adani anlaytic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bd56baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "917a2a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"ADANIPOWER.NS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32c04edc",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-12-13</td>\n",
       "      <td>105.599998</td>\n",
       "      <td>107.400002</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>103.750000</td>\n",
       "      <td>103.750000</td>\n",
       "      <td>4673258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-12-14</td>\n",
       "      <td>103.400002</td>\n",
       "      <td>104.849998</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>103.349998</td>\n",
       "      <td>103.349998</td>\n",
       "      <td>2603876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>104.699997</td>\n",
       "      <td>101.550003</td>\n",
       "      <td>102.199997</td>\n",
       "      <td>102.199997</td>\n",
       "      <td>2600789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-12-16</td>\n",
       "      <td>103.699997</td>\n",
       "      <td>103.949997</td>\n",
       "      <td>100.800003</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>2336205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-12-17</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>100.099998</td>\n",
       "      <td>100.099998</td>\n",
       "      <td>3357266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2022-12-07</td>\n",
       "      <td>325.899994</td>\n",
       "      <td>328.399994</td>\n",
       "      <td>323.250000</td>\n",
       "      <td>324.450012</td>\n",
       "      <td>324.450012</td>\n",
       "      <td>1430486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2022-12-08</td>\n",
       "      <td>326.100006</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>323.049988</td>\n",
       "      <td>323.750000</td>\n",
       "      <td>323.750000</td>\n",
       "      <td>1553350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>325.399994</td>\n",
       "      <td>325.950012</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>316.049988</td>\n",
       "      <td>316.049988</td>\n",
       "      <td>2958588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>307.049988</td>\n",
       "      <td>310.200012</td>\n",
       "      <td>310.200012</td>\n",
       "      <td>2657723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>310.500000</td>\n",
       "      <td>324.799988</td>\n",
       "      <td>310.500000</td>\n",
       "      <td>321.799988</td>\n",
       "      <td>321.799988</td>\n",
       "      <td>2020349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2021-12-13  105.599998  107.400002  103.500000  103.750000  103.750000   \n",
       "1    2021-12-14  103.400002  104.849998  102.500000  103.349998  103.349998   \n",
       "2    2021-12-15  103.500000  104.699997  101.550003  102.199997  102.199997   \n",
       "3    2021-12-16  103.699997  103.949997  100.800003  101.300003  101.300003   \n",
       "4    2021-12-17  101.000000  101.300003   99.000000  100.099998  100.099998   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "245  2022-12-07  325.899994  328.399994  323.250000  324.450012  324.450012   \n",
       "246  2022-12-08  326.100006  329.000000  323.049988  323.750000  323.750000   \n",
       "247  2022-12-09  325.399994  325.950012  311.000000  316.049988  316.049988   \n",
       "248  2022-12-12  315.000000  317.000000  307.049988  310.200012  310.200012   \n",
       "249  2022-12-13  310.500000  324.799988  310.500000  321.799988  321.799988   \n",
       "\n",
       "      Volume  \n",
       "0    4673258  \n",
       "1    2603876  \n",
       "2    2600789  \n",
       "3    2336205  \n",
       "4    3357266  \n",
       "..       ...  \n",
       "245  1430486  \n",
       "246  1553350  \n",
       "247  2958588  \n",
       "248  2657723  \n",
       "249  2020349  \n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4514cb7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "423cbf89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>2.500000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>257.040201</td>\n",
       "      <td>262.928800</td>\n",
       "      <td>251.068600</td>\n",
       "      <td>256.977601</td>\n",
       "      <td>256.977601</td>\n",
       "      <td>2.024498e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>103.418868</td>\n",
       "      <td>104.739161</td>\n",
       "      <td>100.991958</td>\n",
       "      <td>102.989798</td>\n",
       "      <td>102.989798</td>\n",
       "      <td>3.209412e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>97.150002</td>\n",
       "      <td>98.800003</td>\n",
       "      <td>95.099998</td>\n",
       "      <td>97.099998</td>\n",
       "      <td>97.099998</td>\n",
       "      <td>4.024140e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.062500</td>\n",
       "      <td>129.187504</td>\n",
       "      <td>122.300003</td>\n",
       "      <td>124.662502</td>\n",
       "      <td>124.662502</td>\n",
       "      <td>2.790931e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>285.300003</td>\n",
       "      <td>290.149994</td>\n",
       "      <td>277.100006</td>\n",
       "      <td>284.175003</td>\n",
       "      <td>284.175003</td>\n",
       "      <td>7.422374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>338.262504</td>\n",
