Resolved: How to replace by np.nan elements outside a range of float numbers in a row in pandas?

Question:

I have a dataframe:

df = pd.DataFrame([0.05, 0.04, 0.08, 0.8, -0.4, 1.0, -1.0, 1.8, -0.5, -0.05])

I need to replace by np.nan all elements in the row that are outside the limits of mean+3std and mean-3std. In other words, values less than mean-3std, and values higher than mean+3std, should be replaced by np.nan.
I am just putting one column of my dataframe, but I actually have eleven columns where all values outside the specific column range must be replaced by np.nan. All in place within the original df.
Is there a simple way of doing this? I have tried to use lambda functions with where, but it doesn’t work.

Answer:

Use DataFrame.mask

mean = df.mean()
std = df.std()
outliers_mask = (df < mean - 3*std) | (df > mean + 3*std)

# replace outliers with NaN
df = df.mask(outliers_mask)

If you have better answer, please add a comment about this, thank you!

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