Exploratory Data Analysis with Python Cookbook

Good analysis begins before the model: first, you need to understand what the data contains, how it is shaped and where it may mislead. Exploratory Data Analysis with Python Cookbook by Ayodele Oluleye takes that investigative work one practical recipe at a time, moving from basic summaries to analysis of tabular, time-series and textual data.

Across more than 50 recipes, readers encounter techniques for preparing, examining and visualizing data in Python. The cookbook approach makes it useful when you want to follow a particular task, see how it is carried out and understand the choices involved.

From a quick summary to a closer look

The book begins with descriptive statistics—mean, median, mode, variance, standard deviation, percentiles and quartiles—then turns to data preparation. Recipes address grouping, combining and sorting data, changing formats, removing duplicates, and handling missing values. These steps help make the dataset easier to inspect before drawing conclusions from it.

Make patterns visible 📊

Visualization chapters use Python libraries including Matplotlib, Seaborn, GGPLOT and Bokeh. Later material develops univariate, bivariate and multivariate analysis, giving readers ways to inspect individual variables as well as relationships among them. The book also extends its EDA coverage to time-series and text data.

Work through the rough edges

Real-world datasets are not always tidy. The recipes cover outliers and missing values, and the book also introduces automated EDA tools such as pandas profiling, dtale, AutoViz and Sweetviz. Together, these topics give readers a broader view of the practical decisions involved in exploring data—not just producing a chart or summary.

A hands-on reference for Python data work

This cookbook is suited to data analysts, data scientists, researchers and learners who want practical ways to examine datasets with Python. A foundation in Python and basic statistics will help, while readers can use the recipe structure to focus on the tasks most relevant to their work. Find a technique, follow its steps and bring a more informed eye to the data in front of you.