Exercicio3a-checkpoint

Estatística I

•
UFCG

José Simão Trajano
27/07/2021
Esta é uma pré-visualização de arquivo. Entre para ver o arquivo original
{
 "cells": [
 {
 "cell_type": "markdown",
 "id": "77681af1",
 "metadata": {},
 "source": [
 "#Resposta da questão1\n",
 "\n",
 "Listas-Formadas por uma ordem de elementos organizados linearmente. O acesso a cada elemento acontece através do indice; que representa a posição. Utilizam os colchetes na sua delimitação.\n",
 "\n",
 "Dicionários-Possuem dois parâmetros como estrutura básica, valor e chaves. Tem como função facilitar mapeamentos. A indexação e delimitação é feita por chaves.\n",
 "\n",
 "arrays numpy-Estrutura multidimensional com elementos do mesmo tipo caracterizando-a diferente das demais. Possui capacidade de vetorização tornando fácil a manipulação e operações com os dados.\n",
 "\n",
 "Seriespandas-Estrutura de dados da biblioteca pandas formadas por matrizes unidimensionais com capacidade de armazenar dados. Sua principal função é reunir em uma tabela uma quantidade de dados como por exemplo a junção de listas."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "id": "f6fe6b76",
 "metadata": {},
 "outputs": [],
 "source": [
 "import numpy as np\n",
 "import pandas as pd"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 9,
 "id": "08fee3f8",
 "metadata": {
 "scrolled": true
 },
 "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>1</th>\n",
 " <th>2</th>\n",
 " <th>3</th>\n",
 " <th>4</th>\n",
 " <th>5</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>8</td>\n",
 " <td>9</td>\n",
 " <td>15</td>\n",
 " <td>15</td>\n",
 " <td>14</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>27</td>\n",
 " <td>29</td>\n",
 " <td>1</td>\n",
 " <td>33</td>\n",
 " <td>25</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>29</td>\n",
 " <td>17</td>\n",
 " <td>25</td>\n",
 " <td>33</td>\n",
 " <td>6</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>4</th>\n",
 " <td>32</td>\n",
 " <td>9</td>\n",
 " <td>14</td>\n",
 " <td>27</td>\n",
 " <td>16</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>5</th>\n",
 " <td>28</td>\n",
 " <td>2</td>\n",
 " <td>8</td>\n",
 " <td>31</td>\n",
 " <td>32</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>6</th>\n",
 " <td>12</td>\n",
 " <td>6</td>\n",
 " <td>19</td>\n",
 " <td>14</td>\n",
 " <td>31</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>7</th>\n",
 " <td>29</td>\n",
 " <td>20</td>\n",
 " <td>32</td>\n",
 " <td>6</td>\n",
 " <td>19</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "</div>"
 ],
 "text/plain": [
 " 1 2 3 4 5\n",
 "1 8 9 15 15 14\n",
 "2 27 29 1 33 25\n",
 "3 29 17 25 33 6\n",
 "4 32 9 14 27 16\n",
 "5 28 2 8 31 32\n",
 "6 12 6 19 14 31\n",
 "7 29 20 32 6 19"
 ]
 },
 "execution_count": 9,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "#Resposta da questão 2\n",
 "series = pd.DataFrame(np.random.randint(1,35, size=(7,5)),columns=list(\"12345\"),index=[1,2,3,4,5,6,7])\n",
 "series"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 10,
 "id": "fe81154d",
 "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>crim</th>\n",
 " <th>medv</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>0</th>\n",
 " <td>0.00632</td>\n",
 " <td>24.0</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>0.02731</td>\n",
 " <td>21.6</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>0.02729</td>\n",
 " <td>34.7</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>0.03237</td>\n",
 " <td>33.4</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>4</th>\n",
 " <td>0.06905</td>\n",
 " <td>36.2</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>...</th>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>501</th>\n",
 " <td>0.06263</td>\n",
