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 4: Aggregating Pandas DataFrames

In this chapter, we will continue our discussion of data wrangling from Chapter 3, Data Wrangling with Pandas, by addressing the enrichment and aggregation of data. This includes essential skills, such as merging dataframes, creating new columns, performing window calculations, and aggregating by group membership. Calculating aggregations and summaries will help us draw conclusions about our data.

We will also take a look at the additional functionality pandas has for working with time series data, beyond the time series slicing we introduced in previous chapters, including how we can roll up the data with aggregation and select it based on the time of day. Much of the data we will encounter is time series data, so being able to effectively work with time series is paramount. Of course, performing these operations efficiently is important, so we will also review how to write efficient pandas code.

This chapter will get us comfortable with...