{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Science Academy - Python Fundamentos - Capítulo 7\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missão: Analisar o Comportamento de Compra de Consumidores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nível de Dificuldade: Alto" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Você recebeu a tarefa de analisar os dados de compras de um web site! Os dados estão no formato JSON e disponíveis junto com este notebook.\n", "\n", "No site, cada usuário efetua login usando sua conta pessoal e pode adquirir produtos à medida que navega pela lista de produtos oferecidos. Cada produto possui um valor de venda. Dados de idade e sexo de cada usuário foram coletados e estão fornecidos no arquivo JSON.\n", "\n", "Seu trabalho é entregar uma análise de comportamento de compra dos consumidores. Esse é um tipo de atividade comum realizado por Cientistas de Dados e o resultado deste trabalho pode ser usado, por exemplo, para alimentar um modelo de Machine Learning e fazer previsões sobre comportamentos futuros.\n", "\n", "Mas nesta missão você vai analisar o comportamento de compra dos consumidores usando o pacote Pandas da linguagem Python e seu relatório final deve incluir cada um dos seguintes itens:\n", "\n", "** Contagem de Compradores **\n", "\n", "* Número total de compradores\n", "\n", "\n", "** Análise Geral de Compras **\n", "\n", "* Número de itens exclusivos\n", "* Preço médio de compra\n", "* Número total de compras\n", "* Rendimento total\n", "\n", "\n", "** Informações Demográficas Por Gênero **\n", "\n", "* Porcentagem e contagem de compradores masculinos\n", "* Porcentagem e contagem de compradores do sexo feminino\n", "* Porcentagem e contagem de outros / não divulgados\n", "\n", "\n", "** Análise de Compras Por Gênero **\n", "\n", "* Número de compras\n", "* Preço médio de compra\n", "* Valor Total de Compra\n", "* Compras for faixa etária\n", "\n", "\n", "** Identifique os 5 principais compradores pelo valor total de compra e, em seguida, liste (em uma tabela): **\n", "\n", "* Login\n", "* Número de compras\n", "* Preço médio de compra\n", "* Valor Total de Compra\n", "* Itens mais populares\n", "\n", "\n", "** Identifique os 5 itens mais populares por contagem de compras e, em seguida, liste (em uma tabela): **\n", "\n", "* ID do item\n", "* Nome do item\n", "* Número de compras\n", "* Preço do item\n", "* Valor Total de Compra\n", "* Itens mais lucrativos\n", "\n", "\n", "** Identifique os 5 itens mais lucrativos pelo valor total de compra e, em seguida, liste (em uma tabela): **\n", "\n", "* ID do item\n", "* Nome do item\n", "* Número de compras\n", "* Preço do item\n", "* Valor Total de Compra\n", "\n", "\n", "** Como considerações finais: **\n", "\n", "* Seu script deve funcionar para o conjunto de dados fornecido.\n", "* Você deve usar a Biblioteca Pandas e o Jupyter Notebook.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Imports\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IdadeItem IDLoginNome do ItemSexoValor
038165Aelalis34Bone Crushing Silver SkewerMasculino3.37
121119Eolo46Stormbringer, Dark Blade of Ending MiseryMasculino2.32
234174Assastnya25Primitive BladeMasculino2.46
32192Pheusrical25Final CriticMasculino1.36
42363Aela59Stormfury MaceMasculino1.27
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" ], "text/plain": [ " Idade Item ID Login Nome do Item \\\n", "0 38 165 Aelalis34 Bone Crushing Silver Skewer \n", "1 21 119 Eolo46 Stormbringer, Dark Blade of Ending Misery \n", "2 34 174 Assastnya25 Primitive Blade \n", "3 21 92 Pheusrical25 Final Critic \n", "4 23 63 Aela59 Stormfury Mace \n", "\n", " Sexo Valor \n", "0 Masculino 3.37 \n", "1 Masculino 2.32 \n", "2 Masculino 2.46 \n", "3 Masculino 1.36 \n", "4 Masculino 1.27 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Carrega o arquivo\n", "load_file = \"dados_compras.json\"\n", "purchase_file = pd.read_json(load_file, orient = \"records\")\n", "purchase_file.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Informações Sobre os Compradores" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SexoLoginIdade
0MasculinoAelalis3438
1MasculinoEolo4621
2MasculinoAssastnya2534
3MasculinoPheusrical2521
