Prelim5_Encoding_Ft.select(CatVars)_FisherScore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import SelectKBest, SelectPercentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>2</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
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      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0               0                      0.0              0   \n",
       "1               0                      0.0              0   \n",
       "2               0                      0.0              0   \n",
       "3               0                      0.0              0   \n",
       "4               0                      0.0              0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0               1                 0.000000   \n",
       "1                     0.0               2                64.000000   \n",
       "2                     0.0               1                 0.000000   \n",
       "3                     0.0               2                 2.666667   \n",
       "4                     0.0              10               627.500000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
       "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
       "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
       "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
       "4         0.02       0.05         0.0         0.0   Feb                 3   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  \n",
       "3        2       2            4  Returning_Visitor    False    False  \n",
       "4        3       1            4  Returning_Visitor     True    False  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 60)\n",
    "myData=pd.read_csv('imputedData.csv')\n",
    "myData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 57 columns):\n",
      "Administrative                   12330 non-null int64\n",
      "Administrative_Duration          12330 non-null float64\n",
      "Informational                    12330 non-null int64\n",
      "Informational_Duration           12330 non-null float64\n",
      "ProductRelated                   12330 non-null int64\n",
      "ProductRelated_Duration          12330 non-null float64\n",
      "BounceRates                      12330 non-null float64\n",
      "ExitRates                        12330 non-null float64\n",
      "PageValues                       12330 non-null float64\n",
      "TrafficType                      12330 non-null int64\n",
      "SpecialDay                       12330 non-null float64\n",
      "Month_Aug                        12330 non-null uint8\n",
      "Month_Dec                        12330 non-null uint8\n",
      "Month_Feb                        12330 non-null uint8\n",
      "Month_Jul                        12330 non-null uint8\n",
      "Month_June                       12330 non-null uint8\n",
      "Month_Mar                        12330 non-null uint8\n",
      "Month_May                        12330 non-null uint8\n",
      "Month_Nov                        12330 non-null uint8\n",
      "Month_Oct                        12330 non-null uint8\n",
      "Month_Sep                        12330 non-null uint8\n",
      "OperatingSystems_1               12330 non-null uint8\n",
      "OperatingSystems_2               12330 non-null uint8\n",
      "OperatingSystems_3               12330 non-null uint8\n",
      "OperatingSystems_4               12330 non-null uint8\n",
      "OperatingSystems_5               12330 non-null uint8\n",
      "OperatingSystems_6               12330 non-null uint8\n",
      "OperatingSystems_7               12330 non-null uint8\n",
      "OperatingSystems_8               12330 non-null uint8\n",
      "Browser_1                        12330 non-null uint8\n",
      "Browser_2                        12330 non-null uint8\n",
      "Browser_3                        12330 non-null uint8\n",
      "Browser_4                        12330 non-null uint8\n",
      "Browser_5                        12330 non-null uint8\n",
      "Browser_6                        12330 non-null uint8\n",
      "Browser_7                        12330 non-null uint8\n",
      "Browser_8                        12330 non-null uint8\n",
      "Browser_9                        12330 non-null uint8\n",
      "Browser_10                       12330 non-null uint8\n",
      "Browser_11                       12330 non-null uint8\n",
      "Browser_12                       12330 non-null uint8\n",
      "Browser_13                       12330 non-null uint8\n",
      "Region_1                         12330 non-null uint8\n",
      "Region_2                         12330 non-null uint8\n",
      "Region_3                         12330 non-null uint8\n",
      "Region_4                         12330 non-null uint8\n",
      "Region_5                         12330 non-null uint8\n",
      "Region_6                         12330 non-null uint8\n",
      "Region_7                         12330 non-null uint8\n",
