Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory113.4 B

Variable types

Text4
Categorical9

Alerts

강우량계여부 has constant value ""Constant
강우량계측정여부 has constant value ""Constant
내부케이싱구경 has constant value ""Constant
내부케이싱재질내용 is highly overall correlated with 설치일자 and 4 other fieldsHigh correlation
통신방법내용 is highly overall correlated with 설치일자 and 4 other fieldsHigh correlation
외부케이싱재질내용 is highly overall correlated with 설치일자 and 4 other fieldsHigh correlation
설치일자 is highly overall correlated with 관리기관명 and 4 other fieldsHigh correlation
관리기관명 is highly overall correlated with 설치일자 and 4 other fieldsHigh correlation
외부케이싱구경 is highly overall correlated with 설치일자 and 4 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 12:36:45.640631
Analysis finished2023-12-10 12:36:47.076977
Duration1.44 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T21:36:47.255832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2666667
Min length4

Characters and Unicode

Total characters128
Distinct characters35
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

Unique8 ?
Unique (%)26.7%

Sample

1st row안동남후
2nd row안동도산
3rd row안동송천
4th row영양석보
5th row영양섬촌
ValueCountFrequency (%)
울산화산 2
 
6.7%
울산원산 2
 
6.7%
부산송정3 2
 
6.7%
창원천선 2
 
6.7%
부산송정1 2
 
6.7%
창원팔용 2
 
6.7%
부산송정2 2
 
6.7%
창원신촌 2
 
6.7%
부산송정4 2
 
6.7%
창원성산 2
 
6.7%
Other values (9) 10
33.3%
2023-12-10T21:36:47.739332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 24
18.8%
원 10
 
7.8%
송 9
 
7.0%
부 8
 
6.2%
정 8
 
6.2%
창 8
 
6.2%
울 7
 
5.5%
동 3
 
2.3%
도 3
 
2.3%
영 3
 
2.3%
Other values (25) 45
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120
93.8%
Decimal Number 8
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 24
20.0%
원 10
 
8.3%
송 9
 
7.5%
부 8
 
6.7%
정 8
 
6.7%
창 8
 
6.7%
울 7
 
5.8%
동 3
 
2.5%
도 3
 
2.5%
영 3
 
2.5%
Other values (21) 37
30.8%
Decimal Number
ValueCountFrequency (%)
1 2
25.0%
3 2
25.0%
2 2
25.0%
4 2
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120
93.8%
Common 8
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 24
20.0%
원 10
 
8.3%
송 9
 
7.5%
부 8
 
6.7%
정 8
 
6.7%
창 8
 
6.7%
울 7
 
5.8%
동 3
 
2.5%
도 3
 
2.5%
영 3
 
2.5%
Other values (21) 37
30.8%
Common
ValueCountFrequency (%)
1 2
25.0%
3 2
25.0%
2 2
25.0%
4 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120
93.8%
ASCII 8
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 24
20.0%
원 10
 
8.3%
송 9
 
7.5%
부 8
 
6.7%
정 8
 
6.7%
창 8
 
6.7%
울 7
 
5.8%
동 3
 
2.5%
도 3
 
2.5%
영 3
 
2.5%
Other values (21) 37
30.8%
ASCII
ValueCountFrequency (%)
1 2
25.0%
3 2
25.0%
2 2
25.0%
4 2
25.0%

설치일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
20211214
23 
20211216
7 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20211216
2nd row20211216
3rd row20211216
4th row20211216
5th row20211216

Common Values

ValueCountFrequency (%)
20211214 23
76.7%
20211216 7
 
23.3%

Length

2023-12-10T21:36:47.924774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:48.056684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20211214 23
76.7%
20211216 7
 
23.3%

주소
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T21:36:48.326272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length20.533333
Min length18

Characters and Unicode

Total characters616
Distinct characters63
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

Unique8 ?
Unique (%)26.7%

Sample

1st row경상북도 안동시 남후면 광음리 427-1
2nd row경상북도 안동시 도산면 단천리 532
3rd row경상북도 안동시 송천동 1319-101
4th row경상북도 영양군 석보면 요원리 282-1
5th row경상북도 영양군 일월면 섬촌리 1-2
ValueCountFrequency (%)
부산광역시 8
 
