Overview

Dataset statistics

Number of variables10
Number of observations78
Missing cells65
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory83.7 B

Variable types

Categorical2
Text4
Numeric2
DateTime2

Dataset

Description경상북도 상하수도.수질과 관련한 정보입니다.(경상북도 상수원 보호구역 시군별, 명칭, 위치, 면적, 지정일자, 취수장 등의 현황입니다.)
Author경상북도
URLhttps://www.data.go.kr/data/3083973/fileData.do

Alerts

면적(제곱킬로미터) is highly overall correlated with 취수능력(1일당세제곱미터)High correlation
취수능력(1일당세제곱미터) is highly overall correlated with 면적(제곱킬로미터)High correlation
시군 is highly overall correlated with 수원High correlation
수원 is highly overall correlated with 시군High correlation
수원 is highly imbalanced (52.7%)Imbalance
변경일자 has 58 (74.4%) missing valuesMissing
수계 has 7 (9.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:35:13.675459
Analysis finished2023-12-12 22:35:14.926594
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size756.0 B
울릉군
7 
영양군
7 
봉화군
6 
의성군
6 
포항시
6 
Other values (16)
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)3.8%

Sample

1st row경산시
2nd row경주시
3rd row경주시
4th row경주시
5th row경주시

Common Values

ValueCountFrequency (%)
울릉군 7
 
9.0%
영양군 7
 
9.0%
봉화군 6
 
7.7%
의성군 6
 
7.7%
포항시 6
 
7.7%
경주시 6
 
7.7%
청송군 5
 
6.4%
예천군 5
 
6.4%
영주시 4
 
5.1%
상주시 3
 
3.8%
Other values (11) 23
29.5%

Length

2023-12-13T07:35:14.973853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
울릉군 7
 
9.0%
영양군 7
 
9.0%
봉화군 6
 
7.7%
의성군 6
 
7.7%
포항시 6
 
7.7%
경주시 6
 
7.7%
청송군 5
 
6.4%
예천군 5
 
6.4%
영주시 4
 
5.1%
군위군 3
 
3.8%
Other values (11) 23
29.5%

명칭
Text

Distinct73
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size756.0 B
2023-12-13T07:35:15.172728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.3333333
Min length2

Characters and Unicode

Total characters182
Distinct characters96
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

Unique68 ?
Unique (%)87.2%

Sample

1st row경산(운문댐)
2nd row탑동
3rd row보문
4th row보문
5th row안강
ValueCountFrequency (%)
북면 2
 
2.6%
포항제2 2
 
2.6%
보문 2
 
2.6%
구미광역 2
 
2.6%
풍양 2
 
2.6%
금성 1
 
1.3%
일월 1
 
1.3%
영양 1
 
1.3%
수비 1
 
1.3%
신원 1
 
1.3%
Other values (63) 63
80.8%
2023-12-13T07:35:15.490654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 7
 
3.8%
천 6
 
3.3%
문 6
 
3.3%
동 6
 
3.3%
포 5
 
2.7%
영 5
 
2.7%
안 5
 
2.7%
지 5
 
2.7%
풍 4
 
2.2%
산 4
 
2.2%
Other values (86) 129
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 176
96.7%
Decimal Number 2
 
1.1%
Open Punctuation 2
 
1.1%
Close Punctuation 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 7
 
4.0%
천 6
 
3.4%
문 6
 
3.4%
동 6
 
3.4%
포 5
 
2.8%
영 5
 
2.8%
안 5
 
2.8%
지 5
 
2.8%
풍 4
 
2.3%
산 4
 
2.3%
Other values (83) 123
69.9%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176
96.7%
Common 6
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 7
 
4.0%
천 6
 
3.4%
문 6
 
3.4%
동 6
 
3.4%
포 5
 
2.8%
영 5
 
2.8%
안 5
 
2.8%
지 5
 
2.8%
풍 4
 
2.3%
산 4
 
2.3%
Other values (83) 123
69.9%
Common
ValueCountFrequency (%)
2 2
33.3%
( 2
33.3%
) 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176
96.7%
ASCII 6
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 7
 
