Using pandas.DataFrame.interpolate to add rows to DataFrame

Question

I have a Pandas dataframe with the following format:

    Frequency | Value
1   10          2.8
2   20          2.5
3   30          2.2
4   40          2.3

I want to use pandas.DataFrame.interpolate in order to add a line at frequency 35 with a value interpolated linearly between frequencies 30 and 40.

In the user manual the example shows how to replace a Nan but not how to add values in between others (Pandas doc).

What would be the best way to proceed ?

score 9 · Accepted Answer · edited May 23 '17 at 12:16

I think you need first add new value 35 to frequency column by loc, sort_values and then interpolate:

df.loc[-1, 'Frequency'] = 35
df = df.sort_values('Frequency').reset_index(drop=True)
print (df)
   Frequency  Value
0       10.0    2.8
1       20.0    2.5
2       30.0    2.2
3       35.0    NaN
4       40.0    2.3

df = df.interpolate()
print (df)
   Frequency  Value
0       10.0   2.80
1       20.0   2.50
2       30.0   2.20
3       35.0   2.25
4       40.0   2.30

Solution with Series, thank you for idea Rutger Kassies.

DataFrame.squeeze create Series with one column DataFrame.

s = df.set_index('Frequency').squeeze()
s.loc[35] = np.nan
s = s.sort_index().interpolate(method='index')
print (s)
Frequency
10    2.80
20    2.50
30    2.20
35    2.25
40    2.30
Name: Value, dtype: float64

If the frequencies are irregular, setting it as the index and using `df.interpolate(method='index')` would take the actual frequency into account. — Rutger Kassies, Jan 25 '17 at 14:59

Using pandas.DataFrame.interpolate to add rows to DataFrame

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