5.7%
송정동 8
 
5.7%
강서구 8
 
5.7%
경상남도 8
 
5.7%
창원시 8
 
5.7%
울산광역시 7
 
5.0%
경상북도 7
 
5.0%
온산읍 7
 
5.0%
울주군 7
 
5.0%
성산구 6
 
4.3%
Other values (42) 67
47.5%
2023-12-10T21:36:48.841272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111
 
18.0%
산 37
 
6.0%
1 30
 
4.9%
시 26
 
4.2%
동 20
 
3.2%
도 18
 
2.9%
구 16
 
2.6%
광 16
 
2.6%
2 16
 
2.6%
- 15
 
2.4%
Other values (53) 311
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 378
61.4%
Decimal Number 112
 
18.2%
Space Separator 111
 
18.0%
Dash Punctuation 15
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 37
 
9.8%
시 26
 
6.9%
동 20
 
5.3%
도 18
 
4.8%
구 16
 
4.2%
광 16
 
4.2%
경 15
 
4.0%
역 15
 
4.0%
상 15
 
4.0%
울 14
 
3.7%
Other values (41) 186
49.2%
Decimal Number
ValueCountFrequency (%)
1 30
26.8%
2 16
14.3%
5 13
11.6%
7 11
 
9.8%
9 10
 
8.9%
4 10
 
8.9%
3 6
 
5.4%
6 6
 
5.4%
0 5
 
4.5%
8 5
 
4.5%
Space Separator
ValueCountFrequency (%)
111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 378
61.4%
Common 238
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 37
 
9.8%
시 26
 
6.9%
동 20
 
5.3%
도 18
 
4.8%
구 16
 
4.2%
광 16
 
4.2%
경 15
 
4.0%
역 15
 
4.0%
상 15
 
4.0%
울 14
 
3.7%
Other values (41) 186
49.2%
Common
ValueCountFrequency (%)
111
46.6%
1 30
 
12.6%
2 16
 
6.7%
- 15
 
6.3%
5 13
 
5.5%
7 11
 
4.6%
9 10
 
4.2%
4 10
 
4.2%
3 6
 
2.5%
6 6
 
2.5%
Other values (2) 10
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 378
61.4%
ASCII 238
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111
46.6%
1 30
 
12.6%
2 16
 
6.7%
- 15
 
6.3%
5 13
 
5.5%
7 11
 
4.6%
9 10
 
4.2%
4 10
 
4.2%
3 6
 
2.5%
6 6
 
2.5%
Other values (2) 10
 
4.2%
Hangul
ValueCountFrequency (%)
산 37
 
9.8%
시 26
 
6.9%
동 20
 
5.3%
도 18
 
4.8%
구 16
 
4.2%
광 16
 
4.2%
경 15
 
4.0%
역 15
 
4.0%
상 15
 
4.0%
울 14
 
3.7%
Other values (41) 186
49.2%

관리기관명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
환경부. 한국환경공단
23 
환경부. 한국수자원공사
7 

Length

Max length12
Median length11
Mean length11.233333
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row환경부. 한국수자원공사
2nd row환경부. 한국수자원공사
3rd row환경부. 한국수자원공사
4th row환경부. 한국수자원공사
5th row환경부. 한국수자원공사

Common Values

ValueCountFrequency (%)
환경부. 한국환경공단 23
76.7%
환경부. 한국수자원공사 7
 
23.3%

Length

2023-12-10T21:36:49.040816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:49.185513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환경부 30
50.0%
한국환경공단 23
38.3%
한국수자원공사 7
 
11.7%

강우량계여부
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

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 30
100.0%

Length

2023-12-10T21:36:49.346552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:49.481793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

통신방법내용
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
TCP
23 
0
7 

Length

Max length3
Median length3
Mean length2.5333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
TCP 23
76.7%
0 7
 
