Pandas foward fill in between - same values

Solution with ffill and bfill

f = df['col'].ffill()
b = df['col'].bfill()

df['col'].mask(f == b, f)

0      NaN
1      NaN
2     10.0
3     10.0
4     10.0
5     10.0
6     10.0
7      NaN
8      4.0
9      4.0
10     4.0
11     5.0
12     5.0
13     5.0
14     NaN
15     2.0
16     2.0
17     2.0
18     1.0
19     1.0
Name: col, dtype: float64

You can use

m = df['l'].eq(df['l']).cumsum()
out = df.groupby(m).apply(lambda g: g.fillna({'l': g['l'].max()}) if g.name % 2 else g)

Detailed explaination:

Assume you have the dataframe

l = [np.nan, np.nan, 10, np.nan, np.nan, np.nan, 10, np.nan, 4, np.nan, 4, 5, np.nan, 5, np.nan,  2, np.nan, 2, 1, 1, np.nan]

df = pd.DataFrame({'l': l})

print(df)

       l
0    NaN
1    NaN
2   10.0
3    NaN
4    NaN
5    NaN
6   10.0
7    NaN
8    4.0
9    NaN
10   4.0
11   5.0
12   NaN
13   5.0
14   NaN
15   2.0
16   NaN
17   2.0
18   1.0
19   1.0
20   NaN

You can use the feature that NaN is not equal with NaN to create a cumsum.

df['m'] = df['l'].eq(df['l']).cumsum()

print(df)

       l   m
0    NaN   0
1    NaN   0
2   10.0   1
3    NaN   1
4    NaN   1
5    NaN   1
6   10.0   2
7    NaN   2
8    4.0   3
9    NaN   3
10   4.0   4
11   5.0   5
12   NaN   5
13   5.0   6
14   NaN   6
15   2.0   7
16   NaN   7
17   2.0   8
18   1.0   9
19   1.0  10
20   NaN  10

We can notice that the True only occurs in pairs. The first line could only be True or False, so no matter what the first line is

• The cumsum to the start True of first True pair could only be 1
• The cumsum to the end True of first True pair could only be 2

This happens to other True pairs: the cumsum to the start True is odd number, the cumsum to the end True is even number.

With this in mind, we can do a groupby then only fill the odd sum value with the not non value in group.

out = df.groupby(m).apply(lambda g: g.fillna({'l': g['l'].max()}) if g.name % 2 else g)

print(out)

       l
0    NaN
1    NaN
2   10.0
3   10.0
4   10.0
5   10.0
6   10.0
7    NaN
8    4.0
9    4.0
10   4.0
11   5.0
12   5.0
13   5.0
14   NaN
15   2.0
16   2.0
17   2.0
18   1.0
19   1.0
20   NaN