{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Árvores de Decisão com Python: Base de Dados Risco de Crédito\n",
"### Implementando o algoritmo de árvores de decisão usando a base de dados \"Risco de Crédito\" para classificar riscos de empréstimos"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# importando a biblioteca pandas do python\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# atribuindo ao \"dataframe\" todos os registros da base de dados\n",
"dataframe = pd.read_csv(\"risco_credito.csv\", encoding = \"utf-8\", sep = \",\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>historia</th>\n",
" <th>divida</th>\n",
" <th>garantias</th>\n",
" <th>renda</th>\n",
" <th>risco</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ruim</td>\n",
" <td>alta</td>\n",
" <td>nenhuma</td>\n",
" <td>0_15</td>\n",
" <td>alto</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>desconhecida</td>\n",
" <td>alta</td>\n",
" <td>nenhuma</td>\n",
" <td>15_35</td>\n",
" <td>alto</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>desconhecida</td>\n",
" <td>baixa</td>\n",
" <td>nenhuma</td>\n",
" <td>15_35</td>\n",
" <td>moderado</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>desconhecida</td>\n",
" <td>baixa</td>\n",
" <td>nenhuma</td>\n",
" <td>acima_35</td>\n",
" <td>alto</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>desconhecida</td>\n",
" <td>baixa</td>\n",
" <td>nenhuma</td>\n",
" <td>acima_35</td>\n",
" <td>baixo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>desconhecida</td>\n",
" <td>baixa</td>\n",
" <td>adequada</td>\n",
" <td>acima_35</td>\n",
" <td>baixo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ruim</td>\n",
" <td>baixa</td>\n",
" <td>nenhuma</td>\n",
" <td>0_15</td>\n",
" <td>alto</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ruim</td>\n",
" <td>baixa</td>\n",
" <td>adequada</td>\n",
" <td>acima_35</td>\n",
" <td>moderado</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>boa</td>\n",
" <td>baixa</td>\n",
" <td>nenhuma</td>\n",
" <td>acima_35</td>\n",
" <td>baixo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>boa</td>\n",
" <td>alta</td>\n",
" <td>adequada</td>\n",
" <td>acima_35</td>\n",
" <td>baixo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>boa</td>\n",
" <td>alta</td>\n",
" <td>nenhuma</td>\n",
" <td>0_15</td>\n",
" <td>alto</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>boa</td>\n",
" <td>alta</td>\n",
" <td>nenhuma</td>\n",
" <td>15_35</td>\n",
" <td>moderado</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>boa</td>\n",
" <td>alta</td>\n",
" <td>nenhuma</td>\n",
" <td>acima_35</td>\n",
" <td>baixo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ruim</td>\n",
" <td>alta</td>\n",
" <td>nenhuma</td>\n",
" <td>15_35</td>\n",
" <td>alto</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" historia divida garantias renda risco\n",
"0 ruim alta nenhuma 0_15 alto\n",
"1 desconhecida alta nenhuma 15_35 alto\n",
"2 desconhecida baixa nenhuma 15_35 moderado\n",
"3 desconhecida baixa nenhuma acima_35 alto\n",
"4 desconhecida baixa nenhuma acima_35 baixo\n",
"5 desconhecida baixa adequada acima_35 baixo\n",
"6 ruim baixa nenhuma 0_15 alto\n",
"7 ruim baixa adequada acima_35 moderado\n",
"8 boa baixa nenhuma acima_35 baixo\n",
"9 boa alta adequada acima_35 baixo\n",
"10 boa alta nenhuma 0_15 alto\n",
"11 boa alta nenhuma 15_35 moderado\n",
"12 boa alta nenhuma acima_35 baixo\n",
"13 ruim alta nenhuma 15_35 alto"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# visualizando a base de dados\n",
"dataframe"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# separando os atributos previsores do meta classe\n",
"previsores = dataframe.iloc[:, 0:4].values\n",
