Time Series EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91dd09ad",
   "metadata": {},
   "source": [
    "## Time Series EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70d30d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas-datareader in c:\\users\\win10\\anaconda3\\lib\\site-packages (0.10.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from pandas-datareader) (1.3.4)\n",
      "Requirement already satisfied: lxml in c:\\users\\win10\\anaconda3\\lib\\site-packages (from pandas-datareader) (4.6.3)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from pandas-datareader) (2.26.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (1.20.3)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas-datareader) (1.16.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.2)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (1.26.7)\n"
     ]
    }
   ],
   "source": [
    "## Install Pandas Data Reader\n",
    "!pip install pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9aca849",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr\n",
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e044285a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tesla=pdr.get_data_yahoo('TSLA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd43450a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-17</th>\n",
       "      <td>918.500000</td>\n",
       "      <td>874.099976</td>\n",
       "      <td>913.260010</td>\n",
       "      <td>876.349976</td>\n",
       "      <td>18392800.0</td>\n",
       "      <td>876.349976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-18</th>\n",
       "      <td>886.869995</td>\n",
       "      <td>837.609985</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>856.979980</td>\n",
       "      <td>22710500.0</td>\n",
       "      <td>856.979980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-22</th>\n",
       "      <td>856.729980</td>\n",
       "      <td>801.099976</td>\n",
       "      <td>834.130005</td>\n",
       "      <td>821.530029</td>\n",
       "      <td>27762700.0</td>\n",
       "      <td>821.530029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23</th>\n",
       "      <td>835.299988</td>\n",
       "      <td>760.559998</td>\n",
       "      <td>830.429993</td>\n",
       "      <td>764.039978</td>\n",
       "      <td>31752300.0</td>\n",
       "      <td>764.039978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-24</th>\n",
       "      <td>802.479980</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.390015</td>\n",
       "      <td>800.770020</td>\n",
       "      <td>44876700.0</td>\n",
       "      <td>800.770020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2022-02-17  918.500000  874.099976  913.260010  876.349976  18392800.0   \n",
       "2022-02-18  886.869995  837.609985  886.000000  856.979980  22710500.0   \n",
       "2022-02-22  856.729980  801.099976  834.130005  821.530029  27762700.0   \n",
       "2022-02-23  835.299988  760.559998  830.429993  764.039978  31752300.0   \n",
       "2022-02-24  802.479980  700.000000  700.390015  800.770020  44876700.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2022-02-17  876.349976  \n",
       "2022-02-18  856.979980  \n",
       "2022-02-22  821.530029  \n",
       "2022-02-23  764.039978  \n",
       "2022-02-24  800.770020  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42b2ed0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tesla['High'].plot(figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2564b531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## xlimit and y limit\n",
    "df_tesla['High'].plot(xlim=['2020-01-01','2021-09-01'],figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a927152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## xlimit and y limit\n",
    "df_tesla['High'].plot(xlim=['2020-01-01','2021-09-01'],ylim=[0,900],figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a1c26cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## xlimit and y limit and coloring\n",
    "df_tesla['High'].plot(xlim=['2020-01-01','2021-09-01'],ylim=[0,900],figsize=(12,4),ls='--',c='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5abbe0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-02-27', '2017-02-28', '2017-03-01', '2017-03-02',\n",
       "               '2017-03-03', '2017-03-06', '2017-03-07', '2017-03-08',\n",
       "               '2017-03-09', '2017-03-10',\n",
       "               ...\n",
       "               '2022-02-10', '2022-02-11', '2022-02-14', '2022-02-15',\n",
       "               '2022-02-16', '2022-02-17', '2022-02-18', '2022-02-22',\n",
       "               '2022-02-23', '2022-02-24'],\n",
       "              dtype='datetime64[ns]', name='Date', length=1259, freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7214bae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "index=df_tesla.loc['2020-01-01':'2021-09-01'].index\n",
    "share_open=df_tesla.loc['2020-01-01':'2021-09-01']['Open']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d29fec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-02     84.900002\n",
       "2020-01-03     88.099998\n",
       "2020-01-06     88.094002\n",
       "2020-01-07     92.279999\n",
       "2020-01-08     94.739998\n",
       "                 ...    \n",
       "2021-08-26    708.309998\n",
       "2021-08-27    705.000000\n",
       "2021-08-30    714.719971\n",
       "2021-08-31    733.000000\n",
       "2021-09-01    734.080017\n",
       "Name: Open, Length: 421, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a4c4cabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-06', '2020-01-07',\n",
       "               '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-13',\n",
       "               '2020-01-14', '2020-01-15',\n",
       "               ...\n",
       "               '2021-08-19', '2021-08-20', '2021-08-23', '2021-08-24',\n",
       "               '2021-08-25', '2021-08-26', '2021-08-27', '2021-08-30',\n",
       "               '2021-08-31', '2021-09-01'],\n",
       "              dtype='datetime64[ns]', name='Date', length=421, freq=None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dea1b66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5da74d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x217f22dd670>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,axis=plt.subplots()\n",
    "plt.tight_layout()\n",
    "## Preventing overlapping\n",
    "figure.autofmt_xdate()\n",