4.0%
천 6
 
3.4%
문 6
 
3.4%
동 6
 
3.4%
포 5
 
2.8%
영 5
 
2.8%
안 5
 
2.8%
지 5
 
2.8%
풍 4
 
2.3%
산 4
 
2.3%
Other values (83) 123
69.9%
ASCII
ValueCountFrequency (%)
2 2
33.3%
( 2
33.3%
) 2
33.3%

위치
Text

Distinct69
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size756.0 B
2023-12-13T07:35:15.702870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0897436
Min length2

Characters and Unicode

Total characters241
Distinct characters93
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

Unique61 ?
Unique (%)78.2%

Sample

1st row대정동
2nd row탑동
3rd row신평동
4th row덕동
5th row강동면
ValueCountFrequency (%)
울릉읍 3
 
3.8%
강동면 2
 
2.5%
수비면 2
 
2.5%
오천읍 2
 
2.5%
북면 2
 
2.5%
효령면 2
 
2.5%
서면 2
 
2.5%
연일읍 2
 
2.5%
대정동 1
 
1.2%
입암면 1
 
1.2%
Other values (61) 61
76.2%
2023-12-13T07:35:16.015373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 47
 
19.5%
읍 24
 
10.0%
동 12
 
5.0%
천 6
 
2.5%
울 4
 
1.7%
양 4
 
1.7%
고 4
 
1.7%
, 3
 
1.2%
산 3
 
1.2%
흥 3
 
1.2%
Other values (83) 131
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 236
97.9%
Other Punctuation 3
 
1.2%
Space Separator 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 47
 
19.9%
읍 24
 
10.2%
동 12
 
5.1%
천 6
 
2.5%
울 4
 
1.7%
양 4
 
1.7%
고 4
 
1.7%
산 3
 
1.3%
흥 3
 
1.3%
릉 3
 
1.3%
Other values (81) 126
53.4%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 236
97.9%
Common 5
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 47
 
19.9%
읍 24
 
10.2%
동 12
 
5.1%
천 6
 
2.5%
울 4
 
1.7%
양 4
 
1.7%
고 4
 
1.7%
산 3
 
1.3%
흥 3
 
1.3%
릉 3
 
1.3%
Other values (81) 126
53.4%
Common
ValueCountFrequency (%)
, 3
60.0%
2
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 236
97.9%
ASCII 5
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 47
 
19.9%
읍 24
 
10.2%
동 12
 
5.1%
천 6
 
2.5%
울 4
 
1.7%
양 4
 
1.7%
고 4
 
1.7%
산 3
 
1.3%
흥 3
 
1.3%
릉 3
 
1.3%
Other values (81) 126
53.4%
ASCII
ValueCountFrequency (%)
, 3
60.0%
2
40.0%

면적(제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8672051
Minimum0.001
Maximum45.165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-13T07:35:16.134314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0421
Q10.21925
median0.474
Q31.06475
95-th percentile4.872
Maximum45.165
Range45.164
Interquartile range (IQR)0.8455

Descriptive statistics

Standard deviation5.9892786
Coefficient of variation (CV)3.2076168
Kurtosis40.470024
Mean1.8672051
Median Absolute Deviation (MAD)0.313
Skewness6.1666206
Sum145.642
Variance35.871458
MonotonicityNot monotonic
2023-12-13T07:35:16.239544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.301 2
 
2.6%
3.32 2
 
2.6%
1.975 2
 
2.6%
0.147 2
 
2.6%
0.492 2
 
2.6%
4.872 2
 
2.6%
0.176 1
 
1.3%
0.185 1
 
1.3%
0.159 1
 
1.3%
0.062 1
 
1.3%
Other values (62) 62
79.5%
ValueCountFrequency (%)
0.001 1
1.3%
0.002 1
1.3%
0.031 1
1.3%
0.037 1
1.3%
0.043 1
1.3%
0.062 1
1.3%
0.079 1
1.3%
0.081 1
1.3%
0.11 1
1.3%
0.119 1
1.3%
ValueCountFrequency (%)
45.165 1
1.3%
28.179 1
1.3%
7.233 1
1.3%
4.872 2
2.6%
3.861 1
1.3%
3.478 1
1.3%
3.32 2
2.6%
2.603 1
1.3%
2.354 1
1.3%
2.316 1
1.3%

