Exp_17_PCA_real_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfb41842",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8508bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_wine()\n",
    "#data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d58c2b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1333e22",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(data.data, columns = data.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fd75f1b",
   "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  \n",
       "3                          3.45   1480.0  \n",
       "4                          2.93    735.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2ec991e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['class_0', 'class_0', 'class_0', 'class_0', 'class_0', ..., 'class_2', 'class_2', 'class_2', 'class_2', 'class_2']\n",
       "Length: 178\n",
       "Categories (3, object): ['class_0', 'class_1', 'class_2']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = pd.Categorical.from_codes(data.target, data.target_names)\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c3d9f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65800729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "L = LabelEncoder()\n",
    "#df = X.join(pd.Series(Y, name='class'))\n",
    "#y = L.fit_transform(data['class'])\n",
    "y = L.fit_transform(Y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3068e82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "49e3909c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8c363112",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_pca = pca.fit_transform(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5545132e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.18562979e+02,  2.14921307e+01],\n",
       "       [ 3.03097420e+02, -5.36471768e+00],\n",
       "       [ 4.38061133e+02, -6.53730945e+00],\n",
       "       [ 7.33240139e+02,  1.92729032e-01],\n",
       "       [-1.15714285e+01,  1.84899946e+01],\n",
       "       [ 7.03231192e+02, -3.32158674e-01],\n",
       "       [ 5.42971581e+02, -1.35189666e+01],\n",
       "       [ 5.48401860e+02,  1.14494324e+01],\n",
       "       [ 2.98036863e+02, -8.18015784e+00],\n",
       "       [ 2.98049553e+02, -7.10154294e+00],\n",
       "       [ 7.63079712e+02, -8.33431723e+00],\n",
       "       [ 5.32943228e+02, -1.42876338e+01],\n",
       "       [ 5.72834410e+02, -2.10050143e+01],\n",
       "       [ 4.02925358e+02, -1.61026352e+01],\n",
       "       [ 8.00053394e+02, -1.21184472e+01],\n",
       "       [ 5.63245578e+02,  2.21482866e+00],\n",
       "       [ 5.33379651e+02,  1.08048022e+01],\n",
       "       [ 3.83317591e+02,  8.47741982e+00],\n",
       "       [ 9.33118387e+02, -8.35447844e+00],\n",
       "       [ 9.84031775e+01,  1.43926594e+01],\n",
       "       [ 3.35935940e+01,  2.55679565e+01],\n",
       "       [ 2.31464375e+01,  1.81747309e+00],\n",
       "       [ 2.88093030e+02, -3.96304175e+00],\n",
       "       [ 2.67981513e+02, -9.57070401e+00],\n",
       "       [ 9.80198858e+01, -5.49584606e+00],\n",
       "       [ 8.34987440e+01,  2.28916215e+01],\n",
       "       [ 4.47925596e+02, -1.47973313e+01],\n",
       "       [ 5.37919165e+02, -1.53883461e+01],\n",
       "       [ 1.68210468e+02,  4.25531096e+00],\n",
       "       [ 2.88008247e+02, -8.95973155e+00],\n",
       "       [ 5.38026452e+02, -8.21273882e+00],\n",
       "       [ 7.68092939e+02, -7.37989737e+00],\n",
       "       [ 2.43150751e+02, -1.43914928e-01],\n",
       "       [ 4.88601280e+02,  2.35653250e+01],\n",
       "       [ 3.48231007e+02,  4.03808015e+00],\n",
       "       [ 1.73079957e+02, -2.79292165e+00],\n",
       "       [ 1.33286424e+02,  7.77272958e+00],\n",
       "       [ 3.58018559e+02, -8.15798412e+00],\n",
       "       [ 2.73044432e+02, -6.72507431e+00],\n",
       "       [ 1.36430021e+01,  2.78468321e+01],\n",
       "       [ 4.84296422e+01,  1.63219498e+01],\n",
       "       [ 2.87884092e+02, -1.48851185e+01],\n",
