Overview

Dataset statistics

Number of variables15
Number of observations265
Missing cells74
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.7 KiB
Average record size in memory126.5 B

Variable types

Text5
Categorical4
Numeric5
DateTime1

Dataset

Description충청남도 지하수통합정보시스템 지역지하수 관측망 제원입니다. 관측소표준코드, 관측소이름, 시도, 시군구, 읍면동, 리, 번지, 표고, 설치일자, 관측정구분, 굴착심도, 굴착구경, 관측소등록일자, 케이싱높이, 관측방법 등이 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=15&beforeMenuCd=DOM_000000201001001000&publicdatapk=15122723

Alerts

관측방법 is highly overall correlated with 표고 and 5 other fieldsHigh correlation
시도 is highly overall correlated with 시군구High correlation
관측정구분 is highly overall correlated with 굴착심도 and 4 other fieldsHigh correlation
시군구 is highly overall correlated with 굴착구경 and 4 other fieldsHigh correlation
표고 is highly overall correlated with 굴착심도 and 3 other fieldsHigh correlation
굴착심도 is highly overall correlated with 표고 and 4 other fieldsHigh correlation
굴착구경 is highly overall correlated with 표고 and 5 other fieldsHigh correlation
관측소등록일자 is highly overall correlated with 표고 and 5 other fieldsHigh correlation
시도 is highly imbalanced (68.6%)Imbalance
관측소이름 has 5 (1.9%) missing valuesMissing
리 has 54 (20.4%) missing valuesMissing
번지 has 4 (1.5%) missing valuesMissing
표고 has 6 (2.3%) missing valuesMissing
표고 has 92 (34.7%) zerosZeros
굴착심도 has 151 (57.0%) zerosZeros
굴착구경 has 156 (58.9%) zerosZeros
케이싱높이 has 241 (90.9%) zerosZeros

Reproduction

Analysis started2024-01-09 19:51:40.009925
Analysis finished2024-01-09 19:51:43.189407
Duration3.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소표준코드
Text

Distinct257
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-01-10T04:51:43.326823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters3710
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique249 ?
Unique (%)94.0%

Sample

1st rowCN-ASN-G1-0001
2nd rowCN-ASN-G1-0002
3rd rowCN-ASN-G1-0003
4th rowCN-ASN-G1-0004
5th rowCN-ASN-G1-0005
ValueCountFrequency (%)
cn-can-g1-0007 2
 
0.8%
cn-can-g1-0018 2
 
0.8%
cn-can-g1-0003 2
 
0.8%
cn-can-g1-0006 2
 
0.8%
cn-can-g1-0017 2
 
0.8%
cn-can-g1-0010 2
 
0.8%
cn-can-g1-0011 2
 
0.8%
cn-can-g1-0019 2
 
0.8%
cn-can-g1-0080 1
 
0.4%
cn-can-g1-0085 1
 
0.4%
Other values (247) 247
93.2%
2024-01-10T04:51:43.654250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 795
21.4%
0 660
17.8%
N 475
12.8%
C 396
10.7%
1 353
9.5%
G 293
 
7.9%
A 144
 
3.9%
S 123
 
3.3%
2 58
 
1.6%
3 42
 
1.1%
Other values (18) 371
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1590
42.9%
Decimal Number 1325
35.7%
Dash Punctuation 795
21.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 475
29.9%
C 396
24.9%
G 293
18.4%
A 144
 
9.1%
S 123
 
7.7%
J 37
 
2.3%
O 23
 
1.4%
D 20
 
1.3%
Y 14
 
0.9%
B 13
 
0.8%
Other values (7) 52
 
3.3%
Decimal Number
ValueCountFrequency (%)
0 660
49.8%
1 353
26.6%
2 58
 
4.4%
3 42
 
3.2%
4 36
 
2.7%
5 36
 
2.7%
6 36
 
2.7%
8 36
 
2.7%
7 36
 
2.7%
9 32
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 795
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2120
57.1%
Latin 1590
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 475
29.9%
C 396
24.9%
G 293
18.4%
A 144
 