       "      <td>343.774994</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>336.375000</td>\n",
       "      <td>336.375000</td>\n",
       "      <td>2.375525e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>421.000000</td>\n",
       "      <td>432.500000</td>\n",
       "      <td>419.399994</td>\n",
       "      <td>432.500000</td>\n",
       "      <td>432.500000</td>\n",
       "      <td>2.421999e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open        High         Low       Close   Adj Close  \\\n",
       "count  250.000000  250.000000  250.000000  250.000000  250.000000   \n",
       "mean   257.040201  262.928800  251.068600  256.977601  256.977601   \n",
       "std    103.418868  104.739161  100.991958  102.989798  102.989798   \n",
       "min     97.150002   98.800003   95.099998   97.099998   97.099998   \n",
       "25%    125.062500  129.187504  122.300003  124.662502  124.662502   \n",
       "50%    285.300003  290.149994  277.100006  284.175003  284.175003   \n",
       "75%    338.262504  343.774994  330.000000  336.375000  336.375000   \n",
       "max    421.000000  432.500000  419.399994  432.500000  432.500000   \n",
       "\n",
       "             Volume  \n",
       "count  2.500000e+02  \n",
       "mean   2.024498e+07  \n",
       "std    3.209412e+07  \n",
       "min    4.024140e+05  \n",
       "25%    2.790931e+06  \n",
       "50%    7.422374e+06  \n",
       "75%    2.375525e+07  \n",
       "max    2.421999e+08  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb16bc5a",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-12-13</td>\n",
       "      <td>105.599998</td>\n",
       "      <td>107.400002</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>103.750000</td>\n",
       "      <td>103.750000</td>\n",
       "      <td>4673258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-12-14</td>\n",
       "      <td>103.400002</td>\n",
       "      <td>104.849998</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>103.349998</td>\n",
       "      <td>103.349998</td>\n",
       "      <td>2603876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>104.699997</td>\n",
       "      <td>101.550003</td>\n",
       "      <td>102.199997</td>\n",
       "      <td>102.199997</td>\n",
       "      <td>2600789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-12-16</td>\n",
       "      <td>103.699997</td>\n",
       "      <td>103.949997</td>\n",
       "      <td>100.800003</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>2336205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-12-17</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>100.099998</td>\n",
       "      <td>100.099998</td>\n",
       "      <td>3357266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>98.800003</td>\n",
       "      <td>98.800003</td>\n",
       "      <td>95.099998</td>\n",
       "      <td>97.099998</td>\n",
       "      <td>97.099998</td>\n",
       "      <td>4106643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>97.150002</td>\n",
       "      <td>99.900002</td>\n",
       "      <td>97.150002</td>\n",
       "      <td>98.849998</td>\n",
       "      <td>98.849998</td>\n",
       "      <td>2857892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>99.900002</td>\n",
       "      <td>100.949997</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.650002</td>\n",
       "      <td>99.650002</td>\n",
       "      <td>2158306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>100.449997</td>\n",
       "      <td>100.949997</td>\n",
       "      <td>99.099998</td>\n",
       "      <td>99.300003</td>\n",
       "      <td>99.300003</td>\n",
       "      <td>2235524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-12-24</td>\n",
       "      <td>99.949997</td>\n",
       "      <td>99.949997</td>\n",
       "      <td>98.099998</td>\n",
       "      <td>98.650002</td>\n",
       "      <td>98.650002</td>\n",
       "      <td>2572160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>98.800003</td>\n",
       "      <td>100.699997</td>\n",
       "      <td>97.300003</td>\n",
       "      <td>99.449997</td>\n",
       "      <td>99.449997</td>\n",
       "      <td>4643998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>100.300003</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>99.199997</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>7055795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>101.199997</td>\n",
       "      <td>102.400002</td>\n",
       "      <td>99.699997</td>\n",
       "      <td>99.849998</td>\n",
       "      <td>99.849998</td>\n",
       "      <td>3075259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.400002</td>\n",
       "      <td>99.099998</td>\n",
       "      <td>99.449997</td>\n",
       "      <td>99.449997</td>\n",
       "      <td>2092134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>99.900002</td>\n",
       "      <td>101.500000</td>\n",
       "      <td>99.500000</td>\n",
       "      <td>99.699997</td>\n",
       "      <td>99.699997</td>\n",
       "      <td>3351858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>100.150002</td>\n",
       "      <td>103.900002</td>\n",
       "      <td>100.099998</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>101.300003</td>\n",
       "      <td>7352927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>103.550003</td>\n",
       "      <td>100.099998</td>\n",