    "axis.plot(index,share_open)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "83149bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Datetime Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8350ca58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tesla=df_tesla.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6746d682",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1259 entries, 0 to 1258\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       1259 non-null   datetime64[ns]\n",
      " 1   High       1259 non-null   float64       \n",
      " 2   Low        1259 non-null   float64       \n",
      " 3   Open       1259 non-null   float64       \n",
      " 4   Close      1259 non-null   float64       \n",
      " 5   Volume     1259 non-null   float64       \n",
      " 6   Adj Close  1259 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(6)\n",
      "memory usage: 69.0 KB\n"
     ]
    }
   ],
   "source": [
    "df_tesla.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "31c87e86",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tesla=df_tesla.set_index('Date',drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7423d545",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-27</th>\n",
       "      <td>49.672001</td>\n",
       "      <td>48.402000</td>\n",
       "      <td>49.633999</td>\n",
       "      <td>49.245998</td>\n",
       "      <td>57304000.0</td>\n",
       "      <td>49.245998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>50.200001</td>\n",
       "      <td>48.779999</td>\n",
       "      <td>48.838001</td>\n",
       "      <td>49.998001</td>\n",
       "      <td>30390500.0</td>\n",
       "      <td>49.998001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>50.970001</td>\n",
       "      <td>49.821999</td>\n",
       "      <td>50.835999</td>\n",
       "      <td>50.004002</td>\n",
       "      <td>24047500.0</td>\n",
       "      <td>50.004002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-02</th>\n",
       "      <td>50.655998</td>\n",
       "      <td>49.653999</td>\n",
       "      <td>49.942001</td>\n",
       "      <td>50.096001</td>\n",
       "      <td>16759000.0</td>\n",
       "      <td>50.096001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>50.380001</td>\n",
       "      <td>49.799999</td>\n",
       "      <td>50.147999</td>\n",
       "      <td>50.313999</td>\n",
       "      <td>14597000.0</td>\n",
       "      <td>50.313999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close\n",
       "Date                                                                         \n",
       "2017-02-27  49.672001  48.402000  49.633999  49.245998  57304000.0  49.245998\n",
       "2017-02-28  50.200001  48.779999  48.838001  49.998001  30390500.0  49.998001\n",
       "2017-03-01  50.970001  49.821999  50.835999  50.004002  24047500.0  50.004002\n",
       "2017-03-02  50.655998  49.653999  49.942001  50.096001  16759000.0  50.096001\n",
       "2017-03-03  50.380001  49.799999  50.147999  50.313999  14597000.0  50.313999"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ed080582",
   "metadata": {},
   "outputs": [],
   "source": [
    "## datetime\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8bdca59d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 11, 21, 0, 0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime(2021,11,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1f93e9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2022, 2, 25, 19, 34, 3, 931965)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5069c363",
   "metadata": {},
   "outputs": [],
   "source": [
    "date=datetime(2021,11,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "141a0f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 11, 21, 0, 0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "10c90eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2021, 11, 21)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8d5c191c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bff30466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "602b8aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "020b199c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bec9dbb",
   "metadata": {},
   "source": [
    "## Time Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a1dc9a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-27</th>\n",
       "      <td>49.672001</td>\n",
       "      <td>48.402000</td>\n",
       "      <td>49.633999</td>\n",
       "      <td>49.245998</td>\n",
       "      <td>57304000.0</td>\n",
       "      <td>49.245998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>50.200001</td>\n",
       "      <td>48.779999</td>\n",
       "      <td>48.838001</td>\n",
       "      <td>49.998001</td>\n",
       "      <td>30390500.0</td>\n",
       "      <td>49.998001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>50.970001</td>\n",
       "      <td>49.821999</td>\n",
       "      <td>50.835999</td>\n",
       "      <td>50.004002</td>\n",
       "      <td>24047500.0</td>\n",
       "      <td>50.004002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-02</th>\n",
       "      <td>50.655998</td>\n",
       "      <td>49.653999</td>\n",
       "      <td>49.942001</td>\n",
       "      <td>50.096001</td>\n",
       "      <td>16759000.0</td>\n",
       "      <td>50.096001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>50.380001</td>\n",
       "      <td>49.799999</td>\n",
       "      <td>50.147999</td>\n",
       "      <td>50.313999</td>\n",
       "      <td>14597000.0</td>\n",
       "      <td>50.313999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close\n",
       "Date                                                                         \n",
       "2017-02-27  49.672001  48.402000  49.633999  49.245998  57304000.0  49.245998\n",
       "2017-02-28  50.200001  48.779999  48.838001  49.998001  30390500.0  49.998001\n",
       "2017-03-01  50.970001  49.821999  50.835999  50.004002  24047500.0  50.004002\n",
       "2017-03-02  50.655998  49.653999  49.942001  50.096001  16759000.0  50.096001\n",
       "2017-03-03  50.380001  49.799999  50.147999  50.313999  14597000.0  50.313999"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3a52fae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>49.299999</td>\n",
       "      <td>48.402000</td>\n",
       "      <td>48.838001</td>\n",
       "      <td>48.737999</td>\n",
       "      <td>14597000.0</td>\n",
       "      <td>48.737999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>52.066002</td>\n",
       "      <td>48.917999</td>\n",
       "      <td>50.556000</td>\n",
       "      <td>50.112000</td>\n",
       "      <td>15403500.0</td>\n",
       "      <td>50.112000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>37.335999</td>\n",
       "      <td>35.397999</td>\n",
       "      <td>36.220001</td>\n",