"meta_classe = dataframe.iloc[:, 4].values"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['ruim' 'alta' 'nenhuma' '0_15']\n",
" ['desconhecida' 'alta' 'nenhuma' '15_35']\n",
" ['desconhecida' 'baixa' 'nenhuma' '15_35']\n",
" ['desconhecida' 'baixa' 'nenhuma' 'acima_35']\n",
" ['desconhecida' 'baixa' 'nenhuma' 'acima_35']\n",
" ['desconhecida' 'baixa' 'adequada' 'acima_35']\n",
" ['ruim' 'baixa' 'nenhuma' '0_15']\n",
" ['ruim' 'baixa' 'adequada' 'acima_35']\n",
" ['boa' 'baixa' 'nenhuma' 'acima_35']\n",
" ['boa' 'alta' 'adequada' 'acima_35']\n",
" ['boa' 'alta' 'nenhuma' '0_15']\n",
" ['boa' 'alta' 'nenhuma' '15_35']\n",
" ['boa' 'alta' 'nenhuma' 'acima_35']\n",
" ['ruim' 'alta' 'nenhuma' '15_35']]\n"
]
}
],
"source": [
"# visualizando os atributos previsores\n",
"print(previsores)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['alto' 'alto' 'moderado' 'alto' 'baixo' 'baixo' 'alto' 'moderado' 'baixo'\n",
" 'baixo' 'alto' 'moderado' 'baixo' 'alto']\n"
]
}
],
"source": [
"# visualizando os atributos meta classe\n",
"print(meta_classe)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# importando a biblioteca sklearn do python\n",
"from sklearn.preprocessing import LabelEncoder"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# definindo e configurando o objeto \"labelencoder\"\n",
"labelencoder = LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# transformando as variáveis categóricas em numéricas, nos previsores, usando o objeto \"labelencoder\" \n",
"# criado\n",
"previsores[:, 0] = labelencoder.fit_transform(previsores[:, 0])\n",
"previsores[:, 1] = labelencoder.fit_transform(previsores[:, 1])\n",
"previsores[:, 2] = labelencoder.fit_transform(previsores[:, 2])\n",
"previsores[:, 3] = labelencoder.fit_transform(previsores[:, 3])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[2 0 1 0]\n",
" [1 0 1 1]\n",
" [1 1 1 1]\n",
" [1 1 1 2]\n",
" [1 1 1 2]\n",
" [1 1 0 2]\n",
" [2 1 1 0]\n",
" [2 1 0 2]\n",
" [0 1 1 2]\n",
" [0 0 0 2]\n",
" [0 0 1 0]\n",
" [0 0 1 1]\n",
" [0 0 1 2]\n",
" [2 0 1 1]]\n"
]
}
],
"source": [
"# visualizando os atributos previsores\n",
"print(previsores)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# importando a biblioteca sklearn do python\n",
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# configurando e definindo o objeto \"classificador\"\n",
"classificador = DecisionTreeClassifier(criterion = \"entropy\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
" max_depth=None, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=None, splitter='best')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fazendo o treinamento com a base de dados no objeto criado \"classificador\". Nele, será criado a árvore de\n",
"# decisão\n",
"classificador.fit(previsores, meta_classe)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.48015651 0.03885431 0.03885431 0.44213486]\n"
]
}
],
"source": [
"# observando o resultando de ganho para cada um dos campos de dados. O maior valor tem mais relevância na\n",
"# hierárquia da árvore de decisão\n",
"print(classificador.feature_importances_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|**Valor de ganho na hierárquia da árvore de decisões**|\n",
"|:-----------------------------------------------------|\n",
"|1 - Renda|\n",
"|2 - História|\n",
"|3 - Dívida|\n",
"|4 - Garantias|"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# importando a biblioteca sklearn do python\n",
"from sklearn.tree import DecisionTreeClassifier, export"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# criando o arquivo \"arvore.dot\" com códigos renderizáveis no Graphviz\n",
"export.export_graphviz(classificador, out_file = 'arvore.dot', \n",
