pivot tables

import seaborn as sns
import pandas as pd

df = sns.load_dataset('penguins')
df

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns

df.pivot_table(index=['species', 'island', 'sex'],
               values=['body_mass_g', 'flipper_length_mm'],
              aggfunc='mean')

			body_mass_g	flipper_length_mm
species	island	sex
Adelie	Biscoe	Female	3369.318182	187.181818
		Male	4050.000000	190.409091
	Dream	Female	3344.444444	187.851852
		Male	4045.535714	191.928571
	Torgersen	Female	3395.833333	188.291667
		Male	4034.782609	194.913043
Chinstrap	Dream	Female	3527.205882	191.735294
		Male	3938.970588	199.911765
Gentoo	Biscoe	Female	4679.741379	212.706897
		Male	5484.836066	221.540984

df.pivot_table(index=['species', 'island', 'sex'],
               values=['body_mass_g', 'flipper_length_mm'],
              aggfunc='min')

			body_mass_g	flipper_length_mm
species	island	sex
Adelie	Biscoe	Female	2850.0	172.0
		Male	3550.0	180.0
	Dream	Female	2900.0	178.0
		Male	3425.0	178.0
	Torgersen	Female	2900.0	176.0
		Male	3325.0	181.0
Chinstrap	Dream	Female	2700.0	178.0
		Male	3250.0	187.0
Gentoo	Biscoe	Female	3950.0	203.0
		Male	4750.0	208.0