/
Google_Play.ipynb
3231 lines (3231 loc) · 571 KB
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Google_Play.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Google_Play.ipynb",
"provenance": [],
"collapsed_sections": [],
"mount_file_id": "1lFWKDXsT_aeKqqrqT7_n-8D39fjv4WkO",
"authorship_tag": "ABX9TyMHDG9CWAIaISHIzr40wWX/",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/karinnecristina/Data-Science/blob/master/Google_Play.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aq6WvQjWFQ3q",
"colab_type": "text"
},
"source": [
"#**Análise dos dados da Google Play Store**\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g0IPMLAQPgQl",
"colab_type": "text"
},
"source": [
"A Play Store é a loja de aplicativos do Google, é através dela que os usuários de celulares com sistema operacional Android conseguem baixar apps como WhatsApp, Facebook, Spotify, Netflix e vários outros para seus dispositivos. Apesar do uso ser gratuito, ela também conta com ofertas de aplicativos e serviços pagos."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m4FM5u0eRhiO",
"colab_type": "text"
},
"source": [
"##**Objetivo**\n",
"\n",
"Os dados dos aplicativos da Play Store têm um enorme potencial para levar as empresas que desenvolvem essas aplicações ao sucesso.O objetivo deste projeto é analisar esses dados com o intuito de ajudar os desenvolvedores a entender que tipo de aplicativo provavelmente atrairá mais usuários.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IsG7LSeySWZG",
"colab_type": "text"
},
"source": [
" ### **Obtenção dos Dados**\n",
"\n",
"Os dados usados nessa análise são referentes ao ano de 2018 e foram obtidos a partir do site [Kaggle.com](https://www.kaggle.com/lava18/google-play-store-apps)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Bebl4ypjPYlk",
"colab_type": "code",
"colab": {}
},
"source": [
"# Importando os pacotes necessários\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from wordcloud import WordCloud"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "eqJhTyulMIgz",
"colab_type": "code",
"colab": {}
},
"source": [
"# Lendo o arquivo:\n",
"df_app = pd.read_csv('/content/drive/My Drive/Curso_Dataquest/googleplaystore.csv')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q1BtWFN6WECG",
"colab_type": "text"
},
"source": [
" ### **Análise dos Dados**\n",
"\n",
"Esta etapa tem por objetivo criar uma consciência situacional inicial e permitir um entendimento de como os dados estão estruturados."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1diVzypO1iab"
},
"source": [
"<Enter>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Xo1MEbNtcJ8o",
"colab_type": "text"
},
"source": [
"**Dicionário das variáveis**\n",
"\n",
"* App - Nome do aplicativo\n",
"* Category - Categoria à qual o aplicativo pertence\n",
"* Rating - classificação do aplicativo \n",
"* Reviews - Número de avaliações de usuários do aplicativo\n",
"* Size - Tamanho do aplicativo\n",
"* Installs - Número de downloads / instalações de usuário para o aplicativo \n",
"* Type - Pago ou Gratuito\n",
"* Price - preço\n",
"* Content Rating - Faixa etária em que o aplicativo é direcionado - Crianças / maiores de 21 anos / Adulto\n",
"* Genres - Um aplicativo pode pertencer a vários gêneros (além da categoria principal). Por exemplo, um jogo musical familiar pertence aos gêneros Música, Jogo, Família.\n",
"* Last Updated - Data em que o aplicativo foi atualizado pela última vez na Play Store\n",
"* Current Ver - Versão atual do aplicativo disponível na Play Store\n",
"* Android Ver - Versão mínima exigida do Android"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "II-kMx_BezxX",
"colab_type": "text"
},
"source": [
"Vamos dar uma olhada nas primeiras linhas do Dataframe."
