Overview

Dataset statistics

Number of variables9
Number of observations116
Missing cells2
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory76.1 B

Variable types

Categorical2
Text4
Numeric3

Alerts

사업장소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 사업장소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 사업장소재지우편번호 and 2 other fieldsHigh correlation
사업장전화번호 has 2 (1.7%) missing valuesMissing
대상기관명 has unique valuesUnique

Reproduction

Analysis started2024-03-12 23:22:06.629263
Analysis finished2024-03-12 23:22:07.926910
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
안산시
26 
안양시
25 
수원시
10 
군포시
6 
의정부시
6 
Other values (17)
43 

Length

Max length4
Median length3
Mean length3.0775862
Min length3

Unique

Unique6 ?
Unique (%)5.2%

Sample

1st row고양시
2nd row과천시
3rd row광명시
4th row군포시
5th row군포시

Common Values

ValueCountFrequency (%)
안산시 26
22.4%
안양시 25
21.6%
수원시 10
 
8.6%
군포시 6
 
5.2%
의정부시 6
 
5.2%
부천시 5
 
4.3%
성남시 5
 
4.3%
시흥시 5
 
4.3%
화성시 4
 
3.4%
김포시 4
 
3.4%
Other values (12) 20
17.2%

Length

2024-03-13T08:22:07.974534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 26
22.4%
안양시 25
21.6%
수원시 10
 
8.6%
군포시 6
 
5.2%
의정부시 6
 
5.2%
부천시 5
 
4.3%
성남시 5
 
4.3%
시흥시 5
 
4.3%
화성시 4
 
3.4%
김포시 4
 
3.4%
Other values (12) 20
17.2%

기관분류명
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
대기측정대행
90 
수질측정대행
26 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대기측정대행
2nd row수질측정대행
3rd row대기측정대행
4th row수질측정대행
5th row대기측정대행

Common Values

ValueCountFrequency (%)
대기측정대행 90
77.6%
수질측정대행 26
 
22.4%

Length

2024-03-13T08:22:08.074992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:22:08.154527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대기측정대행 90
77.6%
수질측정대행 26
 
22.4%

대상기관명
Text

UNIQUE 

Distinct116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-13T08:22:08.316443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.4827586
Min length2

Characters and Unicode

Total characters984
Distinct characters175
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)100.0%

Sample

1st row새롬환경기술
2nd row한국수자원공사 한강유역본부
3rd row(주)한솔환경산업
4th row워터스생활환경연구소
5th row자연과환경(주)
ValueCountFrequency (%)
주식회사 11
 
8.4%
여수사업소 1
 
0.8%
우현환경컨설팅(주 1
 
0.8%
워트랩생활환경연구원 1
 
0.8%
이산 1
 
0.8%
인수환경 1
 
0.8%
대화환경 1
 
0.8%
송화 1
 
0.8%
주)대현환경 1
 
0.8%
주)신화환경연구원 1
 
0.8%
Other values (111) 111
84.7%
2024-03-13T08:22:08.617026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 83
 
8.4%
) 70
 
7.1%
( 70
 
7.1%
경 66
 
6.7%
환 64
 
6.5%
이 35
 
3.6%
에 19
 
1.9%
기 17
 
1.7%
술 16
 
1.6%
사 16
 
1.6%
Other values (165) 528
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 808
82.1%
Close Punctuation 70
 
7.1%
Open Punctuation 70
 
7.1%
Uppercase Letter 19
 
1.9%
Space Separator 15
 
1.5%
Other Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 83
 
10.3%
경 66
 
8.2%
환 64
 
7.9%
이 35
 
4.3%
에 19
 
2.4%
기 17
 
2.1%
술 16
 
2.0%
사 16
 
2.0%
스 15
 
1.9%
연 14
 
1.7%
Other values (149) 463
57.3%
Uppercase Letter
ValueCountFrequency (%)
E 3
15.8%
A 2
10.5%
H 2
10.5%
K 2
10.5%
I 2
10.5%
T 2
10.5%
P 1
 