4MasculinoAela5923
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" ], "text/plain": [ " Sexo Login Idade\n", "0 Masculino Aelalis34 38\n", "1 Masculino Eolo46 21\n", "2 Masculino Assastnya25 34\n", "3 Masculino Pheusrical25 21\n", "4 Masculino Aela59 23" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "player_demographics = purchase_file.loc[:, [\"Sexo\", \"Login\", \"Idade\"]]\n", "player_demographics.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "573" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Limpeza dos dados e remoção de duplicatas\n", "player_demographics = player_demographics.drop_duplicates()\n", "player_count = player_demographics.count()[0]\n", "player_count" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Total de Jogadores
0573
\n", "
" ], "text/plain": [ " Total de Jogadores\n", "0 573" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Converter saída para DF para uso posterior em análise\n", "pd.DataFrame({\"Total de Jogadores\" : [player_count]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Análise de Compra" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Número de Itens ÚnicosPreço MédioNúmero de ComprasTotal de Vendas
0183$2.93780$2,286.33
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" ], "text/plain": [ " Número de Itens Únicos Preço Médio Número de Compras Total de Vendas\n", "0 183 $2.93 780 $2,286.33" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cálculos básicos\n", "average_item_price = purchase_file[\"Valor\"].mean()\n", "total_item_price = purchase_file[\"Valor\"].sum()\n", "total_item_count = purchase_file[\"Valor\"].count()\n", "item_id = len(purchase_file[\"Item ID\"].unique())\n", "\n", "# Dataframe para os resultados\n", "summary_calculations = pd.DataFrame({\"Número de Itens Únicos\" : item_id,\n", " \"Número de Compras\" : total_item_count, \n", " \"Total de Vendas\" : total_item_price, \n", " \"Preço Médio\" : [average_item_price]})\n", "\n", "# Data Munging\n", "summary_calculations = summary_calculations.round(2)\n", "summary_calculations [\"Preço Médio\"] = summary_calculations[\"Preço Médio\"].map(\"${:,.2f}\".format)\n", "summary_calculations [\"Total de Vendas\"] = summary_calculations[\"Total de Vendas\"].map(\"${:,.2f}\".format)\n", "summary_calculations = summary_calculations.loc[:, [\"Número de Itens Únicos\", \"Preço Médio\", \"Número de Compras\", \"Total de Vendas\"]]\n", "\n", "summary_calculations" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([165, 119, 174, 92, 63, 10, 153, 169, 118, 99, 57, 47, 81,\n", " 77, 44, 96, 123, 59, 91, 177, 78, 3, 11, 183, 65, 132,\n", " 106, 49, 45, 155, 37, 48, 90, 13, 171, 25, 7, 124, 68,\n", " 85, 120, 17, 141, 73, 151, 32, 51, 101, 140, 31, 34, 2,\n", " 86, 39, 28, 160, 134, 83, 38, 158, 110, 122, 54, 105, 87,\n", " 23, 144, 128, 175, 46, 150, 152, 108, 172, 167, 181, 20, 130,\n", " 111, 103, 30, 139, 173, 55, 115, 35, 42, 9, 84, 180, 102,\n", " 53, 18, 74, 126, 50, 62, 125, 121, 129, 149, 12, 71, 14,\n", " 58, 27, 52, 66, 100, 112, 24, 94, 107, 0, 182, 97, 70,\n", " 89, 1, 170, 93, 179, 36, 75, 143, 137, 176, 148, 127, 147,\n", " 161, 154, 157, 116, 61, 131, 41, 145, 60, 162, 135, 8, 40,\n", " 15, 29, 72, 114, 117, 79, 88, 104, 95, 64, 98, 33, 76,\n", " 146, 166, 56, 22, 21, 16, 67, 133, 69, 159, 82, 113, 164,\n", " 6, 163, 5, 19, 168, 136, 80, 26, 142, 178, 156, 109, 43,\n", " 4])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "purchase_file[\"Item ID\"].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Informações Demográficas" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Cálculos básicos\n", "gender_count = player_demographics[\"Sexo\"].value_counts()\n", "gender_percent = (gender_count / player_count) * 100\n", "\n", "# Dataframe para os resultados\n", "gender_demographics = pd.DataFrame({\"Sexo\" : gender_count, \n", " \"%\" : gender_percent})\n", "\n", "# Data Munging\n", "gender_demographics = gender_demographics.round(2)\n", "gender_demographics [\"%\"] = gender_demographics[\"%\"].map(\"{:,.1f}%\".format)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Masculino 465\n", "Feminino 100\n", "Outro / Não Divulgado 8\n", "Name: Sexo, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Output Test\n", "gender_count" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Masculino 81.151832\n", "Feminino 17.452007\n", "Outro / Não Divulgado 1.396161\n", "Name: Sexo, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Output Test\n", "gender_percent" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