9.1%
S 123
 
7.7%
J 37
 
2.3%
O 23
 
1.4%
D 20
 
1.3%
Y 14
 
0.9%
B 13
 
0.8%
Other values (7) 52
 
3.3%
Common
ValueCountFrequency (%)
- 795
37.5%
0 660
31.1%
1 353
16.7%
2 58
 
2.7%
3 42
 
2.0%
4 36
 
1.7%
5 36
 
1.7%
6 36
 
1.7%
8 36
 
1.7%
7 36
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 795
21.4%
0 660
17.8%
N 475
12.8%
C 396
10.7%
1 353
9.5%
G 293
 
7.9%
A 144
 
3.9%
S 123
 
3.3%
2 58
 
1.6%
3 42
 
1.1%
Other values (18) 371
10.0%

관측소이름
Text

MISSING 

Distinct246
Distinct (%)94.6%
Missing5
Missing (%)1.9%
Memory size2.2 KiB
2024-01-10T04:51:43.865680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length17.680769
Min length7

Characters and Unicode

Total characters4597
Distinct characters179
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)90.0%

Sample

1st rowCN-ASN-G1-0001(인주-0001)(폐공)
2nd rowCN-ASN-G1-0002(송악-0002)
3rd rowCN-ASN-G1-0003(영인-0003)
4th rowCN-ASN-G1-0004(인주-0004)
5th rowCN-ASN-G1-0005(실옥-0005)
ValueCountFrequency (%)
보조지하수관측정 17
 
5.5%
입장 4
 
1.3%
풍세(풍서)보조지하수관측정 3
 
1.0%
북면(납안)보조지하수관측정 3
 
1.0%
동면(덕성)보조지하수관측정 2
 
0.6%
북면 2
 
0.6%
풍세 2
 
0.6%
성환 2
 
0.6%
0003 2
 
0.6%
광덕(광덕)보조지하수관측정 2
 
0.6%
Other values (260) 269
87.3%
2024-01-10T04:51:44.164450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 734
 
16.0%
- 502
 
10.9%
1 231
 
5.0%
( 203
 
4.4%
) 203
 
4.4%
N 192
 
4.2%
C 143
 
3.1%
G 131
 
2.8%
정 109
 
2.4%
수 108
 
2.3%
Other values (169) 2041
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1633
35.5%
Decimal Number 1277
27.8%
Uppercase Letter 730
15.9%
Dash Punctuation 502
 
10.9%
Open Punctuation 203
 
4.4%
Close Punctuation 203
 
4.4%
Space Separator 48
 
1.0%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 109
 
6.7%
수 108
 
6.6%
지 103
 
6.3%
보 102
 
6.2%
관 101
 
6.2%
조 100
 
6.1%
하 100
 
6.1%
측 100
 
6.1%
산 55
 
3.4%
면 49
 
3.0%
Other values (136) 706
43.2%
Uppercase Letter
ValueCountFrequency (%)
N 192
26.3%
C 143
19.6%
G 131
17.9%
S 59
 
8.1%
A 44
 
6.0%
B 30
 
4.1%
J 25
 
3.4%
D 20
 
2.7%
L 14
 
1.9%
Y 14
 
1.9%
Other values (8) 58
 
7.9%
Decimal Number
ValueCountFrequency (%)
0 734
57.5%
1 231
 
18.1%
2 79
 
6.2%
3 50
 
3.9%
4 41
 
3.2%
7 32
 
2.5%
6 32
 
2.5%
5 32
 
2.5%
8 25
 
2.0%
9 21
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 502
100.0%
Open Punctuation
ValueCountFrequency (%)
( 203
100.0%
Close Punctuation
ValueCountFrequency (%)
) 203
100.0%
Space Separator
ValueCountFrequency (%)
48
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2234
48.6%
Hangul 1633
35.5%
Latin 730
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 109
 
