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Hands-On Data Analysis with Pandas - Second Edition

You're reading from Hands-On Data Analysis with Pandas - Second Edition

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781800563452

Pages 788 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Stefanie Molin

Table of Contents (21) Chapters

Preface

Section 1: Getting Started with Pandas

Chapter 1: Introduction to Data Analysis

Chapter 2: Working with Pandas DataFrames

Section 2: Using Pandas for Data Analysis

Chapter 3: Data Wrangling with Pandas

Chapter 4: Aggregating Pandas DataFrames

Chapter 5: Visualizing Data with Pandas and Matplotlib

Chapter 6: Plotting with Seaborn and Customization Techniques

Section 3: Applications – Real-World Analyses Using Pandas

Chapter 7: Financial Analysis – Bitcoin and the Stock Market

Chapter 8: Rule-Based Anomaly Detection

Section 4: Introduction to Machine Learning with Scikit-Learn

Chapter 9: Getting Started with Machine Learning in Python

Chapter 10: Making Better Predictions – Optimizing Models

Chapter 11: Machine Learning Anomaly Detection

Section 5: Additional Resources

Chapter 12: The Road Ahead

Solutions

Other Books You May Enjoy

Appendix

Python practice

We have seen throughout this book that working with data in Python isn't just pandas, matplotlib, and numpy; there are many ways our workflow can benefit from us being strong Python programmers in general. With strong Python skills, we can build web applications with Flask, make requests of an API, efficiently iterate over combinations or permutations, and find ways to speed up our code. While this book didn't focus on honing these skills directly, here are some free resources for practicing with Python and thinking like a programmer:

HackerRank: https://www.hackerrank.com
Codewars: https://www.codewars.com
LeetCode: https://www.leetcode.com
CodinGame: https://www.codingame.com

While not free, Python Morsels (https://www.pythonmorsels.com/) provides weekly Python exercises that will help you learn to write more Pythonic code and get more familiar with the Python standard library. Exercises vary in difficulty but can be set to a higher...

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Authors (1)

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.

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