As of pandas 0.25, this is possible with a "Named aggregation".
In [79]: animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
....: 'height': [9.1, 6.0, 9.5, 34.0],
....: 'weight': [7.9, 7.5, 9.9, 198.0]})
....:
In [80]: animals
Out[80]:
kind height weight
0 cat 9.1 7.9
1 dog 6.0 7.5
2 cat 9.5 9.9
3 dog 34.0 198.0
In [82]: animals.groupby("kind").agg(
....: min_height=('height', 'min'),
....: max_height=('height', 'max'),
....: average_weight=('weight', np.mean),
....: )
....:
Out[82]:
min_height max_height average_weight
kind
cat 9.1 9.5 8.90
dog 6.0 34.0 102.75
The previously deprecated version follows:
You can pass a dictionary of dictionaries to .agg
mapping {column: {name: aggfunc}}
, for example
In [46]: df.head()
Out[46]:
Year qtr realgdp realcons realinvs realgovt realdpi cpi_u M1
0 1950 1 1610.5 1058.9 198.1 361.0 1186.1 70.6 110.20
1 1950 2 1658.8 1075.9 220.4 366.4 1178.1 71.4 111.75
2 1950 3 1723.0 1131.0 239.7 359.6 1196.5 73.2 112.95
3 1950 4 1753.9 1097.6 271.8 382.5 1210.0 74.9 113.93
4 1951 1 1773.5 1122.8 242.9 421.9 1207.9 77.3 115.08
tbilrate unemp pop infl realint
0 1.12 6.4 149.461 0.0000 0.0000
1 1.17 5.6 150.260 4.5071 -3.3404
2 1.23 4.6 151.064 9.9590 -8.7290
3 1.35 4.2 151.871 9.1834 -7.8301
4 1.40 3.5 152.393 12.6160 -11.2160
In [47]: df.groupby('qtr').agg({"realgdp": {"mean_gdp": "mean", "std_gdp": "std"},
"unemp": {"mean_unemp": "mean"}})
Out[47]:
realgdp unemp
mean_gdp std_gdp mean_unemp
qtr
1 4506.439216 2104.195963 5.694118
2 4546.043137 2121.824090 5.686275
3 4580.507843 2132.897955 5.662745
4 4617.592157 2158.132698 5.654902
The result has a MultiIndex in the columns. If you don't want that outer level, you can use .columns.droplevel(0)
.
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