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

Pandas data structures

Python has several data structures already, such as tuples, lists, and dictionaries. Pandas provides two main structures to facilitate working with data: Series and DataFrame. The Series and DataFrame data structures each contain another pandas data structure, Index, that we must also be aware of. However, in order to understand these data structures, we need to first take a look at NumPy (, which provides the n-dimensional arrays that pandas builds upon.

The aforementioned data structures are implemented as Python classes; when we actually create one, they are referred to as objects or instances. This is an important distinction, since, as we will see, some actions can be performed using the object itself (a method), whereas others will require that we pass our object in as an argument to some function. Note that, in Python, class names are traditionally written in CapWords, while objects are written in snake_case. (More Python...