Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics

Reliable analysis starts before the modeling or charting: it starts with data that has been collected, examined, and shaped for the question at hand. Roy Jafari’s Hands-On Data Preprocessing in Python connects practical Python work with the reasoning behind those choices, showing how preparation supports meaningful data analytics.

Build the working foundations

The book opens with Jupyter Notebook and useful capabilities of NumPy, Pandas, and Matplotlib, then establishes a grounding in data and databases. That progression gives readers a shared toolkit for exploring datasets, visualizing patterns, and bringing data into their analysis—including through database connections and APIs.

Prepare data with the analytic goal in view

Rather than treating every dataset problem as the same kind of cleanup, the material considers how the intended analysis informs preprocessing decisions. Topics include visualization and prediction, followed by practical work in data cleaning, integration, reduction, and transformation.

• Recognize and address missing values, outliers, and errors.
• Unpack, restructure, and reformulate tables when their current shape gets in the way.
• Consider data fusion and integration challenges when combining sources.
• Explore transformations such as normalization, standardization, discretization, feature extraction, and aggregation.

Learn through Python examples and case studies

Explanations are paired with hands-on examples, chapter exercises, and case studies, connecting individual techniques to broader analytic work. The book also revisits core tools such as Pandas and NumPy in the context of manipulating and preparing data.

A practical starting point for data work

This guide is intended for data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts. It assumes basic programming ability and beginner-level Python familiarity, but does not require previous experience in data preprocessing. For readers who want to understand not only how to adjust a dataset, but why a preparation step matters to the analysis that follows, this is a focused, practice-led reference.