301_HDFCLife_CSVfunctions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c80c53aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "51b40bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/HDFCLIFE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1f495c59",
   "metadata": {
    "scrolled": true
   },
   "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>PREV. CLOSE</th>\n",
       "      <th>LTP</th>\n",
       "      <th>CLOSE</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>52WH</th>\n",
       "      <th>52WL</th>\n",
       "      <th>VOLUME</th>\n",
       "      <th>No of trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>2.490000e+02</td>\n",
       "      <td>249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>655.777912</td>\n",
       "      <td>661.841767</td>\n",
       "      <td>648.138554</td>\n",
       "      <td>654.883735</td>\n",
       "      <td>654.522289</td>\n",
       "      <td>654.513253</td>\n",
       "      <td>654.754739</td>\n",
       "      <td>765.409438</td>\n",
       "      <td>550.952410</td>\n",
       "      <td>3.654682e+06</td>\n",
       "      <td>80194.819277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>62.964291</td>\n",
       "      <td>62.167352</td>\n",
       "      <td>63.428526</td>\n",
       "      <td>62.583853</td>\n",
       "      <td>62.771070</td>\n",
       "      <td>62.739226</td>\n",
       "      <td>62.961371</td>\n",
       "      <td>14.126748</td>\n",
       "      <td>50.048874</td>\n",
       "      <td>3.978281e+06</td>\n",
       "      <td>42973.794420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>514.950000</td>\n",
       "      <td>497.050000</td>\n",
       "      <td>503.500000</td>\n",
       "      <td>503.550000</td>\n",
       "      <td>503.500000</td>\n",
       "      <td>504.890000</td>\n",
       "      <td>746.000000</td>\n",
       "      <td>459.000000</td>\n",
       "      <td>4.091640e+05</td>\n",
       "      <td>24377.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>635.000000</td>\n",
       "      <td>639.850000</td>\n",
       "      <td>622.100000</td>\n",
       "      <td>633.600000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>630.800000</td>\n",
       "      <td>630.080000</td>\n",
       "      <td>746.000000</td>\n",
       "      <td>497.050000</td>\n",
       "      <td>1.943112e+06</td>\n",
       "      <td>54905.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>672.000000</td>\n",
       "      <td>677.450000</td>\n",
       "      <td>666.750000</td>\n",
       "      <td>671.100000</td>\n",
       "      <td>671.000000</td>\n",
       "      <td>671.050000</td>\n",
       "      <td>671.670000</td>\n",
       "      <td>775.650000</td>\n",
       "      <td>555.950000</td>\n",
       "      <td>2.644690e+06</td>\n",
       "      <td>69386.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>696.000000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>688.100000</td>\n",
       "      <td>693.800000</td>\n",
       "      <td>693.850000</td>\n",
       "      <td>693.800000</td>\n",
       "      <td>695.160000</td>\n",
       "      <td>775.650000</td>\n",
       "      <td>590.450000</td>\n",
       "      <td>3.678237e+06</td>\n",
       "      <td>90812.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>759.000000</td>\n",
       "      <td>775.650000</td>\n",
       "      <td>743.500000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>760.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>754.710000</td>\n",
       "      <td>775.650000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>4.108107e+07</td>\n",
       "      <td>327691.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             OPEN        HIGH         LOW  PREV. CLOSE          LTP  \\\n",
       "count  249.000000  249.000000  249.000000    249.000000  249.000000   \n",
       "mean   655.777912  661.841767  648.138554    654.883735  654.522289   \n",
       "std     62.964291   62.167352   63.428526     62.583853   62.771070   \n",
       "min    500.000000  514.950000  497.050000    503.500000  503.550000   \n",
       "25%    635.000000  639.850000  622.100000    633.600000  630.000000   \n",
       "50%    672.000000  677.450000  666.750000    671.100000  671.000000   \n",
       "75%    696.000000  702.000000  688.100000    693.800000  693.850000   \n",
       "max    759.000000  775.650000  743.500000    759.000000  760.000000   \n",
       "\n",
       "            CLOSE        VWAP       52WH        52WL        VOLUME   \\\n",
       "count  249.000000  249.000000  249.000000  249.000000  2.490000e+02   \n",
       "mean   654.513253  654.754739  765.409438  550.952410  3.654682e+06   \n",
       "std     62.739226   62.961371   14.126748   50.048874  3.978281e+06   \n",
       "min    503.500000  504.890000  746.000000  459.000000  4.091640e+05   \n",
       "25%    630.800000  630.080000  746.000000  497.050000  1.943112e+06   \n",
       "50%    671.050000  671.670000  775.650000  555.950000  2.644690e+06   \n",
       "75%    693.800000  695.160000  775.650000  590.450000  3.678237e+06   \n",
       "max    759.000000  754.710000  775.650000  627.000000  4.108107e+07   \n",
       "\n",
       "       No of trades   \n",
       "count     249.000000  \n",
       "mean    80194.819277  \n",
       "std     42973.794420  \n",
       "min     24377.000000  \n",
       "25%     54905.000000  \n",
       "50%     69386.000000  \n",
       "75%     90812.000000  \n",
       "max    327691.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "08ab2b0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "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": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "de014939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "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": [
    "data['OPEN'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d59b91d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 249 entries, 0 to 248\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Date           249 non-null    object \n",
      " 1   series         249 non-null    object \n",
      " 2   OPEN           249 non-null    float64\n",
      " 3   HIGH           249 non-null    float64\n",
      " 4   LOW            249 non-null    float64\n",
      " 5   PREV. CLOSE    249 non-null    float64\n",
      " 6   ltp            249 non-null    float64\n",
      " 7   close          249 non-null    float64\n",
      " 8   vwap           249 non-null    float64\n",
      " 9   52W H          249 non-null    float64\n",
      " 10  52W L          249 non-null    float64\n",
      " 11  VOLUME         249 non-null    int64  \n",
      " 12  VALUE          249 non-null    object \n",
      " 13  No of trades   249 non-null    int64  \n",
      "dtypes: float64(9), int64(2), object(3)\n",
      "memory usage: 27.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f1d7167c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date ', 'SERIES', 'OPEN', 'HIGH', 'LOW', 'PREV. CLOSE ', 'LTP',\n",
       "       'CLOSE', 'VWAP', '52WH ', '52WL ', 'VOLUME ', 'VALUE ',\n",
       "       'No of trades '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8dbaef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              object\n",
       "series            object\n",
       "OPEN             float64\n",
       "HIGH             float64\n",
       "LOW              float64\n",
       "PREV. CLOSE      float64\n",
       "ltp              float64\n",
       "close            float64\n",
       "vwap             float64\n",
       "52W H            float64\n",
       "52W L            float64\n",
       "VOLUME             int64\n",
       "VALUE             object\n",
       "No of trades       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1347f3ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of            Date  series    OPEN    HIGH     LOW   PREV. CLOSE    ltp   close   \\\n",
       "0    29-Apr-2022      EQ  575.35  594.00  573.20        571.40  581.6  582.40   \n",
       "1    28-Apr-2022      EQ  550.90  575.35  546.50        547.65  571.4  571.40   \n",
       "2    27-Apr-2022      EQ  552.00  558.45  537.25        549.85  547.7  547.65   \n",
       "3    26-Apr-2022      EQ  541.10  556.00  537.95        540.00  548.9  549.85   \n",
       "4    25-Apr-2022      EQ  552.00  552.00  536.00        554.05  542.0  540.00   \n",
       "..           ...     ...     ...     ...     ...           ...    ...     ...   \n",
       "244  06-May-2021      EQ  672.00  679.90  669.00        674.40  671.7  671.80   \n",
       "245  05-May-2021      EQ  670.05  675.80  667.00        667.85  673.9  674.40   \n",
       "246  04-May-2021      EQ  677.00  680.85  665.50        671.90  669.0  667.85   \n",
       "247  03-May-2021      EQ  660.00  674.50  657.15        665.10  673.7  671.90   \n",
       "248  30-Apr-2021      EQ  670.00  678.00  660.70        674.65  662.9  665.10   \n",
       "\n",
       "      vwap   52W H   52W L    VOLUME             VALUE   No of trades   \n",
       "0    586.04  775.65  497.05   8916828  5,225,605,076.70         143404  \n",
       "1    559.23  775.65  497.05  14954957  8,363,251,852.20         168212  \n",
       "2    547.19  775.65  497.05   4744055  2,595,920,500.10          97046  \n",
       "3    546.85  775.65  497.05   4153518  2,271,361,912.10          80629  \n",
       "4    541.72  775.65  497.05   3225681  1,747,406,998.60          67671  \n",
       "..      ...     ...     ...       ...               ...            ...  \n",
       "244  672.93  746.00  464.00   2269126  1,526,955,618.85          47123  \n",
       "245  671.15  746.00  464.00   2249063  1,509,451,700.25          53489  \n",
       "246  671.71  746.00  464.00   4118750  2,766,611,158.90         102269  \n",
       "247  666.04  746.00  464.00   4908079  3,268,991,058.95         101245  \n",
       "248  669.95  746.00  459.00   2742166  1,837,121,825.70          79379  \n",
       "\n",
       "[249 rows x 14 columns]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2aeeeb95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 48,
     "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": [
    "data['OPEN'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eac60743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "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": [
    "data['HIGH'].plot(kind='area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "543e51ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "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": [
    "data['LOW'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a1c80939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 51,
     "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": [
    "data['LTP'].plot(kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b90f9040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 53,
     "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": [
    "data['OPEN'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f11d8c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kr7vu+8skTyT5YZLvJ/nHJAs79+1JcnbXse9L8u+n6ueQjse4S5NI8hfAFcAfVtUlwB8ALwBLBjqY1Ac/rEOa3BbgvVX1C4Cq+i1wy0Ankvpk3PVatCTJrq7bbwTu7T4gyVnA66rqqSm+184khzrXXwc8MWNTSifBbRm9Fr1YVWte+gL+tscxAV4+TzjJlUl2dfbZL+s6bm3X9/nzWZ1amgbjLvVQVb8C9ic5v3P7PzoBfxQ4Y5CzSf0w7tLk/gG4LckbAJIEWDzQiaQ+uecuTe424Ezg20kOAr8G/gv43kCnkvrgnx+QpAa5LSNJDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDfp/Uh7RzfvnnnsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['HIGH'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5a5cbd05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "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": [
    "data['HIGH'].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "42a7b36f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 56,
     "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": [
    "data['LTP'].plot(kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e54696",
   "metadata": {},
   "source": [
    "display.max.columns displays all the columns in the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "856e94d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max.columns\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f161fc3a",
