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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Análisis exploratorio" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Importamos las principales librerías y el dataset. Observamos el número de filas (316200), columnas (6) y los primeros registros." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(316200, 6)" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import seaborn as sns\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "df = pd.read_csv(\"../conversion_data.csv\")\n", | |
| "df.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style>\n", | |
| " .dataframe thead tr:only-child th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: left;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>country</th>\n", | |
| " <th>age</th>\n", | |
| " <th>new_user</th>\n", | |
| " <th>source</th>\n", | |
| " <th>total_pages_visited</th>\n", | |
| " <th>converted</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>UK</td>\n", | |
| " <td>25</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Ads</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>US</td>\n", | |
| " <td>23</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Seo</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>US</td>\n", | |
| " <td>28</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Seo</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>China</td>\n", | |
| " <td>39</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Seo</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>US</td>\n", | |
| " <td>30</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Seo</td>\n", | |
| " <td>6</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " country age new_user source total_pages_visited converted\n", | |
| "0 UK 25 1 Ads 1 0\n", | |
| "1 US 23 1 Seo 5 0\n", | |
| "2 US 28 1 Seo 4 0\n", | |
| "3 China 39 1 Seo 5 0\n", | |
| "4 US 30 1 Seo 6 0" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Comprobamos las personas mayores de 100 años" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "Chequeamos si existen celdas vacías. Nos devuelve que todas las filas y columnas contienen algún valor." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0" | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "sum(df.isnull().values.ravel())\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0" | |
| ] | |
| }, | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "sum([True for idx,row in df.iterrows() if any(row.isnull())])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Comprobamos de qué tipo es cada variable y cambiamos su nomenclatura: (integer = numéricas y object = categóricas) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style>\n", | |
| " .dataframe thead tr:only-child th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: left;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Variable</th>\n", | |
| " <th>Tipo</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>country</td>\n", | |
| " <td>object</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>age</td>\n", | |
| " <td>int64</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>new_user</td>\n", | |
| " <td>int64</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>source</td>\n", | |
| " <td>object</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>total_pages_visited</td>\n", | |
| " <td>int64</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>converted</td>\n", | |
| " <td>int64</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Variable Tipo\n", | |
| "0 country object\n", | |
| "1 age int64\n", | |
| "2 new_user int64\n", | |
| "3 source object\n", | |
| "4 total_pages_visited int64\n", | |
| "5 converted int64" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dtype_df = df.dtypes.reset_index()\n", | |
| "dtype_df.columns = [\"Variable\", \"Tipo\"]\n", | |
| "dtype_df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def get_var_category(series): \n", | |
| " unique_count = series.nunique(dropna=False)\n", | |
| " total_count = len(series)\n", | |
| " if pd.api.types.is_numeric_dtype(series): \n", | |
| " return 'Numerical' \n", | |
| " elif pd.api.types.is_datetime64_dtype(series): \n", | |
| " return 'Date' \n", | |
| " elif unique_count==total_count: \n", | |
| " return 'Text (Unique)' \n", | |
| " else: \n", | |
| " return 'Categorical'\n", | |
| "\n", | |
| "def print_categories(df): \n", | |
| " for column_name in df.columns: \n", | |
| " print(column_name, \": \", get_var_category(df[column_name]))\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "country : Categorical\n", | |
| "age : Numerical\n", | |
| "new_user : Numerical\n", | |
| "source : Categorical\n", | |
| "total_pages_visited : Numerical\n", | |
| "converted : Numerical\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print_categories(df)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Analizamos la variable 'edad' y vemos que hay un valor máximo de 123 años." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "count 316200.000000\n", | |
| "mean 30.569858\n", | |
| "std 8.271802\n", | |
| "min 17.000000\n", | |
| "25% 24.000000\n", | |
| "50% 30.000000\n", | |
| "75% 36.000000\n", | |
| "max 123.000000\n", | |
| "Name: age, dtype: float64\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0xbbf3128>" | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xa7f1a20>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "print(df['age'].describe())\n", | |
| "sns.distplot( df[\"age\"] , color=\"y\", bins=12)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style>\n", | |
| " .dataframe thead tr:only-child th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: left;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>country</th>\n", | |
| " <th>age</th>\n", | |
| " <th>new_user</th>\n", | |
| " <th>source</th>\n", | |
| " <th>total_pages_visited</th>\n", | |
| " <th>converted</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>90928</th>\n", | |
| " <td>Germany</td>\n", | |
| " <td>123</td>\n", | |
| " <td>0</td>\n", | |
| " <td>Seo</td>\n", | |
| " <td>15</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>295581</th>\n", | |
| " <td>UK</td>\n", | |
| " <td>111</td>\n", | |
| " <td>0</td>\n", | |
| " <td>Ads</td>\n", | |
| " <td>10</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " country age new_user source total_pages_visited converted\n", | |
| "90928 Germany 123 0 Seo 15 1\n", | |
| "295581 UK 111 0 Ads 10 1" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df[df.age>=100]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Eliminamos los valores superiores a 110 años." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "count 316198.000000\n", | |
| "mean 30.569311\n", | |
| "std 8.268958\n", | |
| "min 17.000000\n", | |
| "25% 24.000000\n", | |
| "50% 30.000000\n", | |
| "75% 36.000000\n", | |
