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

The1st_edition tag contains all materials as they were at time of publishing the first edition.

IMPORTANT NOTE (April 29, 2021):

This is the code repository for thefirst edition. For thesecond edition, usethis repository instead.

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.

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 withpandas,matplotlib, andseaborn
• Apply machine learning algorithms withsklearn to identify patterns and make predictions
• Use Python data science libraries to analyze real-world datasets.
• Usepandas 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

• 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 thepandas library and shows you the basics of working withDataFrames.

• 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 thematplotlib library, and then directly frompandas objects.

• Chapter 6,Plotting with Seaborn and Customization Techniques, continues the discussion on data visualization by teaching you how to use theseaborn 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 aPython 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, coverssimulating 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 thesklearn 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.

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 3.7,set up a virtual environment,activate it, and theninstall the packages listed in requirements.txt. You can then launch JupyterLab and use thech_01/checking_your_setup.ipynb Jupyter notebook to check your setup. Consultthis resource if you have issues with using your virtual environment in Jupyter.

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

Stefanie Molin (@stefmolin) is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She holds a bachelor’s of science degree in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science with minors in Economics and Entrepreneurship and Innovation, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken both among people and computers.

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