From 09775f805f7cf2dc71191713ecc51fa96e1cadaf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9ssica=20Cristina?= Date: Thu, 2 Nov 2023 17:04:19 -0300 Subject: [PATCH 1/6] Criado usando o Colaboratory --- casa.ipynb | 3667 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3667 insertions(+) create mode 100644 casa.ipynb diff --git a/casa.ipynb b/casa.ipynb new file mode 100644 index 0000000..8f327d5 --- /dev/null +++ b/casa.ipynb @@ -0,0 +1,3667 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMqRqGN/LcQ3S0uJrxI097v", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hbyVadJ3YF4O", + "outputId": "85714b42-ba84-4a98-88e0-e0a1ff9b27c6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n", + "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.23.5)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n" + ] + } + ], + "source": [ + "pip install pandas\n" + ] + }, + { + "cell_type": "code", + "source": [ + "pip install matplotlib" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RMen5BHLY1Kq", + "outputId": "006d2fe1-5c18-41cd-d00e-7abb36602b25" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.43.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.23.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#baixando as bibliotecas\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ], + "metadata": { + "id": "wZnf_Fe7Y7I1" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#lendo o arquivo csv\n", + "df = pd.read_csv(\"/content/catsvdogs.csv\", sep=\",\" , encoding= 'UTF-8')" + ], + "metadata": { + "id": "BLFY9IjcZEBe" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#mostrando que tipo é o dataframe=df\n", + "print(type(df))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k2wUblxyZlD4", + "outputId": "5edd815a-3f89-4543-9f1d-65b3e716fa22" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#para ver memória utilizada no df\n", + "df.dtypes" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oX2Rk4PXZpf2", + "outputId": "303dc2b6-1a64-4ac4-80cc-397d5c36c84a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Location object\n", + "Number of Households (in 1000) int64\n", + "Percentage of households with pets float64\n", + "Number of Pet Households (in 1000) int64\n", + "Percentage of Dog Owners float64\n", + "Dog Owning Households (1000s) object\n", + "Mean Number of Dogs per household float64\n", + "Dog Population (in 1000) int64\n", + "Percentage of Cat Owners float64\n", + "Cat Owning Households int64\n", + "Mean Number of Cats float64\n", + "Cat Population int64\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#para ver memória utilizada no df\n", + "df.info(memory_usage=\"deep\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-CORE6-yaWXX", + "outputId": "70ca2039-dc75-46e3-8f4e-d0fec78781e8" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 49 entries, 0 to 48\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Location 49 non-null object \n", + " 1 Number of Households (in 1000) 49 non-null int64 \n", + " 2 Percentage of households with pets 49 non-null float64\n", + " 3 Number of Pet Households (in 1000) 49 non-null int64 \n", + " 4 Percentage of Dog Owners 49 non-null float64\n", + " 5 Dog Owning Households (1000s) 49 non-null object \n", + " 6 Mean Number of Dogs per household 49 non-null float64\n", + " 7 Dog Population (in 1000) 49 non-null int64 \n", + " 8 Percentage of Cat Owners 49 non-null float64\n", + " 9 Cat Owning Households 49 non-null int64 \n", + " 10 Mean Number of Cats 49 non-null float64\n", + " 11 Cat Population 49 non-null int64 \n", + "dtypes: float64(5), int64(5), object(2)\n", + "memory usage: 10.0 KB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#para ver se tem nulos, por exemplo\n", + "df.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pFLGcBFxad4M", + "outputId": "3db8d977-dd88-4279-cd4f-8d91aefa0fb6" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 49 entries, 0 to 48\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Location 49 non-null object \n", + " 1 Number of Households (in 1000) 49 non-null int64 \n", + " 2 Percentage of households with pets 49 non-null float64\n", + " 3 Number of Pet Households (in 1000) 49 non-null int64 \n", + " 4 Percentage of Dog Owners 49 non-null float64\n", + " 5 Dog Owning Households (1000s) 49 non-null object \n", + " 6 Mean Number of Dogs per household 49 non-null float64\n", + " 7 Dog Population (in 1000) 49 non-null int64 \n", + " 8 Percentage of Cat Owners 49 non-null float64\n", + " 9 Cat Owning Households 49 non-null int64 \n", + " 10 Mean Number of Cats 49 non-null float64\n", + " 11 Cat Population 49 non-null int64 \n", + "dtypes: float64(5), int64(5), object(2)\n", + "memory usage: 4.7+ KB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Imprime as 10 primeiras linhas\n", + "df.head(10)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 453 + }, + "id": "fTUQlOSebKt4", + "outputId": "47f245eb-7fdf-4847-e3cd-ca270d215a54" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Location Number of Households (in 1000) \\\n", + "0 Alabama 1828 \n", + "1 Arizona 2515 \n", + "2 Arkansas 1148 \n", + "3 California 12974 \n", + "4 Colorado 1986 \n", + "5 Connecticut 1337 \n", + "6 Delaware 334 \n", + "7 District of Columbia 287 \n", + "8 Florida 7609 \n", + "9 Georgia 3798 \n", + "\n", + " Percentage of households with pets Number of Pet Households (in 1000) \\\n", + "0 59.5 1088 \n", + "1 59.5 1497 \n", + "2 62.4 716 \n", + "3 52.9 6865 \n", + "4 61.3 1217 \n", + "5 54.4 728 \n", + "6 56.6 189 \n", + "7 21.9 63 \n", + "8 54.4 4138 \n", + "9 55.1 2093 \n", + "\n", + " Percentage of Dog Owners Dog Owning Households (1000s) \\\n", + "0 44.1 807 \n", + "1 40.1 1008 \n", + "2 47.9 550 \n", + "3 32.8 426 \n", + "4 42.5 845 \n", + "5 28.3 379 \n", + "6 33.7 113 \n", + "7 13.1 38 \n", + "8 35.7 2718 \n", + "9 40.1 1522 \n", + "\n", + " Mean Number of Dogs per household Dog Population (in 1000) \\\n", + "0 1.7 141 \n", + "1 1.8 1798 \n", + "2 2.0 1097 \n", + "3 1.6 6687 \n", + "4 1.6 1349 \n", + "5 1.3 507 \n", + "6 1.4 163 \n", + "7 1.1 42 \n", + "8 1.5 421 \n", + "9 1.6 2479 \n", + "\n", + " Percentage of Cat Owners Cat Owning Households Mean Number of Cats \\\n", + "0 27.4 501 2.5 \n", + "1 29.6 743 1.9 \n", + "2 30.6 351 2.3 \n", + "3 28.3 3687 1.9 \n", + "4 32.3 642 1.9 \n", + "5 31.9 427 1.9 \n", + "6 33.7 113 1.7 \n", + "7 11.6 33 1.9 \n", + "8 27.3 2079 2.1 \n", + "9 27.3 1037 2.1 \n", + "\n", + " Cat Population \n", + "0 1252 \n", + "1 1438 \n", + "2 810 \n", + "3 7118 \n", + "4 1191 \n", + "5 796 \n", + "6 187 \n", + "7 63 \n", + "8 4375 \n", + "9 2162 " + ], + "text/html": [ + 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LocationNumber of Households (in 1000)Percentage of households with petsNumber of Pet Households (in 1000)Percentage of Dog OwnersDog Owning Households (1000s)Mean Number of Dogs per householdDog Population (in 1000)Percentage of Cat OwnersCat Owning HouseholdsMean Number of CatsCat Population
