
Python Data Analysis Cookbook
Clean, scrape, analyze, and visualize data with the power of Python!
Ivan Idris(Author)
Packt Publishing
Published on 22. July 2016
Book
Paperback/Softback
462 pages
978-1-78528-228-7 (ISBN)
Description
Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps
Key Features
Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types
Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning
Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books
Book DescriptionData analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.
Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.
In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.
By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.What you will learn
Set up reproducible data analysis
Clean and transform data
Apply advanced statistical analysis
Create attractive data visualizations
Web scrape and work with databases, Hadoop, and Spark
Analyze images and time series data
Mine text and analyze social networks
Use machine learning and evaluate the results
Take advantage of parallelism and concurrency
Who this book is forThis book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed.
Key Features
Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types
Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning
Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books
Book DescriptionData analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.
Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.
In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.
By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.What you will learn
Set up reproducible data analysis
Clean and transform data
Apply advanced statistical analysis
Create attractive data visualizations
Web scrape and work with databases, Hadoop, and Spark
Analyze images and time series data
Mine text and analyze social networks
Use machine learning and evaluate the results
Take advantage of parallelism and concurrency
Who this book is forThis book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 25 mm
Weight
856 gr
ISBN-13
978-1-78528-228-7 (9781785282287)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Ivan Idris
Python Data Analysis Cookbook
Clean, scrape, analyze, and visualize data with the power of Python!
E-Book
07/2025
Packt Publishing
from
€41.99
Available for download
Person
Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.
Content
Table of Contents
Laying the foundations for reproducible data analysis
Creating attractive data visualizations
Statistical data analysis and probability
Dealing with data and numerical issues
Web mining, Databases and Big Data
Signal processing and timeseries
Selecting stocks with financial data analysis
Text mining and social network analysis
Ensemble learning and dimension reduction
Evaluating classifiers, regressors and clusters
Analyzing images
Parallelism and performance
Appendix A: Glossary
Appendix B: Function Reference
Appendix C: Online Resources
Appendix D: Tips and Tricks for Command Line and Miscellaneous Tools
Laying the foundations for reproducible data analysis
Creating attractive data visualizations
Statistical data analysis and probability
Dealing with data and numerical issues
Web mining, Databases and Big Data
Signal processing and timeseries
Selecting stocks with financial data analysis
Text mining and social network analysis
Ensemble learning and dimension reduction
Evaluating classifiers, regressors and clusters
Analyzing images
Parallelism and performance
Appendix A: Glossary
Appendix B: Function Reference
Appendix C: Online Resources
Appendix D: Tips and Tricks for Command Line and Miscellaneous Tools