The aim of this guide is to give both an introduction and to motivate the use of the Python programming language in research in the field of cognitive science. Past 10 years, we have seen a rapid development of scientific and numerical libraries in Python. In fact,  Python can now easily be used as a scientific and numerical computing environment and is a contender to propriety products such as MATLAB and Mathematica.  The goal of this guide is to put forward the areas of application and to highlight the advantages and appeals of using Python as the number one programming language in cognitive science.  Given the generality of the tools being discussed, it is  hoped  that this  guide  will  have  widespread  appeal  and relevance. That is, researchers in other fields may also find this guide informative.


Python can, as previously mentioned, be a strong contender with its choices (i.e., MATLAB).  MATLAB has for long been one of the favourite programming environments in cognitive science. Striking purposes of likeness in the middle of Python and Matlab are that both offer an intelligent  interactive array-processing  and  visualization  environment using  high-level  dynamic  programming  languages. Both are intended for quick prototyping and advancement. Both take into consideration consistent augmentation utilizing outer modules composed as a part of ordered languages like C/C++ and Fortran.

Python, be that as it may, incorporate that it is a broadly useful language whose application goes a long ways past numerical array-processing. Python is one of the main five programming languages right now being used all through the world. Python is a strikingly designed object-oriented language whose standard library is vast and extensive. Furthermore, Python is free open-source programming distributed according to an unrestricted software license. Similarly, its substantial arrangement of third-party modules and libraries are likewise, typically, released according to open-source programming licenses.

Numerical and Scientific Python

The essential Python language as presented in the past segment needs n-dimensional numerical arrays and the capacity to effortlessly plot and visualize information. These capacities, notwithstanding countless extraordinary reason investigative libraries are given by the Scipy/Numpy suite of modules. These libraries are consistently incorporated with jupyter to make a rich intelligent exhibit handling and representation environment, tantamount in usefulness to MATLAB and Mathematica.

Jupyter has a lot of really nice functions: Interactive superior parallel computing for clusters and multicore models, an online intuitive Notebook practically identical to that utilized as a part of Mathematica, sql-based searchable summon histories, in-line illustrations, and typical arithmetic with TEX-based yield. Markdown can be used to create reports.

PC based Experiments

PC based brain research and psychophysics analyses are presently verging on universal in cognitive science.  While these undertakings have been customarily taken care of by GUI-based projects like
e-prime, Presentation, and superlab , these projects don't take into account the adaptability and control that is frequently requested by researchers. While programming environments like Matlab are being utilized as a distinct options for GUI-based projects, MATLAB's special-purpose nature is not well suited to the non-numerical  programming  necessary  for  experimental  stimuli presentation and recording. Python, because of the generality of its language, have a broad pool of libraries for creating graphical interfaces (e.g. wx-python, pyGTK, pyQt), and computer game libraries (pyGame,  pyglet), Python takes into account significant adaptability and complexity in the outline test programming.  At present, there are no less than 5 Python-based stimuli presentation programs: PsychoPy, OpenSesame,  ExPyriment, vision-egg, and pyepl. Note, that both PsychoPy and OpenSesame offers GUI-based projects.

To conclude this post, Python can be used for many things. It is a general purpose language so you can, basically, do whatever you want. Althoug, R may be more common when it comes to statistics you can, of course, also analyze your data with Python. My last post cover some jupyter notebooks that teaches you analysis using Python: http://pythondataanalysis.blogspot.se/2016/04/great-resources-for-learning-how-to.html.
I will, however, return with more Python and data analysis-related stuff. Later!