pandas - 5

Group by

데이터에서 정보를 취하기 위해서 그룹별로 묶는 방법에 대해 알아보겠습니다.

student_list = [{'name': 'John', 'major': "Computer Science", 'sex': "male"},
                {'name': 'Nate', 'major': "Computer Science", 'sex': "male"},
                {'name': 'Abraham', 'major': "Physics", 'sex': "male"},
                {'name': 'Brian', 'major': "Psychology", 'sex': "male"},
                {'name': 'Janny', 'major': "Economics", 'sex': "female"},
                {'name': 'Yuna', 'major': "Economics", 'sex': "female"},
                {'name': 'Jeniffer', 'major': "Computer Science", 'sex': "female"},
                {'name': 'Edward', 'major': "Computer Science", 'sex': "male"},
                {'name': 'Zara', 'major': "Psychology", 'sex': "female"},
                {'name': 'Wendy', 'major': "Economics", 'sex': "female"},
                {'name': 'Sera', 'major': "Psychology", 'sex': "female"}
         ]
df = pd.DataFrame(student_list, columns = ['name', 'major', 'sex'])
df

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	name	major	sex
0	John	Computer Science	male
1	Nate	Computer Science	male
2	Abraham	Physics	male
3	Brian	Psychology	male
4	Janny	Economics	female
5	Yuna	Economics	female
6	Jeniffer	Computer Science	female
7	Edward	Computer Science	male
8	Zara	Psychology	female
9	Wendy	Economics	female
10	Sera	Psychology	female

groupby_major = df.groupby('major')

groupby_major.groups

{'Computer Science': Int64Index([0, 1, 6, 7], dtype='int64'),
 'Economics': Int64Index([4, 5, 9], dtype='int64'),
 'Physics': Int64Index([2], dtype='int64'),
 'Psychology': Int64Index([3, 8, 10], dtype='int64')}

here we can see, computer science has mostly man, while economic has mostly woman students

for name, group in groupby_major:
    print(name + ": " + str(len(group)))
    print(group)
    print()

Computer Science: 4
       name             major     sex
0      John  Computer Science    male
1      Nate  Computer Science    male
6  Jeniffer  Computer Science  female
7    Edward  Computer Science    male

Economics: 3
    name      major     sex
4  Janny  Economics  female
5   Yuna  Economics  female
9  Wendy  Economics  female

Physics: 1
      name    major   sex
2  Abraham  Physics  male

Psychology: 3
     name       major     sex
3   Brian  Psychology    male
8    Zara  Psychology  female
10   Sera  Psychology  female

그룹 객체를 다시 데이터프레임으로 생성하는 예제입니다.

df_major_cnt = pd.DataFrame({'count' : groupby_major.size()}).reset_index()
df_major_cnt

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	major	count
0	Computer Science	4
1	Economics	3
2	Physics	1
3	Psychology	3

groupby_sex = df.groupby('sex')

아래의 출력을 통해, 이 학교의 남녀 성비가 균등하다는 정보를 알 수 있습니다.

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

female: 6
        name             major     sex
4      Janny         Economics  female
5       Yuna         Economics  female
6   Jeniffer  Computer Science  female
8       Zara        Psychology  female
9      Wendy         Economics  female
10      Sera        Psychology  female

male: 5
      name             major   sex
0     John  Computer Science  male
1     Nate  Computer Science  male
2  Abraham           Physics  male
3    Brian        Psychology  male
7   Edward  Computer Science  male

df_sex_cnt = pd.DataFrame({'count' : groupby_sex.size()}).reset_index()
df_sex_cnt

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	sex	count
0	female	6
1	male	5