Overview

Dataset statistics

Number of variables11
Number of observations328
Missing cells12
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.6 KiB
Average record size in memory92.4 B

Variable types

Numeric4
Text3
Categorical3
DateTime1

Dataset

Description경상남도 김해시 토양오염 관리 시설 현황(신고번호, 사업장명, 업종, 지번주소, 도로명주소, 완공일자, 토양오염물질저장용량 등)에 관한 데이터를 제공합니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15013379

Alerts

토양오염도검사해당여부(누출검사) is highly overall correlated with 토양오염도검사해당여부(오염도검사)High correlation
토양오염도검사해당여부(오염도검사) is highly overall correlated with 토양오염도검사해당여부(누출검사)High correlation
토양오염도검사해당여부(오염도검사) is highly imbalanced (52.9%)Imbalance
지번주소 has 12 (3.7%) missing valuesMissing

Reproduction

Analysis started2024-03-13 00:08:11.773240
Analysis finished2024-03-13 00:08:13.499605
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

신고번호
Real number (ℝ)

Distinct327
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.39329
Minimum1
Maximum488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-13T09:08:13.555817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32.35
Q1172.5
median290
Q3392.25
95-th percentile469.65
Maximum488
Range487
Interquartile range (IQR)219.75

Descriptive statistics

Standard deviation136.53264
Coefficient of variation (CV)0.49397959
Kurtosis-0.93021125
Mean276.39329
Median Absolute Deviation (MAD)109
Skewness-0.31986904
Sum90657
Variance18641.163
MonotonicityNot monotonic
2024-03-13T09:08:13.662629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
436 2
 
0.6%
1 1
 
0.3%
356 1
 
0.3%
363 1
 
0.3%
362 1
 
0.3%
361 1
 
0.3%
360 1
 
0.3%
359 1
 
0.3%
358 1
 
0.3%
357 1
 
0.3%
Other values (317) 317
96.6%
ValueCountFrequency (%)
1 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
10 1
0.3%
13 1
0.3%
14 1
0.3%
ValueCountFrequency (%)
488 1
0.3%
487 1
0.3%
486 1
0.3%
485 1
0.3%
484 1
0.3%
483 1
0.3%
482 1
0.3%
481 1
0.3%
480 1
0.3%
479 1
0.3%

사업장명
Text

Distinct321
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-13T09:08:13.852940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length7.0670732
Min length2

Characters and Unicode

Total characters2318
Distinct characters308
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)95.7%

Sample

1st row영진주유소㈜유테크 김해지점
2nd row예진주유소
3rd row고속도로관리공단 진영(상)주유소
4th row고속도로관리공단 진영(하)주유소
5th row상동농협직영주유소
ValueCountFrequency (%)
김해지점 4
 
1.1%
㈜에스엘선린 3
 
0.8%
㈜동남 2
 
0.5%
진영휴게소 2
 
0.5%
sk네트웍스㈜ 2
 
0.5%
대양석유㈜ 2
 
0.5%
㈜경진이엔에스 2
 
0.5%
셀프주유소 2
 
0.5%
㈜에스엔디유통 2
 
0.5%
대성주유소 2
 
0.5%
Other values (349) 356
93.9%
2024-03-13T09:08:14.364856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 220
 
9.5%
주 177
 
7.6%
소 173
 
7.5%
㈜ 137
 
5.9%
57
 
2.5%
대 43
 
1.9%
지 38
 
1.6%
에 36
 
1.6%
스 35
 
1.5%
동 35
 
1.5%
Other values (298) 1367
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2002
86.4%
Other Symbol 137
 
5.9%
Space Separator 57
 
2.5%
Uppercase Letter 51
 
2.2%
Decimal Number 29
 
1.3%
Close Punctuation 17
 
0.7%
Open Punctuation 17
 
0.7%
Other Punctuation 6
 
0.3%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 220
 
11.0%
주 177
 
8.8%
소 173
 
8.6%
대 43
 
2.1%
지 38
 
1.9%
에 36
 
1.8%
스 35
 
1.7%
동 35
 
1.7%
해 31
 
1.5%
성 30
 
1.5%
Other values (266) 1184
59.1%
Uppercase Letter
ValueCountFrequency (%)
C 12
23.5%
I 8
15.7%
S 7
13.7%
K 5
9.8%
O 3
 