]
},
{
"cell_type": "code",
"metadata": {
"id": "N651pCxDTuUr",
"colab_type": "code",
"outputId": "8617d6e4-651b-4061-9113-05cb5a010575",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"# Primeiras 5 linhas:\n",
"df_app.head()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"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>App</th>\n",
" <th>Category</th>\n",
" <th>Rating</th>\n",
" <th>Reviews</th>\n",
" <th>Size</th>\n",
" <th>Installs</th>\n",
" <th>Type</th>\n",
" <th>Price</th>\n",
" <th>Content Rating</th>\n",
" <th>Genres</th>\n",
" <th>Last Updated</th>\n",
" <th>Current Ver</th>\n",
" <th>Android Ver</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Photo Editor & Candy Camera & Grid & ScrapBook</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.1</td>\n",
" <td>159</td>\n",
" <td>19M</td>\n",
" <td>10,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design</td>\n",
" <td>January 7, 2018</td>\n",
" <td>1.0.0</td>\n",
" <td>4.0.3 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Coloring book moana</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>3.9</td>\n",
" <td>967</td>\n",
" <td>14M</td>\n",
" <td>500,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design;Pretend Play</td>\n",
" <td>January 15, 2018</td>\n",
" <td>2.0.0</td>\n",
" <td>4.0.3 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.7</td>\n",
" <td>87510</td>\n",
" <td>8.7M</td>\n",
" <td>5,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design</td>\n",
" <td>August 1, 2018</td>\n",
" <td>1.2.4</td>\n",
" <td>4.0.3 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sketch - Draw & Paint</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.5</td>\n",
" <td>215644</td>\n",
" <td>25M</td>\n",
" <td>50,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Teen</td>\n",
" <td>Art & Design</td>\n",
" <td>June 8, 2018</td>\n",
" <td>Varies with device</td>\n",
" <td>4.2 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Pixel Draw - Number Art Coloring Book</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.3</td>\n",
" <td>967</td>\n",
" <td>2.8M</td>\n",
" <td>100,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design;Creativity</td>\n",
" <td>June 20, 2018</td>\n",
" <td>1.1</td>\n",
" <td>4.4 and up</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" App ... Android Ver\n",
"0 Photo Editor & Candy Camera & Grid & ScrapBook ... 4.0.3 and up\n",
"1 Coloring book moana ... 4.0.3 and up\n",
"2 U Launcher Lite – FREE Live Cool Themes, Hide ... ... 4.0.3 and up\n",
"3 Sketch - Draw & Paint ... 4.2 and up\n",
"4 Pixel Draw - Number Art Coloring Book ... 4.4 and up\n",
"\n",
"[5 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BqTSheRTELDs",
"colab_type": "text"
},
"source": [
"###**Quantos linhas e quantas colunas o nosso conjunto de dados possui? Quais os tipos das variáveis?**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DLaxvR0VEcB2",
"colab_type": "code",
"outputId": "71b4f0e2-8e8d-482f-a867-4a8d5b14d013",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 306
}
},
"source": [
"# Tamanho do Dataframe:\n",
"print(f'Número de linhas: {len(df_app.index)}')\n",
"print(f'Número de colunas: {len(df_app.columns)}\\n')\n",
"\n",
"# Identificando o tipo de cada variável:\n",
"display(df_app.dtypes)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Número de linhas: 10841\n",
"Número de colunas: 13\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"App object\n",
"Category object\n",
"Rating float64\n",
"Reviews object\n",
"Size object\n",
"Installs object\n",
"Type object\n",
"Price object\n",
"Content Rating object\n",
"Genres object\n",
"Last Updated object\n",
"Current Ver object\n",
"Android Ver object\n",
"dtype: object"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pTCcchjBCb8m",
"colab_type": "text"
},
"source": [
"#**Limpeza e transformação dos dados**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v9vkmZZJRvT4",
"colab_type": "text"
},
"source": [
"Precisamos garantir que os dados que estamos analisando sejam precisos, caso contrário, os resultados de nossa análise estarão errados."
