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

Types of Graphs


The first type of graph that we will present is the line graph or line chart. A line graph displays data as a series of interconnected points on two axes (x and y), usually Cartesian, ordered commonly by the x-axis. Line charts are useful for demonstrating trends in data, such as in time series, for example.

A graph related to the line graph is the scatter plot. A scatter plot represents the data as points in Cartesian coordinates. Usually, two variables are demonstrated in this graph, although more information can be conveyed if the data is color-coded or size-coded by category, for example. Scatter plots are useful for showing the relationship and possible correlation between variables.

Histograms are useful for representing the distribution of data. Unlike the two previous examples, histograms show only one variable, usually on the x-axis, while the y-axis shows the frequency of occurrence of the data. The process of creating a histogram is a bit more involved than the line...