MIN 오라클 함수 [Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table, DuckDB]

포스팅 목차

* 파이썬 & R 패키지 호출 및 예제 데이터 생성 링크

MIN Oracle Function

MIN함수는 (개별 그룹내의) 사용자가 지정한 인수의 최소값을 반환한다. 집계 함수, 분석 함수 모두 이용 가능하다.

함수설명 : MIN 오라클 함수 링크

1. Oracle(오라클)

Min 함수

다음 예제는 employees 테이블의 개별 부서내에서 최소 급여를 계산한다.

Oracle Programming

SELECT deptno,MIN(sal) sal_min 
FROM   emp 
group  by deptno

Results

DEPTNO	SAL_MIN
------------------
30	950
10	1300
20	800

2. Python Pandas(파이썬)

1) 집계함수(Aggregate function)

Min 함수

Python Programming

emp.groupby('deptno')['sal'].min()

Results

deptno
10    1300
20     800
30     950
Name: sal, dtype: int64

Apply()와 Min 함수

Python Programming

emp.groupby('deptno')['sal'].apply(min)

Results

deptno
10    1300
20     800
30     950
Name: sal, dtype: int64

Rank() 함수

Python Programming

emp.groupby('deptno').apply(lambda x : x[ x['sal'].rank() ==1 ])

Results

		empno	ename	job	mgr	hiredate	sal	comm	deptno
deptno									
10	13	7934	MILLER	CLERK	7782.0	1982/01/23	1300	NaN	10
20	0	7369	SMITH	CLERK	7902.0	1980/12/17	800	NaN	20
30	11	7900	JAMES	CLERK	7698.0	1981/12/03	950	NaN	30

2) 분석함수(Analytic Function)

groupby()와 Min 함수

Python Programming

withmooc = emp.copy()

group_min = withmooc.groupby('deptno')['sal'].min()
withmooc['sal_min'] = withmooc['deptno'].map(group_min)

withmooc.sort_values('deptno').head()

Results

	empno	ename	job		mgr	hiredate	sal	comm	deptno	sal_min
6	7782	CLARK	MANAGER		7839.0	1981/01/09	2450	NaN	10	1300
8	7839	KING	PRESIDENT	NaN	1981/11/17	5000	NaN	10	1300
13	7934	MILLER	CLERK		7782.0	1982/01/23	1300	NaN	10	1300
0	7369	SMITH	CLERK		7902.0	1980/12/17	800	NaN	20	800
3	7566	JONES	MANAGER		7839.0	1981/04/02	2975	NaN	20	800

Transform()과 Min 함수

Python Programming

withmooc = emp.copy()
withmooc['min_value'] = emp.groupby('deptno')['sal'].transform(lambda x:x.min())
withmooc.sort_values('deptno').head()

Results

	empno	ename	job		mgr	hiredate	sal	comm	deptno	min_value
6	7782	CLARK	MANAGER		7839.0	1981/01/09	2450	NaN	10	1300
8	7839	KING	PRESIDENT	NaN	1981/11/17	5000	NaN	10	1300
13	7934	MILLER	CLERK		7782.0	1982/01/23	1300	NaN	10	1300
0	7369	SMITH	CLERK		7902.0	1980/12/17	800	NaN	20	800
3	7566	JONES	MANAGER		7839.0	1981/04/02	2975	NaN	20	800

Transform()과 Min 함수

Python Programming

withmooc = emp.copy()
withmooc['min_value'] = emp.groupby('deptno')['sal'].transform('min')
withmooc.sort_values('deptno').head()

Results

	empno	ename	job		mgr	hiredate	sal	comm	deptno	min_value
6	7782	CLARK	MANAGER		7839.0	1981/01/09	2450	NaN	10	1300
8	7839	KING	PRESIDENT	NaN	1981/11/17	5000	NaN	10	1300
13	7934	MILLER	CLERK		7782.0	1982/01/23	1300	NaN	10	1300
0	7369	SMITH	CLERK		7902.0	1980/12/17	800	NaN	20	800
3	7566	JONES	MANAGER		7839.0	1981/04/02	2975	NaN	20	800

3. R Programming (R Package)

1) 집계함수 (Aggregate function)

