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Examining Data Using Pandas

You don't need to be a data scientist to use Pandas for some basic analysis. Traditionally, people who program in Python use the data types that come with the language, such as integers, strings, lists, tuples and dictionaries. Sure, you can create objects in Python, but those objects typically are built out of those fundamental data structures.
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ONNX: the Open Neural Network Exchange Format

An open-source battle is being waged for the soul of artificial intelligence. It is being fought by industry titans, universities and communities of machine-learning researchers world-wide. This article chronicles one small skirmish in that fight: a standardized file format for neural networks. At stake is the open exchange of data among a multitude of tools instead of competing monolithic frameworks.
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Multiprocessing in Python

Python's "multiprocessing" module feels like threads, but actually launches processes. Many people, when they start to work with Python, are excited to hear that the language supports threading. And, as I've discussed in previous articles, Python does indeed support native-level threads with an easy-to-use and convenient interface.

Using Python for Science

Introducing Anaconda, a Python distribution for scientific research. I've looked at several ways you could use Python to do scientific calculations in the past, but I've never actually covered how to set up and use Python itself in a way that makes scientific work easier. Anaconda does just that.

Visualizing Molecules with Python

Introducing PyMOL, a Python package for studying chemical structures. I've looked at several open-source packages for computational chemistry in the past, but in this article, I cover a package written in Python called PyMOL.

Learning Data Science

In my last few articles, I've written about data science and machine learning. In case my enthusiasm wasn't obvious from my writing, let me say it plainly: it has been a long time since I last encountered a technology that was so poised to revolutionize the world in which we live.

Novelty and Outlier Detection

In my last few articles, I've looked at a number of ways machine learning can help make predictions. The basic idea is that you create a model using existing data and then ask that model to predict an outcome based on new data.

V. Anton Spraul's Think Like a Programmer, Python Edition

What is programming? Sure, it consists of syntax and the assembly of code, but it is essentially a means to solve problems. To study programming, then, is to study the art of problem solving, and a new book from V. Anton Spraul, Think Like a Programmer, Python Edition, is a guide to sharpening skills in both spheres.

Classifying Text

In my last few articles, I've looked at several ways one can apply machine learning, both supervised and unsupervised. This time, I want to bring your attention to a surprisingly simple—but powerful and widespread—use of machine learning, namely document classification.

Unsupervised Learning

In my last few articles, I've looked into machine learning and how you can build a model that describes the world in some way. All of the examples I looked at were of "supervised learning", meaning that you loaded data that already had been categorized or classified in some way, and then created a model that "learned" the ways the inputs mapped to the outputs.

Testing Models

In my last few articles, I've been dipping into the waters of "machine learning"—a powerful idea that has been moving steadily into the mainstream of computing, and that has the potential to change lives in numerous ways.

Pythonic Science in the Browser

In the past, if you wanted a friendly environment for doing Python programming, you would use Ipython. The Ipython project actually consists of three parts: the standard console interface, a Qt-based GUI interface and a web server interface that you can connect to with a web browser.
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