%Sexo
Masculino81.2%465
Feminino17.4%100
Outro / Não Divulgado1.4%8
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" ], "text/plain": [ " % Sexo\n", "Masculino 81.2% 465\n", "Feminino 17.4% 100\n", "Outro / Não Divulgado 1.4% 8" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Output Test\n", "gender_demographics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Análise de Compra Por Gênero" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Agrupamentos\n", "gender_total_item_price = purchase_file.groupby([\"Sexo\"]).sum()[\"Valor\"].rename(\"Total de Vendas\")\n", "gender_average_item_price = purchase_file.groupby([\"Sexo\"]).mean()[\"Valor\"].rename(\"Average Price\")\n", "purchase_count = purchase_file.groupby([\"Sexo\"]).count()[\"Valor\"].rename(\"Número de Compras\")\n", "normalized_total = gender_total_item_price / gender_demographics[\"Sexo\"]\n", "\n", "# Armazenando o resultado em um Dataframe\n", "gender_purchasing_analysis = pd.DataFrame({\"Número de Compras\" : purchase_count, \n", " \"Valor Médio Por Item\" : gender_average_item_price, \n", " \"Total de Vendas\" : gender_total_item_price, \n", " \"Total Normalizado\" : normalized_total})\n", "\n", "# Data Munging\n", "gender_purchasing_analysis = gender_purchasing_analysis.round(2)\n", "gender_purchasing_analysis [\"Valor Médio Por Item\"] = gender_purchasing_analysis[\"Valor Médio Por Item\"].map(\"${:,.2f}\".format)\n", "gender_purchasing_analysis [\"Total de Vendas\"] = gender_purchasing_analysis[\"Total de Vendas\"].map(\"${:,.2f}\".format)\n", "gender_purchasing_analysis [\"Total Normalizado\"] = gender_purchasing_analysis[\"Total Normalizado\"].map(\"${:,.2f}\".format)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Sexo\n", "Feminino 382.91\n", "Masculino 1867.68\n", "Outro / Não Divulgado 35.74\n", "Name: Total de Vendas, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resultado\n", "gender_total_item_price" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Sexo\n", "Feminino 2.815515\n", "Masculino 2.950521\n", "Outro / Não Divulgado 3.249091\n", "Name: Average Price, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resultado\n", "gender_average_item_price" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Número de ComprasTotal NormalizadoTotal de VendasValor Médio Por Item
Sexo
Feminino136$3.83$382.91$2.82
Masculino633$4.02$1,867.68$2.95
Outro / Não Divulgado11$4.47$35.74$3.25
\n", "
" ], "text/plain": [ " Número de Compras Total Normalizado Total de Vendas \\\n", "Sexo \n", "Feminino 136 $3.83 $382.91 \n", "Masculino 633 $4.02 $1,867.68 \n", "Outro / Não Divulgado 11 $4.47 $35.74 \n", "\n", " Valor Médio Por Item \n", "Sexo \n", "Feminino $2.82 \n", "Masculino $2.95 \n", "Outro / Não Divulgado $3.25 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resultado\n", "gender_purchasing_analysis" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Feminino 3.829100\n", "Masculino 4.016516\n", "Outro / Não Divulgado 4.467500\n", "dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resultado\n", "normalized_total" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Análise Demográfica" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SexoLoginIdade
0MasculinoAelalis3438
1MasculinoEolo4621
2MasculinoAssastnya2534
3MasculinoPheusrical2521
4MasculinoAela5923
5MasculinoTanimnya9120
6MasculinoUndjaskla9720
7FemininoIathenudil2929
8MasculinoSondenasta6325
9MasculinoHilaerin9231
10MasculinoChamosia2924
11MasculinoSally6420
12MasculinoIskossa8830
13MasculinoSeorithstilis9023
14MasculinoSundast2940
15MasculinoHaellysu2921
16FemininoSundista8522
17FemininoAenarap3422
18MasculinoIskista8828
19MasculinoAssossa4331
20MasculinoIrith8324
21MasculinoIaralrgue7415
22FemininoDeural4811
23MasculinoChanosia6519
24MasculinoQarwen6711
25MasculinoIdai6121
26MasculinoAerithllora3629
28MasculinoIlariarin4515
29FemininoPhaedai2516
30FemininoEulaeria4021
............
