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)
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications – Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Building a Python package

Building packages is considered good coding practice since it allows for writing modular code and reuse. Modular code is code that is written in many smaller pieces for more pervasive use, without needing to know the underlying implementation details of everything involved in a task. For example, when we use matplotlib to plot something, we don't need to know what the code inside the functions we call is doing exactly—it suffices to simply know what the input and output will be to build on top of it.

Package structure

A module is a single file of Python code that can be imported; window_calc.py from Chapter 4, Aggregating Pandas DataFrames, and viz.py from Chapter 6, Plotting with Seaborn and Customization Techniques, were both modules. A package is a collection of modules organized into directories. Packages can also be imported, but when we import a package we have access to certain modules inside, so we don't have to import each one...