Exploratory Data Analysis with Python Cookbook

Book Description

Exploratory 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 learn

• Perform EDA with leading Python data visualization libraries
• Execute univariate, bivariate, and multivariate analyses on tabular data
• Uncover patterns and relationships within time series data
• Identify hidden patterns within textual data
• Discover different techniques to prepare data for analysis
• Overcome the challenge of outliers and missing values during data analysis
• Leverage automated EDA for fast and efficient analysis

• # Data Processing