| "max 79.000000\n", | |
| "Name: age, dtype: float64" | |
| ] | |
| }, | |
| "execution_count": 39, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df2 = df.drop(df[(df.age > 110)].index)\n", | |
| "df2['age'].describe()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Analizamos la conversión por tipo de tráfico web: primero en números absolutos y después transformamos a %" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 41, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style>\n", | |
| " .dataframe thead tr:only-child th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: left;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>converted</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " <th>All</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>source</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Ads</th>\n", | |
| " <td>85680</td>\n", | |
| " <td>3059</td>\n", | |
| " <td>88739</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Direct</th>\n", | |
| " <td>70380</td>\n", | |
| " <td>2040</td>\n", | |
| " <td>72420</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Seo</th>\n", | |
| " <td>149940</td>\n", | |
| " <td>5099</td>\n", | |
| " <td>155039</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>All</th>\n", | |
| " <td>306000</td>\n", | |
| " <td>10198</td>\n", | |
| " <td>316198</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "converted 0 1 All\n", | |
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| "Ads 85680 3059 88739\n", | |
| "Direct 70380 2040 72420\n", | |
| "Seo 149940 5099 155039\n", | |
| "All 306000 10198 316198" | |
| ] | |
| }, | |
| "execution_count": 41, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pd.crosstab(df2.source,df2.converted, margins=True)" | |
| ] | |
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| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
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| " <td>0.967742</td>\n", | |
| " <td>0.032258</td>\n", | |
| " <td>1.0</td>\n", | |
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| "converted 0 1 All\n", | |
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| "execution_count": 6, | |
| "metadata": {}, | |
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| "def percConvert(ser):\n", | |
| " return ser/float(ser[-1])\n", | |
| "pd.crosstab(df.source,df.converted, margins=True).apply(percConvert, axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Analizamos la tasa de conversión por tipo de usuario: el 70% de las conversiones es por parte de usuarios existentes y el 30% de nuevos usuarios." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
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| " <td>1.0</td>\n", | |
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| " <th>All</th>\n", | |
| " <td>0.314535</td>\n", | |
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| "new_user 0 1 All\n", | |
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| "All 0.314535 0.685465 1.0" | |
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| "execution_count": 7, | |
| "metadata": {}, | |
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| } | |
| ], | |
| "source": [ | |
| "def percConvert(ser):\n", | |
| " return ser/float(ser[-1])\n", | |
| "pd.crosstab(df.converted,df.new_user, margins=True).apply(percConvert, axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Por países, en valores absolutos el mayor volumen de conversión viene de EE.UU (6732), aunque porcentualmente es Alemania el principal protagonista (6%)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
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| " <tr>\n", | |
| " <th>China</th>\n", | |
| " <td>76500</td>\n", | |
| " <td>102</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Germany</th>\n", | |
| " <td>12240</td>\n", | |
| " <td>816</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>UK</th>\n", | |
| " <td>45900</td>\n", | |
| " <td>2550</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>US</th>\n", | |
| " <td>171360</td>\n", | |
| " <td>6732</td>\n", | |
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| ], | |
| "text/plain": [ | |
| "converted 0 1\n", | |
| "country \n", | |
| "China 76500 102\n", | |
| "Germany 12240 816\n", | |
| "UK 45900 2550\n", | |
| "US 171360 6732" | |
| ] | |
| }, | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pais_tabla = pd.crosstab(index=df[\"country\"], \n", | |
| " columns=df[\"converted\"])\n", | |
| "\n", | |
| "pais_tabla" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
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| " <tr>\n", | |
| " <th>country</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
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| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>China</th>\n", | |
| " <td>0.998668</td>\n", | |
| " <td>0.001332</td>\n", | |
| " <td>1.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Germany</th>\n", | |
| " <td>0.937500</td>\n", | |
| " <td>0.062500</td>\n", | |
| " <td>1.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>UK</th>\n", | |
| " <td>0.947368</td>\n", | |
| " <td>0.052632</td>\n", | |
| " <td>1.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>US</th>\n", | |
| " <td>0.962199</td>\n", | |
| " <td>0.037801</td>\n", | |
| " <td>1.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>All</th>\n", | |
| " <td>0.967742</td>\n", | |
| " <td>0.032258</td>\n", | |
| " <td>1.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
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| "</div>" | |
| ], | |
| "text/plain": [ | |
| "converted 0 1 All\n", | |
| "country \n", | |
| "China 0.998668 0.001332 1.0\n", | |
| "Germany 0.937500 0.062500 1.0\n", | |
| "UK 0.947368 0.052632 1.0\n", | |
| "US 0.962199 0.037801 1.0\n", | |
| "All 0.967742 0.032258 1.0" | |
| ] | |
| }, | |
| "execution_count": 50, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "def percConvert(ser):\n", | |
| " return ser/float(ser[-1])\n", | |
| "pd.crosstab(df.country,df.converted, margins=True).apply(percConvert, axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Text(0.5,1,'Conversión por país')" | |
| ] | |
| }, | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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9SpIkjWTe6UCSJKnlVsZZopKkVWjiE+f2uoVWmtfrBqQXkDNskiRJLWdgkyRJ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\nkqSW+/8BVrkY5dP+hmsAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xced7e48>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "pais_tabla.plot(kind=\"bar\", \n", | |
| " figsize=(10,5),\n", | |
| " stacked=True)\n", | |
| "plt.title('Conversión por paÃs')\n" | |
| ] | |
| } | |
| ], | |
| "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": "3.6.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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