   "metadata": {},
   "source": [
    "Head default value is 5 rows we can specify as head(any number) to display the specific number of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c076d348",
   "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>SERIES</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "      <th>PREV. CLOSE</th>\n",
       "      <th>LTP</th>\n",
       "      <th>CLOSE</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>52WH</th>\n",
       "      <th>52WL</th>\n",
       "      <th>VOLUME</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>No of trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29-Apr-22</td>\n",
       "      <td>EQ</td>\n",
       "      <td>575.35</td>\n",
       "      <td>594.00</td>\n",
       "      <td>573.2</td>\n",
       "      <td>571.40</td>\n",
       "      <td>581.6</td>\n",
       "      <td>582.4</td>\n",
       "      <td>586.04</td>\n",
       "      <td>775.65</td>\n",
       "      <td>497.05</td>\n",
       "      <td>8916828</td>\n",
       "      <td>5,22,56,05,076.70</td>\n",
       "      <td>143404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28-Apr-22</td>\n",
       "      <td>EQ</td>\n",
       "      <td>550.90</td>\n",
       "      <td>575.35</td>\n",
       "      <td>546.5</td>\n",
       "      <td>547.65</td>\n",
       "      <td>571.4</td>\n",
       "      <td>571.4</td>\n",
       "      <td>559.23</td>\n",
       "      <td>775.65</td>\n",
       "      <td>497.05</td>\n",
       "      <td>14954957</td>\n",
       "      <td>8,36,32,51,852.20</td>\n",
       "      <td>168212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  SERIES    OPEN    HIGH    LOW  PREV. CLOSE     LTP  CLOSE  \\\n",
       "0  29-Apr-22     EQ  575.35  594.00  573.2        571.40  581.6  582.4   \n",
       "1  28-Apr-22     EQ  550.90  575.35  546.5        547.65  571.4  571.4   \n",
       "\n",
       "     VWAP   52WH    52WL    VOLUME              VALUE   No of trades   \n",
       "0  586.04  775.65  497.05   8916828  5,22,56,05,076.70         143404  \n",
       "1  559.23  775.65  497.05  14954957  8,36,32,51,852.20         168212  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10109e99",
   "metadata": {},
   "source": [
    "name.plot(x=\"\",y=[\"\",\"\"]) gives the plot with specific rows and columns as mentioned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7145861c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f14e51c6",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data.plot(x=\"SERIES\", y=\"OPEN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b791ecf2",
   "metadata": {},
   "source": [
    "Creating plot using matplotlib- plt.plot(df[\"OPEN\"], df[\"CLOSE\"]) or df.plot(x=\"\",y=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618030c7",
   "metadata": {},
   "source": [
    "#Plotting top 5 values by OPEN value in descending order in a bar chart\n",
    "\n",
    "top_5 = df.sort_values(by=\"OPEN\", ascending=False).head()\n",
    "\n",
    "top_5.plot(x=\"Date\", y=\"OPEN\", kind=\"bar\", rot=5, fontsize=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c0850180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dd603a5190>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rrGdKpBcPnhPNqCB36psM3PDeVuysrfjg1nFMjjDNilmy6SArEwu556yoU9rPnkCCuxDipDIaNb+nlrAsNpPV+wvRwKxBvlw/MZjJEd4s+HA7m1JN+52GeTu1Gdi11jz01R6Wx+cwMsidZ68aaZnW2GwwcvcnO4nLLOe1a8dYAjvAOUP9efqXA+RV1NFfZssIIcSJK69p5Mv4bD7emkVmaS1eTnYsmhHBteODCfJ0JKe8lmsWx7I7u8JyTqiXY5vXWrY1i+XxOdw1K4IH5g5qtTvSP77dx6r9hfznkqFcMCKg1Xlzh/jx9C8H+C2xkJsmh56srvZIEtyFEN1Ga82OrAo+js3kx735NDYbGR/qyV/PHsi5w/yxtzHNXFm9v5C/frEbo1Hj42JvWbi0NqmYhR/F8cA5gxjoZxonj88s5z8/JDBrkA/3nz2o1erSF39L5rPt2dw9K5IbJ4Ue1Z5wH2cifZ1ZmVggwV0IIbqqpqGZb3flsiw2i/35hwCYM9iXB8+JbvUws9lg5LmVyZaHp2OC3cmrqLccD/FyZGViIav2F/KPC4Zw4cgA7vw4ngC3frx0zehWD1iXbsnglTWpXBMTxP1zB7bbtrlD/Hh7QzqVtU24Odq2W6+vOWZwV0oNAj5vURQO/BP4yFweCmQAV2uty83nPAIsAAzAvVrrFd3aaiFEj5BUUMWy2Ey+2ZlLdUNzq2Nb0kpJKapikL8LWmuWx+fw4PI9rersyKpo9f3hDIzDBrjh6WTHje9to/BQA/eeFcXncVlkldWSXVZHdnktB0tqmDPYjycvG9Zhrpi5Q/15Y10aa5IKuWx0YPd0vBfoUspfpZQ1kAtMAO4CyrTWTyulHgY8tNYPKaWGAJ8C44H+wCpgoNba0N51JeWvEL1HQ7OBX/cV8HFsFtsyylods7VWnBXtx/kjAliy8SC7zOPpfq72FB5q6PR7DO3vSkLeoaPKXR1sCPZyJMjDkYF+LiyaEUG/YyxSMho1k55ezZhgD968fmyn29AbdGfK37OANK11plLqEmCmufxDYB3wEHAJ8JnWugE4qJRKxRTotxxH24UQPUR2WS2fbMtiWWwmVfWt79JHBrpxxdhApkZ6sze3kjX7C0krrrYc/2NgHxnkzmMXDeGyNza3KrezsSLQox9ezvaWsjfmjyHY05EgT0fc+nV9WMXKSnH2ED++3pFLfZMBB9szY8VqV4P7PEx35QB+Wut8AK11vlLK11w+AIhtcU6OuawVpdRCYCFAcHBwF5shhDiV3tmQzpM/729V5utiz+VjAjl/uD9JBVV8GZfDv75PQGtTmtyzB/sxM9qXusZmnvhxP1Uthm1evmYUX+3IsXy/YGoYt08Lx9fFHisrxfrkYjYkF/PslSM4f3jrGTDHY+4Qf5bFZrEptYSzBvud8PV6g7bX9bZBKWUHXAx8eayqbZQdNfajtV6stY7RWsf4+Ph0thlCiFMov7KO19emHhXYb5wUwts3jKWqvon572zlweV7KKlp4N7ZUdw5M4IANwemRnlz8cj+XDMumB/vndrqfD9XBz7dls3MQT5E+jqzNqkIb2c7ywPTxRvS8HO155JRR90XHpeJLXK8nym6cud+HrBDa334b6dQKRVgvmsPAIrM5TlAUIvzAoE8hBC9Qm1jMysSCvgqPpdNaSVoDeNDPbloZACFhxp4bW0qH23J5KMtmQBcOqo/8yeG0M/Wmsd/TGTrQdM4fFxmOZePMT3AbGw24mxvY3noOvifvwJw8+RQGpuNLFwaz5fxOVw7Pph9uZVsSi3l4fOisbPp9P1nh2ytFVOjvFm1vxCDUWPdRlqDvqYrwf1ajgzJAHwP3AQ8bf76XYvyT5RSL2B6oBoFbDvxpgohTqa04mreWpfGz3vzqWk0EOTZj3tnR3H5mAEUHmrgs+1ZfL3j6E0zViYW8u0u0/2bp5Mdj186jLfWpVHXaCC/so6Xfkvhy/hsnOxt+PGeqVz46kbLudbm8fCxIR68tCqZS0cNYPGGdJztbbhuQueGa41GTVltIwWV9eRX1lNQWWf+Wk9eZZ2lvMG8iXZWWS1h3k7d8DfWs3UquCulHIGzgTtaFD8NfKGUWgBkAVcBaK0TlFJfAIlAM3BXRzNlhBA9w+trU/l2Zy5GbZqjHhPqyfqkYl5enWKp0/Lu+7DaxiP/vW+eHMqVYwJ5c20q3+zM5ee9+Ri1Zv6EEMK8nVifXNzq3BveM933OdhaUd9k5N3f0/lpbz63TgnF1cEWo1FTUt1AfsvAfajeErDzK+sorGyg0WBsdV0bK4WfqwMBbg4MG+DG3KH++Ls6EOHr3O4q2L6mS1MhTxaZCinE6ffyqhReXJXcrdc8f7g/l4wawIu/JXOgoKrVsZYrU//o8K5JhYfqaTa2jlF21lb4uzng72YK3v5uDgS4OhDg3s/yvbeTfZsZJfua7pwKKYTog/Ir61iRUNCq7OwhfqQUVuHaz5Y9OZXHdd2f9xbw894j1/VyssPX1YGK2kaK2gnsAMGejkcCt5sD/m6mwB3g5oCnk12XNrg+U0lwF+IM1WQwsuZAEZ9ty2J9cjF/uEHG0c6ajNJj72HaFaU1jZTWNHZY55c/T2NwgGu3vu+ZSIK7EGeYmoZmXlubyvL4HIqrGvBztefOmZGcM9Sfi1478rDzu10nPslt/oRgbpwUir+rA1UNTfywO59X16S0GqdvaWSgmwT2biLBXYgzzEurknl340HOivZl3rhgZg7ywcbaypIqoDuNCfYwbdCxLYu1SUWW3DFRvs7MivZlSIAr9jZWHCioIrWomj/NjOj2NpypJLgLcQZpMhj5ZmeuKVPiDa2fwx1reuBtU8N4d+PBLr3f/V/uBkwPQadGeXPb1DBmDvIlyLP1jJXzumEVqmite1YICCF6hbUHiiipbuSqsUFHHVOKo+aW3zAxxPLazsaKpQvG89hFQ7r8vm/MH8OSm8dxw6TQowK7ODnkzl2IM8iX8Tl4O9szc9DRKT9u/zDOsrr0sKWxmZbXb6xL4411aX88rUPv3zKOtKJqRgS5HV+DxXGT4C5EH/XJ1izyK+uYHOHNmBB3quqbWXugiFunhmFj3fqX9q3ppUcF9q4I93biicuGMT7UExtrK97beJDHf0xkdJA7swb5HvsCottJcBeij9p6sJTvduXx6ppUywpQgEF+LkflV7lmcWx7l7FYtmACNtaKeYtjuXhkf77fnceHt45nxsCjfwtwNOdYr2004C6jMKeFjLkL0Uc9e+VIy+vDgR1MDznHPP4bdyyN46MtGXwRl93m+eE+Tqx7YCYAkyO8mBrlzQ+787C3sWLh9HAAig7Vt3luy+AuTg+5cxeij7KzsWLVX6cz54UNRx2rrGtiRUIhKzpIgZteXMPM59YBsDmtlIte3cjeXNNK1fjMcoA2V5nWNDSzObUUgPomCe6niwR3IXq5w8m18syJtfIqTAm18irrya+ow93Rlorapi5f99krR9Bk0Pztm70AlsAO8K/vEwAsmRYBtNZ8tyuPp37ZT+GhBi4fM4BIX+cT7J04XhLchejFfk8pZsGHcTQ2t86KaG9jRX9zIq2pkd78uCe/3WvcNCmEu2dH4eVkx4GCKjanlVBQWc/lYwJpMhh5a30a0f4ulNY0UlHbyK/3TafwUD2Fh+otq0n35FTw2PcJ7MiqYGSgG2/MH8vYEI+T2nfRMQnuQvRi3s72GIyaSF9nHjo3mgA3B/q798PD0bTXaF2Tgfc3ZfDjnnwG+jmTXFh91DWWbc3iy/gcbpsWzu3TwritfzhGo+bbXbk8tyKJvMp6xod5sjKxkL+fPxhbaysCPRwJ9HCkqKqef32XYJli+eyVI7hiTOAZkZGxp5PgLkQvUVnbxNyX1uPqYMvgAFeiA1wYHODKxSP7883OXJILq1i9v5Ah/V0ZFeTO5rRSnv7lgOX85MJq5gz2xcXBlm92Htl0Y0KYJ4521ryyOoVlsZmMDfEgs7SG5MJqRgS68cI1o1iZUIitteLyMaZt7xqbjby/6SCvrkmlodnAHTPCuXtWJC4OXd/AWpwcks9diF7CaNQ8sHx3m7shdYWHoy3lfxiDf/6qkWjgAXO6AIBZg3x458YYmo2aiU+tZkqkN69dO5o1B4p44qf9HCyp4axoX/5x4ZAzYmejnkjyuQvRi9U0NHPRaxs5VNeMk731CV/vj4EdjuSAAZga6U3BoXrWJhVz0WubmBTuRUVtE+NDPbn5/e2sTy4mwseJD24Zx0xZoNRjSXAXooeztbaioclIeW0jUyIDGOTnQmpRNeklNd3+Xq9cO5qLR/bHaNT8sCeP51cms2STKVnYv39IwMnehkcvHMKNk0KwtZZlMj2ZBHchejg7Gyv+evZA7v9yN2cN9uPikf0B0xj8pKdXd8tCoUUzInj4vGjL91ZWigtH9Keyrol/fmea9njNuCDunzsIb2f7E34/cfJJcBeiF7h09ADe+T2d51YkMSXCi2WxWby3Mf24AvvoYHeuGhuEp5Mti5btAEybZLS07WAZ//4hgYS8Q4wP9eSfFw1h2ABJ/tWbSHAXohewtlI8fF40N7+/nbFPrLKUjw3xsKwW7YyRgW5cNz6Y/Mp6nvgpERsrxQ2TQphhzhKZV1HHU78c4IfdefR3c+DVa0dz4YgA2bO0F5LgLkQvMWOgD5MjvNicVmop60pgB9idU8nu5XsAOHeoPw+dF02YtxP1TQZeXpXCm+tT0Rr+fFYUi2ZE0M/uxB/gitNDgrsQp0GzwUh9s5GGJgP1zUbqmwzmP6ayhsNlzaayw8ccuzHYZpXVcufHO9DalL6gpLqRC0YE8Mh50QR6SCrH3k6CuxCn0MaUEm77aHurLI3dydfFnggfUz6XLemlbdYZG+KBWz9bTItIFUpBpK8z108MYWK410lplzj1JLgLcQodzqs+OcKLGQN9cLC1xsHWCnsb81dbaxzMrx1srbG3sTLXOVIvt7yO6c+uBUxDNXWNBnZml/PqtaMJ93Emu6yWh77a024bFk4P55yh/qeqy+I0keAuxCk0NsSDQX4uVNU3c8eMiOO6RrCXIzdPDuWDzRmsTy62lB+e+dLWe44KcifY05FQbyemRXof1/uK3kWCuxCnkFKK+ROD+ed3CezJqWBEoPtxXeee2ZF8tCUDozl7yFVjA9mVXUFK0ZHEYMsXTSIm1LMbWi16I1liJsQpdunoAfSztebj2KzjvoaXsz3v3TyOl64ZxeIbxpJcWNUqsH931xQJ7Gc4Ce5CnGKuDrZcMsq0B2llXdubaJRUN2A0tp/Ur6q+iYPFNbzwWzILl8ZTUt1o2RP1f1cMZ2SQ+8louuhFJLgLcRrMnxBCXZOBb3e2zvC4P/8Qo/6zkpgnVrEy8egt8LLLavnPD4lMemoN//kxEV8Xe96YP4YLRgRgMGrumxPFNeOCT1U3RA/WqTF3pZQ78C4wDNDArcA5wO3A4Sc6f9Na/2yu/wiwADAA92qtV3Rvs4Xo3YYHujEi0I2Pt2Zy46QQtOaodL6TI03TErXWbDtYxpJNB/ktsRArpbhwRAC3TAljZJA77208yOIN6cyfEMyfz4o6XV0SPUxnH6i+DPyqtb5SKWUHOGIK7i9qrZ9rWVEpNQSYBwwF+gOrlFIDtdayU644YyXmHeKf3+3DxlrhbG+Do50NiXmHaDZqrn9vK1MivVsF9jX3z8Dexoqv4nNYsukgCXmHcHe05U8zI7hhYij+bg4AfLcrl8d/TOTcof7855JhkiZAWBwzuCulXIHpwM0AWutGoLGDf0SXAJ9prRuAg0qpVGA8sKU7GixEb7QxtZi4zHJiQjzIq6inprEZexsrmhsNbEotZVNq6wVHL69OYXNaKcVVDUT6OvPfy4Zz2egBrdIBbEgu5oEvdzMx3JOX5o2yjLkLAZ27cw/HNPTyvlJqJBAP/Nl87G6l1I1AHHC/1rocGADEtjg/x1zWilJqIbAQIDhYxghF31Zc1YCDrRVfLprU6u76se8TWBqbyVVjA/lse7al/Ltdecwc5MOtU8KYFuV91B357uwKFi2LJ9LXhcU3xuBgKzlgRGudeaBqA4wB3tRajwZqgIeBN4EIYBSQDzxvrt/W7cNRj/211ou11jFa6xgfH5/jaLoQvUdxVQM+LvZHBekrxwZiMGpLYHeys+amSSGs+usMPrhlPNMH+hx1TnpxNbd8sB0vZzs+vGUcrrJvqWhDZ+7cc4AcrfVW8/fLgYe11pZH+Uqpd4AfW9QPanF+IJDXDW0VotcqqW5stclFfmUdH8dm8em2I3PdHzkvmusmBHe4yXThoXpueG8bCvjo1gn4ujqczGaLXuyYwV1rXaCUylZKDdJaJwFnAYlKqQCtdb652mXAPvPr74FPlFIvYHqgGgVsOwltF6LbNBuM2JyEbeOaDEZWJhSyMbUEgNj0Uj7aksGKhEKMWnNWtB/XTwxmepQPVscYM6+sa+KmJduoqG3ks4WTZFNq0aHOzpa5B/jYPFMmHbgFeEUpNQrTkEsGcAeA1jpBKfUFkAg0A3fJTBnRk207WMZ178QyMsid84b5c+4w/xNOeZtdVsuX8Tl8ui2L4qoGS/m8xbG49bPltqlhXD8xhCDPzr1PfZOB2z+KI624mvdvHs/wQNkVSXRMad3+KrhTJSYmRsfFxZ3uZogz1PaMMq56awueTnaU1TQCMHyAG+cO8+e8Yf6Em1PodkRrzYGCKlYkFLAioZD9+YdQCmYO9GFWtK9lH9L/XTGci0cO6NImGAaj5k/L4vltfyGvzBvNReY9VIVQSsVrrWPaOiaJw8QZb5C/CwALpoZx4YgAft1XwC/7Cnh2RRLPrkhikJ+LKdAP92eQn4vlAafBqNmRVc5Kc0DPKqtFKRjs78qlo/pz/9xBBHk6smhpPGDau7Srq0e11vzj232sTCzksYuGSGAXnSZ37kIAU55ew5gQD169drSlLK+ijhUJpkC/PaMMrSHM24lzh/njbG/D+5sOUlJtutMfH+rJpaMHMCbEnQte2YjBqEn49zkUVzUw54X1DA9048s7JnV5XP+F35J5ZXUKd82K4MFzoru1z6L3kzt3IY5hcIALB/IPtSrr796PW6aEccuUMIqrGliZWMCv+wpYvCEdwx+Sem3PLKOuycDfvtlrKTtQUMUHmzOwtbbi7RvGdjmwL92SwSurU7g6JpAH5g46/s6JM5IEdyGAaH9X1iYVU99kaHNBkI+LPfMnhDB/QggVtY38lljIr/sKWH2gCACtYW9uZatzrnhzs+X1cyuSiPR1JsLHmUhfZwI9HDtcUfrTnnz++X0Ccwb78d/LhktaAdFlEtyFAKIDXDAYNalF1Qwb0PFMFHdHO66KCeKqmCCq6ptYc6CIX/cVsDapqM29UYcEuLLmQDFfxOVYyuxsrAj3diLC15lIc8CP9HUmzNuJHZnl/OXzXcSEePDadaNPyhRN0fdJcBcC0507mIZSjhXcW3JxsOWSUQO4ZNQA6hoNrE8u4pd9BXy368i6vUH+Lvx5ThSjgtzJKa8ltaia1KJq0opr2JtTyc978zn86EspsFaKcB8n3r1xnKQVEMdNgrsQQKiXI/Y2VkeNu3dFPztrzh0WwDlD/UktqiYh7xAXj+zP2qQivtmZi6OdNTdMCuGR8wa3Oq++yUB6cQ1pxaagX1nXxKIZEbg5SloBcfwkuAsB2FhbMdDPhaTCqhO+1srEQhLyDvH05cOZNz6YdUlF3PPJTqoamqmoOXrnJQdba4b0d2VIf9cTfm8hDpPgLoRZtL8La5OKKTxUT3JhFR6Odng6mf50dnjEYNQ8uyKJcB8nJkd486dl8fyyr4AQL0devnYUs6P9TnIvhDCR4C6EWXSAK1/G53D9u1tbbTYN0M/WGk8nOzycbC1B38PRzvzaFg8nOzwd7dibW0lqUTVD+7sy96X1ADx4ziAWTA2T8XNxSklwF8JssHmlakpRNReP7M8FIwIor2mkrLbR9LWmifLaRspqGskqq6WsppGq+uY2r5WQd4jzh/vz9wuGMMC936nshhCABHchLA6nIQA4f7g/5wz1P+Y5TQYjFbVN7Mqu4C+f76K6oRlnexveun4sU6O8T2ZzheiQBHchzDyd7CyvxwR7dOocreGjLRm8vT4dW2vF388fzE2TQ7Gzkbnp4vSS4C6E2daDZZbXndkEo7ymkUXL4tl6sIzLRg/gkfOiZfMM0WNIcBdntPKaRjallbAxpcSSSuC8YccejkktqmbBh9vJr6zn5XmjuGTUUdsEC3FaSXAXZ6TY9FKe/Gk/+/IqLatD7czL/CdHdjxWviG5mLs+2YG9jRWf3j6RsSGdG8IR4lSS4C7OSBW1jZTXNmJjpWgymKJ7o8GUF2ZcaPvB+qMtGfz7h0SifJ1596aYE96xSYiTRYK7OCOdOyyAc4cFYDRqSmoaKKisJ7+yHjiSZ6alZoORf/+QyNLYTOYM9uWleaNxtpf/PqLnkn+d4oxmZaXwdXHA18WBEYFt16msa+LuT3bwe0oJd0wP5//Oje4wXa8QPYEEdyE6kFFSw60fbie7rJZnrhjB1eOCTneThOgUCe5CtGNLWil/+jgeBSxbMIEJ4V6nu0lCdJoEdyHa8Pn2LP7+zT5CvZ1476YYQrycTneThOgSCe5CtGAwap76eT/vbjzItChvXp8/BlcHyasueh8J7kKYVdU38efPdrHmQBE3Tw7lHxcMli3uRK8lwV0IILuslts+jCO1uJrHLx3GDRNDTneThDghEtzFGS8+s4yFH8XTaDDywS3jmBblc7qbJMQJk+Auzmhf78jh4a/20t/dgXdvGkekr/PpbpIQ3UKCuzgjGY2a51Ym8ca6NCaGe/Lm/LF4tEj5K0RvJ0+LRI+zJa2Ui17dyNLYTBqaDd1+/drGZu78eAdvrEtj3rggPrp1ggR20ed06s5dKeUOvAsMAzRwK5AEfA6EAhnA1VrrcnP9R4AFgAG4V2u9opvbLfqw1OJq9uZWsje3kjfWpvKnmRFcHRN0XHuQGo2avMo60oprSCuqJq24mi3ppWSU1PCPCwazYGoYSkkqAdH3dHZY5mXgV631lUopO8AR+BuwWmv9tFLqYeBh4CGl1BBgHjAU6A+sUkoN1Fp3/y2Y6JPc+5nmlf/roiH8tCeff36XwOtrU/nTjAjmjQ9uM8jXNRpIL6luFcTTi2tIL6mmvsloqefqYEOUnwuP3jSEWdG+p6xPQpxqxwzuSilXYDpwM4DWuhFoVEpdAsw0V/sQWAc8BFwCfKa1bgAOKqVSgfHAlm5uu+ijPBxNQyRD+7tx8+RQNqeV8vKqFB77IZE31qUxLtSTIf1dKa5qsATx3Io6y/lKQZCHIxE+TkyO8CLC15lwbycifJ3xcrKTO3VxRujMnXs4UAy8r5QaCcQDfwb8tNb5AFrrfKXU4dugAUBsi/NzzGVCdIq7o+nOvby2EaUUUyK9mRzhxZb0Uv7zQyI/7c3np735ONpZE+HjzLhQD+b5BJmCuI8ToV5OxzWEI0Rf0pngbgOMAe7RWm9VSr2MaQimPW3dFumjKim1EFgIEBwc3IlmiDPF4eBeUdtoKVNKMTnCm1/vm87S2EwG+jozPsxT7sKFaEdnZsvkADla663m75djCvaFSqkAAPPXohb1W+ZFDQTy/nhRrfVirXWM1jrGx0cWjYgjDg/LVNQ2tXn8hokhTAj3ksAuRAeOGdy11gVAtlJqkLnoLCAR+B64yVx2E/Cd+fX3wDyllL1SKgyIArZ1a6tFn+ZoZ42ttaK8neAuhDi2zs6WuQf42DxTJh24BdMPhi+UUguALOAqAK11glLqC0w/AJqBu2SmjOgqZ3sbKusaj11RCNGmTgV3rfUuIKaNQ2e1U/9J4Mnjb5Y4k61LKqa8tonhA9xPd1OE6LVkharoUYxGzbMrkgj2dOSqmHY2NRVCHJPklhHdprymkfc3Z7B0Swa21lZE+jpb/owN8WBof7djXuOnvfkk5h/ipWtGYSu51IU4bhLcxQkrqqrnvd8PsjQ2k9pGA3MG++HWz5bU4mq+jMuhrsmAk501ex87Byur9me4NBuMvPBbMoP8XLhoZP9T2AMh+h4J7uK45VXU8fb6ND7bnk2TwchFI/tz58xIBvm7ALAioYAHvtiNu6MtL10zqsPADvDVjhwOltTwzo0xWB+jrhCiYxLcRZdllNTw5ro0vt6Zg9ZwxZhAFs2MIMzbtIl0s8HIsyuTeHt9OiMD3Xh9/hgCPRw7vGZ9k4GXVqUwKsidOYMl54sQJ0qCu+i05MIq3libyve787CxtuK68cEsnBHBAPd+ljpFVfXc++lOYtPLuH5iMI9eOAR7m2OnAvh4axb5lfU8f9VIWZwkRDeQ4C6OKa24mhdWJlvyudw2LZzbpobh6+rQqt72jDLu+ngHh+qbeOHqkVw+pnOzXaobmnljbSpTI72ZHOl9MrogxBlHgrtoV15FHS+vSuHzuGyc7Ky5Z3Ykt04Ja3Nji/c2HuS/P+8nyKMfHy0YT7S/a6ffZ8nGg5TWNPLAOYOOXVkI0SkS3MVRMktruP69rWSXHUmju/7/ZuHtbN9m/ZdXpfDiqmTmDvHjuatH4upg2+n3Kq9p5J0N6cwd4seoIPcTbboQwkyCu6DZYGR3TiUrEgpYvCG91bHBAa5cNyEYr3a2oXt9bSovrkrmijGBPHvliGPOiPmjtzakUd3YLHftQnQzCe5nqOyyWjakFPN7cgnrkota7VYE8ORlw7g6JqjDhUSLN6Tx7IokLhnVn2eOI7AXHqrng00ZXDZqAAP9XI6rH0KItklwP0NU1TexJa2U31NK+D2lmIzS2qPqDA5w5b+XDWN0sMcxr7dk40H++/MBLhgRwPNXjTyueemvrknBYNTcN2dgl88VQnRMgnsfZTBq9uZW8ntyMb+nlLAjq5xmo6afrTUTwz3p796PrQfLMBg1wwe48X/nDmJqpHenpiEu3ZLBf35M5Jyhfrx0zShsjiNNQFZpLZ9ty+ba8cEEe3U8B14I0XUS3PuQ3Io6SzDfmFpCZZ0pH/rwAW4snB7OtCgfmgxGXlyVzM6sCsK9nXjgnEGcN8y/03PLP92WxaPfJTBnsC+vXjvmuPO/vLgqGRtrxT2zI4/rfCFExyS492I1Dc3EppuGWjakFJNeXAOAn6s9Zw325esduQAs/9Mk7G2sufuTHfy4Jx9fF3uevnw4V44N7NJd95dx2fztm73MHOTD6/PHYGdzfIE9qaCKb3flsnB6+FFz5YUQ3UOCey+zL7eS9cnFbEguZkdWOU0GjYOtFRPDvZg/IYTpUd5E+jqjlKLwUD2bUksZ9I9fyXj6AstddkVtE3GZ5QwPdOswU2N9k4GDJTWkF9ewJ7eCxRvSmRrpzVvXj+3UqtP2PL8yCWc7GxZNjzjuawghOibBvRdZmVDAwqXxAAwJcOXWqWFMj/JhbIgHDrZHgm1do4F3fk+37EUKMOeF9Xx063jumhXBh5szWR6fw/L4HCaGe3LrlDDOGuxneSi6ZONBlmw6SG5FHbrF1uazo315/boxrd6rq3ZmlbMysZD7zx7Y5mIoIUT3kODeizQaTNMVly+aREyo51HHtdb8vLeAJ39KJK+yvtWx1KJqJj+9BjsbK4I8+jGkvyvxmeXEppcRm14GwMPnRTMu1JMnf97PyEA3rhwbSLiPMxE+ToR5O+Fod+L/XJ5bmYSXkx23TA074WsJIdonwb0XOXwnbtRHH0surOJf3yWwJb20Vfk5Q/0YF+rJEz/tB+Dikf2pqm8is7QWRztrahuPbG/79C8HLK//edHQbl8xuim1hE2ppTx64RCc7eWfnhAnk/wP60Xc+pmW9ZfXHtk4urKuiZdWJfP+poxWdaN8nRkT7MHncdkUVzVYypfH5/DkZcOYPyEErTUl1Y1kldWQWVrLX7/Ybal32RubOHuwH7dODWNCmOcJZ2rUWvPMiiT6uzkwf0LwCV1LCHFsEtx7EXdHU3CvrG3CaNR8GZ/No98l0Nh8ZHXp6GB3PB3taDJqNqQUA7Ajq6LVdf7+zT4G+rkwLtQTHxd7fFzsGdrfzRLcP1s4kY0pJXy8NZOViYWW8f2LRgYc94PU3xIL2Z1dwf+uGH5CY/ZCiM6RTSp7EXfzsMy65CKmPbOWh77aawns06K8+fT2iXz9p8mkl9SwM7OcCWGePDB3ILbWprvuf100xHKtq97aQmn1kTv6a9+JBWBKpBcTw7144JxBbHnkLJ6+fDjNRiMPfLmbKU+v4cXfklv9JtAZBqPm+ZXJhHs7cUUn0wALIU6MBPdepLaxGYCf9xaQW2HK2HjeMH9+uHsqSxdMYFKEF0opRge5Y2djxYvXjOLu2VHsfewcxoZ48NQvB3j68uGW6326LQuA9OJqdprv7t+/ebzluIOtNfPGB7PivuksWzCBEYHuvLw6hSlPr+H+L3aTkFfZqXZ/vzuXpMIq/jp34HGtZhVCdJ38T+sFmgxGHlq+h/FPrraUXTA8gNX3z+DN68cyPLD1XPXxYZ6U1jSSZl7U5GBrzbs3xhDo3o+nfjnAJ7dPwMPRli/icqisbWL28+sBeP6qkW0uTFJKMTXKmyU3j2PN/TOYNz6In/fmc8ErG7nm7S2sSCjA0NZTXqCx2ciLv6UwJMCV84cFdNdfiRDiGCS4t2FXdgVFVfXHrngKLNl4kKi//8LncdmWsjHB7rw+fwwRPs5tnjMh3AuAbQfLLGUeTnZ8eOt4bK0VD365h6cuH0F+ZR0j/7PSUueKscceMgn3ceY/lwwj9pGz+Nv50eSU13HH0nhmPreW9zYepKq+qVX9L+KyySqr5cFzBnU5a6QQ4vhJcP8DrTXXLo5lytNruOfTncRllKF123elJ1N2WS2Llsbznx8TLWV/mTOQSF9n+tl1/EAy1MsRHxd7ft6bT1aL7I9Bno68f/N4ymsbeXVNCn85+0g2xo0PzepS+9wcbVk4PYL1D87kjflj8HNx4PEfE5n01Br+/UMCmaU11DUaeGV1CuNCPZg5yKdL1xdCnBh1OgLXH8XExOi4uLjT3QyLp37ez9stNq0YHODKTZNCuGTUgGMG1hNV32TgzXVpvLU+DSuluHFSCN7O9ny9M5f9+Ycs9T65fQITw7zavRv+78/7LRtvBHs6Mi3Km2lRPkyK8GJHVjm3fRjXaijl7RvGcs5Q/xNq+56cCt7flMEPu/MwaE2UrzPJhdV8ccckxocdvehKCHFilFLxWuuYNo9JcD9aVX0Ts55bh6+LA/MnBrN0SyYHCqpwdbDh6pggbpgUQoiXU7e+p9aaX/cV8MRP+8mtqOPCEQH87fzB9HfvZzm+L/cQF7220XJOsKcjV8cEcuXYIPzdHI66XnpJDRvN+du3pJVS02jASsGoIHcOFFRZFjANH+DGwZIavr1rCpG+bQ/1dEXhoXqWxWby8dYsYkI8WHxjm//2hBAnSIL7cfh8exYPfbWXV64dzUUjAtieUc6HWzJYsa8Ag9bMHOjDjZNCmTHQ54THklMKq3jshwQ2pZYS7e/Cvy4ayqQIrzbrPv5jIh9szuC5q0bw+fZsYtPLsFIwY6AP14wLYna0X5sPRZsMRnZmVbAxpZgNKSXsyq6wHLs6JpDV+4twc7Tlu7um4NKFPVA7cvg3g+PZyEMIcWwS3I+Dwai55PWNlFY3sub+mZbhmMJD9XyyNYtPtmVRXNVAiJcj108I4aqYQMs89M46VN/ES7+l8OGWDJzsrLl/7iDmTwjucLrgq6tTeP63ZJKfOA87GysyS2v4Ii6b5fE5FB5qwMvJjsvHDOCacUFE+ra9dV1yYRVzX9yAi70NVlaKyrom/nfFcP72zT7OivblrevHysNPIXqBEw7uSqkMoAowAM1a6xil1GPA7UCxudrftNY/m+s/Aiww179Xa72io+v3xOAOsD2jjKve2sJ9c6KO2gqusdnIrwkFLN2SwfaMchxsrbh01ABumBTSYRpdAKNRs3xHDs/8eoDSmkbmjQvmgbkD8XK2P2ablm7J4NHvEtj+9zn4uByp32wwsiGlmM+3Z7N6fxHNRs2YYHeuGRfEhSP642TO5aK1ZuA/fqHJYPrc3R1tuX1aOH+aEcH7mzN4/MdEHpg7kLtnR3X1r0sIcYp1FNy7kn5glta65A9lL2qtn/vDmw0B5gFDgf7AKqXUQK21gV5mXKgnF44I4K31aVwVE8QA8/g3gJ2NFReP7M/FI/uTmHeIpbEZfLMzl8+2ZxMT4sENk0I4b1jAUUMkmaU1/PmzXezKrmBMsDvv3zz+qHnqHXEz/3ZQWdfYKrjbWFsxO9qP2dF+FFc18M3OHD7fns1DX+3lPz8kcuGI/vi7OfDy6hTLOX87P5r5E0Isgf/WKaHsyang+d+SGTrAjVmDfI/r700IcfqdjKmQlwCfaa0btNYHgVRg/DHO6bEeOX8wWrfOmPhHQ/q78tTlI9j6yBz+ccFgiqsb+PNnu5j89Bpe+C2Zghbpd39LLGRXdgX3nhXF8kWTuxTYAdzNycMqapvarePjYs/C6RGs+usMli+ahL2tNZ/HZbcK7BsenMXC6RGWwA6mxUpPXz6CaH9X/vzpTjJKarrUNiFEz9HZO3cNrFRKaeBtrfVic/ndSqkbgTjgfq11OTAAiG1xbo65rBWl1EJgIUBwcM/NEjjAvR+LZkTw8uoUbpwUwrg28qgf5uZoy23Twrl1ShjrU4pZuiWTV9ek8PraVM4d6s+Nk0K4KiaI19amsjOrHCsrxf9+PcCOzHI8HO1wd7TF3fzVw9EWt352eJjLPBxtcXO0tSQP6yi4g+mZwS/78nltTSplNY14OdlRWnMkm+Ts59cxZ7Af14wLYvpAH8tDz3521iy+YSwXvbaRRcvi+frOyd2Sx10IcWp1dsy9v9Y6TynlC/wG3AMkASWYAv/jQIDW+lal1OvAFq31MvO57wE/a62/au/6PXXM/bC6RgOzn1+Hl7Md3981tUsPGzNLa1gWm2la6l/XxCA/F5oMRtJLali2YALbMsp4a31aq8yOHbG3saKh2cjzV41sc0Vps8HId7vyeH1dKunFNUT4OHHXrEjGhXoy98UNzBjow/1zB/L59my+3plLWU0j/q4OXDk2kKtjggj2cgRgQ3IxN7+/jfOHB/DqtaNPOOWvEKL7detsGfOD1OqWY+1KqVDgR631MPPDVLTWT5mPrQAe01pvae+aPT24A3y3K5c/f7aLZ64YwdXjgrp8fl2jge935/Lh5kwSWyxGWn3/DDwc7fgiLptlsZnklNfhYm/D3KH+nD/cH3sbayrqGimvbaKytpGK2ibqmgzcNSvSMgceoKHZwNc7cnljXSrZZXVE+7twz+wozh3mj7WV4s+f7eSXfQWs/usMgjxNAbyx2cjq/YV8HpfNhuRijBomhXtxzbggzh3mz5JNB3nm1yT+fv5gbp8efuJ/iUKIbnVCwV0p5QRYaa2rzK9/A/4D7NZa55vr/AWYoLWep5QaCnyCaZy9P7AaiOrogWpvCO5aa656awsZpTWsfWDmcc8F11qzI6ucK9488rNuWpQ3N00KZcYgH343D+esSy7GSinmDvHjhokhloyPf1TfZOCzbVm8vSGd/Mp6Rga6cc/sKM4a7Gupf3jWzz2zI7l/7qA225VfWcfyuBy+iM8mu6wOVwcbLh7Vny1ppRw0/5YxOdL7uPoshDg5TjS4hwPfmL+1AT7RWj+plFoKjMI0LJMB3NEi2P8duBVoBu7TWv/S0Xv0huAOpuX1F7+2iTtmhPPIeYNP6FpGo2bCU6sprmrA08mOsppGAj36cf3EEK6JCaKqvpmPt2XyxfZsymubiPBx4vqJIVw+JhC3frbUNDTz8dZMFm84SEl1A+NCPbhndhTTorxb/RAwGDUXv7aRsppGVt8/45jj50ajJja9lM/jsvllX4FluMjD0ZYf7plKoIfjCfVbCNF9ZBFTN3rwy918uyuXlX+ZQZj3iaUg2JBczI1LtvG386MJ9HDkoy0ZxKaXYW9jxUUj+3PTpFCi/Jz5aU8+S2Mz2ZVdQT9ba2YP9mVTagkVtU1Mi/Lm7lmRlkyQf/Tptiwe+dq00vbikf271L7K2ia+253L59uzScg7xIKpYTx64ZBjnyiEOCUkuHejoqp6Zj27jppGA0Ge/Qj1ciLS15lIX2cifExfvZzsOv0A8vp3t5KQV8n6/5uFq4MtSQVVfLTFNGe+ttHAqCB3bpocwvnDA0guqGZpbAa/7C1gfJgnd8+OZHSwR7vXrqxtYtbz64j0cebzOyae0EPRlMIqvJ3t8XDq2ipcIcTJI8G9m017Zg3ZZaadkIYNcCWtqIa6piOPFNwdbU2B3seZCF9z8PdxYYBHv1Z5VsprGkktruaqt7Zw58wI/u/caMuxQ/VNfBWfw9ItmaSX1ODlZMe88UHMnxDS6kFqR/79QwIfbs7gh3umHnPVrBCi95Hg3o3iM8u54s3NAET4OLHivulYKUX+oXpSi6pJK6omtbia1KJq0ourKak+Mrfc3saKMG9TsE8prCapsKrVtadGejMhzJNgL0dCvJwI9XLE1cGWTWklfLg5kzUHCgGYO8Q0Z769h6xgutM+9+XfuWZcEP+9bHibdYQQvZsE925SdKieC1/diIOtNffMjuTB5Xv498VDuWlyaLvnVNQ2kmYO9qlF1aQV17DmQFGn39PVwYZQbyeCPR2xsVJsTC2x/MCI8HHipsmhXD4mEOcWK0211tzw3jb25FSw7sFZeMpQihB9kgT3btDYbOS6d2JJyDvE13dOJtrfhevf28q+3EOse2Bmp8ei65sMXPr6JrLKanntutHUNhosgX9yhDeXjR5AVlktmaU1ZJXVklFaQ2ZpLVllteSU17W7VynAzZNDmR3tS35lHQ99tZfHLhrCzVPCuuuvQAjRw3RX4rAz2hM/JRKXWc4r145mcIArAP+8cCjnvbyBl1Yl8+9LhnXqOv/8bh9JhVW8f/M4ZraTmGuQvwuD/I9O19tkMJJXUUdmqSn4Z5bW8v3uPIqqGgD4YHMGH2zOALBMnRRCnJkkuHfC8vgcPtqSyW1Tw1pNJxzk78L1E0NYtjWL6yaEtBmQW/oiLpsv4nK4Z3Zku4G9I7bWVoR4OZl3gTLtSfoP89TE4qoGXluTwodbMgFIK67h7Q3pXD8xBLd+3bP5hhCi95ANso9hb04lf/tmL5PCvXj4vOijjv9lzkCc7W14/MfEDjfS3p9/iEe/3cfkCK+jcsN3BztrK2YP9uOe2ZG4Oph+Zj+7IonnVyZ1+3sJIXo+uXPvQGl1A4uWxePtZMdr141uc4ckDyc7/jInisd+SGTV/iLOHuJ3VJ2q+ibu/HgHbv1seXne6BPedq7ZYCSpsIqdWRWmP9nlpBeb0vMqBYP8XDg/yJ3Rwe4nvOm1EKJ3kuDejmaDkXs+3UlxdQPLF02irsnAq6tTsLJSLJwejm2LQD9/Yggfb83iiZ8SmT7QG3sba8sxrTUPf7WXrLJaPrltQqsNNjqr6FA9O7PNgTyrnD05lZZ59V5OdowOdueKMYGMDnJneKBbt+2BKoTovSS4t+OZFUlsTitl0YwIPtqSybc7c2k2z1R5doVpqGP6QB9unRLKmBAPHr1wCDcu2cb7mzJYNCPCcp0PN2fw0958Hjo3ut0UAS3VNxlIyDvEzqxydmZXsCurgtwK04IpW2vFkP5uXDMuiNHB7owO8iDIs5+k4xVCHEWmQrbhh9153PPpTsA0zGFvY8V140O4fMwALnx1Y5vnBLg5kG/ecenrOyczOsidXdkVXP32FqZH+fDOjTFH5YHXWpNdVsfO7HLz8EoFiXmVlv1NB7j3Y1SwO6OD3Bkd7MHQ/q442Fof9d5CiDOTzHPvgqWxmTz67T4AHGytuG1qOLdMCT1q8+q1B4q45YPt7V7nghEB7MqqAOCne6fi7mhHdUMze7IrzEMspoB+eHekfrbWjAh0Y3Swh/mu3B1fV4eT00khRJ8gwf0YtNasSy7m2V+TLBtp3Dw5lL/OHYhrB+PXjc1Gfk8pZsGH7bf9ugnBGI2aXdkVJBVWcfivO8LHiVFB5kAe7M4gP5c2H9gKIUR7ZBFTOwxGzYqEAl5fm0pC3pHdkTpKj1vT0My6pGJWJBSw9kARVQ3NONpZMzLQHaVgc1ppq/qfbM3CrZ8to4LcOXeYP6ODPRgV6I6bozz0FEKcPGdkcG8yGPl2Zy5vrk8jvbiGcG8nnr1yBPXNRh79dh9jgt3bPffsF9aTV1mPp5Md5w33Z6CfCzuyyvl1XwFGDdZWypIi4P/OHcQ5Q/0J83Lq0r6rQghxos6o4F7fZODz7dks3pBObkUdQwJcef26MZZ9Rl/4LRmlwK+NsW6tNTnldeRV1nPhiAD+evZAXl+bxn9/3o+9jTULpoYxZ7AfwwPdjrnbkRBCnGxnRBSqqm9iaWwmSzYepKS6kZgQD564bBgzB/q0mkaYV1GHn4sDBqPm253ZpJfUWHK4tBy2+XFPPr/sK8DGSrFgahh3zIjA27nr89eFEOJk6fPB/YNNB3n+t2Sq6puZMdCHu2ZFMj7Ms826+ZV1BLg7sC6piAeX72n3mkrBTZNCWTQzHF8XmdEihOh5+nxw/253HjUNzXy5aBLjQtsO6oflVdQT4eNM4aGGduvcNCmEO2dFtjl0I4QQPUWfD+7/d040174Ty+8pJa2Ce1V9E5mlR/KlHyypsfxZtb/wqOtcPzGYO2dGdnqLOyGEOJ36dHCvrG3C0c60ovOV1SlsSi1Ba01maa1l8dBhttZtz2axsVKse3AmgR6OJ729QgjRXXp9cDdtY2d68Jlh3sTi8NeK2qZWdeMzy5kQ5snZQ/wI9TbtURriZdrCbntGGTe/f2TFqbujLRW1Tcwc5CuBXQjR6/Tq4F5QWc+0Z9ZYcrEoZcrHEurlxAXDAwj1ciLEy5FQbydWJhTw3MpkFkwNY26LNLil1Q28sjqFtzekW8qev2okL69Oobq+mXOHScpcIUTv06uDu4+LPTMG+rBqfxH3zI7k7tmRrdLtthTm7cT3u/P49w+JTIvyob7JwOLf0/lwcwb1TQYuGz2A26eFk5Bn2pzDw9GOz++YxNgQj1PcKyGEOHG9PrdMfZOBW97fzraMMt6YP6bDzSli00uZtzgWACc7a2qbDFw0oj/3nhVFkGc/Hvs+kU+3ZTEp3ItXrxstc9eFED1aR7llen2mKgdba965KYbhA9y455OdbEwpabNeZV0TW1rkfQn1dmLFfdN55drR9LOz5uq3tvDptiwWzYhg6YLxEtiFEL1arw/uAM72NnxwyzjCfZy4/aM44jPLLceq6pt4ZXUK0/63hpdXp1jyxvi42BPl68yG5GIufOV30otrePuGsTx8XrRkZxRC9Hq9flimpaKqeq5+awtlNY28d/M4th0sY/GGdCrrmjh7iB/3zYliaH833tt4kMd/TOSCEQH8vDefgb4uvHXDWMK8nbqhN0IIcWqccD53pVQGUAUYgGatdYxSyhP4HAgFMoCrtdbl5vqPAAvM9e/VWq/o6Prdmc89p7yWq97aYtkVaXa0L3+ZM5DhgW6WOs0GIxe+upEDBVVcOqo//718uCT7EkL0Ot0V3GO01iUtyp4ByrTWTyulHgY8tNYPKaWGAJ8C44H+wCpgoNba0N71u3uzjvTiapZsOsiVY4MYFeTeZp2s0lr2Fxxi7hA/2YNUCNErnazNOi4BZppffwisAx4yl3+mtW4ADiqlUjEF+i0n8F5dEu7jzBOXDu+wTrCXI8FesjhJCNE3dfbJoQZWKqXilVILzWV+Wut8APNXX3P5ACC7xbk55jIhhBCnSGfv3KdorfOUUr7Ab0qpAx3UbWuM46ixH/MPiYUAwcHBnWyGEEKIzujUnbvWOs/8tQj4BtMwS6FSKgDA/LXIXD0HCGpxeiCQ18Y1F2utY7TWMT4+PsffAyGEEEc5ZnBXSjkppVwOvwbmAvuA74GbzNVuAr4zv/4emKeUsldKhQFRwLbubrgQQoj2dWZYxg/4xjyjxAb4RGv9q1JqO/CFUmoBkAVcBaC1TlBKfQEkAs3AXR3NlBFCCNH9jhnctdbpwMg2ykuBs9o550ngyRNunRBCiOMi6+yFEKIPkuAuhBB9UI/ILaOUKgYyT+AS3kDb6SD7ljOlnyB97avOlL6eqn6GaK3bnG7YI4L7iVJKxbW3BLcvOVP6CdLXvupM6WtP6KcMywghRB8kwV0IIfqgvhLcF5/uBpwiZ0o/QfraV50pfT3t/ewTY+5CCCFa6yt37kIIIVqQ4C6EEH1QrwjuSqkMpdRepdQupVScucxTKfWbUirF/NWjRf1HlFKpSqkkpdQ5p6/lXddOXx9TSuWay3Yppc5vUb9X9lUp5a6UWq6UOqCU2q+UmtSHP9O2+toXP9NBLfqzSyl1SCl1X1/8XDvoa8/5XLXWPf4Ppj1avf9Q9gzwsPn1w8D/zK+HALsBeyAMSAOsT3cfTrCvjwEPtFG31/YV0+5dt5lf2wHuffgzbauvfe4z/UM/rIECIKSvfq7t9LXHfK694s69HZdg+k+D+eulLco/01o3aK0PAoe3+euLemVflVKuwHTgPQCtdaPWuoI++Jl20Nf29Nq+/sFZQJrWOpM++Ln+Qcu+tueU97W3BPczaZu/tvoKcLdSao9SakmLX2t7a1/DgWLgfaXUTqXUu+a9AvriZ9peX6FvfaZ/NA/41Py6L36uLbXsK/SQz7W3BPcpWusxwHnAXUqp6R3U7dQ2fz1YW319E4gARgH5wPPmur21rzbAGOBNrfVooAbTr+vt6a39hPb72tc+UwullB1wMfDlsaq2Udbb+9pjPtdeEdz1Sdjmr6dqq69a60KttUFrbQTe4civc721rzlAjtZ6q/n75ZgCYF/8TNvsax/8TFs6D9ihtS40f98XP9fDWvW1J32uPT64qzNom7/2+nr4P4bZZZj6D720r1rrAiBbKTXIXHQWpp27+txn2l5f+9pn+gfX0nqYos99ri206muP+lxP95PmTjyJDsf0lHk3kAD83VzuBawGUsxfPVuc83dMT6OTgPNOdx+6oa9Lgb3AHvM/koA+0NdRQJy5T98CHn3xM+2gr33uMzW33REoBdxalPXVz7WtvvaYz1XSDwghRB/U44dlhBBCdJ0EdyGE6IMkuAshRB8kwV0IIfogCe5CCNEHSXAXQog+SIK7EEL0Qf8PVtVsWkAfvTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"OPEN\"], data[\"LTP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4ea5a3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LTP'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_6 = data.sort_values(by=\"OPEN\", ascending=False).head(6)\n",
