Speed-up Pandas Apply 5x with NumPy
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While creating conditional columns in Pandas, we tend to use the 𝐚𝐩𝐩𝐥𝐲() method almost all the time.
However, 𝐚𝐩𝐩𝐥𝐲() in Pandas is nothing but a glorified for-loop. As a result, it misses the whole point of vectorization.
Instead, you should use the 𝐧𝐩.𝐬𝐞𝐥𝐞𝐜𝐭() method to create conditional columns. It does the same job but is extremely fast.
The conditions and the corresponding results are passed as the first two arguments. The last argument is the default result.
Read more here: NumPy docs.
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Find the code for my tips here: GitHub.
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