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
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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