729MasculinoIskichinya8116
730FemininoHiadanurin3610
731FemininoTyaelly5320
732FemininoShidai4223
733MasculinoBartassaya7316
734MasculinoMindosiasya2813
736MasculinoIndirrian5619
737MasculinoSondim6822
739FemininoCosadar5835
741MasculinoLeyirra8324
742MasculinoInguron5526
746MasculinoRalasti4835
747MasculinoLamon2832
748MasculinoIsri4915
750MasculinoEollym9123
752FemininoYalostiphos6815
755FemininoAilaesuir6622
756MasculinoSiasri6722
758MasculinoRyastycal9020
761MasculinoRaeduerin3328
764MasculinoAssassasda8425
766FemininoNitherian5822
768MasculinoChamucosda9321
769MasculinoFrichilsasya7824
770MasculinoAenasu6922
771MasculinoLassista9724
772MasculinoSidap5115
773MasculinoChamadarsda6321
778MasculinoQuelaton8020
779FemininoAlim8523
\n", "

573 rows × 3 columns

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" ], "text/plain": [ " Sexo Login Idade\n", "0 Masculino Aelalis34 38\n", "1 Masculino Eolo46 21\n", "2 Masculino Assastnya25 34\n", "3 Masculino Pheusrical25 21\n", "4 Masculino Aela59 23\n", "5 Masculino Tanimnya91 20\n", "6 Masculino Undjaskla97 20\n", "7 Feminino Iathenudil29 29\n", "8 Masculino Sondenasta63 25\n", "9 Masculino Hilaerin92 31\n", "10 Masculino Chamosia29 24\n", "11 Masculino Sally64 20\n", "12 Masculino Iskossa88 30\n", "13 Masculino Seorithstilis90 23\n", "14 Masculino Sundast29 40\n", "15 Masculino Haellysu29 21\n", "16 Feminino Sundista85 22\n", "17 Feminino Aenarap34 22\n", "18 Masculino Iskista88 28\n", "19 Masculino Assossa43 31\n", "20 Masculino Irith83 24\n", "21 Masculino Iaralrgue74 15\n", "22 Feminino Deural48 11\n", "23 Masculino Chanosia65 19\n", "24 Masculino Qarwen67 11\n", "25 Masculino Idai61 21\n", "26 Masculino Aerithllora36 29\n", "28 Masculino Ilariarin45 15\n", "29 Feminino Phaedai25 16\n", "30 Feminino Eulaeria40 21\n", ".. ... ... ...\n", "729 Masculino Iskichinya81 16\n", "730 Feminino Hiadanurin36 10\n", "731 Feminino Tyaelly53 20\n", "732 Feminino Shidai42 23\n", "733 Masculino Bartassaya73 16\n", "734 Masculino Mindosiasya28 13\n", "736 Masculino Indirrian56 19\n", "737 Masculino Sondim68 22\n", "739 Feminino Cosadar58 35\n", "741 Masculino Leyirra83 24\n", "742 Masculino Inguron55 26\n", "746 Masculino Ralasti48 35\n", "747 Masculino Lamon28 32\n", "748 Masculino Isri49 15\n", "750 Masculino Eollym91 23\n", "752 Feminino Yalostiphos68 15\n", "755 Feminino Ailaesuir66 22\n", "756 Masculino Siasri67 22\n", "758 Masculino Ryastycal90 20\n", "761 Masculino Raeduerin33 28\n", "764 Masculino Assassasda84 25\n", "766 Feminino Nitherian58 22\n", "768 Masculino Chamucosda93 21\n", "769 Masculino Frichilsasya78 24\n", "770 Masculino Aenasu69 22\n", "771 