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Books > Computing & IT > Computer programming > Programming languages
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Python Data Analysis Cookbook (Paperback)
Loot Price: R1,340
Discovery Miles 13 400
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Python Data Analysis Cookbook (Paperback)
Expected to ship within 18 - 22 working days
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Over 140 practical recipes to help you make sense of your data with
ease and build production-ready data apps About This Book * Analyze
Big Data sets, create attractive visualizations, and manipulate and
process various data types * Packed with rich recipes to help you
learn and explore amazing algorithms for statistics and machine
learning * Authored by Ivan Idris, expert in python programming and
proud author of eight highly reviewed books Who This Book Is For
This book teaches Python data analysis at an intermediate level
with the goal of transforming you from journeyman to master. Basic
Python and data analysis skills and affinity are assumed. What You
Will Learn * Set up reproducible data analysis * Clean and
transform data * Apply advanced statistical analysis * Create
attractive data visualizations * Web scrape and work with
databases, Hadoop, and Spark * Analyze images and time series data
* Mine text and analyze social networks * Use machine learning and
evaluate the results * Take advantage of parallelism and
concurrency In Detail Data analysis is a rapidly evolving field and
Python is a multi-paradigm programming language suitable for
object-oriented application development and functional design
patterns. As Python offers a range of tools and libraries for all
purposes, it has slowly evolved as the primary language for data
science, including topics on: data analysis, visualization, and
machine learning. Python Data Analysis Cookbook focuses on
reproducibility and creating production-ready systems. You will
start with recipes that set the foundation for data analysis with
libraries such as matplotlib, NumPy, and pandas. You will learn to
create visualizations by choosing color maps and palettes then dive
into statistical data analysis using distribution algorithms and
correlations. You'll then help you find your way around different
data and numerical problems, get to grips with Spark and HDFS, and
then set up migration scripts for web mining. In this book, you
will dive deeper into recipes on spectral analysis, smoothing, and
bootstrapping methods. Moving on, you will learn to rank stocks and
check market efficiency, then work with metrics and clusters. You
will achieve parallelism to improve system performance by using
multiple threads and speeding up your code. By the end of the book,
you will be capable of handling various data analysis techniques in
Python and devising solutions for problem scenarios. Style and
Approach The book is written in "cookbook" style striving for high
realism in data analysis. Through the recipe-based format, you can
read each recipe separately as required and immediately apply the
knowledge gained.
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