       "      <td>100.550003</td>\n",
       "      <td>100.550003</td>\n",
       "      <td>5914012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>100.650002</td>\n",
       "      <td>101.599998</td>\n",
       "      <td>99.599998</td>\n",
       "      <td>99.849998</td>\n",
       "      <td>99.849998</td>\n",
       "      <td>4250315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022-01-06</td>\n",
       "      <td>99.500000</td>\n",
       "      <td>100.400002</td>\n",
       "      <td>98.650002</td>\n",
       "      <td>99.849998</td>\n",
       "      <td>99.849998</td>\n",
       "      <td>4373449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-01-07</td>\n",
       "      <td>101.400002</td>\n",
       "      <td>102.449997</td>\n",
       "      <td>99.500000</td>\n",
       "      <td>100.199997</td>\n",
       "      <td>100.199997</td>\n",
       "      <td>4759250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date        Open        High         Low       Close   Adj Close  \\\n",
       "0   2021-12-13  105.599998  107.400002  103.500000  103.750000  103.750000   \n",
       "1   2021-12-14  103.400002  104.849998  102.500000  103.349998  103.349998   \n",
       "2   2021-12-15  103.500000  104.699997  101.550003  102.199997  102.199997   \n",
       "3   2021-12-16  103.699997  103.949997  100.800003  101.300003  101.300003   \n",
       "4   2021-12-17  101.000000  101.300003   99.000000  100.099998  100.099998   \n",
       "5   2021-12-20   98.800003   98.800003   95.099998   97.099998   97.099998   \n",
       "6   2021-12-21   97.150002   99.900002   97.150002   98.849998   98.849998   \n",
       "7   2021-12-22   99.900002  100.949997   99.000000   99.650002   99.650002   \n",
       "8   2021-12-23  100.449997  100.949997   99.099998   99.300003   99.300003   \n",
       "9   2021-12-24   99.949997   99.949997   98.099998   98.650002   98.650002   \n",
       "10  2021-12-27   98.800003  100.699997   97.300003   99.449997   99.449997   \n",
       "11  2021-12-28  100.300003  104.000000   99.199997  101.000000  101.000000   \n",
       "12  2021-12-29  101.199997  102.400002   99.699997   99.849998   99.849998   \n",
       "13  2021-12-30  100.000000  100.400002   99.099998   99.449997   99.449997   \n",
       "14  2021-12-31   99.900002  101.500000   99.500000   99.699997   99.699997   \n",
       "15  2022-01-03  100.150002  103.900002  100.099998  101.300003  101.300003   \n",
       "16  2022-01-04  102.500000  103.550003  100.099998  100.550003  100.550003   \n",
       "17  2022-01-05  100.650002  101.599998   99.599998   99.849998   99.849998   \n",
       "18  2022-01-06   99.500000  100.400002   98.650002   99.849998   99.849998   \n",
       "19  2022-01-07  101.400002  102.449997   99.500000  100.199997  100.199997   \n",
       "\n",
       "     Volume  \n",
       "0   4673258  \n",
       "1   2603876  \n",
       "2   2600789  \n",
       "3   2336205  \n",
       "4   3357266  \n",
       "5   4106643  \n",
       "6   2857892  \n",
       "7   2158306  \n",
       "8   2235524  \n",
       "9   2572160  \n",
       "10  4643998  \n",
       "11  7055795  \n",
       "12  3075259  \n",
       "13  2092134  \n",
       "14  3351858  \n",
       "15  7352927  \n",
       "16  5914012  \n",
       "17  4250315  \n",
       "18  4373449  \n",
       "19  4759250  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ca2c992",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>2022-11-16</td>\n",
       "      <td>356.500000</td>\n",
       "      <td>356.850006</td>\n",
       "      <td>342.399994</td>\n",
       "      <td>345.149994</td>\n",
       "      <td>345.149994</td>\n",
       "      <td>1790067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2022-11-17</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>343.950012</td>\n",
       "      <td>334.500000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>1678802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2022-11-18</td>\n",
       "      <td>339.950012</td>\n",
       "      <td>349.799988</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>336.500000</td>\n",
       "      <td>336.500000</td>\n",
       "      <td>1345504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2022-11-21</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>331.200012</td>\n",
       "      <td>331.200012</td>\n",
       "      <td>854114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2022-11-22</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>328.500000</td>\n",
       "      <td>328.500000</td>\n",
       "      <td>1318384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>331.950012</td>\n",
       "      <td>323.000000</td>\n",
       "      <td>325.950012</td>\n",
       "      <td>325.950012</td>\n",
       "      <td>1106098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2022-11-24</td>\n",
       "      <td>327.950012</td>\n",
       "      <td>329.899994</td>\n",
       "      <td>323.000000</td>\n",
       "      <td>324.950012</td>\n",
       "      <td>324.950012</td>\n",
       "      <td>896931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2022-11-25</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>318.500000</td>\n",
       "      <td>323.950012</td>\n",
       "      <td>323.950012</td>\n",
       "      <td>1084355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2022-11-28</td>\n",
       "      <td>327.100006</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>326.899994</td>\n",