Masculino Lassista97 24\n", "772 Masculino Sidap51 15\n", "773 Masculino Chamadarsda63 21\n", "778 Masculino Quelaton80 20\n", "779 Feminino Alim85 23\n", "\n", "[573 rows x 3 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "player_demographics" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Cálculos básicos\n", "age_bins = [0, 9.99, 14.99, 19.99, 24.99, 29.99, 34.99, 39.99, 999]\n", "age_bracket = [\"Menos de 10\", \"10 a 14\", \"15 a 19\", \"20 a 24\", \"25 a 29\", \"30 a 34\", \"35 a 39\", \"Mais de 40\"]\n", "\n", "purchase_file[\"Range de Idades\"] = pd.cut(purchase_file[\"Idade\"], age_bins, labels=age_bracket)\n", "\n", "# Cálculos básicos\n", "age_demographics_count = purchase_file[\"Range de Idades\"].value_counts()\n", "age_demographics_average_item_price = purchase_file.groupby([\"Range de Idades\"]).mean()[\"Valor\"]\n", "age_demographics_total_item_price = purchase_file.groupby([\"Range de Idades\"]).sum()[\"Valor\"]\n", "age_demographics_percent = (age_demographics_count / player_count) * 100\n", "\n", "# Dataframe para os resultados\n", "age_demographics = pd.DataFrame({\"Contagem\": age_demographics_count, \"%\": age_demographics_percent, \"Valor Unitario\": age_demographics_average_item_price, \"Valor Total de Compra\": age_demographics_total_item_price})\n", "\n", "# Data Munging\n", "age_demographics [\"Valor Unitario\"] = age_demographics[\"Valor Unitario\"].map(\"${:,.2f}\".format)\n", "age_demographics [\"Valor Total de Compra\"] = age_demographics[\"Valor Total de Compra\"].map(\"${:,.2f}\".format)\n", "age_demographics [\"%\"] = age_demographics[\"%\"].map(\"{:,.2f}%\".format)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SexoLoginIdade
0MasculinoAelalis3438
1MasculinoEolo4621
2MasculinoAssastnya2534
3MasculinoPheusrical2521
4MasculinoAela5923
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" ], "text/plain": [ " Sexo Login Idade\n", "0 Masculino Aelalis34 38\n", "1 Masculino Eolo46 21\n", "2 Masculino Assastnya25 34\n", "3 Masculino Pheusrical25 21\n", "4 Masculino Aela59 23" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resultado\n", "player_demographics.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
%ContagemValor Total de CompraValor Unitario
10 a 146.11%35$96.95$2.77
15 a 1923.21%133$386.42$2.91
20 a 2458.64%336$978.77$2.91
25 a 2921.82%125$370.33$2.96
30 a 3411.17%64$197.25$3.08
35 a 397.33%42$119.40$2.84
Mais de 402.97%17$53.75$3.16
Menos de 104.89%28$83.46$2.98