지정일자
Date

Distinct57
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Memory size756.0 B
Minimum1962-03-04 00:00:00
Maximum2018-12-14 00:00:00
2023-12-13T07:35:16.337278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:16.661265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

변경일자
Date

MISSING 

Distinct17
Distinct (%)85.0%
Missing58
Missing (%)74.4%
Memory size756.0 B
Minimum1986-10-24 00:00:00
Maximum2014-08-27 00:00:00
2023-12-13T07:35:16.754739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:16.833952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

수원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size756.0 B
복류수
63 
호소수
10 
하천수
 
3
지하수
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row하천수
2nd row복류수
3rd row호소수
4th row호소수
5th row복류수

Common Values

ValueCountFrequency (%)
복류수 63
80.8%
호소수 10
 
12.8%
하천수 3
 
3.8%
지하수 2
 
2.6%

Length

2023-12-13T07:35:16.927655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:35:17.033952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
복류수 63
80.8%
호소수 10
 
12.8%
하천수 3
 
3.8%
지하수 2
 
2.6%

수계
Text

MISSING 

Distinct52
Distinct (%)73.2%
Missing7
Missing (%)9.0%
Memory size756.0 B
2023-12-13T07:35:17.250609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length6.2816901
Min length3

Characters and Unicode

Total characters446
Distinct characters73
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)57.7%

Sample

1st row1지류(금호강)
2nd row형산강
3rd row덕동댐
4th row덕동댐
5th row1지류(기계천)
ValueCountFrequency (%)
1지류(반변천 5
 
7.0%
1지류(내성천 4
 
5.6%
낙동강 4
 
5.6%
1지류(위천 3
 
4.2%
1지류(미천 2
 
2.8%
덕동댐 2
 
2.8%
2지류(용전천 2
 
2.8%
2지류(신원천 2
 
2.8%
2지류(쌍계천 2
 
2.8%
용출수 2
 
2.8%
Other values (42) 43
60.6%
2023-12-13T07:35:17.641750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 62
13.9%
지 49
 
11.0%
류 48
 
10.8%
( 48
 
10.8%
) 48
 
10.8%
1 23
 
5.2%
2 19
 
4.3%
동 9
 
2.0%
강 8
 
1.8%
반 6
 
1.3%
Other values (63) 126
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
67.5%
Open Punctuation 48
 
10.8%
Close Punctuation 48
 
10.8%
Decimal Number 48
 
10.8%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 62
20.6%
지 49
16.3%
류 48
15.9%
동 9
 
3.0%
강 8
 
2.7%
반 6
 
2.0%
남 6
 
2.0%
산 5
 
1.7%
계 5
 
1.7%
내 5
 
1.7%
Other values (57) 98
32.6%
Decimal Number
ValueCountFrequency (%)
1 23
47.9%
2 19
39.6%
3 6
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
67.5%
Common 145
32.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 62
20.6%
지 49
16.3%
류 48
15.9%
동 9
 
3.0%
강 8
 
2.7%
반 6
 
2.0%
남 6
 
2.0%
산 5
 
1.7%
계 5
 
1.7%
내 5
 
1.7%
Other values (57) 98
32.6%
Common
ValueCountFrequency (%)
( 48
33.1%
) 48
33.1%
1 23
15.9%
2 19
 
13.1%
3 6
 
4.1%
, 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
67.5%
ASCII 145
32.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 62
20.6%
지 49
16.3%
류 48
15.9%
동 9
 
3.0%
강 8
 
2.7%
반 6
 
2.0%
남 6
 
2.0%
산 5
 
1.7%
계 5
 
1.7%
내 5
 
1.7%
Other values (57) 98
32.6%
ASCII
ValueCountFrequency (%)
( 48
33.1%
) 48
33.1%
1 23
15.9%
2 19
 
