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Exploratory Data Analysis with Python Cookbook

Name: Exploratory Data Analysis with Python Cookbook
Author: Ayodele Oluleye
ISBN: 9781803231105

by Ayodele Oluleye

June 2023

Beginner to intermediate

382 pages

7h 52m

English

Packt Publishing

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Exploratory Data Analysis with Python Cookbook
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Chapter 1: Generating Summary Statistics
Technical requirementsAnalyzing the mean of a datasetGetting readyHow to do it…How it works...There’s more...Checking the median of a datasetGetting readyHow to do it…How it works...There’s more...Identifying the mode of a datasetGetting readyHow to do it…How it works...There’s more...Checking the variance of a datasetGetting readyHow to do it…How it works...There’s more…Identifying the standard deviation of a datasetGetting readyHow to do it…How it works...There’s more...Generating the range of a datasetGetting readyHow to do it…How it works...There’s more...Identifying the percentiles of a datasetGetting readyHow to do it…How it works...There’s more...Checking the quartiles of a datasetGetting readyHow to do it…How it works...There’s more...Analyzing the interquartile range (IQR) of a datasetGetting readyHow to do it…How it works...
Chapter 2: Preparing Data for EDA
Technical requirementsGrouping dataGetting readyHow to do it…How it works...There’s more...See alsoAppending dataGetting readyHow to do it…How it works...There’s more...Concatenating dataGetting readyHow to do it…How it works...There’s more...See alsoMerging dataGetting readyHow to do it…How it works...There’s more...See alsoSorting dataGetting readyHow to do it…How it works...There’s more...Categorizing dataGetting readyHow to do it…How it works...There’s more...Removing duplicate dataGetting readyHow to do it…How it works...There’s more...Dropping data rows and columnsGetting readyHow to do it…How it works...There’s more...Replacing dataGetting readyHow to do it…How it works...There’s more...See alsoChanging a data formatGetting readyHow to do it…How it works...There’s more...See alsoDealing with missing valuesGetting readyHow to do it…How it works...There’s more...See also
Chapter 3: Visualizing Data in Python
Technical requirementsPreparing for visualizationGetting readyHow to do it…How it works...There’s more...Visualizing data in MatplotlibGetting readyHow to do it…How it works...There’s more...See alsoVisualizing data in SeabornGetting readyHow to do it…How it works...There’s more...See alsoVisualizing data in GGPLOTGetting readyHow to do it…How it works...There’s more...See alsoVisualizing data in BokehGetting readyHow to do it…How it works...There's more...See also
Chapter 4: Performing Univariate Analysis in Python
Technical requirementsPerforming univariate analysis using a histogramGetting readyHow to do it…How it works...Performing univariate analysis using a boxplotGetting readyHow to do it…How it works...There’s more...Performing univariate analysis using a violin plotGetting readyHow to do it…How it works...Performing univariate analysis using a summary tableGetting readyHow to do it…How it works...There’s more...Performing univariate analysis using a bar chartGetting readyHow to do it…How it works...Performing univariate analysis using a pie chartGetting readyHow to do it…How it works...
Chapter 5: Performing Bivariate Analysis in Python
Technical requirementsAnalyzing two variables using a scatter plotGetting readyHow to do it…How it works...There’s more...See also...Creating a crosstab/two-way table on bivariate dataGetting readyHow to do it…How it works...Analyzing two variables using a pivot tableGetting readyHow to do it…How it works...There is more...Generating pairplots on two variablesGetting readyHow to do it…How it works...Analyzing two variables using a bar chartGetting readyHow to do it…How it works...There is more...Generating box plots for two variablesGetting readyHow to do it…How it works...Creating histograms on two variablesGetting readyHow to do it…How it works...Analyzing two variables using a correlation analysisGetting readyHow to do it…How it works...
Chapter 6: Performing Multivariate Analysis in Python
Technical requirementsImplementing Cluster Analysis on multiple variables using KmeansGetting readyHow to do it…How it works...There is more...See also...Choosing the optimal number of clusters in KmeansGetting readyHow to do it…How it works...There is more...See also...Profiling Kmeans clustersGetting readyHow to do it…How it works...There’s more...Implementing principal component analysis on multiple variablesGetting readyHow to do it…How it works...There is more...See also...Choosing the number of principal componentsGetting readyHow to do it…How it works...Analyzing principal componentsGetting readyHow to do it…How it works...There’s more...See also...Implementing factor analysis on multiple variablesGetting readyHow to do it…How it works...There is more...Determining the number of factorsGetting readyHow to do it…How it works...Analyzing the factorsGetting readyHow to do it…How it works...
Chapter 7: Analyzing Time Series Data in Python
Technical requirementsUsing line and boxplots to visualize time series dataGetting readyHow to do it…How it works...Spotting patterns in time seriesGetting readyHow to do it…How it works...Performing time series data decompositionGetting readyHow to do it…How it works...Performing smoothing – moving averageGetting readyHow to do it…How it works…See also...Performing smoothing – exponential smoothingGetting readyHow to do it…How it works...See also...Performing stationarity checks on time series dataGetting readyHow to do it…How it works...See also…Differencing time series dataGetting readyHow to do it…How it works...Getting readyHow to do it…How it works...See also...
Chapter 8: Analysing Text Data in Python
Technical requirementsPreparing text dataGetting readyHow to do it…How it works...There’s more…See also…Dealing with stop wordsGetting readyHow to do it…How it works...There’s more…Analyzing part of speechGetting readyHow to do it…How it works...Performing stemming and lemmatizationGetting readyHow to do it…How it works...Analyzing ngramsGetting readyHow to do it…How it works...Creating word cloudsGetting readyHow to do it…How it works...Checking term frequencyGetting readyHow to do it…How it works...There’s more…See alsoChecking sentimentsGetting readyHow to do it…How it works...There’s more…See alsoPerforming Topic ModelingGetting readyHow to do it…How it works...Choosing an optimal number of topicsGetting readyHow to do it…How it works...

