manipulação_dataframe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import seaborn as sns \n",
    "import random "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "nome_lista = ['Luiz' , 'Zé' , 'João Melão', 'Eduardo' , 'Maria Jose' , 'Vanessa']\n",
    "\n",
    "dados = pd.DataFrame({'Coluna 1': np.random.random_sample(50) , \n",
    "                     'Coluna 2': np.random.randint(50),\n",
    "                     'Coluna 3': np.random.randn(50),\n",
    "                     'Coluna 4': np.random.rand(50),\n",
    "                     'Escolhido': np.random.choice(nome_lista,50)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando a coluna ano de modo não aleatório \n",
    "dados['Ano'] = 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coluna 1</th>\n",
       "      <th>Coluna 2</th>\n",
       "      <th>Coluna 3</th>\n",
       "      <th>Coluna 4</th>\n",
       "      <th>Escolhido</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ano</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>2018</th>\n",
       "      <td>0.850866</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.608010</td>\n",
       "      <td>0.783435</td>\n",
       "      <td>Maria Jose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.584693</td>\n",
       "      <td>9</td>\n",
       "      <td>1.327169</td>\n",
       "      <td>0.013730</td>\n",
       "      <td>Maria Jose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.283182</td>\n",
       "      <td>9</td>\n",
       "      <td>1.176204</td>\n",
       "      <td>0.769360</td>\n",
       "      <td>Vanessa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.372648</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.295688</td>\n",
       "      <td>0.640789</td>\n",
       "      <td>Maria Jose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.727585</td>\n",
       "      <td>9</td>\n",
       "      <td>0.681454</td>\n",
       "      <td>0.227811</td>\n",
       "      <td>Eduardo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Coluna 1  Coluna 2  Coluna 3  Coluna 4   Escolhido\n",
       "Ano                                                     \n",
       "2018  0.850866         9 -0.608010  0.783435  Maria Jose\n",
       "2018  0.584693         9  1.327169  0.013730  Maria Jose\n",
       "2018  0.283182         9  1.176204  0.769360     Vanessa\n",
       "2018  0.372648         9 -0.295688  0.640789  Maria Jose\n",
       "2018  0.727585         9  0.681454  0.227811     Eduardo"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Setando a coluna ano como indexador\n",
    "dados.set_index('Ano').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Coluna 1', 'Coluna 2', 'Coluna 3', 'Coluna 4', 'Escolhido'], dtype='object')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removendo a coluna Ano\n",
    "dados.columns.drop('Ano')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando a coluna ano de modo aleatório \n",
    "ano = ['2015', '2016', '2017', '2018']\n",
    "dados['Ano'] = np.random.choice(ano,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#criando a coluna mes\n",
    "mes = ['Janeiro', 'Fevereiro', 'Maio', 'Abril', 'Março', 'Junho',\n",
    "        'Julho', 'Agosto', 'Setembro', 'Outubro', 'Novembro', 'Dezembro'] \n",
    "dados['Mês'] = np.random.choice(mes,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando coluna com a data especifica \n",
    "data = (['21-05-2018', '21-05-2016' ,'21-04-2016' , '22-07-2018' , '8-03-2015' , '1-03-2017' , '2-5-2016',\n",
    "       '11-03-2018', '2-04-2015' ,'7-05-2016' , '8-09-2015' , '13-3-2018' , '1-05-2017' , '2-7-2016'])\n",
    "dados['Data'] = np.random.choice(data,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coluna 1</th>\n",
       "      <th>Coluna 2</th>\n",
       "      <th>Coluna 3</th>\n",
       "      <th>Coluna 4</th>\n",
       "      <th>Escolhido</th>\n",
       "      <th>Mês</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Dia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.850866</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.608010</td>\n",
       "      <td>0.783435</td>\n",
       "      <td>Maria Jose</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.584693</td>\n",
       "      <td>9</td>\n",
       "      <td>1.327169</td>\n",
       "      <td>0.013730</td>\n",
       "      <td>Maria Jose</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.283182</td>\n",
       "      <td>9</td>\n",
       "      <td>1.176204</td>\n",
       "      <td>0.769360</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.372648</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.295688</td>\n",
       "      <td>0.640789</td>\n",
       "      <td>Maria Jose</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.727585</td>\n",
       "      <td>9</td>\n",
       "      <td>0.681454</td>\n",
       "      <td>0.227811</td>\n",
       "      <td>Eduardo</td>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.836782</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.268974</td>\n",
       "      <td>0.067763</td>\n",
       "      <td>Eduardo</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.553165</td>\n",
       "      <td>9</td>\n",
       "      <td>0.175069</td>\n",
       "      <td>0.602440</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.712521</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.633030</td>\n",
       "      <td>0.843643</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.194926</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.766166</td>\n",
       "      <td>0.886930</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.748323</td>\n",
       "      <td>9</td>\n",
       "      <td>-1.763233</td>\n",
       "      <td>0.659920</td>\n",
       "      <td>Eduardo</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Coluna 1  Coluna 2  Coluna 3  Coluna 4   Escolhido  Mês   Ano  Dia\n",
       "0  0.850866         9 -0.608010  0.783435  Maria Jose    9  2017   19\n",
       "1  0.584693         9  1.327169  0.013730  Maria Jose   10  2015   29\n",
       "2  0.283182         9  1.176204  0.769360     Vanessa    6  2017   21\n",
       "3  0.372648         9 -0.295688  0.640789  Maria Jose    6  2017   24\n",
       "4  0.727585         9  0.681454  0.227811     Eduardo   11  2017   20\n",
