I would also like to point out at this time, that I will only be presenting the advantages of Python over the other softwares I have used, I can not speak to the abilities of other programming languages (MATLAB, R).
Psychopy anaconda how to#
Although learning programming seems like an ominous task, especially if you aren’t especially tech savvy (after all, I am a developmental psychologist, not a computer scientist), with the right tools you can quickly learn how to make the task much more manageable or even automated. Learning scientific computing in Python was my solution for consolidating and streamlining data processing pipelines from several softwares into one (actually 2, but more on that below).
Psychopy anaconda full#
The use of multiple softwares often means having to create multiple copies of your data file, running the risk of a conversion error, sometimes copying data by hand from one system to another, and generally making it harder to keep a clean track of the full processing pipeline. These differences often reflect differences in the nature of the data (i.e., it may not make sense to process fMRI data the same was as survey data or financial time series data) but often are simply a matter of convenience, comfort, or necessity of the individual researcher. With so many options and different capabilities and learning curves for each, it shouldn’t be surprising that there exists so much diversity in processing pipelines across fields, between labs and sometimes even between individuals within labs. These are just a few of the many software programs out there see these Wikipedia articles for much longer lists of available numerical softwares and statistical packages. Throughout my time as a researcher, I’ve relied on various softwares for these steps including Excel (pivot tables are great for summarizing data that is in long/row format), SPSS, E-Prime, Bash scripts, HLM & Python. These tasks can be slowed down by the need to switch softwares, data formats, and at times do manipulations or calculations by hand. Some of the major tasks (and potential major time sucks) for many graduate students and quantitative researchers revolve around data management, processing, analysis and graphing. I hope this blog will present, in a very understandable way, some of the best tools for social scientists looking to take advantage of programming to improve their data processing.
Although learning programming may seem an insurmountable task, with the right tools a few simple lines of code can accomplish wonderful things. Why should psychologists, or social scientists more generally, care about programming? The fact is anyone who uses softwares for data analysis relies on programming and many wonderful tools exist to give researchers improved control over their data for more efficient workflow.
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