Book Image

Python Data Analysis Cookbook

By : Ivan Idris
Book Image

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

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.
Table of Contents (23 chapters)
Python Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Glossary
Index

Introduction


This chapter is light on math, but it is more focused on technical topics. Technology has a lot to offer for data analysts. Databases have been around for a while, but the relational databases that most people are familiar with can be traced back to the 1970s. Edgar Codd came up with a number of ideas that later led to the creation of the relational model and SQL. Relational databases have been a dominant technology since then. In the 1980s, object-oriented programming languages caused a paradigm shift and an unfortunate mismatch with relational databases.

Object-oriented programming languages support concepts such as inheritance, which relational databases and SQL do not support (of course with some exceptions). The Python ecosystem has several object-relational mapping (ORM) frameworks that try to solve this mismatch issue. It is not possible and is unnecessary to cover them all, so I chose SQLAlchemy for the recipes here. We will also have a look at database schema migration...