Overview

Dataset statistics

Number of variables6
Number of observations94
Missing cells100
Missing cells (%)17.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory50.4 B

Variable types

Numeric1
Text3
DateTime1
Categorical1

Dataset

Description경상남도 사천시의 특정토양 오염관리 대상에 관한 현황 정보(상호, 소재지, 완공일자 등)를 공공데이터로 제공합니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15005200

Alerts

비고 has constant value ""Constant
데이터기준일자 has constant value ""Constant
완공일자 has 7 (7.4%) missing valuesMissing
비고 has 93 (98.9%) missing valuesMissing
연번 has unique valuesUnique
상호 has unique valuesUnique
소재지(지번) has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:57:22.659882
Analysis finished2023-12-11 00:57:23.424101
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-11T09:57:23.518588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.65
Q124.25
median47.5
Q370.75
95-th percentile89.35
Maximum94
Range93
Interquartile range (IQR)46.5

Descriptive statistics

Standard deviation27.279418
Coefficient of variation (CV)0.57430354
Kurtosis-1.2
Mean47.5
Median Absolute Deviation (MAD)23.5
Skewness0
Sum4465
Variance744.16667
MonotonicityStrictly increasing
2023-12-11T09:57:23.673176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
61 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
Other values (84) 84
89.4%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%

상호
Text

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2023-12-11T09:57:23.960710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15.5
Mean length8.212766
Min length4

Characters and Unicode

Total characters772
Distinct characters167
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)100.0%

Sample

1st row용장군주유소
2nd row송포새한주유소
3rd row동백제3주유소
4th row믿음주유소
5th row삼일주유소
ValueCountFrequency (%)
주식회사 3
 
2.8%
주)에어로코텍 2
 
1.9%
합동주유소 1
 
0.9%
브리티쉬아메리칸토바코코리아(주 1
 
0.9%
현대모비스(주)진주부품사업소 1
 
0.9%
육군제3718-1부대 1
 
0.9%
주)월성주유소 1
 
0.9%
대동기어(주 1
 
0.9%
유니슨(주 1
 
0.9%
한국앰코스페셜티카톤즈(유 1
 
0.9%
Other values (93) 93
87.7%
2023-12-11T09:57:24.427300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 98
 
12.7%
유 72
 
9.3%
소 65
 
8.4%
) 34
 
4.4%
( 34
 
4.4%
사 21
 
2.7%
천 18
 
2.3%
12
 
1.6%
에 10
 
1.3%
남 10
 
1.3%
Other values (157) 398
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 666
86.3%
Close Punctuation 34
 
4.4%
Open Punctuation 34
 
4.4%
Decimal Number 17
 
2.2%
Space Separator 12
 
1.6%
Uppercase Letter 8
 
1.0%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 98
 
14.7%
유 72
 
10.8%
소 65
 
9.8%
사 21
 
3.2%
천 18
 
2.7%
에 10
 
1.5%
남 10
 
1.5%
포 10
 
1.5%
협 9
 
1.4%
대 9
 
1.4%
Other values (143) 344
51.7%
Decimal Number
ValueCountFrequency (%)
1 5
29.4%
3 4
23.5%
2 3
17.6%
8 2
 
11.8%
9 2
 
11.8%
7 1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
Y 2
25.0%
K 2
25.0%
M 2
25.0%
S 2
25.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 666
86.3%
Common 98
 
12.7%
Latin 8
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 98
 
14.7%
유 72
 
10.8%
소 65
 
9.8%
사 21
 
3.2%
천 18
 
2.7%
에 10
 
1.5%
남 10
 
1.5%
포 10
 
1.5%
협 9
 
1.4%
대 9
 
1.4%
Other values (143) 344
51.7%
Common
ValueCountFrequency (%)
) 34
34.7%
( 34
34.7%
12
 