       "       [ 3.48095348e+02, -5.04342609e+00],\n",
       "       [-6.68146554e+01,  4.38482992e+00],\n",
       "       [ 1.38227010e+02,  4.73068836e+00],\n",
       "       [ 3.33254806e+02,  5.31933116e+00],\n",
       "       [ 3.18111848e+02, -3.48694494e+00],\n",
       "       [ 2.38111485e+02, -3.05152100e+00],\n",
       "       [ 3.13119915e+02, -2.30536316e+00],\n",
       "       [ 5.13187748e+02, -8.63759235e-01],\n",
       "       [ 4.02939017e+02, -1.50533028e+01],\n",
       "       [ 5.17928963e+02, -1.49975549e+01],\n",
       "       [ 4.43266411e+02,  3.26050204e+00],\n",
       "       [ 6.28286071e+02,  4.04114538e+00],\n",
       "       [ 3.13397613e+02,  1.26089135e+01],\n",
       "       [ 3.73333291e+02,  9.66419863e+00],\n",
       "       [ 2.23417106e+02,  1.42168924e+01],\n",
       "       [ 5.23072851e+02, -7.09662526e+00],\n",
       "       [ 5.38182097e+02, -1.35678013e+00],\n",
       "       [-2.27032690e+02, -7.99496797e+00],\n",
       "       [-6.68501220e+01,  2.31986654e+00],\n",
       "       [-2.96824437e+02,  5.46847570e+00],\n",
       "       [-1.16970473e+02, -3.72638342e+00],\n",
       "       [-3.27059126e+02, -6.94813081e+00],\n",
       "       [-3.91752486e+02,  1.11716783e+01],\n",
       "       [-6.89057604e+01, -5.58620537e-01],\n",
       "       [-2.45212524e+02, -1.74936393e+01],\n",
       "       [-2.37241410e+02, -1.75310256e+01],\n",
       "       [ 3.29615599e+00,  1.00958116e+01],\n",
       "       [-2.79661207e+01,  5.16301252e+01],\n",
       "       [ 1.23130138e+02,  1.05983451e+00],\n",
       "       [-3.37104122e+02, -7.62385512e+00],\n",
       "       [-2.75096026e+02, -7.75315213e+00],\n",
       "       [ 2.38716757e+02,  3.52518274e+01],\n",
       "       [ 1.39094057e+02, -1.20747620e+00],\n",
       "       [-3.18876329e+02,  2.81077113e+00],\n",
       "       [-3.55060253e+02, -7.53070183e+00],\n",
       "       [-2.46633597e+02,  1.65584367e+01],\n",
       "       [ 3.77168966e+00,  3.60310924e+01],\n",
       "       [-2.83842248e+02,  6.36186080e+00],\n",
       "       [-4.69059350e+02, -5.45892398e+00],\n",
       "       [-3.31313392e+01, -1.31871050e+01],\n",
       "       [-1.17290392e+02, -1.95786125e+01],\n",
       "       [-2.32132988e+02, -1.05301033e+01],\n",
       "       [-2.26953299e+02, -1.77605879e+00],\n",
       "       [-2.96852152e+02,  4.45280309e+00],\n",
       "       [-2.52046392e+02, -5.21261759e+00],\n",
       "       [-1.85108857e+02, -8.31858202e+00],\n",
       "       [-6.71807518e+01, -1.45246902e+01],\n",
       "       [-1.22431326e+02, -2.75040387e+01],\n",
       "       [-2.67185333e+02, -1.40538901e+01],\n",
       "       [-2.97104415e+02, -8.41503161e+00],\n",
       "       [-2.52213885e+02, -1.52515702e+01],\n",
       "       [-4.57022215e+02, -3.69733793e+00],\n",
       "       [-4.01851274e+02,  5.33140022e+00],\n",
       "       [ 1.91183397e+02,  5.87937624e+01],\n",
       "       [-1.21279178e+02,  3.64175149e+01],\n",
       "       [-3.19088273e+02, -9.19307820e+00],\n",
       "       [-8.70802926e+01, -1.02265727e+01],\n",
       "       [-3.41040900e+02, -5.75056559e+00],\n",
       "       [-3.69316146e+01, -2.17041593e+00],\n",
       "       [-1.85073713e+02, -8.52051902e+00],\n",
       "       [-3.08882387e+02,  3.75165655e+00],\n",
       "       [-3.32089296e+02, -7.88372875e+00],\n",
       "       [-7.51531378e+01, -1.34138279e+01],\n",
       "       [-4.32009742e+02, -2.03294673e+00],\n",
       "       [-2.37206697e+02, -1.55619679e+01],\n",
       "       [-2.59148368e+02, -1.10788163e+01],\n",
       "       [-4.34957780e+02, -5.95260295e-02],\n",
       "       [-6.69906958e+01, -4.57145764e+00],\n",
       "       [-1.84736439e+02,  1.05164632e+01],\n",
       "       [-4.22042874e+02, -4.24492194e+00],\n",
       "       [-1.39818663e+02,  5.73555207e+00],\n",
       "       [-3.13060948e+02, -6.16709401e+00],\n",
       "       [-3.62129633e+02, -9.25929540e+00],\n",
       "       [-3.40115006e+02, -8.68973443e+00],\n",
