You're reading from Python Data Analysis Cookbook Clean, scrape, analyze, and visualize data with the power of Python!

Product type Paperback

Published in Jul 2016

Publisher

ISBN-13 9781785282287

Length 462 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Ivan Idris

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Table of Contents (18) Chapters

Preface

1. Laying the Foundation for Reproducible Data Analysis FREE CHAPTER

2. Creating Attractive Data Visualizations

3. Statistical Data Analysis and Probability

4. Dealing with Data and Numerical Issues

5. Web Mining, Databases, and Big Data

6. Signal Processing and Timeseries

7. Selecting Stocks with Financial Data Analysis

8. Text Mining and Social Network Analysis

9. Ensemble Learning and Dimensionality Reduction

10. Evaluating Classifiers, Regressors, and Clusters

11. Analyzing Images

12. Parallelism and Performance

A. Glossary

B. Function Reference

C. Online Resources

D. Tips and Tricks for Command-Line and Miscellaneous Tools

Index

Setting up database migration scripts

One of the first things that you learn in programming classes is that nobody can get a complex program right the very first time. Software evolves over time, and we hope for the best. Automation in automated testing helps ensure that our programs improve over time. However, when it comes to evolving database schemas, automation doesn't seem to be so obvious. Especially in large enterprises, database schemas are the domain of database administrators and specialists. Of course, there are security and operational issues related to changing schemas, even more so in production databases. In any case, you can always implement database migration in your local test environment and document proposed changes for the production team.

We will use Alembic to demonstrate how you can go about setting up migration scripts. In my opinion, Alembic is the right tool for the job, although it is in beta as of September 2015.