The Python subprocess module is a powerful swiss-army knife for launching and interacting with child processes. It comes with several high-level APIs like call, check_output and (starting with Python 3.5) run that are focused at child processes our program runs and waits to complete. In this post I want to discuss a variation of this task that is less directly addressed - long-running child processes. 

One of the things I’ve been thinking about recently is how to do natural language processing (NLP) effectively with deep neural networks using real world language examples. An example would be to classify the youtube comment 

Embed docs directly on your website with a few lines of code. 

The Union of Concerned Scientists maintains a database of ~1000 Earth satellites. For the majority of satellites, it includes kinematic, material, electrical, political, functional, and economic characteristics, such as dry mass, launch date, orbit type, country of operator, and purpose. The data appears to have been mirrored on other satellite search websites, e.g. http://satellites.findthedata.com/ . This iPython notebook describes a sequence of interactions with a snapshot of this database using the bayeslite implementation of BayesDB, using the Python bayeslite client library. The snapshot includes a population of satellites defined using the UCS data as well as a constellation of generative probabilistic models for this population. 

How you can extract meaningful information from raw text and use it to analyze the networks of individuals hidden within your data set. 

Today, let’s learn how to build a simple linear regression model using Python’s Pandas and Scikit-learn libraries. Our goal is to build a model that analyses customer data and solves a problem for a (simulated) e-commerce business. 

The FAT Python project was started by Victor Stinner in October 2015 to try to solve issues of previous attempts of “static optimizers” for Python. Victor has created a set of changes to CPython (Python Enhancement Proposals or “PEPs”), some example optimizations and benchmarks. We’ll explore those 3 levels in this article. 

It has been a long time coming, but I am now actively migrating existing projects to Python 3. Python 3.6 specifically, because when I am done I will be able to take advantage of my new favourite feature everywhere! That feature is f-strings. 

Seashells lets you pipe output from command-line programs to the web in real-time, even without installing any new software on your machine. You can use it to monitor long-running processes like experiments that print progress to the console. You can also use Seashells to share output with friends! 

This is the first post in a series exploring the Arrange Act Assert pattern and how to apply it to Python tests. 

numpy, 

pandas

, code snippets

In this tutorial I will show you how to use Amazon Web Services (AWS) Lambda service to save the results of an API response to a PostgreSQL database on a recurring schedule. 

Projects