0Alabama182859.5108844.18071.714127.45012.51252
1Arizona251559.5149740.110081.8179829.67431.91438
2Arkansas114862.471647.95502.0109730.63512.3810
3California1297452.9686532.84261.6668728.336871.97118
4Colorado198661.3121742.58451.6134932.36421.91191
5Connecticut133754.472828.33791.350731.94271.9796
6Delaware33456.618933.71131.416333.71131.7187
7District of Columbia28721.96313.1381.14211.6331.963
8Florida760954.4413835.727181.542127.320792.14375
9Georgia379855.1209340.115221.6247927.310372.12162
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[(d['color'], d['value']) for d in dictionarylist]\n", + "\n", + "novas_colunas = []\n", + "for chave, valor in traducoes.items():\n", + " novas_colunas.append(valor)\n", + "\n", + "print(novas_colunas)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R-clmGiDfgsd", + "outputId": "72c487e3-224b-4e67-e025-8bb02438df53" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['Localização', 'Número de famílias (1000)', 'Porcentagem de famílias com Pets (%)', 'Número de famílias com Pets (1000)', 'Porcentagem de donos de cães (%)', 'Famílias com cães (milhares)', 'Média de cães por famílias', 'População canina (1000)', 'Porcentagem de donos de gatos (%)', 'Famílias com gatos', 'Média de gatos por famílias', 'População felina']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# atribuindo os novos nomes das colunas\n", + "df.columns = novas_colunas\n", + "df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "PTKCaWFUic20", + "outputId": "17e50769-1186-4999-af82-960e195dad52" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Localização Número de famílias (1000) \\\n", + "0 Alabama 1828 \n", + "1 Arizona 2515 \n", + "2 Arkansas 1148 \n", + "3 California 12974 \n", + "4 Colorado 1986 \n", + "5 Connecticut 1337 \n", + "6 Delaware 334 \n", + "7 District of Columbia 287 \n", + "8 Florida 7609 \n", + "9 Georgia 3798 \n", + "10 Idaho 568 \n", + "11 Illinois 5026 \n", + "12 Indiana 2478 \n", + "13 Iowa 1219 \n", + "14 Kansas 1133 \n", + "15 Kentucky 1777 \n", + "16 Louisiana 1702 \n", + "17 Maine 548 \n", + "18 Maryland 2169 \n", + "19 Massachusetts 2618 \n", + "20 Michigan 3804 \n", + "21 Minnesota 2163 \n", + "22 Mississippi 1115 \n", + "23 Missouri 2498 \n", + "24 Montana 410 \n", + "25 Nebraska 710 \n", + "26 Nevada 986 \n", + "27 New Hampshire 508 \n", + "28 New Jersey 3177 \n", + "29 New Mexico 773 \n", + "30 New York 7512 \n", + "31 North Carolina 3701 \n", + "32 North Dakota 272 \n", + "33 Ohio 4661 \n", + "34 Oklahoma 1479 \n", + "35 Oregon 1505 \n", + "36 Pennsylvania 5172 \n", + "37 Rhode Island 434 \n", + "38 South Carolina 1759 \n", + "39 South Dakota 333 \n", + "40 Tennessee 2583 \n", + "41 Texas 9002 \n", + "42 Utah 930 \n", + "43 Vermont 265 \n", + "44 Virginia 3017 \n", + "45 Washington 2632 \n", + "46 West Virginia 765 \n", + "47 Wisconsin 235 \n", + "48 Wyoming 221 \n", + "\n", + " Porcentagem de famílias com Pets (%) Número de famílias com Pets (1000) \\\n", + "0 59.5 1088 \n", + "1 59.5 1497 \n", + "2 62.4 716 \n", + "3 52.9 6865 \n", + "4 61.3 1217 \n", + "5 54.4 728 \n", + "6 56.6 189 \n", + "7 21.9 63 \n", + "8 54.4 4138 \n", + "9 55.1 2093 \n", + "10 62.0 352 \n", + "11 51.8 2602 \n", + "12 59.9 1484 \n", + "13 53.6 654 \n", + "14 61.0 691 \n", + "15 61.6 1094 \n", + "16 55.1 937 \n", + "17 62.9 345 \n", + "18 52.3 1134 \n", + "19 50.4 1318 \n", + "20 55.4 2108 \n", + "21 53.0 1146 \n", + "22 56.4 629 \n", + "23 61.4 1534 \n", + "24 61.3 251 \n", + "25 51.3 364 \n", + "26 55.6 548 \n", + "27 56.8 289 \n", + "28 50.7 1611 \n", + "29 67.6 523 \n", + "30 50.6 3802 \n", + "31 56.4 2089 \n", + "32 53.9 147 \n", + "33 57.4 2677 \n", + "34 58.9 872 \n", + "35 63.6 957 \n", + "36 56.9 2942 \n", + "37 53.0 230 \n", + "38 54.1 951 \n", + "39 65.6 219 \n", + "40 59.6 154 \n", + "41 58.5 5265 \n", + "42 51.2 476 \n", + "43 70.8 188 \n", + "44 53.4 1611 \n", + "45 62.7 1649 \n", + "46 62.1 475 \n", + "47 57.5 1352 \n", + "48 61.8 137 \n", + "\n", + " Porcentagem de donos de cães (%) Famílias com cães (milhares) \\\n", + "0 44.1 807 \n", + "1 40.1 1008 \n", + "2 47.9 550 \n", + "3 32.8 426 \n", + "4 42.5 845 \n", + "5 28.3 379 \n", + "6 33.7 113 \n", + "7 13.1 38 \n", + "8 35.7 2718 \n", + "9 40.1 1522 \n", + "10 42.7 242 \n", + "11 32.4 1627 \n", + "12 39.9 989 \n", + "13 33.4 407 \n", + "14 42.3 480 \n", + "15 45.9 816 \n", + "16 36.4 619 \n", + "17 34.6 190 \n", + "18 30.8 667 \n", + "19 23.6 618 \n", + "20 34.6 1318 \n", + "21 31.9 690 \n", + "22 45.2 504 \n", + "23 45.9 1148 \n", + "24 41.2 169 \n", + "25 33.8 240 \n", + "26 37.1 366 \n", + "27 30.3 154 \n", + "28 32.4 1028 \n", + "29 46.0 356 \n", + "30 29.0 2177 \n", + "31 40.3 1491 \n", + "32 36.1 98 \n", + "33 36.6 1708 \n", + "34 43.2 638 \n", + "35 38.8 584 \n", + "36 32.9 1702 \n", + "37 29.3 127 \n", + "38 38.6 678 \n", + "39 42.8 143 \n", + "40 44.1 114 \n", + "41 44.0 3,96 \n", + "42 29.4 273 \n", + "43 37.7 100 \n", + "44 35.4 1069 \n", + "45 36.3 954 \n", + "46 45.8 350 \n", + "47 33.9 796 \n", + "48 38.8 86 \n", + "\n", + " Média de cães por famílias População canina (1000) \\\n", + "0 1.7 141 \n", + "1 1.8 1798 \n", + "2 2.0 1097 \n", + "3 1.6 6687 \n", + "4 1.6 1349 \n", + "5 1.3 507 \n", + "6 1.4 163 \n", + "7 1.1 42 \n", + "8 1.5 421 \n", + "9 1.6 2479 \n", + "10 1.5 357 \n", + "11 1.5 2365 \n", + "12 1.6 1619 \n", + "13 1.5 610 \n", + "14 1.6 774 \n", + "15 1.9 1531 \n", + "16 1.8 1115 \n", + "17 1.6 300 \n", + "18 1.4 915 \n", + "19 1.4 850 \n", + "20 1.5 2036 \n", + "21 1.4 934 \n", + "22 1.7 846 \n", + "23 1.7 1978 \n", + "24 1.7 282 \n", + "25 1.6 374 \n", + "26 1.6 578 \n", + "27 1.4 212 \n", + "28 1.3 134 \n", + "29 2.0 703 \n", + "30 1.4 3054 \n", + "31 1.7 2518 \n", + "32 1.4 139 \n", + "33 1.6 273 \n", + "34 2.1 1327 \n", + "35 1.6 917 \n", + "36 1.5 2485 \n", + "37 1.3 161 \n", + "38 1.8 1191 \n", + "39 1.5 220 \n", + "40 1.9 2157 \n", + "41 1.8 7163 \n", + "42 1.5 410 \n", + "43 1.4 142 \n", + "44 1.6 1699 \n", + "45 1.7 1609 \n", + "46 1.8 648 \n", + "47 1.4 1138 \n", + "48 1.5 125 \n", + "\n", + " Porcentagem de donos de gatos (%) Famílias com gatos \\\n", + "0 27.4 501 \n", + "1 29.6 743 \n", + "2 30.6 351 \n", + "3 28.3 3687 \n", + "4 32.3 642 \n", + "5 31.9 427 \n", + "6 33.7 113 \n", + "7 11.6 33 \n", + "8 27.3 2079 \n", + "9 27.3 1037 \n", + "10 34.6 196 \n", + "11 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LocalizaçãoNúmero de famílias (1000)Porcentagem de famílias com Pets (%)Número de famílias com Pets (1000)Porcentagem de donos de cães (%)Famílias com cães (milhares)Média de cães por famíliasPopulação canina (1000)Porcentagem de donos de gatos (%)Famílias com gatosMédia de gatos por famíliasPopulação felina
