Overview

Dataset statistics

Number of variables4
Number of observations179
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory32.7 B

Variable types

Text4

Dataset

Description2023년 청년일자리도약장려금 사업 운영기관에 대한 데이터로 전국 179개소의 관할고용센터명, 운영기관명, 연락처, 관할지역 항목에 대한 데이터를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15119494/fileData.do

Reproduction

Analysis started2023-12-12 04:02:26.507446
Analysis finished2023-12-12 04:02:27.201256
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관할고용센터명
Text

Distinct54
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T13:02:27.432259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.5251397
Min length6

Characters and Unicode

Total characters1168
Distinct characters56
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

Unique13 ?
Unique (%)7.3%

Sample

1st row서울고용센터
2nd row서울고용센터
3rd row서울고용센터
4th row서울고용센터
5th row서울고용센터
ValueCountFrequency (%)
수원고용센터 8
 
4.5%
인천고용센터 8
 
4.5%
서울강남고용센터 7
 
3.9%
광주고용센터 7
 
3.9%
성남고용센터 7
 
3.9%
서울동부고용센터 6
 
3.4%
대전고용센터 6
 
3.4%
서울관악고용센터 6
 
3.4%
고양고용센터 6
 
3.4%
대구고용센터 5
 
2.8%
Other values (44) 113
63.1%
2023-12-12T13:02:27.969127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 185
15.8%
용 179
15.3%
센 179
15.3%
터 179
15.3%
서 52
 
4.5%
부 50
 
4.3%
울 39
 
3.3%
산 23
 
2.0%
천 23
 
2.0%
주 21
 
1.8%
Other values (46) 238
20.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1168
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 185
15.8%
용 179
15.3%
센 179
15.3%
터 179
15.3%
서 52
 
4.5%
부 50
 
4.3%
울 39
 
3.3%
산 23
 
2.0%
천 23
 
2.0%
주 21
 
1.8%
Other values (46) 238
20.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1168
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 185
15.8%
용 179
15.3%
센 179
15.3%
터 179
15.3%
서 52
 
4.5%
부 50
 
4.3%
울 39
 
3.3%
산 23
 
2.0%
천 23
 
2.0%
주 21
 
1.8%
Other values (46) 238
20.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1168
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 185
15.8%
용 179
15.3%
센 179
15.3%
터 179
15.3%
서 52
 
4.5%
부 50
 
4.3%
울 39
 
3.3%
산 23
 
2.0%
천 23
 
2.0%
주 21
 
1.8%
Other values (46) 238
20.4%

운영기관명
Text

Distinct178
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T13:02:28.309628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length11.290503
Min length5

Characters and Unicode

Total characters2021
Distinct characters192
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)98.9%

Sample

1st row(주)메이크인
2nd row(주)잡모아 동대문지점
3rd row(사)한국경영혁신중소기업협회
4th row(사)여성중앙회종로여성인력개발센터
5th row스탭스(주)
ValueCountFrequency (%)
주)잡모아 14
 
5.4%
사)중소기업기술혁신협회 7
 
2.7%
주)제이엠커리어 7
 
2.7%
주)지에스씨넷 6
 
2.3%
사회적협동조합 5
 
1.9%
주)제니엘 4
 
1.5%
주)명은커리어 4
 
1.5%
주)커리어넷 3
 
1.1%
주식회사 3
 
1.1%
스탭스(주 3
 
1.1%
Other values (186) 205
78.5%
2023-12-12T13:02:28.848274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 125
 
6.2%
) 125
 
6.2%
주 111
 
5.5%
지 95
 
4.7%
사 84
 
4.2%
83
 
4.1%
회 78
 
3.9%
스 48
 
2.4%
소 40
 
2.0%
협 36
 
1.8%
Other values (182) 1196
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1685
83.4%
Open Punctuation 125
 
6.2%
Close Punctuation 125
 
6.2%
Space Separator 83
 
4.1%
Other Symbol 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 111
 
6.6%
지 95
 
5.6%
사 84
 
5.0%
회 78
 
4.6%
스 48
 
2.8%
소 40
 
2.4%
협 36
 
2.1%
잡 33
 
2.0%
점 30
 
1.8%
리 30
 
1.8%
Other values (178) 1100
65.3%
Open Punctuation
ValueCountFrequency (%)
( 125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1688
83.5%
Common 333
 