12.2%
1 5
 
5.1%
3 4
 
4.1%
2 3
 
3.1%
8 2
 
2.0%
9 2
 
2.0%
7 1
 
1.0%
- 1
 
1.0%
Latin
ValueCountFrequency (%)
Y 2
25.0%
K 2
25.0%
M 2
25.0%
S 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 666
86.3%
ASCII 106
 
13.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 98
 
14.7%
유 72
 
10.8%
소 65
 
9.8%
사 21
 
3.2%
천 18
 
2.7%
에 10
 
1.5%
남 10
 
1.5%
포 10
 
1.5%
협 9
 
1.4%
대 9
 
1.4%
Other values (143) 344
51.7%
ASCII
ValueCountFrequency (%)
) 34
32.1%
( 34
32.1%
12
 
11.3%
1 5
 
4.7%
3 4
 
3.8%
2 3
 
2.8%
8 2
 
1.9%
9 2
 
1.9%
Y 2
 
1.9%
K 2
 
1.9%
Other values (4) 6
 
5.7%

소재지(지번)
Text

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2023-12-11T09:57:24.832194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length29
Mean length21.946809
Min length17

Characters and Unicode

Total characters2063
Distinct characters93
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

Unique94 ?
Unique (%)100.0%

Sample

1st row경상남도 사천시 용현면 금문리 49-10
2nd row경상남도 사천시 송포동 174-3
3rd row경상남도 사천시 대방동 162-1
4th row경상남도 사천시 벌리동 25-20
5th row경상남도 사천시 좌룡동 408-2
ValueCountFrequency (%)
경상남도 94
20.8%
사천시 94
20.8%
사남면 18
 
4.0%
사천읍 13
 
2.9%
축동면 9
 
2.0%
송포동 7
 
1.5%
곤양면 6
 
1.3%
유천리 6
 
1.3%
곤명면 6
 
1.3%
용현면 5
 
1.1%
Other values (152) 195
43.0%
2023-12-11T09:57:25.370255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
453
22.0%
사 130
 
6.3%
천 117
 
5.7%
남 116
 
5.6%
도 95
 
4.6%
경 94
 
4.6%
시 94
 
4.6%
상 94
 
4.6%
1 78
 
3.8%
- 76
 
3.7%
Other values (83) 716
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1177
57.1%
Space Separator 453
 
22.0%
Decimal Number 357
 
17.3%
Dash Punctuation 76
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 130
11.0%
천 117
9.9%
남 116
9.9%
도 95
 
8.1%
경 94
 
8.0%
시 94
 
8.0%
상 94
 
8.0%
리 65
 
5.5%
면 50
 
4.2%
동 46
 
3.9%
Other values (71) 276
23.4%
Decimal Number
ValueCountFrequency (%)
1 78
21.8%
3 45
12.6%
2 39
10.9%
4 38
10.6%
6 31
 
8.7%
5 30
 
8.4%
8 27
 
7.6%
9 25
 
7.0%
7 23
 
6.4%
0 21
 
5.9%
Space Separator
ValueCountFrequency (%)
453
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1177
57.1%
Common 886
42.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 130
11.0%
천 117
9.9%
남 116
9.9%
도 95
 
8.1%
경 94
 
8.0%
시 94
 
8.0%
상 94
 
8.0%
리 65
 
5.5%
면 50
 
4.2%
동 46
 
3.9%
Other values (71) 276
23.4%
Common
ValueCountFrequency (%)
453
51.1%
1 78
 
8.8%
- 76
 
8.6%
3 45
 
5.1%
2 39
 
4.4%
4 38
 
4.3%
6 31
 
3.5%
5 30
 
3.4%
8 27
 
3.0%
9 25
 
2.8%
Other values (2) 44
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1177
57.1%
ASCII 886
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
453
51.1%
1 78
 
