포스팅 목차
13. 데이터 프레임 정렬과 중복제거-Sorting & duplicate
1. Proc SQL
- Order by 구분을 이용하여 데이터 소트;
| SAS Programming |
proc sql;
select *
from BACK.mydata
order by workshop;
quit;
| Results |
id workshop gender q1 q2 q3 q4
--------------------------------------------------------------
3 1 f 2 2 4 3
5 1 m 4 5 2 4
7 1 m 5 3 4 4
1 1 f 1 1 5 1
6 2 m 5 4 5 5
2 2 f 2 1 4 1
4 2 f 3 1 . 3
8 2 m 4 5 5 5
| SAS Programming |
proc sql;
select *
from BACK.mydata
order by workshop,gender;
quit;
| Results |
id workshop gender q1 q2 q3 q4
--------------------------------------------------------------
3 1 f 2 2 4 3
1 1 f 1 1 5 1
7 1 m 5 3 4 4
5 1 m 4 5 2 4
2 2 f 2 1 4 1
4 2 f 3 1 . 3
6 2 m 5 4 5 5
8 2 m 4 5 5 5
- Descending를 이용하여 내림차순 소트;
| SAS Programming |
proc sql;
select *
from BACK.mydata
order by workshop,gender descending;
quit;
| Results |
id workshop gender q1 q2 q3 q4
--------------------------------------------------------------
5 1 m 4 5 2 4
7 1 m 5 3 4 4
3 1 f 2 2 4 3
1 1 f 1 1 5 1
6 2 m 5 4 5 5
8 2 m 4 5 5 5
2 2 f 2 1 4 1
4 2 f 3 1 . 3
| SAS Programming |
proc sql;
select *
from BACK.mydata
order by workshop descending,gender descending;
quit;
| Results |
id workshop gender q1 q2 q3 q4
--------------------------------------------------------------
6 2 m 5 4 5 5
8 2 m 4 5 5 5
2 2 f 2 1 4 1
4 2 f 3 1 . 3
7 1 m 5 3 4 4
5 1 m 4 5 2 4
1 1 f 1 1 5 1
3 1 f 2 2 4 3
- 위에서 소트 된 데이터 세트에서 Sub-query를 이용하여 중복된 데이터 제거
| SAS Programming |
proc sql;
select a.*
from BACK.mydata a
where id = ( select max(id)
from BACK.mydata b
where a.workshop = b.workshop
and a.gender = b.gender);
quit;
| Results |
id workshop gender q1 q2 q3 q4
--------------------------------------------------------------
3 1 f 2 2 4 3
4 2 f 3 1 . 3
7 1 m 5 3 4 4
8 2 m 4 5 5 5
2. SAS Programming
- 데이터 소트를 위한 SAS 프로그램.
| SAS Programming |
PROC SORT DATA=BACK.mydata out=mydata;
BY workshop;
RUN;
PROC PRINT DATA=mydata; RUN;
| Results |
OBS id workshop gender q1 q2 q3 q4
1 1 1 f 1 1 5 1
2 3 1 f 2 2 4 3
3 5 1 m 4 5 2 4
4 7 1 m 5 3 4 4
5 2 2 f 2 1 4 1
6 4 2 f 3 1 . 3
7 6 2 m 5 4 5 5
8 8 2 m 4 5 5 5
| SAS Programming |
PROC SORT DATA=BACK.mydata out=mydata;
BY gender workshop;
RUN;
PROC PRINT DATA=mydata; RUN;
| Results |
OBS id workshop gender q1 q2 q3 q4
1 1 1 f 1 1 5 1
2 3 1 f 2 2 4 3
3 2 2 f 2 1 4 1
4 4 2 f 3 1 . 3
5 5 1 m 4 5 2 4
6 7 1 m 5 3 4 4
7 6 2 m 5 4 5 5
8 8 2 m 4 5 5 5
| SAS Programming |
PROC SORT DATA=BACK.mydata out=mydata;
BY workshop descending gender;
RUN;
PROC PRINT DATA=mydata; RUN;
| Results |
OBS id workshop gender q1 q2 q3 q4
1 5 1 m 4 5 2 4
2 7 1 m 5 3 4 4
3 1 1 f 1 1 5 1
4 3 1 f 2 2 4 3
5 6 2 m 5 4 5 5
6 8 2 m 4 5 5 5
7 2 2 f 2 1 4 1
8 4 2 f 3 1 . 3
| SAS Programming |
PROC SORT DATA=BACK.mydata out=mydata;
BY descending workshop descending gender;
RUN;
PROC PRINT DATA=mydata; RUN;
| Results |
OBS id workshop gender q1 q2 q3 q4
1 6 2 m 5 4 5 5
2 8 2 m 4 5 5 5
3 2 2 f 2 1 4 1
4 4 2 f 3 1 . 3
5 5 1 m 4 5 2 4
6 7 1 m 5 3 4 4
7 1 1 f 1 1 5 1
8 3 1 f 2 2 4 3
| SAS Programming |
PROC SORT DATA=BACK.mydata out=mydata ;
BY workshop gender descending id;
RUN;
PROC SORT DATA=mydata nodupkey;
BY workshop gender;
RUN;
PROC PRINT DATA=mydata; RUN;
| Results |
OBS id workshop gender q1 q2 q3 q4
1 3 1 f 2 2 4 3
2 7 1 m 5 3 4 4
3 4 2 f 3 1 . 3
4 8 2 m 4 5 5 5
3. SPSS
- SPSS Program to Sort Data.
