How to fill a particular value with mean value of the column between first row and the corresponding row in pandas dataframe

Question

I have a df like this,

A   B   C   D   E
1   2   3   0   2
2   0   7   1   1
3   4   0   3   0
0   0   3   4   3

I am trying to replace all the 0 with mean() value between the first row and the 0 value row for the corresponding column,

My expected output is,

A       B       C           D       E
1.0     2.00    3.000000    0.0     2.0
2.0     1.00    7.000000    1.0     1.0
3.0     4.00    3.333333    3.0     1.0
1.5     1.75    3.000000    4.0     3.0

Answer 1

IIUC

def f(x):
    for z in range(x.size):
        if x[z] == 0: x[z] = np.mean(x[:z+1])
    return x

df.astype(float).apply(f)

    A   B       C           D   E
0   1.0 2.00    3.000000    0.0 2.0
1   2.0 1.00    7.000000    1.0 1.0
2   3.0 4.00    3.333333    3.0 1.0
3   1.5 1.75    3.000000    4.0 3.0

Answer 2

Here is main problem need previous mean value if multiple 0 per column, so realy problematic create vectorized solution:

def f(x):
    for i, v in enumerate(x):
        if v == 0: 
            x.iloc[i] = x.iloc[:i+1].mean()
    return x

df1 = df.astype(float).apply(f)
print (df1)

     A     B         C    D    E
0  1.0  2.00  3.000000  0.0  2.0
1  2.0  1.00  7.000000  1.0  1.0
2  3.0  4.00  3.333333  3.0  1.0
3  1.5  1.75  3.000000  4.0  3.0

Better solution:

#create indices of zero values to helper DataFrame
a, b = np.where(df.values == 0)
df1 = pd.DataFrame({'rows':a, 'cols':b})
#for first row is not necessary count means
df1 = df1[df1['rows'] != 0]
print (df1)
   rows  cols
1     1     1
2     2     2
3     2     4
4     3     0
5     3     1

#loop by each row of helper df and assign means
for i in df1.itertuples():
    df.iloc[i.rows, i.cols] = df.iloc[:i.rows+1, i.cols].mean()

print (df)
     A     B         C  D    E
0  1.0  2.00  3.000000  0  2.0
1  2.0  1.00  7.000000  1  1.0
2  3.0  4.00  3.333333  3  1.0
3  1.5  1.75  3.000000  4  3.0

Another similar solution (with mean of all pairs):

for i, j in zip(*np.where(df.values == 0)):
    df.iloc[i, j] = df.iloc[:i+1, j].mean()
print (df)

     A     B         C    D    E
0  1.0  2.00  3.000000  0.0  2.0
1  2.0  1.00  7.000000  1.0  1.0
2  3.0  4.00  3.333333  3.0  1.0
3  1.5  1.75  3.000000  4.0  3.0