23.3%

Length

2023-12-10T21:36:49.659111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:49.838407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tcp 23
76.7%
0 7
 
23.3%

강우량계측정여부
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

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 30
100.0%

Length

2023-12-10T21:36:50.035455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:50.164401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

외부케이싱설치구간내용
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T21:36:50.330369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.3
Min length7

Characters and Unicode

Total characters219
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row0.0~5.5
2nd row0.0~7.5
3rd row0.0~8.0
4th row0.0~9.5
5th row0.0~8.4
ValueCountFrequency (%)
0m 23
30.3%
23
30.3%
2m 12
15.8%
24m 2
 
2.6%
89m 1
 
1.3%
0.0~5.5 1
 
1.3%
0.0~5.0 1
 
1.3%
85m 1
 
1.3%
67m 1
 
1.3%
38m 1
 
1.3%
Other values (10) 10
13.2%
2023-12-10T21:36:50.746796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 46
21.0%
46
21.0%
0 40
18.3%
- 23
10.5%
2 15
 
6.8%
. 14
 
6.4%
~ 7
 
3.2%
5 7
 
3.2%
8 5
 
2.3%
4 4
 
1.8%
Other values (5) 12
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83
37.9%
Lowercase Letter 46
21.0%
Space Separator 46
21.0%
Dash Punctuation 23
 
10.5%
Other Punctuation 14
 
6.4%
Math Symbol 7
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
48.2%
2 15
 
18.1%
5 7
 
8.4%
8 5
 
6.0%
4 4
 
4.8%
7 4
 
4.8%
9 3
 
3.6%
1 2
 
2.4%
6 2
 
2.4%
3 1
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
m 46
100.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 173
79.0%
Latin 46
 
21.0%

Most frequent character per script

Common
ValueCountFrequency (%)
46
26.6%
0 40
23.1%
- 23
13.3%
2 15
 
8.7%
. 14
 
8.1%
~ 7
 
4.0%
5 7
 
4.0%
8 5
 
2.9%
4 4
 
2.3%
7 4
 
2.3%
Other values (4) 8
 
4.6%
Latin
ValueCountFrequency (%)
m 46
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 46
21.0%
46
21.0%
0 40
18.3%
- 23
10.5%
2 15
 
6.8%
. 14
 
6.4%
~ 7
 
3.2%
5 7
 
3.2%
8 5
 
2.3%
4 4
 
1.8%
Other values (5) 12
 
5.5%

외부케이싱구경
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
250
23 
200
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row200
2nd row200
3rd row200
4th row200
5th row200

Common Values

ValueCountFrequency (%)
250 23
76.7%
200 7
 
23.3%

Length

2023-12-10T21:36:50.966165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:51.133214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
250 23
76.7%
200 7
 
23.3%

외부케이싱재질내용
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
강관
23 
백관
7 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강관 23
76.7%
백관 7
 
23.3%

Length

2023-12-10T21:36:51.283020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:51.445656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강관 23
76.7%
백관 7
 
23.3%

내부케이싱설치구간내용
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T21:36:51.629488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.2
Min length7

Characters and Unicode

Total characters246
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)50.0%

Sample

1st row0.0~100.0
2nd row0.0~98.0
3rd row0.0~100.0
4th row0.0~98.8
5th row0.0~98.5
ValueCountFrequency (%)
0m 23
30.3%
23
30.3%
20m 5
 
6.6%
60m 4
 
5.3%
0.0~100.0 4
 
5.3%
50m 2
 
2.6%
24m 1
 
1.3%
120m 1
 
1.3%
100m 1
 
1.3%
90m 1
 
1.3%
Other values (11) 11
14.5%
2023-12-10T21:36:52.063573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 70
28.5%
m 46
18.7%
46
18.7%
- 23
 
9.3%
. 14
 
5.7%
1 10
 
4.1%
2 7
 
2.8%
~ 7
 
2.8%
6 5
 
2.0%
9 5
 
2.0%
Other values (5) 13
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110
44.7%
Lowercase Letter 46
18.7%
Space Separator 46
18.7%
Dash Punctuation 23
 