0Alabama182859.5108844.18071.714127.45012.51252
1Arizona251559.5149740.110081.8179829.67431.91438
2Arkansas114862.471647.95502.0109730.63512.3810
3California1297452.9686532.84261.6668728.336871.97118
4Colorado198661.3121742.58451.6134932.36421.91191
5Connecticut133754.472828.33791.350731.94271.9796
6Delaware33456.618933.71131.416333.71131.7187
7District of Columbia28721.96313.1381.14211.6331.963
8Florida760954.4413835.727181.542127.320792.14375
9Georgia379855.1209340.115221.6247927.310372.12162
10Idaho56862.035242.72421.535734.61962.0393
11Illinois502651.8260232.416271.5236526.313211.92453
12Indiana247859.9148439.99891.6161934.48522.21912
13Iowa121953.665433.44071.561030.33702.2805
14Kansas113361.069142.34801.677433.33781.9731
15Kentucky177761.6109445.98161.9153136.86542.11349
16Louisiana170255.193736.46191.8111525.94412.0877
17Maine54862.934534.61901.630046.42541.9498
18Maryland216952.3113430.86671.491529.86452.61677
19Massachusetts261850.4131823.66181.485034.18921.81593
20Michigan380455.4210834.613181.5203631.311922.0242
21Minnesota216353.0114631.96901.493429.76432.01264
22Mississippi111556.462945.25041.784629.13242.1668
23Missouri249861.4153445.911481.7197832.28052.11653
24Montana41061.325141.21691.728233.61382.0277
25Nebraska71051.336433.82401.637431.32222.3514
26Nevada98655.654837.13661.657830.32992.1625
27New Hampshire50856.828930.31541.421234.21741.8309
28New Jersey317750.7161132.410281.313425.38031.81468
29New Mexico77367.652346.03562.070332.02472.2533
30New York751250.6380229.021771.4305429.121891.94261
31North Carolina370156.4208940.314911.7251829.51092.02220
32North Dakota27253.914736.1981.413931.4852.0174
33Ohio466157.4267736.617081.627333.315532.43786
34Oklahoma147958.987243.26382.1132732.64822.21041
35Oregon150563.695738.85841.691740.26052.01185
36Pennsylvania517256.9294232.917021.5248533.817482.03544
37Rhode Island43453.023029.31271.316127.61201.8212
38South Carolina175954.195138.66781.8119127.84902.11039
39South Dakota33365.621942.81431.522039.11302.2290
40Tennessee258359.615444.11141.9215729.87702.31749
41Texas900258.5526544.03,961.8716328.325442.25565
42Utah93051.247629.42731.541024.62292.0455
43Vermont26570.818837.71001.414249.51311.8234
44Virginia301753.4161135.410691.6169929.08762.11855
45Washington263262.7164936.39541.7160939.010281.81844
46West Virginia76562.147545.83501.864838.12912.2628
47Wisconsin23557.5135233.97961.4113833.07761.91510
48Wyoming22161.813738.8861.512533.9751.9144
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LocalizaçãoNúmero de famílias com Pets (1000)
0Alabama1088
1Arizona1497
2Arkansas716
3California6865
4Colorado1217
5Connecticut728
6Delaware189
7District of Columbia63
8Florida4138
9Georgia2093
10Idaho352
11Illinois2602
12Indiana1484
13Iowa654
14Kansas691
15Kentucky1094
16Louisiana937
17Maine345
18Maryland1134
19Massachusetts1318
20Michigan2108
21Minnesota1146
22Mississippi629
23Missouri1534
24Montana251
25Nebraska364
26Nevada548
27New Hampshire289
28New Jersey1611
29New Mexico523
30New York3802
31North Carolina2089
32North Dakota147
33Ohio2677
34Oklahoma872
35Oregon957
36Pennsylvania2942
37Rhode Island230
38South Carolina951
39South Dakota219
40Tennessee154
41Texas5265
42Utah476
43Vermont188
44Virginia1611
45Washington1649
46West Virginia475
47Wisconsin1352
48Wyoming137
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Distributions

\n", + "" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "from matplotlib import pyplot as plt\n", + "_df_0['Número de famílias com Pets (1000)'].plot(kind='hist', bins=20, title='Número de famílias com Pets (1000)')\n", + "plt.gca().spines[['top', 'right',]].set_visible(False)" + ], + "text/html": [ + "
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Values

\n", + "" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "from matplotlib import pyplot as plt\n", + "_df_1['Número de famílias com Pets (1000)'].plot(kind='line', figsize=(8, 4), title='Número de famílias com Pets (1000)')\n", + "plt.gca().spines[['top', 'right']].set_visible(False)" + ], + "text/html": [ + "
\n", + " \n", + "
\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "source": [ + "from matplotlib import pyplot as plt\n", + "_df_0['Número de famílias com Pets (1000)'].plot(kind='hist', bins=20, title='Número de famílias com Pets (1000)')\n", + "plt.gca().spines[['top', 'right',]].set_visible(False)" + ], + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "kyYkCamyoZ5u", + "outputId": "cd771f87-fe5c-4df5-d222-626e7b1c6dbb" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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UKFM4ul5ERITWrl2r5ORkpx8cN46XcZqrcOHCN/3h5orPPvtMlSpV0pIlS5y2P+O3fHf74osvlJKSouXLlzsdLbrxFGGG++67T/fdd5/eeOMNffzxx+rVq5cWLVrkdGroepUrV5Yk7dmz56Y/+CMiIiRde08yTlFm2L9/v2O5O+Vk/7jdfp7TOZSuHbly9SJoSSpbtqxCQkK0devWTMt++OEH1a9f3/G8fv36+s9//qN9+/Y5nRbOuFA/o29OxswQFxen4ODgTEfccPfgVBTcpnLlyurdu7dmz56tU6dOOS0LDAxUcHCw091LkjRjxow8r6N79+46ceKE5syZk2nZn3/+qYsXL97y9V5eXmrfvr0+//xzp29yPX36tD7++GO1aNFCgYGBN339Qw89pKtXrzrdvpyWlqZ33nnHqV9oaKhat26t2bNnZ/kbqKtfBZ9xFOH6owZbtmxxumXWnbKqLzExUXPnznXqd+7cuUxHPjJ+cN3qVEr79u0VEBCg6Ohox+mJDBnjNW7cWKGhoZo1a5bTWKtWrdK+ffsUGRmZ8w3LYznZP4oUKSIp8y8Ors6hJDVr1kx79uzJ9mmrrDz66KNasWKF06nbtWvX6sCBA45b1KVrp+QKFy7s9HlgjNGsWbNUtmxZ3X///TkeM8O2bdvUrFkzl7cB7scRG7jVP//5T82fP1/79+9XrVq1nJYNHjxYEydO1ODBg9W4cWNt3LhRBw4cyPMa+vTpo08//VRPPvmkYmNj1bx5c6WlpemXX37Rp59+qq+++irL20WvN378eK1Zs0YtWrTQ008/LW9vb82ePVspKSmaPHnyLV/buXNnNW/eXFFRUTpy5Ihq1qypJUuWZHl9y/Tp09WiRQvVqVNHQ4YMUaVKlXT69Glt2rRJx48f165du3K8/Q8//LCWLFmibt26KTIyUnFxcZo1a5Zq1qyp5OTkHI+X19q3by8fHx917txZ//jHP5ScnKw5c+YoNDTU6Qf4vHnzNGPGDHXr1k2VK1fWhQsXNGfOHAUGBuqhhx666fiBgYF68803NXjwYN177716/PHHVbx4ce3atUuXLl3SvHnzVLhwYU2aNEkDBgxQq1at1LNnT50+fdpxu/9zzz13J6bitrK7f9SvX19eXl6aNGmSEhMTZbfb9eCDD+rjjz92aQ6la2Hj9ddf14YNG9S+fXunZe+++67Onz/v+IqHL774QsePH5ckPfPMM47rsV555RUtXrxYbdq00fDhw5WcnKwpU6aoTp06GjBggGO8cuXKacSIEZoyZYpSU1N17733atmyZfrmm2/00UcfOZ3yy+6Y0rVrkHbv3q2hQ4e6+A7AI7jhTiwUQNff7n2jjNtjb/zm4UuXLplBgwaZoKAgExAQYLp3724SEhJuerv3mTNnMo1bpEiRTOvL6luOr1y5YiZNmmRq1apl7Ha7KV68uGnUqJEZN26cSUxMdPRTFt/+mmH79u2mQ4cOpmjRosbf39+0adPGfP/997edG2OM+eOPP0yfPn1MYGCgCQoKMn369DE7duzI8tbYQ4cOmb59+5rSpUubwoULm7Jly5qHH37YfPbZZ7ddT1a3e6enp5sJEyaYiIgIY7fbTYMGDcyKFStMv379TEREhKNfxq3ZU6ZMcXp9bGyskWQWL17s1J7Ve+7q7d7Lly83devWNb6+vqZChQpm0qRJ5oMPPjCSTFxcnDHm2vz37NnTlC9f3tjtdhMaGmoefvhhs3Xr1tvOS8Y67r//fuPn52cCAwNNkyZNMn09/yeffGIaNGhg7Ha7KVGihOnVq5c5fvy4U5+c7HfGZP2eZOVW+971srt/zJkzx1SqVMl4eXk5bv3O7RzWrVvXDBo0KMtt1P//kyo3PjLevwx79uwx7du3N/7+/qZYsWKmV69e5tSpU5nGTEtLc+y3Pj4+platWmbBggVZ1pXdMWfOnMmfVLAAmzG5vLIPAABJ8+fP19ChQ3Xs2DHHreN3kwYNGqh169Z688033V0KcoFrbAAAeaJXr14qX7680zdC3y1Wr16tgwcPatSoUe4uBbnEERsAAGAZHLEBAACWQbABAACWQbABAACWQbABAACWYflgY4xRUlJSrv9eDQAA8HyWDzYXLlxQUFCQLly44O5SAABAPrN8sAEAAAUHwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFiGt7sLuJtViFqZb2MfmRiZb2MDAGBVHLEBAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACWQbABAACW4dZgs3HjRnXu3FllypSRzWbTsmXLHMtSU1P18ssvq06dOipSpIjKlCmjvn376rfffnNfwQAAwKO5NdhcvHhR9erV0/Tp0zMtu3TpkrZv367Ro0dr+/btWrJkifbv369HHnnEDZUCAIC7gbc7V96pUyd16tQpy2VBQUFas2aNU9u7776rJk2a6NixYypfvnyWr0tJSVFKSorjeVJSUt4VDAAAPNpddY1NYmKibDabihUrdtM+0dHRCgoKcjzCw8PvXIEAAMCt7ppgc/nyZb388svq2bOnAgMDb9pv1KhRSkxMdDzi4+PvYJUAAMCd3HoqKrtSU1PVvXt3GWM0c+bMW/a12+2y2+13qDIAAOBJPD7YZISao0ePat26dbc8WgMAAAo2jw42GaHm4MGDio2NVcmSJd1dEgAA8GBuDTbJycn69ddfHc/j4uK0c+dOlShRQmFhYfrb3/6m7du3a8WKFUpLS9OpU6ckSSVKlJCPj4+7ygYAAB7KrcFm69atatOmjeP5yJEjJUn9+vXT2LFjtXz5cklS/fr1nV4XGxur1q1b36kyAQDAXcKtwaZ169Yyxtx0+a2WAQAA3Oiuud0bAADgdgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMtwabDZu3KjOnTurTJkystlsWrZsmdNyY4xee+01hYWFyc/PT+3atdPBgwfdUywAAPB4bg02Fy9eVL169TR9+vQsl0+ePFn//ve/NWvWLG3ZskVFihRRhw4ddPny5TtcKQAAuBt4u3PlnTp1UqdOnbJcZozRW2+9pVdffVVdunSRJH344YcqVaqUli1bpr///e93slQAAHAX8NhrbOLi4nTq1Cm1a9fO0RYUFKSmTZtq06ZNN31dSkqKkpKSnB4AAKBg8Nhgc+rUKUlSqVKlnNpLlSrlWJaV6OhoBQUFOR7h4eH5WicAAPAcHhtsXDVq1CglJiY6HvHx8e4uCQAA3CEeG2xKly4tSTp9+rRT++nTpx3LsmK32xUYGOj0AAAABYPHBpuKFSuqdOnSWrt2raMtKSlJW7ZsUbNmzdxYGQAA8FRuvSsqOTlZv/76q+N5XFycdu7cqRIlSqh8+fIaMWKExo8fr6pVq6pixYoaPXq0ypQpo65du7qvaAAA4LHcGmy2bt2qNm3aOJ6PHDlSktSvXz/FxMTopZde0sWLF/XEE0/o/PnzatGihVavXi1fX193lQwAADyYzRhj3F1EfkpKSlJQUJASExPz/HqbClEr83S86x2ZGJlvYwMAYFUee40NAABAThFsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZXi7uwBkrULUynwb+8jEyHwbGwAAd+KIDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyCDQAAsAyPDjZpaWkaPXq0KlasKD8/P1WuXFmvv/66jDHuLg0AAHggb3cXcCuTJk3SzJkzNW/ePNWqVUtbt27VgAEDFBQUpGeffdbd5QEAAA/j0cHm+++/V5cuXRQZGSlJqlChghYuXKgffvjBzZUBAABP5NKpqMOHD+d1HVm6//77tXbtWh04cECStGvXLn377bfq1KnTTV+TkpKipKQkpwcAACgYXAo2VapUUZs2bbRgwQJdvnw5r2tyiIqK0t///ndVr15dhQsXVoMGDTRixAj16tXrpq+Jjo5WUFCQ4xEeHp5v9QEAAM/iUrDZvn276tatq5EjR6p06dL6xz/+kS+nhz799FN99NFH+vjjj7V9+3bNmzdP//rXvzRv3rybvmbUqFFKTEx0POLj4/O8LgAA4JlsJhe3GF29elXLly9XTEyMVq9erXvuuUcDBw5Unz59FBISkuviwsPDFRUVpaFDhzraxo8frwULFuiXX37J1hhJSUkKCgpSYmKiAgMDc13T9SpErczT8e6UIxMj3V0CAAD5Ile3e3t7e+uvf/2rFi9erEmTJunXX3/VCy+8oPDwcPXt21cnT57MVXGXLl1SoULOJXp5eSk9PT1X4wIAAGvKVbDZunWrnn76aYWFhWnatGl64YUXdOjQIa1Zs0a//fabunTpkqviOnfurDfeeEMrV67UkSNHtHTpUk2bNk3dunXL1bgAAMCaXDoVNW3aNM2dO1f79+/XQw89pMGDB+uhhx5yOrpy/PhxVahQQVevXnW5uAsXLmj06NFaunSpEhISVKZMGfXs2VOvvfaafHx8sjUGp6Iy41QUAMCqXAo2VatW1cCBA9W/f3+FhYVl2efKlStauHCh+vXrl+sic4NgkxnBBgBgVS59Qd/Bgwdv28fHx8ftoQYAABQsLl1jM3fuXC1evDhT++LFi295KzYAAEB+cinYREdHKzg4OFN7aGioJkyYkOuiAAAAXOFSsDl27JgqVqyYqT0iIkLHjh3LdVEAAACucCnYhIaGavfu3Znad+3apZIlS+a6KAAAAFe4FGx69uypZ599VrGxsUpLS1NaWprWrVun4cOH6+9//3te1wgAAJAtLt0V9frrr+vIkSNq27atvL2vDZGenq6+fftyjQ0AAHAbl4KNj4+PPvnkE73++uvatWuX/Pz8VKdOHUVEROR1fQAAANnmUrDJcM899+iee+7Jq1oAAAByxaVgk5aWppiYGK1du1YJCQmZ/ijlunXr8qQ4AACAnHAp2AwfPlwxMTGKjIxU7dq1ZbPZ8rouAACAHHMp2CxatEiffvqpHnroobyuBwAAwGUu3e7t4+OjKlWq5HUtAAAAueJSsHn++ef19ttvy4U/DA4AAJBvXDoV9e233yo2NlarVq1SrVq1VLhwYaflS5YsyZPiAAAAcsKlYFOsWDF169Ytr2sBAADIFZeCzdy5c/O6DgAAgFxz6RobSbp69aq+/vprzZ49WxcuXJAk/fbbb0pOTs6z4gAAAHLCpSM2R48eVceOHXXs2DGlpKToL3/5iwICAjRp0iSlpKRo1qxZeV0nAADAbbl0xGb48OFq3Lixzp07Jz8/P0d7t27dtHbt2jwrDgAAICdcOmLzzTff6Pvvv5ePj49Te4UKFXTixIk8KQx3nwpRK/Nt7CMTI/NtbACAdbh0xCY9PV1paWmZ2o8fP66AgIBcFwUAAOAKl4JN+/bt9dZbbzme22w2JScna8yYMfyZBQAA4DYunYqaOnWqOnTooJo1a+ry5ct6/PHHdfDgQQUHB2vhwoV5XSMAAEC2uBRsypUrp127dmnRokXavXu3kpOTNWjQIPXq1cvpYmIAAIA7yaVgI0ne3t7q3bt3XtYCAACQKy4Fmw8//PCWy/v27etSMQAAALnhUrAZPny40/PU1FRdunRJPj4+8vf3J9gAAAC3cOmuqHPnzjk9kpOTtX//frVo0YKLhwEAgNu4/LeiblS1alVNnDgx09EcAACAOyXPgo107YLi3377LS+HBAAAyDaXrrFZvny503NjjE6ePKl3331XzZs3z5PCAAAAcsqlYNO1a1en5zabTSEhIXrwwQc1derUvKgLAAAgx1wKNunp6XldBwAAQK7l6TU2AAAA7uTSEZuRI0dmu++0adNcWQUAAECOuRRsduzYoR07dig1NVXVqlWTJB04cEBeXl5q2LCho5/NZsubKgEAALLBpWDTuXNnBQQEaN68eSpevLika1/aN2DAALVs2VLPP/98nhYJAACQHS5dYzN16lRFR0c7Qo0kFS9eXOPHj+euKAAA4DYuBZukpCSdOXMmU/uZM2d04cKFXBcFAADgCpeCTbdu3TRgwAAtWbJEx48f1/Hjx/Xf//5XgwYN0l//+te8rhEAACBbXLrGZtasWXrhhRf0+OOPKzU19dpA3t4aNGiQpkyZkqcFAgAAZJdLwcbf318zZszQlClTdOjQIUlS5cqVVaRIkTwtDgAAICdy9QV9J0+e1MmTJ1W1alUVKVJExpi8qsvhxIkT6t27t0qWLCk/Pz/VqVNHW7duzfP1AACAu59LR2z++OMPde/eXbGxsbLZbDp48KAqVaqkQYMGqXjx4nl2Z9S5c+fUvHlztWnTRqtWrVJISIgOHjzodDcWAABABpeO2Dz33HMqXLiwjh07Jn9/f0d7jx49tHr16jwrbtKkSQoPD9fcuXPVpEkTVaxYUe3bt1flypXzbB0AAMA6XAo2//vf/zRp0iSVK1fOqb1q1ao6evRonhQmScuXL1fjxo312GOPKTQ0VA0aNNCcOXNu+ZqUlBQlJSU5PQAAQMHg0qmoixcvOh2pyXD27FnZ7fZcF5Xh8OHDmjlzpkaOHKlXXnlFP/74o5599ln5+PioX79+Wb4mOjpa48aNy7Ma4BkqRK3Ml3GPTIzMl3EBAO7h0hGbli1b6sMPP3Q8t9lsSk9P1+TJk9WmTZs8Ky49PV0NGzbUhAkT1KBBAz3xxBMaMmSIZs2addPXjBo1SomJiY5HfHx8ntUDAAA8m0tHbCZPnqy2bdtq69atunLlil566SXt3btXZ8+e1XfffZdnxYWFhalmzZpObTVq1NB///vfm77Gbrfn6VEjAABw93DpiE3t2rV14MABtWjRQl26dNHFixf117/+VTt27MjTC3ubN2+u/fv3O7UdOHBAERERebYOAABgHTk+YpOamqqOHTtq1qxZ+uc//5kfNTk899xzuv/++zVhwgR1795dP/zwg9577z299957+bpeAABwd8rxEZvChQtr9+7d+VFLJvfee6+WLl2qhQsXqnbt2nr99df11ltvqVevXndk/QAA4O7i0qmo3r176/3338/rWrL08MMP66efftLly5e1b98+DRky5I6sFwAA3H1cunj46tWr+uCDD/T111+rUaNGmf5G1LRp0/KkOAAAgJzIUbA5fPiwKlSooD179qhhw4aSrl3Mez2bzZZ31QEAAORAjoJN1apVdfLkScXGxkq69icU/v3vf6tUqVL5UhwAAEBO5Ogamxv/eveqVat08eLFPC0IAADAVS5dPJzhxqADAADgTjkKNjabLdM1NFxTAwAAPEWOrrExxqh///6OP1lw+fJlPfnkk5nuilqyZEneVQgAAJBNOQo2N/5F7d69e+dpMQAAALmRo2Azd+7c/KoDAAAg13J18TAAAIAnIdgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLINgAAADLuKuCzcSJE2Wz2TRixAh3lwIAADzQXRNsfvzxR82ePVt169Z1dykAAMBD3RXBJjk5Wb169dKcOXNUvHhxd5cDAAA81F0RbIYOHarIyEi1a9futn1TUlKUlJTk9AAAAAWDt7sLuJ1FixZp+/bt+vHHH7PVPzo6WuPGjcvnqu5uFaJWursEAADyhUcfsYmPj9fw4cP10UcfydfXN1uvGTVqlBITEx2P+Pj4fK4SAAB4Co8+YrNt2zYlJCSoYcOGjra0tDRt3LhR7777rlJSUuTl5eX0GrvdLrvdfqdLBQAAHsCjg03btm31008/ObUNGDBA1atX18svv5wp1AAAgILNo4NNQECAateu7dRWpEgRlSxZMlM7AACAR19jAwAAkBMefcQmK+vXr3d3CQAAwENxxAYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFgGwQYAAFiGRweb6Oho3XvvvQoICFBoaKi6du2q/fv3u7ssAADgoTw62GzYsEFDhw7V5s2btWbNGqWmpqp9+/a6ePGiu0sDAAAeyNvdBdzK6tWrnZ7HxMQoNDRU27Zt0wMPPOCmqgAAgKfy6GBzo8TERElSiRIlbtonJSVFKSkpjudJSUn5XhcAAPAMNmOMcXcR2ZGenq5HHnlE58+f17fffnvTfmPHjtW4ceMytScmJiowMDBPa6oQtTJPx4O1HJkYmS/j5ud+l181A8Cd4tHX2Fxv6NCh2rNnjxYtWnTLfqNGjVJiYqLjER8ff4cqBAAA7nZXnIoaNmyYVqxYoY0bN6pcuXK37Gu322W32+9QZQAAwJN4dLAxxuiZZ57R0qVLtX79elWsWNHdJQEAAA/m0cFm6NCh+vjjj/X5558rICBAp06dkiQFBQXJz8/PzdUBAABP49HX2MycOVOJiYlq3bq1wsLCHI9PPvnE3aUBAAAP5NFHbO6SG7YAAICH8OgjNgAAADlBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZBsAEAAJZhM8YYdxeRn5KSkhQUFKTExEQFBgbm6dgVolbm6XiAVR2ZGJlvY9+N/w/zcz7uRvn1HjLPzgrKPHPEBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWMZdEWymT5+uChUqyNfXV02bNtUPP/zg7pIAAIAH8vhg88knn2jkyJEaM2aMtm/frnr16qlDhw5KSEhwd2kAAMDDeHywmTZtmoYMGaIBAwaoZs2amjVrlvz9/fXBBx+4uzQAAOBhvN1dwK1cuXJF27Zt06hRoxxthQoVUrt27bRp06YsX5OSkqKUlBTH88TERElSUlJSnteXnnIpz8cErCg//v9luBv/H+bnfNyN8us9ZJ6d3a3zHBAQIJvNlu3+Hh1sfv/9d6WlpalUqVJO7aVKldIvv/yS5Wuio6M1bty4TO3h4eH5UiOA2wt6y90VeBbm485gnu+M/J7nxMREBQYGZru/RwcbV4waNUojR450PE9PT9fZs2dVsmTJHCW+GyUlJSk8PFzx8fE5mmArYi6uYR6uYR6uYR7+D3NxDfNwTW7nISAgIEf9PTrYBAcHy8vLS6dPn3ZqP336tEqXLp3la+x2u+x2u1NbsWLF8qymwMDAAr2DXo+5uIZ5uIZ5uIZ5+D/MxTXMwzV3ah48+uJhHx8fNWrUSGvXrnW0paena+3atWrWrJkbKwMAAJ7Io4/YSNLIkSPVr18/NW7cWE2aNNFbb72lixcvasCAAe4uDQAAeBiPDzY9evTQmTNn9Nprr+nUqVOqX7++Vq9enemC4vxmt9s1ZsyYTKe5CiLm4hrm4Rrm4Rrm4f8wF9cwD9fc6XmwGWPMHVkTAABAPvPoa2wAAABygmADAAAsg2ADAAAsg2ADAAAsg2ADAAAsg2CTTdOnT1eFChXk6+urpk2b6ocffnB3SbmyceNGde7cWWXKlJHNZtOyZcuclhtj9NprryksLEx+fn5q166dDh486NTn7Nmz6tWrlwIDA1WsWDENGjRIycnJTn12796tli1bytfXV+Hh4Zo8eXJ+b1q2RUdH695771VAQIBCQ0PVtWtX7d+/36nP5cuXNXToUJUsWVJFixbVo48+mumbsI8dO6bIyEj5+/srNDRUL774oq5everUZ/369WrYsKHsdruqVKmimJiY/N68HJk5c6bq1q3r+GbQZs2aadWqVY7lBWUebjRx4kTZbDaNGDHC0VYQ5mLs2LGy2WxOj+rVqzuWF4Q5yHDixAn17t1bJUuWlJ+fn+rUqaOtW7c6lheEz0pJqlChQqZ9wmazaejQoZI8bJ8wuK1FixYZHx8f88EHH5i9e/eaIUOGmGLFipnTp0+7uzSXffnll+af//ynWbJkiZFkli5d6rR84sSJJigoyCxbtszs2rXLPPLII6ZixYrmzz//dPTp2LGjqVevntm8ebP55ptvTJUqVUzPnj0dyxMTE02pUqVMr169zJ49e8zChQuNn5+fmT179p3azFvq0KGDmTt3rtmzZ4/ZuXOneeihh0z58uVNcnKyo8+TTz5pwsPDzdq1a83WrVvNfffdZ+6//37H8qtXr5ratWubdu3amR07dpgvv/zSBAcHm1GjRjn6HD582Pj7+5uRI0ean3/+2bzzzjvGy8vLrF69+o5u760sX77crFy50hw4cMDs37/fvPLKK6Zw4cJmz549xpiCMw/X++GHH0yFChVM3bp1zfDhwx3tBWEuxowZY2rVqmVOnjzpeJw5c8axvCDMgTHGnD171kRERJj+/fubLVu2mMOHD5uvvvrK/Prrr44+BeGz0hhjEhISnPaHNWvWGEkmNjbWGONZ+wTBJhuaNGlihg4d6nielpZmypQpY6Kjo91YVd65Mdikp6eb0qVLmylTpjjazp8/b+x2u1m4cKExxpiff/7ZSDI//vijo8+qVauMzWYzJ06cMMYYM2PGDFO8eHGTkpLi6PPyyy+batWq5fMWuSYhIcFIMhs2bDDGXNvmwoULm8WLFzv67Nu3z0gymzZtMsZcC4iFChUyp06dcvSZOXOmCQwMdGz3Sy+9ZGrVquW0rh49epgOHTrk9yblSvHixc1//vOfAjkPFy5cMFWrVjVr1qwxrVq1cgSbgjIXY8aMMfXq1ctyWUGZA2OufV61aNHipssL6melMcYMHz7cVK5c2aSnp3vcPsGpqNu4cuWKtm3bpnbt2jnaChUqpHbt2mnTpk1urCz/xMXF6dSpU07bHBQUpKZNmzq2edOmTSpWrJgaN27s6NOuXTsVKlRIW7ZscfR54IEH5OPj4+jToUMH7d+/X+fOnbtDW5N9iYmJkqQSJUpIkrZt26bU1FSneahevbrKly/vNA916tRx+ibsDh06KCkpSXv37nX0uX6MjD6euv+kpaVp0aJFunjxopo1a1Yg52Ho0KGKjIzMVG9BmouDBw+qTJkyqlSpknr16qVjx45JKlhzsHz5cjVu3FiPPfaYQkND1aBBA82ZM8exvKB+Vl65ckULFizQwIEDZbPZPG6fINjcxu+//660tLRMf8KhVKlSOnXqlJuqyl8Z23WrbT516pRCQ0Odlnt7e6tEiRJOfbIa4/p1eIr09HSNGDFCzZs3V+3atSVdq9HHxyfTX4e/cR5ut40365OUlKQ///wzPzbHJT/99JOKFi0qu92uJ598UkuXLlXNmjUL3DwsWrRI27dvV3R0dKZlBWUumjZtqpiYGK1evVozZ85UXFycWrZsqQsXLhSYOZCkw4cPa+bMmapataq++uorPfXUU3r22Wc1b948SQXzs1KSli1bpvPnz6t///6SPO//hcf/rSjgThg6dKj27Nmjb7/91t2luE21atW0c+dOJSYm6rPPPlO/fv20YcMGd5d1R8XHx2v48OFas2aNfH193V2O23Tq1Mnx77p166pp06aKiIjQp59+Kj8/PzdWdmelp6ercePGmjBhgiSpQYMG2rNnj2bNmqV+/fq5uTr3ef/999WpUyeVKVPG3aVkiSM2txEcHCwvL69MV3efPn1apUuXdlNV+Stju261zaVLl1ZCQoLT8qtXr+rs2bNOfbIa4/p1eIJhw4ZpxYoVio2NVbly5RztpUuX1pUrV3T+/Hmn/jfOw+228WZ9AgMDPeqHhI+Pj6pUqaJGjRopOjpa9erV09tvv12g5mHbtm1KSEhQw4YN5e3tLW9vb23YsEH//ve/5e3trVKlShWYubhesWLFdM899+jXX38tUPtDWFiYatas6dRWo0YNx2m5gvZZKUlHjx7V119/rcGDBzvaPG2fINjcho+Pjxo1aqS1a9c62tLT07V27Vo1a9bMjZXln4oVK6p06dJO25yUlKQtW7Y4trlZs2Y6f/68tm3b5uizbt06paenq2nTpo4+GzduVGpqqqPPmjVrVK1aNRUvXvwObc3NGWM0bNgwLV26VOvWrVPFihWdljdq1EiFCxd2mof9+/fr2LFjTvPw008/OX1wrVmzRoGBgY4PxGbNmjmNkdHH0/ef9PR0paSkFKh5aNu2rX766Sft3LnT8WjcuLF69erl+HdBmYvrJScn69ChQwoLCytQ+0Pz5s0zfQXEgQMHFBERIangfFZeb+7cuQoNDVVkZKSjzeP2CRcviC5QFi1aZOx2u4mJiTE///yzeeKJJ0yxYsWcru6+21y4cMHs2LHD7Nixw0gy06ZNMzt27DBHjx41xly7hbFYsWLm888/N7t37zZdunTJ8hbGBg0amC1btphvv/3WVK1a1ekWxvPnz5tSpUqZPn36mD179phFixYZf39/j7mF8amnnjJBQUFm/fr1TrcxXrp0ydHnySefNOXLlzfr1q0zW7duNc2aNTPNmjVzLM+4hbF9+/Zm586dZvXq1SYkJCTLWxhffPFFs2/fPjN9+nSPu601KirKbNiwwcTFxZndu3ebqKgoY7PZzP/+9z9jTMGZh6xcf1eUMQVjLp5//nmzfv16ExcXZ7777jvTrl07ExwcbBISEowxBWMOjLl2y7+3t7d54403zMGDB81HH31k/P39zYIFCxx9CsJnZYa0tDRTvnx58/LLL2da5kn7BMEmm9555x1Tvnx54+PjY5o0aWI2b97s7pJyJTY21kjK9OjXr58x5tptjKNHjzalSpUydrvdtG3b1uzfv99pjD/++MP07NnTFC1a1AQGBpoBAwaYCxcuOPXZtWuXadGihbHb7aZs2bJm4sSJd2oTbyur7Zdk5s6d6+jz559/mqefftoUL17c+Pv7m27dupmTJ086jXPkyBHTqVMn4+fnZ4KDg83zzz9vUlNTnfrExsaa+vXrGx8fH1OpUiWndXiCgQMHmoiICOPj42NCQkJM27ZtHaHGmIIzD1m5MdgUhLno0aOHCQsLMz4+PqZs2bKmR48eTt/dUhDmIMMXX3xhateubex2u6levbp57733nJYXhM/KDF999ZWRlGn7jPGsfcJmjDE5O8YDAADgmbjGBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWAbBBgAAWMb/A4hmoITgZQTuAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "fvvgmEgvn7Jw" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 7b3c2954526aee586e83cd93257d8aa4fd71cfd0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9ssica=20Cristina?= Date: Thu, 2 Nov 2023 18:14:18 -0300 Subject: [PATCH 2/6] Criado usando o Colaboratory --- casa.ipynb | 3177 +++++++++++++++++----------------------------------- 1 file changed, 1008 insertions(+), 2169 deletions(-) diff --git a/casa.ipynb b/casa.ipynb index 8f327d5..a1549f0 100644 --- a/casa.ipynb +++ b/casa.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyMqRqGN/LcQ3S0uJrxI097v", + "authorship_tag": "ABX9TyPQmrEULLd1sFB0fdLl+4j0", "include_colab_link": true }, "kernelspec": { @@ -805,331 +805,97 @@ "source": [ "# atribuindo os novos nomes das colunas\n", "df.columns = novas_colunas\n", - "df" + "df.head(10)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000 + "height": 450 }, "id": "PTKCaWFUic20", - "outputId": "17e50769-1186-4999-af82-960e195dad52" + "outputId": "b58e3a86-4218-4d8c-a331-94d93b543293" }, - "execution_count": 32, + "execution_count": 42, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - " Localização Número de famílias (1000) \\\n", - "0 Alabama 1828 \n", - "1 Arizona 2515 \n", - "2 Arkansas 1148 \n", - "3 California 12974 \n", - "4 Colorado 1986 \n", - "5 Connecticut 1337 \n", - "6 Delaware 334 \n", - "7 District of Columbia 287 \n", - "8 Florida 7609 \n", - "9 Georgia 3798 \n", - "10 Idaho 568 \n", - "11 Illinois 5026 \n", - "12 Indiana 2478 \n", - "13 Iowa 1219 \n", - "14 Kansas 1133 \n", - "15 Kentucky 1777 \n", - "16 Louisiana 1702 \n", - "17 Maine 548 \n", - "18 Maryland 2169 \n", - "19 Massachusetts 2618 \n", - "20 Michigan 3804 \n", - "21 Minnesota 2163 \n", - "22 Mississippi 1115 \n", - "23 Missouri 2498 \n", - "24 Montana 410 \n", - "25 Nebraska 710 \n", - "26 Nevada 986 \n", - "27 New Hampshire 508 \n", - "28 New Jersey 3177 \n", - "29 New Mexico 773 \n", - "30 New York 7512 \n", - "31 North Carolina 3701 \n", - "32 North Dakota 