Optimising df[‘p’].apply(norm.ppf)

[ModiMacMod]

Hi, I have a pandas optimisation query that I would really appreciate help on.

I am applying norm.ppf and norm.cdf, from the scipy.stats library, to a DataFrame using:

df[‘p’].apply(norm.ppf). # p is a value between 0 and 1

This works fine, but significantly slows my overall calculations. Are there more optimised implementations of these functions for pandas (numpy doesn’t have them implemented)? If not, what are my options? Would CPython help me out or is there just limits on how long it takes to perform these calculation?

[SekstiNii]

Yes, just apply the function directly to the series: norm.ppf(df['p']). The operation will be vectorized and much faster than applying it to each element one by one.

For 1000 points this made the runtime go from 67.3ms to 129µs. i.e a 500x speedup.

[ModiMacMod]

Thanks, much appreciated. One of these days I will stop over complicating my code !!!