "    \n",
      "    >>> index = pd.date_range('1/1/2000', periods=9, freq='T')\n",
      "    >>> series = pd.Series(range(9), index=index)\n",
      "    >>> series\n",
      "    2000-01-01 00:00:00    0\n",
      "    2000-01-01 00:01:00    1\n",
      "    2000-01-01 00:02:00    2\n",
      "    2000-01-01 00:03:00    3\n",
      "    2000-01-01 00:04:00    4\n",
      "    2000-01-01 00:05:00    5\n",
      "    2000-01-01 00:06:00    6\n",
      "    2000-01-01 00:07:00    7\n",
      "    2000-01-01 00:08:00    8\n",
      "    Freq: T, dtype: int64\n",
      "    \n",
      "    Downsample the series into 3 minute bins and sum the values\n",
      "    of the timestamps falling into a bin.\n",
      "    \n",
      "    >>> series.resample('3T').sum()\n",
      "    2000-01-01 00:00:00     3\n",
      "    2000-01-01 00:03:00    12\n",
      "    2000-01-01 00:06:00    21\n",
      "    Freq: 3T, dtype: int64\n",
      "    \n",
      "    Downsample the series into 3 minute bins as above, but label each\n",
      "    bin using the right edge instead of the left. Please note that the\n",
      "    value in the bucket used as the label is not included in the bucket,\n",
      "    which it labels. For example, in the original series the\n",
      "    bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed\n",
      "    value in the resampled bucket with the label ``2000-01-01 00:03:00``\n",
      "    does not include 3 (if it did, the summed value would be 6, not 3).\n",
      "    To include this value close the right side of the bin interval as\n",
      "    illustrated in the example below this one.\n",
      "    \n",
      "    >>> series.resample('3T', label='right').sum()\n",
      "    2000-01-01 00:03:00     3\n",
      "    2000-01-01 00:06:00    12\n",
      "    2000-01-01 00:09:00    21\n",
      "    Freq: 3T, dtype: int64\n",
      "    \n",
      "    Downsample the series into 3 minute bins as above, but close the right\n",
      "    side of the bin interval.\n",
      "    \n",
      "    >>> series.resample('3T', label='right', closed='right').sum()\n",
      "    2000-01-01 00:00:00     0\n",
      "    2000-01-01 00:03:00     6\n",
      "    2000-01-01 00:06:00    15\n",
      "    2000-01-01 00:09:00    15\n",
      "    Freq: 3T, dtype: int64\n",
      "    \n",
      "    Upsample the series into 30 second bins.\n",
      "    \n",
      "    >>> series.resample('30S').asfreq()[0:5]   # Select first 5 rows\n",
      "    2000-01-01 00:00:00   0.0\n",
      "    2000-01-01 00:00:30   NaN\n",
      "    2000-01-01 00:01:00   1.0\n",
      "    2000-01-01 00:01:30   NaN\n",
      "    2000-01-01 00:02:00   2.0\n",
      "    Freq: 30S, dtype: float64\n",
      "    \n",
      "    Upsample the series into 30 second bins and fill the ``NaN``\n",
      "    values using the ``pad`` method.\n",
      "    \n",
      "    >>> series.resample('30S').pad()[0:5]\n",
      "    2000-01-01 00:00:00    0\n",
      "    2000-01-01 00:00:30    0\n",
      "    2000-01-01 00:01:00    1\n",
      "    2000-01-01 00:01:30    1\n",
      "    2000-01-01 00:02:00    2\n",
      "    Freq: 30S, dtype: int64\n",
      "    \n",
      "    Upsample the series into 30 second bins and fill the\n",
      "    ``NaN`` values using the ``bfill`` method.\n",
      "    \n",
      "    >>> series.resample('30S').bfill()[0:5]\n",
      "    2000-01-01 00:00:00    0\n",
      "    2000-01-01 00:00:30    1\n",
      "    2000-01-01 00:01:00    1\n",
      "    2000-01-01 00:01:30    2\n",
      "    2000-01-01 00:02:00    2\n",
      "    Freq: 30S, dtype: int64\n",
      "    \n",
      "    Pass a custom function via ``apply``\n",
      "    \n",
      "    >>> def custom_resampler(arraylike):\n",
      "    ...     return np.sum(arraylike) + 5\n",
      "    ...\n",
      "    >>> series.resample('3T').apply(custom_resampler)\n",
      "    2000-01-01 00:00:00     8\n",
      "    2000-01-01 00:03:00    17\n",
      "    2000-01-01 00:06:00    26\n",
      "    Freq: 3T, dtype: int64\n",
      "    \n",
      "    For a Series with a PeriodIndex, the keyword `convention` can be\n",
      "    used to control whether to use the start or end of `rule`.\n",
      "    \n",
      "    Resample a year by quarter using 'start' `convention`. Values are\n",
      "    assigned to the first quarter of the period.\n",
      "    \n",
      "    >>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',\n",
      "    ...                                             freq='A',\n",
      "    ...                                             periods=2))\n",
      "    >>> s\n",
      "    2012    1\n",
      "    2013    2\n",
      "    Freq: A-DEC, dtype: int64\n",
      "    >>> s.resample('Q', convention='start').asfreq()\n",
      "    2012Q1    1.0\n",
      "    2012Q2    NaN\n",
      "    2012Q3    NaN\n",
      "    2012Q4    NaN\n",
      "    2013Q1    2.0\n",
      "    2013Q2    NaN\n",
      "    2013Q3    NaN\n",
      "    2013Q4    NaN\n",
      "    Freq: Q-DEC, dtype: float64\n",
      "    \n",
      "    Resample quarters by month using 'end' `convention`. Values are\n",
      "    assigned to the last month of the period.\n",
      "    \n",
      "    >>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',\n",
      "    ...                                                   freq='Q',\n",
      "    ...                                                   periods=4))\n",
      "    >>> q\n",
      "    2018Q1    1\n",
      "    2018Q2    2\n",
      "    2018Q3    3\n",
      "    2018Q4    4\n",
      "    Freq: Q-DEC, dtype: int64\n",
      "    >>> q.resample('M', convention='end').asfreq()\n",
      "    2018-03    1.0\n",
      "    2018-04    NaN\n",
      "    2018-05    NaN\n",
      "    2018-06    2.0\n",
      "    2018-07    NaN\n",
      "    2018-08    NaN\n",
      "    2018-09    3.0\n",
      "    2018-10    NaN\n",
      "    2018-11    NaN\n",
      "    2018-12    4.0\n",
      "    Freq: M, dtype: float64\n",
      "    \n",
      "    For DataFrame objects, the keyword `on` can be used to specify the\n",
      "    column instead of the index for resampling.\n",
      "    \n",
      "    >>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],\n",
      "    ...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}\n",
      "    >>> df = pd.DataFrame(d)\n",
      "    >>> df['week_starting'] = pd.date_range('01/01/2018',\n",
      "    ...                                     periods=8,\n",
      "    ...                                     freq='W')\n",
      "    >>> df\n",
      "       price  volume week_starting\n",
      "    0     10      50    2018-01-07\n",
      "    1     11      60    2018-01-14\n",
      "    2      9      40    2018-01-21\n",
      "    3     13     100    2018-01-28\n",
      "    4     14      50    2018-02-04\n",
      "    5     18     100    2018-02-11\n",
      "    6     17      40    2018-02-18\n",
      "    7     19      50    2018-02-25\n",
      "    >>> df.resample('M', on='week_starting').mean()\n",
      "                   price  volume\n",
      "    week_starting\n",
      "    2018-01-31     10.75    62.5\n",
      "    2018-02-28     17.00    60.0\n",
      "    \n",
      "    For a DataFrame with MultiIndex, the keyword `level` can be used to\n",
      "    specify on which level the resampling needs to take place.\n",
      "    \n",
      "    >>> days = pd.date_range('1/1/2000', periods=4, freq='D')\n",
      "    >>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],\n",
      "    ...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}\n",
      "    >>> df2 = pd.DataFrame(\n",
      "    ...     d2,\n",
      "    ...     index=pd.MultiIndex.from_product(\n",
      "    ...         [days, ['morning', 'afternoon']]\n",
      "    ...     )\n",
      "    ... )\n",
      "    >>> df2\n",
      "                          price  volume\n",
      "    2000-01-01 morning       10      50\n",
      "               afternoon     11      60\n",
      "    2000-01-02 morning        9      40\n",
      "               afternoon     13     100\n",
      "    2000-01-03 morning       14      50\n",
      "               afternoon     18     100\n",
      "    2000-01-04 morning       17      40\n",
      "               afternoon     19      50\n",
      "    >>> df2.resample('D', level=0).sum()\n",
      "                price  volume\n",
      "    2000-01-01     21     110\n",
      "    2000-01-02     22     140\n",
      "    2000-01-03     32     150\n",
      "    2000-01-04     36      90\n",
      "    \n",
      "    If you want to adjust the start of the bins based on a fixed timestamp:\n",
      "    \n",
      "    >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'\n",
      "    >>> rng = pd.date_range(start, end, freq='7min')\n",
      "    >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)\n",
      "    >>> ts\n",
      "    2000-10-01 23:30:00     0\n",
      "    2000-10-01 23:37:00     3\n",
      "    2000-10-01 23:44:00     6\n",
      "    2000-10-01 23:51:00     9\n",
      "    2000-10-01 23:58:00    12\n",
      "    2000-10-02 00:05:00    15\n",
      "    2000-10-02 00:12:00    18\n",
      "    2000-10-02 00:19:00    21\n",
      "    2000-10-02 00:26:00    24\n",
      "    Freq: 7T, dtype: int64\n",
      "    \n",
      "    >>> ts.resample('17min').sum()\n",
      "    2000-10-01 23:14:00     0\n",
      "    2000-10-01 23:31:00     9\n",
      "    2000-10-01 23:48:00    21\n",
      "    2000-10-02 00:05:00    54\n",
      "    2000-10-02 00:22:00    24\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    >>> ts.resample('17min', origin='epoch').sum()\n",
      "    2000-10-01 23:18:00     0\n",
      "    2000-10-01 23:35:00    18\n",
      "    2000-10-01 23:52:00    27\n",
      "    2000-10-02 00:09:00    39\n",
      "    2000-10-02 00:26:00    24\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    >>> ts.resample('17min', origin='2000-01-01').sum()\n",