" feature_names = ['história', 'dívidas', 'garantia', 'renda'],\n",
" class_names = ['alto', 'moderado', 'baixo'],\n",
" filled = True, leaves_parallel = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Links para o Graphviz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Graphviz Online](https://dreampuf.github.io/GraphvizOnline/);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Árvore de Decisão para a base de dados risco de crédito](https://dreampuf.github.io/GraphvizOnline/#digraph%20Tree%20%7B%0Anode%20%5Bshape%3Dbox%2C%20style%3D%22filled%22%2C%20color%3D%22black%22%5D%20%3B%0Agraph%20%5Branksep%3Dequally%2C%20splines%3Dpolyline%5D%20%3B%0A0%20%5Blabel%3D%22renda%20%3C%3D%201.5%5Cnentropy%20%3D%201.531%5Cnsamples%20%3D%2014%5Cnvalue%20%3D%20%5B6%2C%205%2C%203%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23fcf1e9%22%5D%20%3B%0A1%20%5Blabel%3D%22hist%C3%B3ria%20%3C%3D%201.5%5Cnentropy%20%3D%200.863%5Cnsamples%20%3D%207%5Cnvalue%20%3D%20%5B5%2C%200%2C%202%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23efb388%22%5D%20%3B%0A0%20-%3E%201%20%5Blabeldistance%3D2.5%2C%20labelangle%3D45%2C%20headlabel%3D%22True%22%5D%20%3B%0A2%20%5Blabel%3D%22renda%20%3C%3D%200.5%5Cnentropy%20%3D%201.0%5Cnsamples%20%3D%204%5Cnvalue%20%3D%20%5B2%2C%200%2C%202%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23ffffff%22%5D%20%3B%0A1%20-%3E%202%20%3B%0A3%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%201%5Cnvalue%20%3D%20%5B1%2C%200%2C%200%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23e58139%22%5D%20%3B%0A2%20-%3E%203%20%3B%0A4%20%5Blabel%3D%22d%C3%ADvidas%20%3C%3D%200.5%5Cnentropy%20%3D%200.918%5Cnsamples%20%3D%203%5Cnvalue%20%3D%20%5B1%2C%200%2C%202%5D%5Cnclass%20%3D%20baixo%22%2C%20fillcolor%3D%22%23c09cf2%22%5D%20%3B%0A2%20-%3E%204%20%3B%0A5%20%5Blabel%3D%22hist%C3%B3ria%20%3C%3D%200.5%5Cnentropy%20%3D%201.0%5Cnsamples%20%3D%202%5Cnvalue%20%3D%20%5B1%2C%200%2C%201%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23ffffff%22%5D%20%3B%0A4%20-%3E%205%20%3B%0A6%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%201%5Cnvalue%20%3D%20%5B0%2C%200%2C%201%5D%5Cnclass%20%3D%20baixo%22%2C%20fillcolor%3D%22%238139e5%22%5D%20%3B%0A5%20-%3E%206%20%3B%0A7%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%201%5Cnvalue%20%3D%20%5B1%2C%200%2C%200%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23e58139%22%5D%20%3B%0A5%20-%3E%207%20%3B%0A8%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%201%5Cnvalue%20%3D%20%5B0%2C%200%2C%201%5D%5Cnclass%20%3D%20baixo%22%2C%20fillcolor%3D%22%238139e5%22%5D%20%3B%0A4%20-%3E%208%20%3B%0A9%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%203%5Cnvalue%20%3D%20%5B3%2C%200%2C%200%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23e58139%22%5D%20%3B%0A1%20-%3E%209%20%3B%0A10%20%5Blabel%3D%22hist%C3%B3ria%20%3C%3D%201.5%5Cnentropy%20%3D%201.149%5Cnsamples%20%3D%207%5Cnvalue%20%3D%20%5B1%2C%205%2C%201%5D%5Cnclass%20%3D%20moderado%22%2C%20fillcolor%3D%22%237beeab%22%5D%20%3B%0A0%20-%3E%2010%20%5Blabeldistance%3D2.5%2C%20labelangle%3D-45%2C%20headlabel%3D%22False%22%5D%20%3B%0A11%20%5Blabel%3D%22hist%C3%B3ria%20%3C%3D%200.5%5Cnentropy%20%3D%200.65%5Cnsamples%20%3D%206%5Cnvalue%20%3D%20%5B1%2C%205%2C%200%5D%5Cnclass%20%3D%20moderado%22%2C%20fillcolor%3D%22%2361ea9a%22%5D%20%3B%0A10%20-%3E%2011%20%3B%0A12%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%203%5Cnvalue%20%3D%20%5B0%2C%203%2C%200%5D%5Cnclass%20%3D%20moderado%22%2C%20fillcolor%3D%22%2339e581%22%5D%20%3B%0A11%20-%3E%2012%20%3B%0A13%20%5Blabel%3D%22garantia%20%3C%3D%200.5%5Cnentropy%20%3D%200.918%5Cnsamples%20%3D%203%5Cnvalue%20%3D%20%5B1%2C%202%2C%200%5D%5Cnclass%20%3D%20moderado%22%2C%20fillcolor%3D%22%239cf2c0%