5.3%
O 1
 
5.3%
C 1
 
5.3%
S 1
 
5.3%
Other values (2) 2
10.5%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 810
82.3%
Common 155
 
15.8%
Latin 19
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 83
 
10.2%
경 66
 
8.1%
환 64
 
7.9%
이 35
 
4.3%
에 19
 
2.3%
기 17
 
2.1%
술 16
 
2.0%
사 16
 
2.0%
스 15
 
1.9%
연 14
 
1.7%
Other values (150) 465
57.4%
Latin
ValueCountFrequency (%)
E 3
15.8%
A 2
10.5%
H 2
10.5%
K 2
10.5%
I 2
10.5%
T 2
10.5%
P 1
 
5.3%
O 1
 
5.3%
C 1
 
5.3%
S 1
 
5.3%
Other values (2) 2
10.5%
Common
ValueCountFrequency (%)
) 70
45.2%
( 70
45.2%
15
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 808
82.1%
ASCII 174
 
17.7%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 83
 
10.3%
경 66
 
8.2%
환 64
 
7.9%
이 35
 
4.3%
에 19
 
2.4%
기 17
 
2.1%
술 16
 
2.0%
사 16
 
2.0%
스 15
 
1.9%
연 14
 
1.7%
Other values (149) 463
57.3%
ASCII
ValueCountFrequency (%)
) 70
40.2%
( 70
40.2%
15
 
8.6%
E 3
 
1.7%
A 2
 
1.1%
H 2
 
1.1%
K 2
 
1.1%
I 2
 
1.1%
T 2
 
1.1%
P 1
 
0.6%
Other values (5) 5
 
2.9%
None
ValueCountFrequency (%)
㈜ 2
100.0%

사업장전화번호
Text

MISSING 

Distinct114
Distinct (%)100.0%
Missing2
Missing (%)1.7%
Memory size1.0 KiB
2024-03-13T08:22:08.821145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.04386
Min length9

Characters and Unicode

Total characters1373
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)100.0%

Sample

1st row031-938-0641
2nd row02-2150-0382
3rd row02-2615-4483
4th row031-687-5970
5th row031-427-5285
ValueCountFrequency (%)
031-239-6625 1
 
0.9%
070-8890-0048 1
 
0.9%
031-360-0808 1
 
0.9%
031-420-5526 1
 
0.9%
1588-7377 1
 
0.9%
031-389-0114 1
 
0.9%
031-476-9402 1
 
0.9%
031-346-1991 1
 
0.9%
031-420-5505 1
 
0.9%
031-389-6055 1
 
0.9%
Other values (104) 104
91.2%
2024-03-13T08:22:09.126606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 226
16.5%
0 217
15.8%
3 195
14.2%
1 178
13.0%
4 102
7.4%
2 96
7.0%
8 94
6.8%
5 75
 
5.5%
6 65
 
4.7%
9 63
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1147
83.5%
Dash Punctuation 226
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 217
18.9%
3 195
17.0%
1 178
15.5%
4 102
8.9%
2 96
8.4%
8 94
8.2%
5 75
 
6.5%
6 65
 
5.7%
9 63
 
5.5%
7 62
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1373
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 226
16.5%
0 217
15.8%
3 195
14.2%
1 178
13.0%
4 102
7.4%
2 96
7.0%
8 94
6.8%
5 75
 
5.5%
6 65
 
4.7%
9 63
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 226
16.5%
0 217
15.8%
3 195
14.2%
1 178
13.0%
4 102
7.4%
2 96
7.0%
8 94
6.8%
5 75
 
5.5%
6 65
 
4.7%
9 63
 
4.6%

사업장소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14724.138
Minimum10047
Maximum18392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T08:22:09.249805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10047
5-th percentile11379.5
Q114057
median15086
Q315807.25
95-th percentile17962.25
Maximum18392
Range8345
Interquartile range (IQR)1750.25

Descriptive statistics

Standard deviation1881.404
Coefficient of variation (CV)0.12777685
Kurtosis0.26555936
Mean14724.138
Median Absolute Deviation (MAD)1023.5
Skewness-0.48312644
Sum1708000
Variance3539681.1
MonotonicityNot monotonic
2024-03-13T08:22:09.367180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14057 4
 