       "      <td>35.793999</td>\n",
       "      <td>12328000.0</td>\n",
       "      <td>35.793999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>80.972000</td>\n",
       "      <td>70.101997</td>\n",
       "      <td>74.940002</td>\n",
       "      <td>72.244003</td>\n",
       "      <td>17357700.0</td>\n",
       "      <td>72.244003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>566.210022</td>\n",
       "      <td>539.489990</td>\n",
       "      <td>552.549988</td>\n",
       "      <td>563.000000</td>\n",
       "      <td>9800600.0</td>\n",
       "      <td>563.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>802.479980</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.390015</td>\n",
       "      <td>764.039978</td>\n",
       "      <td>16909700.0</td>\n",
       "      <td>764.039978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2017-12-31   49.299999   48.402000   48.838001   48.737999  14597000.0   \n",
       "2018-12-31   52.066002   48.917999   50.556000   50.112000  15403500.0   \n",
       "2019-12-31   37.335999   35.397999   36.220001   35.793999  12328000.0   \n",
       "2020-12-31   80.972000   70.101997   74.940002   72.244003  17357700.0   \n",
       "2021-12-31  566.210022  539.489990  552.549988  563.000000   9800600.0   \n",
       "2022-12-31  802.479980  700.000000  700.390015  764.039978  16909700.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2017-12-31   48.737999  \n",
       "2018-12-31   50.112000  \n",
       "2019-12-31   35.793999  \n",
       "2020-12-31   72.244003  \n",
       "2021-12-31  563.000000  \n",
       "2022-12-31  764.039978  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.resample(rule='A').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f6171bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>77.921997</td>\n",
       "      <td>75.870003</td>\n",
       "      <td>77.337997</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>98957000.0</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>77.491997</td>\n",
       "      <td>73.424004</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.914001</td>\n",
       "      <td>168248500.0</td>\n",
       "      <td>75.914001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>87.061996</td>\n",
       "      <td>85.269997</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>86.188004</td>\n",
       "      <td>150030500.0</td>\n",
       "      <td>86.188004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>718.719971</td>\n",
       "      <td>691.119995</td>\n",
       "      <td>699.989990</td>\n",
       "      <td>705.669983</td>\n",
       "      <td>304694000.0</td>\n",
       "      <td>705.669983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>1243.489990</td>\n",
       "      <td>1217.000000</td>\n",
       "      <td>1234.410034</td>\n",
       "      <td>1229.910034</td>\n",
       "      <td>89396500.0</td>\n",
       "      <td>1229.910034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>1208.000000</td>\n",
       "      <td>1136.040039</td>\n",
       "      <td>1189.550049</td>\n",
       "      <td>1199.780029</td>\n",
       "      <td>50521900.0</td>\n",
       "      <td>1199.780029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close       Volume  \\\n",
       "Date                                                                          \n",
       "2017-12-31    77.921997    75.870003    77.337997    77.000000   98957000.0   \n",
       "2018-12-31    77.491997    73.424004    75.000000    75.914001  168248500.0   \n",
       "2019-12-31    87.061996    85.269997    87.000000    86.188004  150030500.0   \n",
       "2020-12-31   718.719971   691.119995   699.989990   705.669983  304694000.0   \n",
       "2021-12-31  1243.489990  1217.000000  1234.410034  1229.910034   89396500.0   \n",
       "2022-12-31  1208.000000  1136.040039  1189.550049  1199.780029   50521900.0   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "2017-12-31    77.000000  \n",
       "2018-12-31    75.914001  \n",
       "2019-12-31    86.188004  \n",
       "2020-12-31   705.669983  \n",
       "2021-12-31  1229.910034  \n",
       "2022-12-31  1199.780029  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.resample(rule='A').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6d8f06e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fBdx1RHERkd5FefUnAhNjOaaIVI27c/tLC1m3fR+TfjCCFplpYYckSUrfzBapo16ck8s/5m3kppG9Ofm4VmGHI0lMiUKkDsrZsoc7X17MiONacsPZPcMOR5KcEoVIHVNYXMoNz39Go7QGPDx6CA30k6YSI91MLVLH/Pr1pXy+aTdPfW8YbZtlhB2O1AHqUYjUIW8u2sSzH6/lmtO7c3aftmGHI3WEEoVIHZG7Yx8/e3E+Aztm8bPz+oQdjtQhShQidUBJaRk3TZ5HmcMjY4aQlqr/2hI/GqMQqQP+953lzFm7g4dHD6Zb68yww5E6Rh87RJLcv1Zs5bH3VvLtYZ0YNVhTh0v8KVGIJLH83UX8aMo8emQ34Rff6B92OFJH6dKTSJIqK3Nu+ft8CvYX89zVw2mcpv/OUjPUoxBJUpP+vY73l+dzx0X96NOuWdjhSB2mRCGShDYVFHLf659zao9WfOfkLmGHI3WcEoVIknF37nh5EQdKy7j3mwM1dbjUOCUKkSTz5qJNzFiymR+f05uurXQrrNQ8JQqRJFKwr5g7py+mf4dmXH1697DDkXpCt0mIJJF731jK9r0H+Mv3TiK1gT7nSe3QK00kSXy0ciuT/72ea87ozoCOWWGHI/WIEoVIEigsLuW2aQvp2qoxN4/sHXY4Us/o0pNIEnjonRWs2baP5685mUZpDcIOR+oZ9ShEEtyiDQX86YNVfHtYJ07t2TrscKQeUqIQSWAlpWWMn7aAFo3TuO2CvmGHI/WULj2JJLCnPlzNog27+P0VQ2neOC3scKSeUo9CJEGt3baXB2cs52t923LBwHZhhyP1mBKFSAJyd257aSGpKSn86pL+mqZDQhVTojCz5mb2opl9bmZLzewUM2tpZjPMbEXw2CKq/gQzyzGzZWZ2buzhi9RNL87J5cOcbdx6fh/aZzUKOxyp52LtUTwMvOnufYATgKXAeGCmu/cCZgbPMbN+wGigP3Ae8JiZ6T4/kSPk7y7inteWMqxrC/5zuGaGlfBVO1GYWTPgK8CTAO5+wN13AqOAZ4JqzwCXBMujgMnuXuTuq4EcYHh1jy9SV939ymL2HyjlvssGkpKiS04Svlh6FMcB+cBfzGyumf3ZzDKBtu6eBxA8tgnqdwTWR22fG5R9iZmNM7PZZjY7Pz8/hhBFkss7Szbz6oI8bji7Jz3bNA07HBEgtkSRCgwFHnf3IcBegstMFSjvo5GXV9Hdn3D3Ye4+LDs7O4YQRZLH7sJi7nh5Eb3bNuHaM3uEHY7IF2JJFLlArrvPCp6/SCRxbDaz9gDB45ao+p2jtu8EbIzh+CJ1ym/eWsamXYXcd9kg0lJ1Q6Ikjmq/Gt19E7DezI4PikYCS4DpwNigbCzwcrA8HRhtZulm1h3oBXxa3eOL1CWz12znuU/WMvaUbgzt0uLoG4jUoli/mX0j8DczSwNWAVcRST5TzOxqYB1wOYC7LzazKUSSSQlwvbuXxnh8kaRXVFLK+GkL6ZDViJ+ce/zRNxCpZTElCnefBwwrZ9XICupPBCbGckyRuuaxd1eSs2UPf7nqJJqka1YdSTy6ECoSouWbd/PYezmMGtyBrx7f5ugbiIRAiUIkJKVlzq1TF9AkPZU7L+oXdjgiFVKiEAnJXz9Zy9x1O7njon60apIedjgiFVKiEAnBhp37eeDNzzmjV2suHVLu905FEoYShUgtc3duf2khZQ6/vnSgZoaVhKdEIVLLXlmQx7vL8rnl673p3LJx2OGIHJUShUgt2rH3AHdPX8wJnbK46rTuYYcjUiW6aVukFt3z2lIK9hfz12tOpoFmhpUkoR6FSC35YEU+Uz/L5doze9C3fbOwwxGpMiUKkVqw70AJt720kONaZ3LD2T3DDkfkmOjSk0gt+N8Zy1m/fT8vjBtBRkP9sKMkF/UoRGrYgtydPPmv1VxxchdOPq5V2OGIHDMlCpEaVFxaxq1TF9K6STrjz+8Tdjgi1aJLTyI16E8frGJp3i7+eOWJNMtoGHY4ItWiHoVIDVm9dS8PvbOC8we049z+7cIOR6TalChEakBZmTN+6gLSU1O4+xv9ww5HJCZKFCI1YMrs9cxavZ2fX9CXNs0ywg5HJCZKFCJxtmVXIRNfX8qI41ryHyd1DjsckZgpUYjE2V3TF1NUUsa93xykmWGlTlCiEImjNxdt4o1Fm7j5a73o3joz7HBE4kKJQiROCvYXc+fLi+jbvhk/OOO4sMMRiRslCpE4uf/Nz9m6p4j7LxtIwwb6ryV1h17NInEwa9U2np+1jqtP786gTs3DDkckrpQoRGJUWFzKhGkL6dyyET86p3fY4YjEnabwEInRo//MYdXWvTx39XAap+m/lNQ9MfcozKyBmc01s1eD5y3NbIaZrQgeW0TVnWBmOWa2zMzOjfXYImFbmreLP/zfSi4b2okzemWHHY5IjYjHpaebgKVRz8cDM929FzAzeI6Z9QNGA/2B84DHzEwT80vSKg2m6chq1JDbL+wbdjgiNSamRGFmnYALgT9HFY8CngmWnwEuiSqf7O5F7r4ayAGGx3J8kTA9/dEa5ucWcNc3+tMiMy3scERqTKw9ioeAnwFlUWVt3T0PIHhsE5R3BNZH1csNykSSzvrt+/jtW8s4u08bLh7UPuxwRGpUtROFmV0EbHH3OVXdpJwyr2Df48xstpnNzs/Pr26IIjXC3fn5PxaRYvCrSwZomg6p82LpUZwGfMPM1gCTgbPN7K/AZjNrDxA8bgnq5wLRM6R1AjaWt2N3f8Ldh7n7sOxsDRBKYvnHvA28vzyfn53Xh47NG4UdjkiNq3aicPcJ7t7J3bsRGaT+p7t/B5gOjA2qjQVeDpanA6PNLN3MugO9gE+rHblICLbtKeKXryxhaJfmfGdE17DDEakVNXHT933AFDO7GlgHXA7g7ovNbAqwBCgBrnf30ho4vkiN+dWrS9hTVMJ9lw2iQYouOUn9EJdE4e7vAe8Fy9uAkRXUmwhMjMcxRWrbu8u28I95G7lpZC96t20adjgitUZTeIhUwd6iEm5/aRE92zThv7/aI+xwRGqV5hsQqYLfvr2MjQX7efHaU0hP1fdEpX5Rj0LkKOau28HTH63hyhFdObFry7DDEal1ShQilThQUsb4qQtp1yyDn557fNjhiIRCl55EKvHH/1vJss27eXLsMJpmNAw7HJFQqEchUoGcLXt45J85XDSoPSP7tg07HJHQKFGIlKOszJkwbQGN0hpw18X9ww5HJFRKFCLleP7Tdfx7zQ7uuKgf2U3Tww5HJFRKFCJH2FRQyH1vfM7pPVtz2VBNcCyiRCESxd25/R+LKCkr49eXDtTMsCIoUYgc5o1Fm3hn6WZuOed4urRqHHY4IglBiUIkULCvmDtfXszAjllcdVq3sMMRSRj6HoVI4NevL2XHvgM88/2TSG2gz1AiB+l/gwjwUc5WXpi9nnFfOY7+HbLCDkckoShRSL1XWFzKhJcW0q1VY24a2SvscEQSji49Sb330DsrWLttH8//4GQyGmpmWJEjqUch9dqiDQX86YNVjD6pM6f2aB12OCIJSYlC6q2S0jLGT1tAy8w0JpzfN+xwRBKWLj1JvfXUh6tZtGEXj//nULIaa2ZYkYqoRyH10tpte3lwxnK+3q8t5w1oF3Y4IglNiULqHXfntpcW0jAlhV+OGqBpOkSOQolC6p0X5+TyYc42xl/Qh3ZZGWGHI5LwlCikXsnfXcQ9ry1leLeWjDmpS9jhiCQFJQqpV+5+ZTH7D5Ry72UDSUnRJSeRqlCikHrjnSWbeXVBHj8c2ZMe2U3CDkckaVQ7UZhZZzN718yWmtliM7spKG9pZjPMbEXw2CJqmwlmlmNmy8zs3HicgEhV7C4s5o6XF9GnXVPGfaVH2OGIJJVYehQlwC3u3hcYAVxvZv2A8cBMd+8FzAyeE6wbDfQHzgMeMzPNlyC14jdvLWPTrkLuu2wQaanqSIsci2r/j3H3PHf/LFjeDSwFOgKjgGeCas8AlwTLo4DJ7l7k7quBHGB4dY8vUlWz12znuU/WctWp3RncuXnY4Ygknbh8tDKzbsAQYBbQ1t3zIJJMgDZBtY7A+qjNcoMykRpTVFLK+GkL6ZDViFu+3jvscESSUsyJwsyaAFOBm919V2VVyynzCvY5zsxmm9ns/Pz8WEOUeuz3764kZ8seJl46gMx0zVgjUh0xJQoza0gkSfzN3acFxZvNrH2wvj2wJSjPBTpHbd4J2Fjeft39CXcf5u7DsrOzYwlR6rFlm3bz+Hs5XDqkI2cd3+boG4hIuWK568mAJ4Gl7v5g1KrpwNhgeSzwclT5aDNLN7PuQC/g0+oeX6QypWXOrVMX0DSjIXdc1C/scESSWix98dOAK4GFZjYvKLsNuA+YYmZXA+uAywHcfbGZTQGWELlj6np3L43h+CIVeu7jNcxbv5OH/mMwLTPTwg5HJKlVO1G4+78of9wBYGQF20wEJlb3mCJVsWHnfh54axln9s5m1OAOYYcjkvR0Q7nUKe7O7S8tBGDipZoZViQelCikTpk+fyPvLsvnJ18/nk4tGocdjkidoEQhdcb2vQe4+5UlnNC5OWNP7RZ2OCJ1hhKF1Bn3vLaEXfuLuf+ygTTQzLAicaNEIXXC+8vzmfbZBq47qwd92jULOxyROkWJQpLevgMl3PbSQo7LzuT6r/YMOxyROkdzGkjSe/Dt5eTu2M+U/zqFjIaakFgk3tSjkKQ2f/1OnvpwNf95cheGd28ZdjgidZIShSSt4tIybp26gOym6dx6fp+wwxGps3TpSZLWE++v4vNNu/nTd4fRLKNh2OGI1FnqUUhSWpW/h4dnruDCge05p1/bsMMRqdOUKCTplJU5E6YtJCM1hbu+oZlhRWqaEoUknRdmr2fW6u3cfmE/2jTNCDsckTpPYxQSE3enuNQpKinlQEkZRV/8lVJUHFk+cPB5VPmB0rJgfemhbYpLo8qj1hWXUVQarC8pI3fnfk7t0YrLh3UK+/RF6oWETxT7DpQwZ+0OzCDFDCN4NCJ/GCkpwePBsqh6h9W1oE5Ql3L3aRUeq7y6YSst88PelA++uR444s36sDfk4E354PKBI9+Uj9xHueWHnnu5P2hbdWaQnppCemqDyGPDyHJag4PLKTRPa0h603TSUlMY0aMVN3y1Z0K0v0h9kPCJYmX+Xi57/KOww6jQkQkkknyiE1dU4omqe6j8y3UrSkrF5XwKLy2L8V0aSEtNOfyNOjUlUtYw8rxJeiqtMr/8Rv5FvYPbNjy0XF552hHHOLj/1BTTm75IAkv4RNG9VSZ//P5wytzBocwdP/hI5NJH5Dk4HnkMyhynrKzyuvihbSLl0fUi2x085hd1yj3+obqHxxk5llca+5frRsdzcJ8NG9gXb7yHPm0f/sZbXnlaOW/kB/eR1iCFFE2gJyKVSPhE0SQjlTN7Z4cdhohIvaW7nkREpFJKFCIiUiklChERqZQShYiIVEqJQkREKqVEISIilVKiEBGRSilRiIhIpcxjnainhpnZbmBZ2HEkiNbA1rCDSBBqi0PUFoeoLQ453t2bxmNHCf/NbGCZuw8LO4hEYGaz1RYRaotD1BaHqC0OMbPZ8dqXLj2JiEillChERKRSyZAongg7gASitjhEbXGI2uIQtcUhcWuLhB/MFhGRcCVDj0JEREKkRCEiIpWq9URhZp3N7F0zW2pmi83spqC8pZnNMLMVwWOLoLxVUH+PmT0atZ+mZjYv6m+rmT1U2+cTi3i1RbBujJktNLMFZvammbUO45yqK85t8R9BOyw2swfCOJ9YVKMtzjGzOcG//xwzOztqXycG5Tlm9jtLst+cjXNbTDSz9Wa2J6zzqa54tYOZNTaz18zs82A/91UpgMjPcdbeH9AeGBosNwWWA/2AB4DxQfl44P5gORM4HbgWeLSS/c4BvlLb55MIbUHk+zBbgNbB8weAX4R9fiG1RStgHZAdPH8GGBn2+dVwWwwBOgTLA4ANUfv6FDgFMOAN4Pywzy/EthgR7G9P2OcVVjsAjYGvBstpwAdVeU0kQgO8DJxD5NvX7aMaZdkR9b5HBYkC6AWsJxicT9a/6rYF0BDIB7oGbwh/AMaFfT4htcVJwDtRz68EHgv7fGqjLYJyA7YB6UGdz6PWjQH+GPb5hNEWR5QnXaKoiXYI1j0M/OBoxwt1jMLMuhHJfLOAtu6eBxA8tjmGXY0BXvDgzJNRLG3h7sXAdcBCYCORTxpP1mS8NSnG10UO0MfMuplZKnAJ0Lnmoq1Z1WiLy4C57l4EdARyo9blBmVJKca2qDPi1Q5m1hy4GJh5tGOGlijMrAkwFbjZ3XfFuLvRwKTYowpHrG1hZg2JJIohQAdgATAhrkHWkljbwt13EGmLF4h0q9cAJfGMsbYca1uYWX/gfuC/DhaVUy0pP0zFoS3qhHi1Q/AhahLwO3dfdbT9hJIogje2qcDf3H1aULzZzNoH69sTueZelX2dAKS6+5waCbaGxaktBgO4+8qgVzUFOLVmIq458XpduPsr7n6yu59CpGu+oqZirinH2hZm1gl4Cfiuu68MinOBTlG77USkx5lU4tQWSS/O7fAEsMLdH6rKscO468mIXBZZ6u4PRq2aDowNlscSuQZXFWNI0t5EHNtiA9DPzLKD5+cAS+MZa02L5+vCzNoEjy2A/wb+HN9oa9axtkVwCeE1YIK7f3iwcnApYreZjQj2+V2q/v8qIcSrLZJdPNvBzO4BsoCbqxxACIMwpxPp/i4A5gV/FxC5W2UmkU9/M4GWUdusAbYDe4h8SuoXtW4V0Ke2zyPR2oLI3T9Lg329ArQK+/xCbItJwJLgb3TY51bTbQHcDuyNqjsPaBOsGwYsAlYCj5JkN3zEuS0eCF4nZcHjL8I+v9puByK9Sg/eKw6WX3O042sKDxERqZS+mS0iIpVSohARkUopUYiISKWUKEREpFJKFCIiUiklCpEoZlZqkdmIF5vZfDP7sZlV+v8kmC7kitqKUaS2KVGIHG6/uw929/5Evrh4AXDXUbbpBihRSJ2l71GIRDGzPe7eJOr5ccC/gdZEZud9jsgU5wA3uPtHZvYJ0BdYTWRa898B9wFnEZnF9ffu/sdaOwmROFOiEIlyZKIIynYAfYDdQJm7F5pZL2CSuw8zs7OAn7j7RUH9cUS+DXyPmaUDHwKXu/vq2jwXkXhJDTsAkSRwcBbWhsCjZjYYKAV6V1D/68AgM/tW8DyLyG+mKFFIUlKiEKlEcOmplMisnHcBm4ETiIzvFVa0GXCju79VK0GK1DANZotUIJiN9w9EfkHPifQM8ty9jMgv5zUIqu4m8vOUB70FXBdMC42Z9TazTESSlHoUIodrZGbziFxmKiEyeH1wWufHgKlmdjnwLpHZOSEyo2eJmc0Hniby85LdgM+C6aHzifzSnkhS0mC2iIhUSpeeRESkUkoUIiJSKSUKERGplBKFiIhUSolCREQqpUQhIiKVUqIQEZFK/T8ZTfC2Le+DdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##year end frequency\n",
    "df_tesla.resample(rule='A').max()['Open'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "52eceae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##quaterly start frequency\n",
    "##https://towardsdatascience.com/resample-function-of-pandas-79b17ec82a78\n",
    "df_tesla.resample(rule='QS').max()['High'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "384d6d83",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>77.921997</td>\n",
       "      <td>75.870003</td>\n",
       "      <td>77.337997</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>98957000.0</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>77.491997</td>\n",
       "      <td>73.424004</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.914001</td>\n",
       "      <td>168248500.0</td>\n",
       "      <td>75.914001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>87.061996</td>\n",
       "      <td>85.269997</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>86.188004</td>\n",
       "      <td>150030500.0</td>\n",
       "      <td>86.188004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>718.719971</td>\n",
       "      <td>691.119995</td>\n",
       "      <td>699.989990</td>\n",
       "      <td>705.669983</td>\n",
       "      <td>304694000.0</td>\n",
       "      <td>705.669983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>1243.489990</td>\n",
       "      <td>1217.000000</td>\n",
       "      <td>1234.410034</td>\n",
       "      <td>1229.910034</td>\n",
