{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![](https://raw.githubusercontent.com/rafneta/CienciaDatosPythonCIDE/master/imagenes/banner.png)\n", "\n", "```{contents}\n", ":depth: 4\n", "```\n", "\n", "# EDA\n", "\n", "## ¿Qué es?\n", "\n", "La EDA (Exploratory Data Anlayisis) nos permite analizar los datos que tenemos en un database de manera intuitiva y grafíca asi de poder mejor comprender sus peculiaridades y llegar a una mejor comprensión así de facilitar las fases sucesivas. \n", "\n", "La definición formal es:\n", "\n", ">En estadística, el análisis exploratorio de datos es un enfoque para analizar conjuntos de datos para resumir sus principales características, a menudo con métodos visuales. Se puede usar o no un modelo estadístico, pero principalmente EDA es para ver qué nos pueden decir los datos más allá del modelado formal o la tarea de prueba de hipótesis. \n", "\n", "\n", "Podemos por lo tanto tener unos insight que nos serán utilzes luego tanto para preparar los datos para las faces sucesivas (removiendo las irregularidades) y tener una visión aproximativa de los mismos. \n", "\n", "Basandonos en este anlísis podemos empezar a toamr algunas decisiones en la estructuración de los datos y en la definición de los modelos a utilizar:\n", "\n", "* si la hacemos bien podemos ahorrarnos mucho trabajo\n", "* si la hacemos mal arriesgamos a perder información valiosa\n", "\n", "Temos 5 fases principales en el EDA:\n", "\n", "1. Recompilación y carga de datos\n", "2. Limpieza datos\n", "3. Analísis univariada\n", "4. Analísis bivariada\n", "5. Analísis multivariada" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recompilación y carga de datos\n", "\n", "Ya vimos en varios laboratorios como podemos cargar los datos de diferentes archivos, con apis e introducirlos manualmente. Cuidados con las fuentes de los datos y como tenemos que citarlos y como lo podemos compartir. \n", "\n", "Subimos por lo tantos los datos que pueden encontrar [aquí](https://raw.githubusercontent.com/Kaushik-Varma/Marketing_Data_Analysis/master/Marketing_Analysis.csv) para replicas." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "db=pd.read_csv(\"Marketing_Analysis.csv\",low_memory=False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(45211, 19)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.shape" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customeridagesalarybalancemaritaljobedutargeteddefaulthousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeresponse
0158.01000002143marriedmanagement,tertiaryyesnoyesnounknown5may, 2017261 sec1-10unknownno
1244.06000029singletechnician,secondaryyesnoyesnounknown5may, 2017151 sec1-10unknownno
2333.01200002marriedentrepreneur,secondaryyesnoyesyesunknown5may, 201776 sec1-10unknownno
3447.0200001506marriedblue-collar,unknownnonoyesnounknown5may, 201792 sec1-10unknownno
4533.001singleunknown,unknownnonononounknown5may, 2017198 sec1-10unknownno
\n", "
" ], "text/plain": [ " customerid age salary balance marital jobedu \\\n", "0 1 58.0 100000 2143 married management,tertiary \n", "1 2 44.0 60000 29 single technician,secondary \n", "2 3 33.0 120000 2 married entrepreneur,secondary \n", "3 4 47.0 20000 1506 married blue-collar,unknown \n", "4 5 33.0 0 1 single unknown,unknown \n", "\n", " targeted default housing loan contact day month duration campaign \\\n", "0 yes no yes no unknown 5 may, 2017 261 sec 1 \n", "1 yes no yes no unknown 5 may, 2017 151 sec 1 \n", "2 yes no yes yes unknown 5 may, 2017 76 sec 1 \n", "3 no no yes no unknown 5 may, 2017 92 sec 1 \n", "4 no no no no unknown 5 may, 2017 198 sec 1 \n", "\n", " pdays previous poutcome response \n", "0 -1 0 unknown no \n", "1 -1 0 unknown no \n", "2 -1 0 unknown no \n", "3 -1 0 unknown no \n", "4 -1 0 unknown no " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.head(5)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "db.set_index(\"customerid\", inplace= True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesalarybalancemaritaljobedutargeteddefaulthousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeresponse
customerid
158.01000002143marriedmanagement,tertiaryyesnoyesnounknown5may, 2017261 sec1-10unknownno
244.06000029singletechnician,secondaryyesnoyesnounknown5may, 2017151 sec1-10unknownno
333.01200002marriedentrepreneur,secondaryyesnoyesyesunknown5may, 201776 sec1-10unknownno