    "\n",
    "top_6.plot(x=\"LTP\", y=\"OPEN\", kind=\"bar\", rot=5, fontsize=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "dded7ffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas_datareader in c:\\anaconda\\lib\\site-packages (0.10.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\anaconda\\lib\\site-packages (from pandas_datareader) (2.26.0)\n",
      "Requirement already satisfied: lxml in c:\\anaconda\\lib\\site-packages (from pandas_datareader) (4.6.3)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\anaconda\\lib\\site-packages (from pandas_datareader) (1.3.4)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (1.20.3)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\anaconda\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas_datareader) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (3.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\anaconda\\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:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (1.26.7)\n"
     ]
    }
   ],
   "source": [
    "! pip install pandas_datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ca5487e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting yfinance\n",
      "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
      "Requirement already satisfied: pandas>=0.24.0 in c:\\anaconda\\lib\\site-packages (from yfinance) (1.3.4)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\anaconda\\lib\\site-packages (from yfinance) (1.20.3)\n",
      "Requirement already satisfied: lxml>=4.5.1 in c:\\anaconda\\lib\\site-packages (from yfinance) (4.6.3)\n",
      "Collecting multitasking>=0.0.7\n",
      "  Downloading multitasking-0.0.10.tar.gz (8.2 kB)\n",
      "Requirement already satisfied: requests>=2.26 in c:\\anaconda\\lib\\site-packages (from yfinance) (2.26.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.24.0->yfinance) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\anaconda\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\anaconda\\lib\\site-packages (from requests>=2.26->yfinance) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\anaconda\\lib\\site-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\anaconda\\lib\\site-packages (from requests>=2.26->yfinance) (3.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\anaconda\\lib\\site-packages (from requests>=2.26->yfinance) (1.26.7)\n",
      "Building wheels for collected packages: multitasking\n",
      "  Building wheel for multitasking (setup.py): started\n",
      "  Building wheel for multitasking (setup.py): finished with status 'done'\n",
      "  Created wheel for multitasking: filename=multitasking-0.0.10-py3-none-any.whl size=8500 sha256=0938a9bb8dda48b334ca9b1a3b0483777d938e939805894fbd45ccbc560ba081\n",
      "  Stored in directory: c:\\users\\nivethitha k s\\appdata\\local\\pip\\cache\\wheels\\f2\\b5\\2c\\59ba95dcf854e542944c75fe3da584e4e3833b319735a0546c\n",
      "Successfully built multitasking\n",
      "Installing collected packages: multitasking, yfinance\n",
      "Successfully installed multitasking-0.0.10 yfinance-0.1.70\n",
      "Collecting plotly\n",
      "  Downloading plotly-5.7.0-py2.py3-none-any.whl (28.8 MB)\n",
      "Requirement already satisfied: six in c:\\anaconda\\lib\\site-packages (from plotly) (1.16.0)\n",
      "Collecting tenacity>=6.2.0\n",
      "  Downloading tenacity-8.0.1-py3-none-any.whl (24 kB)\n",
      "Installing collected packages: tenacity, plotly\n",
      "Successfully installed plotly-5.7.0 tenacity-8.0.1\n"
     ]
    }
   ],
   "source": [
    "! pip install yfinance\n",
    "! pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "730ee1e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas_datareader in c:\\anaconda\\lib\\site-packages (0.10.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\anaconda\\lib\\site-packages (from pandas_datareader) (2.26.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\anaconda\\lib\\site-packages (from pandas_datareader) (1.3.4)\n",
      "Requirement already satisfied: lxml in c:\\anaconda\\lib\\site-packages (from pandas_datareader) (4.6.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (1.20.3)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\anaconda\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas_datareader) (1.16.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (1.26.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (3.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas_datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fcb0290",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the necessary packages\n",
    "import pandas_datareader as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6d48588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>2021-04-28</th>\n",
       "      <td>682.500000</td>\n",
       "      <td>671.150024</td>\n",
       "      <td>682.500000</td>\n",
       "      <td>672.599976</td>\n",
       "      <td>4011121</td>\n",
       "      <td>670.620911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-29</th>\n",
       "      <td>681.349976</td>\n",
       "      <td>672.150024</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>674.650024</td>\n",
       "      <td>3939217</td>\n",
       "      <td>672.664917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>678.000000</td>\n",
       "      <td>660.700012</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>665.099976</td>\n",
       "      <td>2742166</td>\n",
       "      <td>663.142944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-03</th>\n",
       "      <td>674.500000</td>\n",
       "      <td>657.150024</td>\n",
       "      <td>660.000000</td>\n",
       "      <td>671.900024</td>\n",
       "      <td>4908079</td>\n",
       "      <td>669.922974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-04</th>\n",
       "      <td>680.849976</td>\n",
       "      <td>665.500000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>667.849976</td>\n",
       "      <td>4118750</td>\n",
       "      <td>665.884888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-05</th>\n",
       "      <td>675.799988</td>\n",
       "      <td>667.000000</td>\n",
       "      <td>670.049988</td>\n",
       "      <td>674.400024</td>\n",
       "      <td>2249063</td>\n",
       "      <td>672.415649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-06</th>\n",
       "      <td>679.900024</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>671.799988</td>\n",
       "      <td>2269126</td>\n",
       "      <td>669.823242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>680.500000</td>\n",
       "      <td>671.049988</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>672.650024</td>\n",
       "      <td>1913550</td>\n",
       "      <td>670.670776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-10</th>\n",
       "      <td>685.950012</td>\n",
       "      <td>670.250000</td>\n",
       "      <td>670.250000</td>\n",
       "      <td>680.049988</td>\n",
       "      <td>4342679</td>\n",
       "      <td>678.048950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-11</th>\n",
       "      <td>681.500000</td>\n",
       "      <td>669.900024</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>671.250000</td>\n",
       "      <td>2430724</td>\n",
       "      <td>669.274902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-12</th>\n",
       "      <td>673.950012</td>\n",
       "      <td>665.099976</td>\n",
       "      <td>671.250000</td>\n",
       "      <td>668.799988</td>\n",
       "      <td>2428984</td>\n",
       "      <td>666.832092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2021-04-28  682.500000  671.150024  682.500000  672.599976  4011121   \n",
       "2021-04-29  681.349976  672.150024  678.000000  674.650024  3939217   \n",
       "2021-04-30  678.000000  660.700012  670.000000  665.099976  2742166   \n",
       "2021-05-03  674.500000  657.150024  660.000000  671.900024  4908079   \n",
       "2021-05-04  680.849976  665.500000  677.000000  667.849976  4118750   \n",
       "2021-05-05  675.799988  667.000000  670.049988  674.400024  2249063   \n",
       "2021-05-06  679.900024  669.000000  672.000000  671.799988  2269126   \n",
       "2021-05-07  680.500000  671.049988  678.000000  672.650024  1913550   \n",
       "2021-05-10  685.950012  670.250000  670.250000  680.049988  4342679   \n",
       "2021-05-11  681.500000  669.900024  678.000000  671.250000  2430724   \n",
       "2021-05-12  673.950012  665.099976  671.250000  668.799988  2428984   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2021-04-28  670.620911  \n",
       "2021-04-29  672.664917  \n",
       "2021-04-30  663.142944  \n",
       "2021-05-03  669.922974  \n",
       "2021-05-04  665.884888  \n",
       "2021-05-05  672.415649  \n",
       "2021-05-06  669.823242  \n",
       "2021-05-07  670.670776  \n",
       "2021-05-10  678.048950  \n",
       "2021-05-11  669.274902  \n",
       "2021-05-12  666.832092  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting stockdata from yahoo using pandas datareader- date in yyyy-mm-dd format\n",
    "stockdata = pdr.data.get_data_yahoo('hdfclife.ns' , start='2021-04-28' , end='2021-05-13')\n",
    "stockdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f9a1b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 22,
     "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 a specific variable in default chart size\n",
    "stockdata['High'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "016e7fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ydDpShXJF85q80r8Ni7cf5tFP1lJQaJ2OJOWA1pkTERERkQv26vx41uxNZcLt0TSorvPkSsOADvU4kpHjWlTch3/d2BJjjNOxxEEqcyIiIiJyQX7cfIjJixIY3OUirmld2+k4Fdrw7o05nJ7Lu7/upHqgD4/0inQ6kjhIZU5EREREztv+1Cz+8tlaWtQO5qlrmzsdp1IYc3UzDmfk8sYP26ge4MPgiyOcjiQOUZkTERERkfOSV1B0nlxefiHjb4/Gz1vnyZUFYwwv92vN0Yxc/vHVRqoF+HBdmzpOxxIHaAIUERERETkv//5+K3G7j/JS/zY0DAtwOk6l4uXpwduDoom5qBqjP1nDb9tSnI4kDlCZExEREZFz9vOWJN75ZQcDO9XnhrYaFXJCFR9P3ruzI43DAxk+I5Z1ialOR5IypjInIiIiIufk4LFs/vLpWprVCuKf17d0Ok6lVtXfmw/u7kRogA9Dpq1kZ3K605GkDKnMiYiIiEix5RcU8tCs1WTnFfD2IJ0nVx7UDPZj+t2dMMDgKSs4eCzb6UhSRlTmRERERKTY3vhhGyt2HeH5m1rRpEag03HEpVF4IO8P7URqZi53Tl1Oamau05GkDKjMiYiIiEixLNqWzPiF27m5Qz36RddzOo6cpHW9qky+M4ZdKZnc80EsWbkFTkeSUqYyJyIiIiJnlZSWzSMfr6FJeCDP3qjz5Mqrrk3CeOO2dqzac5RRH60ir6DQ6UhSilTmREREROSMCgotD3+8hozcfMbfHo2/j5YqLs+uaV2b525sxU/xSTzx+ToKC63TkaSU6JkoIiIiImf0n5+2sXTnYcYOaENkzSCn40gx3NHlIg6n5zLuh61UD/ThqWtbOB1JSoHKnIiIiIic1pIdKbz54zb6ta/LzR10npw7eeiKJhzOyGHyogTCAn25r0djpyNJCVOZExEREZFTSj6ew8Mfr6FRWADP3dQKY4zTkeQcGGN45vqWHMnI5aXv4qkW4MMtMfWdjiUlSGVORERERP5HYaHl0U/XkJaVx4x7OhHgq7eN7sjDw/D6Le04lpXHk3PWU83fh94tajodS0pIuZgA5eCxbJZsTyE3X7PtiIiIiJQHExZuZ9G2FJ65oSXNagU7HUcugI+XBxPv6ECrOsE88NEqViQccTqSlBBjrfOz2/jVbmpr3fUG/j6edG0cRo+ocHpGhlM/1N/paCIiIiKVzvKdhxk4eRnXtanDm7e10+GVFcTh9Bxufncpycdz+PS+i2leWyW9PDHGxFlrY87pNuWhzEV36GBfmfEtC7cksXBLMvtSswBoFB5Az8ga9IgKp3PDUPy8PR1OKiIiIlKxHU7P4Zq3FuHv48XXD15CoA6vrFASj2YyYOJSCqxlzoiuGjwpR9y2zMXExNjY2FgArLXsSM7gl63J/LI1mWU7D5ObX4iftwddGlWnR2Q4PaNqEFHdX58SiYiIiJSgwkLL0PdXsnTnYb4Y2ZWWdao6HUlKwdZDx7n5naVU8/fm8xFdCQv0dTqSUEHK3MmycgtYlnCYX7YUlbuElAwAGoT60zMqnB6R4VzcuLoWrxQRERG5QBMX7uCVefE8d1MrBne5yOk4Uoridh/h9veW06RGILPu7UKQn7fTkSq9ClnmTrb7cNGo3cItySzdcZisvAJ8PD3o1DDUNWoXTpMagRq1ExERETkHsbuOcOukZVzZsibjB0XrvVQl8HN8EsOmx9IpIpRpQzvqlCaHVYoyd6LsvAJidx1l4ZYkftmazLakdADqhlShe2TRqF23JtX1SYOIiIjIGRzNyOWatxbh7enBfx+6hGC9d6o05qxK5NFP13J1q1q8PSgaTw+VeKdUujJ3ssSjmfy6NYWFW5JYvD2FjNwCvDwMHS6q5pohswbNawfpkyYRERERF2stwz6I5ddtycwe0ZU29UKcjiRl7L1FO3n+m80M6tyAF7Q4vGPOp8wV60QzY0wI8B7QCrDA3UAW8A7gB+QDI621K4wxvYGXAR8gF3jMWvvTuYQ6X/Wq+TOocwMGdW5Abn4hq/YcZaHrXLux87Ywdt4WagT50iMynB5R4VzaJJyq/vrkSURERCqv9xYl8GN8Ev+8voWKXCU17NJGpKTn8s4vOwgL8OHRPlFOR5JiKtbInDHmA2CRtfY9Y4wP4A98Coyz1n5njLkGeNxa29MY0x44ZK3db4xpBcy31tY90/5LamTuTA6lZf8xQ+aircmkZefjYaB9g2p/nGvXqk5VPDS0LCIiIpXEqj1HueWdpVzRvAbv3NFBIzKVmLWWJ2av49PYRJ69oSV3dY1wOlKlUyojc8aYYKA7MATAWpsL5BpjLPD7SoNVgf2u61efcPONgJ8xxtdam3MuwUpazWA/bompzy0x9ckvKGRtYuofo3avL9jK6wu2Uj3A549z7S5tGkZ1TdMqIiIiFdSxzDwe/Gg1tar6MbZ/WxW5Ss4Yw4t9W3MkI49nvt5ItQAfbmhbx+lYchZnHZkzxrQDJgGbgLZAHPAw0ACYDxjAA+hqrd190m0HAPdba3udYr/DgeEADRo06LB79+6TNykzKek5LNqWzC9bkvl1WwpHMnIxBtrUrUqPqBr0iAynXf0QnRAqIiIiFYK1lvtmxPFTfBKf3X8x7RtUczqSlBPZeQXcOWUFq/ceZcpdHekeGe50pEqjVCZAMcbEAMuAbtba5caYN4E0ikbjfrHWzjbG3AIMP7G0GWNaAl8Bfay1O850H2VxmGVxFRRaNuw75hq1S2LN3lQKLVSt4s2lTcP+ON+uRpCf01FFREREzsu0xQk8+/Um/n5tc4Zd2sjpOFLOHMvK49Z3l7LnSCYf3duFdvVDnI5UKZRWmasFLLPWRrh+vhQYA1wChFhrrSkalz9mrQ12bVMP+AkYaq1dfLYQ5anMnSw1M5dF21L+ON8u+XjR0aItage7ZsgMJ/qianh7ejicVEREROTs1iWm0n/iEnpEhjP5zhgdXimnlJSWTf93lpCenc9n93elSY1ApyNVeKW2NIExZhEwzFq7xRjzDBAAXAeMsNYuNMZcAYy11nZwzXz5C/Ava+3s4oQoz2XuRIWFls0H0/5YtDxu91EKCi1Bvl50axJGj6ii8+3qhFRxOqqIiIjI/0jLzuO6t34jv6CQbx++lBB/H6cjSTm2KyWDAe8swcfTg9kju1K7qt7jlqbSLHPtKFqawAfYCQwFWgJvUjSJSjZFSxPEGWP+DjwJbDthF32stUmn27+7lLmTpWXnsWR7yh/l7sCxbAAiawa6ZsisQUxENXy9PB1OKiIiIpWdtZZRH61i/sZDfHpfFzpcFOp0JHEDG/Yd47ZJy6hd1Y/P7r9YHwCUokq/aLiTrLVsS0rnly3JLNyaxMqEo+QWFOLv40nXxtX/KHf1Q/2djioiIiKV0Iylu3h67kbGXN2M+3s0djqOuJElO1IYMnUlreoG8+Gwzvj7FGupajlHKnPlSEZOPkt3HC4atduaxN4jWQA0Cgv443DMLo2q4+etUTsREREpXRv2HaPfhCV0bVKdqXd11Lq6cs7mbTjAyJmr6O4611LzRZQ8lblyylpLQkrGH4djLtt5mJz8Qny9POjSqPofi5Y3DAvQScgiIiJSoo5n53H9f34jO6/oPLnQAB0mJ+fno+V7+NsX6+nbvi7/vrmtPhQoYaWyaLhcOGMMjcIDaRQeyNBuDcnOK2DZzsN/zJD5r/9u4l//hfqhVegZWbSu3cWNqxPgq/89IiIicv6stfztiw3sPZrFrHu7qMjJBRnUuQFHMnJ47futVPP34enrmmsgwmFqCw7w8/akZ1QNekbVAGDP4Ux+2ZbML1uSmL0qkRnLduPj6UHHhtX+ONeuaY1APVlERETknMxasZev1+7nsSuj6NRQE57IhRt1WRNS0nOZujiBsCAfRvZs4nSkSk2HWZYzOfkFxO06ysKtyfyyJZkth44DUKeq3x/n2nVrEkaQn7fDSUVERKQ823wgjZvGL6ZTw1A+GNpJh8RJiSkstDzyyRq+WrufyXfG0LtFTacjVQg6Z64C2p+axa+uc+0Wb0/heE4+Xh6G6Iuq/XGuXYvawRq1ExERkT9k5ORz/du/cTw7n28fupTwIF+nI0kFk5tfyLVvLaKg0PL96O54aUKUC6YyV8HlFRSyek8qC7ck8cvWZDbuTwMgPMiXHpFFo3aXNg3T+h8iIiKVmLWWRz9dy9w1+/hwWGe6Ng5zOpJUUN9vPMjwGXG82Lc1gzo3cDqO21OZq2SS0rL5dVvRouWLtiWTmpmHh4GrWtXilf5tdCimiIhIJfTpyr08Pnsdj/RqyiO9Ip2OIxWYtZYB7yxl75FMFj7WU+vPXSCVuUqsoNCyNjGV+RsP8t6iBBqGBfDenTFEhAU4HU1ERETKyNZDx7nh7d+IblCNGfd0xlPnyUkpW7nrCDe/s5THroxi1GWaDOVCnE+Z08GtFYSnhyG6QTWevLo5M+7uREp6DjdNWMyS7SlORxMREZEykJmbz8iZqwj09eKN29qpyEmZ6BgRSq/mNXln4Q6OZuQ6HafSUZmrgLo2CWPuqG6EBfoyeOoKZizd5XQkERERKWX/mLuRHcnpvHFre2oE+TkdRyqRx6+KIiM3n/E/b3c6SqWjMldBXVQ9gC9GdqVHZDhPz93IU1+sJ6+g0OlYIiIiUgpmxyXyeVwiD1zWhEuaasITKVuRNYMY0KEe05fuJvFoptNxKhWVuQosyM+byXfGcF+PRsxcvoc73lvOEQ1/i4iIVCjbk9J5eu4GOjUM5eErmjodRyqpR3pFgoHXF2x1OkqlojJXwXl6GJ68ujnjbm3L6r2p3Dj+N7YcPO50LBERESkB2XkFPPDRKvy8PXnrtvZa60scUyekCkO7RvDF6n1sPpDmdJxKQ8/4SqJv+3p8MrwL2XmF9JuwmAWbDjkdSURERC7Qs19vJP7gcV6/pS21quo8OXHWiJ6NCfL1Yuy8eKejVBoqc5VI+wbV+OqBbjQKD2T4jFgmLNxOeViaQkRERM7d3DX7mLViLyN6NqZnVA2n44gQ4u/DyMua8POWZJbtPOx0nEpBZa6SqV21Cp/edzHXtanD2HlbeOSTNWTnFTgdS0RERM7BzuR0/jZnPTEXVeMvvbUwuJQfQ7pGUCvYj5e/i9egQRlQmauEqvh48tZt7fhrn0jmrtnPre8u5VBattOxROQcrN5zlC9WJzodQ0QckJ1XwKiPVuPt5cFbA3WenJQvft6ePNo7kjV7U5m/8aDTcSo8PfsrKWMMD1zelHcHd2BbUjo3vP0ba/emOh1LRIrhaEYuwz6IZfQna/ly9T6n44hIGXv+m01sPpDG67e0pU5IFafjiPyPftF1aVIjkLHztpCvpbFKlcpcJXdly1rMHtEVLw8Pbn53KXPX6I2hSHn3wrebSc3Ko2WdYJ6YvY71icecjiQiZeS/6/bz4bI9DO/eiMub1XQ6jsgpeXl68PiVUexMyeDTWB1FUppU5oTmtYP56oFutKsfwsMfr+GVefEUFuoYZ5Hy6LdtKXwel8h93Rvxwd2dqB7gw30zYklJz3E6moiUst2HMxgzez3t6ofw2JVRTscROaPeLWrS4aJqvPHDVrJyNT9DaVGZEwCqB/ry4T2dGdipPhMX7mD4jFjSc/KdjiUiJ8jKLeBvX6ynYVgAD13RlLBAX94dHMPhjFxGzlxFng5lEamwcvILGPXRKjwM/Gdge7x1npyUc8YYxlzdjKTjOUxdnOB0nApL/xLIH3y8PHixb2ueub4FP29Jpt+Exew5nOl0LBFxeeOHrew5kslL/Vrj5+0JQOt6VXm5f2tWJBzh+f9ucjihiJSWl76NZ8O+NF69uS31Q/2djiNSLB0jQunVvCbvLNzB0Yxcp+NUSCpz8ifGGIZ0a8gHQztxKC2HG8f/xtIdWidExGkb9h1j8qKdDOxUny6Nqv/pur7t6zHskoZ8sHQ3n67c61BCESkt8zYc4P0luxjaLYIrW9ZyOo7IOXn8qigycvMZ//N2p6NUSCpzckqXNA3jy1HdCA3wYfCU5Xy4bLfTkUQqrbyCQh7/fB3VA30Zc3XzU24z5upmXNIkjL9/uYHVe46WcUIRKWkFhZa9RzL5YdMhHvt8HW3qVeXJ0zz/RcqzyJpB9I+ux/Slu0k8qiO+SpopD4v5xcTE2NjYWKdjyCmkZefx0KzVLNySzOAuF/GP61voOH2RMvbOLzt4+bt4Jt4ezdWta592u6MZudww/jdy8wv5+oFLqBHsV4YpReRcWWtJPp7DzpQMdqVkkJCSwU7X1z2HM8l1nQcb4u/NV6MuoUF1HV4p7ml/ahY9X1vIdW1q8/ot7ZyOU24ZY+KstTHndBuVOTmbgkLLK/PimfTrTro2rs74QdFUC/BxOpZIpbArJYMr3/iVHpHhvDu4A8aYM26/+UAa/SYsoXntIGYN74Kvl2cZJRWR0zmWlUdCSgYJKekkJGeQcDjzj+8zTpjlz8fLg4jq/jQMCyAiLIBGYQE0DAskqlYQVat4O/gbiFy4l77dzKRFO/n2oUtpXjvY6TjlksqclKrP4xL525z11A7x4707Y2haM8jpSCIVmrWWQZOXs2HfMRY82oNaVYs30vbNugOM+mgVAzs14KV+rUs5pYhA0Wyzuw4XjbD9PrqW4BpxO3zCxA8eBupVKypsDcMCaBQeQET1ou/rhFTB0+PMH9iIuKvUzFy6j/2ZmIhQpg7p6HSccul8ypxXaYWRimdAh3o0DAvgvhlx9J2whLcGttOCpSKl6LPYRJbuPMwLfVsVu8gBXNumNhv3N2bCwh20qhvM7Z0vKsWUIpVHXkEhiUezSEhJZ2dyBrsOu0pbcgb7j2X/aduawb40DAugT8uaruIWSMOwAOqHVtGIuVRKIf4+jLysCS9/F8+ynYf/ZzIvOT8amZNztj81i3unx7LpQBpPXNWM+7o3OuuhXyJybpKOZ9Pr37/QrFYwHw/vgsc5flpfUGi554OVLN6ewkf3dqFjRGgpJRWpWAoLLQfTsk85wrbnSCb5hf//vqlqFe+i0TXXKFvECV8DffV5ucjJsvMK6PnqQmpV9eOLkV31/vEkOsxSykxmbj6PfbaOb9YfoF/7urx4wrpXInLhRn20igUbD/HdI5fSODzwvPZxLCuPm8Yv5nh2Pl8/2I3aVauUcEoR92St5Whm3v+MsP3+fXZe4R/b+nl7uEbV/P80wtYoLEDnj4uch09W7uGJ2et5545ormp1+km9KiOVOSlT1lr+89N2Xl+wlXb1Q5g0uINmzxMpAQs2HeLe6bH8tU8kD1ze9IL2te3QcW4av5gmNQL55L6L9aGLVCoZOfl/jKwlnDBb5K6UDI5l5f2xnZeHoUHo/5/H1jA8gIbVi77WDPI755FxETm9/IJCrnpzEYWFlu9Hd8dLs6T/odTKnDEmBHgPaAVY4G4gC3gH8APygZHW2hWu7Z8E7gEKgIestfPPtH+VOfc2b8MBRn+ylqpVvJl0Zwfa1AtxOpKI2zqenUfv138tmor8gUvw8brwF7n5Gw9y34w4+kfX47Wb2+iwFqlQcvIL2Hsk839G2BJSMkg6nvOnbeuGVCHihBG23w+PrFutipbdESlD3288yPAZcbzYtzWDOjdwOk65UZoToLwJzLPWDjDG+AD+wKfAs9ba74wx1wBjgZ7GmBbAbUBLoA7wgzEm0lpbcLqdi3u7qlVtGoQGcO/0WG5+Zymv3dyW69vWcTqWiFsaO28Lh45nM/GO6BIpcgBXtqzFw1c05c0ft9G6bjBDujUskf2KlJWCQsv+1KxTjrAlHs3khNPYqB7gQ8OwAHpEhv9phO2i0ACq+GhkWqQ86N2iJh0uqsYbP2ylb/u6em5egLOWOWNMMNAdGAJgrc0Fco0xFvh9kYiqwH7X9zcCH1trc4AEY8x2oBOwtGSjS3nSok4wcx/oxogP43hw1mq2HDzOo70jdWiKyDmI3XWEGct2M7RbBO0bVCvRfT98RVM27k/juW82E1kriK6Nw0p0/yIXylpLcnoOCa4Rtp2uWSITUjLYfcIC2gCBvl40DAugbf0Qbmpf908TkGg9NpHyzxjDmKubcfM7S5m6OIFRlzVxOpLbOuthlsaYdsAkYBPQFogDHgYaAPMBA3gAXa21u40xbwPLrLUfum4/BfjOWvv56e5Dh1lWHLn5hTz95QY+id1LnxY1GXdrOwI0o5fIWeXkF3DNm4vIzivk+9HdS+V5czy7aEKUIxm5fPXAJdQP9S/x+xA5m2NZeew6YXTt95kiE1IySM/J/2M7H08PLnItoP3HCJvr+/BAXx0uLFIBDPsgluU7D/Pr45dpQiFK6Zw5Y0wMsAzoZq1dbox5E0ijaDTuF2vtbGPMLcBwa20vY8x4YOlJZe5ba+3sk/Y7HBgO0KBBgw67d+8+l9xSjllrmbZ4F89/s4nImkFMvjNGbxpFzuL1BVt568dtTBvakcuiapTa/exMTufGtxdTP9Sf2SO66tAWKRXZeSctoJ38/4dHnmoB7YgTpvf//T8toC1S8W09dJyr3viVu7s15O/XtXA6juNKq8zVomikLcL186XAGOASIMRaa03Rx2PHrLXBrslPsNa+5Np+PvCMtfa0h1lqZK5i+nVrMqM+WoW3pwcTb4+msxaHFDmlrYeOc+1bi7i2dW3euK19qd/fT/GHuOeDWK5vU4c3b2unEQ4pUd+tP8Ajn6whJ///D4usEVS0gHaj8AAiXCNsjcIDqB/qrwW0RSq5xz5by9w1+/nprz2oV61yf/hfKhOgWGsPGmP2GmOirLVbgCsoOuSyEdADWAhcDmxz3eQr4CNjzOsUTYDSFFhxLqGkYugeGc7cUd0Y9kEst7+3nOduasXATpqxSOREBYWWJ2avI9DXi6fL6FPJy5vV5K99onh1/hZa1Q1mePfGZXK/UvHN23CAB2etpk29qgzt1lALaIvIWY3uHcnctft5fcFWXr+lndNx3E5x/3V9EJjpmslyJzAUmAu8aYzxArJxHTJprd1ojPmUosKXD4zSTJaVV6PwQL4Y1Y0HZ63myTnr2XLwOH+/trnWFBFx+XDZblbvSWXcrW2pHuhbZvc7smdjNu4/xsvfxdOsVjDdI8PL7L6lYpq/8SAPfFRU5D64uxNBfpqIRETOrk5IFYZ2jWDSop0M796IZrWCz34j+YMWDZcykV9QyEvfxTPltwQuaRLG24PaE+KvE12lctuXmkWf13+hQ0QoHwztWOaHO2bk5NN/4hIOHMvmqwe6cVH1gDK9f6k4Fmw6xMiZcbSqW5XpKnIico5SM3PpPvZnYiJCmTqko9NxHHM+h1lqeETKhJenB09f14Kx/duwPOEwN41fzPakdKdjiTjGWsvfv1hPoYUXbmrlyHlrAb5eTBpc9JoxfHocGSfMJChSXD9uLipyLepoRE5Ezk+Ivw8jL2vCT/FJLNt52Ok4bkVlTsrULR3rM+veLqTn5NN3/GJ+3pLkdCQRR3y97gA/b0nmL30iHZ3ttUF1f94e1J5tScf562drKQ9Ha4j7+Cn+ECM+XEXz2sFMv7sTwSpyInKehnSNoFawHy9/F6/XonOgMidlLiYilLkPXEK9UH/ueX8lk3/dqSetVCpHM3J59quNtHVNEuG0S5uG8+TVzfluw0EmLNzhdBxxEz9vSeL+GauIqhXEjLs7a7FuEbkgft6ejO7dlDV7U5m/8aDTcdyGypw4om5IFWaPuJgrW9bihW8389jn68jJ1zw5Ujk8/81mjmXl8VK/NuVmHa1hlzbkxnZ1eO37LfwUf8jpOFLO/bI1mftmxBFZK5AP7+lMVX8VORG5cP2j69GkRiBj528hv6Dw7DcQlTlxjr+PF+MHRfNIr6Z8HpfIwEnLSDqe7XQskVK1aFsys1clcl+PRrSoU35m7DLG8HK/NrSoHczDs9awI1nntMqp/bo1mXunx9IkXEVOREqWl6cHj18Zxc7kDD6LS3Q6jltQmRNHeXgYHukVyYTbo9l0II0b317Mhn3HnI4lUiqycgv42xfraRQWwIOXN3U6zv+o4uPJu4M74O3lwfDpsRzPznM6kpQzv21L4d7psTQOD2TmsM6alVhESlzvFjXpcFE1xi3YSlaujto6G5U5KReuaV2bz+/vigEGvLOEb9YdcDqSSIkb98NW9h7J4sV+rfHz9nQ6zinVq+bP+EHR7DqcyehP1lJYqPNZpcji7Snc88FKGoYFMHNYZ6oFqMiJSMkzxjDm6mYkHc9h6uIEp+OUeypzUm60qluVuQ9cQovawYz6aBWvL9iqN5JSYaxPPMZ7i3YysFMDujSq7nScM7q4cXWevrY5P2w+xJs/bnM6jpQDS3YUFbmI6kVFLlRFTkRKUceIUHo1r8E7C3dwNCPX6TjlmsqclCvhQb7MGt6FAR3q8daP2xg5cxWZuVr7StxbXkEhT8xeR1igL2OubuZ0nGK5q2sEAzrU480ft2lWsUpu6Y7D3P3+ShqE+jPz3s5UD/R1OpKIVAKPXdmMjNx8xv+83eko5ZrKnJQ7vl6evDqgDX+/tjnfbzpI/4lLSTya6XQskfP23qIENh1I4183tnSb6duNMTx/Uyva1qvKo5+sYeuh405HEgcs31lU5OpX8+eje7sQpiInImUkqlYQ/aPrMX3pbr0PPAOVOSmXjDEMu7QRU4d0JPFIJje+vZjYXUecjiVyzhJSMnjjh61c2bImV7Wq7XScc+Ln7ck7gztQxceL4dNjOZapCVEqk5W7jjD0/ZXUrVZFRU5EHDG6dyQYGLdAh/yfjsqclGs9o2rwxahuBFfxZuDkZXy6cq/TkUSKzVrLk3PW4ePpwb9ubOV0nPNSu2oV3rkjmn2pWTz08WoKdB5rpRC76whDpq6gdlU/Prq3M+FBKnIiUvbqhFRhaNcI5qxOJP5gmtNxyiWVOSn3mtQI5MuR3ejSqDqPz17Hv77epIUkxS18GruXZTuP8OQ1zakZ7Od0nPMWExHKMze05Jetybz2/Ran40gpi9t9hLumrqBmsB+z7u1CjSD3/dsVEfc3omdjgny9GDtPrz+nojInbqGqvzfThnRkaLcIpi5OYOj7KzmWpUO+pPxKOp7NC99splPDUG7rWN/pOBfs9s4XMbBTAyYu3MF/1+13Oo6UklV7jnLX1JXUCPZj1vAu1HDjDyFEpGII8fdhRM8m/BSfxLKdh52OU+6ozInb8PL04J/Xt+Tlfq1ZtvMwfccvZkdyutOxRE7p2a82kZ1fyEv9WuPhYZyOUyKeuaEFHS6qxmOfrWPTfh3uUtGs2ZvKXVNWEBbow6x7u7j1aLKIVCxDu0VQK9iPl7+Lx1od7n8ilTlxO7d1asDMYV04lpXHTeMX88vWZKcjifzJ9xsP8s36Azx8RVMahwc6HafE+Hp5MvH2aIKreDF8RqzW/qlA1u5NZfCU5YQG+jBreBdqVVWRE5Hyw8/bk9G9m7Jmb6qWyzmJypy4pU4NQ5n7QDfqhlRh6LQVTPktQZ/USLmQlp3H03M30KxWEMO7N3I6TomrEezHu4NjSErL4YFZq3T+agWwLjGVO6Ysp5p/0Yhc7apVnI4kIvI/+kfXo0mNQMbO36LXnhOozInbqlfNn9kjutK7RU2e++8mnpi9jpz8AqdjSSU3dl48ScdzeLl/G7w9K+Y/se3qh/B831Ys3n6Yl7+LdzqOXIAN+45xx3vLqVrFm1nDu1AnREVORMonL08PHr8yip3JGXwWl+h0nHKjYr7TkEojwNeLibd34KHLm/BpbCK3T15OSnqO07Gkklq56wgfLtvD0K4NaVc/xOk4peqWmPoM6RrBe78l8MVqvai6ow37jnH7e8sJ8vNm1r1dqKsiJyLlXO8WNelwUTXGLdhKVq4+wAeVOakAPDwMj/aJ4j8D27Nh/zFufHsxG/cfczqWVDLZeQWMmb2OuiFV+EufSKfjlImnrm1O54ahjJm9nvWJes65k437j3HHlOUE+nrx8fAu1A/1dzqSiMhZGWMYc3Uzko7nMHVxgtNxygWVOakwrm9bh8/u60qhtQyYuJTv1h9wOpJUIhN+3s6O5Axe6NuKAF8vp+OUCW9PDybcHk1YoC/3zYjVqLib2HwgjTveW46/tyez7lWRExH30jEilF7Na/DOwh2aiAuVOalgWterytwHutGsdhAjZq7izR+2aWIUKXVbDh5nwsId9G1fl55RNZyOU6aqB/ry7uAOHM7IZeTMVeTppPRyLf5gGre/txw/b09mDe9Cg+oqciLifh67shkZufmM/3m701EcpzInFU6NID9m3duFftF1GffDVh74aDWZuflOx5IKqqDQ8sTsdQRX8ebp61o4HccRrepW5ZX+bViRcITn/7vJ6ThyGlsOHmfQ5OX4eHow694uXFQ9wOlIIiLnJapWEP2j6zF96W4Sj2Y6HcdRKnNSIfl5e/Lvm9vyt2ua8e2GA9z8zlL2pWY5HUsqoBlLd7Fmbyr/uK4FoQE+TsdxzE3t63LvpQ35YOluPl251+k4cpKth44zaPIyvDwMs4Z3ISJMRU5E3Nvo3pFgYNyCbU5HcZTKnFRYxhiGd2/M1Ls6sudwJje+/Rtxu484HUsqkH2pWYydv4UekeHc2K6O03Ec98RVzbikSRh//3IDq/ccdTqOuGxzFTlPV5FrqCInIhVAnZAqDO0awZzVicQfTHM6jmNU5qTCu6xZDb4Y1ZUAXy8GTlrOZ7EaNZALZ63l71+sx1p4/qZWGGOcjuQ4L08P/jOwPTWr+nL/h3EkpWU7HanS256UzsDJyzHG8NG9XWgcHuh0JBGREjOiZ2OCfL0YO2+L01EcozInlUKTGkHMHdWNjg2r8djn63jhm00UFGpiFDl/X63dz89bkvnrlVGaDfAE1QJ8mDQ4hrSsfO7/MI6cfK0D5JQdyekMnLwMgFn3dqZJDRU5EalYQvx9GNGzCT/FJ7Fs52Gn4zhCZU4qjRB/H94f2om7Lr6IyYsSuPv9laRl5zkdS9zQkYxcnv16E23rVWVI1win45Q7zWsH89rNbVm1J5V/zt2oGWUdsDM5nYGTlmGtdRW5IKcjiYiUiqHdIqgV7MfL38VXytcblTmpVLw9PXj2xla82Lc1i7encNP4xSSkZDgdS9zM899sIi0rj5f7t8HTQ4dXnsq1bWozsmdjPl65l5nL9zgdp1JJSMlg4ORlFBRaZg7rQtOaKnIiUnH5eXsyundT1uxNZf7GQ07HKXMqc1IpDercgA+HdeZoRi43vv0bi7YlOx1J3MSvW5OZs2of9/doTPPawU7HKdf+0ieKnlHhPPPVRlYkaPKhsrArJYOBk5aRV2CZeW9nomqpyIlIxdc/uh5NagQydn48+ZVsvVOVOam0ujSqzlcPXELtqlUYMm0l0xYnVMrheSm+zNx8nvpyPY3CAnjg8iZOxyn3PD0Mb97Wnvqh/oycGceBY1oepDTtPlw0IpeTX8DMYZ1pVksfNohI5eDl6cHjV0axMzmDz+ISnY5TplTmpFKrH+rP7JFdubxZDZ79ehN/+2I9ufmV6xMdKb5xC7ay90gWL/VrjZ+3p9Nx3ELVKt5MvrMD2XmF3Dcjjuw8TYhSGvYeyWTgpGVk5RUwc1gXjRqLSKXTu0VNOlxUjXELtpKVW3lea4pV5owxIcaYz40x8caYzcaYi40xnxhj1rj+22WMWePa1tsY84ExZr1r2ydL9TcQuUCBvl68e0cHRl3WmFkr9nLHe8s5nJ7jdCwpZ9YlpjLltwQGdW5A50bVnY7jVprUCOL1W9qyLvEYT32xQSPgJWzvkUxum7SMjNyiEbkWdVTkRKTyMcbwxFXNSDqew9TFCU7HKTPFHZl7E5hnrW0GtAU2W2tvtda2s9a2A2YDc1zb3gz4WmtbAx2A+4wxESUbW6RkeXgYHruyGW/e1o61ianc8PZiNh+ovAtQyp/lFRTyxOz1hAX6MubqZk7HcUt9WtbikV5Nmb0qkfeX7HI6ToWReDSTgZOXcTw7j5nDOtOyTlWnI4mIOKZTw1B6Na/BOwt3cDQj1+k4ZeKsZc4YEwx0B6YAWGtzrbWpJ1xvgFuAWa6LLBBgjPECqgC5gN4Vi1u4sV1dPr3vYvILC+k/cQnzNx50OpKUA5MX7WTzgTT+dWMrgv28nY7jth66vCm9W9Tk+W82s2RHitNx3N6+1CwGTl5GWlYeM4d1oVVdFTkRkceubEZGbj4TFm53OkqZKM7IXCMgGZhmjFltjHnPGBNwwvWXAoestdtcP38OZAAHgD3Aa9ZaTWMmbqNt/RC+euASmtYM4r4ZcYz/ebsOC6vEElIyeOOHbVzVshZXtarldBy35uFheP2WtjQMC2DUzFXsPZLpdCS3tT81i4GTlpGamceMezrTup6KnIgIQFStIPpH1+ODJbtJPFrxX2eKU+a8gGhgorW2PUVFbcwJ1w/k/0flADoBBUAdoCHwF2NMo5N3aowZboyJNcbEJidrWngpX2oG+/HJ8C7c0LYOr87fwpjZ68mrZFPdChQWWsbMXoevlwfP3tjS6TgVQpCfN5MGdyC/0HLfjLhKdZJ6STlwrGhE7mhGLjPu6Uzb+iFORxIRKVdG944EA+MWbDv7xm6uOGUuEUi01i53/fw5ReUO16GU/YBPTth+EEXn1+VZa5OAxUDMyTu11k6y1sZYa2PCw8Mv5HcQKRV+3p68eVs7Hrq8CZ/E7mXItBUcy8pzOpaUoU9j97I84Qh/u6Y5NYP9nI5TYTQKD+St29qz+WAaT8xep5Hvc3DwWDaDJi/ncHou0+/pRDsVORGR/1EnpApDukYwZ3Ui8Qcr9tleZy1z1tqDwF5jTJTroiuATa7vewHx1toTF3TYA1xuigQAXYD4EswsUmaMMTzaJ4pXB7Rh+c4jDJi4pFIM2QskpWXz4reb6dwwlFtj6jsdp8K5rFkN/toniq/W7mfyop1Ox3ELh9KyGTR5GUlp2XxwdyfaN6jmdCQRkXJrZM/GBPp6MXbeFqejlKrizmb5IDDTGLMOaAe86Lr8Nv58iCXAeCAQ2ACsBKZZa9ddeFQR59wcU5/pd3fiYFo2N41fwtq9qU5HklL2zNcbyc4v5KV+rfHwME7HqZBG9mzMNa1r8fJ38fy6VYfbn0lSWjYDJy/jkKvIdbhIRU5E5ExC/H0Y2bMJP8UnsXznYafjlBpTHg5viYmJsbGxsU7HEDmr7UnHGTJtJSnpObxxa3tNiFFBzd94kPtmxPHYlVGMuqyJ03EqtIycfPpPXMKBY9l89UA3LqoecPYbVTJJx7MZOGkZB44VFbmOEaFORxIRcQvZeQX0fHUhtUP8mDOiK0WT8Jdfxpg4a+3/nJ52JsUdmRMRihY//nJUN5rVCmbEzDjeW7RT5/tUMGnZefxj7gaa1QpiePf/mbtJSliArxeTBhe9bg2fHkdGTr7DicqX5OM5DJq8nAPHsnl/qIqciMi58PP2ZHTvpqzek8r8jYecjlMqVOZEzlFYoC8fD+/CVS1r8fw3m/nH3I3ka6bLCuOV7+JJPp7Dy/3b4O2pfyLLQoPq/rw9qD3bko7zl0/X6gMSl5T0HAZNXsa+o1lMHdKRTg1V5EREzlX/6Ho0qRHI2PnxFfL9mt6piJwHP29Pxg+K5r4ejZixbDfDpseSrhEFt7ci4Qgzl+9haLeGmiWwjF3aNJwnr27OvI0HGf9z5Vjo9UwOp+dw++Tl7D2ayZQhMXRpVN3pSCIibsnL04PHroxiZ3IGn8Ulnv0GbkZlTuQ8eXgYnry6OS/2bc2ibSnc/M5SDhzLcjqWnKfsvALGzFlHvWpV+EufSKfjVErDLm3Ije3q8O8FW/lxc8U8HKY4jmTkcvt7y9l1OIOpd3Wka+MwpyOJiLi1Pi1qEt0ghHELtla49U1V5kQu0KDODZg6pCN7j2Ry0/jFbNx/zOlIch7G/7ydnckZvNC3Nf4+Xk7HqZSMMbzcrw0tagfzyMdr2JGc7nSkMnc0I5dBk5eRkJLBlLs60rWJipyIyIUyxjDm6uYkHc9h6uIEp+OUKJU5kRLQIzKcz+6/GA9juPmdpfwUX3lHFdxR/ME0Ji7cQb/2dekRGe50nEqtio8n7w7ugLeXB/dOjyUtO8/pSGXmqGtEbmdKBu/dFcMlTVXkRERKSqeGofRqXoN3ftnB0Yxcp+OUGJU5kRLSvHYwX47qRqPwAIZ9EMv0pbucjiTFUFBoGTN7PcFVvPn7dS2cjiNAvWr+TLg9mt2HM3n0kzUUFlb8CVFSM3O5Y8pytienM/nOGC5tqg8VRERK2mNXNiMjJ58JCyvOudkqcyIlqGawH5/edzGXN6vBP+Zu5Ln/bqKgErwRdWfTl+5izd5U/nl9C0IDfJyOIy5dGlXnH9e14IfNSbzx4zan45SqY5l5DJ6ygm2H0nl3cAeNDouIlJKoWkH0j67HB0t2k3g00+k4JUJlTqSE+ft48e7gGIZ0jWDKbwnc/2Ecmbma6bI8Sjyayavzt9AjMpwb2tZxOo6c5M6LL+LmDvV468dtzNtw0Ok4peJYVh6Dpy5ny8HjvDu4A5dF1XA6kohIhTa6dyQYGLegYnxQqDInUgo8PQzP3NCSf17fgh83H+K2SctIOp7tdCw5gbWWv3+5AYAX+rbCGONwIjmZMYbnbmpF2/oh/OXTNWw9dNzpSCUqLTuPO6csZ/OBNCbeEc1lzVTkRERKW52QKgzpGsGc1YnEH0xzOs4FU5kTKUVDuzVk0uAYth1Kp+/4JWw5WLHejLqzr9buZ+GWZP7aJ4p61fydjiOn4eftybt3dKCKjxfDp8dyLLNiTIhSVORWsOlAGhNv78AVzWs6HUlEpNIY2bMxgb5ejJ23xekoF0xlTqSU9WpRk8/uv5i8gkIGTFzCom3JTkeq9I5k5PLs15toWz+Eu7pGOB1HzqJWVT/euSOafalZPPTxarc/D/V4dh53TV3Bhn3HGD8oml4tVORERMpSiL8PI3s24af4JJbvPOx0nAuiMidSBlrVrcqXo7pRt1oVhkxbyccr9jgdqVJ7/r+bSMvK45X+rfH00OGV7iAmIpRnb2jFL1uTee179/0kNT0nnyHTVrI+8RhvD4qmT8taTkcSEamUhnaLoFawHy/Pi8da9/2QUGVOpIzUCanCZ/dfzCVNwhgzZz2vzIuvFFOulze/bE1mzup9jOjZmGa1gp2OI+dgUOcGDOrcgIkLd/DfdfudjnPO0nPyGTJ1BWv2pvKfge25qpWKnIiIU/y8PRnduymr96Qyf6P7rg+sMidShoL8vJlyV8wfb0gfnLWa7LwCp2NVGpm5+Tz1xXoahQcw6rImTseR8/DM9S2Juagaj322jk373efE9YycfO6etpLVe1N567b2XN26ttORREQqvf7R9WgcHsDY+fHkFxQ6Hee8qMyJlDEvTw9euKkVf7umGd9uOMCgycs4nJ7jdKxK4fXvt5J4NIuX+7XBz9vT6ThyHny8PJhwRzTBVbwYPiOWoxm5Tkc6q8zcfIa+v5K4PUd587Z2XNtGRU5EpDzw8vTg8auasTM5g8/iEp2Oc15U5kQcYIxhePfGTBgUzcb9afSdsITtSelOx6rQ1u5NZeriBAZ1bkCnhqFOx5ELUCPIj3cHx5CUlsOoj1aV609TM3Pzufv9lcTuOsK4W9txXRutZygiUp70aVGT6AYhvPHDVrJy3e9oKZU5EQdd3bo2Hw/vQmZuPv0nLmGZm8+oVF7lFRTyxOx1hAX6MubqZk7HkRLQrn4Iz/dtxZIdh3npu3in45xSVm4B97wfy4qEoiKnhelFRMofYwxjrm7OobQcpi1JcDrOOVOZE3FY+wbV+GJkN8KDfBk8ZTlzVrnnMH95NunXncQfPM5zN7Ui2M/b6ThSQm6Jqc+QrhFM+S2h3D1vsvMKGDZ9JcsSDvPvW9pyY7u6TkcSEZHT6NQwlF7NazBx4Q63OHz/RCpzIuVA/VB/Zo/oSseIUB79dC3jFmx162lyy5Odyem8+eM2rm5Viys1DXyF89S1zencMJQxc9azLjHV6ThAUZG7d3osS3Yc5rUBbenbvp7TkURE5Cweu7IZGTn5TFi43eko50RlTqScqFrFm/eHdmJAh3q8+eM2Hv10LTn57nfsdnlSWGh5cs56fL08ePaGlk7HkVLg7enBhNujCQ/05b4ZcSQfd3Yyod+L3G/bU3h1QFv6d1CRExFxB1G1gugXXY8Pluwm8Wim03GKTWVOpBzx8fLg1QFteOzKKL5YvY/BU1aQmulew/3lySexe1mecISnrmlOjWA/p+NIKake6Mu7gztwNDOXUTNXkefQhCjZeQXcNyOO37an8Er/NgxQkRMRcSuje0eCgXELtjkdpdhU5kTKGWMMoy5rwpu3tWPNnlT6TVjCrpQMp2O5naS0bF78djNdGoVya8f6TseRUtaqblVe6d+GFbuO8Nx/N5X5/efkFzDiwzh+2ZrMy/1ac0uM/uZERNxN3ZAqDOkawZzVicQfdI+1TFXmRMqpG9vVZea9nTmamUu/iUuI233E6Uhu5Z9fbSQnv5CX+rXBGON0HCkDN7ary/DujZi+dDefrNxTZvdbVORW8fOWZF7s25pbOzYos/sWEZGSNbJnYwJ9vXh13hanoxSLypxIOdYxIpQ5I7tRtYo3Aycv5+u1+52O5BbmbTjIdxsO8vAVTWkYFuB0HClDj18ZxaVNw3j6y42s2nO01O8vN7+QUTNX8VN8Ei/0bcWgzipyIiLuLMTfh5E9m/BjfBLL3WDJKJU5kXKuYVgAc0Z0pV29EB6ctZrxP2/XTJdncCwrj3/M3UCzWkEM797I6ThSxrw8PfjPwPbUqurH/TPiSErLLrX7ys0vZNRHq/hhcxLP3dSK2ztfVGr3JSIiZWdotwhqBfvx8rz4cv+eS2VOxA1UC/BhxrBO3NiuDq/O38KY2esdm+ShvHtlXjwp6Tm80r8N3p76J64yCvH3YdKdHTienc/9H8aVyqyweQWFPPDRKhZsOsS/bmzJ4C4qciIiFYWftyeP9GrK6j2pzN94yOk4Z6R3OiJuwtfLkzdubcdDVzTlk9i9DJm2gmNZeU7HKleW7zzMR8v3cHe3hrStH+J0HHFQs1rB/PuWtqzak8o/524s0U9W8woKefCj1Xy/6RDPXN+COy+OKLF9i4hI+TCgQz0ahwcwdn48+eX4A3SVORE3Yozh0d6RvHZzW1YkHGHAxCXsPeI+a6GUpuy8Ap6cs5561arwaJ9Ip+NIOXBN69qMuqwxH6/cy8zlJTMhSl5BIQ9/vJp5Gw/y9HUtGNKtYYnsV0REyhcvTw8ev6oZO5Mz+Dwu0ek4p6UyJ+KGBnSoxwd3d+JQWjZ9Jyxh7d5UpyM57u2ftrMzJYMX+7bG38fL6ThSTjzaO4rLosJ55quNrEi4sBlh8wsKeeSTNXy7/iB/v7Y591yiIiciUpH1aVGT6AYhjPthK1m5JX/IfklQmRNxU10bhzFnZFeq+Hhw66SlzNtw0OlIjok/mMY7v+ygX3RdukeGOx1HyhFPD8Mbt7Wnfqg/I2fGceBY1nntJ7+gkNGfruWbdQd46prmDLtUk+uIiFR0xhjGXN2cQ2k5TFuS4HScU1KZE3FjTWoE8cXIbjSvHcyImXG8t2hnuZ91qaQVFFqemL2eqlW8efraFk7HkXKoahVvJt/Zgey8Qu6bEUd23rl9uppfUMijn67l67X7efLqZtyrWVJFRCqNTg1D6dW8BhMX7uBoRq7Tcf5HscqcMSbEGPO5MSbeGLPZGHOxMeYTY8wa13+7jDFrTti+jTFmqTFmozFmvTHGr9R+A5FKLizQl1n3duHqVrV4/pvNPD13Q7k+UbekfbBkF2v3pvKP61tQLcDH6ThSTjWpEcTrt7RlXeIxnvpiQ7E/9CgotPz1s7V8tXY/j18VxX09GpdyUhERKW8eu7IZ6Tn5TFi43eko/6O4I3NvAvOstc2AtsBma+2t1tp21tp2wGxgDoAxxgv4ELjfWtsS6Aloyj2RUuTn7cnbA6O5v0djPly2h2HTY0nPyXc6VqnbeyST177fQs+ocG5oW8fpOFLO9WlZi0d6NWX2qkSmLd511u0LCi2PfbaWL9fs57EroxjZs0nphxQRkXInqlYQ/aPr8cGS3exLPb/D9UvLWcucMSYY6A5MAbDW5lprU0+43gC3ALNcF/UB1llr17q2P2ytLZ9nDIpUIB4ehjFXN+Olfq1ZtC2Fm99Zet7nB7kDay1PfbkBgOdvakXRP0UiZ/bQ5U3p3aImL3y7mSXbU067XUGh5fHP1zFn9T7+0juSUZepyImIVGaje0eCgXELtjod5U+KMzLXCEgGphljVhtj3jPGBJxw/aXAIWvtNtfPkYA1xsw3xqwyxjxewplF5AwGdmrAtCEd2Xskk5vGL2bDvmNORyoVc9fs59etyTx2ZRT1qvk7HUfchIeH4fVb2tIwLIBRH6065dIehYWWMbPXMXtVIqN7RfLgFU0dSCoiIuVJ3ZAqDOkawexVicQfTHM6zh+KU+a8gGhgorW2PZABjDnh+oH8/6jc79tfAtzu+trXGHPFyTs1xgw3xsQaY2KTk5PPN7+InEL3yHA+H3ExnsZwy7tL+Sn+kNORStTh9Bye/Xoj7eqHaMFmOWdBft5MGtyB/ELL8Blxf5puurDQ8uSc9XwWl8jDVzTl4V4qciIiUmRkz8YE+nrx6rwtTkf5Q3HKXCKQaK1d7vr5c4rK3e/nx/UDPjlp+1+stSnW2kzg29+3P5G1dpK1NsZaGxMerqnERUpas1rBfDmqG43DAxn2QSzTl+5yOlKJef6bzRzPzueV/m3w9NDhlXLuGoUH8tbA9sQfTOPx2euw1lJYaPnbF+v5JHYvD17ehEdU5ERE5AQh/j6M7NmEH+OTWL7zsNNxgGKUOWvtQWCvMSbKddEVwCbX972AeGvticuizwfaGGP8XWWvxwnbi0gZqhHsxyf3deHyZjX5x9yN/OvrTRQUuvfSBb9sTeaL1fsY2bMxUbWCnI4jbuyyqBo8dmUUX6/dzzu/7OSpLzfw8cq9jLqsMY/2jtR5mCIi8j+GdI2gZrAvL8+LLxfLQRV3NssHgZnGmHVAO+BF1+W38edDLLHWHgVeB1YCa4BV1tpvSiKsiJw7fx8v3h3cgbu7NWTq4gTu/zCOzFz3nOkyIyefv81ZT+PwAEZdrgkp5MKN6NGYa1vX5pV58cxasYcRPRvz1z5RKnIiInJKVXw8Gd0rktV7Upm/0fnTWEx5aJQxMTE2NjbW6RgiFd4HS3bx7NcbaVmnKlPuiqFGsHstAfncfzcx5bcEPrv/YjpGhDodRyqIzNx8Rny4inb1Q3ikV1MVOREROaP8gkKufONXAOY/0h0vz+KOj52ZMSbOWhtzLrcpmXsWEbdwV9cIJt8Zw47kdPpOWMKWg8edjlRsa/amMm1xArd3bqAiJyXK38eLD+7uxGgdWikiIsXg5enB41c1Y0dyBp/HJZ79BqVIZU6kkrmieU0+ve9i8gsLGTBxCb9uLf+zyeYVFDJm9jrCg3x54upmTscRERGRSq5Pi5pENwhh3A9b/zQrcllTmROphFrVrcqXo7pRt1oVhr6/ko9X7HE60hlN+nUn8QeP89yNrQj283Y6joiIiFRyxhjGXN2cQ2k5TFuS4FgOlTmRSqp21Sp8PqIrlzQJY8yc9bwyL57CcjjT5Y7kdN78cRvXtK5Fn5a1nI4jIiIiAkCnhqFc0awGExfu4GhGriMZVOZEKrFAXy+m3BXD7Z0bMHHhDh6ctZrsPOcOFTjZ7ws4+3l58MwNLZ2OIyIiIvInj1/VjPScfCYs3O7I/avMiVRyXp4ePH9TK566pjnfbjjAoMnLOJye43QsAD5euZcVCUd46trm1Ahyr5k3RUREpOKLqhVE/+h6fLB0N/tSs8r8/lXmRARjDPd2b8TE26PZdCCNvhOWsD0p3dFMh9Kyeem7zVzcqDq3xNR3NIuIiIjI6YzuHQnAuAVby/y+VeZE5A9XtarNx8MvJjM3n34TFrN0x2HHsvxz7kZy8wt5sV9rTRcvIiIi5VbdkCoM6RrB7FWJxB9MK9P7VpkTkT9pVz+EL0Z2o2awH3dOXc5sB9ZPmbfhAPM2HuThXk1pGBZQ5vcvIiIici5G9mxMoK8Xr87bUqb3qzInIv+jfqg/n4/oSqeGofzls7W8vmAr1pbNTJfHsvL4x9yNNK8dzL2XNiqT+xQRERG5ECH+Pozo2Zgf45NYvrPsjmxSmRORU6paxZtpQzpxc4d6vPXjNkZ/soac/NKf6fLl7+JJSc/hlf6t8fbUP1EiIiLiHoZ2bUjNYF9enhdfZh+C652SiJyWj5cHYwe04bEro/hyzX4GT1lBambpraOybOdhZq3Ywz2XNKRNvZBSux8RERGRklbFx5PRvSJZvSeV7zcdKpP7VJkTkTMyxjDqsia8NbA9a/am0m/CEnalZJT4/WTnFfDknPXUD63yx6xQIiIiIu5kQId6NA4PYOy8ePILCkv9/lTmRKRYbmhbh4+GdeZoZi59JywmdteREt3/f37aRkJKBi/2bY2/j1eJ7ltERESkLHh5evD4Vc3YkZzB52UwiZzKnIgUW0xEKF+M7EaIvw+D3lvO12v3l8h+Nx9I491fdtI/uh6XNg0vkX2KiIiIOKFPi5pENwhh3A9bycot3fkGVOZE5JxEhAUwZ0RX2tUL4cFZqxn/8/YLOsm3oNAyZvY6qlbx5u/XNi/BpCIiIiJlzxjDmKubcygth2lLEkr1vlTmROScVQvwYcawTtzUrg6vzt/CE7PXkXeex4W/v2QXaxOP8Y/rW1AtwKeEk4qIiIiUvU4NQ7miWQ0mLtxRqpPHqcyJyHnx9fJk3K3tePiKpnwam8iQaSs4lpV3TvvYeyST1+Zv4bKocG5oW6eUkoqIiIiUvcevakZ6Tj4TFu4otftQmROR82aMYXTvSP59c1tWJBxhwMQl7D2SWazbWmt56ssNGAPP922NMaaU04qIiIiUnahaQfSPrsf7S3axLzWrVO5DZU5ELlj/DvWYfndnDqVl03fCEtbsTT3rbb5cs49ftybz+JVR1A2pUvohRURERMrY78stjVuwtVT2rzInIiXi4sbVmTOyG1V8PLht0lLmbTh42m0Pp+fwr6830b5BCIMvjii7kCIiIiJlqG5IFYZ0jWD2qkTiD6aV+P5V5kSkxDSpEciXI7vRvHYwI2bGMfnXnaec6fK5/24iPSefV/q3wdNDh1eKiIhIxTWyZ2MCfb14dd6WEt+3ypyIlKjqgb7MurcL17SqzQvfbubpuRvIP2Gmy4VbkvhyzX5G9GxCZM0gB5OKiIiIlL4Qfx9G9GzMj/FJrEg4UqL7VpkTkRLn5+3Jfwa2Z0TPxny4bA/DpseSnpNPRk4+T32xgcbhAYy6rLHTMUVERETKxNCuDakZ7MvL322+oPV5T6YyJyKlwsPD8MRVzXipX2sWbUthwMQlPD13A/tSs3i5fxt8vTydjigiIiJSJqr4eDK6VySr9qTy/aZDJbZflTkRKVUDOzXg/aEd2Xc0izmr9nFHlwZ0jAh1OpaIiIhImRrQoR6NwwMYOy/+T6egXAiVOREpdZc2DefzEV0ZdklDnriqmdNxRERERMqcl6cHj1/VjB3JGXwel1gi+1SZE5EyEVUriL9f14IgP2+no4iIiIg4ok+LmkQ3CGHcD1vJyi244P2pzImIiIiIiJQBY4rmFDiUlsO0JQkXvD+VORERERERkTLSuVF1rmhWg4kLd5CamXtB+1KZExERERERKUOPX9WM9Jx8JizccUH7UZkTEREREREpQ1G1gugfXY/3l+xiX2rWee9HZU5ERERERKSMje4dCcC4BVvPex/FKnPGmBBjzOfGmHhjzGZjzMXGmE+MMWtc/+0yxqw56TYNjDHpxpi/nnc6ERERERGRCqhuSBXuuvgiZq9KJP5g2nntw6uY270JzLPWDjDG+AD+1tpbf7/SGPNv4NhJtxkHfHdeqURERERERCq4kT2b8PHKvbw6b8t53f6sI3PGmGCgOzAFwFqba61NPeF6A9wCzDrhspuAncDG80olIiIiIiJSwVUL8GFEz8b8GJ90XrcvzmGWjYBkYJoxZrUx5j1jTMAJ118KHLLWbgNwXfcE8OyZdmqMGW6MiTXGxCYnJ59XeBEREREREXc2tGtDagb7ntdti1PmvIBoYKK1tj2QAYw54fqBnDAqR1GJG2etTT/TTq21k6y1MdbamPDw8HOMLSIiIiIi4v6q+Hgyulfked22OOfMJQKJ1trlrp8/x1XmjDFeQD+gwwnbdwYGGGPGAiFAoTEm21r79nklFBERERERqcAGdKjHwPO43VnLnLX2oDFmrzEmylq7BbgC2OS6uhcQb61NPGH7S3//3hjzDJCuIiciIiIiInJqXp7nt2JccWezfBCY6ZrJcicw1HX5bfz5EEsREREREREpA8Uqc9baNUDMKS4fcpbbPXM+oUREREREROTMzm88T0RERERERBylMiciIiIiIuKGVOZERERERETckMqciIiIiIiIG1KZExERERERcUMqcyIiIiIiIm5IZU5ERERERMQNqcyJiIiIiIi4IWOtdToDxpjjwBanc1QgVYFjToeoQPR4lpwwIMXpEBWI/jZLlh7PkqXHs+TosSxZejxLlh7PkhNlrQ06lxt4lVaSc7TFWhvjdIiKwhgzyVo73OkcFYUez5JjjInVc73k6G+zZOnxLFl6PEuOHsuSpcezZOnxLDnGmNhzvY0Os6yYvnY6QAWjx1PKK/1tliw9niVLj2fJ0WNZsvR4liw9ng4qL4dZ6tN6kUpAz3URERGRUzuf90nlZWRuktMBRKRM6LkuIiIicmrn/D6pXJQ5a63e4J0HY0x9Y8zPxpjNxpiNxpiHXZe3NcYsNcasN8Z8bYwJdjqrOzDG+BljVhhj1roez2ddl4caYxYYY7a5vlZzOqu70nP9/BljrjLGbDHGbDfGjDnpur8aY6wxJsypfO7mVI+nMeYZY8w+Y8wa13/XOJ3TXZzu79MY86Dr8o3GmLFOZnQXp/nb/OSEv8tdxpg1Dsd0G8aYqcaYJGPMhhMu0+v6eTjNY3mz6/ldaIzRkTcX6HzeJ5WLwyzl/BhjagO1rbWrjDFBQBxwE/AB8Fdr7S/GmLuBhtbapx2M6haMMQYIsNamG2O8gd+Ah4F+wBFr7cuuF9Zq1tonnMwqlYsxxhPYCvQGEoGVwEBr7SZjTH3gPaAZ0MFaq9lCz+J0jydwC5BurX3NwXhu5wyPZ03gKeBaa22OMaaGtTbJuaTl35me6yds82/gmLX2X86kdC/GmO5AOjDdWtvKddlY9Lp+zk7zWDYHCoF3KXrvec4TeMiFKfORuTOMJrUzxixzfeoUa4zpVNbZ3I219oC1dpXr++PAZqAuEAX86tpsAdDfmYTuxRZJd/3o7frPAjdSVJBxfb2p7NO5p9N8wvycMWad67n+vTGmjtM53UAnYLu1dqe1Nhf4mKK/S4BxwOMU/a1K8Zzp8ZRzd7rHcwTwsrU2B0BFrljO+Lfp+tDxFmCWQ/ncjrX2V+DISRfrdf08nOqxtNZuttZqebHzcJqRzleNMfGu90lfGGNCzrYfJw6zzAf+Yq1tDnQBRhljWgBjgWette2Af7h+lmIyxkQA7YHlwAbgBtdVNwP1HYrldowxnq7DV5KABdba5UBNa+0BKCrQQA0HI7oN1yfM44GrgRbAQNdz/VVrbRvXc/2/FD3f5czqAntP+DkRqGuMuQHYZ61d60wst3XKx9P1/QOuF9GpOvSq2E73eEYClxpjlhtjfjHGdHQknXs5098mwKXAIWvttjJNVfHodV3Kg/eBq066bAHQylrbhqJR+ifPtpMyL3NnGE2ywO/ndlUF9pd1NndljAkEZgOPWGvTgLspKslxQBCQ62Q+d2KtLXCVjHpAJ2NMK4cjubNTfsLs+hv9XQAaUSoOc4rLfCk6hE1l+Nyd6vG0wESgMdAOOAD8uwwzubPTPZ5eQDWKPrh9DPjUNbIkp3e6x/J3A9GonEiFcJqRzu+ttfmuH5dR9H70jBxdNPyk0aRHgPnGmNcoKpldnUvmPlznds0GZlpr5wBYa+OBPq7rI4FrnUvonqy1qcaYhRR9YnLIGFPbWnvAdZ6iDhUqnlN9wtwZwBjzAnAncAy4rOyjuZ1E/jzCXg/YQ9GhQmtd74/rAauMMZ2stQfLPqJbOdXjud9ae+j3C4wxkykaOZazO+Xj6bp8ji06OX+FMaYQCAOSyz6i2zjdY4kxxouic7g7OJCrotHruriDu4FPzraRY7NZnmI0aQQw2lpbHxgNTHEqm7twfcI5BdhsrX39hMtruL56AH8H3nEmoXsxxoT/fmyyMaYK0AuIB74C7nJtdhcw15GA7ue0nzBba59yPddnAg+UaSr3tBJoaoxpaIzxAW6j6E1yDWtthLU2gqI3gdEqcsVyqsfzK9ebut/1peiQdTm7Uz6ewJfA5fDHB4s+gCboObPTPZbgek2y1iY6lq7i0Ou6lGvGmKcoOjVt5tm2dWRk7lSjSRQ9mR52ff8ZRbOzyZl1AwYD60+YpvhvFL0QjHL9PAeY5kA2d1Qb+MB1rpcH8Km19r/GmKUUHR50D0WjITc7GdKNnPYT5hN8BHwD/LOsQrkja22+MeYBYD7gCUy11m50OJbbOt3jaYyZYYxpR9GHDruA+5xL6T7O8HhuA6a6Tu7PBe6ymkL7jM7yXL8NHWJ5zowxs4CeQJgxJpGi15uX0ev6OTvNY3kE+A8QDnxjjFljrb3SuZTuzxhzF3AdcEVx/s0s86UJXKNJH1A0JewjJ1y+GRhhrV1ojLkCGGut1aEEIm7KdUjQVuAKYB9FnzgPAnJ/P3nfGPMg0MNaO8CxoCIiIiIOcJ1y9t8Tlnq4CnidovdGxTok3YkydwmwCFhP0boUUDSalAa8SdFoYTYw0lobV6bhRKREmaJFl9/g/z9hfsEYM5ui5TMKgd3A/dbafc6lFBERESlbJ450AocoGul8kqIJzg67Nltmrb3/jPvREQ8iIiIiIiLux7EJUEREREREROT8qcyJiIiIiIi4IZU5ERERERERN6QyJyIlwhhjjTEzTvjZyxiTbIzRwssiIiIipUBlTkRKSgbQyrXgOkBvipYkEBEREZFSoDInIiXpO+Ba1/cDOWGBW2NMJ2PMEmPMatfXKNfli1wLNf++3WJjTJuyDC0iIiLijlTmRKQkfQzcZozxA9oAy0+4Lh7obq1tD/wDeNF1+XvAEABjTCTga61dV2aJRURERNyUypyIlBhXCYugaFTu25Ourgp8ZozZAIwDWrou/wy4zhjjDdwNvF8mYUVERETcnMqciJS0r4DXOOEQS5fngJ+tta2A6wE/AGttJrAAuBG4Bfio7KKKiIiIuC8vpwOISIUzFThmrV1vjOl5wuVV+f8JUYacdJv3gK+BRdbaI6UdUERERKQi0MiciJQoa22itfbNU1w1FnjJGLMY8DzpNnFAGjCtDCKKiIiIVAjGWut0BhGp5IwxdYCFQDNrbaHDcURERETcgkbmRMRRxpg7KZr18ikVOREREZHi08iciIiIiIiIG9LInIicN2NMfWPMz8aYzcaYjcaYh12XhxpjFhhjtrm+VnNd3tsYE2eMWe/6evkJ+3rBGLPXGJPu1O8jIiIi4k40Mici580YUxuoba1dZYwJAuKAmyiarfKItfZlY8wYoJq19gljTHvgkLV2vzGmFTDfWlvXta8uwG5gm7U20InfR0RERMSdqMyJSIkxxswF3nb919Nae8BV+BZaa6NO2tYAKUAda23OCZenq8yJiIiInJ0OsxSREmGMiQDaUzSZSU1r7QEA19cap7hJf2D1iUVORERERIpPi4aLyAUzxgQCs4FHrLVpRYNuZ9y+JfAK0KcM4omIiIhUSBqZE5ELYozxpqjIzbTWznFdfMh1eOXv59UlnbB9PeAL4E5r7Y6yzisiIiJSUajMich5c533NgXYbK19/YSrvgLucn1/FzDXtX0I8A3wpLV2cRlGFREREalwNAGKiJw3Y8wlwCJgPfD7gt9/o+i8uU+BBsAe4GZr7RFjzN+BJ4FtJ+ymj7U2yRgzFhgE1AH2A+9Za58pk19ERERExA2pzImIiIiIiLghHWYpIiIiIiLihlTmRERERERE3JDKnIiIiIiIiBtSmRMREREREXFDKnMiIiIiIiJuSGVOREQqLGNMgTFmjTFmozFmrTHmUWPMGV/7jDERxphBZZVRRETkfKnMiYhIRZZlrW1nrW0J9AauAf55lttEULTmoYiISLmmdeZERKTCMsakW2sDT/i5EbASCAMuAmYAAa6rH7DWLjHGLAOaAwnAB8BbwMtAT8AXGG+tfbfMfgkREZHTUJkTEZEK6+Qy57rsKNAMOA4UWmuzjTFNgVnW2hhjTE/gr9ba61zbDwdqWGufN8b4AouBm621CWX5u4iIiJzMy+kAIiIiZcy4vnoDbxtj2gEFQORptu8DtDHGDHD9XBVoStHInYiIiGNU5kREpNJwHWZZACRRdO7cIaAtReeQZ5/uZsCD1tr5ZRJSRESkmDQBioiIVArGmHDgHeBtW3SOQVXggLW2EBgMeLo2PQ4EnXDT+cAIY4y3az+RxpgAREREHKaRORERqciqGGPWUHRIZT5FE5687rpuAjDbGHMz8DOQ4bp8HZBvjFkLvA+8SdEMl6uMMQZIBm4qm/giIiKnpwlQRERERERE3JAOsxQREREREXFDKnMiIiIiIiJuSGVORERERETEDanMiYiIiIiIuCGVORERERERETekMiciIiIiIuKGVOZERERERETckMqciIiIiIiIG/o/z2wc5maBl2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a specific variable in the specified chart size\n",
