Get_price_by_data_ip.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yahooquery import Ticker\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import investpy as ip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ip.get_stock_historical_data(stock='IOC', country=\"India\", from_date='01/01/2009', to_date='30/04/2021', interval='Monthly', as_json=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"name\": \"Indian Oil\", \"historical\": [{\"date\": \"01/01/2009\", \"open\": 50.7, \"high\": 56.73, \"low\": 44.25, \"close\": 55.77, \"volume\": 59752080, \"currency\": \"INR\"}, {\"date\": \"01/02/2009\", \"open\": 56.0, \"high\": 58.36, \"low\": 52.51, \"close\": 54.71, \"volume\": 37573528, \"currency\": \"INR\"}, {\"date\": \"01/03/2009\", \"open\": 53.89, \"high\": 56.34, \"low\": 47.26, \"close\": 48.48, \"volume\": 49605792, \"currency\": \"INR\"}, {\"date\": \"01/04/2009\", \"open\": 49.87, \"high\": 56.88, \"low\": 48.52, \"close\": 55.34, \"volume\": 33818712, \"currency\": \"INR\"}, {\"date\": \"01/05/2009\", \"open\": 55.62, \"high\": 82.75, \"low\": 50.0, \"close\": 76.12, \"volume\": 103288344, \"currency\": \"INR\"}, {\"date\": \"01/06/2009\", \"open\": 77.0, \"high\": 79.12, \"low\": 65.51, \"close\": 66.48, \"volume\": 125103784, \"currency\": \"INR\"}, {\"date\": \"01/07/2009\", \"open\": 66.88, \"high\": 73.33, \"low\": 63.27, \"close\": 68.42, 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\"01/07/2019\", \"open\": 155.9, \"high\": 157.5, \"low\": 132.0, \"close\": 139.3, \"volume\": 443209984, \"currency\": \"INR\"}, {\"date\": \"01/08/2019\", \"open\": 137.95, \"high\": 141.9, \"low\": 116.25, \"close\": 122.45, \"volume\": 244300000, \"currency\": \"INR\"}, {\"date\": \"01/09/2019\", \"open\": 117.75, \"high\": 149.45, \"low\": 116.25, \"close\": 147.4, \"volume\": 315710016, \"currency\": \"INR\"}, {\"date\": \"01/10/2019\", \"open\": 149.8, \"high\": 156.0, \"low\": 140.05, \"close\": 146.8, \"volume\": 259240000, \"currency\": \"INR\"}, {\"date\": \"01/11/2019\", \"open\": 141.0, \"high\": 144.0, \"low\": 128.35, \"close\": 131.4, \"volume\": 191010000, \"currency\": \"INR\"}, {\"date\": \"01/12/2019\", \"open\": 131.5, \"high\": 131.6, \"low\": 121.85, \"close\": 125.55, \"volume\": 126880000, \"currency\": \"INR\"}, {\"date\": \"01/01/2020\", \"open\": 125.95, \"high\": 128.2, \"low\": 111.95, \"close\": 113.45, \"volume\": 611420032, \"currency\": \"INR\"}, {\"date\": \"01/02/2020\", \"open\": 113.35, \"high\": 118.35, \"low\": 100.9, \"close\": 105.75, \"volume\": 277590016, \"currency\": \"INR\"}, {\"date\": \"01/03/2020\", \"open\": 108.45, \"high\": 112.9, \"low\": 74.45, \"close\": 81.65, \"volume\": 421110016, \"currency\": \"INR\"}, {\"date\": \"01/04/2020\", \"open\": 81.65, \"high\": 89.5, \"low\": 76.6, \"close\": 84.2, \"volume\": 246530000, \"currency\": \"INR\"}, {\"date\": \"01/05/2020\", \"open\": 82.65, \"high\": 84.7, \"low\": 71.15, \"close\": 83.2, \"volume\": 294510016, \"currency\": \"INR\"}, {\"date\": \"01/06/2020\", \"open\": 85.3, \"high\": 96.4, \"low\": 83.75, \"close\": 85.35, \"volume\": 538560000, \"currency\": \"INR\"}, {\"date\": \"01/07/2020\", \"open\": 85.65, \"high\": 95.65, \"low\": 82.5, \"close\": 88.45, \"volume\": 615500032, \"currency\": \"INR\"}, {\"date\": \"01/08/2020\", \"open\": 88.35, \"high\": 91.75, \"low\": 84.55, \"close\": 85.8, \"volume\": 464670016, \"currency\": \"INR\"}, {\"date\": \"01/09/2020\", \"open\": 85.8, \"high\": 86.6, \"low\": 71.65, \"close\": 73.9, \"volume\": 401520000, \"currency\": \"INR\"}, {\"date\": \"01/10/2020\", \"open\": 73.95, \"high\": 81.9, \"low\": 73.45, \"close\": 79.55, \"volume\": 396840000, \"currency\": \"INR\"}, {\"date\": \"01/11/2020\", \"open\": 80.05, \"high\": 87.35, \"low\": 76.85, \"close\": 84.6, \"volume\": 340480000, \"currency\": \"INR\"}, {\"date\": \"01/12/2020\", \"open\": 85.0, \"high\": 97.5, \"low\": 84.1, \"close\": 90.95, \"volume\": 576030016, \"currency\": \"INR\"}, {\"date\": \"01/01/2021\", \"open\": 91.3, \"high\": 103.9, \"low\": 90.25, \"close\": 93.25, \"volume\": 543299968, \"currency\": \"INR\"}, {\"date\": \"01/02/2021\", \"open\": 95.8, \"high\": 104.6, \"low\": 93.4, \"close\": 98.05, \"volume\": 715260032, \"currency\": \"INR\"}, {\"date\": \"01/03/2021\", \"open\": 101.0, \"high\": 105.0, \"low\": 90.1, \"close\": 91.85, \"volume\": 567440000, \"currency\": \"INR\"}, {\"date\": \"01/04/2021\", \"open\": 92.2, \"high\": 93.7, \"low\": 86.75, \"close\": 90.85, \"volume\": 29815698, \"currency\": \"INR\"}]}'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ip.get_stock_historical_data(stock='IOC', country=\"India\", from_date='01/01/2009', to_date='30/04/2021', interval='Monthly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Volume</th>\n",
       "      <th>Currency</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>2009-01-01</th>\n",
       "      <td>50.70</td>\n",
       "      <td>56.73</td>\n",
       "      <td>44.25</td>\n",
       "      <td>55.77</td>\n",
       "      <td>59752080</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-01</th>\n",
       "      <td>56.00</td>\n",
       "      <td>58.36</td>\n",
       "      <td>52.51</td>\n",
       "      <td>54.71</td>\n",
       "      <td>37573528</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-01</th>\n",
       "      <td>53.89</td>\n",
       "      <td>56.34</td>\n",
       "      <td>47.26</td>\n",
       "      <td>48.48</td>\n",
       "      <td>49605792</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>49.87</td>\n",
       "      <td>56.88</td>\n",
       "      <td>48.52</td>\n",
       "      <td>55.34</td>\n",
       "      <td>33818712</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-01</th>\n",
       "      <td>55.62</td>\n",
       "      <td>82.75</td>\n",
       "      <td>50.00</td>\n",
       "      <td>76.12</td>\n",
       "      <td>103288344</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume Currency\n",