Aggregate()와 Min

R Programming

%%R

aggregate(sal ~ deptno, data=emp , FUN=min)

Results

  deptno  sal
1     10 1300
2     20  800
3     30  950

by()와 Min

R Programming

%%R

unlist(with(emp, by(sal, deptno, min)))

Results

deptno: 10
[1] 1300
------------------------------------------------------------ 
deptno: 20
[1] 800
------------------------------------------------------------ 
deptno: 30
[1] 950

Map과 Min

R Programming

%%R

do.call(rbind, Map(min, split(emp$sal, emp$deptno)))

Results

purrr::map_dfr()과 min

R Programming

%%R
# purrr::map_chr(split(emp,emp$deptno), ~min(.x$sal))

purrr::map_dfr(split(emp,emp$deptno), ~min(.x$sal))

Results

# A tibble: 1 x 3
   `10`  `20`  `30`
  <dbl> <dbl> <dbl>
1  1300   800   950

purrr::map_dfr()과 min

R Programming

%%R

do.call(rbind, purrr::map_dfr(split(emp,emp$deptno), ~min(.x$sal)) )

Results

lapply()와 Min

R Programming

%%R

do.call(rbind, lapply(split(emp$sal, emp$deptno), min))

Results

2) 분석함수(Analytic Function)

ave()와 min

R Programming

%%R
withmooc <- emp
withmooc['sal_min'] = with(withmooc, ave(x = sal, deptno, FUN=min))   # FUN =function(x) { median(x) }

withmooc[order(withmooc$deptno),][1:10,]

Results

# A tibble: 10 x 9
   empno ename  job         mgr hiredate     sal  comm deptno sal_min
   <dbl> <chr>  <chr>     <dbl> <date>     <dbl> <dbl>  <dbl>   <dbl>
 1  7782 CLARK  MANAGER    7839 1981-01-09  2450    NA     10    1300
 2  7839 KING   PRESIDENT    NA 1981-11-17  5000    NA     10    1300
 3  7934 MILLER CLERK      7782 1982-01-23  1300    NA     10    1300
 4  7369 SMITH  CLERK      7902 1980-12-17   800    NA     20     800
 5  7566 JONES  MANAGER    7839 1981-04-02  2975    NA     20     800
 6  7788 SCOTT  ANALYST    7566 1982-12-09  3000    NA     20     800
 7  7876 ADAMS  CLERK      7788 1983-01-12  1100    NA     20     800
 8  7902 FORD   ANALYST    7566 1981-12-03  3000    NA     20     800
 9  7499 ALLEN  SALESMAN   7698 1981-02-20  1600   300     30     950
10  7521 WARD   SALESMAN   7698 1981-02-22  1250   500     30     950

ave()와 Min

R Programming

%%R

withmooc <- emp

withmooc['sal_median'] = ave(x = withmooc$sal, withmooc$deptno, FUN = min)    # FUN = function (x) {  min(x) }

withmooc[order(withmooc$deptno),][1:10,]

Results

# A tibble: 10 x 9
   empno ename  job         mgr hiredate     sal  comm deptno sal_median
   <dbl> <chr>  <chr>     <dbl> <date>     <dbl> <dbl>  <dbl>      <dbl>
 1  7782 CLARK  MANAGER    7839 1981-01-09  2450    NA     10       1300
 2  7839 KING   PRESIDENT    NA 1981-11-17  5000    NA     10       1300
 3  7934 MILLER CLERK      7782 1982-01-23  1300    NA     10       1300
 4  7369 SMITH  CLERK      7902 1980-12-17   800    NA     20        800
 5  7566 JONES  MANAGER    7839 1981-04-02  2975    NA     20        800
 6  7788 SCOTT  ANALYST    7566 1982-12-09  3000    NA     20        800
 7  7876 ADAMS  CLERK      7788 1983-01-12  1100    NA     20        800
 8  7902 FORD   ANALYST    7566 1981-12-03  3000    NA     20        800
 9  7499 ALLEN  SALESMAN   7698 1981-02-20  1600   300     30        950
10  7521 WARD   SALESMAN   7698 1981-02-22  1250   500     30        950

purrr::map_chr()과 min

R Programming

%%R

withmooc <- emp

Min_var <- with(withmooc, purrr::map_chr(base::split(withmooc,withmooc$deptno), ~min(.x$sal)) ) 

data.frame(withmooc, Min_value =rep(Min_var, table(withmooc$deptno)))[1:10,]

