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 a star schema with fact and dimension tables


The star schema is a database pattern that facilitates reporting. Star schemas are appropriate for the processing of events, such as website visits, ad clicks, or financial transactions. Event information (metrics, such as temperature or purchase amount) is stored in fact tables linked to much smaller dimension tables. Star schemas are denormalized, which places the responsibility of integrity checks to the application code. For this reason, we should only write to the database in a controlled manner. If you use SQLAlchemy for bulk inserts, you should choose the Core API over the ORM API or use straight SQL. You can read more about the reasons at http://docs.sqlalchemy.org/en/rel_1_0/faq/performance.html (retrieved September 2015).

Time is a common dimension in reporting. For instance, we can store dates of daily weather measurements in a dimension table. For each date in our data, we can save the date, year, month, and day of year...