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

Handling Unstructured Data


Unstructured data usually refers to data that doesn’t have a fixed format. CSV files are structured, for example, and JSON files can also be considered structured, although not tabular. Computer logs, on the other hand, don’t have the same structure, as different programs and daemons will output messages without a common pattern. Images are also another example of unstructured data, like free text.

We can leverage Spark’s flexibility for reading data to parse unstructured formats and extract the required information into a more structured format, allowing analysis. This step is usually called pre-processing or data wrangling.

Exercise 23: Parsing Text and Cleaning

In this exercise, we will read a text file, split it into lines and remove the words the and a from the string given string:

  1. Read the text file shake.txt (https://raw.githubusercontent.com/TrainingByPackt/Big-Data-Analysis-with-Python/master/Lesson03/data/shake.txt) into the Spark object using the text method...