      "Region_8                         12330 non-null uint8\n",
      "Region_9                         12330 non-null uint8\n",
      "VisitorType_New_Visitor          12330 non-null uint8\n",
      "VisitorType_Other                12330 non-null uint8\n",
      "VisitorType_Returning_Visitor    12330 non-null uint8\n",
      "Weekend_False                    12330 non-null uint8\n",
      "Weekend_True                     12330 non-null uint8\n",
      "Revenue                          12330 non-null bool\n",
      "dtypes: bool(1), float64(7), int64(4), uint8(45)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "#One hot encoding of categorical variables\n",
    "\n",
    "#Create list with features to be dummified cols.\n",
    "nonum_feats_names = ['Month','OperatingSystems','Browser','Region','VisitorType','Weekend']\n",
    "\n",
    "\n",
    "dataModel = pd.concat([myData[['Administrative', 'Administrative_Duration', 'Informational','Informational_Duration', \n",
    "                              'ProductRelated','ProductRelated_Duration','BounceRates','ExitRates','PageValues',\n",
    "                               'TrafficType','SpecialDay']],\n",
    "                       pd.get_dummies(myData[nonum_feats_names].astype('category')),myData['Revenue']],axis=1)\n",
    "                      \n",
    "dataModel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding of revenue\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "myData['Revenue'] = le.fit_transform(myData['Revenue'])\n",
    "myData['Revenue'].value_counts()\n",
    "\n",
    "myData['Revenue'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x: (12330, 56)\n",
      "Shape of y: (12330,)\n"
     ]
    }
   ],
   "source": [
    "# getting dependent and independent variables\n",
    "\n",
    "x = dataModel\n",
    "# removing the target column revenue from x\n",
    "x = x.drop(['Revenue'], axis = 1)\n",
    "\n",
    "y = myData['Revenue']\n",
    "\n",
    "# checking the shapes\n",
    "print(\"Shape of x:\", x.shape)\n",
    "print(\"Shape of y:\", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train : (8631, 45)\n",
      "Shape of y_train : (8631,)\n",
      "Shape of x_test : (3699, 45)\n",
      "Shape of y_test : (3699,)\n"
     ]
    }
   ],
   "source": [
    "# splitting the data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_baseTrain, x_baseTest, y_baseTrain, y_baseTest = train_test_split(x[['Month_Aug','Month_Dec','Month_Feb','Month_Jul','Month_June','Month_Mar','Month_May','Month_Nov','Month_Oct','Month_Sep',\n",
    " 'OperatingSystems_1','OperatingSystems_2','OperatingSystems_3','OperatingSystems_4','OperatingSystems_5','OperatingSystems_6',\n",
    "'OperatingSystems_7','OperatingSystems_8','Browser_1','Browser_2','Browser_3','Browser_4','Browser_5','Browser_6','Browser_7',\n",
    "'Browser_8','Browser_9','Browser_10','Browser_11','Browser_12','Browser_13','Region_1','Region_2','Region_3','Region_4',\n",
    "'Region_5','Region_6','Region_7','Region_8','Region_9','VisitorType_New_Visitor','VisitorType_Other',\n",
    " 'VisitorType_Returning_Visitor','Weekend_False','Weekend_True']], y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "# checking the shapes\n",
    "\n",
    "print(\"Shape of x_train :\", x_baseTrain.shape)\n",
    "print(\"Shape of y_train :\", y_baseTrain.shape)\n",
    "print(\"Shape of x_test :\", x_baseTest.shape)\n",
    "print(\"Shape of y_test :\", y_baseTest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all feature selection procedures, it is good practice to select the features by examining only the training set. And this is to avoid overfit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.43537495e+00, 7.19558412e+00, 1.94347244e+01, 2.49960273e-05,\n",
       "        5.30015752e+00, 3.91861740e+01, 3.75997879e+01, 1.53865584e+02,\n",
       "        1.00314605e+01, 6.93840988e+00, 8.86097697e-04, 1.28581572e+01,\n",
       "        3.99474948e+01, 1.09482635e+00, 2.79685938e-01, 5.59371876e-01,\n",
       "        5.47958345e-01, 5.75644048e-02, 3.19114059e-01, 6.00243728e-03,\n",
       "        5.07476309e+00, 1.24631735e+00, 1.15114072e+00, 1.99085795e+00,\n",
       "        8.19336788e-03, 1.35416779e-01, 1.82652782e-01, 4.39923848e-01,\n",
       "        2.79685938e-01, 3.16606796e-01, 9.90953061e-01, 1.33818130e+00,\n",
       "        8.67671307e-01, 1.26520991e+00, 1.70828854e-01, 2.30257619e-01,\n",
       "        6.18844314e-04, 1.47349416e-02, 2.86105784e+00, 1.80752490e-03,\n",
       "        8.07580853e+01, 4.20779773e-02, 1.30420113e+01, 1.13187305e+00,\n",
       "        3.80455763e+00]),\n",
       " array([2.30889242e-01, 7.30831978e-03, 1.04097080e-05, 9.96010911e-01,\n",
       "        2.13234959e-02, 3.85258742e-10, 8.68539251e-10, 2.47801670e-35,\n",
       "        1.53888972e-03, 8.43640778e-03, 9.76252564e-01, 3.36011325e-04,\n",
       "        2.60881945e-10, 2.95404313e-01, 5.96907138e-01, 4.54513434e-01,\n",