]
},
{
"cell_type": "code",
"metadata": {
"id": "1urEzkoTHUGE",
"colab_type": "code",
"outputId": "d541ec69-bbdd-43bf-d44d-b481e9201362",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"# Corrigindo o nome das colunas:\n",
"df_app.columns = df_app.columns.str.replace(' ', '_')\n",
"\n",
"# Visualizando os dados novamente:\n",
"df_app.head()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"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",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>App</th>\n",
" <th>Category</th>\n",
" <th>Rating</th>\n",
" <th>Reviews</th>\n",
" <th>Size</th>\n",
" <th>Installs</th>\n",
" <th>Type</th>\n",
" <th>Price</th>\n",
" <th>Content_Rating</th>\n",
" <th>Genres</th>\n",
" <th>Last_Updated</th>\n",
" <th>Current_Ver</th>\n",
" <th>Android_Ver</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Photo Editor & Candy Camera & Grid & ScrapBook</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.1</td>\n",
" <td>159</td>\n",
" <td>19M</td>\n",
" <td>10,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design</td>\n",
" <td>January 7, 2018</td>\n",
" <td>1.0.0</td>\n",
" <td>4.0.3 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Coloring book moana</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>3.9</td>\n",
" <td>967</td>\n",
" <td>14M</td>\n",
" <td>500,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design;Pretend Play</td>\n",
" <td>January 15, 2018</td>\n",
" <td>2.0.0</td>\n",
" <td>4.0.3 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.7</td>\n",
" <td>87510</td>\n",
" <td>8.7M</td>\n",
" <td>5,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design</td>\n",
" <td>August 1, 2018</td>\n",
" <td>1.2.4</td>\n",
" <td>4.0.3 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sketch - Draw & Paint</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.5</td>\n",
" <td>215644</td>\n",
" <td>25M</td>\n",
" <td>50,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Teen</td>\n",
" <td>Art & Design</td>\n",
" <td>June 8, 2018</td>\n",
" <td>Varies with device</td>\n",
" <td>4.2 and up</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Pixel Draw - Number Art Coloring Book</td>\n",
" <td>ART_AND_DESIGN</td>\n",
" <td>4.3</td>\n",
" <td>967</td>\n",
" <td>2.8M</td>\n",
" <td>100,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Art & Design;Creativity</td>\n",
" <td>June 20, 2018</td>\n",
" <td>1.1</td>\n",
" <td>4.4 and up</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" App ... Android_Ver\n",
"0 Photo Editor & Candy Camera & Grid & ScrapBook ... 4.0.3 and up\n",
"1 Coloring book moana ... 4.0.3 and up\n",
"2 U Launcher Lite – FREE Live Cool Themes, Hide ... ... 4.0.3 and up\n",
"3 Sketch - Draw & Paint ... 4.2 and up\n",
"4 Pixel Draw - Number Art Coloring Book ... 4.4 and up\n",
"\n",
"[5 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HastMHr0vsx2",
"colab_type": "text"
},
"source": [
"Vou iniciar a análise verificando se há registos duplicados no Dataframe, pois não nos interessa analisar o mesmo aplicativo mais de uma vez."
