python Pandas : applying different aggregate functions to different columns

Question

我试图理解这个简单的 SQL 语句的等价物是什么:

select mykey, sum(Field1) as sum_of_field1, avg(Field1) as avg_field1, min(field2) as min_field2
from df
group by mykey

我知道我可以将字典传递给 agg() 函数:

  f = {'Field1':'sum',
         'Field2':['max','mean'],
         'Field3':['min','mean','count'],
         'Field4':'count'
         }

    grouped = df.groupby('mykey').agg(f)

但是，生成的列名称似乎由 pandas 自动选择:('Field1','sum') 等

有没有办法为列名传递字符串，以便该字段不是 ('Field1','sum') 而是我可以选择的东西，比如 sum_of_field1 ？

谢谢。我在这里查看了文档:http://pandas.pydata.org/pandas-docs/stable/groupby.html 但找不到答案。

Answer 1

从 pandas 0.25 开始，这可以通过 "Named aggregation" 实现.

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

之前弃用的版本如下:

---

例如，您可以将字典的字典传递给 .agg 映射 {column: {name: aggfunc}}

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

结果的列中有一个 MultiIndex。如果你不想要那个外层，你可以使用 .columns.droplevel(0)。