Book Image

Big Data Analysis with Python

By : Ivan Marin, Ankit Shukla, Sarang VK
Book Image

Big Data Analysis with Python

By: Ivan Marin, Ankit Shukla, Sarang VK

Overview of this book

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.
Table of Contents (11 chapters)
Big Data Analysis with Python
Preface

Which Tool Should Be Used?


Seaborn tries to make the creation of some common analysis graphs easier than using Matplotlib directly. Matplotlib can be considered more low-level than Seaborn, and although this makes it a bit more cumbersome and verbose, it gives analysts much more flexibility. Some graphs, which with Seaborn are created with one function call, would take several lines of code to achieve using Matplotlib.

There is no rule to determine whether an analyst should use only the pandas plotting interface, Matplotlib directly, or Seaborn. Analysts should keep in mind the visualization requirements and the level of configuration required to create the desired graph.

Pandas' plotting interface is easier to use but is more constrained and limited. Seaborn has several graph patterns ready to use, including common statistical graphs such as pair plots and boxplots, but requires that the data is formatted into a tidy format and is more opinionated on how the graphs should look. Matplotlib...