 " <td>22.4</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>502</th>\n",
 " <td>0.04527</td>\n",
 " <td>20.6</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>503</th>\n",
 " <td>0.06076</td>\n",
 " <td>23.9</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>504</th>\n",
 " <td>0.10959</td>\n",
 " <td>22.0</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>505</th>\n",
 " <td>0.04741</td>\n",
 " <td>11.9</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "<p>506 rows × 2 columns</p>\n",
 "</div>"
 ],
 "text/plain": [
 " crim medv\n",
 "0 0.00632 24.0\n",
 "1 0.02731 21.6\n",
 "2 0.02729 34.7\n",
 "3 0.03237 33.4\n",
 "4 0.06905 36.2\n",
 ".. ... ...\n",
 "501 0.06263 22.4\n",
 "502 0.04527 20.6\n",
 "503 0.06076 23.9\n",
 "504 0.10959 22.0\n",
 "505 0.04741 11.9\n",
 "\n",
 "[506 rows x 2 columns]"
 ]
 },
 "execution_count": 10,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "#Resposta da questão 3\n",
 "boston = pd.read_csv(\"https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv\", usecols=[\"crim\",\"medv\"])\n",
 "boston"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 11,
 "id": "49c14d08",
 "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>Manufacturer</th>\n",
 " <th>Model</th>\n",
 " <th>Cartype</th>\n",
 " <th>Min_Price</th>\n",
 " <th>Price</th>\n",
 " <th>Max_Price</th>\n",
 " <th>MPG_city</th>\n",
 " <th>MPG_highway</th>\n",
 " <th>AirBags</th>\n",
 " <th>DriveTrain</th>\n",
 " <th>...</th>\n",
 " <th>Passengers</th>\n",
 " <th>Length</th>\n",
 " <th>Wheelbase</th>\n",
 " <th>Width</th>\n",
 " <th>Turn_circle</th>\n",
 " <th>Rear_seat_room</th>\n",
 " <th>Luggage_room</th>\n",
 " <th>Weight</th>\n",
 " <th>Origin</th>\n",
 " <th>Make</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>0</th>\n",
 " <td>Acura</td>\n",
 " <td>Integra</td>\n",
 " <td>Small</td>\n",
 " <td>12.9</td>\n",
 " <td>15.9</td>\n",
 " <td>18.8</td>\n",
 " <td>25.0</td>\n",
 " <td>31.0</td>\n",
 " <td>None</td>\n",
 " <td>Front</td>\n",
 " <td>...</td>\n",
 " <td>5.0</td>\n",
 " <td>177.0</td>\n",
 " <td>102.0</td>\n",
 " <td>68.0</td>\n",
 " <td>37.0</td>\n",
 " <td>26.5</td>\n",
 " <td>NaN</td>\n",
 " <td>2705.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Acura Integra</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>NaN</td>\n",
 " <td>Legend</td>\n",
 " <td>Midsize</td>\n",
 " <td>29.2</td>\n",
 " <td>33.9</td>\n",
 " <td>38.7</td>\n",
 " <td>18.0</td>\n",
 " <td>25.0</td>\n",
 " <td>Driver &amp; Passenger</td>\n",
 " <td>Front</td>\n",
 " <td>...</td>\n",
 " <td>5.0</td>\n",
 " <td>195.0</td>\n",
 " <td>115.0</td>\n",
 " <td>71.0</td>\n",
 " <td>38.0</td>\n",
 " <td>30.0</td>\n",
 " <td>15.0</td>\n",
 " <td>3560.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Acura Legend</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>Audi</td>\n",
 " <td>90</td>\n",
 " <td>Compact</td>\n",
 " <td>25.9</td>\n",
 " <td>29.1</td>\n",
 " <td>32.3</td>\n",
 " <td>20.0</td>\n",
 " <td>26.0</td>\n",
 " <td>Driver only</td>\n",
 " <td>Front</td>\n",
 " <td>...</td>\n",
 " <td>5.0</td>\n",
 " <td>180.0</td>\n",
 " <td>102.0</td>\n",
 " <td>67.0</td>\n",
 " <td>37.0</td>\n",
 " <td>28.0</td>\n",
 " <td>14.0</td>\n",
 " <td>3375.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Audi 90</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>Audi</td>\n",
 " <td>100</td>\n",
 " <td>Midsize</td>\n",
 " <td>NaN</td>\n",
 " <td>37.7</td>\n",
 " <td>44.6</td>\n",
 " <td>19.0</td>\n",
 " <td>26.0</td>\n",
 " <td>Driver &amp; Passenger</td>\n",
 " <td>NaN</td>\n",
 " <td>...</td>\n",
 " <td>6.0</td>\n",
 " <td>193.0</td>\n",
 " <td>106.0</td>\n",
 " <td>NaN</td>\n",
 " <td>37.0</td>\n",
 " <td>31.0</td>\n",
 " <td>17.0</td>\n",
