Applying a custom groupby aggregate function to output a binary outcome in pandas python

import numpy as np
import pandas as pd

df = pd.DataFrame({'Buy/Sell': [1, 0, 1, 1, 0, 1, 0, 0],
                   'Trader': ['A', 'A', 'B', 'B', 'B', 'C', '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, 0], 
    default=np.nan)
print(result)

yields

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

---

My original answer used a custom aggregator, categorize:

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'})

While calling a custom function may be convenient, performance is often significantly slower when you use a custom function compared to the built-in aggregators (such as groupby/agg/mean). The built-in aggregators are Cythonized, while the custom functions reduce performance to plain Python for-loop speeds.

The difference in speed is particularly significant when the number of groups is large. For example, with a 10000-row DataFrame with 1000 groups,

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, means<0.5], choicelist=[1, 0], 
        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, 'sum', 'count'])
    result = result.rename(columns={'categorize' : 'Buy/Sell'})
    return result

using_select is over 50x faster than using_custom_function:

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