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)
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications – Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Summary

In this chapter, we discussed how to join dataframes, how to determine the data we will lose for each type of join using set operations, and how to query dataframes as we would a database. We then went over some more involved transformations on our columns, such as binning and ranking, and how to do so efficiently with the apply() method. We also learned the importance of vectorized operations in writing efficient pandas code. Then, we explored window calculations and using pipes for cleaner code. Our discussion of window calculations served as a primer for aggregating across whole dataframes and by groups. We also went over how to generate pivot tables and crosstabs. Finally, we looked at some time series-specific functionality in pandas for everything from selection and aggregation to merging.

In the next chapter, we will cover visualization, which pandas implements by providing a wrapper around matplotlib. Data wrangling will play a key role in prepping our data for visualization...