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" ], "text/plain": [ " age salary balance marital jobedu targeted \\\n", "customerid \n", "1 58.0 100000 2143 married management,tertiary yes \n", "2 44.0 60000 29 single technician,secondary yes \n", "3 33.0 120000 2 married entrepreneur,secondary yes \n", "\n", " default housing loan contact day month duration campaign \\\n", "customerid \n", "1 no yes no unknown 5 may, 2017 261 sec 1 \n", "2 no yes no unknown 5 may, 2017 151 sec 1 \n", "3 no yes yes unknown 5 may, 2017 76 sec 1 \n", "\n", " pdays previous poutcome response \n", "customerid \n", "1 -1 0 unknown no \n", "2 -1 0 unknown no \n", "3 -1 0 unknown no " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.head(3)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 45211 entries, 1 to 45211\n", "Data columns (total 18 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 45191 non-null float64\n", " 1 salary 45211 non-null int64 \n", " 2 balance 45211 non-null int64 \n", " 3 marital 45211 non-null object \n", " 4 jobedu 45211 non-null object \n", " 5 targeted 45211 non-null object \n", " 6 default 45211 non-null object \n", " 7 housing 45211 non-null object \n", " 8 loan 45211 non-null object \n", " 9 contact 45211 non-null object \n", " 10 day 45211 non-null int64 \n", " 11 month 45161 non-null object \n", " 12 duration 45211 non-null object \n", " 13 campaign 45211 non-null int64 \n", " 14 pdays 45211 non-null int64 \n", " 15 previous 45211 non-null int64 \n", " 16 poutcome 45211 non-null object \n", " 17 response 45181 non-null object \n", "dtypes: float64(1), int64(6), object(11)\n", "memory usage: 6.6+ MB\n" ] } ], "source": [ "db.info()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['age', 'salary', 'balance', 'marital', 'jobedu', 'targeted', 'default',\n", " 'housing', 'loan', 'contact', 'day', 'month', 'duration', 'campaign',\n", " 'pdays', 'previous', 'poutcome', 'response'],\n", " dtype='object')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora si nada más checkamos valores null:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 20\n", "salary 0\n", "balance 0\n", "marital 0\n", "jobedu 0\n", "targeted 0\n", "default 0\n", "housing 0\n", "loan 0\n", "contact 0\n", "day 0\n", "month 50\n", "duration 0\n", "campaign 0\n", "pdays 0\n", "previous 0\n", "poutcome 0\n", "response 30\n", "dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Limpieza datos\n", "\n", "Ahora procedemos a la limpieza del D:\n", "\n", "1. Eliminamos eventuales columnas/renglones de resume\n", "2. Unimos eventualmetne columna para entender mejor los datos\n", "3. de igual forma dividir eventaules otras columnas\n", "4. Averiguamos que todos las columnas tengan nombre y sobre todo el indice\n", "\n", "En nuestro caso tenemos:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesalarybalancemaritaljobedutargeteddefaulthousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeresponse
customerid
158.01000002143marriedmanagement,tertiaryyesnoyesnounknown5may, 2017261 sec1-10unknownno
244.06000029singletechnician,secondaryyesnoyesnounknown5may, 2017151 sec1-10unknownno
333.01200002marriedentrepreneur,secondaryyesnoyesyesunknown5may, 201776 sec1-10unknownno
447.0200001506marriedblue-collar,unknownnonoyesnounknown5may, 201792 sec1-10unknownno
533.001singleunknown,unknownnonononounknown5may, 2017198 sec1-10unknownno
\n", "
" ], "text/plain": [ " age salary balance marital jobedu targeted \\\n", "customerid \n", "1 58.0 100000 2143 married management,tertiary yes \n", "2 44.0 60000 29 single technician,secondary yes \n", "3 33.0 120000 2 married entrepreneur,secondary yes \n", "4 47.0 20000 1506 married blue-collar,unknown no \n", "5 33.0 0 1 single unknown,unknown no \n", "\n", " default housing loan contact day month duration campaign \\\n", "customerid \n", "1 no yes no unknown 5 may, 2017 261 sec 1 \n", "2 no yes no unknown 5 may, 2017 151 sec 1 \n", "3 no yes yes unknown 5 may, 2017 76 sec 1 \n", "4 no yes no unknown 5 may, 2017 92 sec 1 \n", "5 no no no unknown 5 may, 2017 198 sec 1 \n", "\n", " pdays previous poutcome response \n", "customerid \n", "1 -1 0 unknown no \n", "2 -1 0 unknown no \n", "3 -1 0 unknown no \n", "4 -1 0 unknown no \n", "5 -1 0 unknown no " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Qué notamos?" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesalarybalancemaritaljobedutargeteddefaulthousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeresponsejobeducation
customerid
158.01000002143marriedmanagement,tertiaryyesnoyesnounknown5may, 2017261 sec1-10unknownnomanagementtertiary
244.06000029singletechnician,secondaryyesnoyesnounknown5may, 2017151 sec1-10unknownnotechniciansecondary
333.01200002marriedentrepreneur,secondaryyesnoyesyesunknown5may, 201776 sec1-10unknownnoentrepreneursecondary
447.0200001506marriedblue-collar,unknownnonoyesnounknown5may, 201792 sec1-10unknownnoblue-collarunknown
533.001singleunknown,unknownnonononounknown5may, 2017198 sec1-10unknownnounknownunknown
...............................................................
