python - 如何将自定义函数列表传递给 pandas.Dataframe.aggregate

Question

我正在尝试聚合具有不同自定义函数的 pd.Dataframe，尤其是来自 scipy.stats 的函数。我可以让它与单个函数一起工作，在这种情况下 trim_mean:

import pandas as pd
import numpy as np
from scipy.stats import trim_mean

df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C'], index=pd.date_range('1/1/2000', periods=100))

# this works as expected
df.agg([np.sum, np.mean])

# now with a different function, works also
df.agg(lambda x: trim_mean(x, 0.2))

# apply also works
df.apply(lambda x: trim_mean(x, 0.2))

但是，df.agg([lambda x: trim_mean(x, 0.2)]) 生成一个 IndexError: tuple index out of range' 和 一样df.apply([lambda x: trim_mean(x, 0.2)]).

我找到了一个 old issue on pandas-dev但这对我来说没有意义。

有人帮忙吗？

Answer 1

您需要在函数列表之前使用 lambda，返回 DataFrame 使用 Series:

c = ['trim_mean','mean','sum']
print (df.agg(lambda x: pd.Series([trim_mean(x, 0.2), np.mean(x), np.sum(x)], index=c)))

或者:

print (df.apply(lambda x: pd.Series([trim_mean(x, 0.2), np.mean(x), np.sum(x)], index=c)))

---

                   A         B         C
trim_mean  -0.143219 -0.018430 -0.097768
mean       -0.171887 -0.042308 -0.004843
sum       -17.188738 -4.230797 -0.484343

验证:

print (df.agg([np.sum, np.mean]))
              A         B         C
sum  -17.188738 -4.230797 -0.484343
mean  -0.171887 -0.042308 -0.004843

print(df.agg(lambda x: trim_mean(x, 0.2)))
A   -0.143219
B   -0.018430
C   -0.097768
dtype: float64