5.9%
E 3
 
5.9%
L 2
 
3.9%
R 2
 
3.9%
N 1
 
2.0%
Y 1
 
2.0%
Other values (7) 7
13.7%
Decimal Number
ValueCountFrequency (%)
2 11
37.9%
1 10
34.5%
9 3
 
10.3%
6 2
 
6.9%
3 1
 
3.4%
7 1
 
3.4%
5 1
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 3
50.0%
, 2
33.3%
& 1
 
16.7%
Other Symbol
ValueCountFrequency (%)
㈜ 137
100.0%
Space Separator
ValueCountFrequency (%)
57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2139
92.3%
Common 128
 
5.5%
Latin 51
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 220
 
10.3%
주 177
 
8.3%
소 173
 
8.1%
㈜ 137
 
6.4%
대 43
 
2.0%
지 38
 
1.8%
에 36
 
1.7%
스 35
 
1.6%
동 35
 
1.6%
해 31
 
1.4%
Other values (267) 1214
56.8%
Latin
ValueCountFrequency (%)
C 12
23.5%
I 8
15.7%
S 7
13.7%
K 5
9.8%
O 3
 
5.9%
E 3
 
5.9%
L 2
 
3.9%
R 2
 
3.9%
N 1
 
2.0%
Y 1
 
2.0%
Other values (7) 7
13.7%
Common
ValueCountFrequency (%)
57
44.5%
) 17
 
13.3%
( 17
 
13.3%
2 11
 
8.6%
1 10
 
7.8%
. 3
 
2.3%
9 3
 
2.3%
- 2
 
1.6%
, 2
 
1.6%
6 2
 
1.6%
Other values (4) 4
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2002
86.4%
ASCII 179
 
7.7%
None 137
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 220
 
11.0%
주 177
 
8.8%
소 173
 
8.6%
대 43
 
2.1%
지 38
 
1.9%
에 36
 
1.8%
스 35
 
1.7%
동 35
 
1.7%
해 31
 
1.5%
성 30
 
1.5%
Other values (266) 1184
59.1%
None
ValueCountFrequency (%)
㈜ 137
100.0%
ASCII
ValueCountFrequency (%)
57
31.8%
) 17
 
9.5%
( 17
 
9.5%
C 12
 
6.7%
2 11
 
6.1%
1 10
 
5.6%
I 8
 
4.5%
S 7
 
3.9%
K 5
 
2.8%
. 3
 
1.7%
Other values (21) 32
17.9%

업종
Categorical

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
주유소
173 
산업시설
89 
기타
43 
유독물
23 

Length

Max length4
Median length3
Mean length3.1402439
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주유소 173
52.7%
산업시설 89
27.1%
기타 43
 
13.1%
유독물 23
 
7.0%

Length

2024-03-13T09:08:14.509111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:08:14.597374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주유소 173
52.7%
산업시설 89
27.1%
기타 43
 
13.1%
유독물 23
 
7.0%

지번주소
Text

MISSING 

Distinct305
Distinct (%)96.5%
Missing12
Missing (%)3.7%
Memory size2.7 KiB
2024-03-13T09:08:14.770366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length20.183544
Min length15

Characters and Unicode

Total characters6378
Distinct characters106
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

Unique294 ?
Unique (%)93.0%

Sample

1st row경상남도 김해시 내동 495
2nd row경상남도 김해시 어방동 351-8
3rd row경상남도 김해시 진영읍 우동리 300-3
4th row경상남도 김해시 진영읍 우동리 425
5th row경상남도 김해시 상동면 매리 93-3
ValueCountFrequency (%)
경상남도 316
21.4%
김해시 316
21.4%
진영읍 52
 
3.5%
한림면 43
 
2.9%
상동면 28
 
1.9%
생림면 27
 
1.8%
주촌면 26
 
1.8%
진례면 23
 
1.6%
어방동 22
 
1.5%
삼계동 16
 
1.1%
Other values (371) 605
41.0%
2024-03-13T09:08:15.062035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1158
18.2%
상 347
 
5.4%
남 318
 
5.0%
해 317
 
5.0%
시 317
 
5.0%
김 317
 
5.0%
경 316
 
5.0%
도 316
 
5.0%
1 261
 
4.1%
- 227
 
3.6%
Other values (96) 2484
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3761
59.0%
Decimal Number 1232
 
19.3%
Space Separator 1158
 
18.2%
Dash Punctuation 227
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 347
 
