Overview

Dataset statistics

Number of variables6
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory52.0 B

Variable types

Numeric1
Text3
Categorical2

Dataset

Description한국고용정보원이 취업소개 또는 취업정보 제공 등의 방법으로 구인자 및 구직자에 대한 고용서비스 향상에 기여한 민간고용서비스 기관의 신청을 받아 심사하여 고용서비스 우수기관으로 인증
Author고용노동부
URLhttps://www.data.go.kr/data/15101626/fileData.do

Alerts

연번 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 연번High correlation
분야 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
기관명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:08:41.595829
Analysis finished2023-12-12 22:08:42.159968
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T07:08:42.262995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.15
Q111.75
median22.5
Q333.25
95-th percentile41.85
Maximum44
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation12.845233
Coefficient of variation (CV)0.57089923
Kurtosis-1.2
Mean22.5
Median Absolute Deviation (MAD)11
Skewness0
Sum990
Variance165
MonotonicityStrictly increasing
2023-12-13T07:08:42.397079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 1
 
2.3%
24 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
33 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
44 1
2.3%
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%

기관명
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T07:08:42.622080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13.5
Mean length11.568182
Min length6

Characters and Unicode

Total characters509
Distinct characters118
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row구로여성인력개발센터
2nd row부산희망리본 직업상담센터
3rd row부천여성인력개발센터무료직업소개소
4th row서울시중부여성발전센터 직업소개소
5th row시흥여성인력개발센터
ValueCountFrequency (%)
주)제이엠커리어 6
 
9.1%
주)일로이룸 4
 
6.1%
주식회사 3
 
4.5%
주)지에스씨넷 3
 
4.5%
주)제니엘 3
 
4.5%
주)미래고용정보 2
 
3.0%
대전지점 2
 
3.0%
포항지점 1
 
1.5%
광주지사 1
 
1.5%
대구지점 1
 
1.5%
Other values (40) 40
60.6%
2023-12-13T07:08:42.980317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 36
 
7.1%
( 29
 
5.7%
) 29
 
5.7%
22
 
4.3%
지 20
 
3.9%
터 14
 
2.8%
센 14
 
2.8%
인 13
 
2.6%
리 13
 
2.6%
이 12
 
2.4%
Other values (108) 307
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 429
84.3%
Open Punctuation 29
 
5.7%
Close Punctuation 29
 
5.7%
Space Separator 22
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 36
 
8.4%
지 20
 
4.7%
터 14
 
3.3%
센 14
 
3.3%
인 13
 
3.0%
리 13
 
3.0%
이 12
 
2.8%
제 10
 
2.3%
점 9
 
2.1%
성 9
 
2.1%
Other values (105) 279
65.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 429
84.3%
Common 80
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 36
 
8.4%
지 20
 
4.7%
터 14
 
3.3%
센 14
 
3.3%
인 13
 
3.0%
리 13
 
3.0%
이 12
 
2.8%
제 10
 
2.3%
점 9
 
2.1%
성 9
 
2.1%
Other values (105) 279
65.0%
Common
ValueCountFrequency (%)
( 29
36.2%
) 29
36.2%
22
27.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 429
84.3%
ASCII 80
 
15.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 36
 
8.4%
지 20
 
4.7%
터 14
 
3.3%
센 14
 
3.3%
인 13
 
3.0%
리 13
 
3.0%
이 12
 
2.8%
제 10
 
2.3%
점 9
 
2.1%
성 9
 
2.1%
Other values (105) 279
65.0%
ASCII
ValueCountFrequency (%)
( 29
36.2%
) 29
36.2%
22
27.5%

대표자
Text

Distinct29
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T07:08:43.143641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length3
Mean length3.5909091
Min length3

Characters and Unicode

Total characters158
Distinct characters66
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)52.3%

Sample

1st row서정연
2nd row정웅현
3rd row조정숙
4th row박성아
5th row최정은
ValueCountFrequency (%)
윤종만 6
 
12.5%
최병철 4
 
8.3%
박춘홍 4
 
8.3%
박용수 3
 
6.2%
최창규 2
 
4.2%
김성남 2
 
4.2%
유인순 1
 
2.1%
정웅현 1
 
2.1%
김혜양 1
 
2.1%
김대경 1
 
2.1%
Other values (23) 23
47.9%
2023-12-13T07:08:43.387612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
윤 8
 
5.1%
종 8
 
5.1%
박 8
 
5.1%
최 8
 
5.1%
정 7
 
4.4%
만 6
 
3.8%
김 6
 
3.8%
병 5
 
3.2%
철 5
 
3.2%
춘 4
 
2.5%
Other values (56) 93
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
83.5%
Uppercase Letter 21
 
13.3%
Space Separator 4
 
2.5%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
윤 8
 
6.1%
종 8
 
6.1%
박 8
 
6.1%
최 8
 
6.1%
정 7
 
5.3%
만 6
 
4.5%
김 6
 
4.5%
병 5
 
3.8%
철 5
 
3.8%
춘 4
 
3.0%
Other values (41) 67
50.8%
Uppercase Letter
ValueCountFrequency (%)
P 3
14.3%
R 2
9.5%
L 2
9.5%
E 2
9.5%
I 2
9.5%
A 2
9.5%
N 2
9.5%
U 1
 
4.8%
H 1
 
4.8%
J 1
 
4.8%
Other values (3) 3
14.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
83.5%
Latin 21
 