6.7%
수 108
 
6.6%
지 103
 
6.3%
보 102
 
6.2%
관 101
 
6.2%
조 100
 
6.1%
하 100
 
6.1%
측 100
 
6.1%
산 55
 
3.4%
면 49
 
3.0%
Other values (136) 706
43.2%
Latin
ValueCountFrequency (%)
N 192
26.3%
C 143
19.6%
G 131
17.9%
S 59
 
8.1%
A 44
 
6.0%
B 30
 
4.1%
J 25
 
3.4%
D 20
 
2.7%
L 14
 
1.9%
Y 14
 
1.9%
Other values (8) 58
 
7.9%
Common
ValueCountFrequency (%)
0 734
32.9%
- 502
22.5%
1 231
 
10.3%
( 203
 
9.1%
) 203
 
9.1%
2 79
 
3.5%
3 50
 
2.2%
48
 
2.1%
4 41
 
1.8%
7 32
 
1.4%
Other values (5) 111
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2964
64.5%
Hangul 1633
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 734
24.8%
- 502
16.9%
1 231
 
7.8%
( 203
 
6.8%
) 203
 
6.8%
N 192
 
6.5%
C 143
 
4.8%
G 131
 
4.4%
2 79
 
2.7%
S 59
 
2.0%
Other values (23) 487
16.4%
Hangul
ValueCountFrequency (%)
정 109
 
6.7%
수 108
 
6.6%
지 103
 
6.3%
보 102
 
6.2%
관 101
 
6.2%
조 100
 
6.1%
하 100
 
6.1%
측 100
 
6.1%
산 55
 
3.4%
면 49
 
3.0%
Other values (136) 706
43.2%

시도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
충청남도
218 
충남
37 
세종특별자치시
 
5
충북
 
2
<NA>
 
1
Other values (2)
 
2

Length

Max length7
Median length4
Mean length3.754717
Min length2

Unique

Unique3 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 218
82.3%
충남 37
 
14.0%
세종특별자치시 5
 
1.9%
충북 2
 
0.8%
<NA> 1
 
0.4%
유구읍 1
 
0.4%
계룡면 1
 
0.4%

Length

2024-01-10T04:51:44.276683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T04:51:44.368769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 218
82.3%
충남 37
 
14.0%
세종특별자치시 5
 
1.9%
충북 2
 
0.8%
na 1
 
0.4%
유구읍 1
 
0.4%
계룡면 1
 
0.4%

시군구
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
천안시동남구
77 
서산시
31 
아산시
28 
천안시서북구
23 
당진시
20 
Other values (19)
86 

Length

Max length7
Median length3
Mean length4.2603774
Min length3

Unique

Unique4 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
천안시동남구 77
29.1%
서산시 31
11.7%
아산시 28
 
10.6%
천안시서북구 23
 
8.7%
당진시 20
 
7.5%
계룡시 10
 
3.8%
태안군 8
 
3.0%
공주시 8
 
3.0%
홍성군 8
 
3.0%
부여군 7
 
2.6%
Other values (14) 45
17.0%

Length

2024-01-10T04:51:44.472909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안시동남구 77
28.2%
서산시 31
11.4%
아산시 28
 
10.3%
천안시서북구 23
 
8.4%
당진시 20
 
7.3%
계룡시 10
 
3.7%
홍성군 9
 
3.3%
태안군 8
 
2.9%
공주시 8
 
2.9%
천안시 8
 
2.9%
Other values (14) 51
18.7%

읍면동
Text

Distinct117
Distinct (%)44.3%
Missing1
Missing (%)0.4%
Memory size2.2 KiB
2024-01-10T04:51:44.728651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9166667
Min length2

Characters and Unicode

Total characters770
Distinct characters114
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

Unique76 ?
Unique (%)28.8%

Sample

1st row인주면
2nd row송악면
3rd row영인면
4th row인주면
5th row실옥동
ValueCountFrequency (%)
광덕면 15
 
