Wow! eBook
Exploratory Data Analysis with Python Cookbook:Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data
by: Ayodele Oluleye (Author)
Publisher: Packt Publishing
Publication Date: 2023-06-30
Language: English
Print Length: 382 pages
ISBN-10: 1803231106
ISBN-13: 9781803231105
Book Description
Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guidePurchase of the print or Kindle book includes a free PDF eBookKey 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 DescriptionExploratory data analysis (EDA) is a crucial step in data analysis and machine learning projects as it helps in uncovering relationships and patterns and provides insights into structured and unstructured datasets. With various techniques and libraries available for performing EDA, choosing the right approach can sometimes be challenging. This hands-on guide provides you with practical steps and ready-to-use code for conducting exploratory analysis on tabular, time series, and textual data.The book begins by focusing on preliminary recipes such as summary statistics, data preparation, and data visualization libraries. As you advance, you’ll discover how to implement univariate, bivariate, and multivariate analyses on tabular data. Throughout the chapters, you’ll become well versed in popular Python visualization and data manipulation libraries such as seaborn and pandas.By the end of this book, you will have mastered the various EDA techniques and implemented them efficiently on structured and unstructured data.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 forIf you are a data analyst interested in the practical application of exploratory data analysis in Python, then this book is for you. This book will also benefit data scientists, researchers, and statisticians who are looking for hands-on instructions on how to apply EDA techniques using Python libraries. Basic knowledge of Python programming and a basic understanding of fundamental statistical concepts is a prerequisite.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
Editorial Reviews
Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guidePurchase of the print or Kindle book includes a free PDF eBookKey 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 DescriptionExploratory data analysis (EDA) is a crucial step in data analysis and machine learning projects as it helps in uncovering relationships and patterns and provides insights into structured and unstructured datasets. With various techniques and libraries available for performing EDA, choosing the right approach can sometimes be challenging. This hands-on guide provides you with practical steps and ready-to-use code for conducting exploratory analysis on tabular, time series, and textual data.The book begins by focusing on preliminary recipes such as summary statistics, data preparation, and data visualization libraries. As you advance, you’ll discover how to implement univariate, bivariate, and multivariate analyses on tabular data. Throughout the chapters, you’ll become well versed in popular Python visualization and data manipulation libraries such as seaborn and pandas.By the end of this book, you will have mastered the various EDA techniques and implemented them efficiently on structured and unstructured data.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 forIf you are a data analyst interested in the practical application of exploratory data analysis in Python, then this book is for you. This book will also benefit data scientists, researchers, and statisticians who are looking for hands-on instructions on how to apply EDA techniques using Python libraries. Basic knowledge of Python programming and a basic understanding of fundamental statistical concepts is a prerequisite.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
