Pandas interpolate within a groupby

Solution 1:

>>> df.groupby('filename').apply(lambda group: group.interpolate(method='index'))
    filename  val1  val2
t                       
1  file1.csv     5    10
2  file1.csv    10    15
3  file1.csv    15    20
6  file2.csv   NaN   NaN
7  file2.csv    10    20
8  file2.csv    12    15

Solution 2:

I ran into this as well. Instead of using apply, you can use transform, which will reduce your run time by more than 25% if you have on the order of 1000 groups:

import numpy as np
import pandas as pd

np.random.seed(500)
test_df = pd.DataFrame({
    'a': np.random.randint(low=0, high=1000, size=10000),
    'b': np.random.choice([1, 2, 4, 7, np.nan], size=10000, p=([0.2475]*4 + [0.01]))
})

Tests:

%timeit test_df.groupby('a').transform(pd.DataFrame.interpolate)

Output: 566 ms ± 27.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit test_df.groupby('a').apply(pd.DataFrame.interpolate)

Output: 788 ms ± 10.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit test_df.groupby('a').apply(lambda group: group.interpolate())

Output: 787 ms ± 17.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit test_df.interpolate()

Output: 918 µs ± 16.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

You will still see a significant increase in run-time compared to a fully vectorized call to interpolate on the full DataFrame, but I don't think you can do much better in pandas.

Solution 3:

Considering the long running time of above methods, I suggest use a for loop and interpolate(), which is no more than few lines of codes, but much faster in speed.

for i in range(len(df.filename.unique())):
      mask = df.loc[:,'filename']==df.filename.unique()[i]
      df[mask]=dfs[mask].interpolate(method='index')