Find the in between value within a dataframe

Currently I have the following dataframe:

All those value equal to -1 means N/A and the value should be increasing. Therefore I would like to generate another two columns that should indicate the possible min and possible max value, and the possible min and max is based on the valid value inside the value column.

The exptected output would be like this:

I would use the extra column to find the fillna value using my own matching logic.

>Solution :

Given df:

   value
0      1
1     -1
2     -1
3     -1
4      6
5     -1
6     -1
7     -1
8     10

If something should mean NaN, make it NaN.

df['value'] = df['value'].replace(-1, np.nan)

Now, we can fill your desired values:

df.loc[df['value'].isna(), 'possible_min'] = df['value'].ffill()
df.loc[df['value'].isna(), 'possible_max'] = df['value'].bfill()
print(df)

Bonus, linear interpolation:

df['interpolated'] = df['value'].interpolate()

Output:

   value  possible_min  possible_max  interpolated
0    1.0           NaN           NaN          1.00
1    NaN           1.0           6.0          2.25
2    NaN           1.0           6.0          3.50
3    NaN           1.0           6.0          4.75
4    6.0           NaN           NaN          6.00
5    NaN           6.0          10.0          7.00
6    NaN           6.0          10.0          8.00
7    NaN           6.0          10.0          9.00
8   10.0           NaN           NaN         10.00