4520751.060000825marriedtechnician,tertiaryyesnononocellular17nov, 201716.2833333333333 min3-10unknownyestechniciantertiary
4520871.0550001729divorcedretired,primaryyesnononocellular17nov, 20177.6 min2-10unknownyesretiredprimary
4520972.0550005715marriedretired,secondaryyesnononocellular17nov, 201718.7833333333333 min51843successyesretiredsecondary
4521057.020000668marriedblue-collar,secondaryyesnononotelephone17nov, 20178.46666666666667 min4-10unknownnoblue-collarsecondary
4521137.01200002971marriedentrepreneur,secondaryyesnononocellular17nov, 20176.01666666666667 min218811othernoentrepreneursecondary
\n", "

45211 rows × 20 columns

\n", "
" ], "text/plain": [ " age salary balance marital jobedu targeted \\\n", "customerid \n", "1 58.0 100000 2143 married management,tertiary yes \n", "2 44.0 60000 29 single technician,secondary yes \n", "3 33.0 120000 2 married entrepreneur,secondary yes \n", "4 47.0 20000 1506 married blue-collar,unknown no \n", "5 33.0 0 1 single unknown,unknown no \n", "... ... ... ... ... ... ... \n", "45207 51.0 60000 825 married technician,tertiary yes \n", "45208 71.0 55000 1729 divorced retired,primary yes \n", "45209 72.0 55000 5715 married retired,secondary yes \n", "45210 57.0 20000 668 married blue-collar,secondary yes \n", "45211 37.0 120000 2971 married entrepreneur,secondary yes \n", "\n", " default housing loan contact day month \\\n", "customerid \n", "1 no yes no unknown 5 may, 2017 \n", "2 no yes no unknown 5 may, 2017 \n", "3 no yes yes unknown 5 may, 2017 \n", "4 no yes no unknown 5 may, 2017 \n", "5 no no no unknown 5 may, 2017 \n", "... ... ... ... ... ... ... \n", "45207 no no no cellular 17 nov, 2017 \n", "45208 no no no cellular 17 nov, 2017 \n", "45209 no no no cellular 17 nov, 2017 \n", "45210 no no no telephone 17 nov, 2017 \n", "45211 no no no cellular 17 nov, 2017 \n", "\n", " duration campaign pdays previous poutcome response \\\n", "customerid \n", "1 261 sec 1 -1 0 unknown no \n", "2 151 sec 1 -1 0 unknown no \n", "3 76 sec 1 -1 0 unknown no \n", "4 92 sec 1 -1 0 unknown no \n", "5 198 sec 1 -1 0 unknown no \n", "... ... ... ... ... ... ... \n", "45207 16.2833333333333 min 3 -1 0 unknown yes \n", "45208 7.6 min 2 -1 0 unknown yes \n", "45209 18.7833333333333 min 5 184 3 success yes \n", "45210 8.46666666666667 min 4 -1 0 unknown no \n", "45211 6.01666666666667 min 2 188 11 other no \n", "\n", " job education \n", "customerid \n", "1 management tertiary \n", "2 technician secondary \n", "3 entrepreneur secondary \n", "4 blue-collar unknown \n", "5 unknown unknown \n", "... ... ... \n", "45207 technician tertiary \n", "45208 retired primary \n", "45209 retired secondary \n", "45210 blue-collar secondary \n", "45211 entrepreneur secondary \n", "\n", "[45211 rows x 20 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db['job']= db[\"jobedu\"].apply(lambda x: x.split(\",\")[0])\n", "db['education']= db[\"jobedu\"].apply(lambda x: x.split(\",\")[1])\n", "db" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesalarybalancemaritaltargeteddefaulthousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeresponsejobeducation
customerid
158.01000002143marriedyesnoyesnounknown5may, 2017261 sec1-10unknownnomanagementtertiary
244.06000029singleyesnoyesnounknown5may, 2017151 sec1-10unknownnotechniciansecondary
333.01200002marriedyesnoyesyesunknown5may, 201776 sec1-10unknownnoentrepreneursecondary
447.0200001506marriednonoyesnounknown5may, 201792 sec1-10unknownnoblue-collarunknown
533.001singlenonononounknown5may, 2017198 sec1-10unknownnounknownunknown
............................................................