Results

   empno  ename       job  mgr   hiredate  sal comm deptno   Min_value
1   7369  SMITH     CLERK 7902 1980-12-17  800   NA     20 1300.000000
2   7499  ALLEN  SALESMAN 7698 1981-02-20 1600  300     30 1300.000000
3   7521   WARD  SALESMAN 7698 1981-02-22 1250  500     30 1300.000000
4   7566  JONES   MANAGER 7839 1981-04-02 2975   NA     20  800.000000
5   7654 MARTIN  SALESMAN 7698 1981-09-28 1250 1400     30  800.000000
6   7698  BLAKE   MANAGER 7839 1981-03-01 2850   NA     30  800.000000
7   7782  CLARK   MANAGER 7839 1981-01-09 2450   NA     10  800.000000
8   7788  SCOTT   ANALYST 7566 1982-12-09 3000   NA     20  800.000000
9   7839   KING PRESIDENT   NA 1981-11-17 5000   NA     10  950.000000
10  7844 TURNER  SALESMAN 7698 1981-09-08 1500    0     30  950.000000

tapply와 min

R Programming

%%R

withmooc <- emp

Min_var <- with(withmooc, tapply(sal, deptno, min))

data.frame(emp, min_value =rep(Min_var, table(withmooc$deptno)))

# with(withmooc,  data.frame(emp, min_value=rep(tapply(sal, deptno, min), table(deptno))) )[1:10,]

Results

   empno  ename       job  mgr   hiredate  sal comm deptno min_value
1   7369  SMITH     CLERK 7902 1980-12-17  800   NA     20      1300
2   7499  ALLEN  SALESMAN 7698 1981-02-20 1600  300     30      1300
3   7521   WARD  SALESMAN 7698 1981-02-22 1250  500     30      1300
4   7566  JONES   MANAGER 7839 1981-04-02 2975   NA     20       800
5   7654 MARTIN  SALESMAN 7698 1981-09-28 1250 1400     30       800
6   7698  BLAKE   MANAGER 7839 1981-03-01 2850   NA     30       800
7   7782  CLARK   MANAGER 7839 1981-01-09 2450   NA     10       800
8   7788  SCOTT   ANALYST 7566 1982-12-09 3000   NA     20       800
9   7839   KING PRESIDENT   NA 1981-11-17 5000   NA     10       950
10  7844 TURNER  SALESMAN 7698 1981-09-08 1500    0     30       950
11  7876  ADAMS     CLERK 7788 1983-01-12 1100   NA     20       950
12  7900  JAMES     CLERK 7698 1981-12-03  950   NA     30       950
13  7902   FORD   ANALYST 7566 1981-12-03 3000   NA     20       950
14  7934 MILLER     CLERK 7782 1982-01-23 1300   NA     10       950

plyr::ddply()와 Min

R Programming

%%R

library(plyr)   # dplyr 보다 먼저 로딩
library(dplyr)

plyr::ddply(emp,.(deptno),transform,sal_min = min(sal))[1:10,]

Results

   empno  ename       job  mgr   hiredate  sal comm deptno sal_min
1   7782  CLARK   MANAGER 7839 1981-01-09 2450   NA     10    1300
2   7839   KING PRESIDENT   NA 1981-11-17 5000   NA     10    1300
3   7934 MILLER     CLERK 7782 1982-01-23 1300   NA     10    1300
4   7369  SMITH     CLERK 7902 1980-12-17  800   NA     20     800
5   7566  JONES   MANAGER 7839 1981-04-02 2975   NA     20     800
6   7788  SCOTT   ANALYST 7566 1982-12-09 3000   NA     20     800
7   7876  ADAMS     CLERK 7788 1983-01-12 1100   NA     20     800
8   7902   FORD   ANALYST 7566 1981-12-03 3000   NA     20     800
9   7499  ALLEN  SALESMAN 7698 1981-02-20 1600  300     30     950
10  7521   WARD  SALESMAN 7698 1981-02-22 1250  500     30     950

purrr::map_dfc()와 min

R Programming

%%R

withmooc <- emp

mapping    <- t( purrr::map_dfc( split(withmooc,withmooc$deptno), ~min(.x$sal) ) )

trans_data <- melt(mapping, value.name="sal_min")

merge(emp,trans_data,by.x='deptno',by.y='Var1')[1:10,]