      "    2000-10-01 23:24:00     3\n",
      "    2000-10-01 23:41:00    15\n",
      "    2000-10-01 23:58:00    45\n",
      "    2000-10-02 00:15:00    45\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    If you want to adjust the start of the bins with an `offset` Timedelta, the two\n",
      "    following lines are equivalent:\n",
      "    \n",
      "    >>> ts.resample('17min', origin='start').sum()\n",
      "    2000-10-01 23:30:00     9\n",
      "    2000-10-01 23:47:00    21\n",
      "    2000-10-02 00:04:00    54\n",
      "    2000-10-02 00:21:00    24\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    >>> ts.resample('17min', offset='23h30min').sum()\n",
      "    2000-10-01 23:30:00     9\n",
      "    2000-10-01 23:47:00    21\n",
      "    2000-10-02 00:04:00    54\n",
      "    2000-10-02 00:21:00    24\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    If you want to take the largest Timestamp as the end of the bins:\n",
      "    \n",
      "    >>> ts.resample('17min', origin='end').sum()\n",
      "    2000-10-01 23:35:00     0\n",
      "    2000-10-01 23:52:00    18\n",
      "    2000-10-02 00:09:00    27\n",
      "    2000-10-02 00:26:00    63\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    In contrast with the `start_day`, you can use `end_day` to take the ceiling\n",
      "    midnight of the largest Timestamp as the end of the bins and drop the bins\n",
      "    not containing data:\n",
      "    \n",
      "    >>> ts.resample('17min', origin='end_day').sum()\n",
      "    2000-10-01 23:38:00     3\n",
      "    2000-10-01 23:55:00    15\n",
      "    2000-10-02 00:12:00    45\n",
      "    2000-10-02 00:29:00    45\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    To replace the use of the deprecated `base` argument, you can now use `offset`,\n",
      "    in this example it is equivalent to have `base=2`:\n",
      "    \n",
      "    >>> ts.resample('17min', offset='2min').sum()\n",
      "    2000-10-01 23:16:00     0\n",
      "    2000-10-01 23:33:00     9\n",
      "    2000-10-01 23:50:00    36\n",
      "    2000-10-02 00:07:00    39\n",
      "    2000-10-02 00:24:00    24\n",
      "    Freq: 17T, dtype: int64\n",
      "    \n",
      "    To replace the use of the deprecated `loffset` argument:\n",
      "    \n",
      "    >>> from pandas.tseries.frequencies import to_offset\n",
      "    >>> loffset = '19min'\n",
      "    >>> ts_out = ts.resample('17min').sum()\n",
      "    >>> ts_out.index = ts_out.index + to_offset(loffset)\n",
      "    >>> ts_out\n",
      "    2000-10-01 23:33:00     0\n",
      "    2000-10-01 23:50:00     9\n",
      "    2000-10-02 00:07:00    21\n",
      "    2000-10-02 00:24:00    54\n",
      "    2000-10-02 00:41:00    24\n",
      "    Freq: 17T, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(df.resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bbfbd92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='Price'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_3Months.plot(kind='bar', figsize=(10,6), ylabel='Price')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe9c7d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_3Months.plot(kind='barh', figsize=(9,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8328758b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAHTCAYAAABoXRG9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAjYElEQVR4nO3dcbCddX3n8ffHgIBAEWKILIEmxaCASLARnbHuQlkELUvoWDTaKl1pmW3R2rquwu5M1VnjxN0ZxrqVulRds7YkjVggo4iwsWi1qxiQKhBSogQSg5BGqLAQNOG7f9wn4RICuSH3/p5z7n2/Zu6cc37P85z7PTPfXD78fs/znFQVkiRJLTyv7wIkSdLUYfCQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc3s03cBAC960Ytq9uzZfZchSZLGyc033/zPVTVj5/GBCB6zZ89m1apVfZchSZLGSZJ7djW+26WWJC9Ncuuon58l+eMkhyW5Icld3eOho465JMnaJGuSnDmeH0SSJA2v3QaPqlpTVfOqah7wq8CjwFXAxcDKqpoLrOxek+R4YCFwAnAWcFmSaRNTviRJGiZ7enLp6cAPq+oeYAGwpBtfApzbPV8ALKuqx6vqbmAtcMo41CpJkobcnp7jsRBY2j2fWVX3AVTVfUkO78aPBL496pgN3dhTJLkQuBDg6KOP3sMyJEkaLL/4xS/YsGEDW7Zs6buUpvbff39mzZrFvvvuO6b9xxw8kjwfOAe4ZHe77mLsad9EV1WXA5cDzJ8/32+qkyQNtQ0bNnDwwQcze/Zskl39p3DyqSo2b97Mhg0bmDNnzpiO2ZOlljcAt1TV/d3r+5McAdA9PtCNbwCOGnXcLGDjHvweSZKGzpYtW5g+ffqUCR0ASZg+ffoezfLsSfB4K08uswCsAM7vnp8PXDNqfGGS/ZLMAeYCN+3B75EkaShNpdCx3Z5+5jEFjyQvAM4A/nbU8GLgjCR3ddsWA1TV7cBy4A7gOuCiqtq2R1VJkqQ9loS3v/3tO15v3bqVGTNmcPbZZwNw//33c/bZZ3PSSSdx/PHH88Y3vhGAdevWccABBzBv3rwdPx/+8Id3PJ82bdqO55/4xCf2qsYxneNRVY8C03ca28zIVS672n8RsGivKpMkaYjNvvjL4/p+6xb/xm73OfDAA7ntttt47LHHOOCAA7jhhhs48sgnr+/40z/9U8444wze8573APD9739/x7ZjjjmGW2+99Snv98EPfhCAgw466Gnbniu/q0WSpEnkDW94A1/+8kjoWbp0KW9961t3bLvvvvuYNWvWjteveMUrmtdn8JAkaRJZuHAhy5YtY8uWLXz/+9/n1a9+9Y5tF110ERdccAGnnXYaixYtYuPGJ6/9+OEPf7hjOeWiiy6asPoG4rtaJEnS+HjFK17BunXrWLp06Y5zOLY788wz+dGPfsR1113HV77yFU4++WRuu+02YNdLLRPBGQ9JkiaZc845h/e9731PWWbZ7rDDDuNtb3sbn//853nVq17FN77xjaa1OeOxB05ccmLfJUwqPzj/B32XIEmT0jvf+U4OOeQQTjzxRG688cYd41/72td4zWtewwte8AIefvhhfvjDHza/e7jBQ5KkSWbWrFk7rlwZ7eabb+Zd73oX++yzD0888QS/93u/x6te9SrWrVvXrLZU9X+38vnz59eqVav6LmO3nPEYX854SJpMVq9ezXHHHdd3Gb3Y1WdPcnNVzd95X8/xkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIz3sdDkqRJYtq0aZx44pO3frj66qtZt24dCxYsYM6cOTzxxBMcfvjhXHHFFRx++OG91GjwkCRpInzokHF+v3/Z7S4HHHDA075vZd26dbzuda/jS1/6EgCXXHIJn/zkJ/nwhz88vvWNkUstkiRNEVXFww8/zKGHHtpbDc54SJI0STz22GPMmzcPgDlz5nDVVVcB8Pd///fMmzePzZs3c+CBB/LRj360txoNHtIk4S39x4+389ew2tVSC/CUpZaPfexjvP/97+dTn/pU4+pGuNQiSdIUcs455/CNb3yjt99v8JAkaQr55je/yTHHHNPb73epRZKkSW77OR5VxSGHHMKnP/3p3moxeEiSNBHGcPnreHvkkUeeNnbqqafyL//SvpZn4lKLJElqxuAhSZKaMXhIkqRmDB6SJKkZg4ckSWrG4CFJkpoxeEiSNIlcddVVJOHOO+98yvj3vvc9kvDVr371KePTpk1j3rx5vPzlL+e8887j0UcfBeCggw6akPq8j4ckSRNgvL8/aazfIbR06VJ+7dd+jWXLlvGhD33oaeNLly7lzDPP3DE++vtdfvu3f5tPfepTvPe97x3P0p/CGQ9JkiaJRx55hG9961t85jOfYdmyZTvGq4orr7ySz33uc1x//fVs2bJll8e/7nWvY+3atRNao8FDkqRJ4uqrr+ass87i2GOP5bDDDuOWW24B4Fvf+hZz5szhmGOO4dRTT+Xaa6992rFbt27lK1/5CieeOLHfdG3wkCRpkli6dCkLFy4EYOHChSxduvRZxwEee+wx5s2bx/z58zn66KO54IILJrRGz/GQJGkS2Lx5M1/72te47bbbSMK2bdtIwuLFi/niF7/IihUrWLRoEVXF5s2befjhhzn44IOfco5HC854SJI0CVx55ZW84x3v4J577mHdunWsX7+eOXPm8JGPfISTTjqJ9evXs27dOu655x7e9KY3cfXVV/dSp8FDkqRJYOnSpfzmb/7mU8be9KY38e1vf3uX41dcccWzvt+jjz7KrFmzdvxceuml41Jnqmr3OyUvBD4NvBwo4J3AGuBvgNnAOuDNVfVgt/8lwAXANuCPquqrT3vTUebPn1+rVq16rp+hmfG+NGqqG+ulYRob+3P82Jvja6r05seP/zgvnvPiCf89J7zohAn/HXtq9erVHHfccU8ZS3JzVc3fed+xznj8GXBdVb0MOAlYDVwMrKyqucDK7jVJjgcWAicAZwGXJZn2HD+LJEmaRHYbPJL8EvCvgc8AVNXPq+ohYAGwpNttCXBu93wBsKyqHq+qu4G1wCnjW7YkSRpGY5nx+BVgE/C/knwvyaeTHAjMrKr7ALrHw7v9jwTWjzp+QzcmSZKmuLEEj32AVwJ/UVUnA/+PblnlGWQXY087kSTJhUlWJVm1adOmMRUrSdKgKoqxnDc52ezpZx5L8NgAbKiq73Svr2QkiNyf5AiA7vGBUfsfNer4WcDGXRR6eVXNr6r5M2bM2KOiJUkaNOsfW8/PH/75lAof2+8Jsv/++4/5mN3eQKyqfpJkfZKXVtUa4HTgju7nfGBx93hNd8gK4IoklwL/CpgL3LRHn0SSpCHzl/f+Jb/P73PUAUeRXU7+j4/nbRqsO2Hsv//+zJo1a8z7j/XOpe8G/jrJ84EfAf+ekdmS5UkuAO4FzgOoqtuTLGckmGwFLqqqbWP/CJIkDZ+Htz3MpXePz70uns2wX+49puBRVbcCT7sWl5HZj13tvwhY9NzLkiRJk9FgzddIkqRJzeAhSZKaMXhIkqRmDB6SJKkZg4ckSWrG4CFJkpoxeEiSpGYMHpIkqRmDhyRJasbgIUmSmjF4SJKkZgwekiSpGYOHJElqxuAhSZKaMXhIkqRmDB6SJKkZg4ckSWrG4CFJkpoxeEiSpGYMHpIkqRmDhyRJasbgIUmSmjF4SJKkZgwekiSpGYOHJElqxuAhSZKaMXhIkqRmDB6SJKkZg4ckSWrG4CFJkpoxeEiSpGYMHpIkqRmDhyRJasbgIUmSmjF4SJKkZgwekiSpmX36LmCY/ODue/suQZKkoTamGY8k65L8IMmtSVZ1Y4cluSHJXd3joaP2vyTJ2iRrkpw5UcVLkqThsidLLadV1byqmt+9vhhYWVVzgZXda5IcDywETgDOAi5LMm0ca5YkSUNqb87xWAAs6Z4vAc4dNb6sqh6vqruBtcApe/F7JEnSJDHW4FHA9UluTnJhNzazqu4D6B4P78aPBNaPOnZDNyZJkqa4sZ5c+tqq2pjkcOCGJHc+y77ZxVg9baeRAHMhwNFHHz3GMiRJ0jAb04xHVW3sHh8ArmJk6eT+JEcAdI8PdLtvAI4adfgsYOMu3vPyqppfVfNnzJjx3D+BJEkaGrsNHkkOTHLw9ufA64HbgBXA+d1u5wPXdM9XAAuT7JdkDjAXuGm8C5ckScNnLEstM4Grkmzf/4qqui7Jd4HlSS4A7gXOA6iq25MsB+4AtgIXVdW2CalekiQNld0Gj6r6EXDSLsY3A6c/wzGLgEV7XZ0kSZpUvGW6JElqxlumS5OEt/SXNAyc8ZAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIz3jJdkjShvJ2/RnPGQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1MyYg0eSaUm+l+RL3evDktyQ5K7u8dBR+16SZG2SNUnOnIjCJUnS8NmTGY/3AKtHvb4YWFlVc4GV3WuSHA8sBE4AzgIuSzJtfMqVJEnDbEzBI8ks4DeAT48aXgAs6Z4vAc4dNb6sqh6vqruBtcAp41KtJEkaamOd8fg48H7giVFjM6vqPoDu8fBu/Ehg/aj9NnRjT5HkwiSrkqzatGnTntYtSZKG0G6DR5KzgQeq6uYxvmd2MVZPG6i6vKrmV9X8GTNmjPGtJUnSMNtnDPu8FjgnyRuB/YFfSvJXwP1Jjqiq+5IcATzQ7b8BOGrU8bOAjeNZtCRJGk67nfGoqkuqalZVzWbkpNGvVdXvACuA87vdzgeu6Z6vABYm2S/JHGAucNO4Vy5JkobOWGY8nsliYHmSC4B7gfMAqur2JMuBO4CtwEVVtW2vK5UkSUNvj4JHVd0I3Ng93wyc/gz7LQIW7WVtkiRpkvHOpZIkqRmDhyRJasbgIUmSmjF4SJKkZgwekiSpGYOHJElqxuAhSZKaMXhIkqRmDB6SJKkZg4ckSWrG4CFJkpoxeEiSpGYMHpIkqRmDhyRJasbgIUmSmjF4SJKkZgwekiSpGYOHJElqxuAhSZKaMXhIkqRmDB6SJKkZg4ckSWrG4CFJkpoxeEiSpGYMHpIkqRmDhyRJasbgIUmSmjF4SJKkZgwekiSpGYOHJElqxuAhSZKaMXhIkqRmDB6SJKkZg4ckSWrG4CFJkpoxeEiSpGYMHpIkqZndBo8k+ye5Kck/Jrk9yYe78cOS3JDkru7x0FHHXJJkbZI1Sc6cyA8gSZKGx1hmPB4Hfr2qTgLmAWcleQ1wMbCyquYCK7vXJDkeWAicAJwFXJZk2gTULkmShsxug0eNeKR7uW/3U8ACYEk3vgQ4t3u+AFhWVY9X1d3AWuCU8SxakiQNpzGd45FkWpJbgQeAG6rqO8DMqroPoHs8vNv9SGD9qMM3dGM7v+eFSVYlWbVp06a9+AiSJGlY7DOWnapqGzAvyQuBq5K8/Fl2z67eYhfveTlwOcD8+fOftn0Qzd5yRd8lTCrr+i5AktTcHl3VUlUPATcycu7G/UmOAOgeH+h22wAcNeqwWcDGvS1UkiQNv7Fc1TKjm+kgyQHAvwXuBFYA53e7nQ9c0z1fASxMsl+SOcBc4KZxrluSJA2hsSy1HAEs6a5MeR6wvKq+lOT/AsuTXADcC5wHUFW3J1kO3AFsBS7qlmokSdIUt9vgUVXfB07exfhm4PRnOGYRsGivq5MkSZOKdy6VJEnNGDwkSVIzY7qcVtLg83Lv8bOu7wKkScwZD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIz3kBMkjShvLnd+FrXdwF7yRkPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUzG6DR5KjkvxdktVJbk/ynm78sCQ3JLmrezx01DGXJFmbZE2SMyfyA0iSpOExlhmPrcB/rKrjgNcAFyU5HrgYWFlVc4GV3Wu6bQuBE4CzgMuSTJuI4iVJ0nDZbfCoqvuq6pbu+cPAauBIYAGwpNttCXBu93wBsKyqHq+qu4G1wCnjXLckSRpCe3SOR5LZwMnAd4CZVXUfjIQT4PButyOB9aMO29CN7fxeFyZZlWTVpk2bnkPpkiRp2Iw5eCQ5CPgi8MdV9bNn23UXY/W0garLq2p+Vc2fMWPGWMuQJElDbEzBI8m+jISOv66qv+2G709yRLf9COCBbnwDcNSow2cBG8enXEmSNMzGclVLgM8Aq6vq0lGbVgDnd8/PB64ZNb4wyX5J5gBzgZvGr2RJkjSs9hnDPq8F3g78IMmt3dh/BhYDy5NcANwLnAdQVbcnWQ7cwcgVMRdV1bbxLlySJA2f3QaPqvomuz5vA+D0ZzhmEbBoL+qSJEmTkHculSRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzew2eCT5bJIHktw2auywJDckuat7PHTUtkuSrE2yJsmZE1W4JEkaPmOZ8fgccNZOYxcDK6tqLrCye02S44GFwAndMZclmTZu1UqSpKG22+BRVd8AfrrT8AJgSfd8CXDuqPFlVfV4Vd0NrAVOGZ9SJUnSsHuu53jMrKr7ALrHw7vxI4H1o/bb0I09TZILk6xKsmrTpk3PsQxJkjRMxvvk0uxirHa1Y1VdXlXzq2r+jBkzxrkMSZI0iJ5r8Lg/yREA3eMD3fgG4KhR+80CNj738iRJ0mTyXIPHCuD87vn5wDWjxhcm2S/JHGAucNPelShJkiaLfXa3Q5KlwKnAi5JsAD4ILAaWJ7kAuBc4D6Cqbk+yHLgD2ApcVFXbJqh2SZI0ZHYbPKrqrc+w6fRn2H8RsGhvipIkSZOTdy6VJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1Y/CQJEnNGDwkSVIzBg9JktSMwUOSJDVj8JAkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ1M2HBI8lZSdYkWZvk4on6PZIkaXhMSPBIMg34JPAG4HjgrUmOn4jfJUmShsdEzXicAqytqh9V1c+BZcCCCfpdkiRpSExU8DgSWD/q9YZuTJIkTWH7TND7Zhdj9ZQdkguBC7uXjyRZM0G1TEUvAv657yJ2Jx/ruwL1wN7UILM/x9cv72pwooLHBuCoUa9nARtH71BVlwOXT9Dvn9KSrKqq+X3XIe3M3tQgsz/bmKillu8Cc5PMSfJ8YCGwYoJ+lyRJGhITMuNRVVuTvAv4KjAN+GxV3T4Rv0uSJA2PiVpqoaquBa6dqPfXs3IJS4PK3tQgsz8bSFXtfi9JkqRx4C3TJUlSMwYPSZLUzISd46E2koSRO8Ueyci9UjYCN5VraOqZvalBZn/2x3M8hliS1wOXAXcBP+6GZwEvAf6wqq7vqzZNbfamBpn92S+DxxBLshp4Q1Wt22l8DnBtVR3XS2Ga8uxNDTL7s1+e4zHc9mHkLrE7+zGwb+NapNHsTQ0y+7NHnuMx3D4LfDfJMp78Ur6jGLlT7Gd6q0qyNzXY7M8eudQy5JIcD5zDyAlSYSTFr6iqO3otTFOevalBZn/2x+AhSZKa8RyPIZbkkCSLk9yZZHP3s7obe2Hf9Wnqsjc1yOzPfhk8htty4EHg1KqaXlXTgdOAh4Av9FmYpjx7U4PM/uyRSy1DLMmaqnrpnm6TJpq9qUFmf/bLGY/hdk+S9yeZuX0gycwkH+DJM7WlPtibGmT2Z48MHsPtLcB04OtJHkzyU+BG4DDgzX0WpinP3tQgsz975FKLJElqxhmPSSLJK5/ttdQXe1ODzP5sz+AxefzBbl5LfbE3Ncjsz8ZcapEkSc34XS1DLkmAUxi57W8BG4GbykSpntmbGmT2Z3+c8RhiSV4PXAbcxci3KgLMAl4C/GFVXd9XbZra7E0NMvuzXwaPIZZkNfCGqlq30/gc4NqqOq6XwjTl2ZsaZPZnvzy5dLjtw8g3Ku7sx8C+jWuRRrM3Ncjszx55jsdw+yzw3STLePJue0cBC4HP9FaVZG9qsNmfPXKpZcglOR44h5ETpMJIil9RVXf0WpimPHtTg8z+7I/BQ5IkNeM5HkMsySFJFie5M8nm7md1N/bCvuvT1GVvapDZn/0yeAy35cCDwKlVNb2qpgOnAQ8BX+izME159qYGmf3ZI5dahliSNVX10j3dJk00e1ODzP7slzMew+2eJO9PMnP7QJKZST7Ak2dqS32wNzXI7M8eGTyG21uA6cDXkzyY5KfAjcBhwJv7LExTnr2pQWZ/9silFkmS1IwzHpNEklc+22upL/amBpn92Z7BY/L4g928lvpib2qQ2Z+NudQiSZKa8btahlySAKcwctvfAjYCN5WJUj2zNzXI7M/+OOMxxJK8HrgMuIuRb1UEmAW8BPjDqrq+r9o0tdmbGmT2Z78MHkMsyWrgDVW1bqfxOcC1VXVcL4VpyrM3Ncjsz355culw24eRb1Tc2Y+BfRvXIo1mb2qQ2Z898hyP4fZZ4LtJlvHk3faOAhYCn+mtKsne1GCzP3vkUsuQS3I8cA4jJ0iFkRS/oqru6LUwTXn2pgaZ/dkfg4ckSWrGczyGWJJDkixOcmeSzd3P6m7shX3Xp6nL3tQgsz/7ZfAYbsuBB4FTq2p6VU0HTgMeAr7QZ2Ga8uxNDTL7s0cutQyxJGuq6qV7uk2aaPamBpn92S9nPIbbPUnen2Tm9oEkM5N8gCfP1Jb6YG9qkNmfPTJ4DLe3ANOBryf5aZKfAjcChwFv7rMwTXn2pgaZ/dkjl1okSVIzznhIkqRmDB6SJKkZg4ckSWrG72qZpJKcUVU39F2Hpq4khwBnMXJL6gI2Al+tqof6rEt6Nv7tnHjOeExeftGRepPkHcAtwKnAC4ADGblB083dNmlQ+bdzgnlVyxBLsuKZNgG/XlUHtqxH2i7JGuDVO89uJDkU+E5VHdtLYRL+7eybSy3D7XXA7wCP7DQe4JT25Ug7hJHllZ090W2T+uTfzh4ZPIbbt4FHq+rrO2/o/o9T6ssi4JYk1/PknSCPBs4A/mtvVUkj/NvZI5daJE2IblnlTEZOLg2wgZGTSx/stTBJvTJ4SJKkZryqZYgleVmSryT5cpJjknwuyUNJbkpyXN/1aeqyNzXI7M9+GTyG2+XAZcBfAV8DrgMOZWQN/c97rEuyNzXI7M8eudQyxJJ8r6pO7p6vraqXjNp2S1W9sr/qNJXZmxpk9me/nPEYbtNGPb90p23Pb1mItBN7U4PM/uyRwWO4fTLJQQBVddn2wSQvAf5Pb1VJ9qYGm/3ZI5daJElSM854TDJJbum7BmlX7E0NMvuzHYPH5OPtqDWo7E0NMvuzEYPH5PPlvguQnoG9qUFmfzbiOR6SmkjyyqpyOlua4pzxGGLefU+DKskrd/r5VWBFkpOTeI8E9SrJO0c9n5VkZZIHk/xDkmP7rG0qcMZjiCX5BvDfgYOAxcAHgL8Bzgb+uKpO77E8TWFJnmDkG0AfHzX8mm6squrXeylM4qk3CUuyHFgJ/CWwAHiXfzsnlsFjiHn3PQ2qJL8FvBv4WFVd243dXVVz+q1MelrwuLWq5o3atuPvqiaGSy3DzbvvaSBV1ZXAbwBnJPlCkqMB/y9Hg2JWkk8k+R/AjCT7jtq27zMdpPGxT98FaK98MslBVfWId9/ToKmqR4A/SXIysAQ4uOeSpO3+06jnqxhZrn4wyYuBFf2UNHW41CJpwiUJcHBV/azvWiT1y+Ax5JKcCZwLHMnIVPZG4Jqquq7PuiR7U4PM/uyPwWOIJfk4cCzwv4EN3fAs4B3AXVX1np5K0xRnb2qQ2Z/9MngMsST/VFVPu+a8m9b+p6qa20NZkr2pgWZ/9surWobbliSn7GL8VcCW1sVIo9ibGmT2Z4+8qmW4/S7wF0kO5snpwqOAn3XbpL78LvamBtfvYn/2xqWWSaC7BOxIRr5dcUNV/aTnkiTA3tRgsz/74VLLJFBVP6mqm6tqFfAf+q5H2s7e1CCzP/th8Jh8zum7AOkZ2JsaZPZnIwaPySd9FyA9A3tTg8z+bMRzPCaZJM+rqif6rkPamb2pQWZ/tuOMx5BLcmaSC5LMBtj+DyfJO3stTFOevalBZn/2x+AxxJJ8FPgvwInAyiTvHrX5Xf1UJdmbGmz2Z79cahliSX4AnFxVW5O8ELgCWFNVf5Lke1V1cr8VaqqyNzXI7M9+OeMx3Papqq0AVfUQ8O+AX0ryBeD5fRamKc/e1CCzP3tk8BhuP0zyb7a/qKptVXUBsAY4rr+yJHtTA83+7JFLLUMsyQEAVfXYLrYdWVU/bl+VZG9qsNmf/fK7WobY9n80Sfatql/stPnxHkqSAHtTg83+7JdLLUMsyWlJNgAbk1y//bKwzvU9lSXZmxpo9me/DB7D7b8BZ1bVDOBy4IYkr+m2eRc+9cne1CCzP3vkUstwe35V3Q5QVVcmWQ38bZKLAU/eUZ/sTQ0y+7NHBo/h9oskL97+Vc5VdXuS04EvAcf0W5qmOHtTg8z+7JFLLcPtYmDm6IGq2gCcCizuoyCpY29qkNmfPfJyWkmS1IwzHkMsySFJFie5M8nm7md1N/bCvuvT1GVvapDZn/0yeAy35cCDwKlVNb2qpgOndWNf6LUyTXX2pgaZ/dkjl1qGWJI1VfXSPd0mTTR7U4PM/uyXMx7D7Z4k70+y4ySpJDOTfABY32Ndkr2pQWZ/9sjgMdzeAkwHvp7kp0l+CtwIHAa8uc/CNOXZmxpk9mePXGqRJEnNOOMx5JK8LMnpSQ7cafysvmqSwN7UYLM/+2PwGGJJ/gi4Bng3cHuSBaM2f7SfqiR7U4PN/uyXt0wfbr8P/GpVPdJ9u+KVSWZX1Z/hFx2pX/amBpn92SODx3CbVlWPAFTVuiSnMvIP6JfxH4/6ZW9qkNmfPXKpZbj9JMm87S+6f0hnAy8CTuyrKAl7U4PN/uyRV7UMsSSzgK3bv2Fxp22vrapv9VCWZG9qoNmf/TJ4SJKkZlxqkSRJzRg8JElSMwYPSXslybYktya5Pck/Jnlvkmf925JkdpK3tapR0uAweEjaW49V1byqOgE4A3gj8MHdHDMbMHhIU5Anl0raK0keqaqDRr3+FeC7jFya+MvA54Htt6V+V1X9Q5JvA8cBdwNLgE8Ai4FTgf2AT1bV/2z2ISQ1Y/CQtFd2Dh7d2IPAy4CHgSeqakuSucDSqprf3bDpfVV1drf/hcDhVfWRJPsB3wLOq6q7W34WSRPPO5dKmgjb7/64L/Dn3c2atgHHPsP+rwdekeS3uteHAHMZmRGRNIkYPCSNq26pZRvwACPnetwPnMTIOWVbnukw4N1V9dUmRUrqjSeXSho3SWYAnwL+vEbWcQ8B7quqJ4C3A9O6XR8GDh516FeBP0iyb/c+x+78deWSJgdnPCTtrQOS3MrIsspWRk4mvbTbdhnwxSTnAX8H/L9u/PvA1iT/CHwO+DNGrnS5JUmATcC5bcqX1JInl0qSpGZcapEkSc0YPCRJUjMGD0mS1IzBQ5IkNWPwkCRJzRg8JElSMwYPSZLUjMFDkiQ18/8B6XH5CRp9ZCsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_3Months.plot(kind='bar', stacked=True, figsize=(9,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69f06d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['MSFT', 'FB']].plot(kind='hist', bins=25, figsize=(9,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44d9c334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box', figsize=(9,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2af7a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box', vert=False, figsize=(9,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5711198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='area', figsize=(9,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b96814b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='AAPL'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_3Months.index=['March', 'April', 'May']\n",
    "df_3Months.plot(kind='pie', y='AAPL', legend=False, autopct='%.f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "beab6b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='MSFT'>, <AxesSubplot:ylabel='FB'>,\n",
       "       <AxesSubplot:ylabel='AAPL'>], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_3Months.plot(kind='pie', legend=False, autopct='%.f', subplots=True, figsize=(14,8))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97267262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MSFT', ylabel='AAPL'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='MSFT', y='AAPL', figsize=(9,6), color='Green')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b17d6b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on PlotAccessor in module pandas.plotting._core object:\n",
      "\n",
      "class PlotAccessor(pandas.core.base.PandasObject)\n",
      " |  PlotAccessor(data)\n",
      " |  \n",
      " |  Make plots of Series or DataFrame.\n",
      " |  \n",
      " |  Uses the backend specified by the\n",
      " |  option ``plotting.backend``. By default, matplotlib is used.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  data : Series or DataFrame\n",
      " |      The object for which the method is called.\n",
      " |  x : label or position, default None\n",
      " |      Only used if data is a DataFrame.\n",
      " |  y : label, position or list of label, positions, default None\n",
      " |      Allows plotting of one column versus another. Only used if data is a\n",
      " |      DataFrame.\n",
      " |  kind : str\n",
      " |      The kind of plot to produce:\n",
      " |  \n",
      " |      - 'line' : line plot (default)\n",
      " |      - 'bar' : vertical bar plot\n",
      " |      - 'barh' : horizontal bar plot\n",
      " |      - 'hist' : histogram\n",
      " |      - 'box' : boxplot\n",
      " |      - 'kde' : Kernel Density Estimation plot\n",
      " |      - 'density' : same as 'kde'\n",
      " |      - 'area' : area plot\n",
      " |      - 'pie' : pie plot\n",
      " |      - 'scatter' : scatter plot (DataFrame only)\n",
      " |      - 'hexbin' : hexbin plot (DataFrame only)\n",
      " |  ax : matplotlib axes object, default None\n",
      " |      An axes of the current figure.\n",
      " |  subplots : bool, default False\n",
      " |      Make separate subplots for each column.\n",
      " |  sharex : bool, default True if ax is None else False\n",
      " |      In case ``subplots=True``, share x axis and set some x axis labels\n",
      " |      to invisible; defaults to True if ax is None otherwise False if\n",
      " |      an ax is passed in; Be aware, that passing in both an ax and\n",
      " |      ``sharex=True`` will alter all x axis labels for all axis in a figure.\n",
      " |  sharey : bool, default False\n",
      " |      In case ``subplots=True``, share y axis and set some y axis labels to invisible.\n",
      " |  layout : tuple, optional\n",
      " |      (rows, columns) for the layout of subplots.\n",
      " |  figsize : a tuple (width, height) in inches\n",
      " |      Size of a figure object.\n",
      " |  use_index : bool, default True\n",
      " |      Use index as ticks for x axis.\n",
      " |  title : str or list\n",
      " |      Title to use for the plot. If a string is passed, print the string\n",
      " |      at the top of the figure. If a list is passed and `subplots` is\n",
      " |      True, print each item in the list above the corresponding subplot.\n",
      " |  grid : bool, default None (matlab style default)\n",
      " |      Axis grid lines.\n",
      " |  legend : bool or {'reverse'}\n",
      " |      Place legend on axis subplots.\n",
      " |  style : list or dict\n",
      " |      The matplotlib line style per column.\n",
      " |  logx : bool or 'sym', default False\n",
      " |      Use log scaling or symlog scaling on x axis.\n",
      " |      .. versionchanged:: 0.25.0\n",
      " |  \n",
      " |  logy : bool or 'sym' default False\n",
      " |      Use log scaling or symlog scaling on y axis.\n",
      " |      .. versionchanged:: 0.25.0\n",
      " |  \n",
      " |  loglog : bool or 'sym', default False\n",