4520751.060000825marriedyesnononocellular17nov, 201716.2833333333333 min3-10unknownyestechniciantertiary
4520871.0550001729divorcedyesnononocellular17nov, 20177.6 min2-10unknownyesretiredprimary
4520972.0550005715marriedyesnononocellular17nov, 201718.7833333333333 min51843successyesretiredsecondary
4521057.020000668marriedyesnononotelephone17nov, 20178.46666666666667 min4-10unknownnoblue-collarsecondary
4521137.01200002971marriedyesnononocellular17nov, 20176.01666666666667 min218811othernoentrepreneursecondary
\n", "

45211 rows × 19 columns

\n", "
" ], "text/plain": [ " age salary balance marital targeted default housing loan \\\n", "customerid \n", "1 58.0 100000 2143 married yes no yes no \n", "2 44.0 60000 29 single yes no yes no \n", "3 33.0 120000 2 married yes no yes yes \n", "4 47.0 20000 1506 married no no yes no \n", "5 33.0 0 1 single no no no no \n", "... ... ... ... ... ... ... ... ... \n", "45207 51.0 60000 825 married yes no no no \n", "45208 71.0 55000 1729 divorced yes no no no \n", "45209 72.0 55000 5715 married yes no no no \n", "45210 57.0 20000 668 married yes no no no \n", "45211 37.0 120000 2971 married yes no no no \n", "\n", " contact day month duration campaign pdays \\\n", "customerid \n", "1 unknown 5 may, 2017 261 sec 1 -1 \n", "2 unknown 5 may, 2017 151 sec 1 -1 \n", "3 unknown 5 may, 2017 76 sec 1 -1 \n", "4 unknown 5 may, 2017 92 sec 1 -1 \n", "5 unknown 5 may, 2017 198 sec 1 -1 \n", "... ... ... ... ... ... ... \n", "45207 cellular 17 nov, 2017 16.2833333333333 min 3 -1 \n", "45208 cellular 17 nov, 2017 7.6 min 2 -1 \n", "45209 cellular 17 nov, 2017 18.7833333333333 min 5 184 \n", "45210 telephone 17 nov, 2017 8.46666666666667 min 4 -1 \n", "45211 cellular 17 nov, 2017 6.01666666666667 min 2 188 \n", "\n", " previous poutcome response job education \n", "customerid \n", "1 0 unknown no management tertiary \n", "2 0 unknown no technician secondary \n", "3 0 unknown no entrepreneur secondary \n", "4 0 unknown no blue-collar unknown \n", "5 0 unknown no unknown unknown \n", "... ... ... ... ... ... \n", "45207 0 unknown yes technician tertiary \n", "45208 0 unknown yes retired primary \n", "45209 3 success yes retired secondary \n", "45210 0 unknown no blue-collar secondary \n", "45211 11 other no entrepreneur secondary \n", "\n", "[45211 rows x 19 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.drop(labels='jobedu', axis = 1, inplace = True)\n", "db" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora manejamos los valores faltantes, podemos tener de tres tipos:\n", "\n", "1. MCAR(Missing completely at random): no dependen de alguna caracteristica en particular\n", "2. MAR(Missing at random): pueden depender de alguna caracteristica\n", "3. MNAR(Missing not at random): hay alguna razón para faltar\n", "\n", "Droppamos todos lo que no tienen edad definida:" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(45161, 20)" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db = db[~db.age.isnull()].copy()\n", "db.isnull().sum()\n", "db.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora imputamos los valores para los meses:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'may, 2017'" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "month_mode = db.month.mode()[0]\n", "month_mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora procedemos a substituir este valor en nuestra columna con faltante:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.month.fillna(month_mode, inplace = True)\n", "db.month.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora manejamos los faltantes en nuestra variable dependiente *(response)*:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "salary 0\n", "balance 0\n", "marital 0\n", "targeted 0\n", "default 0\n", "housing 0\n", "loan 0\n", "contact 0\n", "day 0\n", "month 0\n", "duration 0\n", "campaign 0\n", "pdays 0\n", "previous 0\n", "poutcome 0\n", "response 0\n", "job 0\n", "education 0\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db = db[~db.response.isnull()].copy()\n", "db.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podriamos sobstituir nuestro valores faltantes con **NaN** para que no haya afectaciones cuando procedemos al analisís estadistico" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Outliers\n", "\n", "Son unos datos que se discostan remarcablemente del siguiente punto más cercano. Podemos tener:\n", "\n", "1. Outlier univariado\n", "2. Outlier multivariado/bivariado: cuando ploteamos una variable con otra el valor se discosta del valor esperado de manera anormal\n", "\n", "Antes de proceder a eso tenemos que estandardizar los valores para que escalas diferentes no nos lleven a conclusiones equivocadas. Tenemos diferentes tipologias de datos que podemos encontrar \n", "\n", "#### Analisis univariado de variable categorica no ordenada\n", "\n", "* no tienen peso o medida\n", "* no tienen orden" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "blue-collar 9722\n", "management 9451\n", "technician 7589\n", "admin. 