 " <td>3405.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Audi 100</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>4</th>\n",
 " <td>BMW</td>\n",
 " <td>535i</td>\n",
 " <td>Midsize</td>\n",
 " <td>NaN</td>\n",
 " <td>30.0</td>\n",
 " <td>NaN</td>\n",
 " <td>22.0</td>\n",
 " <td>30.0</td>\n",
 " <td>NaN</td>\n",
 " <td>Rear</td>\n",
 " <td>...</td>\n",
 " <td>4.0</td>\n",
 " <td>186.0</td>\n",
 " <td>109.0</td>\n",
 " <td>69.0</td>\n",
 " <td>39.0</td>\n",
 " <td>27.0</td>\n",
 " <td>13.0</td>\n",
 " <td>3640.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>BMW 535i</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>...</th>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>88</th>\n",
 " <td>Volkswagen</td>\n",
 " <td>Eurovan</td>\n",
 " <td>Van</td>\n",
 " <td>16.6</td>\n",
 " <td>19.7</td>\n",
 " <td>22.7</td>\n",
 " <td>17.0</td>\n",
 " <td>21.0</td>\n",
 " <td>None</td>\n",
 " <td>Front</td>\n",
 " <td>...</td>\n",
 " <td>7.0</td>\n",
 " <td>187.0</td>\n",
 " <td>115.0</td>\n",
 " <td>72.0</td>\n",
 " <td>38.0</td>\n",
 " <td>34.0</td>\n",
 " <td>NaN</td>\n",
 " <td>3960.0</td>\n",
 " <td>NaN</td>\n",
 " <td>Volkswagen Eurovan</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>89</th>\n",
 " <td>Volkswagen</td>\n",
 " <td>Passat</td>\n",
 " <td>Compact</td>\n",
 " <td>17.6</td>\n",
 " <td>20.0</td>\n",
 " <td>22.4</td>\n",
 " <td>21.0</td>\n",
 " <td>30.0</td>\n",
 " <td>None</td>\n",
 " <td>Front</td>\n",
 " <td>...</td>\n",
 " <td>5.0</td>\n",
 " <td>180.0</td>\n",
 " <td>103.0</td>\n",
 " <td>67.0</td>\n",
 " <td>35.0</td>\n",
 " <td>31.5</td>\n",
 " <td>14.0</td>\n",
 " <td>2985.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Volkswagen Passat</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>90</th>\n",
 " <td>Volkswagen</td>\n",
 " <td>Corrado</td>\n",
 " <td>Sporty</td>\n",
 " <td>22.9</td>\n",
 " <td>23.3</td>\n",
 " <td>23.7</td>\n",
 " <td>18.0</td>\n",
 " <td>25.0</td>\n",
 " <td>None</td>\n",
 " <td>Front</td>\n",
 " <td>...</td>\n",
 " <td>4.0</td>\n",
 " <td>159.0</td>\n",
 " <td>97.0</td>\n",
 " <td>66.0</td>\n",
 " <td>36.0</td>\n",
 " <td>26.0</td>\n",
 " <td>15.0</td>\n",
 " <td>2810.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Volkswagen Corrado</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>91</th>\n",
 " <td>Volvo</td>\n",
 " <td>240</td>\n",
 " <td>Compact</td>\n",
" <td>21.8</td>\n",
 " <td>22.7</td>\n",
 " <td>23.5</td>\n",
 " <td>21.0</td>\n",
 " <td>28.0</td>\n",
 " <td>Driver only</td>\n",
 " <td>Rear</td>\n",
 " <td>...</td>\n",
 " <td>5.0</td>\n",
 " <td>190.0</td>\n",
 " <td>104.0</td>\n",
 " <td>67.0</td>\n",
 " <td>37.0</td>\n",
 " <td>29.5</td>\n",
 " <td>14.0</td>\n",
 " <td>2985.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Volvo 240</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>92</th>\n",
 " <td>NaN</td>\n",
 " <td>850</td>\n",
 " <td>Midsize</td>\n",
 " <td>24.8</td>\n",
 " <td>26.7</td>\n",
 " <td>28.5</td>\n",
 " <td>20.0</td>\n",
 " <td>28.0</td>\n",
 " <td>Driver &amp; Passenger</td>\n",
 " <td>Front</td>\n",
 " <td>...</td>\n",
 " <td>5.0</td>\n",
 " <td>184.0</td>\n",
 " <td>105.0</td>\n",
 " <td>69.0</td>\n",
 " <td>38.0</td>\n",
 " <td>30.0</td>\n",
 " <td>15.0</td>\n",
 " <td>3245.0</td>\n",
 " <td>non-USA</td>\n",
 " <td>Volvo 850</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "<p>93 rows × 27 columns</p>\n",
 "</div>"
 ],
 "text/plain": [
 " Manufacturer Model Cartype Min_Price Price Max_Price MPG_city \\\n",
 "0 Acura Integra Small 12.9 15.9 18.8 25.0 \n",
 "1 NaN Legend Midsize 29.2 33.9 38.7 18.0 \n",
 "2 Audi 90 Compact 25.9 29.1 32.3 20.0 \n",
 "3 Audi 100 Midsize NaN 37.7 44.6 19.0 \n",
 "4 BMW 535i Midsize NaN 30.0 NaN 22.0 \n",
