Overview

Dataset statistics

Number of variables9
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory81.3 B

Variable types

Numeric4
Text3
Categorical2

Dataset

DescriptionK-water 관리 광역 취수장에 대한 시설용량, 취수구분, 후처리시설 등 시설 제원 정보 데이터 항목을 제공합니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049849/fileData.do

Alerts

후처리시설 has constant value ""Constant
관리번호 is highly overall correlated with 취수구분High correlation
취수용량 is highly overall correlated with 취수구분High correlation
취수구분 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
관리번호 has unique valuesUnique
취수장 명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:41:39.678027
Analysis finished2023-12-12 04:41:42.613187
Duration2.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2926699 × 109
Minimum1302210
Maximum1.9000001 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T13:41:42.722189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1302210
5-th percentile2014010
Q12403201
median1.9 × 109
Q31.9000001 × 109
95-th percentile1.9000001 × 109
Maximum1.9000001 × 109
Range1.8986979 × 109
Interquartile range (IQR)1.8975969 × 109

Descriptive statistics

Standard deviation9.0358442 × 108
Coefficient of variation (CV)0.69900633
Kurtosis-1.4473373
Mean1.2926699 × 109
Median Absolute Deviation (MAD)71
Skewness-0.82190369
Sum3.2316747 × 1010
Variance8.1646481 × 1017
MonotonicityStrictly increasing
2023-12-12T13:41:42.887074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1302210 1
 
4.0%
2012210 1
 
4.0%
1900000149 1
 
4.0%
1900000139 1
 
4.0%
1900000135 1
 
4.0%
1900000118 1
 
4.0%
1900000111 1
 
4.0%
1900000100 1
 
4.0%
1900000097 1
 
4.0%
1900000094 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1302210 1
4.0%
2012210 1
4.0%
2021210 1
4.0%
2101210 1
4.0%
2201220 1
4.0%
2201230 1
4.0%
2403201 1
4.0%
2503210 1
4.0%
1900000002 1
4.0%
1900000015 1
4.0%
ValueCountFrequency (%)
1900000149 1
4.0%
1900000139 1
4.0%
1900000135 1
4.0%
1900000118 1
4.0%
1900000111 1
4.0%
1900000100 1
4.0%
1900000097 1
4.0%
1900000094 1
4.0%
1900000062 1
4.0%
1900000061 1
4.0%

취수장 명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T13:41:43.167865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.88
Min length2

Characters and Unicode

Total characters122
Distinct characters47
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

Unique25 ?
Unique (%)100.0%

Sample

1st row달방댐
2nd row영천댐(안계댐)
3rd row운문댐
4th row안계
5th row사연댐
ValueCountFrequency (%)
달방댐 1
 
4.0%
팔당3취수장 1
 
4.0%
본포취수장 1
 
4.0%
원동취수장 1
 
4.0%
해평취수장 1
 
4.0%
대불취수장 1
 
4.0%
이사천취수장 1
 
4.0%
주암취수장 1
 
4.0%
칠보취수장 1
 
4.0%
현도취수장 1
 
4.0%
Other values (15) 15
60.0%
2023-12-12T13:41:43.568323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
취 17
 
13.9%
장 17
 
13.9%
수 17
 
13.9%
댐 8
 
6.6%
대 3
 
2.5%
당 3
 
2.5%
팔 3
 
2.5%
동 3
 
2.5%
천 3
 
2.5%
) 2
 
1.6%
Other values (37) 46
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
94.3%
Decimal Number 3
 
2.5%
Close Punctuation 2
 
1.6%
Open Punctuation 2
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
취 17
14.8%
장 17
14.8%
수 17
14.8%
댐 8
 
7.0%
대 3
 
2.6%
당 3
 
2.6%
팔 3
 
2.6%
동 3
 
2.6%
천 3
 
2.6%
안 2
 
1.7%
Other values (32) 39
33.9%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
1 1
33.3%
2 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
94.3%
Common 7
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
취 17
14.8%
장 17
14.8%
수 17
14.8%
댐 8
 
