优化groupby agg函数以返回多个结果列

Question

我有这个数据帧

import pandas as pd
import numpy as np
df = pd.DataFrame({
'Client':np.random.choice(['Customer_A', 'Customer_B'], 1000),
'Product':np.random.choice( ['Guns', 'Ammo', 'Armour'], 1000),
'Value':(np.random.randn(1000))
})
Categoricals = ['Client', 'Product']
df[Categoricals] = df[Categoricals].astype('category')
df = df.drop_duplicates()
df

我想要这个结果

# Non-anonymous function for Anomaly limit
def Anomaly (x):
Q3 = np.nanpercentile(x, q = 75)
Q1 = np.nanpercentile(x, q = 25)
IQR = (Q3 - Q1)
return (Q3 + (IQR * 2.0))
# Non-anonymous function for CriticalAnomaly limit
def CriticalAnomaly (x):
Q3 = np.nanpercentile(x, q = 75)
Q1 = np.nanpercentile(x, q = 25)
IQR = (Q3 - Q1)
return (Q3 + (IQR * 3.0))

# Define metrics
Metrics = {'Value':['count', Anomaly, CriticalAnomaly]}
# Groupby has more than 1 grouping column, so agg can only accept non-anonymous functions
Limits = df.groupby(['Client', 'Product']).agg(Metrics)
Limits

但在大型数据集上速度较慢，因为函数"异常"one_answers"临界异常"必须重新计算Q1、Q3和IQR两次，而不是一次。通过将这两个功能结合在一起，可以使速度更快。但是结果输出到1列而不是2列。

# Combined anomaly functions
def CombinedAnom (x):
Q3 = np.nanpercentile(x, q = 75)
Q1 = np.nanpercentile(x, q = 25)
IQR = (Q3 - Q1)
Anomaly = (Q3 + (IQR * 2.0))
CriticalAnomaly = (Q3 + (IQR * 3.0))
return (Anomaly, CriticalAnomaly)
# Define metrics
Metrics = {'Value':['count', CombinedAnom]}
# Groupby has more than 1 grouping column, so agg can only accept non-anonymous functions
Limits = df.groupby(['Client', 'Product']).agg(Metrics)
Limits

如何制作一个组合函数，使结果分为两列

Answer 1

如果使用apply而不是agg，则可以返回一个被解压缩为列的Series：

def f(g):
return pd.Series({
'c1': np.sum(g.b),
'c2': np.prod(g.b)
})
df = pd.DataFrame({'a': list('aabbcc'), 'b': [1,2,3,4,5,6]})
df.groupby('a').apply(f)

这来自：

a  b
0  a  1
1  a  2
2  b  3
3  b  4
4  c  5
5  c  6

至

c1  c2
a        
a   3   2
b   7  12
c  11  30