       "        4.59153115e-01, 8.10387755e-01, 5.72140550e-01, 9.38245362e-01,\n",
       "        2.42765894e-02, 2.64257010e-01, 2.83310484e-01, 1.58251202e-01,\n",
       "        9.27876207e-01, 7.12880550e-01, 6.69103324e-01, 5.07159213e-01,\n",
       "        5.96907138e-01, 5.73654008e-01, 3.19509550e-01, 2.47354954e-01,\n",
       "        3.51600797e-01, 2.60667225e-01, 6.79376227e-01, 6.31332874e-01,\n",
       "        9.80153407e-01, 9.03384064e-01, 9.07478017e-02, 9.66088157e-01,\n",
       "        2.55112090e-19, 8.37471205e-01, 3.04580825e-04, 2.87376166e-01,\n",
       "        5.11132759e-02]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the chi2 p_value between each of the cat variables\n",
    "# and the target\n",
    "# it returns 2 arrays, one contains the F-Scores which are then \n",
    "# evaluated against the chi2 distribution to obtain the pvalue\n",
    "# the pvalues are in the second array, see below\n",
    "\n",
    "f_score = chi2(x_baseTrain, y_baseTrain)\n",
    "f_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month_Jul                        9.960109e-01\n",
       "Region_6                         9.801534e-01\n",
       "OperatingSystems_1               9.762526e-01\n",
       "Region_9                         9.660882e-01\n",
       "Browser_2                        9.382454e-01\n",
       "Browser_7                        9.278762e-01\n",
       "Region_7                         9.033841e-01\n",
       "VisitorType_Other                8.374712e-01\n",
       "OperatingSystems_8               8.103878e-01\n",
       "Browser_8                        7.128806e-01\n",
       "Region_4                         6.793762e-01\n",
       "Browser_9                        6.691033e-01\n",
       "Region_5                         6.313329e-01\n",
       "OperatingSystems_5               5.969071e-01\n",
       "Browser_11                       5.969071e-01\n",
       "Browser_12                       5.736540e-01\n",
       "Browser_1                        5.721405e-01\n",
       "Browser_10                       5.071592e-01\n",
       "OperatingSystems_7               4.591531e-01\n",
       "OperatingSystems_6               4.545134e-01\n",
       "Region_2                         3.516008e-01\n",
       "Browser_13                       3.195095e-01\n",
       "OperatingSystems_4               2.954043e-01\n",
       "Weekend_False                    2.873762e-01\n",
       "Browser_5                        2.833105e-01\n",
       "Browser_4                        2.642570e-01\n",
       "Region_3                         2.606672e-01\n",
       "Region_1                         2.473550e-01\n",
       "Month_Aug                        2.308892e-01\n",
       "Browser_6                        1.582512e-01\n",
       "Region_8                         9.074780e-02\n",
       "Weekend_True                     5.111328e-02\n",
       "Browser_3                        2.427659e-02\n",
       "Month_June                       2.132350e-02\n",
       "Month_Sep                        8.436408e-03\n",
       "Month_Dec                        7.308320e-03\n",
       "Month_Oct                        1.538890e-03\n",
       "OperatingSystems_2               3.360113e-04\n",
       "VisitorType_Returning_Visitor    3.045808e-04\n",
       "Month_Feb                        1.040971e-05\n",
       "Month_May                        8.685393e-10\n",
       "Month_Mar                        3.852587e-10\n",
       "OperatingSystems_3               2.608819e-10\n",
       "VisitorType_New_Visitor          2.551121e-19\n",
       "Month_Nov                        2.478017e-35\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's add the variable names and order it for clearer visualisation\n",
    "\n",
    "pvalues = pd.Series(f_score[1])\n",
    "pvalues.index = x_baseTrain.columns\n",
    "pvalues.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Fisher score, the smaller the p_value, the more significant the feature is to predict the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2666ac28cc8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvalues.sort_values(ascending=False).plot.bar(figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most important categorical variables.\n",
    "\n",
    "Browser_6                        \n",
    "Region_8                         \n",
    "Weekend_True                     \n",
    "Browser_3                        \n",
    "Month_June                       \n",
    "Month_Sep                        \n",
    "Month_Dec                        \n",
    "Month_Oct                        \n",
    "OperatingSystems_2               \n",
    "VisitorType_Returning_Visitor #delete as per corr  \n",
    "Month_Feb                        \n",
    "Month_May                        \n",
    "Month_Mar                        \n",
    "OperatingSystems_3               \n",
    "VisitorType_New_Visitor          \n",
    "Month_Nov       \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}