272 \n", - "33 Ohio 4661 \n", - "34 Oklahoma 1479 \n", - "35 Oregon 1505 \n", - "36 Pennsylvania 5172 \n", - "37 Rhode Island 434 \n", - "38 South Carolina 1759 \n", - "39 South Dakota 333 \n", - "40 Tennessee 2583 \n", - "41 Texas 9002 \n", - "42 Utah 930 \n", - "43 Vermont 265 \n", - "44 Virginia 3017 \n", - "45 Washington 2632 \n", - "46 West Virginia 765 \n", - "47 Wisconsin 235 \n", - "48 Wyoming 221 \n", - "\n", - " Porcentagem de famílias com Pets (%) Número de famílias com Pets (1000) \\\n", - "0 59.5 1088 \n", - "1 59.5 1497 \n", - "2 62.4 716 \n", - "3 52.9 6865 \n", - "4 61.3 1217 \n", - "5 54.4 728 \n", - "6 56.6 189 \n", - "7 21.9 63 \n", - "8 54.4 4138 \n", - "9 55.1 2093 \n", - "10 62.0 352 \n", - "11 51.8 2602 \n", - "12 59.9 1484 \n", - "13 53.6 654 \n", - "14 61.0 691 \n", - "15 61.6 1094 \n", - "16 55.1 937 \n", - "17 62.9 345 \n", - "18 52.3 1134 \n", - "19 50.4 1318 \n", - "20 55.4 2108 \n", - "21 53.0 1146 \n", - "22 56.4 629 \n", - "23 61.4 1534 \n", - "24 61.3 251 \n", - "25 51.3 364 \n", - "26 55.6 548 \n", - "27 56.8 289 \n", - "28 50.7 1611 \n", - "29 67.6 523 \n", - "30 50.6 3802 \n", - "31 56.4 2089 \n", - "32 53.9 147 \n", - "33 57.4 2677 \n", - "34 58.9 872 \n", - "35 63.6 957 \n", - "36 56.9 2942 \n", - "37 53.0 230 \n", - "38 54.1 951 \n", - "39 65.6 219 \n", - "40 59.6 154 \n", - "41 58.5 5265 \n", - "42 51.2 476 \n", - "43 70.8 188 \n", - "44 53.4 1611 \n", - "45 62.7 1649 \n", - "46 62.1 475 \n", - "47 57.5 1352 \n", - "48 61.8 137 \n", - "\n", - " Porcentagem de donos de cães (%) Famílias com cães (milhares) \\\n", - "0 44.1 807 \n", - "1 40.1 1008 \n", - "2 47.9 550 \n", - "3 32.8 426 \n", - "4 42.5 845 \n", - "5 28.3 379 \n", - "6 33.7 113 \n", - "7 13.1 38 \n", - "8 35.7 2718 \n", - "9 40.1 1522 \n", - "10 42.7 242 \n", - "11 32.4 1627 \n", - "12 39.9 989 \n", - "13 33.4 407 \n", - "14 42.3 480 \n", - "15 45.9 816 \n", - "16 36.4 619 \n", - "17 34.6 190 \n", - "18 30.8 667 \n", - "19 23.6 618 \n", - "20 34.6 1318 \n", - "21 31.9 690 \n", - "22 45.2 504 \n", - "23 45.9 1148 \n", - "24 41.2 169 \n", - "25 33.8 240 \n", - "26 37.1 366 \n", - "27 30.3 154 \n", - "28 32.4 1028 \n", - "29 46.0 356 \n", - "30 29.0 2177 \n", - "31 40.3 1491 \n", - "32 36.1 98 \n", - "33 36.6 1708 \n", - "34 43.2 638 \n", - "35 38.8 584 \n", - "36 32.9 1702 \n", - "37 29.3 127 \n", - "38 38.6 678 \n", - "39 42.8 143 \n", - "40 44.1 114 \n", - "41 44.0 3,96 \n", - "42 29.4 273 \n", - "43 37.7 100 \n", - "44 35.4 1069 \n", - "45 36.3 954 \n", - "46 45.8 350 \n", - "47 33.9 796 \n", - "48 38.8 86 \n", - "\n", - " Média de cães por famílias População canina (1000) \\\n", - "0 1.7 141 \n", - "1 1.8 1798 \n", - "2 2.0 1097 \n", - "3 1.6 6687 \n", - "4 1.6 1349 \n", - "5 1.3 507 \n", - "6 1.4 163 \n", - "7 1.1 42 \n", - "8 1.5 421 \n", - "9 1.6 2479 \n", - "10 1.5 357 \n", - "11 1.5 2365 \n", - "12 1.6 1619 \n", - "13 1.5 610 \n", - "14 1.6 774 \n", - "15 1.9 1531 \n", - "16 1.8 1115 \n", - "17 1.6 300 \n", - "18 1.4 915 \n", - "19 1.4 850 \n", - "20 1.5 2036 \n", - "21 1.4 934 \n", - "22 1.7 846 \n", - "23 1.7 1978 \n", - "24 1.7 282 \n", - "25 1.6 374 \n", - "26 1.6 578 \n", - "27 1.4 212 \n", - "28 1.3 134 \n", - "29 2.0 703 \n", - "30 1.4 3054 \n", - "31 1.7 2518 \n", - "32 1.4 139 \n", - "33 1.6 273 \n", - "34 2.1 1327 \n", - "35 1.6 917 \n", - "36 1.5 2485 \n", - "37 1.3 161 \n", - "38 1.8 1191 \n", - "39 1.5 220 \n", - "40 1.9 2157 \n", - "41 1.8 7163 \n", - "42 1.5 410 \n", - "43 1.4 142 \n", - "44 1.6 1699 \n", - "45 1.7 1609 \n", - "46 1.8 648 \n", - "47 1.4 1138 \n", - "48 1.5 125 \n", - "\n", - " Porcentagem de donos de gatos (%) Famílias com gatos \\\n", - "0 27.4 501 \n", - "1 29.6 743 \n", - "2 30.6 351 \n", - "3 28.3 3687 \n", - "4 32.3 642 \n", - "5 31.9 427 \n", - "6 33.7 113 \n", - "7 11.6 33 \n", - "8 27.3 2079 \n", - "9 27.3 1037 \n", - "10 34.6 196 \n", - "11 26.3 1321 \n", - "12 34.4 852 \n", - "13 30.3 370 \n", - "14 33.3 378 \n", - "15 36.8 654 \n", - "16 25.9 441 \n", - "17 46.4 254 \n", - "18 29.8 645 \n", - "19 34.1 892 \n", - "20 31.3 1192 \n", - "21 29.7 643 \n", - "22 29.1 324 \n", - "23 32.2 805 \n", - "24 33.6 138 \n", - "25 31.3 222 \n", - "26 30.3 299 \n", - "27 34.2 174 \n", - "28 25.3 803 \n", - "29 32.0 247 \n", - "30 29.1 2189 \n", - "31 29.5 109 \n", - "32 31.4 85 \n", - "33 33.3 1553 \n", - "34 32.6 482 \n", - "35 40.2 605 \n", - "36 33.8 1748 \n", - "37 27.6 120 \n", - "38 27.8 490 \n", - "39 39.1 130 \n", - "40 29.8 770 \n", - "41 28.3 2544 \n", - "42 24.6 229 \n", - "43 49.5 131 \n", - "44 29.0 876 \n", - "45 39.0 1028 \n", - "46 38.1 291 \n", - "47 33.0 776 \n", - "48 33.9 75 \n", - "\n", - " Média de gatos por famílias População felina \n", - "0 2.5 1252 \n", - "1 1.9 1438 \n", - "2 2.3 810 \n", - "3 1.9 7118 \n", - "4 1.9 1191 \n", - "5 1.9 796 \n", - "6 1.7 187 \n", - "7 1.9 63 \n", - "8 2.1 4375 \n", - "9 2.1 2162 \n", - "10 2.0 393 \n", - "11 1.9 2453 \n", - "12 2.2 1912 \n", - "13 2.2 805 \n", - "14 1.9 731 \n", - "15 2.1 1349 \n", - "16 2.0 877 \n", - "17 1.9 498 \n", - "18 2.6 1677 \n", - "19 1.8 1593 \n", - "20 2.0 242 \n", - "21 2.0 1264 \n", - "22 2.1 668 \n", - "23 2.1 1653 \n", - "24 2.0 277 \n", - "25 2.3 514 \n", - "26 2.1 625 \n", - "27 1.8 309 \n", - "28 1.8 1468 \n", - "29 2.2 533 \n", - "30 1.9 4261 \n", - "31 2.0 2220 \n", - "32 2.0 174 \n", - "33 2.4 3786 \n", - "34 2.2 1041 \n", - "35 2.0 1185 \n", - "36 2.0 3544 \n", - "37 1.8 212 \n", - "38 2.1 1039 \n", - "39 2.2 290 \n", - "40 2.3 1749 \n", - "41 2.2 5565 \n", - "42 2.0 455 \n", - "43 1.8 234 \n", - "44 2.1 1855 \n", - "45 1.8 1844 \n", - "46 2.2 628 \n", - "47 1.9 1510 \n", - "48 1.9 144 " + " Localização Número de famílias (1000) \\\n", + "0 Alabama 1828 \n", + "1 Arizona 2515 \n", + "2 Arkansas 1148 \n", + "3 California 12974 \n", + "4 Colorado 1986 \n", + "5 Connecticut 1337 \n", + "6 Delaware 334 \n", + "7 District of Columbia 287 \n", + "8 Florida 7609 \n", + "9 Georgia 3798 \n", + "\n", + " Porcentagem de famílias com Pets (%) Número de famílias com Pets (1000) \\\n", + "0 59.5 1088 \n", + "1 59.5 1497 \n", + "2 62.4 716 \n", + "3 52.9 6865 \n", + "4 61.3 1217 \n", + "5 54.4 728 \n", + "6 56.6 189 \n", + "7 21.9 63 \n", + "8 54.4 4138 \n", + "9 55.1 2093 \n", + "\n", + " Porcentagem de donos de cães (%) Famílias com cães (milhares) \\\n", + "0 44.1 807 \n", + "1 40.1 1008 \n", + "2 47.9 550 \n", + "3 32.8 426 \n", + "4 42.5 845 \n", + "5 28.3 379 \n", + "6 33.7 113 \n", + "7 13.1 38 \n", + "8 35.7 2718 \n", + "9 40.1 1522 \n", + "\n", + " Média de cães por famílias População canina (1000) \\\n", + "0 1.7 141 \n", + "1 1.8 1798 \n", + "2 2.0 1097 \n", + "3 1.6 6687 \n", + "4 1.6 1349 \n", + "5 1.3 507 \n", + "6 1.4 163 \n", + "7 1.1 42 \n", + "8 1.5 421 \n", + "9 1.6 2479 \n", + "\n", + " Porcentagem de donos de gatos (%) Famílias com gatos \\\n", + "0 27.4 501 \n", + "1 29.6 743 \n", + "2 30.6 351 \n", + "3 28.3 3687 \n", + "4 32.3 642 \n", + "5 31.9 427 \n", + "6 33.7 113 \n", + "7 11.6 33 \n", + "8 27.3 2079 \n", + "9 27.3 1037 \n", + "\n", + " Média de gatos por famílias População felina \n", + "0 2.5 1252 \n", + "1 1.9 1438 \n", + "2 2.3 810 \n", + "3 1.9 7118 \n", + "4 1.9 1191 \n", + "5 1.9 796 \n", + "6 1.7 187 \n", + "7 1.9 63 \n", + "8 2.1 4375 \n", + "9 2.1 2162 " ], "text/html": [ "\n", - "