]
},
{
"cell_type": "code",
"metadata": {
"id": "I3j3DLpwiztF",
"colab_type": "code",
"outputId": "239d1022-6952-400d-f2f2-522e7c38dc6f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Verificando valores duplicados:\n",
"if any(df_app.App.duplicated()) is True:\n",
" print(f'Existe valores duplicados na coluna \"App\"? {True}')\n",
"else:\n",
" print(f'Existe valores duplicados na coluna \"App\"? {False}')"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Existe valores duplicados na coluna \"App\"? True\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TvXL33nRMUdO",
"colab_type": "text"
},
"source": [
"Bom, infelizmente temos registros duplicados, portanto, precisamos remover essas entradas duplicadas e manter apenas uma entrada por aplicativo. Uma coisa que poderíamos fazer é remover as linhas duplicadas aleatoriamente, mas vamos encontrar uma maneira melhor."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J9aTYnOvwV87",
"colab_type": "text"
},
"source": [
"Quanto maior o número de \"Reviews\", mais recentes devem ser os dados. Em vez de remover duplicatas aleatoriamente, manteremos apenas a linha com o maior número de avaliações e removeremos as outras entradas."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UiDVYGJ1NEyG",
"colab_type": "text"
},
"source": [
"Não sabemos a ordem dos registros e se não ordenarmos podemos correr o risco de eliminar um aplicativo que tem uma avaliação alta. Para ordenar pelo número de \"Reviews\" precisamos mudar seu tipo de dado para numérico. Quando tentei fazer a transformação recebi o seguinte erro **\"ValueError: invalid literal for int() with base 10: '3.0M'\"**.\n",
"Vamos identificar quais linhas têm esse problema:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iEejF9opJbZ-",
"colab_type": "code",
"outputId": "5c0d48b7-c82a-48e7-c5a3-b7c5dd10cdb2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
}
},
"source": [
"# Identificando registro:\n",
"df_app[~df_app.Reviews.str.isnumeric()]"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"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>App</th>\n",
" <th>Category</th>\n",
" <th>Rating</th>\n",
" <th>Reviews</th>\n",
" <th>Size</th>\n",
" <th>Installs</th>\n",
" <th>Type</th>\n",
" <th>Price</th>\n",
" <th>Content_Rating</th>\n",
" <th>Genres</th>\n",
" <th>Last_Updated</th>\n",
" <th>Current_Ver</th>\n",
" <th>Android_Ver</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10472</th>\n",
" <td>Life Made WI-Fi Touchscreen Photo Frame</td>\n",
" <td>1.9</td>\n",
" <td>19.0</td>\n",
" <td>3.0M</td>\n",
" <td>1,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>NaN</td>\n",
" <td>February 11, 2018</td>\n",
" <td>1.0.19</td>\n",
" <td>4.0 and up</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" App Category ... Current_Ver Android_Ver\n",
"10472 Life Made WI-Fi Touchscreen Photo Frame 1.9 ... 4.0 and up NaN\n",
"\n",
"[1 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5QWuNw2lTbIX",
"colab_type": "text"
},
"source": [
"Apenas um dos registros possue esse problema além disso os registros das outras colunas estão inconsistentes, na coluna \"Category\" por exemplo essa linha possui um valor de 1.9, então vou remover essa linha e transformar o tipo do dado.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VROUWKDjJf8S",
"colab_type": "code",
"colab": {}
},
"source": [
"# Removendo o registro:\n",
"df_app.drop(df_app.index[10472], inplace=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YHFD6iEvJ-7k",
"colab_type": "code",
"colab": {}
},
"source": [
"# Convertendo o tipo dos dados:\n",
"df_app.Reviews = pd.to_numeric(df_app.Reviews)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ixwdFvTWVV0-",
"colab_type": "text"
},
"source": [
"Agora vou remover os valores duplicados mantendo os registros que tem a avaliação mais alta."