       "      <td>89396500.0</td>\n",
       "      <td>1229.910034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>1208.000000</td>\n",
       "      <td>1136.040039</td>\n",
       "      <td>1189.550049</td>\n",
       "      <td>1199.780029</td>\n",
       "      <td>50521900.0</td>\n",
       "      <td>1199.780029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close       Volume  \\\n",
       "Date                                                                          \n",
       "2017-12-29    77.921997    75.870003    77.337997    77.000000   98957000.0   \n",
       "2018-12-31    77.491997    73.424004    75.000000    75.914001  168248500.0   \n",
       "2019-12-31    87.061996    85.269997    87.000000    86.188004  150030500.0   \n",
       "2020-12-31   718.719971   691.119995   699.989990   705.669983  304694000.0   \n",
       "2021-12-31  1243.489990  1217.000000  1234.410034  1229.910034   89396500.0   \n",
       "2022-12-30  1208.000000  1136.040039  1189.550049  1199.780029   50521900.0   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "2017-12-29    77.000000  \n",
       "2018-12-31    75.914001  \n",
       "2019-12-31    86.188004  \n",
       "2020-12-31   705.669983  \n",
       "2021-12-31  1229.910034  \n",
       "2022-12-30  1199.780029  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Business End Frequency\n",
    "##https://towardsdatascience.com/resample-function-of-pandas-79b17ec82a78\n",
    "df_tesla.resample(rule='BA').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0820f1b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>56.400002</td>\n",
       "      <td>55.442001</td>\n",
       "      <td>55.745998</td>\n",
       "      <td>55.660000</td>\n",
       "      <td>57304000.0</td>\n",
       "      <td>55.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>77.398003</td>\n",
       "      <td>75.870003</td>\n",
       "      <td>77.337997</td>\n",
       "      <td>76.690002</td>\n",
       "      <td>86307000.0</td>\n",
       "      <td>76.690002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03</th>\n",
       "      <td>77.921997</td>\n",
       "      <td>75.536003</td>\n",
       "      <td>76.050003</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>96622500.0</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02</th>\n",
       "      <td>72.599998</td>\n",
       "      <td>70.825996</td>\n",
       "      <td>71.396004</td>\n",
       "      <td>71.930000</td>\n",
       "      <td>98957000.0</td>\n",
       "      <td>71.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>72.099998</td>\n",
       "      <td>70.472000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>71.484001</td>\n",
       "      <td>105007000.0</td>\n",
       "      <td>71.484001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02</th>\n",
       "      <td>74.746002</td>\n",
       "      <td>70.900002</td>\n",
       "      <td>73.031998</td>\n",
       "      <td>74.166000</td>\n",
       "      <td>111737000.0</td>\n",
       "      <td>74.166000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>77.491997</td>\n",
       "      <td>73.424004</td>\n",
       "      <td>73.818001</td>\n",
       "      <td>75.914001</td>\n",
       "      <td>168248500.0</td>\n",
       "      <td>75.914001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>75.898003</td>\n",
       "      <td>73.349998</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.358002</td>\n",
       "      <td>137127500.0</td>\n",
       "      <td>75.358002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>70.400002</td>\n",
       "      <td>68.830002</td>\n",
       "      <td>69.241997</td>\n",
       "      <td>69.461998</td>\n",
       "      <td>120754000.0</td>\n",
       "      <td>69.461998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>59.234001</td>\n",
       "      <td>57.433998</td>\n",
       "      <td>57.660000</td>\n",
       "      <td>58.362000</td>\n",
       "      <td>132735500.0</td>\n",
       "      <td>58.362000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>53.214001</td>\n",
       "      <td>51.632000</td>\n",
       "      <td>51.834000</td>\n",
       "      <td>52.976002</td>\n",
       "      <td>112091500.0</td>\n",
       "      <td>52.976002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>87.061996</td>\n",
       "      <td>85.269997</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>86.188004</td>\n",
       "      <td>150030500.0</td>\n",
       "      <td>86.188004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>193.798004</td>\n",
       "      <td>180.203995</td>\n",
       "      <td>184.699997</td>\n",
       "      <td>183.483994</td>\n",
       "      <td>304694000.0</td>\n",
       "      <td>183.483994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>217.537994</td>\n",
       "      <td>200.746002</td>\n",
       "      <td>202.556000</td>\n",
       "      <td>215.962006</td>\n",
       "      <td>162659000.0</td>\n",
       "      <td>215.962006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>502.489990</td>\n",
       "      <td>470.510010</td>\n",
       "      <td>502.140015</td>\n",
       "      <td>498.320007</td>\n",
       "      <td>194927000.0</td>\n",
       "      <td>498.320007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>718.719971</td>\n",
       "      <td>691.119995</td>\n",
       "      <td>699.989990</td>\n",
       "      <td>705.669983</td>\n",
       "      <td>222126200.0</td>\n",
       "      <td>705.669983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>900.400024</td>\n",
       "      <td>871.599976</td>\n",
       "      <td>891.380005</td>\n",
       "      <td>883.090027</td>\n",
       "      <td>89396500.0</td>\n",
       "      <td>883.090027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>780.789978</td>\n",
       "      <td>732.609985</td>\n",
       "      <td>770.700012</td>\n",
       "      <td>762.320007</td>\n",
       "      <td>49017400.0</td>\n",
       "      <td>762.320007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>799.000000</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>787.200012</td>\n",
       "      <td>791.359985</td>\n",
       "      <td>33615800.0</td>\n",
       "      <td>791.359985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>1243.489990</td>\n",
       "      <td>1217.000000</td>\n",
       "      <td>1234.410034</td>\n",
       "      <td>1229.910034</td>\n",
       "      <td>62852100.0</td>\n",
       "      <td>1229.910034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>1208.000000</td>\n",
       "      <td>1136.040039</td>\n",
       "      <td>1189.550049</td>\n",
       "      <td>1199.780029</td>\n",
