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

Implementing association tables


The association table acts as a bridge between database tables, which have a many-to-many relationship. The table contains foreign keys that are linked to the primary keys of the tables it connects.

In this recipe, we will associate web pages with links within the page. A page has many links, and links can be in many pages. We will concern ourselves only with links to other websites, but this is not a requirement. If you are trying to reproduce a website on your local machine for testing or analysis, you will want to store image and JavaScript links as well. Have a look at the following relational schema diagram:

Getting ready

I installed SQLAlchemy 0.9.9 with Anaconda, as follows:

$ conda install sqlalchemy

If you prefer, you can also install SQLAlchemy with the following command:

$ pip install sqlalchemy

How to do it…

The following code from the impl_association.py file in this book's code bundle implements the association table pattern:

  1. The imports are as follows...