]
},
{
"cell_type": "code",
"metadata": {
"id": "eMDxULD4pbEe",
"colab_type": "code",
"colab": {}
},
"source": [
"# Removendo os aplicativos duplicados:\n",
"df_app = df_app.sort_values(by='Reviews', ascending=False).drop_duplicates('App', keep='first')\n",
"\n",
"# Reorganizando os indices:\n",
"df_app = df_app.reset_index(drop=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Psgq8OUQ2fC3",
"colab_type": "code",
"outputId": "552d38bd-b623-48f6-d0a1-e526fd4720ee",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"df_app.head()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"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>App</th>\n",
" <th>Category</th>\n",
" <th>Rating</th>\n",
" <th>Reviews</th>\n",
" <th>Size</th>\n",
" <th>Installs</th>\n",
" <th>Type</th>\n",
" <th>Price</th>\n",
" <th>Content_Rating</th>\n",
" <th>Genres</th>\n",
" <th>Last_Updated</th>\n",
" <th>Current_Ver</th>\n",
" <th>Android_Ver</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Facebook</td>\n",
" <td>SOCIAL</td>\n",
" <td>4.1</td>\n",
" <td>78158306</td>\n",
" <td>Varies with device</td>\n",
" <td>1,000,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Teen</td>\n",
" <td>Social</td>\n",
" <td>August 3, 2018</td>\n",
" <td>Varies with device</td>\n",
" <td>Varies with device</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>WhatsApp Messenger</td>\n",
" <td>COMMUNICATION</td>\n",
" <td>4.4</td>\n",
" <td>69119316</td>\n",
" <td>Varies with device</td>\n",
" <td>1,000,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Communication</td>\n",
" <td>August 3, 2018</td>\n",
" <td>Varies with device</td>\n",
" <td>Varies with device</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Instagram</td>\n",
" <td>SOCIAL</td>\n",
" <td>4.5</td>\n",
" <td>66577446</td>\n",
" <td>Varies with device</td>\n",
" <td>1,000,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Teen</td>\n",
" <td>Social</td>\n",
" <td>July 31, 2018</td>\n",
" <td>Varies with device</td>\n",
" <td>Varies with device</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Messenger – Text and Video Chat for Free</td>\n",
" <td>COMMUNICATION</td>\n",
" <td>4.0</td>\n",
" <td>56646578</td>\n",
" <td>Varies with device</td>\n",
" <td>1,000,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone</td>\n",
" <td>Communication</td>\n",
" <td>August 1, 2018</td>\n",
" <td>Varies with device</td>\n",
" <td>Varies with device</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Clash of Clans</td>\n",
" <td>GAME</td>\n",
" <td>4.6</td>\n",
" <td>44893888</td>\n",
" <td>98M</td>\n",
" <td>100,000,000+</td>\n",
" <td>Free</td>\n",
" <td>0</td>\n",
" <td>Everyone 10+</td>\n",
" <td>Strategy</td>\n",
" <td>July 15, 2018</td>\n",
" <td>10.322.16</td>\n",
" <td>4.1 and up</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" App ... Android_Ver\n",
"0 Facebook ... Varies with device\n",
"1 WhatsApp Messenger ... Varies with device\n",
"2 Instagram ... Varies with device\n",
"3 Messenger – Text and Video Chat for Free ... Varies with device\n",
"4 Clash of Clans ... 4.1 and up\n",
"\n",
"[5 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "07hZDxIGYQPX",
"colab_type": "text"
},
"source": [
"Vamos analisar mais a fundo cada um dos atributos."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RPjqfFgLbzK9",
"colab_type": "text"
},
"source": [
"##*Size*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sPbSVaAgdHK1",
"colab_type": "code",
"outputId": "bbafa95b-7cd7-4703-d0ed-f3394e717337",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 986
}
},
"source": [
"# Valores únicos:\n",
"display(df_app.Size.unique())"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"array(['Varies with device', '98M', '76M', '97M', '74M', '40M', '52M',\n",
" '14M', '15M', '88M', '24M', '85M', '94M', '99M', '63M', '92M',\n",
" '17M', '62M', '71M', '34M', '95M', '67M', '58M', '100M', '59M',\n",
" '53M', '96M', '16M', '26M', '41M', '9.9M', '51M', '32M', '11M',\n",
" '55M', '60M', '79M', '69M', '42M', '77M', '57M', '82M', '75M',\n",
" '36M', '3.3M', '68M', '22M', '7.6M', '50M', '33M', '7.4M', '89M',\n",
" '18M', '25M', '78M', '46M', '49M', '9.7M', '37M', '72M', '39M',\n",
" '87M', '6.1M', '5.1M', '70M', '28M', '29M', '61M', '7.1M', '48M',\n",
" '21M', '35M', '54M', '12M', '56M', '3.8M', '8.7M', '91M', '27M',\n",