Overview

Dataset statistics

Number of variables8
Number of observations410
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.2 KiB
Average record size in memory65.3 B

Variable types

Numeric1
Categorical5
Text2

Dataset

Description2023년 7월 26일 기준, 지방공기업 설립 유형, 운영구분, 기관명 등 지방공기업에 대한 관련 정보를 제공합니다.
Author행정안전부 지방공기업평가원
URLhttps://www.data.go.kr/data/15048282/fileData.do

Alerts

지역 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
운영구분 is highly overall correlated with 공기업유형High correlation
공기업유형 is highly overall correlated with 구분1 and 1 other fieldsHigh correlation
구분2 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 구분1 and 2 other fieldsHigh correlation
구분1 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:53:27.026518
Analysis finished2023-12-12 01:53:27.883444
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct410
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.5
Minimum1
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T10:53:27.970327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21.45
Q1103.25
median205.5
Q3307.75
95-th percentile389.55
Maximum410
Range409
Interquartile range (IQR)204.5

Descriptive statistics

Standard deviation118.50105
Coefficient of variation (CV)0.57664747
Kurtosis-1.2
Mean205.5
Median Absolute Deviation (MAD)102.5
Skewness0
Sum84255
Variance14042.5
MonotonicityStrictly increasing
2023-12-12T10:53:28.143366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
271 1
 
0.2%
281 1
 
0.2%
280 1
 
0.2%
279 1
 
0.2%
278 1
 
0.2%
277 1
 
0.2%
276 1
 
0.2%
275 1
 
0.2%
274 1
 
0.2%
Other values (400) 400
97.6%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
410 1
0.2%
409 1
0.2%
408 1
0.2%
407 1
0.2%
406 1
0.2%
405 1
0.2%
404 1
0.2%
403 1
0.2%
402 1
0.2%
401 1
0.2%

구분1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
기초
341 
광역
69 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기초 341
83.2%
광역 69
 
16.8%

Length

2023-12-12T10:53:28.294408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:53:28.419567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기초 341
83.2%
광역 69
 
16.8%

구분2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
비수도권
257 
수도권
153 

Length

Max length4
Median length4
Mean length3.6268293
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비수도권 257
62.7%
수도권 153
37.3%

Length

2023-12-12T10:53:28.577614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:53:28.712511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비수도권 257
62.7%
수도권 153
37.3%

지역
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
경기도
105 
경상남도
39 
경상북도
39 
충청남도
36 
강원도
32 
Other values (12)
159 

Length

Max length7
Median length5
Mean length3.9560976
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 105
25.6%
경상남도 39
 
9.5%
경상북도 39
 
9.5%
충청남도 36
 
8.8%
강원도 32
 
7.8%
서울특별시 32
 
7.8%
충청북도 21
 
5.1%
전라남도 21
 
5.1%
전라북도 20
 
4.9%
인천광역시 16
 
3.9%
Other values (7) 49
12.0%

Length

2023-12-12T10:53:28.866654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 105
25.6%
경상남도 39
 
9.5%
경상북도 39
 
9.5%
충청남도 36
 
8.8%
강원도 32
 
7.8%
서울특별시 32
 
7.8%
충청북도 21
 
5.1%
전라남도 21
 
5.1%
전라북도 20
 
4.9%
인천광역시 16
 
3.9%
Other values (7) 49
12.0%

설립기관
Text

Distinct178
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-12T10:53:29.318752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.2926829
Min length3

Characters and Unicode

Total characters2990
Distinct characters126
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)16.6%

Sample

1st row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시
ValueCountFrequency (%)
경기도 105
 
14.0%
경상북도 39
 
5.2%
경상남도 39
 
5.2%
충청남도 35
 
4.7%
서울특별시 32
 
4.3%
강원도 32
 
4.3%
충청북도 21
 
2.8%
전라남도 21
 
2.8%
전라북도 20
 
2.7%
인천광역시 16
 
2.1%
Other values (162) 391
52.1%
2023-12-12T10:53:30.029860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
341
 