5165\n", "services 4148\n", "retired 2262\n", "self-employed 1574\n", "entrepreneur 1484\n", "unemployed 1302\n", "housemaid 1238\n", "student 938\n", "unknown 288\n", "Name: job, dtype: int64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.job.value_counts()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "blue-collar 0.215274\n", "management 0.209273\n", "technician 0.168043\n", "admin. 0.114369\n", "services 0.091849\n", "retired 0.050087\n", "self-employed 0.034853\n", "entrepreneur 0.032860\n", "unemployed 0.028830\n", "housemaid 0.027413\n", "student 0.020770\n", "unknown 0.006377\n", "Name: job, dtype: float64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.job.value_counts(normalize=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Podemo plotear nuestro analisis:" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "db.job.value_counts(normalize=True).plot.barh()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Analisis univariada Variable categorica ordenada\n", "\n", "Estas variables tienen un orden natural, por ejemplo:\n", "\n", "* Meses\n", "* Educación" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "db.education.value_counts(normalize=True)\n" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "db.education.value_counts().plot.pie()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si tenemos una variable con valores numericos podemos calcular " ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 45161.000000\n", "mean 57004.849317\n", "std 32087.698810\n", "min 0.000000\n", "25% 20000.000000\n", "50% 60000.000000\n", "75% 70000.000000\n", "max 120000.000000\n", "Name: salary, dtype: float64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.salary.describe()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finalmente podemos plotear una densidad de la distribución de la varaible numerica:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(db['salary'])" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(db['balance'])\n", "plt.xlim(-8019, 102127)\n", "plt.ylim(0,3000)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 45161.000000\n", "mean 1362.850690\n", "std 3045.939589\n", "min -8019.000000\n", "25% 72.000000\n", "50% 448.000000\n", "75% 1428.000000\n", "max 102127.000000\n", "Name: balance, dtype: float64" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.balance.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analisis bivariada\n", "\n", "Cuando hacemos un analisis bivariada el uso de graficas nos puede ser extremadamente utíl. En particular los más ocupados son:\n", "\n", "* scatter plot\n", "* pair plot\n", "* matriz de correlación\n", "\n", "#### Scatter plot" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(db.salary,db.balance)\n", "plt.show()\n", "\n", "db.plot.scatter(x=\"age\",y=\"balance\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Pair plot " ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(data = db, vars=['salary','balance','age'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### QQ-Plot\n", "\n", "Es un método gráfico para el diagnóstico de diferencias entre la distribución de probabilidad de una población de la que se ha extraído una muestra aleatoria y una distribución usada para la comparación. " ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.stats as stats\n", "iris = sns.load_dataset('iris')\n", "\n", "\n", "rvs = stats.norm(loc=0, scale=1)\n", "normal_sample = rvs.rvs(size=100000)\n", "sns.set()\n", "sns.distplot(normal_sample)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "normal_sample = rvs.rvs(size=10000)\n", "stats.probplot(normal_sample, dist=\"norm\", plot=plt)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(stats.expon().rvs(size=10000))" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "((array([-4.34602155, -4.14724578, -4.03913009, ..., 4.03913009,\n", " 4.14724578, 4.34602155]),\n", " array([2.16825700e-06, 1.57624189e-05, 2.93375619e-05, ...,\n", " 1.16652885e+01, 1.21702078e+01, 1.34591407e+01])),\n", " (0.8996537488205563, 0.9969476608219366, 0.9033984542371438))" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expon_rvs = stats.expon().rvs(size=100000)\n", "normal_rvs = stats.norm().rvs(size=100000)\n", "stats.probplot(x=expon_rvs, dist=stats.norm(), plot=plt)" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expon_rvs = stats.expon().rvs(size=100000)\n", "stats.probplot(x=expon_rvs, dist=\"expon\", plot=plt)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Matrices de correlación\n", "\n", "Cuando queremos ver en un unico grafico las relaciones entre más de una variable de forma sintetica" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesalarybalance
age1.0000000.0245130.097710
salary0.0245131.0000000.055489
balance0.0977100.0554891.000000
\n", "