9.2%
남 318
 
8.5%
해 317
 
8.4%
시 317
 
8.4%
김 317
 
8.4%
경 316
 
8.4%
도 316
 
8.4%
리 207
 
5.5%
동 173
 
4.6%
면 157
 
4.2%
Other values (84) 976
26.0%
Decimal Number
ValueCountFrequency (%)
1 261
21.2%
2 159
12.9%
3 137
11.1%
4 112
9.1%
0 112
9.1%
5 102
 
8.3%
8 91
 
7.4%
9 90
 
7.3%
7 88
 
7.1%
6 80
 
6.5%
Space Separator
ValueCountFrequency (%)
1158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3761
59.0%
Common 2617
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 347
 
9.2%
남 318
 
8.5%
해 317
 
8.4%
시 317
 
8.4%
김 317
 
8.4%
경 316
 
8.4%
도 316
 
8.4%
리 207
 
5.5%
동 173
 
4.6%
면 157
 
4.2%
Other values (84) 976
26.0%
Common
ValueCountFrequency (%)
1158
44.2%
1 261
 
10.0%
- 227
 
8.7%
2 159
 
6.1%
3 137
 
5.2%
4 112
 
4.3%
0 112
 
4.3%
5 102
 
3.9%
8 91
 
3.5%
9 90
 
3.4%
Other values (2) 168
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3761
59.0%
ASCII 2617
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1158
44.2%
1 261
 
10.0%
- 227
 
8.7%
2 159
 
6.1%
3 137
 
5.2%
4 112
 
4.3%
0 112
 
4.3%
5 102
 
3.9%
8 91
 
3.5%
9 90
 
3.4%
Other values (2) 168
 
6.4%
Hangul
ValueCountFrequency (%)
상 347
 
9.2%
남 318
 
8.5%
해 317
 
8.4%
시 317
 
8.4%
김 317
 
8.4%
경 316
 
8.4%
도 316
 
8.4%
리 207
 
5.5%
동 173
 
4.6%
면 157
 
4.2%
Other values (84) 976
26.0%

도로명주소
Text

Distinct321
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-13T09:08:15.222906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length22.740854
Min length14

Characters and Unicode

Total characters7459
Distinct characters114
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)95.7%

Sample

1st row경상남도 김해시 금관대로 1279 (내동)
2nd row경상남도 김해시 인제로 145 (어방동)
3rd row경상남도 김해시 진영읍 하계로96번길 44-49
4th row경상남도 김해시 진영읍 하계로96번길 94-2
5th row경상남도 김해시 상동면 동북로 481
ValueCountFrequency (%)
경상남도 328
20.1%
김해시 328
20.1%
진영읍 52
 
3.2%
김해대로 49
 
3.0%
한림면 40
 
2.5%
주촌면 29
 
1.8%
상동면 27
 
1.7%
생림면 26
 
1.6%
서부로 26
 
1.6%
진례면 23
 
1.4%
Other values (431) 703
43.1%
2024-03-13T09:08:15.537711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1317
17.7%
해 390
 
5.2%
김 389
 
5.2%
상 374
 
5.0%
경 328
 
4.4%
남 328
 
4.4%
도 328
 
4.4%
시 328
 
4.4%
로 316
 
4.2%
1 251
 
3.4%
Other values (104) 3110
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4509
60.5%
Decimal Number 1369
 
18.4%
Space Separator 1317
 
17.7%
Close Punctuation 93
 
1.2%
Open Punctuation 93
 
1.2%
Dash Punctuation 77
 
1.0%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 390
 
8.6%
김 389
 
8.6%
상 374
 
8.3%
경 328
 
7.3%
남 328
 
7.3%
도 328
 
7.3%
시 328
 
7.3%
로 316
 
7.0%
동 167
 
3.7%
면 155
 
3.4%
Other values (89) 1406
31.2%
Decimal Number
ValueCountFrequency (%)
1 251
18.3%
2 185
13.5%
5 137
10.0%
3 132
9.6%
4 131
9.6%
9 129
9.4%
6 128
9.3%
7 103
7.5%
0 90
 
6.6%
8 83
 
6.1%
Space Separator
ValueCountFrequency (%)
1317
100.0%
Close Punctuation
ValueCountFrequency (%)
) 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4509
60.5%
Common 2950
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 390
 
8.6%
김 389
 
8.6%
상 374
 
8.3%
경 328
 
7.3%
남 328
 
7.3%
도 328
 
7.3%
시 328
 
7.3%
로 316
 
7.0%
동 167
 
3.7%
면 155
 
3.4%
Other values (89) 1406
31.2%
Common
ValueCountFrequency (%)
1317
44.6%
1 251
 