16.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 111
 
6.6%
지 95
 
5.6%
사 84
 
5.0%
회 78
 
4.6%
스 48
 
2.8%
소 40
 
2.4%
협 36
 
2.1%
잡 33
 
2.0%
점 30
 
1.8%
리 30
 
1.8%
Other values (179) 1103
65.3%
Common
ValueCountFrequency (%)
( 125
37.5%
) 125
37.5%
83
24.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1685
83.4%
ASCII 333
 
16.5%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 125
37.5%
) 125
37.5%
83
24.9%
Hangul
ValueCountFrequency (%)
주 111
 
6.6%
지 95
 
5.6%
사 84
 
5.0%
회 78
 
4.6%
스 48
 
2.8%
소 40
 
2.4%
협 36
 
2.1%
잡 33
 
2.0%
점 30
 
1.8%
리 30
 
1.8%
Other values (178) 1100
65.3%
None
ValueCountFrequency (%)
㈜ 3
100.0%

연락처
Text

Distinct175
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T13:02:29.210971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length12.050279
Min length11

Characters and Unicode

Total characters2157
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)95.5%

Sample

1st row02-2291-7070
2nd row02-2293-8883
3rd row02-2230-2120
4th row02-742-1326
5th row02-2178-8088
ValueCountFrequency (%)
02-6011-1444 3
 
1.7%
033-743-2991~4 2
 
1.1%
033-813-0013 2
 
1.1%
02-2188-6791 2
 
1.1%
055-794-1012 1
 
0.6%
053-572-3434 1
 
0.6%
053-350-1098 1
 
0.6%
02-2291-7070 1
 
0.6%
053-811-3031 1
 
0.6%
055-904-3558 1
 
0.6%
Other values (165) 165
91.7%
2023-12-12T13:02:29.797380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 358
16.6%
0 329
15.3%
1 226
10.5%
3 218
10.1%
2 214
9.9%
4 170
7.9%
5 163
7.6%
9 124
 
5.7%
6 120
 
5.6%
8 118
 
5.5%
Other values (4) 117
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1793
83.1%
Dash Punctuation 358
 
16.6%
Math Symbol 3
 
0.1%
Other Punctuation 2
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 329
18.3%
1 226
12.6%
3 218
12.2%
2 214
11.9%
4 170
9.5%
5 163
9.1%
9 124
 
6.9%
6 120
 
6.7%
8 118
 
6.6%
7 111
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 358
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 358
16.6%
0 329
15.3%
1 226
10.5%
3 218
10.1%
2 214
9.9%
4 170
7.9%
5 163
7.6%
9 124
 
5.7%
6 120
 
5.6%
8 118
 
5.5%
Other values (4) 117
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 358
16.6%
0 329
15.3%
1 226
10.5%
3 218
10.1%
2 214
9.9%
4 170
7.9%
5 163
7.6%
9 124
 
5.7%
6 120
 
5.6%
8 118
 
5.5%
Other values (4) 117
 
5.4%

관할지역
Text

Distinct55
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T13:02:30.294226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length43
Mean length24.860335
Min length3

Characters and Unicode

Total characters4450
Distinct characters140
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)7.8%

Sample

1st row서울시 종로구, 중구, 동대문구
2nd row서울시 종로구, 중구, 동대문구
3rd row서울시 종로구, 중구, 동대문구
4th row서울시 종로구, 중구, 동대문구
5th row서울시 종로구, 중구, 동대문구
ValueCountFrequency (%)
경기도 44
 
4.6%
서울시 40
 
4.1%
중구 22
 
2.3%
동구 17
 
1.8%
충청남도 15
 
1.6%
경상남도 12
 
1.2%
부산시 11
 
1.1%
강원도 11
 
1.1%
서구 10
 
1.0%
인천시 10
 
1.0%
Other values (208) 775
80.1%
2023-12-12T13:02:30.975373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
788
17.7%
, 602
 
13.5%
시 357
 
8.0%
구 285
 
6.4%
군 247
 
5.6%
도 143
 
3.2%
남 87
 
2.0%
경 87
 
2.0%
주 85
 
1.9%
천 79
 
1.8%
Other values (130) 1690
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3007
67.6%
Space Separator 788
 
