Data To Fish was born in an effort to facilitate the application of data science using various tools such as Python, R, Julia and SQL.

We are passionate about data, and strive to provide you the most up-to-date and accurate information about common data-related problems.

The content provided on this website is constantly reviewed. Yet, if you do come across any errors in the content, please feel free to reach us at datatofish@gmail.com. Please note that due to the high volume of requests, we can no longer accommodate personal requests of code reviews. Please also refrain from including any email attachments.

OpenTAXII is a robust Python implementation of TAXII Services with a rich feature set and extensible, code-level APIs.

Python 2.7 will not be maintained past 2020. Originally, there was no official date. Recently, that date has been updated to January 1, 2020.

Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Our mission is to provide researchers high-quality, domain-specific training covering the full lifecycle of data-driven research. Data Carpentry is now a lesson project within The Carpentries, having merged with Software Carpentry in January, 2018. Data Carpentry's focus is on the introductory computational skills needed for data management and analysis in all domains of research. Our lessons are domain-specific, and build on the existing knowledge of learners to enable them to quickly apply skills learned to their own research. Our initial target audience is learners who have little to no prior computational experience. We create a friendly environment for learning to empower researchers and enable data driven discovery.

What are wheels?

Wheels are the new standard of Python distribution and are intended to replace eggs. Support is offered in pip >= 1.4 and setuptools >= 0.8.
Advantages of wheels

Faster installation for pure Python and native C extension packages.
Avoids arbitrary code execution for installation. (Avoids setup.py)
Installation of a C extension does not require a compiler on Windows or macOS.
Allows better caching for testing and continuous integration.
Creates .pyc files as part of installation to ensure they match the Python interpreter used.
More consistent installs across platforms and machines

This collection is a presentation of fairly small Python programs. They are aimed at intermediate programmers; people who have studied Python and are fairly comfortable with basic recursion and object oriented techniques. Most programs are very short, not more than a couple of pages and all projects are accompanied with a write-up.