13.3%
Common 5
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
윤 8
 
6.1%
종 8
 
6.1%
박 8
 
6.1%
최 8
 
6.1%
정 7
 
5.3%
만 6
 
4.5%
김 6
 
4.5%
병 5
 
3.8%
철 5
 
3.8%
춘 4
 
3.0%
Other values (41) 67
50.8%
Latin
ValueCountFrequency (%)
P 3
14.3%
R 2
9.5%
L 2
9.5%
E 2
9.5%
I 2
9.5%
A 2
9.5%
N 2
9.5%
U 1
 
4.8%
H 1
 
4.8%
J 1
 
4.8%
Other values (3) 3
14.3%
Common
ValueCountFrequency (%)
4
80.0%
, 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
83.5%
ASCII 26
 
16.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
윤 8
 
6.1%
종 8
 
6.1%
박 8
 
6.1%
최 8
 
6.1%
정 7
 
5.3%
만 6
 
4.5%
김 6
 
4.5%
병 5
 
3.8%
철 5
 
3.8%
춘 4
 
3.0%
Other values (41) 67
50.8%
ASCII
ValueCountFrequency (%)
4
15.4%
P 3
11.5%
R 2
 
7.7%
L 2
 
7.7%
E 2
 
7.7%
I 2
 
7.7%
A 2
 
7.7%
N 2
 
7.7%
, 1
 
3.8%
U 1
 
3.8%
Other values (5) 5
19.2%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
신규
27 
재인증
17 

Length

Max length3
Median length2
Mean length2.3863636
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row재인증
2nd row재인증
3rd row재인증
4th row신규
5th row재인증

Common Values

ValueCountFrequency (%)
신규 27
61.4%
재인증 17
38.6%

Length

2023-12-13T07:08:43.492306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:08:43.576982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 27
61.4%
재인증 17
38.6%

지역
Text

Distinct22
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T07:08:43.696081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters88
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)31.8%

Sample

1st row서울
2nd row부산
3rd row부천
4th row서울
5th row시흥
ValueCountFrequency (%)
서울 12
27.3%
대구 6
13.6%
천안 2
 
4.5%
고양 2
 
4.5%
김포 2
 
4.5%
부산 2
 
4.5%
광주 2
 
4.5%
대전 2
 
4.5%
구미 1
 
2.3%
용인 1
 
2.3%
Other values (12) 12
27.3%
2023-12-13T07:08:43.919108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
울 13
14.8%
서 12
13.6%
대 8
 
9.1%
구 7
 
8.0%
주 6
 
6.8%
천 4
 
4.5%
포 3
 
3.4%
전 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
Other values (20) 26
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
울 13
14.8%
서 12
13.6%
대 8
 
9.1%
구 7
 
8.0%
주 6
 
6.8%
천 4
 
4.5%
포 3
 
3.4%
전 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
Other values (20) 26
29.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
울 13
14.8%
서 12
13.6%
대 8
 
9.1%
구 7
 
8.0%
주 6
 
6.8%
천 4
 
4.5%
포 3
 
3.4%
전 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
Other values (20) 26
29.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
울 13
14.8%
서 12
13.6%
대 8
 
9.1%
구 7
 
8.0%
주 6
 
6.8%
천 4
 
4.5%
포 3
 
3.4%
전 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
Other values (20) 26
29.5%

분야
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
유료
33 
무료
10 
직업정보제공
 
1

Length

Max length6
Median length2
Mean length2.0909091
Min length2

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row무료
2nd row무료
3rd row무료
4th row무료
5th row무료

Common Values

ValueCountFrequency (%)
유료 33
75.0%
무료 10
 
22.7%
직업정보제공 1
 
2.3%

Length

2023-12-13T07:08:44.024163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:08:44.111501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유료 33
75.0%
무료 10
 
22.7%
직업정보제공 1
 
2.3%

Interactions

2023-12-13T07:08:41.906316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:08:44.165869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번기관명대표자구분지역분야
연번1.0001.0000.9460.9180.5680.770
기관명1.0001.0001.0001.0001.0001.000
대표자0.9461.0001.0000.8930.0001.000
구분0.9181.0000.8931.0000.0000.262
지역0.5681.0000.0000.0001.0000.000
분야0.7701.0001.0000.2620.0001.000
2023-12-13T07:08:44.240145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야구분
분야1.0000.420
구분0.4201.000
2023-12-13T07:08:44.300133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분분야
연번1.0000.6830.588
구분0.6831.0000.420
분야0.5880.4201.000

Missing values

2023-12-13T07:08:41.998828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:08:42.111516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번기관명대표자구분지역분야
01구로여성인력개발센터서정연재인증서울무료
12부산희망리본 직업상담센터정웅현재인증부산무료
23부천여성인력개발센터무료직업소개소조정숙재인증부천무료
34서울시중부여성발전센터 직업소개소박성아신규서울무료
45시흥여성인력개발센터최정은재인증시흥무료
56은평여성인력개발센터임정진재인증서울무료
67인천서구여성인력개발센터조민정재인증인천무료
78전라북도장애인복지관강병은재인증전주무료
89중랑여성인력개발센터이보섭신규서울무료
910충북여성새로일하기지원본부오경숙재인증청주무료
연번기관명대표자구분지역분야
3435(주)취업인김대경신규부산유료
3536(주)코비아컨설팅오견성재인증서울유료
3637(주)한국커리어잡스유인순재인증천안유료
3738두리잡인력파출김두일재인증성남유료
3839에이치알컨설팅(주)전용화, 강정대재인증서울유료
3940주식회사 미래일자리센터손일조신규대구유료
4041주식회사 스카우트문영철신규서울유료
4142(주)조인스잡최창규신규평택유료
4243주식회사 조인스잡 고양센터최창규신규고양유료
4344중소기업은행윤종원신규서울직업정보제공