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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 Stefanie Molin
Profile icon Stefanie Molin
LinkedIn
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.
See other products by 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

Further reading

Those with an R and/or SQL background may find it helpful to see how the pandas syntax compares:

  • Comparison with R / R Libraries: https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_r.html
  • Comparison with SQL: https://pandas.pydata.org/pandas-docs/stable/comparison_with_sql.html
  • SQL Queries: https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sql.html

The following are some resources on working with serialized data:

  • Pickle in Python: Object Serialization: https://www.datacamp.com/community/tutorials/pickle-python-tutorial
  • Read RData/RDS files into pandas.DataFrame objects (pyreader): https://github.com/ofajardo/pyreadr

Additional resources for working with APIs are as follows:

  • Documentation for the requests package: https://requests.readthedocs.io/en/master/
  • HTTP Methods: https://restfulapi.net/http-methods/
  • HTTP Status Codes: https://restfulapi...
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Authors (1)

Profile icon Stefanie Molin
LinkedIn
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.
Read more
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