Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas - Second Edition

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

Practicing working with data

Throughout this book, we have worked with various datasets from different sources with step-by-step instructions. It doesn't have to stop here, though. This section is dedicated to some resources that can be used to continue with guided instruction and, eventually, work toward building a model for a predefined problem.

Kaggle ( offers content for learning data science, datasets for exploration that are shared by members of the community, and competitions that have been posted by companies—perhaps the Netflix recommendation contest sounds familiar ( These contests are a great way for you to practice your machine learning skills and become more visible in the community (especially to potential employers).

Important note

Kaggle isn't the only place you can participate in data science competitions. Some additional ones are listed at https://towardsdatascience...