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

Creating a pandas DataFrame

Now that we understand the data structures we will be working with, we can discuss the different ways we can create them. Before we dive into the code however, it's important to know how to get help right from Python. Should we ever find ourselves unsure of how to use something in Python, we can utilize the built-in help() function. We simply run help(), passing in the package, module, class, object, method, or function that we want to read the documentation on. We can, of course, look up the documentation online; however, in most cases, the docstrings (the documentation text written in the code) that are returned with help() will be equivalent to this since they are used to generate the documentation.

Assuming we first ran import pandas as pd, we can run help(pd) to display information about the pandas package; help(pd.DataFrame) for all the methods and attributes of DataFrame objects (note we can also pass in a DataFrame object instead); and help...