      " |      Use log scaling or symlog scaling on both x and y axes.\n",
      " |      .. versionchanged:: 0.25.0\n",
      " |  \n",
      " |  xticks : sequence\n",
      " |      Values to use for the xticks.\n",
      " |  yticks : sequence\n",
      " |      Values to use for the yticks.\n",
      " |  xlim : 2-tuple/list\n",
      " |      Set the x limits of the current axes.\n",
      " |  ylim : 2-tuple/list\n",
      " |      Set the y limits of the current axes.\n",
      " |  xlabel : label, optional\n",
      " |      Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the\n",
      " |      x-column name for planar plots.\n",
      " |  \n",
      " |      .. versionadded:: 1.1.0\n",
      " |  \n",
      " |      .. versionchanged:: 1.2.0\n",
      " |  \n",
      " |         Now applicable to planar plots (`scatter`, `hexbin`).\n",
      " |  \n",
      " |  ylabel : label, optional\n",
      " |      Name to use for the ylabel on y-axis. Default will show no ylabel, or the\n",
      " |      y-column name for planar plots.\n",
      " |  \n",
      " |      .. versionadded:: 1.1.0\n",
      " |  \n",
      " |      .. versionchanged:: 1.2.0\n",
      " |  \n",
      " |         Now applicable to planar plots (`scatter`, `hexbin`).\n",
      " |  \n",
      " |  rot : int, default None\n",
      " |      Rotation for ticks (xticks for vertical, yticks for horizontal\n",
      " |      plots).\n",
      " |  fontsize : int, default None\n",
      " |      Font size for xticks and yticks.\n",
      " |  colormap : str or matplotlib colormap object, default None\n",
      " |      Colormap to select colors from. If string, load colormap with that\n",
      " |      name from matplotlib.\n",
      " |  colorbar : bool, optional\n",
      " |      If True, plot colorbar (only relevant for 'scatter' and 'hexbin'\n",
      " |      plots).\n",
      " |  position : float\n",
      " |      Specify relative alignments for bar plot layout.\n",
      " |      From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5\n",
      " |      (center).\n",
      " |  table : bool, Series or DataFrame, default False\n",
      " |      If True, draw a table using the data in the DataFrame and the data\n",
      " |      will be transposed to meet matplotlib's default layout.\n",
      " |      If a Series or DataFrame is passed, use passed data to draw a\n",
      " |      table.\n",
      " |  yerr : DataFrame, Series, array-like, dict and str\n",
      " |      See :ref:`Plotting with Error Bars <visualization.errorbars>` for\n",
      " |      detail.\n",
      " |  xerr : DataFrame, Series, array-like, dict and str\n",
      " |      Equivalent to yerr.\n",
      " |  stacked : bool, default False in line and bar plots, and True in area plot\n",
      " |      If True, create stacked plot.\n",
      " |  sort_columns : bool, default False\n",
      " |      Sort column names to determine plot ordering.\n",
      " |  secondary_y : bool or sequence, default False\n",
      " |      Whether to plot on the secondary y-axis if a list/tuple, which\n",
      " |      columns to plot on secondary y-axis.\n",
      " |  mark_right : bool, default True\n",
      " |      When using a secondary_y axis, automatically mark the column\n",
      " |      labels with \"(right)\" in the legend.\n",
      " |  include_bool : bool, default is False\n",
      " |      If True, boolean values can be plotted.\n",
      " |  backend : str, default None\n",
      " |      Backend to use instead of the backend specified in the option\n",
      " |      ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to\n",
      " |      specify the ``plotting.backend`` for the whole session, set\n",
      " |      ``pd.options.plotting.backend``.\n",
      " |  \n",
      " |      .. versionadded:: 1.0.0\n",
      " |  \n",
      " |  **kwargs\n",
      " |      Options to pass to matplotlib plotting method.\n",
      " |  \n",
      " |  Returns\n",
      " |  -------\n",
      " |  :class:`matplotlib.axes.Axes` or numpy.ndarray of them\n",
      " |      If the backend is not the default matplotlib one, the return value\n",
      " |      will be the object returned by the backend.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  - See matplotlib documentation online for more on this subject\n",
      " |  - If `kind` = 'bar' or 'barh', you can specify relative alignments\n",
      " |    for bar plot layout by `position` keyword.\n",
      " |    From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5\n",
      " |    (center)\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      PlotAccessor\n",
      " |      pandas.core.base.PandasObject\n",
      " |      pandas.core.accessor.DirNamesMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, *args, **kwargs)\n",
      " |      Make plots of Series or DataFrame.\n",
      " |      \n",
      " |      Uses the backend specified by the\n",
      " |      option ``plotting.backend``. By default, matplotlib is used.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      data : Series or DataFrame\n",
      " |          The object for which the method is called.\n",
      " |      x : label or position, default None\n",
      " |          Only used if data is a DataFrame.\n",
      " |      y : label, position or list of label, positions, default None\n",
      " |          Allows plotting of one column versus another. Only used if data is a\n",
      " |          DataFrame.\n",
      " |      kind : str\n",
      " |          The kind of plot to produce:\n",
      " |      \n",
      " |          - 'line' : line plot (default)\n",
      " |          - 'bar' : vertical bar plot\n",
      " |          - 'barh' : horizontal bar plot\n",
      " |          - 'hist' : histogram\n",
      " |          - 'box' : boxplot\n",
      " |          - 'kde' : Kernel Density Estimation plot\n",
      " |          - 'density' : same as 'kde'\n",
      " |          - 'area' : area plot\n",
      " |          - 'pie' : pie plot\n",
      " |          - 'scatter' : scatter plot (DataFrame only)\n",
      " |          - 'hexbin' : hexbin plot (DataFrame only)\n",
      " |      ax : matplotlib axes object, default None\n",
      " |          An axes of the current figure.\n",
      " |      subplots : bool, default False\n",
      " |          Make separate subplots for each column.\n",
      " |      sharex : bool, default True if ax is None else False\n",
      " |          In case ``subplots=True``, share x axis and set some x axis labels\n",
      " |          to invisible; defaults to True if ax is None otherwise False if\n",
      " |          an ax is passed in; Be aware, that passing in both an ax and\n",
      " |          ``sharex=True`` will alter all x axis labels for all axis in a figure.\n",
      " |      sharey : bool, default False\n",
      " |          In case ``subplots=True``, share y axis and set some y axis labels to invisible.\n",
      " |      layout : tuple, optional\n",
      " |          (rows, columns) for the layout of subplots.\n",
      " |      figsize : a tuple (width, height) in inches\n",
      " |          Size of a figure object.\n",
      " |      use_index : bool, default True\n",
      " |          Use index as ticks for x axis.\n",
      " |      title : str or list\n",
      " |          Title to use for the plot. If a string is passed, print the string\n",
      " |          at the top of the figure. If a list is passed and `subplots` is\n",
      " |          True, print each item in the list above the corresponding subplot.\n",
      " |      grid : bool, default None (matlab style default)\n",
      " |          Axis grid lines.\n",
      " |      legend : bool or {'reverse'}\n",
      " |          Place legend on axis subplots.\n",
      " |      style : list or dict\n",
      " |          The matplotlib line style per column.\n",
      " |      logx : bool or 'sym', default False\n",
      " |          Use log scaling or symlog scaling on x axis.\n",
      " |          .. versionchanged:: 0.25.0\n",
      " |      \n",
      " |      logy : bool or 'sym' default False\n",
      " |          Use log scaling or symlog scaling on y axis.\n",
      " |          .. versionchanged:: 0.25.0\n",
      " |      \n",
      " |      loglog : bool or 'sym', default False\n",
      " |          Use log scaling or symlog scaling on both x and y axes.\n",
      " |          .. versionchanged:: 0.25.0\n",
      " |      \n",
      " |      xticks : sequence\n",
      " |          Values to use for the xticks.\n",
      " |      yticks : sequence\n",
      " |          Values to use for the yticks.\n",
      " |      xlim : 2-tuple/list\n",
      " |          Set the x limits of the current axes.\n",
      " |      ylim : 2-tuple/list\n",
      " |          Set the y limits of the current axes.\n",
      " |      xlabel : label, optional\n",
      " |          Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the\n",
      " |          x-column name for planar plots.\n",
      " |      \n",
      " |          .. versionadded:: 1.1.0\n",
      " |      \n",
      " |          .. versionchanged:: 1.2.0\n",
      " |      \n",
      " |             Now applicable to planar plots (`scatter`, `hexbin`).\n",
      " |      \n",
      " |      ylabel : label, optional\n",
      " |          Name to use for the ylabel on y-axis. Default will show no ylabel, or the\n",
      " |          y-column name for planar plots.\n",
      " |      \n",
      " |          .. versionadded:: 1.1.0\n",
      " |      \n",
      " |          .. versionchanged:: 1.2.0\n",
      " |      \n",
      " |             Now applicable to planar plots (`scatter`, `hexbin`).\n",
      " |      \n",
      " |      rot : int, default None\n",
      " |          Rotation for ticks (xticks for vertical, yticks for horizontal\n",
      " |          plots).\n",
      " |      fontsize : int, default None\n",
