应用自定义groupby聚合函数在pandas python中输出二进制结果

import numpy as np
import pandas as pd

df = pd.DataFrame({'Buy/Sell': [1,1,0],'Trader': ['A','A','B','C','C']})

grouped = df.groupby(['Trader'])
result = grouped['Buy/Sell'].agg(['sum','count'])
means = grouped['Buy/Sell'].mean()
result['Buy/Sell'] = np.select(condlist=[means>0.5,means<0.5],choicelist=[1,default=np.nan)
print(result)

产量

Buy/Sell  sum  count
Trader                      
A            NaN    1      2
B              1    2      3
C              0    1      3

我的原始答案使用了自定义聚合器,分类：

def categorize(x):
    m = x.mean()
    return 1 if m > 0.5 else 0 if m < 0.5 else np.nan
result = df.groupby(['Trader'])['Buy/Sell'].agg([categorize,'sum','count'])
result = result.rename(columns={'categorize' : 'Buy/Sell'})

虽然调用自定义函数可能很方便,但通常会有性能
与内置功能相比,使用自定义功能时速度明显变慢
聚合器(例如groupby / agg / mean).内置聚合器是
Cythonized,而自定义函数降低了普通Python的性能
for-loop速度.

当组数为时,速度的差异尤为显着
大.例如,对于具有1000个组的10000行DataFrame,

import numpy as np
import pandas as pd
np.random.seed(2017)
N = 10000
df = pd.DataFrame({
    'Buy/Sell': np.random.randint(2,size=N),'Trader': np.random.randint(1000,size=N)})

def using_select(df):
    grouped = df.groupby(['Trader'])
    result = grouped['Buy/Sell'].agg(['sum','count'])
    means = grouped['Buy/Sell'].mean()
    result['Buy/Sell'] = np.select(condlist=[means>0.5,default=np.nan)
    return result

def categorize(x):
    m = x.mean()
    return 1 if m > 0.5 else 0 if m < 0.5 else np.nan

def using_custom_function(df):
    result = df.groupby(['Trader'])['Buy/Sell'].agg([categorize,'count'])
    result = result.rename(columns={'categorize' : 'Buy/Sell'})
    return result

using_select比using_custom_function快50倍：

In [69]: %timeit using_custom_function(df)
10 loops,best of 3: 132 ms per loop

In [70]: %timeit using_select(df)
100 loops,best of 3: 2.46 ms per loop

In [71]: 132/2.46
Out[71]: 53.65853658536585