Quality Python sources

The Python community is exceptional at sharing specified resources and supporting beginners discover ways to code with the language. There are so many of sources obtainable although that it can be tough  to locate them all of them.

This post page aggregates the exceptional Python assets with descriptions of what they offer to readers.

Python for specific occupations

Python is powerful for many professions. in case you‘re in search of to use Python in a particular subject, this type of pages may be appropriate for you.

  • Python for Social Scientists here you can find a textbook, a course and slides for a university course that teaches social scientists to use Python. Pretty awesome for Psychologists (or other Social scientists, of course!)
  • Practical Business Python is a blog that covers topics such as automation of the creation of large Excel spreadsheets. Also covers how to perform analysis when your data is locked in Microsoft Office files.
  • Python for the Humanities is a basic Python textbook and course. It covers text processing. It will pretty quick become hard so after you have read the first chapter you may want to consult another introductory Python course or book at the same time.

Some iPython/Jupyter Notebooks:

I have found some useful iPython/Jupyter notebooks for learning Python. They will be categorized into fields of research.

Psychology/Cognitive Neuroscience

First we start with some specific on Psychology and neuroscience:

  • Python for Vision Research. Here you will find a three-day course for vision researchers. Focus is on building experiments with PsychoPy and psychopy_ext, learning the fMRI multi-voxel pattern analysis with PyMVPA, and understanding image processing in Python.
  • Modeling psychophysical data with non-linear functions by Ariel Rokem. General knowledge in Python is needed. You will learn a definition of modeling and get to know why models are useful, different fitting strategies, how to fit a simple model, and model selection, and more.

Statistics-related notebooks

Bayesian Data Analysis Using PyMC3

Introduction to Linear Regression – Linear regression is a commonly used statistical method in social sciences (e.g., Psychology)

Great packages/libraries to use for Data Analysis

I think that I also need to mention som great Python packages that makes data analysis way more easier in Python.

First, there is IPython. IPython is a command shell for interactive computing in multiple programming languages. It was, however, first developed for the Python programming language. For doing Scientific computing IPython is really a must (replacing the interpreter that comes with installation of Python). It offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history. You can also, as been seen above, create IPython notebooks that are, basically, html. Great! iPython is known as juPyter nowadays: Jupyter.

Here is a user manual that I got from asking a question at a blogJupyter Manual. Looks very promising.

pandas is an open source, BSD-licensed library presenting highperformance, easy-to-use statistics structures and data analysis equipment for the Python. Pandas is enabling you to carry out your data analysis workflow in Python while not having to have an extra domain specific language like R. Pandas let you do summary statistics using Python very easy. If you are familiar with R you will like the methods head, describe, and so on.

matplotlib is a 2D plotting library which enables you to create figures in publication quality. You can also create interactive environments across platforms. atplotlib can be used in python scripts, the python and iPython shell (similar to MATLAB or Mathematica), web application servers, and more. matplotlib aims to make easy things easy and hard things possible. Generation of plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, can be carried out with just a few lines of code. For simple plotting the pyplot interface is very similar MATLAB.  Especially, when used within IPython, Spyder, or Rodeo (the last two are great Python IDEs).


There are, of course, a plethora of Python blogs. I will mention a few of them that are related to cognitive science.

There are so many more resources. Hopefully, I will update this post or make a new one later. Hope it helps you in the Python jungle, though!




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