Someone on this platform has already helped me with generating the following code:
col,row = (100,1000) a = np.random.uniform(0,10,size=col*row).round(6).reshape(row,col) mask = (a*1e6+1).astype(int)%10<2 a[mask] += 2e-6
This code ensures that the last decimal is not a 0 or a 9. However, I would like to have no 0’s nor 9’s in all the decimals that are generated (such that it will not be possible to have a number 1.963749 or 3.459007).
It would be fine for all 0’s and 9’s to be, for example, replaced by a 2 (1.263742 and 3.452227 considering the example above). I know that the function replace (0, 2) does not work for the decimal numbers. Is there a way to replace these numbers or should the code be rewritten to make this work?
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Answer
Solution using strings (I find it non-elegant but it works).
Assuming this input as pandas DataFrame df
:
col1 col2 0 1.234567 9.999909 1 1.999999 0.120949
You can stack and replace as string:
def rand(x): import random return random.choice(list('12345678')) df2 = (df .stack() .astype(str) .str.replace(r'[09](?!.*.)', rand) .astype(float) .unstack() )
Output:
col1 col2 0 1.234567 9.665236 1 1.184731 0.128345