| SPSS Programming |
SORT CASES BY workshop (A).
LIST.
EXECUTE.
SORT CASES BY gender (A) workshop (A).
LIST.
EXECUTE.
SORT CASES BY workshop (D) gender (A).
LIST.
EXECUTE.
4. R Programming (R-PROJECT)
| R Programming |
from rpy2.robjects import r
%load_ext rpy2.ipython
| Results |
The rpy2.ipython extension is already loaded. To reload it, use:
%reload_ext rpy2.ipython
| R Programming |
%%R
library(tidyverse)
library(psych)
library(reshape)
library(reshape2)
mydata <- read_csv("C:/work/data/mydata.csv",
col_types = cols( id = col_double(),
workshop = col_character(),
gender = col_character(),
q1 = col_double(),
q2 = col_double(),
q3 = col_double(),
q4 = col_double()
)
)
withmooc = mydata
attach(withmooc) # mydata를 기본 데이터 세트로 지정.
withmooc
| Results |
R[write to console]: The following objects are masked from withmooc (pos = 3):
gender, id, q1, q2, q3, q4, workshop
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 2 2 f 2 1 4 1
3 3 1 f 2 2 4 3
4 4 2 f 3 1 NA 3
5 5 1 m 4 5 2 4
6 6 2 m 5 4 5 5
7 7 1 m 5 3 4 4
8 8 2 m 4 5 5 5
- 인덱스를 이용한 데이터 프레임 소트
- 처음 4개의 관측치를 출력.
| R Programming |
%%R
withmooc[ c(1,2,3,4), ]
| Results |
# A tibble: 4 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 2 2 f 2 1 4 1
3 3 1 f 2 2 4 3
4 4 2 f 3 1 NA 3
- 인덱스 값을 거꾸로 삽입하여서 역순으로 출력한다.
| R Programming |
%%R
withmooc[ c(4,3,2,1), ]
| Results |
# A tibble: 4 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 4 2 f 3 1 NA 3
2 3 1 f 2 2 4 3
3 2 2 f 2 1 4 1
4 1 1 f 1 1 5 1
- 변수 workshop(1과2)를 기준으로 정렬.
- Order함수는 정렬할 인덱스를 발견한다.
| R Programming |
%%R
mydataSorted<-withmooc[ order(mydata$workshop), ]
mydataSorted
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 3 1 f 2 2 4 3
3 5 1 m 4 5 2 4
4 7 1 m 5 3 4 4
5 2 2 f 2 1 4 1
6 4 2 f 3 1 NA 3
7 6 2 m 5 4 5 5
8 8 2 m 4 5 5 5
- 성별,workshop변수를 기준으로 정렬.
| R Programming |
%%R
mydataSorted<-withmooc[ order( mydata$gender, mydata$workshop ), ]
mydataSorted
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 3 1 f 2 2 4 3
3 2 2 f 2 1 4 1
4 4 2 f 3 1 NA 3
5 5 1 m 4 5 2 4
6 7 1 m 5 3 4 4
7 6 2 m 5 4 5 5
8 8 2 m 4 5 5 5
- 기본 소트 순서는 올림차순이다. 수치형 변수는 앞에 음수(-)를 넣어서 내림차순으로 정렬할 수 있다.