 ".. ... ... ... ... ... ... ... \n",
 "88 Volkswagen Eurovan Van 16.6 19.7 22.7 17.0 \n",
 "89 Volkswagen Passat Compact 17.6 20.0 22.4 21.0 \n",
 "90 Volkswagen Corrado Sporty 22.9 23.3 23.7 18.0 \n",
 "91 Volvo 240 Compact 21.8 22.7 23.5 21.0 \n",
 "92 NaN 850 Midsize 24.8 26.7 28.5 20.0 \n",
 "\n",
 " MPG_highway AirBags DriveTrain ... Passengers Length \\\n",
 "0 31.0 None Front ... 5.0 177.0 \n",
 "1 25.0 Driver & Passenger Front ... 5.0 195.0 \n",
 "2 26.0 Driver only Front ... 5.0 180.0 \n",
 "3 26.0 Driver & Passenger NaN ... 6.0 193.0 \n",
 "4 30.0 NaN Rear ... 4.0 186.0 \n",
 ".. ... ... ... ... ... ... \n",
 "88 21.0 None Front ... 7.0 187.0 \n",
 "89 30.0 None Front ... 5.0 180.0 \n",
 "90 25.0 None Front ... 4.0 159.0 \n",
 "91 28.0 Driver only Rear ... 5.0 190.0 \n",
 "92 28.0 Driver & Passenger Front ... 5.0 184.0 \n",
 "\n",
 " Wheelbase Width Turn_circle Rear_seat_room Luggage_room Weight \\\n",
 "0 102.0 68.0 37.0 26.5 NaN 2705.0 \n",
 "1 115.0 71.0 38.0 30.0 15.0 3560.0 \n",
 "2 102.0 67.0 37.0 28.0 14.0 3375.0 \n",
 "3 106.0 NaN 37.0 31.0 17.0 3405.0 \n",
 "4 109.0 69.0 39.0 27.0 13.0 3640.0 \n",
 ".. ... ... ... ... ... ... \n",
 "88 115.0 72.0 38.0 34.0 NaN 3960.0 \n",
 "89 103.0 67.0 35.0 31.5 14.0 2985.0 \n",
 "90 97.0 66.0 36.0 26.0 15.0 2810.0 \n",
 "91 104.0 67.0 37.0 29.5 14.0 2985.0 \n",
 "92 105.0 69.0 38.0 30.0 15.0 3245.0 \n",
 "\n",
 " Origin Make \n",
 "0 non-USA Acura Integra \n",
 "1 non-USA Acura Legend \n",
 "2 non-USA Audi 90 \n",
 "3 non-USA Audi 100 \n",
 "4 non-USA BMW 535i \n",
 ".. ... ... \n",
 "88 NaN Volkswagen Eurovan \n",
 "89 non-USA Volkswagen Passat \n",
 "90 non-USA Volkswagen Corrado \n",
 "91 non-USA Volvo 240 \n",
 "92 non-USA Volvo 850 \n",
 "\n",
 "[93 rows x 27 columns]"
 ]
 },
 "execution_count": 11,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "dados = pd.read_csv(\"https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv\")\n",
 "dados.columns=dados.columns.str.replace(\".\",\"_\",regex=True)\n",
 "dados.rename(columns={\"Type\" : \"Cartype\"}, inplace=True)\n",
 "dados\n"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 18,
 "id": "7cd769cf",
 "metadata": {},
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "n\\--------------- total de valores nulos: 138------------------------\n",
 "\n"
 ]
 },
 {
 "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>Manufacturer</th>\n",
 " <th>Model</th>\n",
 " <th>Type</th>\n",
 " <th>Min.Price</th>\n",
 " <th>Price</th>\n",
 " <th>Max.Price</th>\n",
 " <th>MPG.city</th>\n",
 " <th>MPG.highway</th>\n",
 " <th>AirBags</th>\n",
 " <th>DriveTrain</th>\n",
 " <th>...</th>\n",
 " <th>Passengers</th>\n",
 " <th>Length</th>\n",
 " <th>Wheelbase</th>\n",
 " <th>Width</th>\n",
 " <th>Turn.circle</th>\n",
 " <th>Rear.seat.room</th>\n",
 " <th>Luggage.room</th>\n",
 " <th>Weight</th>\n",
 " <th>Origin</th>\n",
 " <th>Make</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>0</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
" <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>4</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>...</th>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " <td>...</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>88</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>89</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>90</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>91</th>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>92</th>\n",
 " <td>True</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>...</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " <td>False</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "<p>93 rows × 27 columns</p>\n",
 "</div>"
 ],
 "text/plain": [
