![]() And the speed of those languages will be faster, yes, it's true. It's so versatile, it's really gonna be here to stayĮdit: some things are easier and faster to write in R than in Python, sure that comment applies to lots of languages. ![]() I finally got around to learning R and Python properly and realized I can do Matlab and R functions inside Python. Even my elderly professor who looks like he's 90 decided to learn Python. Years ago my professors and colleagues all said R. Shoot, that gap gets even broader when you include that R can hot-wrap compiled languages like C/++ and Java to add some time on the front end for a bigger time saving on the computational end Python can’t do that. Meanwhile, R’s better storage capacity and reduced overhead mean that it doesn’t need as much database exchange (if at all, depending on your data scale), and probably won’t need to do any chunking beyond what the native optimization engine does.ĭon’t get me wrong, I’d write Python code over R for every single task if given the option, but large data handling is one of the few areas where P圓 rather overtly doesn’t outperform R. With Py, if you’re going to be working with large-scale datasets (>250k rows), you’re either going to need to do database exchanging to get all of the data, and you’ll probably also need to do internal chunking on the in-mem data to speed up those computations. You can incorporate Dask/Vaex/PySpark to parallelize these computations for speed, but those ecosystems aren’t even remotely as mature as what R offers for dataframes. Python’s options for working with tabular data are either Pandas or PySpark, and Pandas has a much lower storage capacity than R dataframes due to the extraneous metadata memory requirements, and by-row operations on these dataframes will also take much longer than the same operations in R due to the Python type-checking overhead. However, it also seems like overkill for someone that isn’t doing computer science or AI stuff? Just like R seems like overkill for someone doing t-tests? I’m slightly biased against Matlab because I prefer open source but my lab does have a license.Ī bit late to the party here, but why would you use Python for very-large datasets? Python’s always going to be slower than R, with 10x being the usual rule of thumb that we toss around in industry (although naturally there are exceptions for the well-optimized C reference code that Python likes to wrap for numerical computing in SKL and the SciPy derivatives). So far, I’m leaning towards Python because it seems like it would be the most flexible down the line, in case my needs change. No one in my lab uses automation or coding, I’m the only one doing semi-automated analyses (so far with ImageJ macros only) so I’m truly open to either program but also worried about ease of use. The statistics are super basic, think t-test or ANOVA. csv files that I sort through on Excel and then use with Prism to do the statistics and graphs. Use case: I do a lot of microscopy and analysis (mostly with ImageJ). I’d like to learn a programming language to make analysis and data visualisation easier down the line so I was wondering if anyone had experience with one (or more) of these programs and could give me their opinion on which one would be more useful for me.
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