df
key1 key2 data1 data2
0 a one -0.204708 1.393406
1 a two 0.478943 0.092908
2 b one -0.519439 0.281746
3 b two -0.555730 0.769023
4 a one 1.965781 1.246435이때 key1을 기준으로 그룹화 한 뒤, data1에 해당하는 데이터를 연산할 수 있습니다.
grouped = df.groupby('key1')
grouped['data1'].quantile(0.9)
key1
a 1.668413
b -0.523068
Name: data1, dtype: float64grouped.describe()
data1 data2
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
key1
a 3.0 0.746672 1.109736 -0.204708 0.137118 0.478943 1.222362 1.965781 3.0 0.910916 0.712217 0.092908 0.669671 1.246435 1.319920 1.393406
b 2.0 -0.537585 0.025662 -0.555730 -0.546657 -0.537585 -0.528512 -0.519439 2.0 0.525384 0.344556 0.281746 0.403565 0.525384 0.647203 0.769023Column-Wise and Multiple Function Application
기존의 데이터를 불러오겠습니다.
tips = pd.read_csv('examples/tips.csv')
# Add tip percentage of total bill
tips['tip_pct'] = tips['tip'] / tips['total_bill']
tips[:6]
total_bill tip smoker day time size tip_pct
0 16.99 1.01 No Sun Dinner 2 0.059447
1 10.34 1.66 No Sun Dinner 3 0.160542
2 21.01 3.50 No Sun Dinner 3 0.166587
3 23.68 3.31 No Sun Dinner 2 0.139780
4 24.59 3.61 No Sun Dinner 4 0.146808
5 25.29 4.71 No Sun Dinner 4 0.186240grouped = tips.groupby(['day', 'smoker'])
grouped_pct = grouped['tip_pct']
grouped_pct.agg('mean')
day smoker
Fri No 0.151650
Yes 0.174783
Sat No 0.158048
Yes 0.147906
Sun No 0.160113
Yes 0.187250
Thur No 0.160298
Yes 0.163863
Name: tip_pct, dtype: float64그런 다음 day, smoker로 그룹화하고, 그 그룹에서 tip_pct에 해당하는 열의 데이터를 불러왔습니다.
이후 .agg() 함수를 통해 평균에 대한 종합을 불러왔습니다.
다른 예시를 보겠습니다.
def peak_to_peak(arr):
return arr.max() - arr.min()
grouped.agg(peak_to_peak)
grouped_pct.agg(['mean', 'std', peak_to_peak])
mean std peak_to_peak
day smoker
Fri No 0.151650 0.028123 0.067349
Yes 0.174783 0.051293 0.159925
Sat No 0.158048 0.039767 0.235193
Yes 0.147906 0.061375 0.290095
Sun No 0.160113 0.042347 0.193226
Yes 0.187250 0.154134 0.644685
Thur No 0.160298 0.038774 0.193350
Yes 0.163863 0.039389 0.151240mean, std, peak_to_peak() 함수를 불러왔습니다.
이때 (name, function) 형태의 리스트를 넘기면, 그에 해당하는 이름이 컬럼 이름으로 되면서, 해당 함수를 실행합니다.
grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])
foo bar
day smoker
Fri No 0.151650 0.028123
Yes 0.174783 0.051293
Sat No 0.158048 0.039767
Yes 0.147906 0.061375
Sun No 0.160113 0.042347
Yes 0.187250 0.154134
Thur No 0.160298 0.038774
Yes 0.163863 0.039389
functions = ['count', 'mean', 'max']
result = grouped['tip_pct', 'total_bill'].agg(functions)
result
tip_pct total_bill
count mean max count mean max
day smoker
Fri No 4 0.151650 0.187735 4 18.420000 22.75
Yes 15 0.174783 0.263480 15 16.813333 40.17
Sat No 45 0.158048 0.291990 45 19.661778 48.33
Yes 42 0.147906 0.325733 42 21.276667 50.81
Sun No 57 0.160113 0.252672 57 20.506667 48.17
Yes 19 0.187250 0.710345 19 24.120000 45.35
Thur No 45 0.160298 0.266312 45 17.113111 41.19
Yes 17 0.163863 0.241255 17 19.190588 43.11result['tip_pct']
count mean max
day smoker
Fri No 4 0.151650 0.187735
Yes 15 0.174783 0.263480
Sat No 45 0.158048 0.291990
Yes 42 0.147906 0.325733
Sun No 57 0.160113 0.252672
Yes 19 0.187250 0.710345
Thur No 45 0.160298 0.266312
Yes 17 0.163863 0.241255이해가 되시나요?
또 다른 예시입니다.
ftuples = [('Durchschnitt', 'mean'), ('Abweichung', np.var)]
grouped['tip_pct', 'total_bill'].agg(ftuples)
tip_pct total_bill
Durchschnitt Abweichung Durchschnitt Abweichung
day smoker
Fri No 0.151650 0.000791 18.420000 25.596333
Yes 0.174783 0.002631 16.813333 82.562438
Sat No 0.158048 0.001581 19.661778 79.908965
Yes 0.147906 0.003767 21.276667 101.387535
Sun No 0.160113 0.001793 20.506667 66.099980
Yes 0.187250 0.023757 24.120000 109.046044
Thur No 0.160298 0.001503 17.113111 59.625081
Yes 0.163863 0.001551 19.190588 69.808518이 뿐만 아니라, {name : function}에 해당하는 딕셔너리를 넣어서 실행할 수 있습니다.
grouped.agg({'tip_pct' : ['min', 'max', 'mean', 'std'],
'size' : 'sum'})
tip_pct size
min max mean std sum
day smoker
Fri No 0.120385 0.187735 0.151650 0.028123 9
Yes 0.103555 0.263480 0.174783 0.051293 31
Sat No 0.056797 0.291990 0.158048 0.039767 115
Yes 0.035638 0.325733 0.147906 0.061375 104
Sun No 0.059447 0.252672 0.160113 0.042347 167
Yes 0.065660 0.710345 0.187250 0.154134 49
Thur No 0.072961 0.266312 0.160298 0.038774 112
Yes 0.090014 0.241255 0.163863 0.039389 40Returning Aggregated Data Without Row Indexes
이처럼 인덱스 없이 불러올 수 있습니다.
tips.groupby(['day', 'smoker'], as_index=False).mean()
day smoker total_bill tip size tip_pct
0 Fri No 18.420000 2.812500 2.250000 0.151650
1 Fri Yes 16.813333 2.714000 2.066667 0.174783
2 Sat No 19.661778 3.102889 2.555556 0.158048
3 Sat Yes 21.276667 2.875476 2.476190 0.147906
4 Sun No 20.506667 3.167895 2.929825 0.160113
5 Sun Yes 24.120000 3.516842 2.578947 0.187250
6 Thur No 17.113111 2.673778 2.488889 0.160298
7 Thur Yes 19.190588 3.030000 2.352941 0.163863원래라면,
tips.groupby(['day', 'smoker']).mean()
total_bill tip size tip_pct
day smoker
Fri No 18.420000 2.812500 2.250000 0.151650
Yes 16.813333 2.714000 2.066667 0.174783
Sat No 19.661778 3.102889 2.555556 0.158048
Yes 21.276667 2.875476 2.476190 0.147906
Sun No 20.506667 3.167895 2.929825 0.160113
Yes 24.120000 3.516842 2.578947 0.187250
Thur No 17.113111 2.673778 2.488889 0.160298
Yes 19.190588 3.030000 2.352941 0.163863이렇게 불러와야 합니다.