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

Performing database-style operations on DataFrames

DataFrame objects are analogous to tables in a database: each has a name we refer to it by, is composed of rows, and contains columns of specific data types. Consequently, pandas allows us to carry out database-style operations on them. Traditionally, databases support a minimum of four operations, called CRUD: Create, Read, Update, and Delete.

A database query language—most commonly SQL (pronounced sequel or S-Q-L), which stands for Structured Query Language—is used to ask the database to perform these operations. Knowledge of SQL is not required for this book; however, we will look at the SQL equivalent for the pandas operations that will be discussed in this section since it may aid the understanding of those familiar with SQL. Many data professionals have some familiarity with basic SQL, so consult the Further reading section for resources that provide a more formal introduction.

For this section, we will be...