       "      <td>50521900.0</td>\n",
       "      <td>1199.780029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close       Volume  \\\n",
       "Date                                                                          \n",
       "2017-01-02    56.400002    55.442001    55.745998    55.660000   57304000.0   \n",
       "2017-04-03    77.398003    75.870003    77.337997    76.690002   86307000.0   \n",
       "2017-07-03    77.921997    75.536003    76.050003    77.000000   96622500.0   \n",
       "2017-10-02    72.599998    70.825996    71.396004    71.930000   98957000.0   \n",
       "2018-01-01    72.099998    70.472000    72.000000    71.484001  105007000.0   \n",
       "2018-04-02    74.746002    70.900002    73.031998    74.166000  111737000.0   \n",
       "2018-07-02    77.491997    73.424004    73.818001    75.914001  168248500.0   \n",
       "2018-10-01    75.898003    73.349998    75.000000    75.358002  137127500.0   \n",
       "2019-01-01    70.400002    68.830002    69.241997    69.461998  120754000.0   \n",
       "2019-04-01    59.234001    57.433998    57.660000    58.362000  132735500.0   \n",
       "2019-07-01    53.214001    51.632000    51.834000    52.976002  112091500.0   \n",
       "2019-10-01    87.061996    85.269997    87.000000    86.188004  150030500.0   \n",
       "2020-01-01   193.798004   180.203995   184.699997   183.483994  304694000.0   \n",
       "2020-04-01   217.537994   200.746002   202.556000   215.962006  162659000.0   \n",
       "2020-07-01   502.489990   470.510010   502.140015   498.320007  194927000.0   \n",
       "2020-10-01   718.719971   691.119995   699.989990   705.669983  222126200.0   \n",
       "2021-01-01   900.400024   871.599976   891.380005   883.090027   89396500.0   \n",
       "2021-04-01   780.789978   732.609985   770.700012   762.320007   49017400.0   \n",
       "2021-07-01   799.000000   775.000000   787.200012   791.359985   33615800.0   \n",
       "2021-10-01  1243.489990  1217.000000  1234.410034  1229.910034   62852100.0   \n",
       "2022-01-03  1208.000000  1136.040039  1189.550049  1199.780029   50521900.0   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "2017-01-02    55.660000  \n",
       "2017-04-03    76.690002  \n",
       "2017-07-03    77.000000  \n",
       "2017-10-02    71.930000  \n",
       "2018-01-01    71.484001  \n",
       "2018-04-02    74.166000  \n",
       "2018-07-02    75.914001  \n",
       "2018-10-01    75.358002  \n",
       "2019-01-01    69.461998  \n",
       "2019-04-01    58.362000  \n",
       "2019-07-01    52.976002  \n",
       "2019-10-01    86.188004  \n",
       "2020-01-01   183.483994  \n",
       "2020-04-01   215.962006  \n",
       "2020-07-01   498.320007  \n",
       "2020-10-01   705.669983  \n",
       "2021-01-01   883.090027  \n",
       "2021-04-01   762.320007  \n",
       "2021-07-01   791.359985  \n",
       "2021-10-01  1229.910034  \n",
       "2022-01-03  1199.780029  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.resample(rule='BQS').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8b259548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##plotting\n",
    "df_tesla['Open'].resample(rule='A').mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "843faaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tesla['Open'].resample(rule='M').max().plot(kind='bar',figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ba11eeb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2017-02-27          NaN\n",
       "2017-02-28          NaN\n",
       "2017-03-01          NaN\n",
       "2017-03-02          NaN\n",
       "2017-03-03          NaN\n",
       "2017-03-06          NaN\n",
       "2017-03-07          NaN\n",
       "2017-03-08          NaN\n",
       "2017-03-09          NaN\n",
       "2017-03-10          NaN\n",
       "2017-03-13    50.970001\n",
       "2017-03-14    51.624001\n",
       "2017-03-15    52.200001\n",
       "2017-03-16    53.150002\n",
       "2017-03-17    53.150002\n",
       "2017-03-20    53.150002\n",
       "2017-03-21    53.150002\n",
       "2017-03-22    53.150002\n",
       "2017-03-23    53.150002\n",
       "2017-03-24    53.150002\n",
       "Name: High, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla['High'].rolling(11).max().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1507d710",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-27</th>\n",
       "      <td>49.672001</td>\n",
       "      <td>48.402000</td>\n",
       "      <td>49.633999</td>\n",
       "      <td>49.245998</td>\n",
       "      <td>57304000.0</td>\n",
       "      <td>49.245998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>50.200001</td>\n",
       "      <td>48.779999</td>\n",
       "      <td>48.838001</td>\n",
       "      <td>49.998001</td>\n",
       "      <td>30390500.0</td>\n",
       "      <td>49.998001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>50.970001</td>\n",
       "      <td>49.821999</td>\n",
       "      <td>50.835999</td>\n",
       "      <td>50.004002</td>\n",
       "      <td>24047500.0</td>\n",
       "      <td>50.004002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-02</th>\n",
       "      <td>50.655998</td>\n",
       "      <td>49.653999</td>\n",
       "      <td>49.942001</td>\n",
       "      <td>50.096001</td>\n",
       "      <td>16759000.0</td>\n",
       "      <td>50.096001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>50.380001</td>\n",
       "      <td>49.799999</td>\n",
       "      <td>50.147999</td>\n",
       "      <td>50.313999</td>\n",
       "      <td>14597000.0</td>\n",
       "      <td>50.313999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close\n",
       "Date                                                                         \n",
       "2017-02-27  49.672001  48.402000  49.633999  49.245998  57304000.0  49.245998\n",
       "2017-02-28  50.200001  48.779999  48.838001  49.998001  30390500.0  49.998001\n",
       "2017-03-01  50.970001  49.821999  50.835999  50.004002  24047500.0  50.004002\n",
       "2017-03-02  50.655998  49.653999  49.942001  50.096001  16759000.0  50.096001\n",
       "2017-03-03  50.380001  49.799999  50.147999  50.313999  14597000.0  50.313999"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f51c43cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tesla['Open:30 days rolling']=df_tesla['Open'].rolling(30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b8275481",
   "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",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Open:30 days rolling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-27</th>\n",