13.1%
3 6
 
4.1%
, 1
 
0.7%

취수장 명칭
Text

Distinct77
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size756.0 B
2023-12-13T07:35:17.890963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.3717949
Min length2

Characters and Unicode

Total characters185
Distinct characters107
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

Unique76 ?
Unique (%)97.4%

Sample

1st row경산
2nd row탑동
3rd row보문
4th row불국
5th row안강
ValueCountFrequency (%)
수비 2
 
2.6%
영덕 1
 
1.3%
예천 1
 
1.3%
점곡 1
 
1.3%
단촌 1
 
1.3%
의성 1
 
1.3%
사벌매호 1
 
1.3%
풍양 1
 
1.3%
감천 1
 
1.3%
용문 1
 
1.3%
Other values (67) 67
85.9%
2023-12-13T07:35:18.292761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 6
 
3.2%
지 6
 
3.2%
수 5
 
2.7%
천 5
 
2.7%
동 5
 
2.7%
해 4
 
2.2%
성 4
 
2.2%
포 4
 
2.2%
영 4
 
2.2%
안 4
 
2.2%
Other values (97) 138
74.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 178
96.2%
Close Punctuation 3
 
1.6%
Open Punctuation 3
 
1.6%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 6
 
3.4%
지 6
 
3.4%
수 5
 
2.8%
천 5
 
2.8%
동 5
 
2.8%
해 4
 
2.2%
성 4
 
2.2%
포 4
 
2.2%
영 4
 
2.2%
안 4
 
2.2%
Other values (94) 131
73.6%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 178
96.2%
Common 7
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 6
 
3.4%
지 6
 
3.4%
수 5
 
2.8%
천 5
 
2.8%
동 5
 
2.8%
해 4
 
2.2%
성 4
 
2.2%
포 4
 
2.2%
영 4
 
2.2%
안 4
 
2.2%
Other values (94) 131
73.6%
Common
ValueCountFrequency (%)
) 3
42.9%
( 3
42.9%
2 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 178
96.2%
ASCII 7
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 6
 
3.4%
지 6
 
3.4%
수 5
 
2.8%
천 5
 
2.8%
동 5
 
2.8%
해 4
 
2.2%
성 4
 
2.2%
포 4
 
2.2%
영 4
 
2.2%
안 4
 
2.2%
Other values (94) 131
73.6%
ASCII
ValueCountFrequency (%)
) 3
42.9%
( 3
42.9%
2 1
 
14.3%

취수능력(1일당세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29774.231
Minimum300
Maximum464000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-13T07:35:18.449312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile500
Q11000
median3350
Q312750
95-th percentile130610
Maximum464000
Range463700
Interquartile range (IQR)11750

Descriptive statistics

Standard deviation84123.627
Coefficient of variation (CV)2.8253837
Kurtosis16.689137
Mean29774.231
Median Absolute Deviation (MAD)2750
Skewness4.0947018
Sum2322390
Variance7.0767847 × 109
MonotonicityNot monotonic
2023-12-13T07:35:18.565852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 5
 
