Python-遍历行和列

Question

First of all, please pardon my skills. I am trying to get into Python, I learn just for fun, let's say, I don't use it professionally and I am quite bad, to be honest. Probably there will be basic errors on my question.

Anyway, I am trying to go over a dataframe's rows and columns. I want to check if the values of the columns (except the first one) are NaNs. If they are, then they should change to the value of the first one.

import math

for index, row in rawdata3.iterrows():
    test = row[0]
    for column in row:
        if math.isnan(row.loc[column]) == True:
            row.loc[column] = test

The error I get is something like this:

the label [4.0] is not in the [columns]

I also had other errors with slightly different code like:

cannot do label indexing on class pandas.core.indexes.base.Index with these indexers class float

Could you give me a hand, please?

Thanks in advance!

Cheers.

Answer 1

Where df is:

   A    B    C
0  5  NaN  2.0
1  6  5.0  NaN
2  9  NaN  NaN
3  2  4.0  6.0

Use transpose and fillna :

Due to fillna "NotImplementedEerror" NotImplementedError: Currently only can fill with dict/Series column by column df.fillna(value=df.A, axis=1) will not work. Therefore we use:

df.T.fillna(df.A).T

Output:

     A    B    C
0  5.0  5.0  2.0
1  6.0  5.0  6.0
2  9.0  9.0  9.0
3  2.0  4.0  6.0

Answer 2

I don't know if there is a better way but this works fine:

for i in df.columns:
    df.loc[df[i].isnull(), i] = df.loc[df[i].isnull(), 'A']

output:

   A    B    C
0  5  5.0  2.0
1  6  5.0  6.0
2  9  9.0  9.0
3  2  4.0  6.0