      " |          Font size for xticks and yticks.\n",
      " |      colormap : str or matplotlib colormap object, default None\n",
      " |          Colormap to select colors from. If string, load colormap with that\n",
      " |          name from matplotlib.\n",
      " |      colorbar : bool, optional\n",
      " |          If True, plot colorbar (only relevant for 'scatter' and 'hexbin'\n",
      " |          plots).\n",
      " |      position : float\n",
      " |          Specify relative alignments for bar plot layout.\n",
      " |          From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5\n",
      " |          (center).\n",
      " |      table : bool, Series or DataFrame, default False\n",
      " |          If True, draw a table using the data in the DataFrame and the data\n",
      " |          will be transposed to meet matplotlib's default layout.\n",
      " |          If a Series or DataFrame is passed, use passed data to draw a\n",
      " |          table.\n",
      " |      yerr : DataFrame, Series, array-like, dict and str\n",
      " |          See :ref:`Plotting with Error Bars <visualization.errorbars>` for\n",
      " |          detail.\n",
      " |      xerr : DataFrame, Series, array-like, dict and str\n",
      " |          Equivalent to yerr.\n",
      " |      stacked : bool, default False in line and bar plots, and True in area plot\n",
      " |          If True, create stacked plot.\n",
      " |      sort_columns : bool, default False\n",
      " |          Sort column names to determine plot ordering.\n",
      " |      secondary_y : bool or sequence, default False\n",
      " |          Whether to plot on the secondary y-axis if a list/tuple, which\n",
      " |          columns to plot on secondary y-axis.\n",
      " |      mark_right : bool, default True\n",
      " |          When using a secondary_y axis, automatically mark the column\n",
      " |          labels with \"(right)\" in the legend.\n",
      " |      include_bool : bool, default is False\n",
      " |          If True, boolean values can be plotted.\n",
      " |      backend : str, default None\n",
      " |          Backend to use instead of the backend specified in the option\n",
      " |          ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to\n",
      " |          specify the ``plotting.backend`` for the whole session, set\n",
      " |          ``pd.options.plotting.backend``.\n",
      " |      \n",
      " |          .. versionadded:: 1.0.0\n",
      " |      \n",
      " |      **kwargs\n",
      " |          Options to pass to matplotlib plotting method.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      :class:`matplotlib.axes.Axes` or numpy.ndarray of them\n",
      " |          If the backend is not the default matplotlib one, the return value\n",
      " |          will be the object returned by the backend.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      - See matplotlib documentation online for more on this subject\n",
      " |      - If `kind` = 'bar' or 'barh', you can specify relative alignments\n",
      " |        for bar plot layout by `position` keyword.\n",
      " |        From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5\n",
      " |        (center)\n",
      " |  \n",
      " |  __init__(self, data)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  area(self, x=None, y=None, **kwargs)\n",
      " |      Draw a stacked area plot.\n",
      " |      \n",
      " |      An area plot displays quantitative data visually.\n",
      " |      This function wraps the matplotlib area function.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      x : label or position, optional\n",
      " |          Coordinates for the X axis. By default uses the index.\n",
      " |      y : label or position, optional\n",
      " |          Column to plot. By default uses all columns.\n",
      " |      stacked : bool, default True\n",
      " |          Area plots are stacked by default. Set to False to create a\n",
      " |          unstacked plot.\n",
      " |      **kwargs\n",
      " |          Additional keyword arguments are documented in\n",
      " |          :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      matplotlib.axes.Axes or numpy.ndarray\n",
      " |          Area plot, or array of area plots if subplots is True.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      DataFrame.plot : Make plots of DataFrame using matplotlib / pylab.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      Draw an area plot based on basic business metrics:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> df = pd.DataFrame({\n",
      " |          ...     'sales': [3, 2, 3, 9, 10, 6],\n",
      " |          ...     'signups': [5, 5, 6, 12, 14, 13],\n",
      " |          ...     'visits': [20, 42, 28, 62, 81, 50],\n",
      " |          ... }, index=pd.date_range(start='2018/01/01', end='2018/07/01',\n",
      " |          ...                        freq='M'))\n",
      " |          >>> ax = df.plot.area()\n",
      " |      \n",
      " |      Area plots are stacked by default. To produce an unstacked plot,\n",
      " |      pass ``stacked=False``:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = df.plot.area(stacked=False)\n",
      " |      \n",
      " |      Draw an area plot for a single column:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = df.plot.area(y='sales')\n",
      " |      \n",
      " |      Draw with a different `x`:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> df = pd.DataFrame({\n",
      " |          ...     'sales': [3, 2, 3],\n",
      " |          ...     'visits': [20, 42, 28],\n",
      " |          ...     'day': [1, 2, 3],\n",
      " |          ... })\n",
      " |          >>> ax = df.plot.area(x='day')\n",
      " |  \n",
      " |  bar(self, x=None, y=None, **kwargs)\n",
      " |      Vertical bar plot.\n",
      " |      \n",
      " |      A bar plot is a plot that presents categorical data with\n",
      " |      rectangular bars with lengths proportional to the values that they\n",
      " |      represent. A bar plot shows comparisons among discrete categories. One\n",
      " |      axis of the plot shows the specific categories being compared, and the\n",
      " |      other axis represents a measured value.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      x : label or position, optional\n",
      " |          Allows plotting of one column versus another. If not specified,\n",
      " |          the index of the DataFrame is used.\n",
      " |      y : label or position, optional\n",
      " |          Allows plotting of one column versus another. If not specified,\n",
      " |          all numerical columns are used.\n",
      " |      color : str, array-like, or dict, optional\n",
      " |          The color for each of the DataFrame's columns. Possible values are:\n",
      " |      \n",
      " |          - A single color string referred to by name, RGB or RGBA code,\n",
      " |              for instance 'red' or '#a98d19'.\n",
      " |      \n",
      " |          - A sequence of color strings referred to by name, RGB or RGBA\n",
      " |              code, which will be used for each column recursively. For\n",
      " |              instance ['green','yellow'] each column's bar will be filled in\n",
      " |              green or yellow, alternatively. If there is only a single column to\n",
      " |              be plotted, then only the first color from the color list will be\n",
      " |              used.\n",
      " |      \n",
      " |          - A dict of the form {column name : color}, so that each column will be\n",
      " |              colored accordingly. For example, if your columns are called `a` and\n",
      " |              `b`, then passing {'a': 'green', 'b': 'red'} will color bars for\n",
      " |              column `a` in green and bars for column `b` in red.\n",
      " |      \n",
      " |          .. versionadded:: 1.1.0\n",
      " |      \n",
      " |      **kwargs\n",
      " |          Additional keyword arguments are documented in\n",
      " |          :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      matplotlib.axes.Axes or np.ndarray of them\n",
      " |          An ndarray is returned with one :class:`matplotlib.axes.Axes`\n",
      " |          per column when ``subplots=True``.\n",
      " |      \n",
      " |              See Also\n",
      " |              --------\n",
      " |              DataFrame.plot.barh : Horizontal bar plot.\n",
      " |              DataFrame.plot : Make plots of a DataFrame.\n",
      " |              matplotlib.pyplot.bar : Make a bar plot with matplotlib.\n",
      " |      \n",
      " |              Examples\n",
      " |              --------\n",
      " |              Basic plot.\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})\n",
      " |                  >>> ax = df.plot.bar(x='lab', y='val', rot=0)\n",
      " |      \n",
      " |              Plot a whole dataframe to a bar plot. Each column is assigned a\n",
      " |              distinct color, and each row is nested in a group along the\n",
      " |              horizontal axis.\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]\n",
      " |                  >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]\n",
      " |                  >>> index = ['snail', 'pig', 'elephant',\n",
      " |                  ...          'rabbit', 'giraffe', 'coyote', 'horse']\n",
      " |                  >>> df = pd.DataFrame({'speed': speed,\n",
      " |                  ...                    'lifespan': lifespan}, index=index)\n",
      " |                  >>> ax = df.plot.bar(rot=0)\n",
      " |      \n",
      " |              Plot stacked bar charts for the DataFrame\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> ax = df.plot.bar(stacked=True)\n",
      " |      \n",
      " |              Instead of nesting, the figure can be split by column with\n",