       "      <td>328.250000</td>\n",
       "      <td>328.250000</td>\n",
       "      <td>2544419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2022-11-29</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>332.899994</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>324.700012</td>\n",
       "      <td>324.700012</td>\n",
       "      <td>1783244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>335.899994</td>\n",
       "      <td>322.500000</td>\n",
       "      <td>332.750000</td>\n",
       "      <td>332.750000</td>\n",
       "      <td>3528568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>336.500000</td>\n",
       "      <td>339.899994</td>\n",
       "      <td>328.500000</td>\n",
       "      <td>330.399994</td>\n",
       "      <td>330.399994</td>\n",
       "      <td>3402035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2022-12-02</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>330.350006</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>325.299988</td>\n",
       "      <td>325.299988</td>\n",
       "      <td>2332809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2022-12-05</td>\n",
       "      <td>326.950012</td>\n",
       "      <td>326.950012</td>\n",
       "      <td>322.049988</td>\n",
       "      <td>323.049988</td>\n",
       "      <td>323.049988</td>\n",
       "      <td>1801476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2022-12-06</td>\n",
       "      <td>322.950012</td>\n",
       "      <td>331.700012</td>\n",
       "      <td>321.899994</td>\n",
       "      <td>324.799988</td>\n",
       "      <td>324.799988</td>\n",
       "      <td>2679257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2022-12-07</td>\n",
       "      <td>325.899994</td>\n",
       "      <td>328.399994</td>\n",
       "      <td>323.250000</td>\n",
       "      <td>324.450012</td>\n",
       "      <td>324.450012</td>\n",
       "      <td>1430486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2022-12-08</td>\n",
       "      <td>326.100006</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>323.049988</td>\n",
       "      <td>323.750000</td>\n",
       "      <td>323.750000</td>\n",
       "      <td>1553350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>325.399994</td>\n",
       "      <td>325.950012</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>316.049988</td>\n",
       "      <td>316.049988</td>\n",
       "      <td>2958588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>307.049988</td>\n",
       "      <td>310.200012</td>\n",
       "      <td>310.200012</td>\n",
       "      <td>2657723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>310.500000</td>\n",
       "      <td>324.799988</td>\n",
       "      <td>310.500000</td>\n",
       "      <td>321.799988</td>\n",
       "      <td>321.799988</td>\n",
       "      <td>2020349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "230  2022-11-16  356.500000  356.850006  342.399994  345.149994  345.149994   \n",
       "231  2022-11-17  343.000000  343.950012  334.500000  336.000000  336.000000   \n",
       "232  2022-11-18  339.950012  349.799988  329.000000  336.500000  336.500000   \n",
       "233  2022-11-21  338.000000  338.000000  330.000000  331.200012  331.200012   \n",
       "234  2022-11-22  332.000000  335.000000  325.000000  328.500000  328.500000   \n",
       "235  2022-11-23  329.000000  331.950012  323.000000  325.950012  325.950012   \n",
       "236  2022-11-24  327.950012  329.899994  323.000000  324.950012  324.950012   \n",
       "237  2022-11-25  326.000000  327.000000  318.500000  323.950012  323.950012   \n",
       "238  2022-11-28  327.100006  337.000000  326.899994  328.250000  328.250000   \n",
       "239  2022-11-29  330.000000  332.899994  323.500000  324.700012  324.700012   \n",
       "240  2022-11-30  325.000000  335.899994  322.500000  332.750000  332.750000   \n",
       "241  2022-12-01  336.500000  339.899994  328.500000  330.399994  330.399994   \n",
       "242  2022-12-02  329.000000  330.350006  324.000000  325.299988  325.299988   \n",
       "243  2022-12-05  326.950012  326.950012  322.049988  323.049988  323.049988   \n",
       "244  2022-12-06  322.950012  331.700012  321.899994  324.799988  324.799988   \n",
       "245  2022-12-07  325.899994  328.399994  323.250000  324.450012  324.450012   \n",
       "246  2022-12-08  326.100006  329.000000  323.049988  323.750000  323.750000   \n",
       "247  2022-12-09  325.399994  325.950012  311.000000  316.049988  316.049988   \n",
       "248  2022-12-12  315.000000  317.000000  307.049988  310.200012  310.200012   \n",
       "249  2022-12-13  310.500000  324.799988  310.500000  321.799988  321.799988   \n",
       "\n",
       "      Volume  \n",
       "230  1790067  \n",
       "231  1678802  \n",
       "232  1345504  \n",
       "233   854114  \n",
       "234  1318384  \n",
       "235  1106098  \n",
       "236   896931  \n",
       "237  1084355  \n",
       "238  2544419  \n",
       "239  1783244  \n",
       "240  3528568  \n",
       "241  3402035  \n",
       "242  2332809  \n",
       "243  1801476  \n",
       "244  2679257  \n",
       "245  1430486  \n",
       "246  1553350  \n",
       "247  2958588  \n",
       "248  2657723  \n",
       "249  2020349  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8d079597",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 250 entries, 0 to 249\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       250 non-null    object \n",