| R Programming |
%%R
mydataSorted<- withmooc[ order( withmooc$gender, - withmooc$q1 ), ]
mydataSorted
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 4 2 f 3 1 NA 3
2 2 2 f 2 1 4 1
3 3 1 f 2 2 4 3
4 1 1 f 1 1 5 1
5 6 2 m 5 4 5 5
6 7 1 m 5 3 4 4
7 5 1 m 4 5 2 4
8 8 2 m 4 5 5 5
- 음수 대신에 REV함수를 사용하여서 내림차순으로 정렬할수 있다.
| R Programming |
%%R
mydataSorted<- mydata[ order( mydata$workshop,rev(mydata$gender) ), ]
mydataSorted
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 5 1 m 4 5 2 4
2 7 1 m 5 3 4 4
3 1 1 f 1 1 5 1
4 3 1 f 2 2 4 3
5 6 2 m 5 4 5 5
6 8 2 m 4 5 5 5
7 2 2 f 2 1 4 1
8 4 2 f 3 1 NA 3
5. R - Tidyverse
| R Programming |
from rpy2.robjects import r
%load_ext rpy2.ipython
| Results |
The rpy2.ipython extension is already loaded. To reload it, use:
%reload_ext rpy2.ipython
| R Programming |
%%R
library(tidyverse)
library(psych)
mydata <- read_csv("C:/work/data/mydata.csv",
col_types = cols( id = col_double(),
workshop = col_character(),
gender = col_character(),
q1 = col_double(),
q2 = col_double(),
q3 = col_double(),
q4 = col_double()
)
)
withmooc = mydata
attach(withmooc) # mydata를 기본 데이터 세트로 지정.
withmooc
| Results |
R[write to console]: The following objects are masked from withmooc (pos = 4):
gender, id, q1, q2, q3, q4, workshop
R[write to console]: The following objects are masked from withmooc (pos = 5):
gender, id, q1, q2, q3, q4, workshop
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 2 2 f 2 1 4 1
3 3 1 f 2 2 4 3
4 4 2 f 3 1 NA 3
5 5 1 m 4 5 2 4
6 6 2 m 5 4 5 5
7 7 1 m 5 3 4 4
8 8 2 m 4 5 5 5
- 처음 4개의 관측치를 출력.
| R Programming |
%%R
withmooc %>%
slice(1:4) %>%
arrange(id)
| Results |
# A tibble: 4 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 2 2 f 2 1 4 1
3 3 1 f 2 2 4 3
4 4 2 f 3 1 NA 3
- 인덱스 값을 거꾸로 삽입하여서 역순으로 출력한다.
| R Programming |
%%R
withmooc %>%
slice(1:4) %>%
arrange(desc(id))
| Results |
# A tibble: 4 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 4 2 f 3 1 NA 3
2 3 1 f 2 2 4 3
3 2 2 f 2 1 4 1
4 1 1 f 1 1 5 1
- 변수 workshop(1과2)를 기준으로 정렬.
- arrange를 사용하여 정렬
| R Programming |
%%R
withmooc %>%
arrange(workshop)
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 3 1 f 2 2 4 3
3 5 1 m 4 5 2 4
4 7 1 m 5 3 4 4
5 2 2 f 2 1 4 1
6 4 2 f 3 1 NA 3
7 6 2 m 5 4 5 5
8 8 2 m 4 5 5 5
- 성별,workshop변수를 기준으로 정렬.
| R Programming |
%%R
withmooc %>%
arrange(gender, workshop)
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 f 1 1 5 1
2 3 1 f 2 2 4 3
3 2 2 f 2 1 4 1
4 4 2 f 3 1 NA 3
5 5 1 m 4 5 2 4
6 7 1 m 5 3 4 4
7 6 2 m 5 4 5 5
8 8 2 m 4 5 5 5
- 기본 소트 순서는 올림차순이다. 변수 앞에 음수(-)를 넣으면 내림차순으로 정렬한다.