      " |              ``subplots=True``. In this case, a :class:`numpy.ndarray` of\n",
      " |              :class:`matplotlib.axes.Axes` are returned.\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> axes = df.plot.bar(rot=0, subplots=True)\n",
      " |                  >>> axes[1].legend(loc=2)  # doctest: +SKIP\n",
      " |      \n",
      " |              If you don't like the default colours, you can specify how you'd\n",
      " |              like each column to be colored.\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> axes = df.plot.bar(\n",
      " |                  ...     rot=0, subplots=True, color={\"speed\": \"red\", \"lifespan\": \"green\"}\n",
      " |                  ... )\n",
      " |                  >>> axes[1].legend(loc=2)  # doctest: +SKIP\n",
      " |      \n",
      " |              Plot a single column.\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> ax = df.plot.bar(y='speed', rot=0)\n",
      " |      \n",
      " |              Plot only selected categories for the DataFrame.\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> ax = df.plot.bar(x='lifespan', rot=0)\n",
      " |  \n",
      " |  barh(self, x=None, y=None, **kwargs)\n",
      " |      Make a horizontal bar plot.\n",
      " |      \n",
      " |      A horizontal bar plot is a plot that presents quantitative data with\n",
      " |      rectangular bars with lengths proportional to the values that they\n",
      " |      represent. A bar plot shows comparisons among discrete categories. One\n",
      " |      axis of the plot shows the specific categories being compared, and the\n",
      " |      other axis represents a measured value.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      x : label or position, optional\n",
      " |          Allows plotting of one column versus another. If not specified,\n",
      " |          the index of the DataFrame is used.\n",
      " |      y : label or position, optional\n",
      " |          Allows plotting of one column versus another. If not specified,\n",
      " |          all numerical columns are used.\n",
      " |      color : str, array-like, or dict, optional\n",
      " |          The color for each of the DataFrame's columns. Possible values are:\n",
      " |      \n",
      " |          - A single color string referred to by name, RGB or RGBA code,\n",
      " |              for instance 'red' or '#a98d19'.\n",
      " |      \n",
      " |          - A sequence of color strings referred to by name, RGB or RGBA\n",
      " |              code, which will be used for each column recursively. For\n",
      " |              instance ['green','yellow'] each column's bar will be filled in\n",
      " |              green or yellow, alternatively. If there is only a single column to\n",
      " |              be plotted, then only the first color from the color list will be\n",
      " |              used.\n",
      " |      \n",
      " |          - A dict of the form {column name : color}, so that each column will be\n",
      " |              colored accordingly. For example, if your columns are called `a` and\n",
      " |              `b`, then passing {'a': 'green', 'b': 'red'} will color bars for\n",
      " |              column `a` in green and bars for column `b` in red.\n",
      " |      \n",
      " |          .. versionadded:: 1.1.0\n",
      " |      \n",
      " |      **kwargs\n",
      " |          Additional keyword arguments are documented in\n",
      " |          :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      matplotlib.axes.Axes or np.ndarray of them\n",
      " |          An ndarray is returned with one :class:`matplotlib.axes.Axes`\n",
      " |          per column when ``subplots=True``.\n",
      " |      \n",
      " |              See Also\n",
      " |              --------\n",
      " |              DataFrame.plot.bar: Vertical bar plot.\n",
      " |              DataFrame.plot : Make plots of DataFrame using matplotlib.\n",
      " |              matplotlib.axes.Axes.bar : Plot a vertical bar plot using matplotlib.\n",
      " |      \n",
      " |              Examples\n",
      " |              --------\n",
      " |              Basic example\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> df = pd.DataFrame({'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]})\n",
      " |                  >>> ax = df.plot.barh(x='lab', y='val')\n",
      " |      \n",
      " |              Plot a whole DataFrame to a horizontal bar plot\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]\n",
      " |                  >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]\n",
      " |                  >>> index = ['snail', 'pig', 'elephant',\n",
      " |                  ...          'rabbit', 'giraffe', 'coyote', 'horse']\n",
      " |                  >>> df = pd.DataFrame({'speed': speed,\n",
      " |                  ...                    'lifespan': lifespan}, index=index)\n",
      " |                  >>> ax = df.plot.barh()\n",
      " |      \n",
      " |              Plot stacked barh charts for the DataFrame\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> ax = df.plot.barh(stacked=True)\n",
      " |      \n",
      " |              We can specify colors for each column\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> ax = df.plot.barh(color={\"speed\": \"red\", \"lifespan\": \"green\"})\n",
      " |      \n",
      " |              Plot a column of the DataFrame to a horizontal bar plot\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]\n",
      " |                  >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]\n",
      " |                  >>> index = ['snail', 'pig', 'elephant',\n",
      " |                  ...          'rabbit', 'giraffe', 'coyote', 'horse']\n",
      " |                  >>> df = pd.DataFrame({'speed': speed,\n",
      " |                  ...                    'lifespan': lifespan}, index=index)\n",
      " |                  >>> ax = df.plot.barh(y='speed')\n",
      " |      \n",
      " |              Plot DataFrame versus the desired column\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]\n",
      " |                  >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]\n",
      " |                  >>> index = ['snail', 'pig', 'elephant',\n",
      " |                  ...          'rabbit', 'giraffe', 'coyote', 'horse']\n",
      " |                  >>> df = pd.DataFrame({'speed': speed,\n",
      " |                  ...                    'lifespan': lifespan}, index=index)\n",
      " |                  >>> ax = df.plot.barh(x='lifespan')\n",
      " |  \n",
      " |  box(self, by=None, **kwargs)\n",
      " |      Make a box plot of the DataFrame columns.\n",
      " |      \n",
      " |      A box plot is a method for graphically depicting groups of numerical\n",
      " |      data through their quartiles.\n",
      " |      The box extends from the Q1 to Q3 quartile values of the data,\n",
      " |      with a line at the median (Q2). The whiskers extend from the edges\n",
      " |      of box to show the range of the data. The position of the whiskers\n",
      " |      is set by default to 1.5*IQR (IQR = Q3 - Q1) from the edges of the\n",
      " |      box. Outlier points are those past the end of the whiskers.\n",
      " |      \n",
      " |      For further details see Wikipedia's\n",
      " |      entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot>`__.\n",
      " |      \n",
      " |      A consideration when using this chart is that the box and the whiskers\n",
      " |      can overlap, which is very common when plotting small sets of data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      by : str or sequence\n",
      " |          Column in the DataFrame to group by.\n",
      " |      **kwargs\n",
      " |          Additional keywords are documented in\n",
      " |          :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      :class:`matplotlib.axes.Axes` or numpy.ndarray of them\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      DataFrame.boxplot: Another method to draw a box plot.\n",
      " |      Series.plot.box: Draw a box plot from a Series object.\n",
      " |      matplotlib.pyplot.boxplot: Draw a box plot in matplotlib.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      Draw a box plot from a DataFrame with four columns of randomly\n",
      " |      generated data.\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> data = np.random.randn(25, 4)\n",
      " |          >>> df = pd.DataFrame(data, columns=list('ABCD'))\n",
      " |          >>> ax = df.plot.box()\n",
      " |  \n",
      " |  density = kde(self, bw_method=None, ind=None, **kwargs)\n",
      " |  \n",
      " |  hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None, **kwargs)\n",
      " |      Generate a hexagonal binning plot.\n",
      " |      \n",
      " |      Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`\n",
      " |      (the default), this is a histogram of the number of occurrences\n",
      " |      of the observations at ``(x[i], y[i])``.\n",
      " |      \n",
      " |      If `C` is specified, specifies values at given coordinates\n",
      " |      ``(x[i], y[i])``. These values are accumulated for each hexagonal\n",
      " |      bin and then reduced according to `reduce_C_function`,\n",
      " |      having as default the NumPy's mean function (:meth:`numpy.mean`).\n",
      " |      (If `C` is specified, it must also be a 1-D sequence\n",
      " |      of the same length as `x` and `y`, or a column label.)\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      x : int or str\n",
      " |          The column label or position for x points.\n",
      " |      y : int or str\n",
      " |          The column label or position for y points.\n",
      " |      C : int or str, optional\n",
      " |          The column label or position for the value of `(x, y)` point.\n",