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" ], "text/plain": [ " % Contagem Valor Total de Compra Valor Unitario\n", "10 a 14 6.11% 35 $96.95 $2.77\n", "15 a 19 23.21% 133 $386.42 $2.91\n", "20 a 24 58.64% 336 $978.77 $2.91\n", "25 a 29 21.82% 125 $370.33 $2.96\n", "30 a 34 11.17% 64 $197.25 $3.08\n", "35 a 39 7.33% 42 $119.40 $2.84\n", "Mais de 40 2.97% 17 $53.75 $3.16\n", "Menos de 10 4.89% 28 $83.46 $2.98" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resultado\n", "age_demographics = age_demographics.sort_index()\n", "age_demographics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Top Spenders" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Número de ComprasValor Médio de CompraValor Total de Compra
Login
Qarwen674$2.49$9.97
Sondim433$3.13$9.38
Tillyrin303$3.06$9.19
Lisistaya473$3.06$9.19
Tyisriphos582$4.59$9.18
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" ], "text/plain": [ " Número de Compras Valor Médio de Compra Valor Total de Compra\n", "Login \n", "Qarwen67 4 $2.49 $9.97\n", "Sondim43 3 $3.13 $9.38\n", "Tillyrin30 3 $3.06 $9.19\n", "Lisistaya47 3 $3.06 $9.19\n", "Tyisriphos58 2 $4.59 $9.18" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cálculos básicos\n", "user_total = purchase_file.groupby([\"Login\"]).sum()[\"Valor\"].rename(\"Valor Total de Compra\")\n", "user_average = purchase_file.groupby([\"Login\"]).mean()[\"Valor\"].rename(\"Valor Médio de Compra\")\n", "user_count = purchase_file.groupby([\"Login\"]).count()[\"Valor\"].rename(\"Número de Compras\")\n", "\n", "# Dataframe para os resultados\n", "user_data = pd.DataFrame({\"Valor Total de Compra\": user_total, \"Valor Médio de Compra\": user_average, \"Número de Compras\": user_count})\n", "\n", "# Data Munging\n", "user_data [\"Valor Total de Compra\"] = user_data[\"Valor Total de Compra\"].map(\"${:,.2f}\".format)\n", "user_data [\"Valor Médio de Compra\"] = user_data[\"Valor Médio de Compra\"].map(\"${:,.2f}\".format)\n", "user_data.sort_values(\"Valor Total de Compra\", ascending=False).head(5)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Número de ComprasValor Médio de CompraValor Total de Compra
Login
Adairialis761$2.46$2.46
Aduephos783$2.23$6.70
Aeduera683$1.93$5.80
Aela491$2.46$2.46
Aela591$1.27$1.27
Aelalis342$2.53$5.06
Aelin321$3.14$3.14
Aeliriam772$3.36$6.72
Aeliriarin931$2.04$2.04
Aeliru632$4.49$8.98
Aellyria801$4.32$4.32
Aellyrialis391$3.15$3.15
Aellysup381$3.61$3.61
Aelollo591$1.55$1.55
Aenarap341$1.65$1.65
Aenasu691$3.27$3.27
Aeral431$2.72$2.72
Aeral851$4.25$4.25
Aeral971$2.35$2.35
Aeri842$3.30$6.60
Aerillorin701$1.88$1.88
Aerithllora363$3.48$10.45
Aerithnucal562$1.59$3.18
Aerithnuphos611$1.69$1.69
Aerithriaphos451$2.38$2.38
Aesty511$1.82$1.82
Aesur961$4.66$4.66
Aethe801$2.32$2.32
Aethedru701$2.97$2.97
Aidain512$3.42$6.84
............
