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Python High Performance, Second Edition

You're reading from   Python High Performance, Second Edition Build high-performing, concurrent, and distributed applications

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787282896
Length 270 pages
Edition 2nd Edition
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Author (1):
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Dr. Gabriele Lanaro Dr. Gabriele Lanaro
Author Profile Icon Dr. Gabriele Lanaro
Dr. Gabriele Lanaro
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Table of Contents (10) Chapters Close

Preface Benchmarking and Profiling FREE CHAPTER Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Distributed Processing Designing for High Performance

Pandas

Pandas is a library originally developed by Wes McKinney, which was designed to analyze datasets in a seamless and performant way. In recent years, this powerful library has seen an incredible growth and huge adoption by the Python community. In this section, we will introduce the main concepts and tools provided in this library, and we will use it to increase performance of various usecases that can't otherwise be addressed with NumPy's vectorized operations and broadcasting.

Pandas fundamentals

While NumPy deals mostly with arrays, Pandas main data structures are pandas.Series, pandas.DataFrame, and pandas.Panel. In the rest of this chapter, we will abbreviate pandas with pd.

The main difference between a pd.Series object and an np.array is that...

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