groupby and aggregation

import seaborn as sns
df_penguins = sns.load_dataset('penguins')
df_penguins

	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

# group by
grouped = df_penguins.groupby(['species', 'island', 'sex'])
grouped.agg(['mean', 'median'])

			bill_length_mm		bill_depth_mm		flipper_length_mm		body_mass_g
			mean	median	mean	median	mean	median	mean	median
species	island	sex
Adelie	Biscoe	Female	37.359091	37.75	17.704545	17.70	187.181818	187.0	3369.318182	3375.0
	Biscoe	Male	40.590909	40.80	19.036364	18.90	190.409091	191.0	4050.000000	4000.0
	Dream	Female	36.911111	36.80	17.618519	17.80	187.851852	188.0	3344.444444	3400.0
	Dream	Male	40.071429	40.25	18.839286	18.65	191.928571	190.5	4045.535714	3987.5
	Torgersen	Female	37.554167	37.60	17.550000	17.45	188.291667	189.0	3395.833333	3400.0
	Torgersen	Male	40.586957	41.10	19.391304	19.20	194.913043	195.0	4034.782609	4000.0
Chinstrap	Dream	Female	46.573529	46.30	17.588235	17.65	191.735294	192.0	3527.205882	3550.0
Chinstrap	Dream	Male	51.094118	50.95	19.252941	19.30	199.911765	200.5	3938.970588	3950.0
Gentoo	Biscoe	Female	45.563793	45.50	14.237931	14.25	212.706897	212.0	4679.741379	4700.0
Gentoo	Biscoe	Male	49.473770	49.50	15.718033	15.70	221.540984	221.0	5484.836066	5500.0

grouped = df_penguins.groupby(['island'])
grouped['body_mass_g'].agg(['mean', 'std'])

grouped = df_penguins.groupby(['species'])
grouped['body_mass_g'].agg(['mean', 'std', 'max'])