Results

   deptno empno  ename       job  mgr   hiredate  sal comm Var2 sal_min
1      10  7782  CLARK   MANAGER 7839 1981-01-09 2450   NA    1    1300
2      10  7839   KING PRESIDENT   NA 1981-11-17 5000   NA    1    1300
3      10  7934 MILLER     CLERK 7782 1982-01-23 1300   NA    1    1300
4      20  7369  SMITH     CLERK 7902 1980-12-17  800   NA    1     800
5      20  7876  ADAMS     CLERK 7788 1983-01-12 1100   NA    1     800
6      20  7566  JONES   MANAGER 7839 1981-04-02 2975   NA    1     800
7      20  7902   FORD   ANALYST 7566 1981-12-03 3000   NA    1     800
8      20  7788  SCOTT   ANALYST 7566 1982-12-09 3000   NA    1     800
9      30  7499  ALLEN  SALESMAN 7698 1981-02-20 1600  300    1     950
10     30  7698  BLAKE   MANAGER 7839 1981-03-01 2850   NA    1     950

4. R Dplyr Package

1) 집계함수(Aggregate function)

dplyr::summarize()와 Min

R Programming

%%R

emp %>%
  dplyr::group_by(deptno) %>%
  dplyr::summarize(sal_min = min(sal)) %>%
  head()

Results

`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  deptno sal_min
   <dbl>   <dbl>
1     10    1300
2     20     800
3     30     950

map()과 min

R Programming

%%R

emp %>%
  tbl_df() %>%
  nest(-deptno) %>%
  mutate(sal_min = map(data, ~ min(.$sal))) %>% 
  unnest(sal_min) %>%
  head()

Results

# A tibble: 3 x 3
  deptno data             sal_min
   <dbl> <list>             <dbl>
1     20 <tibble [5 x 7]>     800
2     30 <tibble [6 x 7]>     950
3     10 <tibble [3 x 7]>    1300

2) 분석함수(Analytic Function)

min() 함수

R Programming

%%R

emp %>%
  dplyr::group_by(deptno) %>%
  dplyr::mutate(sal_min = min(sal)) %>%
  dplyr::arrange(deptno) %>%
  head()

Results

# A tibble: 6 x 9
# Groups:   deptno [2]
  empno ename  job         mgr hiredate     sal  comm deptno sal_min
  <dbl> <chr>  <chr>     <dbl> <date>     <dbl> <dbl>  <dbl>   <dbl>
1  7782 CLARK  MANAGER    7839 1981-01-09  2450    NA     10    1300
2  7839 KING   PRESIDENT    NA 1981-11-17  5000    NA     10    1300
3  7934 MILLER CLERK      7782 1982-01-23  1300    NA     10    1300
4  7369 SMITH  CLERK      7902 1980-12-17   800    NA     20     800
5  7566 JONES  MANAGER    7839 1981-04-02  2975    NA     20     800
6  7788 SCOTT  ANALYST    7566 1982-12-09  3000    NA     20     800

purrr:::map_dfr()과 min()

R Programming

%%R

withmooc <- emp %>% mutate(deptno = as.character(deptno))

withmooc %>%
  dplyr::left_join( ( purrr::map_dfr(split(withmooc,withmooc$deptno), ~min(.x$sal)) %>%
                      gather(key = deptno, value = sal_min )
                    ),
                    id = "deptno"
                  )              %>%
  dplyr::arrange(deptno)         %>%
  head()