5.7%
입장면 13
 
4.9%
북면 12
 
4.5%
풍세면 12
 
4.5%
동면 11
 
4.2%
성남면 11
 
4.2%
성환읍 8
 
3.0%
병천면 7
 
2.7%
수신면 6
 
2.3%
홍성읍 6
 
2.3%
Other values (107) 163
61.7%
2024-01-10T04:51:45.087585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 204
26.5%
읍 44
 
5.7%
성 33
 
4.3%
동 32
 
4.2%
천 20
 
2.6%
신 18
 
2.3%
덕 17
 
2.2%
산 17
 
2.2%
남 17
 
2.2%
광 17
 
2.2%
Other values (104) 351
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 770
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 204
26.5%
읍 44
 
5.7%
성 33
 
4.3%
동 32
 
4.2%
천 20
 
2.6%
신 18
 
2.3%
덕 17
 
2.2%
산 17
 
2.2%
남 17
 
2.2%
광 17
 
2.2%
Other values (104) 351
45.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 770
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 204
26.5%
읍 44
 
5.7%
성 33
 
4.3%
동 32
 
4.2%
천 20
 
2.6%
신 18
 
2.3%
덕 17
 
2.2%
산 17
 
2.2%
남 17
 
2.2%
광 17
 
2.2%
Other values (104) 351
45.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 770
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 204
26.5%
읍 44
 
5.7%
성 33
 
4.3%
동 32
 
4.2%
천 20
 
2.6%
신 18
 
2.3%
덕 17
 
2.2%
산 17
 
2.2%
남 17
 
2.2%
광 17
 
2.2%
Other values (104) 351
45.6%

리
Text

MISSING 

Distinct166
Distinct (%)78.7%
Missing54
Missing (%)20.4%
Memory size2.2 KiB
2024-01-10T04:51:45.348708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0047393
Min length2

Characters and Unicode

Total characters634
Distinct characters132
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

Unique136 ?
Unique (%)64.5%

Sample

1st row냉정리
2nd row유곡리
3rd row신봉리
4th row문방리
5th row시전리
ValueCountFrequency (%)
용정리 5
 
2.4%
광덕리 4
 
1.9%
호당리 4
 
1.9%
봉양리 3
 
1.4%
이호리 3
 
1.4%
대평리 3
 
1.4%
농소리 3
 
1.4%
동산리 3
 
1.4%
신덕리 3
 
1.4%
납안리 3
 
1.4%
Other values (156) 177
83.9%
2024-01-10T04:51:45.727367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 210
33.1%
신 16
 
2.5%
정 14
 
2.2%
산 14
 
2.2%
곡 11
 
1.7%
덕 11
 
1.7%
대 10
 
1.6%
봉 10
 
1.6%
소 10
 
1.6%
평 9
 
1.4%
Other values (122) 319
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 634
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 210
33.1%
신 16
 
2.5%
정 14
 
2.2%
산 14
 
2.2%
곡 11
 
1.7%
덕 11
 
1.7%
대 10
 
1.6%
봉 10
 
1.6%
소 10
 
1.6%
평 9
 
1.4%
Other values (122) 319
50.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 634
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 210
33.1%
신 16
 
2.5%
정 14
 
2.2%
산 14
 
2.2%
곡 11
 
1.7%
덕 11
 
1.7%
대 10
 
1.6%
봉 10
 
1.6%
소 10
 
1.6%
평 9
 
1.4%
Other values (122) 319
50.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 634
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 210
33.1%
신 16
 
2.5%
정 14
 
2.2%
산 14
 
2.2%
곡 11
 
1.7%
덕 11
 
1.7%
대 10
 
1.6%
봉 10
 
1.6%
소 10
 
1.6%
평 9
 
1.4%
Other values (122) 319
50.3%

번지
Text

MISSING 

Distinct255
Distinct (%)97.7%
Missing4
Missing (%)1.5%
Memory size2.2 KiB
2024-01-10T04:51:45.972365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.1417625
Min length2

Characters and Unicode

Total characters1342
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique250 ?
Unique (%)95.8%