3.4%
15458 4
 
3.4%
14084 3
 
2.6%
15455 3
 
2.6%
15426 3
 
2.6%
14059 3
 
2.6%
15434 3
 
2.6%
16690 2
 
1.7%
14118 2
 
1.7%
16006 2
 
1.7%
Other values (76) 87
75.0%
ValueCountFrequency (%)
10047 1
0.9%
10048 1
0.9%
10049 1
0.9%
10071 1
0.9%
10442 1
0.9%
11168 1
0.9%
11450 1
0.9%
11610 2
1.7%
11625 1
0.9%
11653 1
0.9%
ValueCountFrequency (%)
18392 1
0.9%
18343 1
0.9%
18284 1
0.9%
18244 1
0.9%
18105 1
0.9%
18104 1
0.9%
17915 1
0.9%
17590 1
0.9%
17558 1
0.9%
17015 1
0.9%

사업장소재지도로명주소
Text

Distinct101
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-13T08:22:09.531759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length19.931034
Min length14

Characters and Unicode

Total characters2312
Distinct characters138
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

Unique89 ?
Unique (%)76.7%

Sample

1st row경기도 고양시 일산동구 일산로 138
2nd row경기도 과천시 교육원로 11
3rd row경기도 광명시 안양천로502번길 15
4th row경기도 군포시 공단로140번길 46
5th row경기도 군포시 고산로 166
ValueCountFrequency (%)
경기도 116
 
21.5%
안산시 26
 
4.8%
안양시 25
 
4.6%
단원구 24
 
4.4%
동안구 20
 
3.7%
수원시 10
 
1.9%
25 6
 
1.1%
의정부시 6
 
1.1%
군포시 6
 
1.1%
광덕4로 6
 
1.1%
Other values (201) 295
54.6%
2024-03-13T08:22:09.806641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
424
18.3%
시 125
 
5.4%
기 122
 
5.3%
도 121
 
5.2%
경 119
 
5.1%
로 110
 
4.8%
1 103
 
4.5%
안 89
 
3.8%
구 71
 
3.1%
2 61
 
2.6%
Other values (128) 967
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1433
62.0%
Decimal Number 437
 
18.9%
Space Separator 424
 
18.3%
Dash Punctuation 18
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 125
 
8.7%
기 122
 
8.5%
도 121
 
8.4%
경 119
 
8.3%
로 110
 
7.7%
안 89
 
6.2%
구 71
 
5.0%
원 49
 
3.4%
길 47
 
3.3%
번 41
 
2.9%
Other values (116) 539
37.6%
Decimal Number
ValueCountFrequency (%)
1 103
23.6%
2 61
14.0%
5 53
12.1%
4 43
9.8%
3 43
9.8%
6 42
9.6%
0 26
 
5.9%
8 25
 
5.7%
7 24
 
5.5%
9 17
 
3.9%
Space Separator
ValueCountFrequency (%)
424
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1433
62.0%
Common 879
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 125
 
8.7%
기 122
 
8.5%
도 121
 
8.4%
경 119
 
8.3%
로 110
 
7.7%
안 89
 
6.2%
구 71
 
5.0%
원 49
 
3.4%
길 47
 
3.3%
번 41
 
2.9%
Other values (116) 539
37.6%
Common
ValueCountFrequency (%)
424
48.2%
1 103
 
11.7%
2 61
 
6.9%
5 53
 
6.0%
4 43
 
4.9%
3 43
 
4.9%
6 42
 
4.8%
0 26
 
3.0%
8 25
 
2.8%
7 24
 
2.7%
Other values (2) 35
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1433
62.0%
ASCII 879
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
424
48.2%
1 103
 
