Selecting first row from each subgroup (pandas)

Question

How to select the subset of rows where distance is lowest, grouping by date and p columns?

df
    v       p       distance    date
0   14.6    sst     22454.1     2021-12-30
1   14.9    sst     24454.1     2021-12-30
2   14.8    sst     33687.4     2021-12-30
3   1.67    wvht    23141.8     2021-12-30
4   1.9     wvht    24454.1     2021-12-30
5   1.8     wvht    24454.1     2021-12-30
6   1.7     wvht    23141.4     2021-12-31
7   2.1     wvht    24454.1     2021-12-31

Ideally, the returned dataframe should contain:

df
    v       p       distance    date
0   14.6    sst     22454.1     2021-12-30
3   1.67    wvht    23141.8     2021-12-30
6   1.7     wvht    23141.4     2021-12-31

Answer 1

One way is to use groupby + idxmin to get the index of the smallest distance per group, then use loc to get the desired output:

out = df.loc[df.groupby(['date', 'p'])['distance'].idxmin()]

Output:

       v     p  distance        date
0  14.60   sst   22454.1  2021-12-30
3   1.67  wvht   23141.8  2021-12-30
6   1.70  wvht   23141.4  2021-12-31

Answer 2

sort values by p and distance. Drop any duplicates keeping first occurance in each p and date

df.sort_values(by=['p', 'distance']).drop_duplicates(subset=['p','date'],keep='first')



 v     p  distance        date
0  14.60   sst   22454.1  2021-12-30
6   1.70  wvht   23141.4  2021-12-31
3   1.67  wvht   23141.8  2021-12-30

Answer 3

If you don't need original indexes then you can use .first() or .min('distance') and later reset_index().

df.groupby(['date', 'p']).first().reset_index()

---

import pandas as pd

text = '''v       p       distance    date
0   14.6    sst     22454.1     2021-12-30
1   14.9    sst     24454.1     2021-12-30
2   14.8    sst     33687.4     2021-12-30
3   1.67    wvht    23141.8     2021-12-30
4   1.9     wvht    24454.1     2021-12-30
5   1.8     wvht    24454.1     2021-12-30
6   1.7     wvht    23141.4     2021-12-31
7   2.1     wvht    24454.1     2021-12-31'''

import io

df = pd.read_csv(io.StringIO(text), sep='\s+')

df.groupby(['date', 'p']).first().reset_index()

Result:

         date     p      v  distance
0  2021-12-30   sst  14.60   22454.1
1  2021-12-30  wvht   1.67   23141.8
2  2021-12-31  wvht   1.70   23141.4