Sample

1st row245
2nd row597-11
3rd row1066-2
4th row184-5
5th row418-13
ValueCountFrequency (%)
856-3 3
 
1.1%
613-4 2
 
0.8%
483-1번지 2
 
0.8%
1055-1 2
 
0.8%
355-1 2
 
0.8%
359-1번지 1
 
0.4%
29-1번지 1
 
0.4%
85-4번지 1
 
0.4%
11번지 1
 
0.4%
350-49번지 1
 
0.4%
Other values (245) 245
93.9%
2024-01-10T04:51:46.310949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 171
12.7%
- 160
11.9%
2 123
9.2%
3 119
8.9%
4 111
8.3%
5 102
7.6%
번 100
7.5%
지 100
7.5%
8 78
 
5.8%
6 78
 
5.8%
Other values (4) 200
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 973
72.5%
Other Letter 209
 
15.6%
Dash Punctuation 160
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 171
17.6%
2 123
12.6%
3 119
12.2%
4 111
11.4%
5 102
10.5%
8 78
8.0%
6 78
8.0%
7 69
7.1%
9 63
 
6.5%
0 59
 
6.1%
Other Letter
ValueCountFrequency (%)
번 100
47.8%
지 100
47.8%
산 9
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1133
84.4%
Hangul 209
 
15.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 171
15.1%
- 160
14.1%
2 123
10.9%
3 119
10.5%
4 111
9.8%
5 102
9.0%
8 78
6.9%
6 78
6.9%
7 69
6.1%
9 63
 
5.6%
Hangul
ValueCountFrequency (%)
번 100
47.8%
지 100
47.8%
산 9
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1133
84.4%
Hangul 209
 
15.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 171
15.1%
- 160
14.1%
2 123
10.9%
3 119
10.5%
4 111
9.8%
5 102
9.0%
8 78
6.9%
6 78
6.9%
7 69
6.1%
9 63
 
5.6%
Hangul
ValueCountFrequency (%)
번 100
47.8%
지 100
47.8%
산 9
 
4.3%

표고
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct116
Distinct (%)44.8%
Missing6
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean43.145135
Minimum0
Maximum297
Zeros92
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-01-10T04:51:46.430778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26.06
Q372.5
95-th percentile150.1
Maximum297
Range297
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation51.666161
Coefficient of variation (CV)1.1974968
Kurtosis2.0890983
Mean43.145135
Median Absolute Deviation (MAD)26.06
Skewness1.4118116
Sum11174.59
Variance2669.3922
MonotonicityNot monotonic
2024-01-10T04:51:46.531915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 92
34.7%
28.0 6
 
2.3%
44.0 4
 
1.5%
15.0 4
 
1.5%
78.0 4
 
1.5%
24.0 3
 
1.1%
30.0 3
 
1.1%
49.0 3
 
1.1%
122.0 3
 
1.1%
100.0 3
 
1.1%
Other values (106) 134
50.6%
(Missing) 6
 
2.3%
ValueCountFrequency (%)
0.0 92
34.7%
2.0 1
 
0.4%
3.0 1
 
0.4%
4.0 1
 
0.4%
5.0 1
 
0.4%
6.0 1
 
0.4%
8.0 2
 
0.8%
9.0 3
 
1.1%
10.0 1
 
0.4%
10.98 1
 
0.4%
ValueCountFrequency (%)
297.0 1
0.4%
194.0 1
0.4%
187.0 1
0.4%
185.0 1
0.4%
179.0 1
0.4%
176.0 1
0.4%
175.0 1
0.4%
167.0 1
0.4%
165.0 1
0.4%
164.0 1
0.4%

설치일자
Date

Distinct106
Distinct (%)40.2%
Missing1
Missing (%)0.4%
Memory size2.2 KiB
Minimum2004-01-01 00:00:00
Maximum2022-11-30 00:00:00
2024-01-10T04:51:46.654873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:46.783147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관측정구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0
150 
2
105 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 150
56.6%
2 105
39.6%
1 10
 
