Exploratory Data Analysis with Python Cookbook: Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

Description

Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesGain practical experience in conducting EDA on a single variable of interest in PythonLearn the different techniques for analyzing and exploring tabular, time series, and textual data in PythonGet well versed in data visualization using leading Python libraries like Matplotlib and seabornBook Description In today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights. What you will learnPerform EDA with leading Python data visualization librariesExecute univariate, bivariate, and multivariate analyses on tabular dataUncover patterns and relationships within time series dataIdentify hidden patterns within textual dataDiscover different techniques to prepare data for analysisOvercome the challenge of outliers and missing values during data analysisLeverage automated EDA for fast and efficient analysisWho this book is for Whether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience. Table of ContentsGenerating Summary StatisticsPreparing Data for EDAVisualising Data in PythonPerforming Univariate Analysis in PythonPerforming Bivariate analysis in PythonPerforming Multivariate analysis in PythonAnalysing Time Series dataAnalysing Text dataDealing with Outliers and Missing valuesPerforming Automated EDA in Python Cover Title Page Copyright and Credits Dedication Contributors Table of Contents Preface Chapter 1: Generating Summary Statistics Technical requirements Analyzing the mean of a dataset Getting ready How to do it… How it works... There’s more... Checking the median of a dataset Getting ready How to do it… How it works... There’s more... Identifying the mode of a dataset Getting ready How to do it… How it works... There’s more... Checking the variance of a dataset Getting ready How to do it… How it works... There’s more… Identifying the standard deviation of a dataset Getting ready How to do it… How it works... There’s more... Generating the range of a dataset Getting ready How to do it… How it works... There’s more... Identifying the percentiles of a dataset Getting ready How to do it… How it works... There’s more... Checking the quartiles of a dataset Getting ready How to do it… How it works... There’s more... Analyzing the interquartile range (IQR) of a dataset Getting ready How to do it… How it works... Chapter 2: Preparing Data for EDA Technical requirements Grouping data Getting ready How to do it… How it works... There’s more... See also Appending data Getting ready How to do it… How it works... There’s more... Concatenating data Getting ready How to do it… How it works... There’s more... See also Merging data Getting ready How to do it… How it works... There’s more... See also Sorting data Getting ready How to do it… How it works... There’s more... Categorizing data Getting ready How to do it… How it works... There’s more... Removing duplicate data Getting ready How to do it… How it works... There’s more... Dropping data rows and columns Getting ready How to do it… How it works... There’s more... Replacing data Getting ready How to do it… How it works... There’s more... See also Changing a data format Getting ready How to do it… How it works... There’s more... See also Dealing with missing values Getting ready How to do it… How it works... There’s more... See also Chapter 3: Visualizing Data in Python Technical requirements Preparing for visualization Getting ready How to do it… How it works... There’s more... Visualizing data in Matplotlib Getting ready How to do it… How it works... There’s more... See also Visualizing data in Seaborn Getting ready How to do it… How it works... There’s more... See also Visualizing data in GGPLOT Getting ready How to do it… How it works... There’s more... See also Visualizing data in Bokeh Getting ready How to do it… How it works... There's more... See also Chapter 4: Performing Univariate Analysis in Python Technical requirements Performing univariate analysis using a histogram Getting ready How to do it… How it works... Performing univariate analysis using a boxplot Getting ready How to do it… How it works... There’s more... Performing univariate analysis using a violin plot Getting ready How to do it… How it works... Performing univariate analysis using a summary table Getting ready How to do it… How it works... There’s more... Performing univariate analysis using a bar chart Getting ready How to do it… How it works... Performing univariate analysis using a pie chart Getting ready How to do it… How it works... Chapter 5: Performing Bivariate Analysis in Python Technical requirements Analyzing two variables using a scatter plot Getting ready How to do it… How it works... There’s more... See also... Creating a crosstab/two-way table on bivariate data Getting ready How to do it… How it works... Analyzing two variables using a pivot table Getting ready How to do it… How it works... There is more... Generating pairplots on two variables Getting ready How to do it… How it works... Analyzing two variables using a bar chart Getting ready How to do it… How it works... There is more... Generating box plots for two variables Getting ready How to do it… How it works... Creating histograms on two variables Getting ready How to do it… How it works... Analyzing two variables using a correlation analysis Getting ready How to do it… How it works... Chapter 6: Performing Multivariate Analysis in Python Technical requirements Implementing Cluster Analysis on multiple variables using Kmeans Getting ready How to do it… How it works... There is more... See also... Choosing the optimal number of clusters in Kmeans Getting ready How to do it… How it works... There is more... See also... Profiling Kmeans clusters Getting ready How to do it… How it works... There’s more... Implementing principal component analysis on multiple variables Getting ready How to do it… How it works... There is more... See also... Choosing the number of principal components Getting ready How to do it… How it works... Analyzing principal components Getting ready How to do it… How it works... There’s more... See also... Implementing factor analysis on multiple variables Getting ready How to do it… How it works... There is more... Determining the number of factors Getting ready How to do it… How it works... Analyzing the factors Getting ready How to do it… How it works... Chapter 7: Analyzing Time Series Data in Python Technical requirements Using line and boxplots to visualize time series data Getting ready How to do it… How it works... Spotting patterns in time series Getting ready How to do it… How it works... Performing time series data decomposition Getting ready How to do it… How it works... Performing smoothing – moving average Getting ready How to do it… How it works… See also... Performing smoothing – exponential smoothing Getting ready How to do it… How it works... See also... Performing stationarity checks on time series data Getting ready How to do it… How it works... See also… Differencing time series data Getting ready How to do it… How it works... Getting ready How to do it… How it works... See also... Chapter 8: Analysing Text Data in Python Technical requirements Preparing text data Getting ready How to do it… How it works... There’s more… See also… Dealing with stop words Getting ready How to do it… How it works... There’s more… Analyzing part of speech Getting ready How to do it… How it works... Performing stemming and lemmatization Getting ready How to do it… How it works... Analyzing ngrams Getting ready How to do it… How it works... Creating word clouds Getting ready How to do it… How it works... Checking term frequency Getting ready How to do it… How it works... There’s more… See also Checking sentiments Getting ready How to do it… How it works... There’s more… See also Performing Topic Modeling Getting ready How to do it… How it works... Choosing an optimal number of topics Getting ready How to do it… How it works... Chapter 9: Dealing with Outliers and Missing Values Technical requirements Identifying outliers Getting ready How to do it… How it works... Spotting univariate outliers Getting ready How to do it… How it works... Finding bivariate outliers Getting ready How to do it… How it works... Identifying multivariate outliers Getting ready How to do it… How it works... See also Flooring and capping outliers Getting ready How to do it… How it works... Removing outliers Getting ready How to do it… How it works... Replacing outliers Getting ready How to do it… How it works... Identifying missing values Getting ready How to do it… How it works... Dropping missing values Getting ready How to do it… How it works... Replacing missing values Getting ready How to do it… How it works... Imputing missing values using machine learning models Getting ready How to do it… How it works... Chapter 10: Performing Automated Exploratory Data Analysis in Python Technical requirements Doing Automated EDA using pandas profiling Getting ready How to do it… How it works... See also… Performing Automated EDA using dtale Getting ready How to do it… How it works... See also Doing Automated EDA using AutoViz Getting ready How to do it… How it works... See also Performing Automated EDA using Sweetviz Getting ready How to do it… How it works... See also Implementing Automated EDA using custom functions Getting ready How to do it… How it works... There’s more… Index About Packt Other Books You May Enjoy