| R Programming |
%%R
withmooc %>%
arrange(workshop, desc(gender))
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 5 1 m 4 5 2 4
2 7 1 m 5 3 4 4
3 1 1 f 1 1 5 1
4 3 1 f 2 2 4 3
5 6 2 m 5 4 5 5
6 8 2 m 4 5 5 5
7 2 2 f 2 1 4 1
8 4 2 f 3 1 NA 3
| R Programming |
%%R
withmooc %>%
arrange(desc(workshop), desc(gender))
| Results |
# A tibble: 8 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 6 2 m 5 4 5 5
2 8 2 m 4 5 5 5
3 2 2 f 2 1 4 1
4 4 2 f 3 1 NA 3
5 5 1 m 4 5 2 4
6 7 1 m 5 3 4 4
7 1 1 f 1 1 5 1
8 3 1 f 2 2 4 3
- 데이터 중복 제거 하기
| R Programming |
%%R
withmooc %>%
arrange(workshop, gender,desc(id)) %>%
distinct(workshop, gender, .keep_all = TRUE)
| Results |
# A tibble: 4 x 7
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 3 1 f 2 2 4 3
2 7 1 m 5 3 4 4
3 4 2 f 3 1 NA 3
4 8 2 m 4 5 5 5
| R Programming |
%%R
mydata %>%
group_by(workshop, gender) %>%
arrange(desc(id),.by_group=TRUE) %>%
distinct(workshop, gender, .keep_all = TRUE)
| Results |
# A tibble: 4 x 7
# Groups: workshop, gender [4]
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 3 1 f 2 2 4 3
2 7 1 m 5 3 4 4
3 4 2 f 3 1 NA 3
4 8 2 m 4 5 5 5
| R Programming |
%%R
mydata %>%
arrange(workshop, gender, desc(id)) %>%
group_by(workshop, gender) %>%
slice(1)
| Results |
# A tibble: 4 x 7
# Groups: workshop, gender [4]
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 3 1 f 2 2 4 3
2 7 1 m 5 3 4 4
3 4 2 f 3 1 NA 3
4 8 2 m 4 5 5 5
| R Programming |
%%R
mydata %>%
group_by(workshop, gender) %>%
arrange(desc(id),.by_group=TRUE) %>%
slice(1)
| Results |
# A tibble: 4 x 7
# Groups: workshop, gender [4]
id workshop gender q1 q2 q3 q4
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 3 1 f 2 2 4 3
2 7 1 m 5 3 4 4
3 4 2 f 3 1 NA 3
4 8 2 m 4 5 5 5
6. Python - Pandas
| Python Programming |
import pandas as pd
import numpy as np
import sweetviz as sv
mydata = pd.read_csv("C:/work/data/mydata.csv",sep=",",
dtype={'id':object,'workshop':object,
'q1':int, 'q2':int, 'q3':float, 'q4':int},
na_values=['NaN'],skipinitialspace =True)
withmooc= mydata.copy()
withmooc
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
1 2 2 f 2 1 4.0 1
2 3 1 f 2 2 4.0 3
3 4 2 f 3 1 NaN 3
4 5 1 m 4 5 2.0 4
5 6 2 m 5 4 5.0 5
6 7 1 m 5 3 4.0 4
7 8 2 m 4 5 5.0 5
| Python Programming |
withmooc= mydata.copy()
# 처음 4개의 관측치를 출력.
withmooc.iloc[[0,1,2,3],]
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
1 2 2 f 2 1 4.0 1
2 3 1 f 2 2 4.0 3
3 4 2 f 3 1 NaN 3
- 인덱스 값을 거꾸로 삽입하여서 역순으로 출력한다.
| Python Programming |
withmooc.iloc[[3,2,1,0],]
| Results |
id workshop gender q1 q2 q3 q4
3 4 2 f 3 1 NaN 3
2 3 1 f 2 2 4.0 3
1 2 2 f 2 1 4.0 1
0 1 1 f 1 1 5.0 1
- 변수 workshop(1과2)를 기준으로 정렬.