      " 1   Open       250 non-null    float64\n",
      " 2   High       250 non-null    float64\n",
      " 3   Low        250 non-null    float64\n",
      " 4   Close      250 non-null    float64\n",
      " 5   Adj Close  250 non-null    float64\n",
      " 6   Volume     250 non-null    int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 13.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3c621fe",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date   Open   High    Low  Close  Adj Close  Volume\n",
       "0    False  False  False  False  False      False   False\n",
       "1    False  False  False  False  False      False   False\n",
       "2    False  False  False  False  False      False   False\n",
       "3    False  False  False  False  False      False   False\n",
       "4    False  False  False  False  False      False   False\n",
       "..     ...    ...    ...    ...    ...        ...     ...\n",
       "245  False  False  False  False  False      False   False\n",
       "246  False  False  False  False  False      False   False\n",
       "247  False  False  False  False  False      False   False\n",
       "248  False  False  False  False  False      False   False\n",
       "249  False  False  False  False  False      False   False\n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bffed690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21425fa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: missingno in c:\\users\\acer\\appdata\\roaming\\python\\python39\\site-packages (0.5.1)\n",
      "Requirement already satisfied: scipy in c:\\programdata\\anaconda3\\lib\\site-packages (from missingno) (1.7.3)\n",
      "Requirement already satisfied: seaborn in c:\\programdata\\anaconda3\\lib\\site-packages (from missingno) (0.11.2)\n",
      "Requirement already satisfied: matplotlib in c:\\programdata\\anaconda3\\lib\\site-packages (from missingno) (3.5.1)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from missingno) (1.21.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (21.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (4.25.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (3.0.4)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (1.3.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (9.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (0.11.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\programdata\\anaconda3\\lib\\site-packages (from seaborn->missingno) (1.4.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn->missingno) (2021.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "115b82b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f07a926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "missingno.bar(df, color = 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2dea5fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f4efe9a970>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df[\"Volume\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2d4bb718",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Volume', ylabel='Density'>"
      ]
     },
     "execution_count": 26,
     "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": [
    "sns.distplot(df[\"Volume\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f23c570",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Volume', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "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": [
    "sns.distplot(df[\"Volume\"], hist_kws = dict(linewidth = 15, edgecolor = 'g'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "48631e7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, <AxesSubplot:xlabel='Volume', ylabel='Density'>)"
      ]
     },
     "execution_count": 34,
     "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": [
    "sns.set_style(\"whitegrid\"),sns.distplot(df[\"Volume\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6d733695",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Volume', ylabel='Density'>"
      ]
     },
     "execution_count": 41,
     "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": [
    "sns.distplot(df[\"Volume\"],hist=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3c4617c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mean\n",
    "x,y = plt.subplots(figsize=(8,8))\n",
    "sns.distplot(df['Volume'],hist=False,\n",
    "             ax =y)\n",
    "y.axvline(df[\"Volume\"].mean(),color = \"r\",ls =\"--\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fb598e06",
   "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>TotaluniqueName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adj Close</th>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           TotaluniqueName\n",
       "Date                   250\n",
       "Open                   224\n",
       "High                   241\n",
       "Low                    232\n",
       "Close                  238\n",
       "Adj Close              238\n",
       "Volume                 250"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniqueName = []\n",
    "for i in df.columns:\n",
    "    x = df[i].value_counts().count()\n",
    "    uniqueName.append(x)\n",
    "\n",
    "pd.DataFrame(uniqueName,index=df.columns,columns=[\"TotaluniqueName\"])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc1cb5f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}