Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources

Chapter materials

The materials for this chapter can be found on GitHub at There are four notebooks that we will work through, each numbered according to when they will be used. The text will prompt you to switch. We will begin with the 1-querying_and_merging.ipynb notebook to learn about querying and merging dataframes. Then, we will move on to the 2-dataframe_operations.ipynb notebook to discuss data enrichment through operations such as binning, window functions, and pipes. For this section, we will also use the Python file, which contains a function for performing window calculations using pipes.


The understanding_window_calculations.ipynb notebook contains some interactive visualizations for understanding window functions. This may require some additional setup, but the instructions are in the notebook.

Next, in the 3-aggregations.ipynb notebook, we will discuss...