" ], "text/plain": [ " age salary balance\n", "age 1.000000 0.024513 0.097710\n", "salary 0.024513 1.000000 0.055489\n", "balance 0.097710 0.055489 1.000000" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db[['age','salary','balance']].corr()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(db[['age','salary','balance']].corr(), annot=True, cmap = 'Blues')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### variables categoricas numericas\n", "\n", "Normalmente se analizan con base en su media, mediana y un instrumento particularmente util es el *boxplot*. \n", "\n", "Calculamolos a manita:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "response\n", "no 56769.510482\n", "yes 58780.510880\n", "Name: salary, dtype: float64" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.groupby('response')['salary'].mean()\n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "response\n", "no 60000\n", "yes 60000\n", "Name: salary, dtype: int64" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.groupby('response')['salary'].median()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pareciera no haber grandes diferencias, pero analizando los boxplot descubrimos que:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=db.response, y=db.salary)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 1]), [Text(0, 0, 'no'), Text(1, 0, 'yes')])" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.boxplot(x='response', y='salary', data=db)\n", "plt.setp(ax.artists, alpha=.5, linewidth=2, edgecolor=\"k\")\n", "plt.xticks(rotation=45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Analisis variables categoricas\n", "\n", "Queremos analizar como diferentes varbles categoricas (como la educación) tienen impacto en la variable dependiente.. Para hacer eso necesitamos convertir *Response* en una variable binaria. " ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 39876\n", "1 5285\n", "Name: response_rate, dtype: int64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db['response_rate'] = np.where(db.response=='yes',1,0)\n", "db.response_rate.value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Analizando ahora como se distribuye frente distintos valores de la una variable categorica tendremos:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "db.groupby('marital')['response_rate'].mean().plot.bar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analisis multivariado\n", "\n", "Antes tenemos crear una tabla pivot con en las columnas los diferentes status civiles:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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education
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secondary0.1035590.0946500.129271
tertiary0.1374150.1298350.183737
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" ], "text/plain": [ "marital divorced married single\n", "education \n", "primary 0.138852 0.075601 0.106808\n", "secondary 0.103559 0.094650 0.129271\n", "tertiary 0.137415 0.129835 0.183737\n", "unknown 0.142012 0.122519 0.162879" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result = pd.pivot_table(data=db, index='education', columns='marital',values='response_rate')\n", "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora como antes vamos a imprimir una mapa de calor. " ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(result, annot=True, cmap = 'RdYlGn', center=0.117)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apis y carca de datos\n", "\n", "Podemos cargar los datos de local, commo hicimos antes en nuestro ejercicio, por medio de una url o con unas APIs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#metodo URL\n", "\n", "url = \"http://climatedataapi.worldbank.org/climateweb/rest/v1/country/cru/tas/year/ROU.csv\"\n", "df = pd.read_csv(url)\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pero ahora sí pasamos a las APis\n", "\n", ">Blockquote\n", "Una API, o interfaz de programación de aplicaciones, es un servidor que puede utilizar para recuperar y enviar datos mediante código. Las API se utilizan con mayor frecuencia para recuperar datos. \n", "\n", "¿Por que usar una API?\n", "\n", "1. Los datos cambía rápido, y no tiene sentido estar generando un dataset y bajandolo cada munuto.\n", "2. Necesitas acceder a sólo una parte de los datos\n", "3. Hay una computación repetida involucrada\n", "\n", "Podemos hacer diferentes acciones con las API:\n", "\n", "* GET\n", "* POST\n", "* PUT \n", "* DELETE" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import requests\n", "response = requests.get('https://google.com/')\n", "print(response)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](response.PNG)\n", "\n", "La libreria request tiene algunas funciones especificas:" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n" ] } ], "source": [ "print(response.status_code)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Request is successful.\n" ] } ], "source": [ "if response:\n", " print('Request is successful.')\n", "else:\n", " print('Request returned an error.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El endpoint es una dirección, usuaklmente, hacía donde apunta nuestro pedido. Normalmente el nombre de esta dirección es autodescriptivo." ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n" ] } ], "source": [ "response = requests.get(\"http://api.open-notify.org/astros.json\")\n", "print(response.status_code)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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namecraft
0Mark Vande HeiISS
