For data manipulation, many businesses use Python with Pandas; however, in my experience, R’s data.table is more faster and requires less code. A straightforward task in data.table, for example, is shorter than one in Pandas, which is typically more verbose. Polars and other Python alternatives are available, but they don’t mesh as well with Python’s ecosystem as Pandas does. Despite Pandas’ slower performance when compared to R’s data.table, Python is the industry standard due to its compatibility with larger libraries. Why Python is still the best option for data manipulation in particular, aside from things like creating microservices, intrigues me.
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If you have devops, they would much rather use Python code.
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Anyone who needs to scale that process up, including Devops, will need it to be R-compatible.
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More persons who are proficient in Python are able to collaborate “smoothly.”
Python is also much more versatile.
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Additionally, all you need to do is import OpenAI if the higher-ups request AI-related items.
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I’m sorry, but this is rather disrespectful.
Actually, it comes from the transformer import pipeline. Much more sophisticated. I believe I didn’t count because I’m a data scientist and not a mathematician, but that’s around twice as many words.