[파이썬]Pandas groupby를 이용해 같은 항목을 묶어보자

groupby¶

파이썬 데이터프레임에서 같은 셀을 가지는 항목끼리 묶어서 사용할 수 있는 groupby함수를 사용해보도록 하겠습니다

In [61]:

import pandas as pd
df = pd.read_csv('titanic.csv')
df

Out[61]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

편의를 위해 파일에서 이름, 성별, 형제수, 운임료만을 뽑아서 사용하겠습니다.

In [62]:

df = df[['Name', 'Sex', 'SibSp', 'Fare']]
df

Out[62]:

	Name	Sex	SibSp	Fare
0	Braund, Mr. Owen Harris	male	1	7.2500
1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	1	71.2833
2	Heikkinen, Miss. Laina	female	0	7.9250
3	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	1	53.1000
4	Allen, Mr. William Henry	male	0	8.0500
...	...	...	...	...
886	Montvila, Rev. Juozas	male	0	13.0000
887	Graham, Miss. Margaret Edith	female	0	30.0000
888	Johnston, Miss. Catherine Helen "Carrie"	female	1	23.4500
889	Behr, Mr. Karl Howell	male	0	30.0000
890	Dooley, Mr. Patrick	male	0	7.7500

891 rows × 4 columns

성별을 남자, 여자로 나눠 각 항목에 해당하는 정보들을 출력해보도록 하겠습니다.

In [64]:

groupby_sex = df.groupby('Sex')
groupby_sex.groups

Out[64]:

{'female': [1, 2, 3, 8, 9, 10, 11, 14, 15, 18, 19, 22, 24, 25, 28, 31, 32, 38, 39, 40, 41, 43, 44, 47, 49, 52, 53, 56, 58, 61, 66, 68, 71, 79, 82, 84, 85, 88, 98, 100, 106, 109, 111, 113, 114, 119, 123, 128, 132, 133, 136, 140, 141, 142, 147, 151, 156, 161, 166, 167, 172, 177, 180, 184, 186, 190, 192, 194, 195, 198, 199, 205, 208, 211, 215, 216, 218, 229, 230, 233, 235, 237, 240, 241, 246, 247, 251, 254, 255, 256, 257, 258, 259, 264, 268, 269, 272, 274, 275, 276, ...], 'male': [0, 4, 5, 6, 7, 12, 13, 16, 17, 20, 21, 23, 26, 27, 29, 30, 33, 34, 35, 36, 37, 42, 45, 46, 48, 50, 51, 54, 55, 57, 59, 60, 62, 63, 64, 65, 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 80, 81, 83, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 107, 108, 110, 112, 115, 116, 117, 118, 120, 121, 122, 124, 125, 126, 127, 129, 130, 131, 134, 135, 137, 138, 139, 143, 144, 145, 146, 148, 149, 150, 152, 153, 154, 155, ...]}

먼저 성별을 기준으로 나눠주었구요

In [66]:

for name, group in groupby_sex:
    print(name + ' : ' + str(len(group)))
    print(group)
    print()

female : 314
                                                  Name     Sex  SibSp     Fare
1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1  71.2833
2                               Heikkinen, Miss. Laina  female      0   7.9250
3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      1  53.1000
8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female      0  11.1333
9                  Nasser, Mrs. Nicholas (Adele Achem)  female      1  30.0708
..                                                 ...     ...    ...      ...
880       Shelley, Mrs. William (Imanita Parrish Hall)  female      0  26.0000
882                       Dahlberg, Miss. Gerda Ulrika  female      0  10.5167
885               Rice, Mrs. William (Margaret Norton)  female      0  29.1250
887                       Graham, Miss. Margaret Edith  female      0  30.0000
888           Johnston, Miss. Catherine Helen "Carrie"  female      1  23.4500

[314 rows x 4 columns]

male : 577
                               Name   Sex  SibSp     Fare
0           Braund, Mr. Owen Harris  male      1   7.2500
4          Allen, Mr. William Henry  male      0   8.0500
5                  Moran, Mr. James  male      0   8.4583
6           McCarthy, Mr. Timothy J  male      0  51.8625
7    Palsson, Master. Gosta Leonard  male      3  21.0750
..                              ...   ...    ...      ...
883   Banfield, Mr. Frederick James  male      0  10.5000
884          Sutehall, Mr. Henry Jr  male      0   7.0500
886           Montvila, Rev. Juozas  male      0  13.0000
889           Behr, Mr. Karl Howell  male      0  30.0000
890             Dooley, Mr. Patrick  male      0   7.7500

[577 rows x 4 columns]

groupby를 사용하면 여러 통계정보도 알 수 있는데요 대표적으로 평균, 표준편차등을 알 수 있습니다.

In [67]:

df.groupby('Sex').mean()

Out[67]:

	SibSp	Fare
Sex
female	0.694268	44.479818
male	0.429809	25.523893

In [68]:

df.groupby(['Sex', 'SibSp']).mean()

Out[68]:

		Fare
Sex	SibSp
female	0	38.759867
	1	49.401219
	2	63.750315
	3	67.018191
	4	27.402083
	5	46.900000
	8	69.550000
male	0	20.452848
	1	38.740497
	2	41.356667
	3	73.068340
	4	34.082292
	5	46.900000
	8	69.550000

In [69]:

df.groupby('Sex').size()

Out[69]:

Sex
female    314
male      577
dtype: int64

또한 agg(aggregation)함수를 이용해 좀더 활용도 높게 사용할 수 있습니다.
직접 함수를 만들어 대입해줄수도 있습니다.

In [73]:

df.groupby('Sex').agg(np.mean)

Out[73]:

	SibSp	Fare
Sex
female	0.694268	44.479818
male	0.429809	25.523893

In [74]:

def my_mean(s):
    return np.mean(s)+1

df.groupby('Sex').agg({'SibSp': my_mean, 'Fare': my_mean})

Out[74]:

	SibSp	Fare
Sex
female	1.694268	45.479818
male	1.429809	26.523893