22%5D%20%3B%0A11%20-%3E%2013%20%3B%0A14%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%201%5Cnvalue%20%3D%20%5B0%2C%201%2C%200%5D%5Cnclass%20%3D%20moderado%22%2C%20fillcolor%3D%22%2339e581%22%5D%20%3B%0A13%20-%3E%2014%20%3B%0A15%20%5Blabel%3D%22entropy%20%3D%201.0%5Cnsamples%20%3D%202%5Cnvalue%20%3D%20%5B1%2C%201%2C%200%5D%5Cnclass%20%3D%20alto%22%2C%20fillcolor%3D%22%23ffffff%22%5D%20%3B%0A13%20-%3E%2015%20%3B%0A16%20%5Blabel%3D%22entropy%20%3D%200.0%5Cnsamples%20%3D%201%5Cnvalue%20%3D%20%5B0%2C%200%2C%201%5D%5Cnclass%20%3D%20baixo%22%2C%20fillcolor%3D%22%238139e5%22%5D%20%3B%0A10%20-%3E%2016%20%3B%0A%7Brank%3Dsame%20%3B%200%7D%20%3B%0A%7Brank%3Dsame%20%3B%201%3B%2010%7D%20%3B%0A%7Brank%3Dsame%20%3B%202%3B%2011%7D%20%3B%0A%7Brank%3Dsame%20%3B%204%3B%2013%7D%20%3B%0A%7Brank%3Dsame%20%3B%205%7D%20%3B%0A%7Brank%3Dsame%20%3B%203%3B%206%3B%207%3B%208%3B%209%3B%2012%3B%2014%3B%2015%3B%2016%7D%20%3B%0A%7D);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|**Cliente com Baixo Risco**|\n",
"|:-------------------------------------|\n",
"|História de Crédito: **Boa** (0)|\n",
"|Dívida: **Alta** (0)|\n",
"|Garantias: **Nenhuma** (1)|\n",
"|Renda: **>35** (2)|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|**Cliente de Alto Risco**|\n",
"|:-------------------------------------|\n",
"|História de Crédito: **Ruim** (2)|\n",
"|Dívida: **Alta** (0)|\n",
"|Garantias: **Adequada** (0)|\n",
"|Renda: **<15** (0)|"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# avaliando o noss algoritmo para os dois cliente com os registros acima\n",
"resultado = classificador.predict([[0,0,1,2], [2,0,0,0]])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['baixo' 'alto']\n"
]
}
],
"source": [
"# observando so resultados para os dois cliente acima\n",
"print(resultado)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['alto' 'baixo' 'moderado']\n"
]
}
],
"source": [
"# observando as classes do objeto \"classificador\"\n",
"print(classificador.classes_)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# função para análise de riscos em empréstimos\n",
"def risco_creditos(historia, divida, garantia, renda):\n",
" \n",
" if(historia == \"boa\"):\n",
" historia = 0\n",
" elif(historia == \"ruim\"):\n",
" historia = 2\n",
" elif(historia == \"desconhecida\"):\n",
" historia = 1\n",
" \n",
" if(divida == \"alta\"):\n",
" divida = 0\n",
" elif(divida == \"baixa\"):\n",
" divida = 1\n",
" \n",
" if(garantia == \"adequada\"):\n",
" garantia = 1\n",
" elif(garantia == \"nenhuma\"):\n",
" garantia = 0\n",
" \n",
" if(renda == \"0_15\"):\n",
" renda = 0\n",
" elif(renda == \"15_35\"):\n",
" renda = 1\n",
" elif(renda == \"acima_35\"):\n",
" renda = 2\n",
" \n",
" retorno = classificador.predict([[historia, divida, garantia, renda]])\n",
" \n",
" print(\"Risco de Empréstimo: {}\".format(retorno))\n",
" \n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Risco de Empréstimo: ['baixo']\n"
]
}
],
"source": [
"# usando a função criada para fazer a predição do primeiro usuário\n",
"risco_creditos(historia = \"boa\", divida = \"alta\", garantia = \"nenhuma\", renda = \"acima_35\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Risco de Empréstimo: ['alto']\n"
]
}
],
"source": [
"# usando a função criada para fazer a predição do segundo usuário\n",
"risco_creditos(historia = \"ruim\", divida = \"alta\", garantia = \"adequada\", renda = \"0_15\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Alguma dúvida? Entre em contato comigo:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Me envie um e-mail](mailto:alysson.barbosa@ee.ufcg.edu.br)"
]
}
],
"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",
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