8.5%
2 185
 
6.3%
5 137
 
4.6%
3 132
 
4.5%
4 131
 
4.4%
9 129
 
4.4%
6 128
 
4.3%
7 103
 
3.5%
) 93
 
3.2%
Other values (5) 344
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4509
60.5%
ASCII 2950
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1317
44.6%
1 251
 
8.5%
2 185
 
6.3%
5 137
 
4.6%
3 132
 
4.5%
4 131
 
4.4%
9 129
 
4.4%
6 128
 
4.3%
7 103
 
3.5%
) 93
 
3.2%
Other values (5) 344
 
11.7%
Hangul
ValueCountFrequency (%)
해 390
 
8.6%
김 389
 
8.6%
상 374
 
8.3%
경 328
 
7.3%
남 328
 
7.3%
도 328
 
7.3%
시 328
 
7.3%
로 316
 
7.0%
동 167
 
3.7%
면 155
 
3.4%
Other values (89) 1406
31.2%

완공일자
Date

Distinct306
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum1974-04-20 00:00:00
Maximum2020-09-22 00:00:00
2024-03-13T09:08:15.639724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:15.743449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

토양오염도검사해당여부(누출검사)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
적용
283 
면제
45 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
적용 283
86.3%
면제 45
 
13.7%

Length

2024-03-13T09:08:15.844665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:08:15.918137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적용 283
86.3%
면제 45
 
13.7%

토양오염도검사해당여부(오염도검사)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
적용
295 
면제
33 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
적용 295
89.9%
면제 33
 
10.1%

Length

2024-03-13T09:08:15.996923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:08:16.108627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적용 295
89.9%
면제 33
 
10.1%

토양오염물질저장용량(L)
Real number (ℝ)

Distinct149
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246109.93
Minimum20000
Maximum4369000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-13T09:08:16.215884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile25635
Q163582.5
median160000
Q3280000
95-th percentile734608
Maximum4369000
Range4349000
Interquartile range (IQR)216417.5

Descriptive statistics

Standard deviation378264.62
Coefficient of variation (CV)1.5369742
Kurtosis51.384702
Mean246109.93
Median Absolute Deviation (MAD)100000
Skewness6.059139
Sum80724058
Variance1.4308412 × 1011
MonotonicityNot monotonic
2024-03-13T09:08:16.326155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 26
 
7.9%
300000 25
 
7.6%
250000 18
 
5.5%
160000 14
 
4.3%
40000 11
 
3.4%
100000 10
 
3.0%
150000 10
 
3.0%
400000 9
 
2.7%
350000 9
 
2.7%
20000 7
 
2.1%
Other values (139) 189
57.6%
ValueCountFrequency (%)
20000 7
2.1%
20400 1
 
0.3%
20800 1
 
0.3%
21000 2
 
0.6%
21460 1
 
0.3%
21700 1
 
0.3%
24000 2
 
0.6%
25000 1
 
0.3%
25600 1
 
0.3%
25700 1
 
0.3%
ValueCountFrequency (%)
4369000 1
0.3%
2612580 1
0.3%
2246000 1
0.3%
1996780 1
0.3%
1638000 1
0.3%
1600000 1
0.3%
1320000 1
0.3%
1278000 1
0.3%
1104000 1
0.3%
1090000 1
0.3%

위도
Real number (ℝ)

Distinct318
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.269669
Minimum35.193323
Maximum35.349799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-13T09:08:16.431067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.193323
5-th percentile35.216964
Q135.234489
median35.271218
Q335.304058
95-th percentile35.326926
Maximum35.349799
Range0.1564761
Interquartile range (IQR)0.06956921

Descriptive statistics

Standard deviation0.038205843
Coefficient of variation (CV)0.0010832493
Kurtosis-1.2175002
Mean35.269669
Median Absolute Deviation (MAD)0.035198915
Skewness0.12027999
Sum11568.451
Variance0.0014596864
MonotonicityNot monotonic
2024-03-13T09:08:16.544524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.27132093 2
 
