Hands-On Data Analysis with Pandas

This is the code repository for my book Hands-On Data Analysis with Pandas, published by Packt on July 26, 2019.

The 1st_edition tag contains all materials as they were at time of publishing the first edition.

Book Description

Data analysis has become an essential skill in a variety of domains where knowing how to work with data and extract insights can generate significant value.

Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with 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 powerful 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.

By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analysis across multiple domains.

What You Will Learn

Prerequisite: Basic knowledge of Python or past experience with another language (R, SAS, MATLAB, etc.).

• Understand how data analysts and scientists gather and analyze data
• Perform data analysis and data wrangling in Python
• Combine, group, and aggregate data from multiple sources
• Create data visualizations with pandas, matplotlib, and seaborn
• Apply machine learning algorithms with sklearn to identify patterns and make predictions
• Use Python data science libraries to analyze real-world datasets.
• Use pandas to solve several common data representation and analysis problems
• Collect data from APIs
• Build Python scripts, modules, and packages for reusable analysis code.
• Utilize computer science concepts and algorithms to write more efficient code for data analysis
• Write and run simulations

Table of Contents

• Chapter 1, Introduction to Data Analysis, will teach you the fundamentals of data analysis, give you a foundation in statistics, and get your environment set up for working with data in Python and using Jupyter Notebooks.

• Chapter 2, Working with Pandas DataFrames, introduces you to the pandas library and shows you the basics of working with DataFrames.

• Chapter 3, Data Wrangling with Pandas, discusses the process of data manipulation, shows you how to explore an API to gather data, and guides you through data cleaning and reshaping with pandas.

• Chapter 4, Aggregating Pandas DataFrames, teaches you how to query and merge DataFrames, perform complex operations on them, including rolling calculations and aggregations, and how to work effectively with time series data.

• Chapter 5, Visualizing Data with Pandas and Matplotlib, shows you how to create your own data visualizations in Python, first using the matplotlib library, and then directly from pandas objects.

• Chapter 6, Plotting with Seaborn and Customization Techniques, continues the discussion on data visualization by teaching you how to use the seaborn library for visualizing your long form data and giving you the tools you need to customize your visualizations, making them presentation-ready.

• Chapter 7, Financial Analysis: Bitcoin and the Stock Market, walks you through the creation of a Python package for analyzing stocks, building upon everything learned in chapters 1-6 and applying it to a financial application.

• Chapter 8, Rule-Based Anomaly Detection, covers simulating data and applying everything learned in chapters 1-6 to catching hackers attempting to authenticate to a website, using rule-based strategies for anomaly detection.

• Chapter 9, Getting Started with Machine Learning in Python, introduces you to machine learning and building models using the sklearn library.

• Chapter 10, Making Better Predictions: Optimizing Models, shows you strategies for improving the performance of your machine learning models.

• Chapter 11, Machine Learning Anomaly Detection, revisits anomaly detection on login attempt data, using machine learning techniques, all while giving you a taste of how the workflow looks in practice.

• Chapter 12, The Road Ahead, contains resources for taking your skills to the next level and further avenues for exploration.

Notes on Environment Setup

Environment setup instructions are in the chapter 1 of the text. If you don't have the book, you must install Python 3.6 or higher, set up a virtual environment, activate it, and then install the packages listed in requirements.txt. You can then launch JupyterLab and use the ch_01/checking_your_setup.ipynb Jupyter notebook to check your setup. Consult this resource if you have issues with using your virtual environment in Jupyter.

Solutions

Each chapter comes with exercises. The solutions for chapters 1-11 can be found here.

About the Author

Stefanie Molin (@stefmolin) is a data scientist and software engineer at Bloomberg L.P. in NYC (and hacker in training) tackling tough problems in Information Security particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in Operations Research from Columbia University’s Fu Foundation School of Engineering and Applied Science with minors in Economics and Entrepreneurship and Innovation.

In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken both among people and computers.

Acknowledgements

Since the book limited the acknowledgements to 450 characters, the full version is here.