7.0%
대 3
 
2.6%
당 3
 
2.6%
팔 3
 
2.6%
동 3
 
2.6%
천 3
 
2.6%
안 2
 
1.7%
Other values (32) 39
33.9%
Common
ValueCountFrequency (%)
) 2
28.6%
( 2
28.6%
3 1
14.3%
1 1
14.3%
2 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
94.3%
ASCII 7
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
취 17
14.8%
장 17
14.8%
수 17
14.8%
댐 8
 
7.0%
대 3
 
2.6%
당 3
 
2.6%
팔 3
 
2.6%
동 3
 
2.6%
천 3
 
2.6%
안 2
 
1.7%
Other values (32) 39
33.9%
ASCII
ValueCountFrequency (%)
) 2
28.6%
( 2
28.6%
3 1
14.3%
1 1
14.3%
2 1
14.3%

건설일
Real number (ℝ)

Distinct19
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.36
Minimum1965
Maximum2008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T13:41:43.723695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1965
5-th percentile1967.4
Q11977
median1989
Q31994
95-th percentile2001.4
Maximum2008
Range43
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.137682
Coefficient of variation (CV)0.0061105149
Kurtosis-0.97447895
Mean1986.36
Median Absolute Deviation (MAD)9
Skewness-0.31306878
Sum49659
Variance147.32333
MonotonicityNot monotonic
2023-12-12T13:41:43.847627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1971 3
 
12.0%
1994 2
 
8.0%
1989 2
 
8.0%
1999 2
 
8.0%
1992 2
 
8.0%
1990 1
 
4.0%
1998 1
 
4.0%
1987 1
 
4.0%
1967 1
 
4.0%
1977 1
 
4.0%
Other values (9) 9
36.0%
ValueCountFrequency (%)
1965 1
 
4.0%
1967 1
 
4.0%
1969 1
 
4.0%
1971 3
12.0%
1977 1
 
4.0%
1979 1
 
4.0%
1980 1
 
4.0%
1987 1
 
4.0%
1988 1
 
4.0%
1989 2
8.0%
ValueCountFrequency (%)
2008 1
4.0%
2002 1
4.0%
1999 2
8.0%
1998 1
4.0%
1997 1
4.0%
1994 2
8.0%
1992 2
8.0%
1991 1
4.0%
1990 1
4.0%
1989 2
8.0%

주소
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T13:41:44.166808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length21.28
Min length14

Characters and Unicode

Total characters532
Distinct characters100
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

Unique25 ?
Unique (%)100.0%

Sample

1st row강원도 동해시 달방동 산131-8
2nd row경상북도 영천시 자양면 성곡리 51
3rd row경상북도 청도군 운문면 대천리 산133
4th row경북 경주시 강동읍 유금리
5th row울산광역시 울주군 범서읍 사연리 산93-2
ValueCountFrequency (%)
경상북도 4
 
3.3%
경기도 4
 
3.3%
경상남도 4
 
3.3%
배알미동 3
 
2.5%
하남시 3
 
2.5%
전라남도 3
 
2.5%
경주시 3
 
2.5%
강원도 2
 
1.7%
경북 2
 
1.7%
순천시 2
 
1.7%
Other values (86) 90
75.0%
2023-12-12T13:41:44.677048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
98
 
18.4%
1 25
 
4.7%
시 23
 
4.3%
도 23
 
4.3%
리 20
 
3.8%
경 17
 
3.2%
산 17
 
3.2%
면 14
 
2.6%
- 13
 
2.4%
남 11
 
2.1%
Other values (90) 271
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 341
64.1%
Space Separator 98
 
18.4%
Decimal Number 80
 
15.0%
Dash Punctuation 13
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 23
 
6.7%
도 23
 
6.7%
리 20
 
5.9%
경 17
 
5.0%
산 17
 
5.0%
면 14
 
4.1%
남 11
 
3.2%
동 11
 
3.2%
지 9
 
2.6%
번 9
 
2.6%
Other values (78) 187
54.8%
Decimal Number
ValueCountFrequency (%)
1 25
31.2%
3 9
 