Results

Joining, by = "deptno"
# A tibble: 6 x 9
  empno ename  job         mgr hiredate     sal  comm deptno sal_min
  <dbl> <chr>  <chr>     <dbl> <date>     <dbl> <dbl> <chr>    <dbl>
1  7782 CLARK  MANAGER    7839 1981-01-09  2450    NA 10        1300
2  7839 KING   PRESIDENT    NA 1981-11-17  5000    NA 10        1300
3  7934 MILLER CLERK      7782 1982-01-23  1300    NA 10        1300
4  7369 SMITH  CLERK      7902 1980-12-17   800    NA 20         800
5  7566 JONES  MANAGER    7839 1981-04-02  2975    NA 20         800
6  7788 SCOTT  ANALYST    7566 1982-12-09  3000    NA 20         800

5. R sqldf Package

1) 집계함수 (Aggregate function)

Min 함수

R Programming

%%R

sqldf(" SELECT deptno,MIN(sal) sal_min FROM emp group by deptno; ")

Results

  deptno sal_min
1     10    1300
2     20     800
3     30     950

6. Python pandasql Package

1) 집계함수 (Aggregate function)

Min 함수

Python Programming

ps.sqldf(" SELECT deptno,MIN(sal) sal_min FROM emp group by deptno; ")

Results

	deptno	sal_min
0	10	1300
1	20	800
2	30	950

7. R data.table Package

1) 집계함수 (Aggregate function)

Min 함수

R Programming

%%R

DT          <- data.table(emp)
dept_DT     <- data.table(dept)

DT[, min(sal), by =deptno]

Results

   deptno   V1
1:     20  800
2:     30  950
3:     10 1300

2) 분석함수 (Analytic Function)

Min 함수

R Programming

%%R

DT          <- data.table(emp)
dept_DT     <- data.table(dept)

DT[, sal_min := min(sal), by = deptno][order(deptno),][1:10, ]

Results

    empno  ename       job  mgr   hiredate  sal comm deptno sal_min
 1:  7782  CLARK   MANAGER 7839 1981-01-09 2450   NA     10    1300
 2:  7839   KING PRESIDENT   NA 1981-11-17 5000   NA     10    1300
 3:  7934 MILLER     CLERK 7782 1982-01-23 1300   NA     10    1300
 4:  7369  SMITH     CLERK 7902 1980-12-17  800   NA     20     800
 5:  7566  JONES   MANAGER 7839 1981-04-02 2975   NA     20     800
 6:  7788  SCOTT   ANALYST 7566 1982-12-09 3000   NA     20     800
 7:  7876  ADAMS     CLERK 7788 1983-01-12 1100   NA     20     800
 8:  7902   FORD   ANALYST 7566 1981-12-03 3000   NA     20     800
 9:  7499  ALLEN  SALESMAN 7698 1981-02-20 1600  300     30     950
10:  7521   WARD  SALESMAN 7698 1981-02-22 1250  500     30     950

Min 함수

R Programming

%%R

DT          <- data.table(emp)
dept_DT     <- data.table(dept)

DT[, `:=` ( sal_min  = min(unlist(.SD)) ), 
     by = deptno,
     .SDcols = c("sal")][order(deptno),][1:10, ]

Results

    empno  ename       job  mgr   hiredate  sal comm deptno sal_min
 1:  7782  CLARK   MANAGER 7839 1981-01-09 2450   NA     10    1300
 2:  7839   KING PRESIDENT   NA 1981-11-17 5000   NA     10    1300
 3:  7934 MILLER     CLERK 7782 1982-01-23 1300   NA     10    1300
 4:  7369  SMITH     CLERK 7902 1980-12-17  800   NA     20     800
 5:  7566  JONES   MANAGER 7839 1981-04-02 2975   NA     20     800
 6:  7788  SCOTT   ANALYST 7566 1982-12-09 3000   NA     20     800
 7:  7876  ADAMS     CLERK 7788 1983-01-12 1100   NA     20     800
 8:  7902   FORD   ANALYST 7566 1981-12-03 3000   NA     20     800
 9:  7499  ALLEN  SALESMAN 7698 1981-02-20 1600  300     30     950
10:  7521   WARD  SALESMAN 7698 1981-02-22 1250  500     30     950

8. Python Duckdb의 SQL

1) 집계함수 (Aggregate function)