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Número de famílias com Pets (1000)49.01314.3061221368.44410163.0951.06097.06865.0
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LocalizaçãoNúmero de famílias com Pets (1000)
3California6865
41Texas5265
8Florida4138
30New York3802
36Pennsylvania2942
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "6WddP6mx1Yr0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "p1SsP8sY1Yaz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "C7ucAhDH1YDd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**5 CIDADES AS FAMÍLIAS TÊM MAIS CÃES**" + ], + "metadata": { + "id": "PZA_2q2-0He3" + } + }, + { + "cell_type": "code", + "source": [ + "# 5 cidades com mais famílias com gatos\n", + "top5_gatos = df.loc[:, [\"Localização\", \"Famílias com gatos\"]].sort_values(by='Famílias com gatos', ascending=False).head(5)\n", + "top5_gatos" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "XuAXw_nRyf7S", + "outputId": "238ace61-d7eb-468d-879b-f45b1e717ba0" + }, + "execution_count": 96, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Localização Famílias com gatos\n", + "3 California 3687\n", + "41 Texas 2544\n", + "30 New York 2189\n", + "8 Florida 2079\n", + "36 Pennsylvania 1748" + ], + "text/html": [ + "\n", + "
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LocalizaçãoFamílias com gatos
3California3687
41Texas2544
46West Virginia76562.147545.83501.864838.12912.262830New York2189
47Wisconsin23557.5135233.97961.4113833.07761.915108Florida2079
48Wyoming22161.813738.8861.512533.9751.914436Pennsylvania1748
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LocalizaçãoNúmero de famílias (1000)Porcentagem de famílias com Pets (%)Número de famílias com Pets (1000)Porcentagem de donos de cães (%)Famílias com cães (milhares)Média de cães por famíliasPopulação canina (1000)Porcentagem de donos de gatos (%)Famílias com gatosMédia de gatos por famíliasPopulação felina
0Alabama182859.5108844.18071.714127.45012.51252
1Arizona251559.5149740.110081.8179829.67431.91438
2Arkansas114862.471647.95502.0109730.63512.3810
3California1297452.9686532.84261.6668728.336871.97118
4Colorado198661.3121742.58451.6134932.36421.91191
5Connecticut133754.472828.33791.350731.94271.9796
6Delaware33456.618933.71131.416333.71131.7187
7District of Columbia28721.96313.1381.14211.6331.963
8Florida760954.4413835.727181.542127.320792.14375
9Georgia379855.1209340.115221.6247927.310372.12162
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countmeanstdmin50%99%max
Número de famílias com Pets (1000)49.01314.3061221368.44410163.0951.06097.06865.0
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LocalizaçãoNúmero de famílias com Pets (1000)
3California6865
41Texas5265
8Florida4138
30New York3802
36Pennsylvania2942
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "6WddP6mx1Yr0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "p1SsP8sY1Yaz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "C7ucAhDH1YDd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**5 CIDADES AS FAMÍLIAS TÊM MAIS CÃES**" + ], + "metadata": { + "id": "PZA_2q2-0He3" + } + }, + { + "cell_type": "code", + "source": [ + "# 5 cidades com mais famílias com gatos\n", + "top5_gatos = df.loc[:, [\"Localização\", \"Famílias com gatos\"]].sort_values(by='Famílias com gatos', ascending=False).head(5)\n", + "top5_gatos" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "XuAXw_nRyf7S", + "outputId": "238ace61-d7eb-468d-879b-f45b1e717ba0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Localização Famílias com gatos\n", + "3 California 3687\n", + "41 Texas 2544\n", + "30 New York 2189\n", + "8 Florida 2079\n", + "36 Pennsylvania 1748" + ], + "text/html": [ + "\n", + "
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LocalizaçãoFamílias com gatos
3California3687
41Texas2544
30New York2189
8Florida2079
36Pennsylvania1748
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LocalizaçãoFamílias com cães (milhares)
43Vermont100
1Arizona1008
28New Jersey1028
44Virginia1069
6Delaware113
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"name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hbyVadJ3YF4O", - "outputId": "85714b42-ba84-4a98-88e0-e0a1ff9b27c6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n", - "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.23.5)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n" - ] - } - ], - "source": [ - "pip install pandas\n" - ] - }, - { - "cell_type": "code", - "source": [ - "pip install matplotlib" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RMen5BHLY1Kq", - "outputId": "006d2fe1-5c18-41cd-d00e-7abb36602b25" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.1.1)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.43.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n", - "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.23.5)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n", - "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "#baixando as bibliotecas\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ], - "metadata": { - "id": "wZnf_Fe7Y7I1" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "#lendo o arquivo csv\n", - "df = pd.read_csv(\"/content/catsvdogs.csv\", sep=\",\" , encoding= 'UTF-8')" - ], - "metadata": { - "id": "BLFY9IjcZEBe" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "#mostrando que tipo é o dataframe=df\n", - "print(type(df))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "k2wUblxyZlD4", - "outputId": "5edd815a-3f89-4543-9f1d-65b3e716fa22" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "#para ver memória utilizada no df\n", - "df.dtypes" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oX2Rk4PXZpf2", - "outputId": "303dc2b6-1a64-4ac4-80cc-397d5c36c84a" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Location object\n", - "Number of Households (in 1000) int64\n", - "Percentage of households with pets float64\n", - "Number of Pet Households (in 1000) int64\n", - "Percentage of Dog Owners float64\n", - "Dog Owning Households (1000s) object\n", - "Mean