       "      <td>49.672001</td>\n",
       "      <td>48.402000</td>\n",
       "      <td>49.633999</td>\n",
       "      <td>49.245998</td>\n",
       "      <td>57304000.0</td>\n",
       "      <td>49.245998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>50.200001</td>\n",
       "      <td>48.779999</td>\n",
       "      <td>48.838001</td>\n",
       "      <td>49.998001</td>\n",
       "      <td>30390500.0</td>\n",
       "      <td>49.998001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>50.970001</td>\n",
       "      <td>49.821999</td>\n",
       "      <td>50.835999</td>\n",
       "      <td>50.004002</td>\n",
       "      <td>24047500.0</td>\n",
       "      <td>50.004002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-02</th>\n",
       "      <td>50.655998</td>\n",
       "      <td>49.653999</td>\n",
       "      <td>49.942001</td>\n",
       "      <td>50.096001</td>\n",
       "      <td>16759000.0</td>\n",
       "      <td>50.096001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>50.380001</td>\n",
       "      <td>49.799999</td>\n",
       "      <td>50.147999</td>\n",
       "      <td>50.313999</td>\n",
       "      <td>14597000.0</td>\n",
       "      <td>50.313999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-06</th>\n",
       "      <td>50.340000</td>\n",
       "      <td>49.501999</td>\n",
       "      <td>49.582001</td>\n",
       "      <td>50.242001</td>\n",
       "      <td>16777500.0</td>\n",
       "      <td>50.242001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-07</th>\n",
       "      <td>50.778000</td>\n",
       "      <td>49.664001</td>\n",
       "      <td>50.383999</td>\n",
       "      <td>49.717999</td>\n",
       "      <td>17297500.0</td>\n",
       "      <td>49.717999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-08</th>\n",
       "      <td>50.014000</td>\n",
       "      <td>49.063999</td>\n",
       "      <td>49.400002</td>\n",
       "      <td>49.374001</td>\n",
       "      <td>18626000.0</td>\n",
       "      <td>49.374001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-09</th>\n",
       "      <td>49.731998</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>49.526001</td>\n",
       "      <td>48.980000</td>\n",
       "      <td>19396500.0</td>\n",
       "      <td>48.980000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-10</th>\n",
       "      <td>49.299999</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>49.242001</td>\n",
       "      <td>48.737999</td>\n",
       "      <td>15331500.0</td>\n",
       "      <td>48.737999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-13</th>\n",
       "      <td>49.369999</td>\n",
       "      <td>48.556000</td>\n",
       "      <td>48.964001</td>\n",
       "      <td>49.234001</td>\n",
       "      <td>15113000.0</td>\n",
       "      <td>49.234001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-14</th>\n",
       "      <td>51.624001</td>\n",
       "      <td>49.203999</td>\n",
       "      <td>49.222000</td>\n",
       "      <td>51.599998</td>\n",
       "      <td>37992000.0</td>\n",
       "      <td>51.599998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-15</th>\n",
       "      <td>52.200001</td>\n",
       "      <td>50.854000</td>\n",
       "      <td>51.400002</td>\n",
       "      <td>51.146000</td>\n",
       "      <td>26654000.0</td>\n",
       "      <td>51.146000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-16</th>\n",
       "      <td>53.150002</td>\n",
       "      <td>51.812000</td>\n",
       "      <td>52.480000</td>\n",
       "      <td>52.410000</td>\n",
       "      <td>35661000.0</td>\n",
       "      <td>52.410000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-17</th>\n",
       "      <td>53.066002</td>\n",
       "      <td>52.240002</td>\n",
       "      <td>52.799999</td>\n",
       "      <td>52.299999</td>\n",
       "      <td>32487500.0</td>\n",
       "      <td>52.299999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-20</th>\n",
       "      <td>52.910000</td>\n",
       "      <td>51.764000</td>\n",
       "      <td>52.119999</td>\n",
       "      <td>52.383999</td>\n",
       "      <td>18071500.0</td>\n",
       "      <td>52.383999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21</th>\n",
       "      <td>52.959999</td>\n",
       "      <td>50.048000</td>\n",
       "      <td>52.566002</td>\n",
       "      <td>50.136002</td>\n",
       "      <td>34543000.0</td>\n",
       "      <td>50.136002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-22</th>\n",
       "      <td>51.014000</td>\n",
       "      <td>50.102001</td>\n",
       "      <td>50.312000</td>\n",
       "      <td>51.001999</td>\n",
       "      <td>20296500.0</td>\n",
       "      <td>51.001999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-23</th>\n",
       "      <td>51.534000</td>\n",
       "      <td>50.660000</td>\n",
       "      <td>51.077999</td>\n",
       "      <td>50.956001</td>\n",
       "      <td>16601000.0</td>\n",
       "      <td>50.956001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-24</th>\n",
       "      <td>52.778000</td>\n",
       "      <td>51.001999</td>\n",
       "      <td>51.139999</td>\n",
       "      <td>52.632000</td>\n",
       "      <td>28236500.0</td>\n",
       "      <td>52.632000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-27</th>\n",
       "      <td>54.113998</td>\n",
       "      <td>51.950001</td>\n",
       "      <td>52.119999</td>\n",
       "      <td>54.043999</td>\n",
       "      <td>31154000.0</td>\n",
       "      <td>54.043999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-28</th>\n",
       "      <td>56.136002</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.403999</td>\n",
       "      <td>55.490002</td>\n",
       "      <td>39938000.0</td>\n",
       "      <td>55.490002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-29</th>\n",
       "      <td>55.919998</td>\n",
       "      <td>55.108002</td>\n",
       "      <td>55.667999</td>\n",
       "      <td>55.476002</td>\n",
       "      <td>18381000.0</td>\n",
       "      <td>55.476002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-30</th>\n",
       "      <td>56.400002</td>\n",
       "      <td>55.442001</td>\n",
       "      <td>55.608002</td>\n",
       "      <td>55.584000</td>\n",
       "      <td>20742000.0</td>\n",
       "      <td>55.584000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>55.936001</td>\n",
       "      <td>55.264000</td>\n",
       "      <td>55.745998</td>\n",
       "      <td>55.660000</td>\n",
       "      <td>16473000.0</td>\n",