- sort_values를 사용하여 데이터 정렬
| Python Programming |
mydataSorted = withmooc.sort_values(by='workshop')
mydataSorted
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
2 3 1 f 2 2 4.0 3
4 5 1 m 4 5 2.0 4
6 7 1 m 5 3 4.0 4
1 2 2 f 2 1 4.0 1
3 4 2 f 3 1 NaN 3
5 6 2 m 5 4 5.0 5
7 8 2 m 4 5 5.0 5
- 성별,workshop변수를 기준으로 정렬.
| Python Programming |
mydataSorted = withmooc.sort_values(by=['gender','workshop'])
mydataSorted
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
2 3 1 f 2 2 4.0 3
1 2 2 f 2 1 4.0 1
3 4 2 f 3 1 NaN 3
4 5 1 m 4 5 2.0 4
6 7 1 m 5 3 4.0 4
5 6 2 m 5 4 5.0 5
7 8 2 m 4 5 5.0 5
- 기본 소트 순서는 올림차순이다. ascending인자를 사용하여 정렬 방향 결정
| Python Programming |
mydataSorted = withmooc.sort_values(['workshop','gender'],ascending=[False,True])
mydataSorted
| Results |
id workshop gender q1 q2 q3 q4
1 2 2 f 2 1 4.0 1
3 4 2 f 3 1 NaN 3
5 6 2 m 5 4 5.0 5
7 8 2 m 4 5 5.0 5
0 1 1 f 1 1 5.0 1
2 3 1 f 2 2 4.0 3
4 5 1 m 4 5 2.0 4
6 7 1 m 5 3 4.0 4
- 데이터 중복 제거 하기;
| Python Programming |
mydataSorted = withmooc.sort_values(['workshop','gender','id'],ascending=[True,True,False])
mydataSorted
| Results |
id workshop gender q1 q2 q3 q4
2 3 1 f 2 2 4.0 3
0 1 1 f 1 1 5.0 1
6 7 1 m 5 3 4.0 4
4 5 1 m 4 5 2.0 4
3 4 2 f 3 1 NaN 3
1 2 2 f 2 1 4.0 1
7 8 2 m 4 5 5.0 5
5 6 2 m 5 4 5.0 5
- 사전에 정렬된 데이터의 결과를 가지고 중복 데이터 제거;
| Python Programming |
mydataSorted.drop_duplicates(subset=['workshop','gender'])
| Results |
id workshop gender q1 q2 q3 q4
2 3 1 f 2 2 4.0 3
6 7 1 m 5 3 4.0 4
3 4 2 f 3 1 NaN 3
7 8 2 m 4 5 5.0 5
| Python Programming |
mydataSorted.drop_duplicates(subset=['workshop'],keep='last')
| Results |
id workshop gender q1 q2 q3 q4
4 5 1 m 4 5 2.0 4
5 6 2 m 5 4 5.0 5
7. Python - dfply
| Python Programming |
import pandas as pd
from dfply import *
mydata = pd.read_csv("c:/work/data/mydata.csv",sep=",",
dtype={'id':object,'workshop':object,
'q1':int, 'q2':int, 'q3':float, 'q4':int},
na_values=['NaN'],skipinitialspace =True)
withmooc= mydata.copy()
# 모든 변수 선택하기.
withmooc
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
1 2 2 f 2 1 4.0 1
2 3 1 f 2 2 4.0 3
3 4 2 f 3 1 NaN 3
4 5 1 m 4 5 2.0 4
5 6 2 m 5 4 5.0 5
6 7 1 m 5 3 4.0 4
7 8 2 m 4 5 5.0 5- 처음 4개의 관측치를 출력.
| Python Programming |
withmooc >> \
row_slice(list(range(0,4))) >> \
arrange(X.id)
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
1 2 2 f 2 1 4.0 1
2 3 1 f 2 2 4.0 3
3 4 2 f 3 1 NaN 3- 인덱스 값을 거꾸로 삽입하여서 역순으로 출력한다.
| Python Programming |
withmooc >> \
row_slice(list(range(0,4))) >> \
arrange(X.id, ascending=False)
| Results |
id workshop gender q1 q2 q3 q4
3 4 2 f 3 1 NaN 3
2 3 1 f 2 2 4.0 3
1 2 2 f 2 1 4.0 1
0 1 1 f 1 1 5.0 1- 변수 workshop(1과2)를 기준으로 정렬.