      " |      reduce_C_function : callable, default `np.mean`\n",
      " |          Function of one argument that reduces all the values in a bin to\n",
      " |          a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`).\n",
      " |      gridsize : int or tuple of (int, int), default 100\n",
      " |          The number of hexagons in the x-direction.\n",
      " |          The corresponding number of hexagons in the y-direction is\n",
      " |          chosen in a way that the hexagons are approximately regular.\n",
      " |          Alternatively, gridsize can be a tuple with two elements\n",
      " |          specifying the number of hexagons in the x-direction and the\n",
      " |          y-direction.\n",
      " |      **kwargs\n",
      " |          Additional keyword arguments are documented in\n",
      " |          :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      matplotlib.AxesSubplot\n",
      " |          The matplotlib ``Axes`` on which the hexbin is plotted.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      DataFrame.plot : Make plots of a DataFrame.\n",
      " |      matplotlib.pyplot.hexbin : Hexagonal binning plot using matplotlib,\n",
      " |          the matplotlib function that is used under the hood.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      The following examples are generated with random data from\n",
      " |      a normal distribution.\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> n = 10000\n",
      " |          >>> df = pd.DataFrame({'x': np.random.randn(n),\n",
      " |          ...                    'y': np.random.randn(n)})\n",
      " |          >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)\n",
      " |      \n",
      " |      The next example uses `C` and `np.sum` as `reduce_C_function`.\n",
      " |      Note that `'observations'` values ranges from 1 to 5 but the result\n",
      " |      plot shows values up to more than 25. This is because of the\n",
      " |      `reduce_C_function`.\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> n = 500\n",
      " |          >>> df = pd.DataFrame({\n",
      " |          ...     'coord_x': np.random.uniform(-3, 3, size=n),\n",
      " |          ...     'coord_y': np.random.uniform(30, 50, size=n),\n",
      " |          ...     'observations': np.random.randint(1,5, size=n)\n",
      " |          ...     })\n",
      " |          >>> ax = df.plot.hexbin(x='coord_x',\n",
      " |          ...                     y='coord_y',\n",
      " |          ...                     C='observations',\n",
      " |          ...                     reduce_C_function=np.sum,\n",
      " |          ...                     gridsize=10,\n",
      " |          ...                     cmap=\"viridis\")\n",
      " |  \n",
      " |  hist(self, by=None, bins=10, **kwargs)\n",
      " |      Draw one histogram of the DataFrame's columns.\n",
      " |      \n",
      " |      A histogram is a representation of the distribution of data.\n",
      " |      This function groups the values of all given Series in the DataFrame\n",
      " |      into bins and draws all bins in one :class:`matplotlib.axes.Axes`.\n",
      " |      This is useful when the DataFrame's Series are in a similar scale.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      by : str or sequence, optional\n",
      " |          Column in the DataFrame to group by.\n",
      " |      bins : int, default 10\n",
      " |          Number of histogram bins to be used.\n",
      " |      **kwargs\n",
      " |          Additional keyword arguments are documented in\n",
      " |          :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      class:`matplotlib.AxesSubplot`\n",
      " |          Return a histogram plot.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      DataFrame.hist : Draw histograms per DataFrame's Series.\n",
      " |      Series.hist : Draw a histogram with Series' data.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      When we draw a dice 6000 times, we expect to get each value around 1000\n",
      " |      times. But when we draw two dices and sum the result, the distribution\n",
      " |      is going to be quite different. A histogram illustrates those\n",
      " |      distributions.\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> df = pd.DataFrame(\n",
      " |          ...     np.random.randint(1, 7, 6000),\n",
      " |          ...     columns = ['one'])\n",
      " |          >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)\n",
      " |          >>> ax = df.plot.hist(bins=12, alpha=0.5)\n",
      " |  \n",
      " |  kde(self, bw_method=None, ind=None, **kwargs)\n",
      " |      Generate Kernel Density Estimate plot using Gaussian kernels.\n",
      " |      \n",
      " |      In statistics, `kernel density estimation`_ (KDE) is a non-parametric\n",
      " |      way to estimate the probability density function (PDF) of a random\n",
      " |      variable. This function uses Gaussian kernels and includes automatic\n",
      " |      bandwidth determination.\n",
      " |      \n",
      " |      .. _kernel density estimation:\n",
      " |          https://en.wikipedia.org/wiki/Kernel_density_estimation\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      bw_method : str, scalar or callable, optional\n",
      " |          The method used to calculate the estimator bandwidth. This can be\n",
      " |          'scott', 'silverman', a scalar constant or a callable.\n",
      " |          If None (default), 'scott' is used.\n",
      " |          See :class:`scipy.stats.gaussian_kde` for more information.\n",
      " |      ind : NumPy array or int, optional\n",
      " |          Evaluation points for the estimated PDF. If None (default),\n",
      " |          1000 equally spaced points are used. If `ind` is a NumPy array, the\n",
      " |          KDE is evaluated at the points passed. If `ind` is an integer,\n",
      " |          `ind` number of equally spaced points are used.\n",
      " |      **kwargs\n",
      " |          Additional keyword arguments are documented in\n",
      " |          :meth:`pandas.%(this-datatype)s.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      matplotlib.axes.Axes or numpy.ndarray of them\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      scipy.stats.gaussian_kde : Representation of a kernel-density\n",
      " |          estimate using Gaussian kernels. This is the function used\n",
      " |          internally to estimate the PDF.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      Given a Series of points randomly sampled from an unknown\n",
      " |      distribution, estimate its PDF using KDE with automatic\n",
      " |      bandwidth determination and plot the results, evaluating them at\n",
      " |      1000 equally spaced points (default):\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])\n",
      " |          >>> ax = s.plot.kde()\n",
      " |      \n",
      " |      A scalar bandwidth can be specified. Using a small bandwidth value can\n",
      " |      lead to over-fitting, while using a large bandwidth value may result\n",
      " |      in under-fitting:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = s.plot.kde(bw_method=0.3)\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = s.plot.kde(bw_method=3)\n",
      " |      \n",
      " |      Finally, the `ind` parameter determines the evaluation points for the\n",
      " |      plot of the estimated PDF:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])\n",
      " |      \n",
      " |      For DataFrame, it works in the same way:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> df = pd.DataFrame({\n",
      " |          ...     'x': [1, 2, 2.5, 3, 3.5, 4, 5],\n",
      " |          ...     'y': [4, 4, 4.5, 5, 5.5, 6, 6],\n",
      " |          ... })\n",
      " |          >>> ax = df.plot.kde()\n",
      " |      \n",
      " |      A scalar bandwidth can be specified. Using a small bandwidth value can\n",
      " |      lead to over-fitting, while using a large bandwidth value may result\n",
      " |      in under-fitting:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = df.plot.kde(bw_method=0.3)\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = df.plot.kde(bw_method=3)\n",
      " |      \n",
      " |      Finally, the `ind` parameter determines the evaluation points for the\n",
      " |      plot of the estimated PDF:\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])\n",
      " |  \n",
      " |  line(self, x=None, y=None, **kwargs)\n",
      " |      Plot Series or DataFrame as lines.\n",
      " |      \n",
      " |      This function is useful to plot lines using DataFrame's values\n",
      " |      as coordinates.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      x : label or position, optional\n",
      " |          Allows plotting of one column versus another. If not specified,\n",
      " |          the index of the DataFrame is used.\n",
      " |      y : label or position, optional\n",
      " |          Allows plotting of one column versus another. If not specified,\n",
      " |          all numerical columns are used.\n",
      " |      color : str, array-like, or dict, optional\n",
      " |          The color for each of the DataFrame's columns. Possible values are:\n",
      " |      \n",
      " |          - A single color string referred to by name, RGB or RGBA code,\n",
      " |              for instance 'red' or '#a98d19'.\n",
      " |      \n",
      " |          - A sequence of color strings referred to by name, RGB or RGBA\n",
      " |              code, which will be used for each column recursively. For\n",
      " |              instance ['green','yellow'] each column's line will be filled in\n",
      " |              green or yellow, alternatively. If there is only a single column to\n",