11.7%
2 61
 
6.9%
5 53
 
6.0%
4 43
 
4.9%
3 43
 
4.9%
6 42
 
4.8%
0 26
 
3.0%
8 25
 
2.8%
7 24
 
2.7%
Other values (2) 35
 
4.0%
Hangul
ValueCountFrequency (%)
시 125
 
8.7%
기 122
 
8.5%
도 121
 
8.4%
경 119
 
8.3%
로 110
 
7.7%
안 89
 
6.2%
구 71
 
5.0%
원 49
 
3.4%
길 47
 
3.3%
번 41
 
2.9%
Other values (116) 539
37.6%

사업장소재지지번주소
Text

Distinct101
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-13T08:22:10.062120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length21.517241
Min length17

Characters and Unicode

Total characters2496
Distinct characters111
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

Unique89 ?
Unique (%)76.7%

Sample

1st row경기도 고양시 일산동구 백석동 1141-1번지
2nd row경기도 과천시 갈현동 649-1번지
3rd row경기도 광명시 철산동 626-3번지
4th row경기도 군포시 당정동 181-42번지
5th row경기도 군포시 당정동 522번지
ValueCountFrequency (%)
경기도 116
21.5%
안산시 26
 
4.8%
안양시 25
 
4.6%
단원구 24
 
4.4%
동안구 20
 
3.7%
고잔동 10
 
1.9%
수원시 10
 
1.9%
관양동 9
 
1.7%
호계동 7
 
1.3%
의정부시 6
 
1.1%
Other values (182) 287
53.1%
2024-03-13T08:22:10.412872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
424
17.0%
동 131
 
5.2%
시 125
 
5.0%
지 121
 
4.8%
도 121
 
4.8%
기 119
 
4.8%
번 116
 
4.6%
경 116
 
4.6%
1 92
 
3.7%
안 85
 
3.4%
Other values (101) 1046
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1516
60.7%
Decimal Number 473
 
19.0%
Space Separator 424
 
17.0%
Dash Punctuation 83
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 131
 
8.6%
시 125
 
8.2%
지 121
 
8.0%
도 121
 
8.0%
기 119
 
7.8%
번 116
 
7.7%
경 116
 
7.7%
안 85
 
5.6%
구 71
 
4.7%
양 49
 
3.2%
Other values (89) 462
30.5%
Decimal Number
ValueCountFrequency (%)
1 92
19.5%
2 63
13.3%
4 53
11.2%
7 45
9.5%
3 43
9.1%
5 41
8.7%
8 38
8.0%
6 36
 
7.6%
9 36
 
7.6%
0 26
 
5.5%
Space Separator
ValueCountFrequency (%)
424
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1516
60.7%
Common 980
39.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 131
 
8.6%
시 125
 
8.2%
지 121
 
8.0%
도 121
 
8.0%
기 119
 
7.8%
번 116
 
7.7%
경 116
 
7.7%
안 85
 
5.6%
구 71
 
4.7%
양 49
 
3.2%
Other values (89) 462
30.5%
Common
ValueCountFrequency (%)
424
43.3%
1 92
 
9.4%
- 83
 
8.5%
2 63
 
6.4%
4 53
 
5.4%
7 45
 
4.6%
3 43
 
4.4%
5 41
 
4.2%
8 38
 
3.9%
6 36
 
3.7%
Other values (2) 62
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1516
60.7%
ASCII 980
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
424
43.3%
1 92
 
9.4%
- 83
 
8.5%
2 63
 
6.4%
4 53
 
5.4%
7 45
 
4.6%
3 43
 
4.4%
5 41
 
4.2%
8 38
 
3.9%
6 36
 
3.7%
Other values (2) 62
 
6.3%
Hangul
ValueCountFrequency (%)
동 131
 
8.6%
시 125
 
8.2%
지 121
 
8.0%
도 121
 
8.0%
기 119
 
7.8%
번 116
 
7.7%
경 116
 
7.7%
안 85
 
5.6%
구 71
 
4.7%
양 49
 
3.2%
Other values (89) 462
30.5%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.390222
Minimum36.987609
Maximum37.842704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T08:22:10.530373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.987609
5-th percentile37.215616
Q137.305801
median37.372609
Q337.425178
95-th percentile37.737646
Maximum37.842704
Range0.85509508
Interquartile range (IQR)0.11937688

