Ivan Idris - Python Data Analysis Cookbook [2016, PDF, ENG]

Python Data Analysis Cookbook

Год издания: 2016
Автор: Ivan Idris
Жанр или тематика: Программирование

Издательство: Packt Publishing
ISBN: 9781785282287
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 462

Описание: Data 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