Undjaskla971$4.57$4.57
Undjasksya561$4.53$4.53
Undotesta331$3.90$3.90
Wailin721$2.04$2.04
Whaestysu861$4.08$4.08
Yadacal261$1.93$1.93
Yadaisuir652$4.28$8.56
Yadanun743$3.03$9.09
Yalaeria911$1.88$1.88
Yaliru881$3.71$3.71
Yalo711$2.41$2.41
Yalostiphos681$2.37$2.37
Yaralnura482$2.10$4.19
Yararmol431$1.55$1.55
Yarirarn351$2.88$2.88
Yaristi641$1.24$1.24
Yarithllodeu721$2.19$2.19
Yarithphos281$2.35$2.35
Yarithsurgue622$2.41$4.81
Yarmol791$2.91$2.91
Yarolwen772$3.49$6.98
Yasriphos603$3.47$10.40
Yasrisu921$2.60$2.60
Yasur351$2.78$2.78
Yasur851$2.04$2.04
Yasurra521$3.14$3.14
Yathecal722$3.88$7.77
Yathecal821$2.41$2.41
Zhisrisu832$1.23$2.46
Zontibe811$3.71$3.71
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573 rows × 3 columns

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" ], "text/plain": [ " Número de Compras Valor Médio de Compra Valor Total de Compra\n", "Login \n", "Adairialis76 1 $2.46 $2.46\n", "Aduephos78 3 $2.23 $6.70\n", "Aeduera68 3 $1.93 $5.80\n", "Aela49 1 $2.46 $2.46\n", "Aela59 1 $1.27 $1.27\n", "Aelalis34 2 $2.53 $5.06\n", "Aelin32 1 $3.14 $3.14\n", "Aeliriam77 2 $3.36 $6.72\n", "Aeliriarin93 1 $2.04 $2.04\n", "Aeliru63 2 $4.49 $8.98\n", "Aellyria80 1 $4.32 $4.32\n", "Aellyrialis39 1 $3.15 $3.15\n", "Aellysup38 1 $3.61 $3.61\n", "Aelollo59 1 $1.55 $1.55\n", "Aenarap34 1 $1.65 $1.65\n", "Aenasu69 1 $3.27 $3.27\n", "Aeral43 1 $2.72 $2.72\n", "Aeral85 1 $4.25 $4.25\n", "Aeral97 1 $2.35 $2.35\n", "Aeri84 2 $3.30 $6.60\n", "Aerillorin70 1 $1.88 $1.88\n", "Aerithllora36 3 $3.48 $10.45\n", "Aerithnucal56 2 $1.59 $3.18\n", "Aerithnuphos61 1 $1.69 $1.69\n", "Aerithriaphos45 1 $2.38 $2.38\n", "Aesty51 1 $1.82 $1.82\n", "Aesur96 1 $4.66 $4.66\n", "Aethe80 1 $2.32 $2.32\n", "Aethedru70 1 $2.97 $2.97\n", "Aidain51 2 $3.42 $6.84\n", "... ... ... ...\n", "Undjaskla97 1 $4.57 $4.57\n", "Undjasksya56 1 $4.53 $4.53\n", "Undotesta33 1 $3.90 $3.90\n", "Wailin72 1 $2.04 $2.04\n", "Whaestysu86 1 $4.08 $4.08\n", "Yadacal26 1 $1.93 $1.93\n", "Yadaisuir65 2 $4.28 $8.56\n", "Yadanun74 3 $3.03 $9.09\n", "Yalaeria91 1 $1.88 $1.88\n", "Yaliru88 1 $3.71 $3.71\n", "Yalo71 1 $2.41 $2.41\n", "Yalostiphos68 1 $2.37 $2.37\n", "Yaralnura48 2 $2.10 $4.19\n", "Yararmol43 1 $1.55 $1.55\n", "Yarirarn35 1 $2.88 $2.88\n", "Yaristi64 1 $1.24 $1.24\n", "Yarithllodeu72 1 $2.19 $2.19\n", "Yarithphos28 1 $2.35 $2.35\n", "Yarithsurgue62 2 $2.41 $4.81\n", "Yarmol79 1 $2.91 $2.91\n", "Yarolwen77 2 $3.49 $6.98\n", "Yasriphos60 3 $3.47 $10.40\n", "Yasrisu92 1 $2.60 $2.60\n", "Yasur35 1 $2.78 $2.78\n", "Yasur85 1 $2.04 $2.04\n", "Yasurra52 1 $3.14 $3.14\n", "Yathecal72 2 $3.88 $7.77\n", "Yathecal82 1 $2.41 $2.41\n", "Zhisrisu83 2 $1.23 $2.46\n", "Zontibe81 1 $3.71 $3.71\n", "\n", "[573 rows x 3 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resultado \n", "user_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Itens Mais Populares" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Número de ComprasValor Médio de CompraValor Total de Compra
Nome do Item
Final Critic14$2.76$38.60
Arcane Gem11$2.23$24.53
Betrayal, Whisper of Grieving Widows11$2.35$25.85
Stormcaller10$3.46$34.65
Woeful Adamantite Claymore9$1.24$11.16
\n", "
