Python pandas idxmax for multiple indexes in a dataframe

Question

I have a series that looks like this:

            delivery
2007-04-26  706           23
2007-04-27  705           10
            706         1089
            708         


        

Answer 1

Suppose you have this series:

            delivery
2001-01-02  0           2
            1           3
            6           2
            7           2
            9           3
2001-01-03  3           2
            6           1
            7           1
            8           3
            9           1
dtype: int64

If you want one delivery per date with the maximum value, you could use idxmax:

dates = series.index.get_level_values(0)
series.loc[series.groupby(dates).idxmax()]

yields

            delivery
2001-01-02  1           3
2001-01-03  8           3
dtype: int64

If you want all deliveries per date with the maximum value, use transform to generate a boolean mask:

mask = series.groupby(dates).transform(lambda x: x==x.max()).astype('bool')
series.loc[mask]

yields

            delivery
2001-01-02  1           3
            9           3
2001-01-03  8           3
dtype: int64

---

This is the code I used to generate series:

import pandas as pd
import numpy as np

np.random.seed(1)
N = 20
rng = pd.date_range('2001-01-02', periods=N//2, freq='4H')
rng = np.random.choice(rng, N, replace=True)
rng.sort()
df = pd.DataFrame(np.random.randint(10, size=(N,)), columns=['delivery'], index=rng)
series = df.groupby([df.index.date, 'delivery']).size()