Python Programming

%%sql
  SELECT deptno,
         MIN(sal) sal_min
  FROM   emp
  group  by deptno

Python Programming

duckdb.sql(" SELECT deptno,                   \
                    MIN(sal) sal_min          \
             FROM   emp                       \
             group  by deptno ").df()

Results

   deptno  sal_min
0      20      800
1      30      950
2      10     1300

2) 분석함수 (Analytic Function)

Python Programming

%%sql
  SELECT MGR, HIREDATE, SAL, EMPNO, ENAME, 
         MIN(SAL) OVER (PARTITION BY MGR ORDER BY HIREDATE) MGR_MIN
  FROM   emp
  ORDER  BY MGR, HIREDATE
  LIMIT  7

Python Programming

duckdb.sql(" SELECT MGR, HIREDATE, SAL, EMPNO, ENAME,                                \
                    MIN(SAL) OVER (PARTITION BY MGR ORDER BY HIREDATE) MGR_MIN       \
             FROM   emp                                                              \
             ORDER  BY MGR, HIREDATE                                                 \
             LIMIT  7 ").df()

Results

      mgr    hiredate   sal  empno   ename  MGR_MIN
0  7566.0  1981/12/03  3000   7902    FORD     3000
1  7566.0  1982/12/09  3000   7788   SCOTT     3000
2  7698.0  1981/02/20  1600   7499   ALLEN     1600
3  7698.0  1981/02/22  1250   7521    WARD     1250
4  7698.0  1981/09/08  1500   7844  TURNER     1250
5  7698.0  1981/09/28  1250   7654  MARTIN     1250
6  7698.0  1981/12/03   950   7900   JAMES      950

Cheers! A couple of soju shots along with some delicious Korean BBQ. (https://unsplash.com/photos/JWqQFv0n5U4)Min Function

--------------------------------------------

[Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table] 오라클 함수와 R & Python 비교 사전 목록 링크

오라클 SQL 함수(Oracle SQL Function) 목록 리스트 링크

[SQL, Pandas, R Prog, Dplyr, SQLDF, PANDASQL, DATA.TABLE] SQL EMP 예제로 만나는 테이블 데이터 처리 방법 리스트 링크 링크

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MAX 오라클 함수 [Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table] (0)	2021.12.03
LTRIM 오라클 함수 [Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table] (0)	2021.11.30

MIN 오라클 함수 [Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table, DuckDB]

MIN Oracle Function

1. Oracle(오라클)

2. Python Pandas(파이썬)

1) 집계함수(Aggregate function)

2) 분석함수(Analytic Function)

3. R Programming (R Package)

1) 집계함수 (Aggregate function)

2) 분석함수(Analytic Function)

4. R Dplyr Package

1) 집계함수(Aggregate function)

2) 분석함수(Analytic Function)

5. R sqldf Package

1) 집계함수 (Aggregate function)

6. Python pandasql Package

1) 집계함수 (Aggregate function)

7. R data.table Package

1) 집계함수 (Aggregate function)

2) 분석함수 (Analytic Function)

8. Python Duckdb의 SQL

1) 집계함수 (Aggregate function)

2) 분석함수 (Analytic Function)

'통계프로그램 비교 시리즈 > 오라클함수 비교(R & Python)' 카테고리의 다른 글

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MIN 오라클 함수 [Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table, DuckDB]

MIN Oracle Function

1. Oracle(오라클)

2. Python Pandas(파이썬)

1) 집계함수(Aggregate function)

2) 분석함수(Analytic Function)

3. R Programming (R Package)

1) 집계함수 (Aggregate function)

2) 분석함수(Analytic Function)

4. R Dplyr Package

1) 집계함수(Aggregate function)

2) 분석함수(Analytic Function)

5. R sqldf Package

1) 집계함수 (Aggregate function)

6. Python pandasql Package

1) 집계함수 (Aggregate function)

7. R data.table Package

1) 집계함수 (Aggregate function)

2) 분석함수 (Analytic Function)

8. Python Duckdb의 SQL

1) 집계함수 (Aggregate function)

2) 분석함수 (Analytic Function)

'통계프로그램 비교 시리즈 > 오라클함수 비교(R & Python)' 카테고리의 다른 글

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