1-pesos-alturas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estos dos comandos evitan que haya que hacer reload cada vez que se modifica un paquete\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nativo de python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('data/alturas-pesos-mils-train.csv')\n",
    "texto = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "',Genero,Peso,Altura\\n7620,Mujer,61.235,162.40200000000004\\n1837,Hombre,97.432,181.908\\n3311,Hombre,73.324,172.459\\n9478,Mujer,55.19300000000001,157.748\\n7805,Mujer,56.886,151.798\\n6737,Mujer,59.834,158.623\\n4413,Hombre,77.618,172.705\\n6018,Mujer,68.889,169.225\\n5165,Mujer,53.874,155.487\\n600,Hombre,83.24799999999998,177.908\\n7822,Mujer,60.75100000000001,166.041\\n188,Hombre,81.27199999999998,166.769\\n4503,Hombre,95.204,182.764\\n7672,Mujer,73.255,164.09\\n7332,Mujer,62.572,167.602\\n2278,Hombre,87.171,178.373\\n7807,'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texto[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240527"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(texto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('data/alturas-pesos-mils-train.csv')\n",
    "lista = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[',Genero,Peso,Altura\\n',\n",
       " '7620,Mujer,61.235,162.40200000000004\\n',\n",
       " '1837,Hombre,97.432,181.908\\n',\n",
       " '3311,Hombre,73.324,172.459\\n',\n",
       " '9478,Mujer,55.19300000000001,157.748\\n',\n",
       " '7805,Mujer,56.886,151.798\\n',\n",
       " '6737,Mujer,59.834,158.623\\n',\n",
       " '4413,Hombre,77.618,172.705\\n',\n",
       " '6018,Mujer,68.889,169.225\\n',\n",
       " '5165,Mujer,53.874,155.487\\n']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8001"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "nparray = np.loadtxt('data/alturas-pesos-mils-train.csv', dtype=str, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['', 'Genero', 'Peso', 'Altura'],\n",
       "       ['7620', 'Mujer', '61.235', '162.40200000000004'],\n",
       "       ['1837', 'Hombre', '97.432', '181.908'],\n",
       "       ...,\n",
       "       ['3476', 'Hombre', '75.461', '170.91'],\n",
       "       ['7084', 'Mujer', '65.461', '166.137'],\n",
       "       ['3346', 'Hombre', '80.32300000000002', '171.575']], dtype='<U18')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8001, 4)\n"
     ]
    }
   ],
   "source": [
    "print(nparray.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/alturas-pesos-mils-train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7620</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>61.235</td>\n",
       "      <td>162.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1837</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>97.432</td>\n",
       "      <td>181.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3311</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>73.324</td>\n",
       "      <td>172.459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9478</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>55.193</td>\n",
       "      <td>157.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7805</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>56.886</td>\n",
       "      <td>151.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6737</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>59.834</td>\n",
       "      <td>158.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4413</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>77.618</td>\n",
       "      <td>172.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6018</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>68.889</td>\n",
       "      <td>169.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5165</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>53.874</td>\n",
       "      <td>155.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>600</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>83.248</td>\n",
       "      <td>177.908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Genero    Peso   Altura\n",
       "0        7620   Mujer  61.235  162.402\n",
       "1        1837  Hombre  97.432  181.908\n",
       "2        3311  Hombre  73.324  172.459\n",
       "3        9478   Mujer  55.193  157.748\n",
       "4        7805   Mujer  56.886  151.798\n",
       "5        6737   Mujer  59.834  158.623\n",
       "6        4413  Hombre  77.618  172.705\n",
       "7        6018   Mujer  68.889  169.225\n",
       "8        5165   Mujer  53.874  155.487\n",
       "9         600  Hombre  83.248  177.908"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    61.235\n",
       "1    97.432\n",
       "2    73.324\n",
       "3    55.193\n",
       "4    56.886\n",
       "5    59.834\n",
       "6    77.618\n",
       "7    68.889\n",
       "8    53.874\n",
       "9    83.248\n",
       "Name: Peso, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Peso'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([61.235, 97.432, 73.324, ..., 75.461, 65.461, 80.323])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Peso'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['Peso'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexado en pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Unnamed: 0</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>3909</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>111.793</td>\n",
       "      <td>195.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>994</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>115.459</td>\n",
       "      <td>197.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>4077</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>112.413</td>\n",
       "      <td>190.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>1610</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>116.483</td>\n",
       "      <td>194.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>3918</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>110.445</td>\n",
       "      <td>194.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2846</th>\n",
       "      <td>4331</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>115.018</td>\n",
       "      <td>195.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>1896</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>111.146</td>\n",
       "      <td>193.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4421</th>\n",
       "      <td>3669</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>114.215</td>\n",
