Hands-On Data Analysis with Pandas

4 (5 reviews total)
By Stefanie Molin
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  1. Section 1: Getting Started with Pandas

About this book

Data analysis has become a necessary 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 be able 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 analyses across multiple datasets.

Publication date:
July 2019
Publisher
Packt
Pages
740
ISBN
9781789615326

 

Section 1: Getting Started with Pandas

Our journey begins with an introduction to data analysis and statistics, which will lay a strong foundation for the concepts we will cover throughout the book. Then, we will set up our Python data science environment, which contains everything we will need to work through the examples, and get started with learning the basics of pandas.

The following chapters are included in this section:

About the Author

  • Stefanie Molin

    Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, 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 among both people and computers.

    Browse publications by this author

Latest Reviews

(5 reviews total)
I had to reorder because I purchased ebooks (PAC-20-0001667361-0100393477) and not paper books. I had customer support cancel that order. That went off without a hitch, but the second order (PAC-20-0001667361-0100394158) for paper books has not shipped. The order is still in the Processing stage according to your website. Is there something I am missing? Please help! Thank you
I'm a DS at a well-known tech company and I work primarily in R. I could work in python if needed but wanted to get better. I've been through a few python/pandas books and this is hands down the best I've read. Really good example datasets and problems, and well-written code.
Step by step tutorial diving deep into Pandas. Thats one single book to move one big step toward data science with python.

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