This book shows how to use the open source Pandas library with Python to automate and perform data analysis tasks. Author Daniel  Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Using a realistic dataset, the book covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization.

<ASIN:0134546938>

Data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes, are also covered. The book also shows how to fit models for prediction, clustering, inference, and exploration. 

Author: Daniel Chen
Publisher: Addison-Wesley
Date: Jan 2018
Pages: 416
ISBN: 978-0134546933
Print: 0134546938
Kindle: B0789WKTKJ
Audience: Python developers
Level: intermediate
Category: Data Science

• Work with DataFrames and Series, and import or export data
• Create plots with matplotlib, seaborn, and pandas
• Combine datasets and handle missing data
• Reshape, tidy, and clean datasets so they’re easier to work with
• Convert data types and manipulate text strings
• Apply functions to scale data manipulations
• Aggregate, transform, and filter large datasets with groupby
• Use Pandas’ advanced date and time capabilities
• Fit linear models using statsmodels and scikit-learn libraries
• Use generalized linear modeling to fit models with different response variables
• Compare multiple models to select the “best”
• Regularize to overcome overfitting and improve performance
• Use clustering in unsupervised machine learning

For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

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