    "stockdata['High'].plot(figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "798fd65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a multiple variables in the specified chart size- additional square paranthesis are required\n",
    "stockdata[['High','Low']].plot(figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6821f6f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a specific range of multiple variables in the specified chart size- using loc(dates within quotes)\n",
    "stockdata.loc['2021-05-01':'2021-05-04',['High','Low']].plot(figsize=(15,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0147d935",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nivethitha K S\\AppData\\Local\\Temp/ipykernel_7004/3370798085.py:2: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  stockdata['2021-04']\n"
     ]
    },
    {
     "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>2021-04-28</th>\n",
       "      <td>682.500000</td>\n",
       "      <td>671.150024</td>\n",
       "      <td>682.5</td>\n",
       "      <td>672.599976</td>\n",
       "      <td>4011121</td>\n",
       "      <td>670.620911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-29</th>\n",
       "      <td>681.349976</td>\n",
       "      <td>672.150024</td>\n",
       "      <td>678.0</td>\n",
       "      <td>674.650024</td>\n",
       "      <td>3939217</td>\n",
       "      <td>672.664917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>678.000000</td>\n",
       "      <td>660.700012</td>\n",
       "      <td>670.0</td>\n",
       "      <td>665.099976</td>\n",
       "      <td>2742166</td>\n",
       "      <td>663.142944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low   Open       Close   Volume   Adj Close\n",
       "Date                                                                      \n",
       "2021-04-28  682.500000  671.150024  682.5  672.599976  4011121  670.620911\n",
       "2021-04-29  681.349976  672.150024  678.0  674.650024  3939217  672.664917\n",
       "2021-04-30  678.000000  660.700012  670.0  665.099976  2742166  663.142944"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#slicing a specific month \n",
    "stockdata['2021-04']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e1634097",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://query1.finance.yahoo.com/v7/finance/download/HDFCLIFE.NS?period1=1626719400&period2=1645727400&interval=1d&events=history\n",
      "1626719400 1645727400\n"
     ]
    }
   ],
   "source": [
    "#importing stock data using a defined url- Yahoo finance\n",
    "\n",
    "#import necessary packages\n",
    "import datetime\n",
    "import pandas as pd\n",
    "\n",
    "#define the key variables- .NS is not removed so it takes the NSE stock data\n",
    "stockcode ='HDFCLIFE'\n",
    "\n",
    "#converting the date into an epoch number\n",
    "ts1 = str(int(datetime.datetime(2021, 7, 20).timestamp())) \n",
    "ts2 = str(int(datetime.datetime(2022, 2, 25).timestamp()))\n",
    "\n",
    "interval = '1d'\n",
    "#interval = '1wk'\n",
    "#interval = '1mo'\n",
    "\n",
    "events = 'history'\n",
    "#events = 'div'\n",
    "#events = 'split'\n",
    "\n",
    "#https://query1.finance.yahoo.com/v7/finance/download/HDFCBANK.NS?period1=1620962369&period2=1652498369&interval=1d&events=history&includeAdjustedClose=true\n",
    "#unchanged parts with quotes and addition of variable is done with a '+' sign\n",
    "url = 'https://query1.finance.yahoo.com/v7/finance/download/'\\\n",
    "      + stockcode + '.NS?period1=' + ts1 + '&period2=' + ts2 + '&interval='\\\n",
    "      + interval + '&events=' + events\n",
    "\n",
    "print(url)\n",
    "print (ts1, ts2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bceb6078",
   "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>2021-07-20</td>\n",
       "      <td>678.700012</td>\n",
       "      <td>681.500000</td>\n",
       "      <td>661.950012</td>\n",
       "      <td>663.450012</td>\n",
       "      <td>663.450012</td>\n",
       "      <td>3629414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>669.400024</td>\n",
       "      <td>661.099976</td>\n",
       "      <td>662.099976</td>\n",
       "      <td>662.099976</td>\n",
       "      <td>4062964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-07-23</td>\n",
       "      <td>662.099976</td>\n",
       "      <td>667.950012</td>\n",
       "      <td>661.799988</td>\n",
       "      <td>665.349976</td>\n",
       "      <td>665.349976</td>\n",
       "      <td>2796725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-07-26</td>\n",
       "      <td>665.000000</td>\n",
       "      <td>665.400024</td>\n",
       "      <td>660.349976</td>\n",
       "      <td>663.099976</td>\n",
       "      <td>663.099976</td>\n",
       "      <td>2503714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-07-27</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>674.799988</td>\n",
       "      <td>662.900024</td>\n",
       "      <td>668.750000</td>\n",
       "      <td>668.750000</td>\n",
       "      <td>3512513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-07-28</td>\n",
       "      <td>674.450012</td>\n",
       "      <td>674.450012</td>\n",
       "      <td>661.150024</td>\n",
       "      <td>664.849976</td>\n",
       "      <td>664.849976</td>\n",
       "      <td>2686658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>666.049988</td>\n",
       "      <td>669.950012</td>\n",
       "      <td>661.000000</td>\n",
       "      <td>662.650024</td>\n",
       "      <td>662.650024</td>\n",
       "      <td>1981009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2021-07-20  678.700012  681.500000  661.950012  663.450012  663.450012   \n",
       "1  2021-07-22  664.000000  669.400024  661.099976  662.099976  662.099976   \n",
       "2  2021-07-23  662.099976  667.950012  661.799988  665.349976  665.349976   \n",
       "3  2021-07-26  665.000000  665.400024  660.349976  663.099976  663.099976   \n",
       "4  2021-07-27  666.000000  674.799988  662.900024  668.750000  668.750000   \n",
       "5  2021-07-28  674.450012  674.450012  661.150024  664.849976  664.849976   \n",
       "6  2021-07-29  666.049988  669.950012  661.000000  662.650024  662.650024   \n",
       "\n",
       "    Volume  \n",
       "0  3629414  \n",
       "1  4062964  \n",
       "2  2796725  \n",
       "3  2503714  \n",
       "4  3512513  \n",
       "5  2686658  \n",
       "6  1981009  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the csv file from the url\n",
    "stockdata = pd.read_csv(url)\n",
    "stockdata.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c44416a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 62,
     "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": [
    "stockdata.plot(x=\"Date\", y=\"High\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3a118b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nsepy in c:\\anaconda\\lib\\site-packages (0.8)\n",
      "Requirement already satisfied: click in c:\\anaconda\\lib\\site-packages (from nsepy) (8.0.3)\n",
      "Requirement already satisfied: lxml in c:\\anaconda\\lib\\site-packages (from nsepy) (4.6.3)\n",
      "Requirement already satisfied: requests in c:\\anaconda\\lib\\site-packages (from nsepy) (2.26.0)\n",
      "Requirement already satisfied: numpy in c:\\anaconda\\lib\\site-packages (from nsepy) (1.20.3)\n",
      "Requirement already satisfied: six in c:\\anaconda\\lib\\site-packages (from nsepy) (1.16.0)\n",
      "Requirement already satisfied: beautifulsoup4 in c:\\anaconda\\lib\\site-packages (from nsepy) (4.10.0)\n",
      "Requirement already satisfied: pandas in c:\\anaconda\\lib\\site-packages (from nsepy) (1.3.4)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\anaconda\\lib\\site-packages (from beautifulsoup4->nsepy) (2.2.1)\n",
      "Requirement already satisfied: colorama in c:\\anaconda\\lib\\site-packages (from click->nsepy) (0.4.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\anaconda\\lib\\site-packages (from pandas->nsepy) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\anaconda\\lib\\site-packages (from pandas->nsepy) (2021.3)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\anaconda\\lib\\site-packages (from requests->nsepy) (3.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\anaconda\\lib\\site-packages (from requests->nsepy) (1.26.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\anaconda\\lib\\site-packages (from requests->nsepy) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\anaconda\\lib\\site-packages (from requests->nsepy) (2.0.4)\n",
      "Requirement already satisfied: datetime in c:\\anaconda\\lib\\site-packages (4.4)\n",
      "Requirement already satisfied: pytz in c:\\anaconda\\lib\\site-packages (from datetime) (2021.3)\n",
      "Requirement already satisfied: zope.interface in c:\\anaconda\\lib\\site-packages (from datetime) (5.4.0)\n",
      "Requirement already satisfied: setuptools in c:\\anaconda\\lib\\site-packages (from zope.interface->datetime) (58.0.4)\n"
     ]
    }
   ],
   "source": [
    "#Get stock data from NSE\n",
    "!pip install nsepy\n",
    "!pip install datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "11db7477",
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Trades</th>\n",
       "      <th>Deliverable Volume</th>\n",
       "      <th>%Deliverble</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",
       "      <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>2022-05-02</th>\n",
       "      <td>HDFC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>2229.70</td>\n",
       "      <td>2194.10</td>\n",
       "      <td>2270.0</td>\n",
       "      <td>2184.00</td>\n",
       "      <td>2259.00</td>\n",
       "      <td>2264.00</td>\n",
       "      <td>2249.42</td>\n",
       "      <td>3878583</td>\n",
       "      <td>8.724549e+14</td>\n",
       "      <td>175503</td>\n",
       "      <td>2015503</td>\n",
       "      <td>0.5196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-04</th>\n",
       "      <td>HDFC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>2264.00</td>\n",
       "      <td>2271.30</td>\n",
       "      <td>2278.0</td>\n",
       "      <td>2192.00</td>\n",
       "      <td>2209.30</td>\n",
       "      <td>2206.95</td>\n",
       "      <td>2242.40</td>\n",
       "      <td>4754704</td>\n",
