3 Frequent Mistakes Data Scientists Make In Their Code

Author(s): Brandon Walker

Originally published on Towards AI.

These are some of the most common mistakes I’ve come across. Eliminating them will make you write faster code, less code, and code with a clear workflow.

No matter what language you’re using, if you are iterating through a data frame, you’re probably doing something wrong. Your computer can handle doing multiple things at the same time, iterating with a for or while loop means that everything is done one at a time. Here are two ways you can apply the same function to a data set.

def function_to_apply(x): y = x**2 + 5*x + 10 return y

Slow:

for row_number in df.index: df.iloc[row_number]… Read the full blog for free on Medium.

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Published via Towards AI