问题描述
我有一个正常的df
A = pd.DataFrame([[1, 5, 2], [2, 4, 4], [3, 3, 1], [4, 2, 2], [5, 1, 4]],
columns=['A', 'B', 'C'], index=[1, 2, 3, 4, 5])
以下，我得到了我想要的结果。
Following this recipe, I got the the results I wanted.
In [62]: A.groupby((A['A'] > 2)).apply(lambda x: pd.Series(dict(
up_B=(x.B >= 0).sum(), down_B=(x.B < 0).sum(), mean_B=(x.B).mean(), std_B=(x.B).std(),
up_C=(x.C >= 0).sum(), down_C=(x.C < 0).sum(), mean_C=(x.C).mean(), std_C=(x.C).std())))
Out[62]:
down_B down_C mean_B mean_C std_B std_C up_B up_C
A
False 0 0 4.5 3.000000 0.707107 1.414214 2 2
True 0 0 2.0 2.333333 1.000000 1.527525 3 3
这种方法很好，但是想象一下，你必须这样做大量的列（15-100），那么你必须在公式中输入所有的东西，这可能很麻烦。
This approach is fine, but imagine you had to do this for a large number of columns (15-100), then you have to type all that stuff in the formula, which can be cumbersome.
给定相同的公式应用于所有列。有没有一个有效的方式来做这个大量的列？
Given that the same formulas are applied to ALL columns. Is there an efficient way to do this for a large number of columns?.
谢谢
推荐答案
由于您将每个分组列汇总成一个值可以使用 agg 而不是 apply 。 可以功能列表作为输入。这些功能将被应用到每一列：
Since you are aggregating each grouped column into one value, you can use agg instead of apply. The agg method can take a list of functions as input. The functions will be applied to each column:
def up(x):
return (x >= 0).sum()
def down(x):
return (x < 0).sum()
result = A.loc[:, 'B':'C'].groupby((A['A'] > 2)).agg(
[up, down, 'mean', 'std'])
print(result)
收到
B C
up down mean std up down mean std
A
False 2 0 4.5 0.707107 2 0 3.000000 1.414214
True 3 0 2.0 1.000000 3 0 2.333333 1.527525
result has hierarchical（ MultiIndexed）列。要选择某列（或列），您可以使用：
result has hierarchical ("MultiIndexed") columns. To select a certain column (or columns), you could use:
In [39]: result['B','mean']
Out[39]:
A
False 4.5
True 2.0
Name: (B, mean), dtype: float64
In [46]: result[[('B', 'mean'), ('C', 'mean')]]
Out[46]:
B C
mean mean
A
False 4.5 3.000000
True 2.0 2.333333
或您可以将MultiIndex的一个级别移动到索引：
or you could move one level of the MultiIndex to the index:
In [40]: result.stack()
Out[40]:
B C
A
False up 2.000000 2.000000
down 0.000000 0.000000
mean 4.500000 3.000000
std 0.707107 1.414214
True up 3.000000 3.000000
down 0.000000 0.000000
mean 2.000000 2.333333
std 1.000000 1.527525
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