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

Reshaping data

Data isn't always given to us in the format that's most convenient for our analysis. Therefore, we need to be able to restructure data into both wide and long formats, depending on the analysis we want to perform. For many analyses, we will want wide format data so that we can look at the summary statistics easily and share our results in that format.

However, this isn't always as black and white as going from long format to wide format or vice versa. Consider the following data from the Exercises section:

Figure 3.31 – Data with some long and some wide format columns

It's possible to have data where some of the columns are in wide format (open, high, low, close, volume), but others are in long format (ticker). Summary statistics using describe() on this data aren't helpful unless we first filter on ticker. This format makes it easy to compare the stocks; however, as we briefly discussed when we learned about...