8.8%
- 76
 
8.6%
3 45
 
5.1%
2 39
 
4.4%
4 38
 
4.3%
6 31
 
3.5%
5 30
 
3.4%
8 27
 
3.0%
9 25
 
2.8%
Other values (2) 44
 
5.0%
Hangul
ValueCountFrequency (%)
사 130
11.0%
천 117
9.9%
남 116
9.9%
도 95
 
8.1%
경 94
 
8.0%
시 94
 
8.0%
상 94
 
8.0%
리 65
 
5.5%
면 50
 
4.2%
동 46
 
3.9%
Other values (71) 276
23.4%

완공일자
Date

MISSING 

Distinct86
Distinct (%)98.9%
Missing7
Missing (%)7.4%
Memory size884.0 B
Minimum1988-01-16 00:00:00
Maximum2022-06-27 00:00:00
2023-12-11T09:57:25.546728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:25.715068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing93
Missing (%)98.9%
Memory size884.0 B
2023-12-11T09:57:25.812477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
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

Unique1 ?
Unique (%)100.0%

Sample

1st row휴업
ValueCountFrequency (%)
휴업 1
100.0%
2023-12-11T09:57:26.023239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
휴 1
50.0%
업 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
휴 1
50.0%
업 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
휴 1
50.0%
업 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
휴 1
50.0%
업 1
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
2023-06-01
94 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-01
2nd row2023-06-01
3rd row2023-06-01
4th row2023-06-01
5th row2023-06-01

Common Values

ValueCountFrequency (%)
2023-06-01 94
100.0%

Length

2023-12-11T09:57:26.164434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:57:26.269517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-01 94
100.0%

Interactions

2023-12-11T09:57:23.019914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:57:26.336078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호소재지(지번)완공일자
연번1.0001.0001.0000.935
상호1.0001.0001.0001.000
소재지(지번)1.0001.0001.0001.000
완공일자0.9351.0001.0001.000

Missing values

2023-12-11T09:57:23.139561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:57:23.261410image/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.
2023-12-11T09:57:23.367501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번상호소재지(지번)완공일자비고데이터기준일자
01용장군주유소경상남도 사천시 용현면 금문리 49-101992-10-26<NA>2023-06-01
12송포새한주유소경상남도 사천시 송포동 174-31995-05-26<NA>2023-06-01
23동백제3주유소경상남도 사천시 대방동 162-11995-03-25<NA>2023-06-01
34믿음주유소경상남도 사천시 벌리동 25-201994-08-26<NA>2023-06-01
45삼일주유소경상남도 사천시 좌룡동 408-21993-12-20<NA>2023-06-01
56남일주유소경상남도 사천시 향촌동 562-31994-06-16휴업2023-06-01
67노랑주유소경상남도 사천시 서포면 구랑리 811-10 노랑주유소1992-12-19<NA>2023-06-01
78용머리주유소경상남도 사천시 용현면 송지리 836-61994-10-15<NA>2023-06-01
89목화주유소경상남도 사천시 용현면 주문리 4-21991-04-16<NA>2023-06-01
910경서대로주유소경상남도 사천시 곤명면 신흥리 4-31993-08-17<NA>2023-06-01
연번상호소재지(지번)완공일자비고데이터기준일자
8485아주주유소경상남도 사천시 송포동 15-32004-02-10<NA>2023-06-01
8586SK포유주유소경상남도 사천시 선구동 314-22007-09-06<NA>2023-06-01
8687남척주유소경상남도 사천시 송포동 430-11990-05-26<NA>2023-06-01
8788사천농협주유소경상남도 사천시 사천읍 정의리 413-102006-12-04<NA>2023-06-01
8889삼천포수협늑도분급소경상남도 사천시 늑도동 29-12009-11-27<NA>2023-06-01
8990청양주유소경상남도 사천시 향촌동 996-81994-08-01<NA>2023-06-01
9091한국전기통신공사 사천전화국경상남도 사천시 사천읍 수석리 280-101990-07-27<NA>2023-06-01
9192사천신흥주유소경상남도 사천시 용현면 송지리 12622010-11-11<NA>2023-06-01
9293동금석유경상남도 사천시 향촌동 925-162005-06-07<NA>2023-06-01
9394남포주유소경상남도 사천시 이금동 99-12004-10-14<NA>2023-06-01