0.6%
35.30405809 2
 
0.6%
35.22967883 2
 
0.6%
35.26946457 2
 
0.6%
35.3156605 2
 
0.6%
35.2961226 2
 
0.6%
35.31014928 2
 
0.6%
35.20432151 2
 
0.6%
35.29195243 2
 
0.6%
35.28009429 2
 
0.6%
Other values (308) 308
93.9%
ValueCountFrequency (%)
35.19332273 1
0.3%
35.19406362 1
0.3%
35.20291017 1
0.3%
35.20401893 1
0.3%
35.20432151 2
0.6%
35.20700997 1
0.3%
35.2082137 1
0.3%
35.21035047 1
0.3%
35.2104419 1
0.3%
35.21241995 1
0.3%
ValueCountFrequency (%)
35.34979883 1
0.3%
35.34767365 1
0.3%
35.34670998 1
0.3%
35.34561295 1
0.3%
35.34415727 1
0.3%
35.34376992 1
0.3%
35.34115837 1
0.3%
35.34112675 1
0.3%
35.34001409 1
0.3%
35.33913276 1
0.3%

경도
Real number (ℝ)

Distinct318
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.84027
Minimum128.70768
Maximum129.0024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-13T09:08:16.653472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.70768
5-th percentile128.73331
Q1128.77735
median128.8477
Q3128.89711
95-th percentile128.95107
Maximum129.0024
Range0.2947135
Interquartile range (IQR)0.11976195

Descriptive statistics

Standard deviation0.067670287
Coefficient of variation (CV)0.00052522621
Kurtosis-0.78099438
Mean128.84027
Median Absolute Deviation (MAD)0.05572275
Skewness-0.014753629
Sum42259.609
Variance0.0045792677
MonotonicityNot monotonic
2024-03-13T09:08:16.762174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7760131 2
 
0.6%
128.8014759 2
 
0.6%
128.7991381 2
 
0.6%
129.0023976 2
 
0.6%
128.7474005 2
 
0.6%
128.7104373 2
 
0.6%
128.9661478 2
 
0.6%
128.83855 2
 
0.6%
128.7838918 2
 
0.6%
128.7161157 2
 
0.6%
Other values (308) 308
93.9%
ValueCountFrequency (%)
128.7076841 1
0.3%
128.7083055 1
0.3%
128.7088237 1
0.3%
128.7089623 1
0.3%
128.7095672 1
0.3%
128.7104373 2
0.6%
128.7147667 1
0.3%
128.7158797 1
0.3%
128.7161157 2
0.6%
128.7170975 1
0.3%
ValueCountFrequency (%)
129.0023976 2
0.6%
128.9827572 1
0.3%
128.982744 1
0.3%
128.9809918 1
0.3%
128.971853 1
0.3%
128.9709581 1
0.3%
128.9680786 1
0.3%
128.9678661 1
0.3%
128.9661478 2
0.6%
128.9611878 1
0.3%

Interactions

2024-03-13T09:08:13.033108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.219622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.487334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.756526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:13.098446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.280870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.551355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.823076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:13.163970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.342602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.610580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.889339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:13.236216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.420421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.688810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:08:12.960977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:08:16.836326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고번호업종토양오염도검사해당여부(누출검사)토양오염도검사해당여부(오염도검사)토양오염물질저장용량(L)위도경도
신고번호1.0000.5290.2790.2040.0000.1470.407
업종0.5291.0000.6540.5230.2920.2220.265
토양오염도검사해당여부(누출검사)0.2790.6541.0000.9620.1680.0000.236
토양오염도검사해당여부(오염도검사)0.2040.5230.9621.0000.0000.0000.000
토양오염물질저장용량(L)0.0000.2920.1680.0001.0000.0000.000
위도0.1470.2220.0000.0000.0001.0000.730
경도0.4070.2650.2360.0000.0000.7301.000
2024-03-13T09:08:16.917977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토양오염도검사해당여부(누출검사)업종토양오염도검사해당여부(오염도검사)
토양오염도검사해당여부(누출검사)1.0000.4560.823
업종0.4561.0000.355
토양오염도검사해당여부(오염도검사)0.8230.3551.000
2024-03-13T09:08:16.991318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고번호토양오염물질저장용량(L)위도경도업종토양오염도검사해당여부(누출검사)토양오염도검사해당여부(오염도검사)
신고번호1.000-0.0330.032-0.0700.3410.2110.155
토양오염물질저장용량(L)-0.0331.000-0.106-0.0490.2010.1760.000
위도0.032-0.1061.000-0.2620.1330.0000.000
경도-0.070-0.049-0.2621.0000.1610.1790.000
업종0.3410.2010.1330.1611.0000.4560.355
토양오염도검사해당여부(누출검사)0.2110.1760.0000.1790.4561.0000.823
토양오염도검사해당여부(오염도검사)0.1550.0000.0000.0000.3550.8231.000

Missing values

2024-03-13T09:08:13.330842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:08:13.450166image/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

신고번호사업장명업종지번주소도로명주소완공일자토양오염도검사해당여부(누출검사)토양오염도검사해당여부(오염도검사)토양오염물질저장용량(L)위도경도
01영진주유소㈜유테크 김해지점주유소경상남도 김해시 내동 495경상남도 김해시 금관대로 1279 (내동)1995-08-02적용적용12000035.238926128.85985
13예진주유소주유소경상남도 김해시 어방동 351-8경상남도 김해시 인제로 145 (어방동)1996-06-14적용적용16000035.241726128.903738
24고속도로관리공단 진영(상)주유소주유소경상남도 김해시 진영읍 우동리 300-3경상남도 김해시 진영읍 하계로96번길 44-491999-11-30적용적용27000035.279587128.714767
35고속도로관리공단 진영(하)주유소주유소경상남도 김해시 진영읍 우동리 425경상남도 김해시 진영읍 하계로96번길 94-21990-10-29적용적용11000035.278292128.717097
46상동농협직영주유소주유소경상남도 김해시 상동면 매리 93-3경상남도 김해시 상동면 동북로 4811997-09-11적용적용10000035.31561128.967866
57부영주유소주유소경상남도 김해시 진영읍 진영리 319-121경상남도 김해시 진영읍 진산대로 1871994-11-11적용적용19600035.3205128.722983
68해안주유소주유소경상남도 김해시 명법동 202-2경상남도 김해시 금관대로 820-16 (명법동)1993-01-06적용적용16000035.204019128.838288
710믿음가득주유소주유소경상남도 김해시 어방동 1116-3경상남도 김해시 김해대로 2537 (어방동)1993-06-16적용적용15600035.228943128.904401
813송진주유소주유소경상남도 김해시 한림면 신천리 285-2경상남도 김해시 한림면 김해대로 15671987-03-05적용적용13000035.27561128.842721
914현대주유소주유소경상남도 김해시 한림면 명동리 62-3경상남도 김해시 한림면 한림로 1311995-09-07적용적용12000035.30453128.807889
신고번호사업장명업종지번주소도로명주소완공일자토양오염도검사해당여부(누출검사)토양오염도검사해당여부(오염도검사)토양오염물질저장용량(L)위도경도
318479한국전력공사산업시설경상남도 김해시 한림면 명동리 272-3경상남도 김해시 한림면 한림로 632019-05-21적용적용35690035.298529128.809588
319480디아이시스템㈜2공장산업시설경상남도 김해시 한림면 신천리 103경상남도 김해시 한림면 용덕로 48-332019-04-23적용적용3080035.282527128.836851
320481대한정유산업시설경상남도 김해시 주촌면 내삼리 552경상남도 김해시 주촌면 서부로1499번길 22-1022019-06-14적용적용2880035.23622128.815543
321482(주)흥아포밍산업시설경상남도 김해시 주촌면 망덕리 873-4경상남도 김해시 주촌면 골든루트로130번길 572019-08-02면제면제3420035.220024128.830493
322483㈜동성티씨에스산업시설경상남도 김해시 진례면 송현리 1183경상남도 김해시 진례면 고모로134번길 812012-07-10면제면제11700035.238793128.777597
323484내트럭㈜김해사업소주유소경상남도 김해시 진영읍 김해대로 243경상남도 김해시 진영읍 진영리 651-1번지 외 11필지2020-01-15적용적용40000035.306753128.71588
324485선경유화㈜산업시설경상남도 김해시 삼계동 1311경상남도 김해시 김해대로 1685번길 17-12020-01-23적용적용6660035.271963128.851916
325486㈜클린코리아 안강공장산업시설경상남도 김해시 한림면 가산리 52-5경상남도 김해시 한림면 한림로 9252020-06-08적용적용25400035.333497128.758406
326487㈜에스엔디유통 진영휴게소 제1주유소주유소경상남도 김해시 진영읍 우동리 295경상남도 김해시 진영읍 하계로96번길 68일원(부산방향)2020-09-22적용적용56000035.280094128.716116
327488㈜에스엔디유통 진영휴게소 제2주유소주유소경상남도 김해시 진영읍 우동리 295경상남도 김해시 진영읍 하계로96번길 68일원(부산방향)2020-09-22적용적용18000035.280094128.716116