 " Manufacturer Model Type Min.Price Price Max.Price MPG.city \\\n",
 "0 False False False False False False False \n",
 "1 True False False False False False False
\n",
 "2 False False False False False False False \n",
 "3 False False False True False False False \n",
 "4 False False False True False True False \n",
 ".. ... ... ... ... ... ... ... \n",
 "88 False False False False False False False \n",
 "89 False False False False False False False \n",
 "90 False False False False False False False \n",
 "91 False False False False False False False \n",
 "92 True False False False False False False \n",
 "\n",
 " MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase \\\n",
 "0 False False False ... False False False \n",
 "1 False False False ... False False False \n",
 "2 False False False ... False False False \n",
 "3 False False True ... False False False \n",
 "4 False True False ... False False False \n",
 ".. ... ... ... ... ... ... ... \n",
 "88 False False False ... False False False \n",
 "89 False False False ... False False False \n",
 "90 False False False ... False False False \n",
 "91 False False False ... False False False \n",
 "92 False False False ... False False False \n",
 "\n",
 " Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make \n",
 "0 False False False True False False False \n",
 "1 False False False False False False False \n",
 "2 False False False False False False False \n",
 "3 True False False False False False False \n",
 "4 False False False False False False False \n",
 ".. ... ... ... ... ... ... ... \n",
 "88 False False False True False True False \n",
 "89 False False False False False False False \n",
 "90 False False False False False False False \n",
 "91 False False False False False False False \n",
 "92 False False False False False False False \n",
 "\n",
 "[93 rows x 27 columns]"
 ]
 },
 "execution_count": 18,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "df = pd.read_csv(\"https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv\")\n",
 "print (\"n\\--------------- total de valores nulos: {}------------------------\\n\".format(df.isnull().sum().sum()))\n",
 "df.isnull()"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 12,
 "id": "52fda6ad",
 "metadata": {},
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "Na tabela Luggage.room contém o maior número de valores nulos que é 19.\n"
 ]
 }
 ],
 "source": [
 "dados2 = pd.read_csv(\"https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv\")\n",
 "print(\"Na tabela {} contém o maior número de valores nulos que é {}.\".format(dados2.isnull().sum().idxmax(), dados2.isnull().sum().max()))"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 13,
 "id": "b2ab82e0",
 "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>a</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>0</th>\n",
 " <td>0</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>5</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>10</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>15</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "</div>"
 ],
 "text/plain": [
 " a\n",
 "0 0\n",
 "1 5\n",
 "2 10\n",
 "3 15"
 ]
 },
 "execution_count": 13,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "dados3 = pd.DataFrame(np.arange(20).reshape(-1,5), columns=list('abcde'))\n",
 "dados3 = pd.DataFrame(dados3['a'])\n",
 "dados3"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 15,
 "id": "d055de7b",
 "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>c</th>\n",
 " <th>b</th>\n",
 " <th>a</th>\n",
 " <th>d</th>\n",
 " <th>e</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>0</th>\n",
 " <td>2</td>\n",
 " <td>1</td>\n",
 " <td>0</td>\n",
 " <td>3</td>\n",
