Replace specific values in a dataframe by column mean in pandas

I’m a python beginner and I’m trying to do some operations with dataframes that I usually do with R language.

I Have a large dataframe with 2592 rows and 205 columns and I want to replace the 0.0 values by half the minimum value of its column.

An example with a random dataframe would be:

>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(1)
>>> df = pd.DataFrame(np.random.randint(0,10, size=(3,5)), columns = ['A', 'B', 'C', 'D', 'E'])
>>> print(df)
   A  B  C  D  E
0  5  8  9  5  0
1  0  1  7  6  9
2  2  4  5  2  4

And the result I’m looking for is:

   A  B  C  D  E
0  5  8  9  5  2
1  1  1  7  6  9
2  2  4  5  2  4

Intuitively I would do it like this:

>>> for column in df:
        for element in column:
            if element == 0:
                element = df[column].min()/2

But it doesn’t work… any help?

Thank you!

>Solution :

Use DataFrame.mask with replace minimum values without 0 divide by 2:

df1 = df.mask(df.eq(0), df.replace(0, np.nan).min().div(2), axis=1)
print(df1)
   A  B  C  D  E
0  5  8  9  5  2
1  1  1  7  6  9
2  2  4  5  2  4

For more efficient solution is possible use (thanks @mozway):

m = df.eq(0) 
df1 = df.mask(m, df[~m].min().div(2), axis=1)