       "      <td>55.660000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>59.799999</td>\n",
       "      <td>56.916000</td>\n",
       "      <td>57.380001</td>\n",
       "      <td>59.703999</td>\n",
       "      <td>69443000.0</td>\n",
       "      <td>59.703999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>60.962002</td>\n",
       "      <td>58.905998</td>\n",
       "      <td>59.377998</td>\n",
       "      <td>60.740002</td>\n",
       "      <td>50673000.0</td>\n",
       "      <td>60.740002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>60.976002</td>\n",
       "      <td>58.840000</td>\n",
       "      <td>60.408001</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>39404500.0</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>60.388000</td>\n",
       "      <td>58.820000</td>\n",
       "      <td>59.375999</td>\n",
       "      <td>59.740002</td>\n",
       "      <td>27603000.0</td>\n",
       "      <td>59.740002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>60.537998</td>\n",
       "      <td>59.430000</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>60.507999</td>\n",
       "      <td>22898000.0</td>\n",
       "      <td>60.507999</td>\n",
       "      <td>52.673400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>62.745998</td>\n",
       "      <td>61.742001</td>\n",
       "      <td>61.830002</td>\n",
       "      <td>62.478001</td>\n",
       "      <td>38322500.0</td>\n",
       "      <td>62.478001</td>\n",
       "      <td>53.079933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close  \\\n",
       "Date                                                                            \n",
       "2017-02-27  49.672001  48.402000  49.633999  49.245998  57304000.0  49.245998   \n",
       "2017-02-28  50.200001  48.779999  48.838001  49.998001  30390500.0  49.998001   \n",
       "2017-03-01  50.970001  49.821999  50.835999  50.004002  24047500.0  50.004002   \n",
       "2017-03-02  50.655998  49.653999  49.942001  50.096001  16759000.0  50.096001   \n",
       "2017-03-03  50.380001  49.799999  50.147999  50.313999  14597000.0  50.313999   \n",
       "2017-03-06  50.340000  49.501999  49.582001  50.242001  16777500.0  50.242001   \n",
       "2017-03-07  50.778000  49.664001  50.383999  49.717999  17297500.0  49.717999   \n",
       "2017-03-08  50.014000  49.063999  49.400002  49.374001  18626000.0  49.374001   \n",
       "2017-03-09  49.731998  48.599998  49.526001  48.980000  19396500.0  48.980000   \n",
       "2017-03-10  49.299999  48.599998  49.242001  48.737999  15331500.0  48.737999   \n",
       "2017-03-13  49.369999  48.556000  48.964001  49.234001  15113000.0  49.234001   \n",
       "2017-03-14  51.624001  49.203999  49.222000  51.599998  37992000.0  51.599998   \n",
       "2017-03-15  52.200001  50.854000  51.400002  51.146000  26654000.0  51.146000   \n",
       "2017-03-16  53.150002  51.812000  52.480000  52.410000  35661000.0  52.410000   \n",
       "2017-03-17  53.066002  52.240002  52.799999  52.299999  32487500.0  52.299999   \n",
       "2017-03-20  52.910000  51.764000  52.119999  52.383999  18071500.0  52.383999   \n",
       "2017-03-21  52.959999  50.048000  52.566002  50.136002  34543000.0  50.136002   \n",
       "2017-03-22  51.014000  50.102001  50.312000  51.001999  20296500.0  51.001999   \n",
       "2017-03-23  51.534000  50.660000  51.077999  50.956001  16601000.0  50.956001   \n",
       "2017-03-24  52.778000  51.001999  51.139999  52.632000  28236500.0  52.632000   \n",
       "2017-03-27  54.113998  51.950001  52.119999  54.043999  31154000.0  54.043999   \n",
       "2017-03-28  56.136002  55.000000  55.403999  55.490002  39938000.0  55.490002   \n",
       "2017-03-29  55.919998  55.108002  55.667999  55.476002  18381000.0  55.476002   \n",
       "2017-03-30  56.400002  55.442001  55.608002  55.584000  20742000.0  55.584000   \n",
       "2017-03-31  55.936001  55.264000  55.745998  55.660000  16473000.0  55.660000   \n",
       "2017-04-03  59.799999  56.916000  57.380001  59.703999  69443000.0  59.703999   \n",
       "2017-04-04  60.962002  58.905998  59.377998  60.740002  50673000.0  60.740002   \n",
       "2017-04-05  60.976002  58.840000  60.408001  59.000000  39404500.0  59.000000   \n",
       "2017-04-06  60.388000  58.820000  59.375999  59.740002  27603000.0  59.740002   \n",
       "2017-04-07  60.537998  59.430000  59.500000  60.507999  22898000.0  60.507999   \n",
       "2017-04-10  62.745998  61.742001  61.830002  62.478001  38322500.0  62.478001   \n",
       "\n",
       "            Open:30 days rolling  \n",
       "Date                              \n",
       "2017-02-27                   NaN  \n",
       "2017-02-28                   NaN  \n",
       "2017-03-01                   NaN  \n",
       "2017-03-02                   NaN  \n",
       "2017-03-03                   NaN  \n",
       "2017-03-06                   NaN  \n",
       "2017-03-07                   NaN  \n",
       "2017-03-08                   NaN  \n",
       "2017-03-09                   NaN  \n",
       "2017-03-10                   NaN  \n",
       "2017-03-13                   NaN  \n",
       "2017-03-14                   NaN  \n",
       "2017-03-15                   NaN  \n",
       "2017-03-16                   NaN  \n",
       "2017-03-17                   NaN  \n",
       "2017-03-20                   NaN  \n",
       "2017-03-21                   NaN  \n",
       "2017-03-22                   NaN  \n",
       "2017-03-23                   NaN  \n",
       "2017-03-24                   NaN  \n",
       "2017-03-27                   NaN  \n",
       "2017-03-28                   NaN  \n",
       "2017-03-29                   NaN  \n",
       "2017-03-30                   NaN  \n",
       "2017-03-31                   NaN  \n",
       "2017-04-03                   NaN  \n",
       "2017-04-04                   NaN  \n",
       "2017-04-05                   NaN  \n",
       "2017-04-06                   NaN  \n",
       "2017-04-07             52.673400  \n",
       "2017-04-10             53.079933  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head(31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ee4f2520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tesla[['Open','Open:30 days rolling']].plot(figsize=(12,5))"
   ]
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    "##Assignment\n",
    "##news\n",
    "1. Read the Microsoft Data using Pandas Data reader\n",
    "2. Get the maximum price of the share from 2017 to 2022\n",
    "3. Which is the date of the highest price of the stock?\n",
    "4. Which is the date of the lowest price of the stock?"
   ]
  }
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