       "5  0.836782         9 -0.268974  0.067763     Eduardo    8  2016   14\n",
       "6  0.553165         9  0.175069  0.602440     Vanessa    3  2016    2\n",
       "7  0.712521         9 -0.633030  0.843643     Vanessa    1  2015   13\n",
       "8  0.194926         9 -0.766166  0.886930     Vanessa    2  2018   13\n",
       "9  0.748323         9 -1.763233  0.659920     Eduardo    3  2016   21"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exibindo nosso dataframe construído \n",
    "dados.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## E se quisermos gerar as datas de maneira aleatória como seria? \n",
    "## Let´s gooo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "ano = np.random.randint(2015,2019, 50)\n",
    "mes = np.random.randint(1,13 , 50)\n",
    "dia = np.random.randint(1, 30 , 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2017, 2015, 2017, 2017, 2017, 2016, 2016, 2015, 2018, 2016, 2015,\n",
       "       2016, 2018, 2015, 2018, 2017, 2015, 2017, 2016, 2016, 2016, 2018,\n",
       "       2015, 2017, 2018, 2015, 2015, 2017, 2017, 2016, 2017, 2017, 2016,\n",
       "       2018, 2017, 2017, 2018, 2015, 2016, 2015, 2018, 2016, 2015, 2017,\n",
       "       2015, 2017, 2016, 2016, 2017, 2018])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ano "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 10,  6,  6, 11,  8,  3,  1,  2,  3,  9,  8,  9,  4,  4, 12,  6,\n",
       "        9,  4,  1,  1,  2,  4,  2,  4,  6,  6,  1,  9,  9,  4,  3,  3,  6,\n",
       "        9,  9,  8,  5,  6,  7,  5,  3,  1,  8,  2, 10,  3,  4,  3,  7])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 29, 21, 24, 20, 14,  2, 13, 13, 21, 15, 22, 25, 12, 13, 27, 21,\n",
       "       27, 26,  8, 12, 13, 22, 21, 26, 15, 16,  4, 12, 16, 11,  8, 17, 28,\n",
       "       10, 29, 19, 13, 17,  4,  7,  6, 22,  9,  9, 20, 23, 27, 13,  4])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agora que temos nossas listas de datas criadas, vamos remover a colunas criadas anteriormente e preencher com a criadas acima \n",
    "## Let's go "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Coluna 1', 'Coluna 2', 'Coluna 3', 'Coluna 4', 'Escolhido', 'Ano',\n",
       "       'Mês', 'Data'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = dados.drop(columns=['Mês', 'Data', 'Ano'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Coluna 1', 'Coluna 2', 'Coluna 3', 'Coluna 4', 'Escolhido']"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados['Mês'] = mes\n",
    "dados['Ano'] = ano\n",
    "dados['Dia'] = dia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.850866</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.608010</td>\n",
       "      <td>0.783435</td>\n",
       "      <td>Maria Jose</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.584693</td>\n",
       "      <td>9</td>\n",
       "      <td>1.327169</td>\n",
       "      <td>0.013730</td>\n",
       "      <td>Maria Jose</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.283182</td>\n",
       "      <td>9</td>\n",
       "      <td>1.176204</td>\n",
       "      <td>0.769360</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.372648</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.295688</td>\n",
       "      <td>0.640789</td>\n",
       "      <td>Maria Jose</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.727585</td>\n",
       "      <td>9</td>\n",
       "      <td>0.681454</td>\n",
       "      <td>0.227811</td>\n",
       "      <td>Eduardo</td>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.836782</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.268974</td>\n",
       "      <td>0.067763</td>\n",
       "      <td>Eduardo</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.553165</td>\n",
       "      <td>9</td>\n",
       "      <td>0.175069</td>\n",
       "      <td>0.602440</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.712521</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.633030</td>\n",
       "      <td>0.843643</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.194926</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.766166</td>\n",
       "      <td>0.886930</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.748323</td>\n",
       "      <td>9</td>\n",
       "      <td>-1.763233</td>\n",
       "      <td>0.659920</td>\n",
       "      <td>Eduardo</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Coluna 1  Coluna 2  Coluna 3  Coluna 4   Escolhido  Mês   Ano  Dia\n",
       "0  0.850866         9 -0.608010  0.783435  Maria Jose    9  2017   19\n",
       "1  0.584693         9  1.327169  0.013730  Maria Jose   10  2015   29\n",
       "2  0.283182         9  1.176204  0.769360     Vanessa    6  2017   21\n",
       "3  0.372648         9 -0.295688  0.640789  Maria Jose    6  2017   24\n",
       "4  0.727585         9  0.681454  0.227811     Eduardo   11  2017   20\n",
       "5  0.836782         9 -0.268974  0.067763     Eduardo    8  2016   14\n",
       "6  0.553165         9  0.175069  0.602440     Vanessa    3  2016    2\n",
       "7  0.712521         9 -0.633030  0.843643     Vanessa    1  2015   13\n",
       "8  0.194926         9 -0.766166  0.886930     Vanessa    2  2018   13\n",
       "9  0.748323         9 -1.763233  0.659920     Eduardo    3  2016   21"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dados['Coluna 1']\n",
    "y = dados['Ano']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134732c7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['green', 'red', 'blue', 'yellow']\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y,x, marker='o' , s=200, alpha=0.5, color=colors)\n",
    "\n",
    "plt.xlabel(\"Ano\")\n",
    "plt.ylabel(\"Valores \\n Coluna 1\")\n",
    "plt.title(\"Distribuição de valores por ano\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1346f917c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist('Escolhido', data=dados, density=10, bins=20,color='green', alpha=0.5)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Nome')\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title('Frequência do nome escolhido \\n ')\n",
    "plt.grid(False)\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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}