17.7%
Other Punctuation 602
 
13.5%
Close Punctuation 23
 
0.5%
Open Punctuation 23
 
0.5%
Decimal Number 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 357
 
11.9%
구 285
 
9.5%
군 247
 
8.2%
도 143
 
4.8%
남 87
 
2.9%
경 87
 
2.9%
주 85
 
2.8%
천 79
 
2.6%
서 74
 
2.5%
산 70
 
2.3%
Other values (125) 1493
49.7%
Space Separator
ValueCountFrequency (%)
788
100.0%
Other Punctuation
ValueCountFrequency (%)
, 602
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Decimal Number
ValueCountFrequency (%)
3 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3007
67.6%
Common 1443
32.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 357
 
11.9%
구 285
 
9.5%
군 247
 
8.2%
도 143
 
4.8%
남 87
 
2.9%
경 87
 
2.9%
주 85
 
2.8%
천 79
 
2.6%
서 74
 
2.5%
산 70
 
2.3%
Other values (125) 1493
49.7%
Common
ValueCountFrequency (%)
788
54.6%
, 602
41.7%
) 23
 
1.6%
( 23
 
1.6%
3 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3007
67.6%
ASCII 1443
32.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
788
54.6%
, 602
41.7%
) 23
 
1.6%
( 23
 
1.6%
3 7
 
0.5%
Hangul
ValueCountFrequency (%)
시 357
 
11.9%
구 285
 
9.5%
군 247
 
8.2%
도 143
 
4.8%
남 87
 
2.9%
경 87
 
2.9%
주 85
 
2.8%
천 79
 
2.6%
서 74
 
2.5%
산 70
 
2.3%
Other values (125) 1493
49.7%

Correlations

2023-12-12T13:02:31.122434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할고용센터명관할지역
관할고용센터명1.0001.000
관할지역1.0001.000

Missing values

2023-12-12T13:02:26.992848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:02:27.150711image/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

관할고용센터명운영기관명연락처관할지역
0서울고용센터(주)메이크인02-2291-7070서울시 종로구, 중구, 동대문구
1서울고용센터(주)잡모아 동대문지점02-2293-8883서울시 종로구, 중구, 동대문구
2서울고용센터(사)한국경영혁신중소기업협회02-2230-2120서울시 종로구, 중구, 동대문구
3서울고용센터(사)여성중앙회종로여성인력개발센터02-742-1326서울시 종로구, 중구, 동대문구
4서울고용센터스탭스(주)02-2178-8088서울시 종로구, 중구, 동대문구
5서초고용센터(주)지에스씨넷서울지점02-597-1949서울시 서초구
6서초고용센터(주)제니엘02-580-0192서울시 서초구
7서초고용센터(사)국제직업능력개발교류협회02-6949-3930서울시 서초구
8서초고용센터서초여성인력개발센터02-6929-0011서울시 서초구
9서울강남고용센터(주)스카우트02-2188-6791서울시 강남구
관할고용센터명운영기관명연락처관할지역
169천안고용센터충남북부상공회의소041-559-5763충청남도 천안시, 아산시, 예산군, 당진시
170천안고용센터(사)중소기업기술혁신협회 대전세종충남지회041-553-1193충청남도 천안시, 아산시, 예산군, 당진시
171천안고용센터(주)한국커리어잡스 천안지사041-556-2829충청남도 천안시, 아산시, 예산군, 당진시
172천안고용센터(주)제이엠커리어 천안지사041-522-7980충청남도 천안시, 아산시, 예산군, 당진시
173천안고용센터당진상공회의소041-357-2500충청남도 천안시, 아산시, 예산군, 당진시
174충주고용센터충주상공회의소043-843-7004충청북도 충주시, 음성군
175충주고용센터음성상공회의소043-873-9911충청북도 충주시, 음성군
176제천고용센터제천단양상공회의소043-642-3114충청북도 제천시, 단양군
177보령고용센터(주)지이라인041-933-9655충청남도 보령시, 서천군, 부여군, 홍성군, 청양군
178서산고용센터서산상공회의소041-663-3063충청남도 서산시, 태안군