Descriptive statistics

Standard deviation0.15605474
Coefficient of variation (CV)0.0041736778
Kurtosis1.4066197
Mean37.390222
Median Absolute Deviation (MAD)0.063153675
Skewness0.73143124
Sum4337.2658
Variance0.024353082
MonotonicityNot monotonic
2024-03-13T08:22:10.635335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.39261891 3
 
2.6%
37.39162226 3
 
2.6%
37.32331226 3
 
2.6%
37.24495264 2
 
1.7%
37.35025782 2
 
1.7%
37.40153103 2
 
1.7%
37.51106464 2
 
1.7%
37.40179652 2
 
1.7%
37.30105039 2
 
1.7%
37.3151201 2
 
1.7%
Other values (92) 93
80.2%
ValueCountFrequency (%)
36.9876094 1
0.9%
36.99317945 1
0.9%
37.00641561 1
0.9%
37.15153299 1
0.9%
37.18959056 1
0.9%
37.21502093 1
0.9%
37.21581395 1
0.9%
37.22328538 1
0.9%
37.24271995 1
0.9%
37.24495264 2
1.7%
ValueCountFrequency (%)
37.84270448 1
0.9%
37.82767088 1
0.9%
37.76012563 1
0.9%
37.75968395 1
0.9%
37.75143172 1
0.9%
37.75087348 1
0.9%
37.73323723 1
0.9%
37.73298746 1
0.9%
37.65078501 1
0.9%
37.64403651 1
0.9%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93387
Minimum126.5943
Maximum127.27844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T08:22:10.739604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5943
5-th percentile126.7088
Q1126.81526
median126.9532
Q3127.03758
95-th percentile127.17632
Maximum127.27844
Range0.6841405
Interquartile range (IQR)0.22231858

Descriptive statistics

Standard deviation0.14608647
Coefficient of variation (CV)0.0011508865
Kurtosis-0.45432032
Mean126.93387
Median Absolute Deviation (MAD)0.11192755
Skewness-0.0017396195
Sum14724.329
Variance0.021341256
MonotonicityNot monotonic
2024-03-13T08:22:10.867806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9388641 3
 
2.6%
126.9730112 3
 
2.6%
126.7851124 3
 
2.6%
127.0599959 2
 
1.7%
126.7091866 2
 
1.7%
126.967575 2
 
1.7%
126.7790032 2
 
1.7%
126.9910176 2
 
1.7%
126.7869593 2
 
1.7%
126.8252535 2
 
1.7%
Other values (92) 93
80.2%
ValueCountFrequency (%)
126.5942983 1
0.9%
126.6181342 1
0.9%
126.6233957 1
0.9%
126.6270068 1
0.9%
126.6982958 1
0.9%
126.7076386 1
0.9%
126.7091866 2
1.7%
126.7411857 1
0.9%
126.7415631 1
0.9%
126.7419266 1
0.9%
ValueCountFrequency (%)
127.2784388 1
0.9%
127.2107747 1
0.9%
127.1956873 1
0.9%
127.1936926 1
0.9%
127.182717 1
0.9%
127.1778843 1
0.9%
127.1758006 1
0.9%
127.1751478 1
0.9%
127.1742138 1
0.9%
127.1698572 1
0.9%

Interactions

2024-03-13T08:22:07.361051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:06.951256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:07.161087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:07.427486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:07.008904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:07.233421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:07.494556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:07.079965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:07.293545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:22:10.949887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명기관분류명사업장소재지우편번호WGS84위도WGS84경도
시군명1.0000.4490.9920.9710.959
기관분류명0.4491.0000.3690.2720.275
사업장소재지우편번호0.9920.3691.0000.9510.933
WGS84위도0.9710.2720.9511.0000.888
WGS84경도0.9590.2750.9330.8881.000
2024-03-13T08:22:11.028652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관분류명시군명
기관분류명1.0000.321
시군명0.3211.000
2024-03-13T08:22:11.095132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장소재지우편번호WGS84위도WGS84경도시군명기관분류명
사업장소재지우편번호1.000-0.890-0.0310.8970.277
WGS84위도-0.8901.0000.0690.7950.200
WGS84경도-0.0310.0691.0000.7460.202
시군명0.8970.7950.7461.0000.321
기관분류명0.2770.2000.2020.3211.000

