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Understand and implement big data analysis solutions in pandas with
an emphasis on performance. This book strengthens your intuition
for working with pandas, the Python data analysis library, by
exploring its underlying implementation and data structures.
Thinking in Pandas introduces the topic of big data and
demonstrates concepts by looking at exciting and impactful projects
that pandas helped to solve. From there, you will learn to assess
your own projects by size and type to see if pandas is the
appropriate library for your needs. Author Hannah Stepanek explains
how to load and normalize data in pandas efficiently, and reviews
some of the most commonly used loaders and several of their most
powerful options. You will then learn how to access and transform
data efficiently, what methods to avoid, and when to employ more
advanced performance techniques. You will also go over basic data
access and munging in pandas and the intuitive dictionary syntax.
Choosing the right DataFrame format, working with multi-level
DataFrames, and how pandas might be improved upon in the future are
also covered. By the end of the book, you will have a solid
understanding of how the pandas library works under the hood. Get
ready to make confident decisions in your own projects by utilizing
pandas-the right way. What You Will Learn Understand the underlying
data structure of pandas and why it performs the way it does under
certain circumstances Discover how to use pandas to extract,
transform, and load data correctly with an emphasis on performance
Choose the right DataFrame so that the data analysis is simple and
efficient. Improve performance of pandas operations with other
Python libraries Who This Book Is ForSoftware engineers with basic
programming skills in Python keen on using pandas for a big data
analysis project. Python software developers interested in big
data.
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