      " |              be plotted, then only the first color from the color list will be\n",
      " |              used.\n",
      " |      \n",
      " |          - A dict of the form {column name : color}, so that each column will be\n",
      " |              colored accordingly. For example, if your columns are called `a` and\n",
      " |              `b`, then passing {'a': 'green', 'b': 'red'} will color lines for\n",
      " |              column `a` in green and lines for column `b` in red.\n",
      " |      \n",
      " |          .. versionadded:: 1.1.0\n",
      " |      \n",
      " |      **kwargs\n",
      " |          Additional keyword arguments are documented in\n",
      " |          :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      matplotlib.axes.Axes or np.ndarray of them\n",
      " |          An ndarray is returned with one :class:`matplotlib.axes.Axes`\n",
      " |          per column when ``subplots=True``.\n",
      " |      \n",
      " |              See Also\n",
      " |              --------\n",
      " |              matplotlib.pyplot.plot : Plot y versus x as lines and/or markers.\n",
      " |      \n",
      " |              Examples\n",
      " |              --------\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  >>> s = pd.Series([1, 3, 2])\n",
      " |                  >>> s.plot.line()\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  The following example shows the populations for some animals\n",
      " |                  over the years.\n",
      " |      \n",
      " |                  >>> df = pd.DataFrame({\n",
      " |                  ...    'pig': [20, 18, 489, 675, 1776],\n",
      " |                  ...    'horse': [4, 25, 281, 600, 1900]\n",
      " |                  ...    }, index=[1990, 1997, 2003, 2009, 2014])\n",
      " |                  >>> lines = df.plot.line()\n",
      " |      \n",
      " |              .. plot::\n",
      " |                 :context: close-figs\n",
      " |      \n",
      " |                 An example with subplots, so an array of axes is returned.\n",
      " |      \n",
      " |                 >>> axes = df.plot.line(subplots=True)\n",
      " |                 >>> type(axes)\n",
      " |                 <class 'numpy.ndarray'>\n",
      " |      \n",
      " |              .. plot::\n",
      " |                 :context: close-figs\n",
      " |      \n",
      " |                 Let's repeat the same example, but specifying colors for\n",
      " |                 each column (in this case, for each animal).\n",
      " |      \n",
      " |                 >>> axes = df.plot.line(\n",
      " |                 ...     subplots=True, color={\"pig\": \"pink\", \"horse\": \"#742802\"}\n",
      " |                 ... )\n",
      " |      \n",
      " |              .. plot::\n",
      " |                  :context: close-figs\n",
      " |      \n",
      " |                  The following example shows the relationship between both\n",
      " |                  populations.\n",
      " |      \n",
      " |                  >>> lines = df.plot.line(x='pig', y='horse')\n",
      " |  \n",
      " |  pie(self, **kwargs)\n",
      " |      Generate a pie plot.\n",
      " |      \n",
      " |      A pie plot is a proportional representation of the numerical data in a\n",
      " |      column. This function wraps :meth:`matplotlib.pyplot.pie` for the\n",
      " |      specified column. If no column reference is passed and\n",
      " |      ``subplots=True`` a pie plot is drawn for each numerical column\n",
      " |      independently.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      y : int or label, optional\n",
      " |          Label or position of the column to plot.\n",
      " |          If not provided, ``subplots=True`` argument must be passed.\n",
      " |      **kwargs\n",
      " |          Keyword arguments to pass on to :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      matplotlib.axes.Axes or np.ndarray of them\n",
      " |          A NumPy array is returned when `subplots` is True.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      Series.plot.pie : Generate a pie plot for a Series.\n",
      " |      DataFrame.plot : Make plots of a DataFrame.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      In the example below we have a DataFrame with the information about\n",
      " |      planet's mass and radius. We pass the 'mass' column to the\n",
      " |      pie function to get a pie plot.\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> df = pd.DataFrame({'mass': [0.330, 4.87 , 5.97],\n",
      " |          ...                    'radius': [2439.7, 6051.8, 6378.1]},\n",
      " |          ...                   index=['Mercury', 'Venus', 'Earth'])\n",
      " |          >>> plot = df.plot.pie(y='mass', figsize=(5, 5))\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> plot = df.plot.pie(subplots=True, figsize=(11, 6))\n",
      " |  \n",
      " |  scatter(self, x, y, s=None, c=None, **kwargs)\n",
      " |      Create a scatter plot with varying marker point size and color.\n",
      " |      \n",
      " |      The coordinates of each point are defined by two dataframe columns and\n",
      " |      filled circles are used to represent each point. This kind of plot is\n",
      " |      useful to see complex correlations between two variables. Points could\n",
      " |      be for instance natural 2D coordinates like longitude and latitude in\n",
      " |      a map or, in general, any pair of metrics that can be plotted against\n",
      " |      each other.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      x : int or str\n",
      " |          The column name or column position to be used as horizontal\n",
      " |          coordinates for each point.\n",
      " |      y : int or str\n",
      " |          The column name or column position to be used as vertical\n",
      " |          coordinates for each point.\n",
      " |      s : str, scalar or array-like, optional\n",
      " |          The size of each point. Possible values are:\n",
      " |      \n",
      " |          - A string with the name of the column to be used for marker's size.\n",
      " |      \n",
      " |          - A single scalar so all points have the same size.\n",
      " |      \n",
      " |          - A sequence of scalars, which will be used for each point's size\n",
      " |            recursively. For instance, when passing [2,14] all points size\n",
      " |            will be either 2 or 14, alternatively.\n",
      " |      \n",
      " |            .. versionchanged:: 1.1.0\n",
      " |      \n",
      " |      c : str, int or array-like, optional\n",
      " |          The color of each point. Possible values are:\n",
      " |      \n",
      " |          - A single color string referred to by name, RGB or RGBA code,\n",
      " |            for instance 'red' or '#a98d19'.\n",
      " |      \n",
      " |          - A sequence of color strings referred to by name, RGB or RGBA\n",
      " |            code, which will be used for each point's color recursively. For\n",
      " |            instance ['green','yellow'] all points will be filled in green or\n",
      " |            yellow, alternatively.\n",
      " |      \n",
      " |          - A column name or position whose values will be used to color the\n",
      " |            marker points according to a colormap.\n",
      " |      \n",
      " |      **kwargs\n",
      " |          Keyword arguments to pass on to :meth:`DataFrame.plot`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      :class:`matplotlib.axes.Axes` or numpy.ndarray of them\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      matplotlib.pyplot.scatter : Scatter plot using multiple input data\n",
      " |          formats.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      Let's see how to draw a scatter plot using coordinates from the values\n",
      " |      in a DataFrame's columns.\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n",
      " |          ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],\n",
      " |          ...                   columns=['length', 'width', 'species'])\n",
      " |          >>> ax1 = df.plot.scatter(x='length',\n",
      " |          ...                       y='width',\n",
      " |          ...                       c='DarkBlue')\n",
      " |      \n",
      " |      And now with the color determined by a column as well.\n",
      " |      \n",
      " |      .. plot::\n",
      " |          :context: close-figs\n",
      " |      \n",
      " |          >>> ax2 = df.plot.scatter(x='length',\n",
      " |          ...                       y='width',\n",
      " |          ...                       c='species',\n",
      " |          ...                       colormap='viridis')\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from pandas.core.base.PandasObject:\n",
      " |  \n",
      " |  __repr__(self) -> 'str'\n",
      " |      Return a string representation for a particular object.\n",
      " |  \n",
      " |  __sizeof__(self) -> 'int'\n",
      " |      Generates the total memory usage for an object that returns\n",
      " |      either a value or Series of values\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from pandas.core.base.PandasObject:\n",
      " |  \n",
      " |  __annotations__ = {'_cache': 'dict[str, Any]'}\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from pandas.core.accessor.DirNamesMixin:\n",
      " |  \n",
      " |  __dir__(self) -> 'list[str]'\n",
      " |      Provide method name lookup and completion.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Only provide 'public' methods.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from pandas.core.accessor.DirNamesMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(df.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d13a739",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}