6.4%
800 4
 
5.1%
600 4
 
5.1%
500 4
 
5.1%
3000 3
 
3.8%
40000 3
 
3.8%
8000 2
 
2.6%
20000 2
 
2.6%
2000 2
 
2.6%
9000 2
 
2.6%
Other values (42) 47
60.3%
ValueCountFrequency (%)
300 1
 
1.3%
330 1
 
1.3%
400 1
 
1.3%
500 4
5.1%
600 4
5.1%
700 1
 
1.3%
800 4
5.1%
880 1
 
1.3%
990 1
 
1.3%
1000 5
6.4%
ValueCountFrequency (%)
464000 1
 
1.3%
400000 1
 
1.3%
376000 1
 
1.3%
237200 1
 
1.3%
111800 1
 
1.3%
100000 1
 
1.3%
69000 1
 
1.3%
53900 1
 
1.3%
40000 3
3.8%
35000 2
2.6%

Interactions

2023-12-13T07:35:14.501421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:14.384018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:14.565859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:14.442569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:35:18.648596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명칭위치면적(제곱킬로미터)지정일자변경일자수원수계취수장 명칭취수능력(1일당세제곱미터)
시군1.0001.0001.0000.7520.9981.0000.8630.9771.0000.832
명칭1.0001.0000.9931.0001.0001.0001.0001.0000.9950.000
위치1.0000.9931.0001.0000.9281.0000.9890.9761.0000.000
면적(제곱킬로미터)0.7521.0001.0001.0001.0001.0000.7071.0001.0000.480
지정일자0.9981.0000.9281.0001.0001.0000.9950.9920.9990.838
변경일자1.0001.0001.0001.0001.0001.0001.0000.9751.0000.000
수원0.8631.0000.9890.7070.9951.0001.0000.9521.0000.632
수계0.9771.0000.9761.0000.9920.9750.9521.0001.0000.000
취수장 명칭1.0000.9951.0001.0000.9991.0001.0001.0001.0001.000
취수능력(1일당세제곱미터)0.8320.0000.0000.4800.8380.0000.6320.0001.0001.000
2023-12-13T07:35:18.756400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군수원
시군1.0000.586
수원0.5861.000
2023-12-13T07:35:18.846975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱킬로미터)취수능력(1일당세제곱미터)시군수원
면적(제곱킬로미터)1.0000.5020.4500.348
취수능력(1일당세제곱미터)0.5021.0000.4990.455
시군0.4500.4991.0000.586
수원0.3480.4550.5861.000

Missing values

2023-12-13T07:35:14.650193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:35:14.791839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T07:35:14.890999image/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

시군명칭위치면적(제곱킬로미터)지정일자변경일자수원수계취수장 명칭취수능력(1일당세제곱미터)
0경산시경산(운문댐)대정동0.9831980-04-162014-08-27하천수1지류(금호강)경산100000
1경주시탑동탑동0.3571978-09-14<NA>복류수형산강탑동35000
2경주시보문신평동4.8721981-06-01<NA>호소수덕동댐보문40000
3경주시보문덕동4.8721981-06-01<NA>호소수덕동댐불국20000
4경주시안강강동면1.7571981-09-17<NA>복류수1지류(기계천)안강8000
5경주시건천건천읍0.1641987-03-30<NA>복류수건천천건천3500
6경주시안계댐강동면3.4781987-10-17<NA>호소수<NA>안계댐(광역)237200
7구미시구미광역해평면3.321985-10-112012-06-04하천수낙동강해평464000
8구미시구미광역고아읍3.321985-10-112012-06-04하천수낙동강구미광역(해평취수)400000
9김천시황금황금동1.0781987-03-23<NA>복류수1지류(감천)황금53900
시군명칭위치면적(제곱킬로미터)지정일자변경일자수원수계취수장 명칭취수능력(1일당세제곱미터)
68울릉군내수전울릉읍0.6162008-08-03<NA>복류수내수전천내수전300
69울릉군북면북면0.3012011-12-30<NA>지하수용출수북면1000
70울릉군북면북면0.3012011-12-30<NA>지하수용출수통합2200
71청도군운문댐운문면45.1651997-12-052006-08-24호소수운문댐(2지류)운문댐(광역)376000
72청도군풍각풍각면0.1471986-07-092012-01-26복류수청도천풍각600
73청송군청송청송읍0.4691999-02-08<NA>복류수2지류(용전천)청송3300
74청송군부동주왕산면0.0371994-12-31<NA>복류수3지류(주산천)부동330
75청송군부남부남면0.0791994-12-31<NA>복류수2지류(용전천)부남990
76청송군안덕안덕면0.2582001-02-15<NA>복류수2지류(길안천)안덕2640
77청송군진보진보면0.4791999-02-08<NA>복류수1지류(반변천)진보3300