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

Question

I have a dataset of trader transactions where the variable of interest is Buy/Sell which is binary and takes on the value of 1 f the transaction was a buy and 0 if it is a sell. An example looks as follows:

Trader     Buy/Sell
  A           1
  A           0
  B           1
  B           1
  B           0
  C           1
  C           0
  C           0

I would like to calculate the net Buy/Sell for each trader such that if the trader had more than 50% of trades as a buy, he would have a Buy/Sell of 1, if he had less than 50% buy then he would have a Buy/Sell of 0 and if it were exactly 50% he would have NA (and would be disregarded in future calculations).

So for trader A, the buy proportion is (number of buys)/(total number of trader) = 1/2 = 0.5 which gives NA.

For trader B it is 2/3 = 0.67 which gives a 1

For trader C it is 1/3 = 0.33 which gives a 0

The table should look like this:

Trader     Buy/Sell
  A           NA
  B           1
  C           0 

Ultimately i want to compute the total aggregated number of buys, which in this case is 1, and the aggregated total number of trades (disregarding NAs) which in this case is 2. I am not interested in the second table, I am just interested in the aggregated number of buys and the aggregated total number (count) of Buy/Sell .

How can I do this in Pandas?

Answer 1

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

Answer 2

Pandas cut() provides an improvement in @unutbu's answer by getting the result in half the time.

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 using_cut(df):
    grouped = df.groupby(['Trader'])
    result = grouped['Buy/Sell'].agg(['sum', 'count', 'mean'])
    result['Buy/Sell'] = pd.cut(result['mean'], [0, 0.5, 1], labels=[0, 1], include_lowest=True)
    result['Buy/Sell']=np.where(result['mean']==0.5,np.nan, result['Buy/Sell'])
    return result

using_cut() runs in 5.21 ms average per loop in my system whereas using_select() runs in 10.4 ms average per loop.

%timeit using_select(df)
10.4 ms ± 1.07 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit using_cut(df)
5.21 ms ± 147 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)