In [1]:

from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important;}</style>"))

조건¶

Query 사용 and DataFrame[[컬럼명]][조건]¶

In [12]:

import pandas as pd

emp = pd.read_csv("c:/data/emp3.csv")
emp

Out[12]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10

In [14]:

emp.query('sal<=3000')
emp[emp.sal<=3000]

Out[14]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10

In [18]:

emp.query('job in ["SALESMAN"]')
emp[emp.job.isin(["SALESMAN"])]

Out[18]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30

In [20]:

emp.query('job not in ["SALESMAN"]')
emp[~emp.job.isin(['SALESMAN'])] # not in == ~

Out[20]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10

대문자 소문자 바꾸기¶

In [34]:

emp[['ename']] = emp['ename'].str.lower()
emp

Out[34]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1	7839	king	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10
1	2	7698	blake	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	clark	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	jones	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
4	5	7654	martin	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	allen	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	turner	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
7	8	7900	james	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
8	9	7521	ward	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30
9	10	7902	ford	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	smith	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	scott	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	adams	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	miller	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10

In [37]:

emp[['ename']] = emp.ename.str.upper()
emp

Out[37]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10

In [38]:

# Series로 출력
emp['ename']

Out[38]:

0       KING
1      BLAKE
2      CLARK
3      JONES
4     MARTIN
5      ALLEN
6     TURNER
7      JAMES
8       WARD
9       FORD
10     SMITH
11     SCOTT
12     ADAMS
13    MILLER
Name: ename, dtype: object

In [39]:

# DataFrame으로 출력
emp[['ename']]

Out[39]:

	ename
0	KING
1	BLAKE
2	CLARK
3	JONES
4	MARTIN
5	ALLEN
6	TURNER
7	JAMES
8	WARD
9	FORD
10	SMITH
11	SCOTT
12	ADAMS
13	MILLER

합치기(concat,append)¶

In [103]:

new = [15, 7321, 'DEWY', 'MANAGER', np.NaN, '2020-07-16', 3000, np.nan, 50]

# index, columns는 반드시 리스트 형으로 넣어줘야함
# 참고 : https://freedata.tistory.com/53
new_df = pd.DataFrame(new,index=emp.columns,columns=['A']).T
new_df

Out[103]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
A	15	7321	DEWY	MANAGER	NaN	2020-07-16	3000	NaN	50

In [104]:

# 데이터프레임을 데이터프레임에 넣으면 append나 concat이나 같은결과가 나온다.
emp.append(new_df,ignore_index=True)

Out[104]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10
14	15	7321	DEWY	MANAGER	NaN	2020-07-16	3000	NaN	50

In [107]:

pd.concat([emp,new_df],axis=0,ignore_index=False)

Out[107]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10
A	15	7321	DEWY	MANAGER	NaN	2020-07-16	3000	NaN	50

일반 리스트형을 append하면 결과값 이상하게 나옴 시리즈도 마찬가지...¶

In [91]:

new2 = pd.Series([15, 7321, 'DEWY', 'MANAGER', np.NaN, '2020-07-16', 3000, np.nan, 50])

emp.append(new2,ignore_index=True)
pd.concat([emp,new_df],axis=1)

Out[91]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1.0	7839.0	KING	PRESIDENT	NaN	1981-11-17 0:00	5000.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2.0	7698.0	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850.0	NaN	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	3.0	7782.0	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	4.0	7566.0	JONES	MANAGER	7839.0	1981-04-01 0:00	2975.0	NaN	20.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	5.0	7654.0	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250.0	1400.0	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	6.0	7499.0	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600.0	300.0	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
6	7.0	7844.0	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500.0	0.0	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	8.0	7900.0	JAMES	CLERK	7698.0	1981-12-11 0:00	950.0	NaN	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
8	9.0	7521.0	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250.0	500.0	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9	10.0	7902.0	FORD	ANALYST	7566.0	1981-12-11 0:00	3000.0	NaN	20.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10	11.0	7369.0	SMITH	CLERK	7902.0	1980-12-09 0:00	800.0	NaN	20.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11	12.0	7788.0	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000.0	NaN	20.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
12	13.0	7876.0	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100.0	NaN	20.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
13	14.0	7934.0	MILLER	CLERK	7782.0	1982-01-11 0:00	1300.0	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
15	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	15	7321	DEWY	MANAGER	NaN	2020-07-16	3000	NaN	50

그룹화(groupby)¶

In [116]:

emp.groupby('job').sal.sum().reset_index()

Out[116]:

	job	sal
0	ANALYST	6000
1	CLERK	4150
2	MANAGER	8275
3	PRESIDENT	5000
4	SALESMAN	5600

함수 적용(apply)¶

In [165]:

dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
df

Out[165]:

	A	B	C	D
2013-01-01	0.181765	0.421731	-0.391242	-0.949458
2013-01-02	-1.293275	-0.690032	1.194403	-0.189533
2013-01-03	0.471382	1.351683	0.648124	-0.128018
2013-01-04	-0.748874	-0.648833	-0.393539	0.435896
2013-01-05	0.098486	-2.092289	1.048243	-1.068479
2013-01-06	-1.919552	1.020284	0.426926	-0.297642

In [166]:

df.apply(lambda x: (x-x.min()) / (x.max() - x.min()))

Out[166]:

	A	B	C	D
2013-01-01	0.878869	0.729977	0.001447	0.079117
2013-01-02	0.261938	0.407163	1.000000	0.584260
2013-01-03	1.000000	1.000000	0.655983	0.625151
2013-01-04	0.489632	0.419125	0.000000	1.000000
2013-01-05	0.844038	0.000000	0.907956	0.000000
2013-01-06	0.000000	0.903774	0.516685	0.512397

Join¶

In [132]:

emp2 = emp.append(new_df,ignore_index=True)
emp2

Out[132]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10
14	15	7321	DEWY	MANAGER	NaN	2020-07-16	3000	NaN	50

In [126]:

dept = pd.read_csv("c:/data/dept.csv")
dept

Out[126]:

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

In [139]:

pd.merge(emp2,dept,on='deptno') # default : how = 'inner'

Out[139]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno	dname	loc
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10	ACCOUNTING	NEW YORK
1	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10	ACCOUNTING	NEW YORK
2	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10	ACCOUNTING	NEW YORK
3	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30	SALES	CHICAGO
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30	SALES	CHICAGO
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30	SALES	CHICAGO
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30	SALES	CHICAGO
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30	SALES	CHICAGO
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30	SALES	CHICAGO
9	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20	RESEARCH	DALLAS
10	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20	RESEARCH	DALLAS
11	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20	RESEARCH	DALLAS
12	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20	RESEARCH	DALLAS
13	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20	RESEARCH	DALLAS

In [141]:

pd.merge(emp2,dept,on='deptno',how='left')

Out[141]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno	dname	loc
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10	ACCOUNTING	NEW YORK
1	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30	SALES	CHICAGO
2	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10	ACCOUNTING	NEW YORK
3	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20	RESEARCH	DALLAS
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30	SALES	CHICAGO
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30	SALES	CHICAGO
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30	SALES	CHICAGO
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30	SALES	CHICAGO
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30	SALES	CHICAGO
9	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20	RESEARCH	DALLAS
10	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20	RESEARCH	DALLAS
11	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20	RESEARCH	DALLAS
12	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20	RESEARCH	DALLAS
13	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10	ACCOUNTING	NEW YORK
14	15	7321	DEWY	MANAGER	NaN	2020-07-16	3000	NaN	50	NaN	NaN

In [142]:

pd.merge(emp2,dept,on='deptno',how='right')

Out[142]:

	index	empno	ename	job	mgr	hiredate	sal	comm	deptno	dname	loc
0	1	7839	KING	PRESIDENT	NaN	1981-11-17 0:00	5000	NaN	10.0	ACCOUNTING	NEW YORK
1	3	7782	CLARK	MANAGER	7839.0	1981-05-09 0:00	2450	NaN	10.0	ACCOUNTING	NEW YORK
2	14	7934	MILLER	CLERK	7782.0	1982-01-11 0:00	1300	NaN	10.0	ACCOUNTING	NEW YORK
3	2	7698	BLAKE	MANAGER	7839.0	1981-05-01 0:00	2850	NaN	30.0	SALES	CHICAGO
4	5	7654	MARTIN	SALESMAN	7698.0	1981-09-10 0:00	1250	1400.0	30.0	SALES	CHICAGO
5	6	7499	ALLEN	SALESMAN	7698.0	1981-02-11 0:00	1600	300.0	30.0	SALES	CHICAGO
6	7	7844	TURNER	SALESMAN	7698.0	1981-08-21 0:00	1500	0.0	30.0	SALES	CHICAGO
7	8	7900	JAMES	CLERK	7698.0	1981-12-11 0:00	950	NaN	30.0	SALES	CHICAGO
8	9	7521	WARD	SALESMAN	7698.0	1981-02-23 0:00	1250	500.0	30.0	SALES	CHICAGO
9	4	7566	JONES	MANAGER	7839.0	1981-04-01 0:00	2975	NaN	20.0	RESEARCH	DALLAS
10	10	7902	FORD	ANALYST	7566.0	1981-12-11 0:00	3000	NaN	20.0	RESEARCH	DALLAS
11	11	7369	SMITH	CLERK	7902.0	1980-12-09 0:00	800	NaN	20.0	RESEARCH	DALLAS
12	12	7788	SCOTT	ANALYST	7566.0	1982-12-22 0:00	3000	NaN	20.0	RESEARCH	DALLAS
13	13	7876	ADAMS	CLERK	7788.0	1983-01-15 0:00	1100	NaN	20.0	RESEARCH	DALLAS
14	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.0	OPERATIONS	BOSTON

압축(stack)¶

In [152]:

tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
                     'foo', 'foo', 'qux', 'qux'],
                    ['one', 'two', 'one', 'two',
                     'one', 'two', 'one', 'two']]))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
df2 = df[:4]
df2

Out[152]:

		A	B
first	second
bar	one	-0.298864	0.247955
bar	two	-0.045396	0.610319
baz	one	-0.681461	-0.217126
baz	two	0.706999	0.656189

In [155]:

stacked = df2.stack()
stacked

Out[155]:

first  second   
bar    one     A   -0.298864
               B    0.247955
       two     A   -0.045396
               B    0.610319
baz    one     A   -0.681461
               B   -0.217126
       two     A    0.706999
               B    0.656189
dtype: float64

In [156]:

stacked.unstack()

Out[156]:

		A	B
first	second
bar	one	-0.298864	0.247955
bar	two	-0.045396	0.610319
baz	one	-0.681461	-0.217126
baz	two	0.706999	0.656189

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공부하려고 만든 블로그

판다스 이해하기 - 조건문, concat, append, 그룹화, 함수적용, join

조건¶