- arrange함수를 사용하여 데이터 정렬
| Python Programming |
withmooc >> \
arrange(X.workshop)
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
2 3 1 f 2 2 4.0 3
4 5 1 m 4 5 2.0 4
6 7 1 m 5 3 4.0 4
1 2 2 f 2 1 4.0 1
3 4 2 f 3 1 NaN 3
5 6 2 m 5 4 5.0 5
7 8 2 m 4 5 5.0 5- 성별,workshop변수를 기준으로 정렬.
| Python Programming |
withmooc >> \
arrange(X.gender, X.workshop)
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
2 3 1 f 2 2 4.0 3
1 2 2 f 2 1 4.0 1
3 4 2 f 3 1 NaN 3
4 5 1 m 4 5 2.0 4
6 7 1 m 5 3 4.0 4
5 6 2 m 5 4 5.0 5
7 8 2 m 4 5 5.0 5- 기본 소트 순서는 올림차순이다. ascending인자를 사용하여 정렬 방향 결정
| Python Programming |
withmooc >> \
arrange(X.workshop, X.gender, ascending=[True,False])
| Results |
id workshop gender q1 q2 q3 q4
4 5 1 m 4 5 2.0 4
6 7 1 m 5 3 4.0 4
0 1 1 f 1 1 5.0 1
2 3 1 f 2 2 4.0 3
5 6 2 m 5 4 5.0 5
7 8 2 m 4 5 5.0 5
1 2 2 f 2 1 4.0 1
3 4 2 f 3 1 NaN 3
| Python Programming |
mydata >> \
arrange(X.workshop, X.gender, ascending=[False,False])
| Results |
id workshop gender q1 q2 q3 q4
5 6 2 m 5 4 5.0 5
7 8 2 m 4 5 5.0 5
1 2 2 f 2 1 4.0 1
3 4 2 f 3 1 NaN 3
4 5 1 m 4 5 2.0 4
6 7 1 m 5 3 4.0 4
0 1 1 f 1 1 5.0 1
2 3 1 f 2 2 4.0 3- 데이터 중복 제거 하기;
| Python Programming |
mydata >> \
arrange(X.workshop, X.gender,X.id , ascending=[True,True,False]) >> \
distinct(X.workshop, X.gender,keep='first')
| Results |
id workshop gender q1 q2 q3 q4
2 3 1 f 2 2 4.0 3
6 7 1 m 5 3 4.0 4
3 4 2 f 3 1 NaN 3
7 8 2 m 4 5 5.0 5
| Python Programming |
mydata >> \
arrange(X.workshop, X.gender,X.id , ascending=[True,True,False]) >> \
distinct(X.workshop, X.gender,keep='last')
| Results |
id workshop gender q1 q2 q3 q4
0 1 1 f 1 1 5.0 1
4 5 1 m 4 5 2.0 4
1 2 2 f 2 1 4.0 1
5 6 2 m 5 4 5.0 5
| Python Programming |
mydata >> \
arrange(X.workshop, X.gender,X.id , ascending=[True,True,False]) >> \
filter_by(~X[['workshop', 'gender']].duplicated( keep='first'))
| Results |
id workshop gender q1 q2 q3 q4
2 3 1 f 2 2 4.0 3
6 7 1 m 5 3 4.0 4
3 4 2 f 3 1 NaN 3
7 8 2 m 4 5 5.0 5
| Python Programming |
mydata >> \
arrange(X.workshop, X.gender,X.id , ascending=[True,True,False]) >> \
mask(~X[['workshop', 'gender']].duplicated())
| Results |
id workshop gender q1 q2 q3 q4
2 3 1 f 2 2 4.0 3
6 7 1 m 5 3 4.0 4
3 4 2 f 3 1 NaN 3
7 8 2 m 4 5 5.0 5
| 통계프로그램 비교 목록(Proc sql, SAS, SPSS, R 프로그래밍, R Tidyverse, Python Pandas, Python Dfply) |
| [Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table] 오라클 함수와 R & Python 비교 사전 목록 링크 |
| [SQL, Pandas, R Prog, Dplyr, SQLDF, PANDASQL, DATA.TABLE] SQL EMP 예제로 만나는 테이블 데이터 처리 방법 리스트 링크 |
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'통계프로그램 비교 시리즈 > 데이터 전처리 비교' 카테고리의 다른 글
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| [데이터 관리] 11. Aggregating Or Summarizing 데이터 (0) | 2022.01.15 |
| [데이터 관리] 10. Joining & Merging 데이터 프레임 (0) | 2022.01.14 |
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