1Oleg NovitskiyISS
2Pyotr DubrovISS
3Thomas PesquetISS
4Megan McArthurISS
5Shane KimbroughISS
6Akihiko HoshideISS
\n", "
" ], "text/plain": [ " name craft\n", "0 Mark Vande Hei ISS\n", "1 Oleg Novitskiy ISS\n", "2 Pyotr Dubrov ISS\n", "3 Thomas Pesquet ISS\n", "4 Megan McArthur ISS\n", "5 Shane Kimbrough ISS\n", "6 Akihiko Hoshide ISS" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(response.json()[\"people\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Muchas veces necesitamos una llave para poder acceder a los datos que requerimos. Igual hay un verdadero mercado y sitio especializados para esto. \n", "\n", "Por medio de los encabezaados HTTPS se manejan algunas informaciones sobre las APIs\n" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Content-Encoding': 'gzip', 'Content-Type': 'application/json; charset=utf-8', 'Date': 'Fri, 07 May 2021 12:19:43 GMT', 'Server': 'Apache/2.4.46 (Amazon)', 'Strict-Transport-Security': 'max-age=15552000; includeSubDomains', 'Vary': 'Origin,Accept-Encoding', 'X-Content-Type-Options': 'nosniff', 'X-DNS-Prefetch-Control': 'off', 'X-Download-Options': 'noopen', 'X-Frame-Options': 'SAMEORIGIN', 'X-Response-Time': '2ms', 'X-XSS-Protection': '1; mode=block', 'Content-Length': '265', 'Connection': 'keep-alive'}" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response = requests.get(\"https://api.thedogapi.com/v1/breeds/1\")\n", "response.headers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ya sabiendo que es un json, podemos acceder a el atravez de:" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Affenpinscher'" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response.json()[\"name\"]" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La Divina Comedia ['Dante Alighieri'] \n", "La divina comedia ['Dante Alghieri'] \n", "La Divina Comedia por Dante Alighieri ['Dante Alighieri'] \n" ] } ], "source": [ "endpoint = \"https://www.googleapis.com/books/v1/volumes\"\n", "query = \"la divina comedia\"\n", "\n", "params = {\"q\": query, \"maxResults\": 3}\n", "response = requests.get(endpoint, params=params).json()\n", "for book in response[\"items\"]:\n", " volume = book[\"volumeInfo\"]\n", " title = volume[\"title\"]\n", " published = volume[\"authors\"]\n", " print(f\"{title} {published} \")" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'kind': 'books#volumes',\n", " 'totalItems': 734,\n", " 'items': [{'kind': 'books#volume',\n", " 'id': 'WrPCDwAAQBAJ',\n", " 'etag': 'j6ynahmNPaE',\n", " 'selfLink': 'https://www.googleapis.com/books/v1/volumes/WrPCDwAAQBAJ',\n", " 'volumeInfo': {'title': 'La Divina Comedia',\n", " 'authors': ['Dante Alighieri'],\n", " 'publisher': 'Good Press',\n", " 'publishedDate': '2019-11-11',\n", " 'description': '\"La Divina Comedia\" de Dante Alighieri (traducido por Manuel Aranda y Sanjuan) de la Editorial Good Press. Good Press publica una gran variedad de títulos que abarca todos los géneros. Van desde los títulos clásicos famosos, novelas, textos documentales y crónicas de la vida real, hasta temas ignorados o por ser descubiertos de la literatura universal. Editorial Good Press divulga libros que son una lectura imprescindible. Cada publicación de Good Press ha sido corregida y formateada al detalle, para elevar en gran medida su facilidad de lectura en todos los equipos y programas de lectura electrónica. Nuestra meta es la producción de Libros electrónicos que sean versátiles y accesibles para el lector y para todos, en un formato digital de alta calidad.',\n", " 'industryIdentifiers': [{'type': 'OTHER',\n", " 'identifier': 'EAN:4057664122674'}],\n", " 'readingModes': {'text': True, 'image': True},\n", " 'pageCount': 941,\n", " 'printType': 'BOOK',\n", " 'categories': ['Fiction'],\n", " 'maturityRating': 'NOT_MATURE',\n", " 'allowAnonLogging': True,\n", " 'contentVersion': '1.5.5.0.preview.3',\n", " 'panelizationSummary': {'containsEpubBubbles': False,\n", " 'containsImageBubbles': False},\n", " 'imageLinks': {'smallThumbnail': 'http://books.google.com/books/content?id=WrPCDwAAQBAJ&printsec=frontcover&img=1&zoom=5&edge=curl&source=gbs_api',\n", " 'thumbnail': 'http://books.google.com/books/content?id=WrPCDwAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&source=gbs_api'},\n", " 'language': 'es',\n", " 'previewLink': 'http://books.google.com.mx/books?id=WrPCDwAAQBAJ&pg=PP1&dq=la+divina+comedia&hl=&cd=1&source=gbs_api',\n", " 'infoLink': 'https://play.google.com/store/books/details?id=WrPCDwAAQBAJ&source=gbs_api',\n", " 'canonicalVolumeLink': 'https://play.google.com/store/books/details?id=WrPCDwAAQBAJ'},\n", " 'saleInfo': {'country': 'MX',\n", " 'saleability': 'FOR_SALE',\n", " 'isEbook': True,\n", " 'listPrice': {'amount': 25, 'currencyCode': 'MXN'},\n", " 'retailPrice': {'amount': 25, 'currencyCode': 'MXN'},\n", " 'buyLink': 'https://play.google.com/store/books/details?id=WrPCDwAAQBAJ&rdid=book-WrPCDwAAQBAJ&rdot=1&source=gbs_api',\n", " 'offers': [{'finskyOfferType': 1,\n", " 'listPrice': {'amountInMicros': 25000000, 'currencyCode': 'MXN'},\n", " 'retailPrice': {'amountInMicros': 25000000, 'currencyCode': 'MXN'},\n", " 'giftable': True}]},\n", " 'accessInfo': {'country': 'MX',\n", " 'viewability': 'PARTIAL',\n", " 'embeddable': True,\n", " 'publicDomain': False,\n", " 'textToSpeechPermission': 'ALLOWED',\n", " 'epub': {'isAvailable': True},\n", " 'pdf': {'isAvailable': True},\n", " 'webReaderLink': 'http://play.google.com/books/reader?id=WrPCDwAAQBAJ&hl=&printsec=frontcover&source=gbs_api',\n", " 'accessViewStatus': 'SAMPLE',\n", " 'quoteSharingAllowed': False},\n", " 'searchInfo': {'textSnippet': '"La Divina Comedia" de Dante Alighieri (traducido por Manuel Aranda y Sanjuan) de la Editorial Good Press.'