3.8%

Length

2024-01-10T04:51:46.905836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T04:51:46.998491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 150
56.6%
2 105
39.6%
1 10
 
3.8%

굴착심도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)8.4%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean48.825095
Minimum0
Maximum256
Zeros151
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-01-10T04:51:47.092273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100
95-th percentile182
Maximum256
Range256
Interquartile range (IQR)100

Descriptive statistics

Standard deviation67.108547
Coefficient of variation (CV)1.3744683
Kurtosis0.14014003
Mean48.825095
Median Absolute Deviation (MAD)0
Skewness1.1411392
Sum12841
Variance4503.5571
MonotonicityNot monotonic
2024-01-10T04:51:47.212495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 151
57.0%
100 36
 
13.6%
180 17
 
6.4%
70 12
 
4.5%
80 12
 
4.5%
200 11
 
4.2%
40 5
 
1.9%
110 4
 
1.5%
182 2
 
0.8%
256 1
 
0.4%
Other values (12) 12
 
4.5%
(Missing) 2
 
0.8%
ValueCountFrequency (%)
0 151
57.0%
14 1
 
0.4%
16 1
 
0.4%
20 1
 
0.4%
22 1
 
0.4%
26 1
 
0.4%
40 5
 
1.9%
45 1
 
0.4%
52 1
 
0.4%
70 12
 
4.5%
ValueCountFrequency (%)
256 1
 
0.4%
254 1
 
0.4%
200 11
 
4.2%
182 2
 
0.8%
180 17
6.4%
175 1
 
0.4%
120 1
 
0.4%
110 4
 
1.5%
102 1
 
0.4%
100 36
13.6%

굴착구경
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.15094
Minimum0
Maximum350
Zeros156
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-01-10T04:51:47.315292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3300
95-th percentile350
Maximum350
Range350
Interquartile range (IQR)300

Descriptive statistics

Standard deviation151.70039
Coefficient of variation (CV)1.2219028
Kurtosis-1.7093829
Mean124.15094
Median Absolute Deviation (MAD)0
Skewness0.45953794
Sum32900
Variance23013.007
MonotonicityNot monotonic
2024-01-10T04:51:47.417991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 156
58.9%
300 62
 
23.4%
350 32
 
12.1%
200 7
 
2.6%
250 5
 
1.9%
150 3
 
1.1%
ValueCountFrequency (%)
0 156
58.9%
150 3
 
1.1%
200 7
 
2.6%
250 5
 
1.9%
300 62
 
23.4%
350 32
 
12.1%
ValueCountFrequency (%)
350 32
 
12.1%
300 62
 
23.4%
250 5
 
1.9%
200 7
 
2.6%
150 3
 
1.1%
0 156
58.9%

관측소등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)38.6%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean42118.856
Minimum38261
Maximum44895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-01-10T04:51:47.544818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38261
5-th percentile41005.55
Q141264
median41508
Q343090
95-th percentile44358.85
Maximum44895
Range6634
Interquartile range (IQR)1826

Descriptive statistics

Standard deviation1221.5482
Coefficient of variation (CV)0.029002408
Kurtosis-0.48793724
Mean42118.856
Median Absolute Deviation (MAD)290
Skewness0.51194217
Sum11119378
Variance1492180.1
MonotonicityNot monotonic
2024-01-10T04:51:47.694255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41264 100
37.7%
43321 7
 