       "      <td>1.066193e+15</td>\n",
       "      <td>221302</td>\n",
       "      <td>3029406</td>\n",
       "      <td>0.6371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>HDFC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>2206.95</td>\n",
       "      <td>2230.35</td>\n",
       "      <td>2251.9</td>\n",
       "      <td>2207.00</td>\n",
       "      <td>2215.55</td>\n",
       "      <td>2215.40</td>\n",
       "      <td>2228.94</td>\n",
       "      <td>2661611</td>\n",
       "      <td>5.932563e+14</td>\n",
       "      <td>93119</td>\n",
       "      <td>1590663</td>\n",
       "      <td>0.5976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>HDFC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>2215.40</td>\n",
       "      <td>2170.00</td>\n",
       "      <td>2190.0</td>\n",
       "      <td>2142.75</td>\n",
       "      <td>2159.00</td>\n",
       "      <td>2150.65</td>\n",
       "      <td>2160.17</td>\n",
       "      <td>3862931</td>\n",
       "      <td>8.344598e+14</td>\n",
       "      <td>176903</td>\n",
       "      <td>2396534</td>\n",
       "      <td>0.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-09</th>\n",
       "      <td>HDFC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>2150.65</td>\n",
       "      <td>2115.00</td>\n",
       "      <td>2181.0</td>\n",
       "      <td>2113.60</td>\n",
       "      <td>2170.20</td>\n",
       "      <td>2168.30</td>\n",
       "      <td>2148.80</td>\n",
       "      <td>3969231</td>\n",
       "      <td>8.529090e+14</td>\n",
       "      <td>163188</td>\n",
       "      <td>2450142</td>\n",
       "      <td>0.6173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>HDFC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>2168.30</td>\n",
       "      <td>2163.45</td>\n",
       "      <td>2221.8</td>\n",
       "      <td>2160.25</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>2201.30</td>\n",
       "      <td>2200.26</td>\n",
       "      <td>4205193</td>\n",
       "      <td>9.252533e+14</td>\n",
       "      <td>172317</td>\n",
       "      <td>2558844</td>\n",
       "      <td>0.6085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Symbol Series  Prev Close     Open    High      Low     Last  \\\n",
       "Date                                                                      \n",
       "2022-05-02   HDFC     EQ     2229.70  2194.10  2270.0  2184.00  2259.00   \n",
       "2022-05-04   HDFC     EQ     2264.00  2271.30  2278.0  2192.00  2209.30   \n",
       "2022-05-05   HDFC     EQ     2206.95  2230.35  2251.9  2207.00  2215.55   \n",
       "2022-05-06   HDFC     EQ     2215.40  2170.00  2190.0  2142.75  2159.00   \n",
       "2022-05-09   HDFC     EQ     2150.65  2115.00  2181.0  2113.60  2170.20   \n",
       "2022-05-10   HDFC     EQ     2168.30  2163.45  2221.8  2160.25  2200.00   \n",
       "\n",
       "              Close     VWAP   Volume      Turnover  Trades  \\\n",
       "Date                                                          \n",
       "2022-05-02  2264.00  2249.42  3878583  8.724549e+14  175503   \n",
       "2022-05-04  2206.95  2242.40  4754704  1.066193e+15  221302   \n",
       "2022-05-05  2215.40  2228.94  2661611  5.932563e+14   93119   \n",
       "2022-05-06  2150.65  2160.17  3862931  8.344598e+14  176903   \n",
       "2022-05-09  2168.30  2148.80  3969231  8.529090e+14  163188   \n",
       "2022-05-10  2201.30  2200.26  4205193  9.252533e+14  172317   \n",
       "\n",
       "            Deliverable Volume  %Deliverble  \n",
       "Date                                         \n",
       "2022-05-02             2015503       0.5196  \n",
       "2022-05-04             3029406       0.6371  \n",
       "2022-05-05             1590663       0.5976  \n",
       "2022-05-06             2396534       0.6204  \n",
       "2022-05-09             2450142       0.6173  \n",
       "2022-05-10             2558844       0.6085  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting stock data from NSE\n",
    "from nsepy import get_history\n",
    "from datetime import date\n",
    "\n",
    "#Define the variables\n",
    "start_date = date(2022,5,1)\n",
    "end_date = date(2022,5,10)\n",
    "expiry = date(2022,5,13)\n",
    "stock_symbol = \"HDFC\"\n",
    "\n",
    "stockdata_nse = get_history(symbol=stock_symbol , start=start_date , end=end_date)\n",
    "stockdata_nse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "10887532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Expiry</th>\n",
       "      <th>Option Type</th>\n",
       "      <th>Strike Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Last</th>\n",
       "      <th>Settle Price</th>\n",
       "      <th>Number of Contracts</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Premium Turnover</th>\n",
       "      <th>Open Interest</th>\n",
       "      <th>Change in OI</th>\n",
       "      <th>Underlying</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",
       "      <th></th>\n",
       "      <th></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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Symbol, Expiry, Option Type, Strike Price, Open, High, Low, Close, Last, Settle Price, Number of Contracts, Turnover, Premium Turnover, Open Interest, Change in OI, Underlying]\n",
       "Index: []"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting stockoption data\n",
    "Opdata_nse = get_history(symbol=\"NIFTY\" , start=start_date , end=end_date, index=True, option_type=\"CE\", expiry_date=expiry , strike_price=15800)\n",
    "Opdata_nse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62075d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting stockdata using Pandas Datareader\n",
    "#Pandas Datareader is a Python package to create a pandas DataFrame by using data sources available on the internet\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d0e9fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>2022-01-03</th>\n",
       "      <td>654.650024</td>\n",
       "      <td>646.299988</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>650.500000</td>\n",
       "      <td>1892087</td>\n",
       "      <td>650.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>655.000000</td>\n",
       "      <td>649.049988</td>\n",
       "      <td>652.400024</td>\n",
       "      <td>653.150024</td>\n",
       "      <td>2293391</td>\n",
       "      <td>653.150024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>658.250000</td>\n",
       "      <td>650.650024</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>653.549988</td>\n",
       "      <td>3326875</td>\n",
       "      <td>653.549988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>651.950012</td>\n",
       "      <td>644.900024</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>646.900024</td>\n",
       "      <td>2548339</td>\n",
       "      <td>646.900024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>662.950012</td>\n",
       "      <td>646.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>660.299988</td>\n",
       "      <td>2168995</td>\n",
       "      <td>660.299988</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-09</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>557.500000</td>\n",
       "      <td>558.150024</td>\n",
       "      <td>563.849976</td>\n",
       "      <td>2774773</td>\n",
       "      <td>563.849976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>572.900024</td>\n",
       "      <td>558.000000</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>559.200012</td>\n",
       "      <td>3953646</td>\n",
       "      <td>559.200012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-11</th>\n",
       "      <td>575.700012</td>\n",
       "      <td>559.500000</td>\n",
       "      <td>559.500000</td>\n",
       "      <td>563.549988</td>\n",
       "      <td>4283207</td>\n",
       "      <td>563.549988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-12</th>\n",
       "      <td>564.650024</td>\n",
       "      <td>544.299988</td>\n",
       "      <td>560.099976</td>\n",
       "      <td>547.250000</td>\n",
       "      <td>3079554</td>\n",
       "      <td>547.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-13</th>\n",
       "      <td>565.849976</td>\n",
       "      <td>548.049988</td>\n",
       "      <td>555.000000</td>\n",
       "      <td>550.750000</td>\n",
       "      <td>3154166</td>\n",
       "      <td>550.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2022-01-03  654.650024  646.299988  654.000000  650.500000  1892087   \n",
       "2022-01-04  655.000000  649.049988  652.400024  653.150024  2293391   \n",
       "2022-01-05  658.250000  650.650024  655.000000  653.549988  3326875   \n",
       "2022-01-06  651.950012  644.900024  651.000000  646.900024  2548339   \n",
       "2022-01-07  662.950012  646.000000  647.000000  660.299988  2168995   \n",
       "...                ...         ...         ...         ...      ...   \n",
       "2022-05-09  569.000000  557.500000  558.150024  563.849976  2774773   \n",
       "2022-05-10  572.900024  558.000000  561.000000  559.200012  3953646   \n",
       "2022-05-11  575.700012  559.500000  559.500000  563.549988  4283207   \n",
       "2022-05-12  564.650024  544.299988  560.099976  547.250000  3079554   \n",
       "2022-05-13  565.849976  548.049988  555.000000  550.750000  3154166   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2022-01-03  650.500000  \n",
       "2022-01-04  653.150024  \n",
       "2022-01-05  653.549988  \n",
       "2022-01-06  646.900024  \n",
       "2022-01-07  660.299988  \n",
       "...                ...  \n",
       "2022-05-09  563.849976  \n",
       "2022-05-10  559.200012  \n",
       "2022-05-11  563.549988  \n",
       "2022-05-12  547.250000  \n",
       "2022-05-13  550.750000  \n",
       "\n",
       "[89 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define the variables\n",
    "start_date = \"2022-01-1\"\n",
    "end_date = \"2022-12-31\"\n",
    "stockname = \"HDFCLIFE.ns\"\n",
    "datasource = \"yahoo\"\n",
    "#datasource = \"stooq\" - Google finance\n",
    "\n",
    "stockdata = web.DataReader(name=stockname, data_source=datasource, start=start_date, end=end_date)\n",
    "stockdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb856d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'High')"
      ]
     },
     "execution_count": 22,
     "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": [
    "#Plot the stock data\n",
    "stock=stockdata['High'].plot(title=stockname)\n",
    "stock.set_xlabel('Date')\n",
    "stock.set_ylabel('High')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "92f9dc79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                High       Low      Open     Close    Volume  Adj Close\n",
      "Date                                                                   \n",
      "2022-01-03       NaN       NaN       NaN       NaN       NaN        NaN\n",
      "2022-01-04  0.000535  0.004255 -0.002446  0.004074  0.212096   0.004074\n",
      "2022-01-05  0.004962  0.002465  0.003985  0.000612  0.450636   0.000612\n",
      "2022-01-06 -0.009571 -0.008837 -0.006107 -0.010175 -0.234014  -0.010175\n",
      "2022-01-07  0.016872  0.001706 -0.006144  0.020714 -0.148859   0.020714\n",
      "...              ...       ...       ...       ...       ...        ...\n",
      "2022-05-09 -0.008279  0.004505  0.005676 -0.003711 -0.300908  -0.003711\n",
      "2022-05-10  0.006854  0.000897  0.005106 -0.008247  0.424854  -0.008247\n",
      "2022-05-11  0.004887  0.002688 -0.002674  0.007779  0.083356   0.007779\n",
      "2022-05-12 -0.019194 -0.027167  0.001072 -0.028924 -0.281017  -0.028924\n",
      "2022-05-13  0.002125  0.006890 -0.009105  0.006396  0.024228   0.006396\n",
      "\n",
      "[89 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 30,
     "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": [
    "#To determine the percentage change in stock price\n",
    "returns= stockdata.pct_change()\n",
    "print(returns)\n",
    "returns.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "399869d9",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}