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Full disclosure — I’m currently working as a Python Engineer, and therefore you can consider me biased. But I want to unravel some critics about Python and reflect on whether the speed concerns are valid regarding day-to-day work using Python for data engineering, data science, and analytics.

Is Python too slow?

From my perspective, such questions should be asked based on a specific context or use case. Is Python slow in crunching numbers compared to compiled languages such as C? Yes, it is. This fact has been known for years, and that’s the reason why Python libraries for which speed plays an important role, such as numpy, leverage C under the hood.

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But is Python that much slower than other (more difficult to learn and use) languages for all use cases? If you look at the performance benchmarks of many Python libraries optimized to solve a specific problem, they do decently well compared to compiled languages. For instance, look at the FastAPI performance benchmark — obviously, Go as a compiled language is much faster than Python. Still, FastAPI beats some of Go’s libraries for building REST APIs:

Web Framework Benchmarks — image by the author

Side note: the above list does not include C++ and Java web frameworks, which had even higher performance.

Similarly, when comparing Dask (written in Python) against Spark (written in Scala) for data-intensive neuroimaging pipelines [2], the authors concluded:

Overall, our results show no substantial performance difference between the engines.

The question we should be asking ourselves is what speed do we really need. If you run an ETL job triggered only once per day, you may not care whether it takes 20 seconds or 200 seconds. You may then prefer to make the code easy to understand, package, and maintain, especially given that the compute resources are becoming increasingly affordable as compared to the costly engineering time.

Code speed vs. practicality

From a pragmatic standpoint, there are many different questions that we need to answer when choosing a programming language for day-to-day work.

Can you reliably solve multiple business problems with this language?

If all you care about is speed, then don’t use Python, period. There are much faster alternatives for all sorts of use cases. Python’s main benefits lie in its readability, ease of use, and a wide range of problems that can be solved with it. Python can be used as a glue that ties together a myriad of different systems, services, and use cases.

Can you find enough employees that know this language?

Since Python is so easy to learn and use, the number of Python users is constantly growing. Business users, who were previously crunching numbers in Excel, can now quickly learn to code in Pandas and thus learn to be self-sufficient without constantly relying on IT resources. At the same time, this eliminates the burden of the IT and analytics departments. It also improves the time to value.

These days, it’s easier to find data engineers who know Python and can maintain a Spark data processing application in this language, rather than those who could do the same in Java or Scala. Many organizations are gradually switching to Python for many use cases simply because of the higher chances of finding employees who “speak” that language.

In contrast, I know companies who desperately need Java or C# developers to maintain their existing applications, but those languages are difficult (take years to master) and seem unattractive for new programmers who can potentially earn more in jobs that leverage much easier languages such as Go or Python.