11.4%
도 321
 
10.7%
시 318
 
10.6%
경 191
 
6.4%
남 111
 
3.7%
기 106
 
3.5%
군 85
 
2.8%
북 84
 
2.8%
상 80
 
2.7%
광 76
 
2.5%
Other values (116) 1277
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2649
88.6%
Space Separator 341
 
11.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 321
 
12.1%
시 318
 
12.0%
경 191
 
7.2%
남 111
 
4.2%
기 106
 
4.0%
군 85
 
3.2%
북 84
 
3.2%
상 80
 
3.0%
광 76
 
2.9%
천 68
 
2.6%
Other values (115) 1209
45.6%
Space Separator
ValueCountFrequency (%)
341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2649
88.6%
Common 341
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 321
 
12.1%
시 318
 
12.0%
경 191
 
7.2%
남 111
 
4.2%
기 106
 
4.0%
군 85
 
3.2%
북 84
 
3.2%
상 80
 
3.0%
광 76
 
2.9%
천 68
 
2.6%
Other values (115) 1209
45.6%
Common
ValueCountFrequency (%)
341
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2649
88.6%
ASCII 341
 
11.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
341
100.0%
Hangul
ValueCountFrequency (%)
도 321
 
12.1%
시 318
 
12.0%
경 191
 
7.2%
남 111
 
4.2%
기 106
 
4.0%
군 85
 
3.2%
북 84
 
3.2%
상 80
 
3.0%
광 76
 
2.9%
천 68
 
2.6%
Other values (115) 1209
45.6%

운영구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
직영
252 
간접
158 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row직영
2nd row직영
3rd row간접
4th row간접
5th row간접

Common Values

ValueCountFrequency (%)
직영 252
61.5%
간접 158
38.5%

Length

2023-12-12T10:53:30.202753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:53:30.340662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직영 252
61.5%
간접 158
38.5%

공기업유형
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
상수도
122 
하수도
104 
지방공단
88 
기타공사
48 
공영개발
25 
Other values (3)
23 

Length

Max length6
Median length3
Mean length3.5585366
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row상수도
2nd row하수도
3rd row도시철도공사
4th row도시개발공사
5th row기타공사

Common Values

ValueCountFrequency (%)
상수도 122
29.8%
하수도 104
25.4%
지방공단 88
21.5%
기타공사 48
 
11.7%
공영개발 25
 
6.1%
도시개발공사 16
 
3.9%
도시철도공사 6
 
1.5%
자동차운송 1
 
0.2%

Length

2023-12-12T10:53:30.488859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:53:30.666579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도 122
29.8%
하수도 104
25.4%
지방공단 88
21.5%
기타공사 48
 
11.7%
공영개발 25
 
6.1%
도시개발공사 16
 
3.9%
도시철도공사 6
 
1.5%
자동차운송 1
 
0.2%

지방공기업명
Text

Distinct409
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-12T10:53:31.010975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length6
Mean length7.3390244
Min length4

Characters and Unicode

Total characters3009
Distinct characters171
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

Unique408 ?
Unique (%)99.5%

Sample

1st row서울특별시상수도
2nd row서울특별시하수도
3rd row서울교통공사
4th row서울주택도시공사
5th row서울에너지공사
ValueCountFrequency (%)
시설관리공단 3
 
0.7%
고성군상수도 2
 
0.5%
계룡시공영개발 1
 
0.2%
홍성군상수도 1
 
0.2%
서천군상수도 1
 
0.2%
부여군시설관리공단 1
 
0.2%
부여군하수도 1
 
0.2%
부여군상수도 1
 
0.2%
금산군하수도 1
 
0.2%
금산군상수도 1
 
0.2%
Other values (402) 402
96.9%
2023-12-12T10:53:31.528929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 359
 
