Replace all non NaN values with the initial value per row

Question

I have the following dataframe:

Periods            0      1      2      3      4      5      6      7     8     9     10    11    12   13   14   15   16   17   18   19
Item 1           89.0   89.0   88.0   86.0   73.0   73.0   67.0    NaN   NaN   NaN   NaN   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN
Item 2          197.0  197.0  157.0  157.0  156.0  156.0  153.0  153.0   3.0   3.0   2.0   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN
Item 3          105.0  105.0   98.0   47.0   40.0   21.0   20.0   15.0  13.0  11.0  11.0  11.0  10.0  7.0  7.0  5.0  5.0  3.0  2.0  1.0

It has several NaNs, and columns from a range between 0 - 20. What i wanted, is to replicate the first column to the next columns, until this value is not NaN. Let me show you an example of what it should look like:

Periods                0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15     16     17     18     19
Item 1               89.0   89.0   89.0   89.0   89.0   89.0   89.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN
Item 2               197.0  197.0  197.0  197.0  197.0  197.0  197.0  197.0  197.0  197.0  197.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN
Item 3               105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0  105.0

But I can't figure out a way to do this. I wish i could post some code, but all I tried was to search for those methods: backfill, ffill. None of them looked like what I needed.

Any ideas? Thanks in advance.

Answer 1

Here's another idea, just for the record: using map:

df.iloc[0] = df.iloc[0].notna().map({True: df.iloc[0, 0]})

Answer 2

I will do mul

df.notna().mul(df.iloc[:,0],axis=0).mask(df.isna())