9.3%
Other Punctuation 14
 
5.7%
Math Symbol 7
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70
63.6%
1 10
 
9.1%
2 7
 
6.4%
6 5
 
4.5%
9 5
 
4.5%
8 5
 
4.5%
5 3
 
2.7%
4 2
 
1.8%
3 2
 
1.8%
7 1
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
m 46
100.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 200
81.3%
Latin 46
 
18.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70
35.0%
46
23.0%
- 23
 
11.5%
. 14
 
7.0%
1 10
 
5.0%
2 7
 
3.5%
~ 7
 
3.5%
6 5
 
2.5%
9 5
 
2.5%
8 5
 
2.5%
Other values (4) 8
 
4.0%
Latin
ValueCountFrequency (%)
m 46
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70
28.5%
m 46
18.7%
46
18.7%
- 23
 
9.3%
. 14
 
5.7%
1 10
 
4.1%
2 7
 
2.8%
~ 7
 
2.8%
6 5
 
2.0%
9 5
 
2.0%
Other values (5) 13
 
5.3%

내부케이싱구경
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
150
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row150
2nd row150
3rd row150
4th row150
5th row150

Common Values

ValueCountFrequency (%)
150 30
100.0%

Length

2023-12-10T21:36:52.287374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:52.437923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
150 30
100.0%

내부케이싱재질내용
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
HI-PVC
23 
P.V.C(VG1)
7 

Length

Max length10
Median length6
Mean length6.9333333
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP.V.C(VG1)
2nd rowP.V.C(VG1)
3rd rowP.V.C(VG1)
4th rowP.V.C(VG1)
5th rowP.V.C(VG1)

Common Values

ValueCountFrequency (%)
HI-PVC 23
76.7%
P.V.C(VG1) 7
 
23.3%

Length

2023-12-10T21:36:52.645898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:52.829517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
hi-pvc 23
76.7%
p.v.c(vg1 7
 
23.3%

Correlations

2023-12-10T21:36:52.949595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명설치일자주소관리기관명통신방법내용외부케이싱설치구간내용외부케이싱구경외부케이싱재질내용내부케이싱설치구간내용내부케이싱재질내용
관측소명1.0001.0001.0001.0001.0000.8491.0001.0000.0001.000
설치일자1.0001.0001.0000.9890.9891.0000.9890.9891.0000.989
주소1.0001.0001.0001.0001.0000.9091.0001.0000.0001.000
관리기관명1.0000.9891.0001.0000.9891.0000.9890.9891.0000.989
통신방법내용1.0000.9891.0000.9891.0001.0000.9890.9891.0000.989
외부케이싱설치구간내용0.8491.0000.9091.0001.0001.0001.0001.0000.7781.000
외부케이싱구경1.0000.9891.0000.9890.9891.0001.0000.9891.0000.989
외부케이싱재질내용1.0000.9891.0000.9890.9891.0000.9891.0001.0000.989
내부케이싱설치구간내용0.0001.0000.0001.0001.0000.7781.0001.0001.0001.000
내부케이싱재질내용1.0000.9891.0000.9890.9891.0000.9890.9891.0001.000
2023-12-10T21:36:53.142546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
내부케이싱재질내용통신방법내용외부케이싱재질내용설치일자관리기관명외부케이싱구경
내부케이싱재질내용1.0000.9030.9030.9030.9030.903
통신방법내용0.9031.0000.9030.9030.9030.903
외부케이싱재질내용0.9030.9031.0000.9030.9030.903
설치일자0.9030.9030.9031.0000.9030.903
관리기관명0.9030.9030.9030.9031.0000.903
외부케이싱구경0.9030.9030.9030.9030.9031.000
2023-12-10T21:36:53.308002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치일자관리기관명통신방법내용외부케이싱구경외부케이싱재질내용내부케이싱재질내용
설치일자1.0000.9030.9030.9030.9030.903
관리기관명0.9031.0000.9030.9030.9030.903
통신방법내용0.9030.9031.0000.9030.9030.903
외부케이싱구경0.9030.9030.9031.0000.9030.903
외부케이싱재질내용0.9030.9030.9030.9031.0000.903
내부케이싱재질내용0.9030.9030.9030.9030.9031.000