 " <td>4</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>7</td>\n",
 " <td>6</td>\n",
 " <td>5</td>\n",
 " <td>8</td>\n",
 " <td>9</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>12</td>\n",
 " <td>11</td>\n",
 " <td>10</td>\n",
 " <td>13</td>\n",
 " <td>14</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>17</td>\n",
 " <td>16</td>\n",
 " <td>15</td>\n",
 " <td>18</td>\n",
 " <td>19</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "</div>"
 ],
 "text/plain": [
 " c b a d e\n",
 "0 2 1 0 3 4\n",
 "1 7 6 5 8 9\n",
 "2 12 11 10 13 14\n",
 "3 17 16 15 18 19"
 ]
 },
 "execution_count": 15,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "# Questão 8a\n",
 "dados3 = pd.DataFrame(np.arange(20).reshape(-1,5), columns=list('abcde'))\n",
 "dados3 = pd.DataFrame(dados3[['c','b','a','d','e']])\n",
 "dados3"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 25,
 "id": "b897281d",
"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>e</th>\n",
 " <th>b</th>\n",
 " <th>c</th>\n",
 " <th>d</th>\n",
 " <th>a</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>0</th>\n",
 " <td>4</td>\n",
 " <td>1</td>\n",
 " <td>2</td>\n",
 " <td>3</td>\n",
 " <td>0</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>9</td>\n",
 " <td>6</td>\n",
 " <td>7</td>\n",
 " <td>8</td>\n",
 " <td>5</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>14</td>\n",
 " <td>11</td>\n",
 " <td>12</td>\n",
 " <td>13</td>\n",
 " <td>10</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>19</td>\n",
 " <td>16</td>\n",
 " <td>17</td>\n",
 " <td>18</td>\n",
 " <td>15</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "</div>"
 ],
 "text/plain": [
 " e b c d a\n",
 "0 4 1 2 3 0\n",
 "1 9 6 7 8 5\n",
 "2 14 11 12 13 10\n",
 "3 19 16 17 18 15"
 ]
 },
 "execution_count": 25,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "#Questão 8b\n",
 "dados4 = pd.DataFrame(np.arange(20).reshape(-1,5), columns=list('abcde'))\n",
 "def trocar_posicao(dataframe, colA, colB):\n",
 " coluna = dataframe.columns.tolist() #voltando os nomes das colunas em forma de listas\n",
 " a = coluna[colB]\n",
 " coluna[colB] = coluna[colA]\n",
 " coluna[colA] = a\n",
 " dataframe = dataframe.reindex(columns = coluna)\n",
 " return dataframe\n",
 "dados4 = trocar_posicao(dados4,4,0)\n",
 "dados4\n"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 21,
 "id": "ebacf654",
 "metadata": {},
 "outputs": [
 {
 "ename": "NameError",
 "evalue": "name 'dados5_reverse' is not defined",
 "output_type": "error",
 "traceback": [
 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
 "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
 "\u001b[1;32m<ipython-input-21-ce428cb1a13f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdados5\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'abcde'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdados5_reverse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mdados5\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdados5\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdados5_reverse\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mdados5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
 "\u001b[1;31mNameError\u001b[0m: name 'dados5_reverse' is not defined"
 ]
 }
 ],
 "source": [
 "dados5 = pd.DataFrame(np.arange(20).reshape(-1, 5), columns = list('abcde'))\n",
 "dados5_reverse.sorted(reverse=True)\n",
 "dados5 = dados5[dados5_reverse]\n",
 "dados5"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": null,
 "id": "a04608b0",
 "metadata": {},
 "outputs": [],
 "source": []
 }
 ],
 "metadata": {
 "kernelspec": {
 "display_name": "Python 3 (ipykernel)",
 "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": "3.8.0"
 }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}