11.9%
도 290
 
9.6%
수 236
 
7.8%
공 181
 
6.0%
상 127
 
4.2%
하 107
 
3.6%
관 93
 
3.1%
단 89
 
3.0%
리 84
 
2.8%
설 83
 
2.8%
Other values (161) 1360
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2980
99.0%
Close Punctuation 12
 
0.4%
Open Punctuation 12
 
0.4%
Space Separator 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 359
 
12.0%
도 290
 
9.7%
수 236
 
7.9%
공 181
 
6.1%
상 127
 
4.3%
하 107
 
3.6%
관 93
 
3.1%
단 89
 
3.0%
리 84
 
2.8%
설 83
 
2.8%
Other values (158) 1331
44.7%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2980
99.0%
Common 29
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 359
 
12.0%
도 290
 
9.7%
수 236
 
7.9%
공 181
 
6.1%
상 127
 
4.3%
하 107
 
3.6%
관 93
 
3.1%
단 89
 
3.0%
리 84
 
2.8%
설 83
 
2.8%
Other values (158) 1331
44.7%
Common
ValueCountFrequency (%)
) 12
41.4%
( 12
41.4%
5
17.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2980
99.0%
ASCII 29
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 359
 
12.0%
도 290
 
9.7%
수 236
 
7.9%
공 181
 
6.1%
상 127
 
4.3%
하 107
 
3.6%
관 93
 
3.1%
단 89
 
3.0%
리 84
 
2.8%
설 83
 
2.8%
Other values (158) 1331
44.7%
ASCII
ValueCountFrequency (%)
) 12
41.4%
( 12
41.4%
5
17.2%

Interactions

2023-12-12T10:53:27.530218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:53:31.677912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분1구분2지역운영구분공기업유형
연번1.0000.7110.9550.9640.5950.424
구분10.7111.0000.0000.7370.3810.725
구분20.9550.0001.0001.0000.2880.304
지역0.9640.7371.0001.0000.5180.545
운영구분0.5950.3810.2880.5181.0001.000
공기업유형0.4240.7250.3040.5451.0001.000
2023-12-12T10:53:31.831710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역운영구분공기업유형구분1구분2
지역1.0000.4590.2600.6660.981
운영구분0.4591.0000.9930.2490.186
공기업유형0.2600.9931.0000.5510.226
구분10.6660.2490.5511.0000.000
구분20.9810.1860.2260.0001.000
2023-12-12T10:53:31.960056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분1구분2지역운영구분공기업유형
연번1.0000.5500.8110.8300.4550.217
구분10.5501.0000.0000.6660.2490.551
구분20.8110.0001.0000.9810.1860.226
지역0.8300.6660.9811.0000.4590.260
운영구분0.4550.2490.1860.4591.0000.993
공기업유형0.2170.5510.2260.2600.9931.000

Missing values

2023-12-12T10:53:27.681673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:53:27.828636image/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

연번구분1구분2지역설립기관운영구분공기업유형지방공기업명
01광역수도권서울특별시서울특별시직영상수도서울특별시상수도
12광역수도권서울특별시서울특별시직영하수도서울특별시하수도
23광역수도권서울특별시서울특별시간접도시철도공사서울교통공사
34광역수도권서울특별시서울특별시간접도시개발공사서울주택도시공사
45광역수도권서울특별시서울특별시간접기타공사서울에너지공사
56광역수도권서울특별시서울특별시간접기타공사서울농수산식품공사
67광역수도권서울특별시서울특별시간접지방공단서울시설공단
78광역수도권서울특별시서울특별시간접지방공단서울물재생시설공단
89기초수도권서울특별시서울특별시 종로구간접지방공단종로구시설관리공단
910기초수도권서울특별시서울특별시 중구간접지방공단중구시설관리공단
연번구분1구분2지역설립기관운영구분공기업유형지방공기업명
400401기초비수도권경상남도경상남도 합천군간접지방공단합천군 시설관리공단
401402기초비수도권경상남도경상남도 밀양시간접지방공단밀양시시설관리공단
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