}},\n", " {'kind': 'books#volume',\n", " 'id': 'wGPSDgAAQBAJ',\n", " 'etag': 'NOQVX9XaqiA',\n", " 'selfLink': 'https://www.googleapis.com/books/v1/volumes/wGPSDgAAQBAJ',\n", " 'volumeInfo': {'title': 'La divina comedia',\n", " 'subtitle': '',\n", " 'authors': ['Dante Alghieri'],\n", " 'publisher': 'NoBooks Editorial',\n", " 'publishedDate': '1976',\n", " 'readingModes': {'text': True, 'image': True},\n", " 'pageCount': 441,\n", " 'printType': 'BOOK',\n", " 'maturityRating': 'NOT_MATURE',\n", " 'allowAnonLogging': True,\n", " 'contentVersion': 'preview-1.0.0',\n", " 'panelizationSummary': {'containsEpubBubbles': False,\n", " 'containsImageBubbles': False},\n", " 'imageLinks': {'smallThumbnail': 'http://books.google.com/books/content?id=wGPSDgAAQBAJ&printsec=frontcover&img=1&zoom=5&edge=curl&source=gbs_api',\n", " 'thumbnail': 'http://books.google.com/books/content?id=wGPSDgAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&source=gbs_api'},\n", " 'language': 'es',\n", " 'previewLink': 'http://books.google.com.mx/books?id=wGPSDgAAQBAJ&printsec=frontcover&dq=la+divina+comedia&hl=&cd=2&source=gbs_api',\n", " 'infoLink': 'https://play.google.com/store/books/details?id=wGPSDgAAQBAJ&source=gbs_api',\n", " 'canonicalVolumeLink': 'https://play.google.com/store/books/details?id=wGPSDgAAQBAJ'},\n", " 'saleInfo': {'country': 'MX',\n", " 'saleability': 'FOR_SALE',\n", " 'isEbook': True,\n", " 'listPrice': {'amount': 28.84, 'currencyCode': 'MXN'},\n", " 'retailPrice': {'amount': 28.84, 'currencyCode': 'MXN'},\n", " 'buyLink': 'https://play.google.com/store/books/details?id=wGPSDgAAQBAJ&rdid=book-wGPSDgAAQBAJ&rdot=1&source=gbs_api',\n", " 'offers': [{'finskyOfferType': 1,\n", " 'listPrice': {'amountInMicros': 28840000, 'currencyCode': 'MXN'},\n", " 'retailPrice': {'amountInMicros': 28840000, 'currencyCode': 'MXN'},\n", " 'giftable': True}]},\n", " 'accessInfo': {'country': 'MX',\n", " 'viewability': 'PARTIAL',\n", " 'embeddable': True,\n", " 'publicDomain': False,\n", " 'textToSpeechPermission': 'ALLOWED',\n", " 'epub': {'isAvailable': True},\n", " 'pdf': {'isAvailable': True},\n", " 'webReaderLink': 'http://play.google.com/books/reader?id=wGPSDgAAQBAJ&hl=&printsec=frontcover&source=gbs_api',\n", " 'accessViewStatus': 'SAMPLE',\n", " 'quoteSharingAllowed': False}},\n", " {'kind': 'books#volume',\n", " 'id': 'O0ESIQu9_7wC',\n", " 'etag': 'bzi0xAGR2sk',\n", " 'selfLink': 'https://www.googleapis.com/books/v1/volumes/O0ESIQu9_7wC',\n", " 'volumeInfo': {'title': 'La Divina Comedia por Dante Alighieri',\n", " 'subtitle': 'El Infierno',\n", " 'authors': ['Dante Alighieri'],\n", " 'publishedDate': '1870',\n", " 'industryIdentifiers': [{'type': 'OTHER',\n", " 'identifier': 'IBNF:CF005685501'}],\n", " 'readingModes': {'text': False, 'image': False},\n", " 'pageCount': 232,\n", " 'printType': 'BOOK',\n", " 'maturityRating': 'NOT_MATURE',\n", " 'allowAnonLogging': False,\n", " 'contentVersion': '0.2.1.0.preview.0',\n", " 'panelizationSummary': {'containsEpubBubbles': False,\n", " 'containsImageBubbles': False},\n", " 'imageLinks': {'smallThumbnail': 'http://books.google.com/books/content?id=O0ESIQu9_7wC&printsec=frontcover&img=1&zoom=5&source=gbs_api',\n", " 'thumbnail': 'http://books.google.com/books/content?id=O0ESIQu9_7wC&printsec=frontcover&img=1&zoom=1&source=gbs_api'},\n", " 'language': 'es',\n", " 'previewLink': 'http://books.google.com.mx/books?id=O0ESIQu9_7wC&q=la+divina+comedia&dq=la+divina+comedia&hl=&cd=3&source=gbs_api',\n", " 'infoLink': 'http://books.google.com.mx/books?id=O0ESIQu9_7wC&dq=la+divina+comedia&hl=&source=gbs_api',\n", " 'canonicalVolumeLink': 'https://books.google.com/books/about/La_Divina_Comedia_por_Dante_Alighieri.html?hl=&id=O0ESIQu9_7wC'},\n", " 'saleInfo': {'country': 'MX',\n", " 'saleability': 'NOT_FOR_SALE',\n", " 'isEbook': False},\n", " 'accessInfo': {'country': 'MX',\n", " 'viewability': 'NO_PAGES',\n", " 'embeddable': False,\n", " 'publicDomain': False,\n", " 'textToSpeechPermission': 'ALLOWED',\n", " 'epub': {'isAvailable': False},\n", " 'pdf': {'isAvailable': False},\n", " 'webReaderLink': 'http://play.google.com/books/reader?id=O0ESIQu9_7wC&hl=&printsec=frontcover&source=gbs_api',\n", " 'accessViewStatus': 'NONE',\n", " 'quoteSharingAllowed': False}}]}" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fuentes:\n", "* [TWS](https://towardsdatascience.com/exploratory-data-analysis-eda-python-87178e35b14)" ] } ], "metadata": { "celltoolbar": "Edit 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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }