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INSERT¶

CONCAT(db:union) : 붙이기 (위 + 아래 axis=0를 주로 씀) 조인이 되긴하지만 쓰지마라
MERGE(db:join)

In [145]:

import pandas as pd
import numpy as np
df=pd.read_csv("./emp2.csv")  #csv파일 불러서 df변수에 저장
df= df.fillna(0)
df['COMM']=df['COMM'].astype(int)
df['MGR']=df['MGR'].astype(int)
df

Out[145]:

	EMPNO	ENAME	JOB	MGR	HIRDDATE	SAL	COMM	DEPTNO
0	7369	SMITH	CLERK	7902	1980-12-17	1800	0	30
1	7499	ALLEN	SALESMAN	7698	1981-02-20	1600	300	30
2	7521	WARD	SALESMAN	7698	1981-02-22	1250	500	30
3	7566	JONES	MANAGER	7839	1981-04-02	2975	0	20
4	7654	MARTIN	SALESMAN	7698	1981-09-28	1250	1400	30
5	7698	BLAKE	MANAGER	7839	1981-05-01	2850	0	30
6	7782	CLARK	MANAGER	7839	1981-06-09	2450	0	10
7	7788	SCOTT	ANALYST	7566	1987-07-13	3000	0	20
8	7839	KING	PRESIDENT	0	1981-11-17	5000	0	10
9	7844	TURNER	SALESMAN	7698	1981-09-08	1500	0	30
10	7876	ADAMS	CLERK	7788	1987-07-13	1100	0	20
11	7900	JAMES	CLERK	7698	1981-12-03	950	0	30
12	7902	FORD	ANALYST	7566	1981-12-03	6000	0	20
13	7934	MILLER	CLERK	7782	1982-01-23	1300	0	10

In [147]:

list=[col.lower() for col in df.columns.tolist()]
print(list)
df.columns=list
df

['empno', 'ename', 'job', 'mgr', 'hirddate', 'sal', 'comm', 'deptno']

Out[147]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
0	7369	SMITH	CLERK	7902	1980-12-17	1800	0	30
1	7499	ALLEN	SALESMAN	7698	1981-02-20	1600	300	30
2	7521	WARD	SALESMAN	7698	1981-02-22	1250	500	30
3	7566	JONES	MANAGER	7839	1981-04-02	2975	0	20
4	7654	MARTIN	SALESMAN	7698	1981-09-28	1250	1400	30
5	7698	BLAKE	MANAGER	7839	1981-05-01	2850	0	30
6	7782	CLARK	MANAGER	7839	1981-06-09	2450	0	10
7	7788	SCOTT	ANALYST	7566	1987-07-13	3000	0	20
8	7839	KING	PRESIDENT	0	1981-11-17	5000	0	10
9	7844	TURNER	SALESMAN	7698	1981-09-08	1500	0	30
10	7876	ADAMS	CLERK	7788	1987-07-13	1100	0	20
11	7900	JAMES	CLERK	7698	1981-12-03	950	0	30
12	7902	FORD	ANALYST	7566	1981-12-03	6000	0	20
13	7934	MILLER	CLERK	7782	1982-01-23	1300	0	10

In [66]:

df10=df[df['deptno']==10].copy()
df10

Out[66]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
6	7782	CLARK	MANAGER	7839.0	1981-06-09	2450	NaN	10
8	7839	KING	PRESIDENT	NaN	1981-11-17	5000	NaN	10
13	7934	MILLER	CLERK	7782.0	1982-01-23	1300	NaN	10

In [15]:

df20=df[df['deptno']==20].copy()
df20

Out[15]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
3	7566	JONES	MANAGER	7839.0	1981-04-02	2975	NaN	20
7	7788	SCOTT	ANALYST	7566.0	1987-07-13	3000	NaN	20
10	7876	ADAMS	CLERK	7788.0	1987-07-13	1100	NaN	20
12	7902	FORD	ANALYST	7566.0	1981-12-03	6000	NaN	20

pd.concat([df명,df명], axis=0 or 1, ignore_index=True)
axis=0 -> 아래로
axis=1 -> 옆으로
ignore_index=True -> 합치고 인덱스 재정렬
concat axis=1로 했다 -> outerjoin

CONCAT() - UNION¶

DF 합치기¶

위아래 합치기 (CONCAT)¶

In [27]:

df1020_1 = pd.concat([df10,df20], axis=0) #위 아래로 합치기 (로우를 늘린다)
df1020_1

Out[27]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
6	7782	CLARK	MANAGER	7839.0	1981-06-09	2450	NaN	10
8	7839	KING	PRESIDENT	NaN	1981-11-17	5000	NaN	10
13	7934	MILLER	CLERK	7782.0	1982-01-23	1300	NaN	10
3	7566	JONES	MANAGER	7839.0	1981-04-02	2975	NaN	20
7	7788	SCOTT	ANALYST	7566.0	1987-07-13	3000	NaN	20
10	7876	ADAMS	CLERK	7788.0	1987-07-13	1100	NaN	20
12	7902	FORD	ANALYST	7566.0	1981-12-03	6000	NaN	20

In [26]:

df1020_1 = pd.concat([df10,df20], axis=0, ignore_index=True) #ignore_index=True 인덱스 재정렬
df1020_1

Out[26]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
0	7782	CLARK	MANAGER	7839.0	1981-06-09	2450	NaN	10
1	7839	KING	PRESIDENT	NaN	1981-11-17	5000	NaN	10
2	7934	MILLER	CLERK	7782.0	1982-01-23	1300	NaN	10
3	7566	JONES	MANAGER	7839.0	1981-04-02	2975	NaN	20
4	7788	SCOTT	ANALYST	7566.0	1987-07-13	3000	NaN	20
5	7876	ADAMS	CLERK	7788.0	1987-07-13	1100	NaN	20
6	7902	FORD	ANALYST	7566.0	1981-12-03	6000	NaN	20

좌우합치기 (CONCAT)¶

In [25]:

df1020_2 = pd.concat([df10,df20], axis=1) #좌, 우 옆으로 합치기 (컬럼을 늘린다)
df1020_2
# 없으면 없는 대로 NaN처리 -> outerjoin (일반적이지 않음)

Out[25]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno	empno	ename	job	mgr	hirddate	sal	comm	deptno
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7566.0	JONES	MANAGER	7839.0	1981-04-02	2975.0	NaN	20.0
6	7782.0	CLARK	MANAGER	7839.0	1981-06-09	2450.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7788.0	SCOTT	ANALYST	7566.0	1987-07-13	3000.0	NaN	20.0
8	7839.0	KING	PRESIDENT	NaN	1981-11-17	5000.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7876.0	ADAMS	CLERK	7788.0	1987-07-13	1100.0	NaN	20.0
12	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7902.0	FORD	ANALYST	7566.0	1981-12-03	6000.0	NaN	20.0
13	7934.0	MILLER	CLERK	7782.0	1982-01-23	1300.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

In [24]:

df1020_2 = pd.concat([df10,df20], axis=1, ignore_index=True) #ignore_index=True 인덱스 재정렬
df1020_2

Out[24]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7566.0	JONES	MANAGER	7839.0	1981-04-02	2975.0	NaN	20.0
6	7782.0	CLARK	MANAGER	7839.0	1981-06-09	2450.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7788.0	SCOTT	ANALYST	7566.0	1987-07-13	3000.0	NaN	20.0
8	7839.0	KING	PRESIDENT	NaN	1981-11-17	5000.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7876.0	ADAMS	CLERK	7788.0	1987-07-13	1100.0	NaN	20.0
12	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7902.0	FORD	ANALYST	7566.0	1981-12-03	6000.0	NaN	20.0
13	7934.0	MILLER	CLERK	7782.0	1982-01-23	1300.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

join=inner (CONCAT)¶

In [30]:

df1020_2 = pd.concat([df,df10], axis=1, join='inner') #겹치는 컬럼이 있으면 join
df1020_2 #deptno <- 겹치는 부분 으로 join

Out[30]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno	empno	ename	job	mgr	hirddate	sal	comm	deptno
6	7782	CLARK	MANAGER	7839.0	1981-06-09	2450	NaN	10	7782	CLARK	MANAGER	7839.0	1981-06-09	2450	NaN	10
8	7839	KING	PRESIDENT	NaN	1981-11-17	5000	NaN	10	7839	KING	PRESIDENT	NaN	1981-11-17	5000	NaN	10
13	7934	MILLER	CLERK	7782.0	1982-01-23	1300	NaN	10	7934	MILLER	CLERK	7782.0	1982-01-23	1300	NaN	10

Series 합치기 (CONCAT)¶

pd.Series()
pd.DataFrame()

pd.Series()¶

In [32]:

s1 = pd.Series(data=[1,2,3,4,5]) #파라미터   속성 : data index name
s1

Out[32]:

0    1
1    2
2    3
3    4
4    5
dtype: int64

In [34]:

#인덱스 변경 (defalt = 0,1,2,..)
s1 = pd.Series(data=[1,2,3,4,5], index=[10,20,30,40,50]) #파라미터   속성 : data index name
s1

Out[34]:

10    1
20    2
30    3
40    4
50    5
dtype: int64

In [35]:

# 네임 설정(Series의 이름 설정) -> CONCAT시 칼럼명으로 활용
s1 = pd.Series(data=[1,2,3,4,5], index=[10,20,30,40,50], name='s1name') #파라미터   속성 : data index name
s1

Out[35]:

10    1
20    2
30    3
40    4
50    5
Name: s1name, dtype: int64

In [39]:

s1 = pd.Series([1,2,3,4,5], [10,20,30,40,50]) #파라미터 명시하지말고 그냥 이렇게 써라
s1

Out[39]:

10    1
20    2
30    3
40    4
50    5
dtype: int64

In [41]:

s1= pd.Series([1,2,3,4,5])
s2= pd.Series([10,20,30,40,50])

s12= pd.concat([s1,s2]) #defalt: axis=0
s12

Out[41]:

0     1
1     2
2     3
3     4
4     5
0    10
1    20
2    30
3    40
4    50
dtype: int64

In [44]:

s12= pd.concat([s1,s2], axis=1)
print(type(s12)) #Series 합치면 df가 된다
s12.columns = ['col1','col2']
s12

<class 'pandas.core.frame.DataFrame'>

Out[44]:

	col1	col2
0	1	10
1	2	20
2	3	30
3	4	40
4	5	50

In [45]:

s1= pd.Series(data=[1,2,3,4,5], name='col1') #name이 컬럼명으로 들어간다.
s2= pd.Series(data=[10,20,30,40,50], name='col2')
s12= pd.concat([s1,s2], axis=1)
s12

Out[45]:

	col1	col2
0	1	10
1	2	20
2	3	30
3	4	40
4	5	50

DataFrame에 List 합치기¶

list + DF¶

In [122]:

list_data = [8000,'CHOI','CLERK','7782.0','1995-05-31',2000,np.NaN,10]
list_data

Out[122]:

[8000, 'CHOI', 'CLERK', '7782.0', '1995-05-31', 2000, nan, 10]

In [123]:

df_c=pd.DataFrame(data=[list_data], columns=df10.columns) #columns = df10.columns -> df10의 컬럼을 가져와라
df_c

Out[123]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
0	8000	CHOI	CLERK	7782.0	1995-05-31	2000	NaN	10

In [124]:

df2= pd.concat([df10,df_c], axis=0)
df2

Out[124]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
6	7782	CLARK	MANAGER	7839.0	1981-06-09	2450	NaN	10
8	7839	KING	PRESIDENT	NaN	1981-11-17	5000	NaN	10
13	7934	MILLER	CLERK	7782.0	1982-01-23	1300	NaN	10
0	8000	CHOI	CLERK	7782.0	1995-05-31	2000	NaN	10

In [125]:

list_data2 = [8000,'CHOI','CLERK','7782.0','1995-05-31',2000,np.NaN,10]
df_c2=pd.DataFrame(data=list_data2)
df_c2

Out[125]:

	0
0	8000
1	CHOI
2	CLERK
3	7782.0
4	1995-05-31
5	2000
6	NaN
7	10

In [126]:

df_c2.T #세로를 가로로 만들기

Out[126]:

	0	1	2	3	4	5	6	7
0	8000	CHOI	CLERK	7782.0	1995-05-31	2000	NaN	10

MERGE() - JOIN¶

how : {'left','right','outer','inner'}--> {왼쪽기준outer,오른쪽기준outer,풀outer,이너[defalt]}
키가 동일하면 : on
좌 우 프레임의 조인할 키가 다를 경우 : left_on, right_on

In [159]:

# 기준열 이름이 같을 때
#pd.merge(left, right, on = '기준열', how = '조인방식')
# 기준열 이름이 다를 때
#pd.merge(left, right, left_on = '왼쪽 열', right_on = '오른쪽 열', how = '조인방식')

In [167]:

emp=df.copy()
emp

Out[167]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno
0	7369	SMITH	CLERK	7902	1980-12-17	1800	0	30
1	7499	ALLEN	SALESMAN	7698	1981-02-20	1600	300	30
2	7521	WARD	SALESMAN	7698	1981-02-22	1250	500	30
3	7566	JONES	MANAGER	7839	1981-04-02	2975	0	20
4	7654	MARTIN	SALESMAN	7698	1981-09-28	1250	1400	30
5	7698	BLAKE	MANAGER	7839	1981-05-01	2850	0	30
6	7782	CLARK	MANAGER	7839	1981-06-09	2450	0	10
7	7788	SCOTT	ANALYST	7566	1987-07-13	3000	0	20
8	7839	KING	PRESIDENT	0	1981-11-17	5000	0	10
9	7844	TURNER	SALESMAN	7698	1981-09-08	1500	0	30
10	7876	ADAMS	CLERK	7788	1987-07-13	1100	0	20
11	7900	JAMES	CLERK	7698	1981-12-03	950	0	30
12	7902	FORD	ANALYST	7566	1981-12-03	6000	0	20
13	7934	MILLER	CLERK	7782	1982-01-23	1300	0	10

In [177]:

df_join = pd.merge(emp,emp[['empno','ename']], left_on='mgr',right_on='empno',how='left')
df_join

Out[177]:

	empno_x	ename_x	job	mgr	hirddate	sal	comm	deptno	empno_y	ename_y
0	7369	SMITH	CLERK	7902	1980-12-17	1800	0	30	7902.0	FORD
1	7499	ALLEN	SALESMAN	7698	1981-02-20	1600	300	30	7698.0	BLAKE
2	7521	WARD	SALESMAN	7698	1981-02-22	1250	500	30	7698.0	BLAKE
3	7566	JONES	MANAGER	7839	1981-04-02	2975	0	20	7839.0	KING
4	7654	MARTIN	SALESMAN	7698	1981-09-28	1250	1400	30	7698.0	BLAKE
5	7698	BLAKE	MANAGER	7839	1981-05-01	2850	0	30	7839.0	KING
6	7782	CLARK	MANAGER	7839	1981-06-09	2450	0	10	7839.0	KING
7	7788	SCOTT	ANALYST	7566	1987-07-13	3000	0	20	7566.0	JONES
8	7839	KING	PRESIDENT	0	1981-11-17	5000	0	10	NaN	NaN
9	7844	TURNER	SALESMAN	7698	1981-09-08	1500	0	30	7698.0	BLAKE
10	7876	ADAMS	CLERK	7788	1987-07-13	1100	0	20	7788.0	SCOTT
11	7900	JAMES	CLERK	7698	1981-12-03	950	0	30	7698.0	BLAKE
12	7902	FORD	ANALYST	7566	1981-12-03	6000	0	20	7566.0	JONES
13	7934	MILLER	CLERK	7782	1982-01-23	1300	0	10	7782.0	CLARK

In [161]:

dept_list = [[10,'ACCOUNTING','NEW YORK'],[20,'RESEARCH','DALLAS'],[30,'SALES','CHICAGO'],[40,'OPERATIONS','BOSTON']]
dept_col = ['deptno','dname','loc']
dept_df = pd.DataFrame(data=dept_list, columns=dept_col)
dept_df

Out[161]:

	deptno	dname	loc
0	10	ACCOUNTING	NEW YORK
1	20	RESEARCH	DALLAS
2	30	SALES	CHICAGO
3	40	OPERATIONS	BOSTON

In [166]:

df_join2 = pd.merge(df,dept_df, on='deptno',how='right')
df_join2

Out[166]:

	empno	ename	job	mgr	hirddate	sal	comm	deptno	dname	loc
0	7782.0	CLARK	MANAGER	7839.0	1981-06-09	2450.0	0.0	10	ACCOUNTING	NEW YORK
1	7839.0	KING	PRESIDENT	0.0	1981-11-17	5000.0	0.0	10	ACCOUNTING	NEW YORK
2	7934.0	MILLER	CLERK	7782.0	1982-01-23	1300.0	0.0	10	ACCOUNTING	NEW YORK
3	7566.0	JONES	MANAGER	7839.0	1981-04-02	2975.0	0.0	20	RESEARCH	DALLAS
4	7788.0	SCOTT	ANALYST	7566.0	1987-07-13	3000.0	0.0	20	RESEARCH	DALLAS
5	7876.0	ADAMS	CLERK	7788.0	1987-07-13	1100.0	0.0	20	RESEARCH	DALLAS
6	7902.0	FORD	ANALYST	7566.0	1981-12-03	6000.0	0.0	20	RESEARCH	DALLAS
7	7369.0	SMITH	CLERK	7902.0	1980-12-17	1800.0	0.0	30	SALES	CHICAGO
8	7499.0	ALLEN	SALESMAN	7698.0	1981-02-20	1600.0	300.0	30	SALES	CHICAGO
9	7521.0	WARD	SALESMAN	7698.0	1981-02-22	1250.0	500.0	30	SALES	CHICAGO
10	7654.0	MARTIN	SALESMAN	7698.0	1981-09-28	1250.0	1400.0	30	SALES	CHICAGO
11	7698.0	BLAKE	MANAGER	7839.0	1981-05-01	2850.0	0.0	30	SALES	CHICAGO
12	7844.0	TURNER	SALESMAN	7698.0	1981-09-08	1500.0	0.0	30	SALES	CHICAGO
13	7900.0	JAMES	CLERK	7698.0	1981-12-03	950.0	0.0	30	SALES	CHICAGO
14	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40	OPERATIONS	BOSTON

In [175]:

emp2 = emp.rename(columns={"deptno":"DNO"})
emp2

Out[175]:

	empno	ename	job	mgr	hirddate	sal	comm	DNO
0	7369	SMITH	CLERK	7902	1980-12-17	1800	0	30
1	7499	ALLEN	SALESMAN	7698	1981-02-20	1600	300	30
2	7521	WARD	SALESMAN	7698	1981-02-22	1250	500	30
3	7566	JONES	MANAGER	7839	1981-04-02	2975	0	20
4	7654	MARTIN	SALESMAN	7698	1981-09-28	1250	1400	30
5	7698	BLAKE	MANAGER	7839	1981-05-01	2850	0	30
6	7782	CLARK	MANAGER	7839	1981-06-09	2450	0	10
7	7788	SCOTT	ANALYST	7566	1987-07-13	3000	0	20
8	7839	KING	PRESIDENT	0	1981-11-17	5000	0	10
9	7844	TURNER	SALESMAN	7698	1981-09-08	1500	0	30
10	7876	ADAMS	CLERK	7788	1987-07-13	1100	0	20
11	7900	JAMES	CLERK	7698	1981-12-03	950	0	30
12	7902	FORD	ANALYST	7566	1981-12-03	6000	0	20
13	7934	MILLER	CLERK	7782	1982-01-23	1300	0	10

In [179]:

emp_dept2 = pd.merge(emp2, dept_df, how="inner", left_on="DNO", right_on='deptno')
emp_dept2

Out[179]:

	empno	ename	job	mgr	hirddate	sal	comm	DNO	deptno	dname	loc
0	7369	SMITH	CLERK	7902	1980-12-17	1800	0	30	30	SALES	CHICAGO
1	7499	ALLEN	SALESMAN	7698	1981-02-20	1600	300	30	30	SALES	CHICAGO
2	7521	WARD	SALESMAN	7698	1981-02-22	1250	500	30	30	SALES	CHICAGO
3	7654	MARTIN	SALESMAN	7698	1981-09-28	1250	1400	30	30	SALES	CHICAGO
4	7698	BLAKE	MANAGER	7839	1981-05-01	2850	0	30	30	SALES	CHICAGO
5	7844	TURNER	SALESMAN	7698	1981-09-08	1500	0	30	30	SALES	CHICAGO
6	7900	JAMES	CLERK	7698	1981-12-03	950	0	30	30	SALES	CHICAGO
7	7566	JONES	MANAGER	7839	1981-04-02	2975	0	20	20	RESEARCH	DALLAS
8	7788	SCOTT	ANALYST	7566	1987-07-13	3000	0	20	20	RESEARCH	DALLAS
9	7876	ADAMS	CLERK	7788	1987-07-13	1100	0	20	20	RESEARCH	DALLAS
10	7902	FORD	ANALYST	7566	1981-12-03	6000	0	20	20	RESEARCH	DALLAS
11	7782	CLARK	MANAGER	7839	1981-06-09	2450	0	10	10	ACCOUNTING	NEW YORK
12	7839	KING	PRESIDENT	0	1981-11-17	5000	0	10	10	ACCOUNTING	NEW YORK
13	7934	MILLER	CLERK	7782	1982-01-23	1300	0	10	10	ACCOUNTING	NEW YORK

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(데이터 분석) 파이썬 - Pandas DataFrame SQL처럼 활용하기(Join,Union)

INSERT¶

CONCAT() - UNION¶

DF 합치기¶

위아래 합치기 (CONCAT)¶

좌우합치기 (CONCAT)¶

join=inner (CONCAT)¶

Series 합치기 (CONCAT)¶

pd.Series()¶

DataFrame에 List 합치기¶

list + DF¶

MERGE() - JOIN¶

'Python > 데이터 분석' 카테고리의 다른 글

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(데이터 분석) 파이썬 - Pandas DataFrame SQL처럼 활용하기(Join,Union)

INSERT¶

CONCAT() - UNION¶

DF 합치기¶

위아래 합치기 (CONCAT)¶

좌우합치기 (CONCAT)¶

join=inner (CONCAT)¶

Series 합치기 (CONCAT)¶

pd.Series()¶

DataFrame에 List 합치기¶

list + DF¶

MERGE() - JOIN¶

'Python > 데이터 분석' 카테고리의 다른 글

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