Missing values

2024-03-13T08:22:07.589928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:22:07.886411image/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

시군명기관분류명대상기관명사업장전화번호사업장소재지우편번호사업장소재지도로명주소사업장소재지지번주소WGS84위도WGS84경도
0고양시대기측정대행새롬환경기술031-938-064110442경기도 고양시 일산동구 일산로 138경기도 고양시 일산동구 백석동 1141-1번지37.650785126.794625
1과천시수질측정대행한국수자원공사 한강유역본부02-2150-038213841경기도 과천시 교육원로 11경기도 과천시 갈현동 649-1번지37.422884126.988139
2광명시대기측정대행(주)한솔환경산업02-2615-448314223경기도 광명시 안양천로502번길 15경기도 광명시 철산동 626-3번지37.492766126.8685
3군포시수질측정대행워터스생활환경연구소<NA>15847경기도 군포시 공단로140번길 46경기도 군포시 당정동 181-42번지37.356999126.954723
4군포시대기측정대행자연과환경(주)031-687-597015850경기도 군포시 고산로 166경기도 군포시 당정동 522번지37.348425126.952516
5군포시대기측정대행(주)비앤지기술연구소031-427-528515807경기도 군포시 엘에스로182번길 3-15경기도 군포시 산본동 18-14번지37.376791126.942619
6군포시대기측정대행(주)유일환경이앤씨031-477-841115808경기도 군포시 엘에스로 175경기도 군포시 산본동 1026-30번지37.375244126.943398
7군포시대기측정대행(주)엔솔파트너스031-8086-723615850경기도 군포시 고산로148번길 17경기도 군포시 당정동 1045번지37.349665126.953501
8군포시대기측정대행(주)한국유로핀즈 분석서비스031-361-772215849경기도 군포시 산본로101번길 13경기도 군포시 당정동 352-18번지37.352897126.956661
9김포시대기측정대행크린환경 주식회사031-986-602710071경기도 김포시 김포한강10로133번길 127경기도 김포시 구래동 6871-7번지37.644037126.618134
시군명기관분류명대상기관명사업장전화번호사업장소재지우편번호사업장소재지도로명주소사업장소재지지번주소WGS84위도WGS84경도
106의정부시대기측정대행(주)에코인사이트031-928-479511653경기도 의정부시 경의로 69경기도 의정부시 의정부동 564-1번지37.733237127.043175
107의정부시대기측정대행가온환경과학(주)031-836-927111757경기도 의정부시 부용로95번길 10경기도 의정부시 금오동 471-3번지37.751432127.068638
108평택시대기측정대행(주)이앤031-651-490217915경기도 평택시 조개터로2번길 91경기도 평택시 합정동 931-3번지36.987609127.104262
109포천시수질측정대행(주)에버그린탑02-866-420411168경기도 포천시 가산면 시우동6길 15경기도 포천시 가산면 가산리 305-2번지37.827671127.174214
110하남시수질측정대행(재)경기환경과학연구원031-699-013112982경기도 하남시 하남대로 947경기도 하남시 풍산동 618번지37.545867127.193693
111하남시대기측정대행미령환경개발(주)02-422-646212918경기도 하남시 미사강변서로 25경기도 하남시 풍산동 489번지37.552652127.182717
112화성시수질측정대행주흥환경(주)031-236-121518392경기도 화성시 병점동로164번길 39경기도 화성시 진안동 859-1번지37.215814127.039596
113화성시대기측정대행청담환경기술031-8055-051318244경기도 화성시 수노을2로 7경기도 화성시 새솔동 76-2번지37.285505126.816617
114화성시대기측정대행풀빛환경031-233-666018343경기도 화성시 융건로 21경기도 화성시 기안동 457-542번지37.215021126.982853
115화성시대기측정대행KD환경031-8011-230518284경기도 화성시 비봉면 현대기아로830번길 25-22경기도 화성시 비봉면 양노리 715번지37.223285126.859043