Number of Dogs per household float64\n", - "Dog Population (in 1000) int64\n", - "Percentage of Cat Owners float64\n", - "Cat Owning Households int64\n", - "Mean Number of Cats float64\n", - "Cat Population int64\n", - "dtype: object" - ] - }, - "metadata": {}, - "execution_count": 11 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#para ver memória utilizada no df\n", - "df.info(memory_usage=\"deep\")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-CORE6-yaWXX", - "outputId": "70ca2039-dc75-46e3-8f4e-d0fec78781e8" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "RangeIndex: 49 entries, 0 to 48\n", - "Data columns (total 12 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Location 49 non-null object \n", - " 1 Number of Households (in 1000) 49 non-null int64 \n", - " 2 Percentage of households with pets 49 non-null float64\n", - " 3 Number of Pet Households (in 1000) 49 non-null int64 \n", - " 4 Percentage of Dog Owners 49 non-null float64\n", - " 5 Dog Owning Households (1000s) 49 non-null object \n", - " 6 Mean Number of Dogs per household 49 non-null float64\n", - " 7 Dog Population (in 1000) 49 non-null int64 \n", - " 8 Percentage of Cat Owners 49 non-null float64\n", - " 9 Cat Owning Households 49 non-null int64 \n", - " 10 Mean Number of Cats 49 non-null float64\n", - " 11 Cat Population 49 non-null int64 \n", - "dtypes: float64(5), int64(5), object(2)\n", - "memory usage: 10.0 KB\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "#para ver se tem nulos, por exemplo\n", - "df.info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pFLGcBFxad4M", - "outputId": "3db8d977-dd88-4279-cd4f-8d91aefa0fb6" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "RangeIndex: 49 entries, 0 to 48\n", - "Data columns (total 12 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Location 49 non-null object \n", - " 1 Number of Households (in 1000) 49 non-null int64 \n", - " 2 Percentage of households with pets 49 non-null float64\n", - " 3 Number of Pet Households (in 1000) 49 non-null int64 \n", - " 4 Percentage of Dog Owners 49 non-null float64\n", - " 5 Dog Owning Households (1000s) 49 non-null object \n", - " 6 Mean Number of Dogs per household 49 non-null float64\n", - " 7 Dog Population (in 1000) 49 non-null int64 \n", - " 8 Percentage of Cat Owners 49 non-null float64\n", - " 9 Cat Owning Households 49 non-null int64 \n", - " 10 Mean Number of Cats 49 non-null float64\n", - " 11 Cat Population 49 non-null int64 \n", - "dtypes: float64(5), int64(5), object(2)\n", - "memory usage: 4.7+ KB\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "#Imprime as 10 primeiras linhas\n", - "df.head(10)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 453 - }, - "id": "fTUQlOSebKt4", - "outputId": "47f245eb-7fdf-4847-e3cd-ca270d215a54" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Location Number of Households (in 1000) \\\n", - "0 Alabama 1828 \n", - "1 Arizona 2515 \n", - "2 Arkansas 1148 \n", - "3 California 12974 \n", - "4 Colorado 1986 \n", - "5 Connecticut 1337 \n", - "6 Delaware 334 \n", - "7 District of Columbia 287 \n", - "8 Florida 7609 \n", - "9 Georgia 3798 \n", - "\n", - " Percentage of households with pets Number of Pet Households (in 1000) \\\n", - "0 59.5 1088 \n", - "1 59.5 1497 \n", - "2 62.4 716 \n", - "3 52.9 6865 \n", - "4 61.3 1217 \n", - "5 54.4 728 \n", - "6 56.6 189 \n", - "7 21.9 63 \n", - "8 54.4 4138 \n", - "9 55.1 2093 \n", - "\n", - " Percentage of Dog Owners Dog Owning Households (1000s) \\\n", - "0 44.1 807 \n", - "1 40.1 1008 \n", - "2 47.9 550 \n", - "3 32.8 426 \n", - "4 42.5 845 \n", - "5 28.3 379 \n", - "6 33.7 113 \n", - "7 13.1 38 \n", - "8 35.7 2718 \n", - "9 40.1 1522 \n", - "\n", - " Mean Number of Dogs per household Dog Population (in 1000) \\\n", - "0 1.7 141 \n", - "1 1.8 1798 \n", - "2 2.0 1097 \n", - "3 1.6 6687 \n", - "4 1.6 1349 \n", - "5 1.3 507 \n", - "6 1.4 163 \n", - "7 1.1 42 \n", - "8 1.5 421 \n", - "9 1.6 2479 \n", - "\n", - " Percentage of Cat Owners Cat Owning Households Mean Number of Cats \\\n", - "0 27.4 501 2.5 \n", - "1 29.6 743 1.9 \n", - "2 30.6 351 2.3 \n", - "3 28.3 3687 1.9 \n", - "4 32.3 642 1.9 \n", - "5 31.9 427 1.9 \n", - "6 33.7 113 1.7 \n", - "7 11.6 33 1.9 \n", - "8 27.3 2079 2.1 \n", - "9 27.3 1037 2.1 \n", - "\n", - " Cat Population \n", - "0 1252 \n", - "1 1438 \n", - "2 810 \n", - "3 7118 \n", - "4 1191 \n", - 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3California1297452.9686532.84261.6668728.336871.97118
4Colorado198661.3121742.58451.6134932.36421.91191
5Connecticut133754.472828.33791.350731.94271.9796
6Delaware33456.618933.71131.416333.71131.7187
7District of Columbia28721.96313.1381.14211.6331.963
8Florida760954.4413835.727181.542127.320792.14375
9Georgia379855.1209340.115221.6247927.310372.12162
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LocalizaçãoNúmero de famílias (1000)Porcentagem de famílias com Pets (%)Número de famílias com Pets (1000)Porcentagem de donos de cães (%)Famílias com cães (milhares)Média de cães por famíliasPopulação canina (1000)Porcentagem de donos de gatos (%)Famílias com gatosMédia de gatos por famíliasPopulação felina
0Alabama182859.5108844.18071.714127.45012.51252
1Arizona251559.5149740.110081.8179829.67431.91438
2Arkansas114862.471647.95502.0109730.63512.3810
3California1297452.9686532.84261.6668728.336871.97118
4Colorado198661.3121742.58451.6134932.36421.91191
5Connecticut133754.472828.33791.350731.94271.9796
6Delaware33456.618933.71131.416333.71131.7187
7District of Columbia28721.96313.1381.14211.6331.963
8Florida760954.4413835.727181.542127.320792.14375
9Georgia379855.1209340.115221.6247927.310372.12162
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countmeanstdmin50%99%max
Número de famílias com Pets (1000)49.01314.3061221368.44410163.0951.06097.06865.0
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LocalizaçãoNúmero de famílias com Pets (1000)
3California6865
41Texas5265
8Florida4138
30New York3802
36Pennsylvania2942
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"execution_count": 96, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Localização Famílias com gatos\n", - "3 California 3687\n", - "41 Texas 2544\n", - "30 New York 2189\n", - "8 Florida 2079\n", - "36 Pennsylvania 1748" - ], - "text/html": [ - "\n", - "
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LocalizaçãoFamílias com gatos
3California3687
41Texas2544
30New York2189
8Florida2079
36Pennsylvania1748
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LocalizaçãoFamílias com cães (milhares)
43Vermont100
1Arizona1008
28New Jersey1028
44Virginia1069
6Delaware113
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