2.6%
44504 7
 
2.6%
42879 5
 
1.9%
41584 5
 
1.9%
41969 5
 
1.9%
43818 4
 
1.5%
39917 4
 
1.5%
40686 3
 
1.1%
44258 3
 
1.1%
Other values (92) 121
45.7%
ValueCountFrequency (%)
38261 1
 
0.4%
39913 1
 
0.4%
39916 1
 
0.4%
39917 4
1.5%
40283 2
0.8%
40284 1
 
0.4%
40686 3
1.1%
40994 1
 
0.4%
41071 2
0.8%
41142 1
 
0.4%
ValueCountFrequency (%)
44895 1
 
0.4%
44789 1
 
0.4%
44719 1
 
0.4%
44523 2
 
0.8%
44504 7
2.6%
44490 1
 
0.4%
44362 1
 
0.4%
44341 2
 
0.8%
44319 1
 
0.4%
44258 3
1.1%

케이싱높이
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55811321
Minimum0
Maximum20
Zeros241
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-01-10T04:51:47.818036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5303349
Coefficient of variation (CV)4.5337306
Kurtosis33.161032
Mean0.55811321
Median Absolute Deviation (MAD)0
Skewness5.5575015
Sum147.9
Variance6.4025949
MonotonicityNot monotonic
2024-01-10T04:51:47.941705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 241
90.9%
0.5 4
 
1.5%
4.0 4
 
1.5%
0.3 2
 
0.8%
14.0 2
 
0.8%
5.0 2
 
0.8%
10.0 1
 
0.4%
7.0 1
 
0.4%
20.0 1
 
0.4%
5.5 1
 
0.4%
Other values (6) 6
 
2.3%
ValueCountFrequency (%)
0.0 241
90.9%
0.2 1
 
0.4%
0.3 2
 
0.8%
0.5 4
 
1.5%
0.6 1
 
0.4%
4.0 4
 
1.5%
5.0 2
 
0.8%
5.5 1
 
0.4%
6.0 1
 
0.4%
7.0 1
 
0.4%
ValueCountFrequency (%)
20.0 1
0.4%
19.0 1
0.4%
15.0 1
0.4%
14.0 2
0.8%
10.0 1
0.4%
8.0 1
0.4%
7.0 1
0.4%
6.0 1
0.4%
5.5 1
0.4%
5.0 2
0.8%

관측방법
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
자동
165 
수동
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동 165
62.3%
수동 100
37.7%

Length

2024-01-10T04:51:48.065039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T04:51:48.156333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동 165
62.3%
수동 100
37.7%

Interactions

2024-01-10T04:51:42.249135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:40.630414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:40.974665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.345438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.841638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:42.320666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:40.696978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.047114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.442463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.931759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:42.387283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:40.761451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.116337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.552941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:42.015694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:42.672518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:40.830357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.193777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.645480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:42.092553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:42.746725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:40.902688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.265880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:41.744973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:42.166531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T04:51:48.221610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도시군구표고관측정구분굴착심도굴착구경관측소등록일자케이싱높이관측방법
시도1.0000.9980.0000.3800.1600.6060.5250.0000.464
시군구0.9981.0000.4950.8710.5950.8320.8230.0001.000
표고0.0000.4951.0000.6100.6770.5400.5340.1410.884
관측정구분0.3800.8710.6101.0000.7550.9560.6770.5370.636
굴착심도0.1600.5950.6770.7551.0000.7620.6520.5070.984
굴착구경0.6060.8320.5400.9560.7621.0000.6090.2341.000
관측소등록일자0.5250.8230.5340.6770.6520.6091.0000.0000.963
케이싱높이0.0000.0000.1410.5370.5070.2340.0001.0000.273
관측방법0.4641.0000.8840.6360.9841.0000.9630.2731.000
2024-01-10T04:51:48.341577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측방법시도관측정구분시군구
관측방법1.0000.3320.9000.959
시도0.3321.0000.1690.961
관측정구분0.9000.1691.0000.685
시군구0.9590.9610.6851.000
2024-01-10T04:51:48.449444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표고굴착심도굴착구경관측소등록일자케이싱높이시도시군구관측정구분관측방법
표고1.0000.7180.719-0.5960.1140.0000.2190.4750.700
굴착심도0.7181.0000.946-0.6220.2060.0880.2770.6530.878
굴착구경0.7190.9461.000-0.6280.2650.2570.5460.7400.987
관측소등록일자-0.596-0.622-0.6281.000-0.1930.4310.5090.5510.819
케이싱높이0.1140.2060.265-0.1931.0000.0000.0000.4600.283
시도0.0000.0880.2570.4310.0001.0000.9610.1690.332
시군구0.2190.2770.5460.5090.0000.9611.0000.6850.959
관측정구분0.4750.6530.7400.5510.4600.1690.6851.0000.900
관측방법0.7000.8780.9870.8190.2830.3320.9590.9001.000

