Fill NaN based on previous value of row

Question

I have a data frame (sample, not real):

df =

    A   B   C    D   E     F       

0   3   4   NaN  NaN NaN   NaN  
1   9   8   NaN  NaN NaN   NaN      
2   5   9   4    7   NaN   NaN  
3   5   7   6    3   NaN   NaN  
4   2   6   4    3   NaN   NaN  

Now I want to fill NaN values with previous couple(!!!) values of row (fill Nan with left existing couple of numbers and apply to the whole row) and apply this to the whole dataset.

• There are a lot of answers concerning filling the columns. But in this case I need to fill based on rows.
• There are also answers related to fill NaN based on other column, but in my case number of columns are more than 2000. This is sample data

Desired output is:

df =

   A  B   C  D  E  F  

0  3  4   3  4  3  4  
1  9  8   9  8  9  8  
2  5  9   4  7  4  7      
3  5  7   6  3  6  3  
4  2  6   4  3  4  3  

Answer 1

IIUC, a quick solution without reshaping the data:

df.iloc[:,::2] = df.iloc[:,::2].ffill(1)
df.iloc[:,1::2] = df.iloc[:,1::2].ffill(1)
df

Output:

   A  B  C  D  E  F
0  3  4  3  4  3  4
1  9  8  9  8  9  8
2  5  9  4  7  4  7
3  5  7  6  3  6  3
4  2  6  4  3  4  3

Answer 2

Idea is reshape DataFrame for possible forward and back filling missing values with stack and modulo and integer division of 2 of array by length of columns:

c = df.columns 
a = np.arange(len(df.columns))
df.columns = [a // 2, a % 2]

#if possible some pairs missing remove .astype(int)
df1 = df.stack().ffill(axis=1).bfill(axis=1).unstack().astype(int)
df1.columns = c
print (df1)
   A  B  C  D  E  F
0  3  4  3  4  3  4
1  9  8  9  8  9  8
2  5  9  4  7  4  7
3  5  7  6  3  6  3
4  2  6  4  3  4  3

Detail:

print (df.stack())
     0    1   2
0 0  3  NaN NaN
  1  4  NaN NaN
1 0  9  NaN NaN
  1  8  NaN NaN
2 0  5  4.0 NaN
  1  9  7.0 NaN
3 0  5  6.0 NaN
  1  7  3.0 NaN
4 0  2  4.0 NaN
  1  6  3.0 NaN