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

Generating Statistical Measurements


Python is a general-purpose language with statistical modules. A lot of statistical analysis, such as carrying out descriptive analysis, which includes identifying the distribution of data for numeric variables, generating a correlation matrix, the frequency of levels in categorical variables with identifying mode and so on, can be carried out in Python. The following is an example of correlation:

Figure 8.12: Segment numeric data and correlation matrix output

Identifying the distribution of data and normalizing it is important for parametric models such as linear regression and support vector machines. These algorithms assume the data to be normally distributed. If data is not normally distributed, it can lead to bias in the data. In the following example, we will identify the distribution of data through a normality test and then apply a transformation using the yeo-johnson method to normalize the data:

Figure 8.13: Identifying the distribution of the data...