Missing values

2024-01-10T04:51:42.852130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:51:42.995026image/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.
2024-01-10T04:51:43.113481image/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

관측소표준코드관측소이름시도시군구읍면동리번지표고설치일자관측정구분굴착심도굴착구경관측소등록일자케이싱높이관측방법
0CN-ASN-G1-0001CN-ASN-G1-0001(인주-0001)(폐공)충청남도아산시인주면냉정리245107.02012-10-080400411900.0자동
1CN-ASN-G1-0002CN-ASN-G1-0002(송악-0002)충청남도아산시송악면유곡리597-1195.02012-10-080400411900.0자동
2CN-ASN-G1-0003CN-ASN-G1-0003(영인-0003)충청남도아산시영인면신봉리1066-217.02013-04-22000413860.0자동
3CN-ASN-G1-0004CN-ASN-G1-0004(인주-0004)충청남도아산시인주면문방리184-537.02013-04-22000413860.0자동
4CN-ASN-G1-0005CN-ASN-G1-0005(실옥-0005)충청남도아산시실옥동<NA>418-1312.02013-05-060400414000.0자동
5CN-ASN-G1-0006CN-ASN-G1-0006(도고-0006)충청남도아산시도고면시전리581-4212.02013-05-060400414000.0자동
6CN-ASN-G1-0007CN-ASN-G1-0007(신창-0007)충청남도아산시신창면남성리25630.02013-05-060400414000.0자동
7CN-ASN-G1-0008CN-ASN-G1-0008(둔포-0008)충청남도아산시둔포면신항리215-2024.02013-08-22000415080.0자동
8CN-ASN-G1-0009CN-ASN-G1-0009(음봉-0009)충청남도아산시음봉면원남리37040.02013-08-22000415080.0자동
9CN-ASN-G1-0010CN-ASN-G1-0010(배방-0010)충청남도아산시배방읍북수리87148.02014-04-04000417330.0자동
관측소표준코드관측소이름시도시군구읍면동리번지표고설치일자관측정구분굴착심도굴착구경관측소등록일자케이싱높이관측방법
255CN-CAN-G1-0062풍세(풍서)보조지하수관측정충청남도천안시동남구풍세면풍서리483-1번지44.02006-01-012100300412640.0수동
256CN-CAN-G1-0063풍세(가송)보조지하수관측정충청남도천안시동남구풍세면가송리187-11번지36.02006-01-012200300412640.0수동
257CN-CAN-G1-0064풍세(풍서)보조지하수관측정충청남도천안시동남구풍세면풍서리612-2번지74.02006-01-012100300412640.0수동
258CN-CAN-G1-0066목천(덕전)보조지하수관측정충청남도천안시동남구목천읍덕전리92번지297.02006-01-01280300412640.0수동
259CN-CAN-G1-0067목천(송전)보조지하수관측정충청남도천안시동남구목천읍송전리98번지194.02006-01-01280300412640.0수동
260CN-CAN-G1-0048풍세면보조지하수관측정0048충청남도천안시동남구풍세면남관리436번지28.02004-01-012100300412640.0수동
261CN-CAN-G1-0049광덕(광덕)보조지하수관측정충청남도천안시동남구광덕면광덕리12번지150.02010-01-012100300412640.0수동
262CN-CAN-G1-0068병천(매성)보조지하수관측정충청남도천안시동남구병천면매성리150-3번지100.02006-01-012100250412640.0수동
263CN-CAN-G1-0069병천(봉항)보조지하수관측정충청남도천안시동남구병천면봉항리628-1번지137.02006-01-01280350412640.0수동
264CN-CAN-G1-0070병천(봉항)보조지하수관측정충청남도천안시동남구병천면봉항리97-1번지164.02010-01-012100300412640.0수동