       "Date                                                      \n",
       "2009-01-01  50.70  56.73  44.25  55.77   59752080      INR\n",
       "2009-02-01  56.00  58.36  52.51  54.71   37573528      INR\n",
       "2009-03-01  53.89  56.34  47.26  48.48   49605792      INR\n",
       "2009-04-01  49.87  56.88  48.52  55.34   33818712      INR\n",
       "2009-05-01  55.62  82.75  50.00  76.12  103288344      INR"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.85"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Close'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.55"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Close'][-7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfj = ip.get_stock_historical_data(stock='IOC', country=\"India\", from_date='01/01/2020', to_date='30/04/2021', interval='Monthly', as_json=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfj = json.loads(dfj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfj = dfj['historical']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'date': '01/01/2020',\n",
       " 'open': 125.95,\n",
       " 'high': 128.2,\n",
       " 'low': 111.95,\n",
       " 'close': 113.45,\n",
       " 'volume': 611420032,\n",
       " 'currency': 'INR'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfj[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'01/01/2020'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfj[0]['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'01/04/2021'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfj[-1]['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2009-01-01 00:00:00')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('20130101', periods=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(dates, pd.DatetimeIndex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20000101', periods=6)\n",
    "isinstance(dates, pd.DatetimeIndex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date exist\n"
     ]
    }
   ],
   "source": [
    "dtt = pd.to_datetime('2021-01-01')\n",
    "if (dtt in df.index.values):\n",
    "    print ('date exist')\n",
    "else:\n",
    "    print('nope')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date exist\n"
     ]
    }
   ],
   "source": [
    "dates = pd.date_range(start='1/1/2018', end='1/08/2018')\n",
    "if (dtt in df.index.values):\n",
    "    print('date exist')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = df['Close'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_db = df['Close'][-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_wb = df['Close'][-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = datetime.datetime.now()\n",
    "dt_mb = dt - datetime.timedelta(days=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2021, 4, 4)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_mb.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no\n"
     ]
    }
   ],
   "source": [
    "if (dt_mb.date() in df.index.values):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ip.get_stock_historical_data(stock='IOC', country=\"India\", from_date='01/04/2020', to_date='01/05/2021', interval='Daily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = df['Close'][-1]\n",
    "p_db = df['Close'][-2]\n",
    "p_wb = df['Close'][-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no\n"
     ]
    }
   ],
   "source": [
    "if (dt_mb in df.index.values):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2021-03-16T00:00:00.000000000')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.values[-30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_mb = dt - datetime.timedelta(days=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_mb = dt_mb.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dt_mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_mb = pd.to_datetime(dt_mb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-04-04 00:00:00')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no\n"
     ]
    }
   ],
   "source": [
    "if (dt_mb in df.index.values):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = datetime.datetime.now()\n",
    "dt_yb = dt - datetime.timedelta(days=35)\n",
    "dt_yb = pd.to_datetime(dt_yb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no\n"
     ]
    }
   ],
   "source": [
    "if (dt_yb in df.index.values):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-03-30 03:00:53.984930')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_yb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "dt_mb = '2020-04-09'\n",
    "dt_mb = pd.to_datetime(dt_mb)\n",
    "if (dt_mb in df.index.values):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('no')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtt = dt - datetime.timedelta(days=390)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 4, 9, 3, 0, 53, 984930)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtt = dtt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-04-09 00:00:00')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtt = pd.to_datetime(dtt)\n",
    "dtt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "if (dtt in df.index.values):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    dtt = dt - datetime.timedelta(days=33)\n",
    "except:\n",
    "    dtt = dt - datetime.timedelta(days=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtt = dtt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-04-01 00:00:00')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtt = pd.to_datetime(dtt)\n",
    "dtt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "if (dtt in df.index.values):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open           92.00\n",
       "High           93.35\n",
       "Low            91.20\n",
       "Close          93.15\n",
       "Volume      14843941\n",
       "Currency         INR\n",
       "Name: 2021-04-01 00:00:00, dtype: object"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dtt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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