Chapter 9: Dealing with Outliers and Missing Values
Technical requirementsIdentifying outliersGetting readyHow to do it…How it works...Spotting univariate outliersGetting readyHow to do it…How it works...Finding bivariate outliersGetting readyHow to do it…How it works...Identifying multivariate outliersGetting readyHow to do it…How it works...See alsoFlooring and capping outliersGetting readyHow to do it…How it works...Removing outliersGetting readyHow to do it…How it works...Replacing outliersGetting readyHow to do it…How it works...Identifying missing valuesGetting readyHow to do it…How it works...Dropping missing valuesGetting readyHow to do it…How it works...Replacing missing valuesGetting readyHow to do it…How it works...Imputing missing values using machine learning modelsGetting readyHow to do it…How it works...
Chapter 10: Performing Automated Exploratory Data Analysis in Python
Technical requirementsDoing Automated EDA using pandas profilingGetting readyHow to do it…How it works...See also…Performing Automated EDA using dtaleGetting readyHow to do it…How it works...See alsoDoing Automated EDA using AutoVizGetting readyHow to do it…How it works...See alsoPerforming Automated EDA using SweetvizGetting readyHow to do it…How it works...See alsoImplementing Automated EDA using custom functionsGetting readyHow to do it…How it works...There’s more…
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Content preview from Exploratory Data Analysis with Python Cookbook

9 Dealing with Outliers and Missing Values

Outliers and missing values are common issues we will encounter when analyzing various forms of data. They can lead to inaccurate or biased conclusions when not handled properly in our dataset. Hence, it is important to appropriately address them before analyzing our data.

Outliers are unusually high or low values within a dataset that deviate significantly from the rest of the data points in the dataset. Outliers occur due to a wide variety of reasons; the common reasons are covered in this chapter. On the other hand, missing values refer to the absence of data points within a specific variable or observation in our dataset. There are several reasons why they occur; the common reasons are also covered ...

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Exploratory Data Analysis with Python Cookbook

by Ayodele Oluleye

9

Dealing with Outliers and Missing Values

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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