Missing values

2023-12-10T21:36:46.762969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:36:46.983440image/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

관측소명설치일자주소관리기관명강우량계여부통신방법내용강우량계측정여부외부케이싱설치구간내용외부케이싱구경외부케이싱재질내용내부케이싱설치구간내용내부케이싱구경내부케이싱재질내용
0안동남후20211216경상북도 안동시 남후면 광음리 427-1환경부. 한국수자원공사0000.0~5.5200백관0.0~100.0150P.V.C(VG1)
1안동도산20211216경상북도 안동시 도산면 단천리 532환경부. 한국수자원공사0000.0~7.5200백관0.0~98.0150P.V.C(VG1)
2안동송천20211216경상북도 안동시 송천동 1319-101환경부. 한국수자원공사0000.0~8.0200백관0.0~100.0150P.V.C(VG1)
3영양석보20211216경상북도 영양군 석보면 요원리 282-1환경부. 한국수자원공사0000.0~9.5200백관0.0~98.8150P.V.C(VG1)
4영양섬촌20211216경상북도 영양군 일월면 섬촌리 1-2환경부. 한국수자원공사0000.0~8.4200백관0.0~98.5150P.V.C(VG1)
5영양일월20211216경상북도 영양군 일월면 용화리 542-3환경부. 한국수자원공사0000.0~5.2200백관0.0~100.0150P.V.C(VG1)
6울산방도20211214울산광역시 울주군 온산읍 방도리 92환경부. 한국환경공단0TCP00m - 2m250강관0m - 7m150HI-PVC
7울산방도20211214울산광역시 울주군 온산읍 방도리 92환경부. 한국환경공단0TCP00m - 7m250강관0m - 50m150HI-PVC
8울산산암20211214울산광역시 울주군 온산읍 산암리 4-4환경부. 한국환경공단0TCP00m - 2m250강관0m - 60m150HI-PVC
9울산원산20211214울산광역시 울주군 온산읍 원산리 757환경부. 한국환경공단0TCP00m - 9m250강관0m - 60m150HI-PVC
관측소명설치일자주소관리기관명강우량계여부통신방법내용강우량계측정여부외부케이싱설치구간내용외부케이싱구경외부케이싱재질내용내부케이싱설치구간내용내부케이싱구경내부케이싱재질내용
20창원팔용20211214경상남도 창원시 의창구 팔용동 10-1환경부. 한국환경공단0TCP00m - 2m250강관0m - 30m150HI-PVC
21창원팔용20211214경상남도 창원시 의창구 팔용동 10-1환경부. 한국환경공단0TCP00m - 40m250강관0m - 60m150HI-PVC
22부산송정120211214부산광역시 강서구 송정동 1456환경부. 한국환경공단0TCP00m - 2m250강관0m - 20m150HI-PVC
23부산송정120211214부산광역시 강서구 송정동 1456환경부. 한국환경공단0TCP00m - 38m250강관0m - 90m150HI-PVC
24부산송정220211214부산광역시 강서구 송정동 1499-2환경부. 한국환경공단0TCP00m - 67m250강관0m - 100m150HI-PVC
25부산송정220211214부산광역시 강서구 송정동 1499-2환경부. 한국환경공단0TCP00m - 2m250강관0m - 20m150HI-PVC
26부산송정320211214부산광역시 강서구 송정동 1718환경부. 한국환경공단0TCP00m - 85m250강관0m - 120m150HI-PVC
27부산송정320211214부산광역시 강서구 송정동 1718환경부. 한국환경공단0TCP00m - 2m250강관0m - 20m150HI-PVC
28부산송정420211214부산광역시 강서구 송정동 1718환경부. 한국환경공단0TCP00m - 2m250강관0m - 20m150HI-PVC
29부산송정420211214부산광역시 강서구 송정동 1718환경부. 한국환경공단0TCP00m - 71m250강관0m - 110m150HI-PVC