       "       [-2.52109472e+02, -9.27587015e+00],\n",
       "       [-4.01695973e+02,  1.54258903e+01],\n",
       "       [-3.75171092e+02, -1.31850503e+01],\n",
       "       [-1.83097929e+02, -9.55354432e+00],\n",
       "       [-1.21945241e+02, -1.58889428e+00],\n",
       "       [-2.81534569e+02,  2.45082612e+01],\n",
       "       [-3.81826778e+02,  9.18910976e+00],\n",
       "       [-3.67089380e+02, -7.18821589e+00],\n",
       "       [-3.67157175e+02, -1.11963202e+01],\n",
       "       [-3.69103601e+02, -8.17580491e+00],\n",
       "       [-3.95078836e+02, -6.67983187e+00],\n",
       "       [-2.81030369e+02, -2.53586007e+00],\n",
       "       [-4.05061288e+02, -4.45107367e+00],\n",
       "       [-1.67237642e+02, -1.67311857e+01],\n",
       "       [-1.16473000e+02,  2.42660201e+01],\n",
       "       [-2.16784540e+02,  8.13044956e+00],\n",
       "       [-1.86915112e+02,  1.71005736e+00],\n",
       "       [-1.46769530e+02,  8.92033411e+00],\n",
       "       [-9.71329736e+01, -1.30582838e+01],\n",
       "       [-5.19820469e+01, -4.81229310e+00],\n",
       "       [-2.70979307e+01, -1.02299962e+01],\n",
       "       [-2.31951534e+02,  5.34907410e-01],\n",
       "       [-1.67076246e+02, -8.75672311e+00],\n",
       "       [-1.56868045e+02,  4.16316249e+00],\n",
       "       [-1.46946490e+02, -1.09672896e+00],\n",
       "       [ 3.29042927e+01, -1.13053656e+01],\n",
       "       [-2.26926210e+02,  1.38827837e+00],\n",
       "       [-1.97004347e+02, -4.22842870e+00],\n",
       "       [ 1.08312677e+02,  1.03513304e+01],\n",
       "       [ 8.31182626e+01,  8.06869695e-01],\n",
       "       [-3.32195840e+02, -1.38132989e+01],\n",
       "       [-1.22126092e+02, -1.14699259e+01],\n",
       "       [-9.70193893e+01, -5.90644258e+00],\n",
       "       [-1.96624807e+02,  1.68731139e+01],\n",
       "       [-2.46449199e+02,  2.78100954e+01],\n",
       "       [-2.66628788e+02,  1.71502320e+01],\n",
       "       [-3.21570741e+02,  2.21643401e+01],\n",
       "       [-7.18976587e+01, -3.99656400e-01],\n",
       "       [-1.06816618e+02,  5.20612307e+00],\n",
       "       [-2.20202568e+01, -6.22943925e+00],\n",
       "       [-2.67031732e+02, -5.92308914e+00],\n",
       "       [ 1.32999841e+02, -4.86770544e+00],\n",
       "       [-8.69139986e+01,  7.96357772e-02],\n",
       "       [-1.27061290e+02, -8.29141684e+00],\n",
       "       [-2.27068836e+02, -7.61863030e+00],\n",
       "       [-6.67554312e+01,  8.47028990e+00],\n",
       "       [-1.76765098e+02,  9.47319000e+00],\n",
       "       [-7.17683979e+01,  7.51063784e+00],\n",
       "       [-1.32048248e+02, -7.25503031e+00],\n",
       "       [-2.27077845e+02, -7.59337930e+00],\n",
       "       [-5.16895065e+01,  1.23580850e+01],\n",
       "       [-6.20850111e+01, -1.05540153e+01],\n",
       "       [ 3.18276465e+00,  5.39136150e+00],\n",
       "       [-1.16674818e+02,  1.45333703e+01],\n",
       "       [-2.36921208e+02,  4.63036998e-01],\n",
       "       [-2.77083578e+02, -8.74033191e+00],\n",
       "       [-8.70274026e+01, -7.10459575e+00],\n",
       "       [-6.98021096e+00, -4.54113657e+00],\n",
       "       [ 3.13160468e+00,  2.33519051e+00],\n",
       "       [ 8.84580737e+01,  1.87762846e+01],\n",
       "       [ 9.34562419e+01,  1.86708191e+01],\n",
       "       [-1.86943190e+02, -2.13330803e-01]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_pca = pca.transform(X)\n",
    "transformed_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "694b1931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c351464460>"
      ]
     },
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(\n",
    "    X_pca[:,0],\n",
    "    X_pca[:,1],\n",
    "    c=y,\n",
    "    cmap='rainbow',\n",
    "    alpha=0.7,\n",
    "    edgecolors='b'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcf437e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5167846b",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}