11.2%
2 9
 
11.2%
9 7
 
8.8%
4 7
 
8.8%
0 7
 
8.8%
5 5
 
6.2%
7 4
 
5.0%
8 4
 
5.0%
6 3
 
3.8%
Space Separator
ValueCountFrequency (%)
98
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 341
64.1%
Common 191
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 23
 
6.7%
도 23
 
6.7%
리 20
 
5.9%
경 17
 
5.0%
산 17
 
5.0%
면 14
 
4.1%
남 11
 
3.2%
동 11
 
3.2%
지 9
 
2.6%
번 9
 
2.6%
Other values (78) 187
54.8%
Common
ValueCountFrequency (%)
98
51.3%
1 25
 
13.1%
- 13
 
6.8%
3 9
 
4.7%
2 9
 
4.7%
9 7
 
3.7%
4 7
 
3.7%
0 7
 
3.7%
5 5
 
2.6%
7 4
 
2.1%
Other values (2) 7
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 341
64.1%
ASCII 191
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
98
51.3%
1 25
 
13.1%
- 13
 
6.8%
3 9
 
4.7%
2 9
 
4.7%
9 7
 
3.7%
4 7
 
3.7%
0 7
 
3.7%
5 5
 
2.6%
7 4
 
2.1%
Other values (2) 7
 
3.7%
Hangul
ValueCountFrequency (%)
시 23
 
6.7%
도 23
 
6.7%
리 20
 
5.9%
경 17
 
5.0%
산 17
 
5.0%
면 14
 
4.1%
남 11
 
3.2%
동 11
 
3.2%
지 9
 
2.6%
번 9
 
2.6%
Other values (78) 187
54.8%

읍면동 코드
Real number (ℝ)

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42879912
Minimum11215830
Maximum48330320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T13:41:44.848357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11215830
5-th percentile31710254
Q141450510
median46150350
Q347190340
95-th percentile48310360
Maximum48330320
Range37114490
Interquartile range (IQR)5739830

Descriptive statistics

Standard deviation8000644.2
Coefficient of variation (CV)0.18658257
Kurtosis10.203979
Mean42879912
Median Absolute Deviation (MAD)2160020
Skewness-2.9416252
Sum1.0719978 × 109
Variance6.4010308 × 1013
MonotonicityNot monotonic
2023-12-12T13:41:44.998640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
41450510 3
 
12.0%
47130370 2
 
8.0%
42170650 1
 
4.0%
43111525 1
 
4.0%
48310320 1
 
4.0%
48121310 1
 
4.0%
48330320 1
 
4.0%
47190340 1
 
4.0%
46840320 1
 
4.0%
46150400 1
 
4.0%
Other values (12) 12
48.0%
ValueCountFrequency (%)
11215830 1
 
4.0%
31710253 1
 
4.0%
31710259 1
 
4.0%
41360250 1
 
4.0%
41450510 3
12.0%
42170650 1
 
4.0%
42230320 1
 
4.0%
43111330 1
 
4.0%
43111525 1
 
4.0%
45180420 1
 
4.0%
ValueCountFrequency (%)
48330320 1
4.0%
48310370 1
4.0%
48310320 1
4.0%
48121310 1
4.0%
47820350 1
4.0%
47230360 1
4.0%
47190340 1
4.0%
47130370 2
8.0%
47130250 1
4.0%
46840320 1
4.0%

취수구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
용수댐
11 
하천
11 
다목적댐
3 

Length

Max length4
Median length3
Mean length2.68
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
용수댐 11
44.0%
하천 11
44.0%
다목적댐 3
 
12.0%

Length

2023-12-12T13:41:45.134855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:41:45.244138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용수댐 11
44.0%
하천 11
44.0%
다목적댐 3
 
