Big Data Analysis with Python

Book Description

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.

Key Features

  • Get a hands-on, fast-paced introduction to the Python data science stack
  • Explore ways to create useful metrics and statistics from large datasets
  • Create detailed reports with real-world data

Book Description

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big 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 .

The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with 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.

What you will learn

  • Use Python to read and transform data into different formats
  • Generate basic statistics and metrics using data on disk
  • Work with computing tasks distributed over a cluster
  • Convert data from various sources into storage or querying formats
  • Prepare data for statistical analysis, , and machine learning
  • Present data in the form of effective visuals

Who this book is for

Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational will help you to understand various concepts explained in this book.

Table of Contents

  1. The Python Data Science Stack
  2. Statistical Visualizations
  3. Working with Big Data Frameworks
  4. Diving Deeper with Spark
  5. Handling Missing Values and Correlation Analysis
  6. Exploratory Data Analysis
  7. Reproducibility in Big Data Analysis
  8. Creating a Full Analysis Report

Book Details