       "      <td>194.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4744</th>\n",
       "      <td>2014</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>121.687</td>\n",
       "      <td>199.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5743</th>\n",
       "      <td>3285</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>114.927</td>\n",
       "      <td>198.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6751</th>\n",
       "      <td>3757</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>110.850</td>\n",
       "      <td>198.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6795</th>\n",
       "      <td>2971</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>112.818</td>\n",
       "      <td>190.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7194</th>\n",
       "      <td>3228</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>111.924</td>\n",
       "      <td>193.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7543</th>\n",
       "      <td>1750</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>113.410</td>\n",
       "      <td>189.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7856</th>\n",
       "      <td>1506</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>112.710</td>\n",
       "      <td>191.993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  Genero     Peso   Altura\n",
       "53          3909  Hombre  111.793  195.127\n",
       "622          994  Hombre  115.459  197.799\n",
       "1071        4077  Hombre  112.413  190.070\n",
       "1083        1610  Hombre  116.483  194.222\n",
       "2048        3918  Hombre  110.445  194.160\n",
       "2846        4331  Hombre  115.018  195.374\n",
       "3358        1896  Hombre  111.146  193.870\n",
       "4421        3669  Hombre  114.215  194.553\n",
       "4744        2014  Hombre  121.687  199.824\n",
       "5743        3285  Hombre  114.927  198.887\n",
       "6751        3757  Hombre  110.850  198.565\n",
       "6795        2971  Hombre  112.818  190.402\n",
       "7194        3228  Hombre  111.924  193.300\n",
       "7543        1750  Hombre  113.410  189.062\n",
       "7856        1506  Hombre  112.710  191.993"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Peso'] > 110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9633</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>91.056</td>\n",
       "      <td>177.523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>5662</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>85.432</td>\n",
       "      <td>174.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>7921</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>88.153</td>\n",
       "      <td>178.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>8635</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>87.020</td>\n",
       "      <td>183.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2263</th>\n",
       "      <td>8748</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>87.941</td>\n",
       "      <td>178.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3093</th>\n",
       "      <td>7311</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>86.295</td>\n",
       "      <td>186.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3637</th>\n",
       "      <td>8382</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>85.307</td>\n",
       "      <td>176.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5048</th>\n",
       "      <td>6258</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>88.239</td>\n",
       "      <td>179.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5377</th>\n",
       "      <td>7856</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>85.046</td>\n",
       "      <td>182.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6919</th>\n",
       "      <td>9846</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>86.757</td>\n",
       "      <td>178.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>8736</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>87.422</td>\n",
       "      <td>180.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7555</th>\n",
       "      <td>7923</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>87.694</td>\n",
       "      <td>176.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>7892</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>86.835</td>\n",
       "      <td>175.474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 Genero    Peso   Altura\n",
       "75          9633  Mujer  91.056  177.523\n",
       "363         5662  Mujer  85.432  174.364\n",
       "549         7921  Mujer  88.153  178.695\n",
       "2041        8635  Mujer  87.020  183.182\n",
       "2263        8748  Mujer  87.941  178.990\n",
       "3093        7311  Mujer  86.295  186.469\n",
       "3637        8382  Mujer  85.307  176.988\n",
       "5048        6258  Mujer  88.239  179.407\n",
       "5377        7856  Mujer  85.046  182.819\n",
       "6919        9846  Mujer  86.757  178.608\n",
       "7071        8736  Mujer  87.422  180.126\n",
       "7555        7923  Mujer  87.694  176.804\n",
       "7658        7892  Mujer  86.835  175.474"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['Genero'] == 'Mujer') & (data['Peso'] > 85)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 97.432 181.908]\n",
      " [ 73.324 172.459]\n",
      " [ 77.618 172.705]\n",
      " ...\n",
      " [ 83.84  177.866]\n",
      " [ 75.461 170.91 ]\n",
      " [ 80.323 171.575]]\n"
     ]
    }
   ],
   "source": [
    "data_hombres = data[data['Genero']=='Hombre'][['Peso','Altura']].values\n",
    "print(data_hombres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 61.235 162.402]\n",
      " [ 55.193 157.748]\n",
      " [ 56.886 151.798]\n",
      " ...\n",
      " [ 53.528 155.991]\n",
      " [ 62.215 160.083]\n",
      " [ 65.461 166.137]]\n"
     ]
    }
   ],
   "source": [
    "data_mujeres = data[data['Genero']=='Mujer'][['Peso','Altura']].values\n",
    "print(data_mujeres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 589,
       "width": 1171
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(20,10))\n",
    "plt.scatter(data_hombres[:,0], data_hombres[:,1], color='b', s=2, label='Hombres')\n",
    "plt.scatter(data_mujeres[:,0], data_mujeres[:,1], color='r', s=2, label='Mujeres')\n",
    "plt.xlabel('Pesos [cms]')\n",
    "plt.ylabel('Alturas [cms]')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}