12.0%

취수 명
Text

Distinct17
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T13:41:45.409626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.2
Min length3

Characters and Unicode

Total characters80
Distinct characters32
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

Unique13 ?
Unique (%)52.0%

Sample

1st row달방댐
2nd row안계댐
3rd row운문댐
4th row대곡댐
5th row사연댐
ValueCountFrequency (%)
팔당호 5
20.0%
낙동강 3
12.0%
대청댐 2
 
8.0%
안계댐 2
 
8.0%
동진강 1
 
4.0%
달방댐 1
 
4.0%
영산강(영산호 1
 
4.0%
이사천 1
 
4.0%
주암댐 1
 
4.0%
광동댐 1
 
4.0%
Other values (7) 7
28.0%
2023-12-12T13:41:45.693605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
댐 14
17.5%
호 6
 
7.5%
팔 5
 
6.2%
당 5
 
6.2%
동 5
 
6.2%
강 5
 
6.2%
대 4
 
5.0%
낙 3
 
3.8%
산 2
 
2.5%
영 2
 
2.5%
Other values (22) 29
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
97.5%
Close Punctuation 1
 
1.2%
Open Punctuation 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
댐 14
17.9%
호 6
 
7.7%
팔 5
 
6.4%
당 5
 
6.4%
동 5
 
6.4%
강 5
 
6.4%
대 4
 
5.1%
낙 3
 
3.8%
산 2
 
2.6%
영 2
 
2.6%
Other values (20) 27
34.6%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
97.5%
Common 2
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
댐 14
17.9%
호 6
 
7.7%
팔 5
 
6.4%
당 5
 
6.4%
동 5
 
6.4%
강 5
 
6.4%
대 4
 
5.1%
낙 3
 
3.8%
산 2
 
2.6%
영 2
 
2.6%
Other values (20) 27
34.6%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
97.5%
ASCII 2
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
댐 14
17.9%
호 6
 
7.7%
팔 5
 
6.4%
당 5
 
6.4%
동 5
 
6.4%
강 5
 
6.4%
대 4
 
5.1%
낙 3
 
3.8%
산 2
 
2.6%
영 2
 
2.6%
Other values (20) 27
34.6%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

취수용량
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567120.74
Minimum18.463
Maximum2830000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T13:41:45.832496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.463
5-th percentile6800
Q157500
median250000
Q3540000
95-th percentile2556000
Maximum2830000
Range2829981.5
Interquartile range (IQR)482500

Descriptive statistics

Standard deviation830641.27
Coefficient of variation (CV)1.4646639
Kurtosis2.7923101
Mean567120.74
Median Absolute Deviation (MAD)214000
Skewness1.9557254
Sum14178018
Variance6.8996492 × 1011
MonotonicityNot monotonic
2023-12-12T13:41:45.993232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
250000.0 2
 
8.0%
40000.0 1
 
4.0%
2380000.0 1
 
4.0%
20000.0 1
 
4.0%
285000.0 1
 
4.0%
1275000.0 1
 
4.0%
464000.0 1
 
4.0%
57500.0 1
 
4.0%
540000.0 1
 
4.0%
596000.0 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
18.463 1
4.0%
4500.0 1
4.0%
16000.0 1
4.0%
20000.0 1
4.0%
40000.0 1
4.0%
50000.0 1
4.0%
57500.0 1
4.0%
70000.0 1
4.0%
90000.0 1
4.0%
100000.0 1
4.0%
ValueCountFrequency (%)
2830000.0 1
4.0%
2600000.0 1
4.0%
2380000.0 1
4.0%
1275000.0 1
4.0%
994000.0 1
4.0%
596000.0 1
4.0%
540000.0 1
4.0%
475000.0 1
4.0%
464000.0 1
4.0%
376000.0 1
4.0%

후처리시설
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
없음
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
없음 25
100.0%

Length

2023-12-12T13:41:46.126131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:41:46.222504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 25
100.0%