" ], "text/plain": [ " Número de Compras Valor Médio de Compra \\\n", "Nome do Item \n", "Final Critic 14 $2.76 \n", "Arcane Gem 11 $2.23 \n", "Betrayal, Whisper of Grieving Widows 11 $2.35 \n", "Stormcaller 10 $3.46 \n", "Woeful Adamantite Claymore 9 $1.24 \n", "\n", " Valor Total de Compra \n", "Nome do Item \n", "Final Critic $38.60 \n", "Arcane Gem $24.53 \n", "Betrayal, Whisper of Grieving Widows $25.85 \n", "Stormcaller $34.65 \n", "Woeful Adamantite Claymore $11.16 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cálculos básicos\n", "user_total = purchase_file.groupby([\"Nome do Item\"]).sum()[\"Valor\"].rename(\"Valor Total de Compra\")\n", "user_average = purchase_file.groupby([\"Nome do Item\"]).mean()[\"Valor\"].rename(\"Valor Médio de Compra\")\n", "user_count = purchase_file.groupby([\"Nome do Item\"]).count()[\"Valor\"].rename(\"Número de Compras\")\n", "\n", "# Dataframe para os resultados\n", "user_data = pd.DataFrame({\"Valor Total de Compra\": user_total, \"Valor Médio de Compra\": user_average, \"Número de Compras\": user_count})\n", "\n", "# Data Munging\n", "user_data [\"Valor Total de Compra\"] = user_data[\"Valor Total de Compra\"].map(\"${:,.2f}\".format)\n", "user_data [\"Valor Médio de Compra\"] = user_data[\"Valor Médio de Compra\"].map(\"${:,.2f}\".format)\n", "user_data.sort_values(\"Número de Compras\", ascending=False).head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Itens Mais Lucrativos" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Número de ComprasValor Médio de CompraValor Total de Compra
Nome do Item
Shadowsteel5$1.98$9.90
Souleater3$3.27$9.81
Shadow Strike, Glory of Ending Hope5$1.93$9.65
Heartseeker, Reaver of Souls3$3.21$9.63
Agatha5$1.91$9.55
\n", "
" ], "text/plain": [ " Número de Compras Valor Médio de Compra \\\n", "Nome do Item \n", "Shadowsteel 5 $1.98 \n", "Souleater 3 $3.27 \n", "Shadow Strike, Glory of Ending Hope 5 $1.93 \n", "Heartseeker, Reaver of Souls 3 $3.21 \n", "Agatha 5 $1.91 \n", "\n", " Valor Total de Compra \n", "Nome do Item \n", "Shadowsteel $9.90 \n", "Souleater $9.81 \n", "Shadow Strike, Glory of Ending Hope $9.65 \n", "Heartseeker, Reaver of Souls $9.63 \n", "Agatha $9.55 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cálculos básicos\n", "user_total = purchase_file.groupby([\"Nome do Item\"]).sum()[\"Valor\"].rename(\"Valor Total de Compra\")\n", "user_average = purchase_file.groupby([\"Nome do Item\"]).mean()[\"Valor\"].rename(\"Valor Médio de Compra\")\n", "user_count = purchase_file.groupby([\"Nome do Item\"]).count()[\"Valor\"].rename(\"Número de Compras\")\n", "\n", "# Dataframe para os resultados\n", "user_data = pd.DataFrame({\"Valor Total de Compra\": user_total, \"Valor Médio de Compra\": user_average, \"Número de Compras\": user_count})\n", "\n", "# Data Munging\n", "user_data [\"Valor Total de Compra\"] = user_data[\"Valor Total de Compra\"].map(\"${:,.2f}\".format)\n", "user_data [\"Valor Médio de Compra\"] = user_data[\"Valor Médio de Compra\"].map(\"${:,.2f}\".format)\n", "user_data.sort_values(\"Valor Total de Compra\", ascending=False).head(5)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Fim" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Obrigado - Data Science Academy - facebook.com/dsacademybr" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }