01_introducao_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minicurso: (Introdução à) Análise de Dados com Pandas\n",
    "\n",
    "Roteiro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Bibliotecas úteis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abrir o conjunto de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018 = pd.read_csv('bases/ks-projects-201801-5000.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métodos de Visualização dos Dados\n",
    "\n",
    "#### -- Visualizar os 5 primeiros exemplos: df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre os 10 primeiros exemplos\n",
    "\n",
    "ks_proj2018.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Visualizar as colunas com dados numéricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018.select_dtypes(include=[np.number]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Identifique as colunas com dados numéricos, atribua-as a uma variável e em seguida imprima na tela\n",
    "\n",
    "numeric_columns = ks_proj2018.select_dtypes(include=[np.number]).columns\n",
    "numeric_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Visualizar as colunas com dados categóricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.select_dtypes(include=[np.object]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Identifique as colunas com dados categóricos, atribua-as a uma variável e em seguida imprima na tela\n",
    "\n",
    "string_columns = ks_proj2018.select_dtypes(include=[np.object]).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "string_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Média"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018[['goal', 'pledged']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre a média para todas as colunas de dados numéricos\n",
    "\n",
    "ks_proj2018.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Desvio padrão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['goal'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre o desvio padrão para todas as colunas de dados numéricos\n",
    "\n",
    "ks_proj2018.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Máximo e mínimo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['goal'].max(), ks_proj2018['goal'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre o valor máximo para todas as colunas de dados numéricos\n",
    "\n",
    "ks_proj2018.select_dtypes(include=[np.number]).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre o valor mínimo para todas as colunas de dados numéricos\n",
    "\n",
    "ks_proj2018.select_dtypes(include=[np.number]).min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Ou... ver tudo junto!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['goal'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Descreva o conjunto de dados\n",
    "ks_proj2018.select_dtypes(include=[np.number]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Saber a quantidade de projetos por categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.value_counts(ks_proj2018['category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Mostre a quantidade de projetos por estado (coluna 'state')\n",
    "\n",
    "pd.value_counts(ks_proj2018['state'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Calcular a simetria dos dados em relação à média"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['goal'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Calcule a simetria de todos os dados numéricos em relação à média\n",
    "ks_proj2018.skew()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Calcular a correlação entre os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O que podemos inferir em relação à correlação entre os dados?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordenação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Ordenar o conjunto de dados por data de lançamento do projeto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018.sort_values(by='launched', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Ordene o dataset pelo 'name'\n",
    "\n",
    "ks_proj2018.sort_values(by='name', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Ordenar pelo número do índice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos dar uma olhada no conjunto de dados para ver se a mudança persistiu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ihh, parece que não!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Ordene o conjunto de dados pelo número do índice de forma definitiva\n",
    "### Use o parâmetro 'inplace=True'\n",
    "\n",
    "ks_proj2018.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### --Resetar a indexação do conjunto de dados, iniciando a contagem de 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos dar uma olhada no conjunto de dados para ver se a mudança persistiu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ihh, parece que não!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Resete a indexação do conjunto de dados de forma definitiva\n",
    "### Use o parâmetro 'inplace==True'\n",
    "\n",
    "ks_proj2018.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleção, atribuição e operações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Índices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Mude o nome do índice de 'ID' para 'id'\n",
    "### Use o atributo name do índice --> df.index.name\n",
    "ks_proj2018.index.name = 'id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Nomes das colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Formato do conjunto de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ks_proj2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -- Selecionar células utilizando método da biblioteca numpy: df['coluna']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['name'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Selecione a coluna 'pledged'\n",
    "\n",
    "ks_proj2018['pledged'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['name'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Selecione o 5o item da coluna 'pledged'\n",
    "\n",
    "ks_proj2018['pledged'][5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018[4:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Selecione os itens 25 a 37\n",
    "\n",
    "ks_proj2018[25:37]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Operação de subtração: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018['gap'] = ks_proj2018['goal'] - ks_proj2018['usd pledged']\n",
    "ks_proj2018['gap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Mostre a diferença entre o arrecadado real em USD ('usd_pldeged_real') e o objetivo real\n",
    "### de arrecadação em USD ('usd_goal_real')\n",
    "\n",
    "ks_proj2018['usd_goal_real'] - ks_proj2018['usd_pledged_real']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Atribuição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['gap'][0] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vish... o que esse warning significa?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utilizando métodos da biblioteca pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.loc[:,'name'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Selecione a coluna 'pledged'\n",
    "\n",
    "ks_proj2018.loc[:,'pledged'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.loc[:,['name', 'category']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Selecione as colunas com dados numéricos. Lembre-se da lista numeric_columns\n",
    "\n",
    "ks_proj2018.loc[:, numeric_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.loc[14:29,'main_category':'backers'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Selecione as colunas com dados categóricos de 100 a 125. Lembre-se da lista string_columns\n",
    "\n",
    "\n",
    "ks_proj2018.loc[100:125, string_columns].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018.loc[:,'pledged']>500000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018[ks_proj2018.loc[:,'pledged']>500000].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018[((ks_proj2018.loc[:, 'backers']>4) & (ks_proj2018.loc[:, 'backers']<80))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Selecione os projetos com 'backers' entre 4 e 80.\n",
    "\n",
    "\n",
    "ks_proj2018[((ks_proj2018.loc[:, 'backers']>4) & (ks_proj2018.loc[:, 'backers']<80))].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def maximo(linha):\n",
    "    return linha.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018[numeric_columns].apply(maximo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018[numeric_columns].apply(lambda coluna: coluna.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre a média das colunas numéricas utilizando apply()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Padronizando a coluna 'name'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Ordene os projetos pelo nome em ordem alfabética. Lembre-se da função sort_values().\n",
    "### A seguir, imprima os 5 primeiros itens do conjunto de dados.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018.loc[:,string_columns] = ks_proj2018[string_columns].apply(lambda coluna: coluna.str.replace('\\\"', ''))\n",
    "\n",
    "ks_proj2018[string_columns].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mude todas as strings para iniciarem com letra maiúscula (função capitalize())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dados Temporais (Time Series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Vamos verificar o tipo das colunas que representam datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['launched'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Verifique se o tipo da coluna 'deadline'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Passar as colunas de datas para o tipo correto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['launched_parsed'] = pd.to_datetime(ks_proj2018['launched'])\n",
    "ks_proj2018['launched_parsed'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Passe a coluna 'deadline' para o tipo datetime, atribua a uma nova coluna 'deadline_parsed\n",
    "### e imprima os primeiros itens\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos verificar o tipo da nova coluna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018['launched_parsed'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Certo, agora 'launched_parsed' é do tipo datetime!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Verifique se a coluna 'deadline_parsed' está com o tipo data\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Pegar parte da data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "launched_parsed_days = ks_proj2018['launched_parsed'].dt.day\n",
    "launched_parsed_days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Pegue o dia da coluna 'deadline_parsed' e atribua a uma variável\n",
    "### Imprima a variável\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Plotar datas para verificar se o parsing ocorreu corretamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "launched_parsed_days.hist(bins=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Plote o histograma de frequência dos 31 dias do mês da coluna 'deadline_parsed'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dados categóricos\n",
    "\n",
    "#### -- Mudando dados nominais para categóricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = ['main_category', 'currency', 'state', 'country']\n",
    "ks_proj2018[categorical_columns] = ks_proj2018[categorical_columns].apply(lambda coluna: coluna.astype(\"category\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['main_category'].cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre as categorias da coluna 'state'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Mostre as categorias da coluna 'currency'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -- Re-ordenando a importância dos estados dos projetos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018['state'].cat.set_categories(['canceled', 'failed', 'suspended','live', 'successful'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos dar uma olhada no conjunto de dados para ver se a mudança persistiu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez! \n",
    "### Mostre os 5 primeiros itens do conjunto de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ihh, parece que não!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Modifique a ordem da importância dos estados de maneira definitiva\n",
    "### Use use inplace=True\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agrupamento "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.groupby('state').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Agrupe o conjunto de dados por moeda mostre o tamanho.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.groupby('state').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Agrupe o conjunto de dados por moeda mostre a mediana.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.groupby('state').count().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Agrupe o conjunto de dados por moeda mostre quantos pertencem a cada moeda.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)\n",
    "ks_proj2018.groupby('state').describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez! Agrupe pela moeda e descreva o conjunto de dados.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018['goal'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Plote apenas os exemplos com 'goal' menor que 1000\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.loc[ks_proj2018['usd pledged']<100000, 'usd pledged'].plot.hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Plote apenas os exemplos com 'usd pledged' menor que 40000\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ks_proj2018[ks_proj2018['goal'] < 100000].boxplot('goal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Faça um boxplot com 'usd pledged' menor que 10000\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.plot(x='backers',y='pledged',kind='scatter', title='Backers x Pledged',color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sua vez!\n",
    "### Plote o 'backers' pelo 'gap'\n",
    "\n",
    "ks_proj2018.plot(x='backers',y='gap',kind='scatter', title='Backers x Pledged',color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Salvar dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks_proj2018.to_csv('ks-projects-201801-v-processada.csv')"
   ]
  }
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