Interactions

2023-12-12T13:41:41.848588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.163685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.673554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:41.097512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:41.976469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.302688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.785122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:41.227367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:42.082001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.408052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.870560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:41.318592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:42.190768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.542351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:40.976292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:41:41.430067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:41:46.287817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호취수장 명건설일주소읍면동 코드취수구분취수 명취수용량
관리번호1.0001.0000.3841.0000.3280.4780.9680.000
취수장 명1.0001.0001.0001.0001.0001.0001.0001.000
건설일0.3841.0001.0001.0000.7870.6900.5560.745
주소1.0001.0001.0001.0001.0001.0001.0001.000
읍면동 코드0.3281.0000.7871.0001.0000.3441.0000.000
취수구분0.4781.0000.6901.0000.3441.0001.0000.707
취수 명0.9681.0000.5561.0001.0001.0001.0000.000
취수용량0.0001.0000.7451.0000.0000.7070.0001.000
2023-12-12T13:41:46.403890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호건설일읍면동 코드취수용량취수구분
관리번호1.0000.1980.2530.3650.733
건설일0.1981.000-0.1530.2400.338
읍면동 코드0.253-0.1531.000-0.2270.000
취수용량0.3650.240-0.2271.0000.558
취수구분0.7330.3380.0000.5581.000

Missing values

2023-12-12T13:41:42.342028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:41:42.542842image/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

관리번호취수장 명건설일주소읍면동 코드취수구분취수 명취수용량후처리시설
01302210달방댐1990강원도 동해시 달방동 산131-842170650용수댐달방댐40000.0없음
12012210영천댐(안계댐)1971경상북도 영천시 자양면 성곡리 5147230360용수댐안계댐195000.0없음
22021210운문댐1994경상북도 청도군 운문면 대천리 산13347820350용수댐운문댐376000.0없음
32101210안계1971경북 경주시 강동읍 유금리47130370용수댐대곡댐18.463없음
42201220사연댐1965울산광역시 울주군 범서읍 사연리 산93-231710259용수댐사연댐220000.0없음
52201230대암댐(원동구)1969울산광역시 울주군 언양읍 구수리 산18531710253용수댐대암댐50000.0없음
62403201감포댐2008경상북도 경주시 감포읍 오류리 산17-247130250용수댐감포댐4500.0없음
72503210연초댐1979경상남도 거제시 연초면 덕치리 산25-148310370용수댐연초댐16000.0없음
81900000002부조취수장1971경북 경주시 강동면 국당리 산10-1번지47130370용수댐안계댐100000.0없음
91900000015광동취수장1989강원도 삼척시 하장면 숙암리 산103-142230320용수댐광동댐70000.0없음
관리번호취수장 명건설일주소읍면동 코드취수구분취수 명취수용량후처리시설
151900000061대청취수장1989충청북도 청원군 가덕면 국전리 21043111330다목적댐대청댐250000.0없음
161900000062현도취수장2002충청북도 청주시서원구 현도면 하석리 383-143111525다목적댐대청댐994000.0없음
171900000094칠보취수장1992전라북도 정읍시 칠보면 시산리 429-1번지45180420하천동진강90000.0없음
181900000097주암취수장1998전라남도 순천시 주암면 광천리 46846150350다목적댐주암댐596000.0없음
191900000100이사천취수장1991전라남도 순천시 상사면 용계리 67-146150400하천이사천540000.0없음
201900000111대불취수장1994전라남도 무안군 몽탄면 몽강리 16946840320하천영산강(영산호)57500.0없음
211900000118해평취수장1997경상북도 구미시 해평면 문량2리 704-54번지47190340하천낙동강464000.0없음
221900000135원동취수장1977경상남도 양산시 원동면 화제리 2945-1번지48330320하천낙동강1275000.0없음
231900000139본포취수장1967경상남도 창원시 의창구 동읍 본포리 210번지48121310하천낙동강285000.0없음
241900000149구천취수장1987경상남도 거제시 동부면 구천리 산10-948310320용수댐구천댐20000.0없음