Overview

Dataset statistics

Number of variables9
Number of observations163
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory75.8 B

Variable types

Numeric3
Categorical3
Text3

Dataset

Description광주광역시 내 민방위 비상급수시설 현황입니다. 시설종류, 용도, 시설명, 시설유형, 소재지주소, 급수용량 등의 정보를 제공합니다. ○ 정부지원시설 : 정부지원금으로 설치한 시설 * 행정안전부(민방위과)에 의해 비상급수를 목적으로 예산 지원 및 구축이 이루어진 시설 ○ 지자체시설 : 지방자치단체가 설치한 시설 * 지자체(시도 및 시군구) 민방위 업무 담당부서의 예산이 투입된 시설 ○ 공공용시설 : 민간 및 정부⋅공공기관 등의 양수시설 중 유사 시 비상급수시설로 전환이 가능한 시설로 소유자⋅관리자 또는 점유자(이하 “시설주”)의 동의를 얻어 주민에게 용수 공급이 가능토록 지정한 시설
URLhttps://www.data.go.kr/data/15059988/fileData.do

Alerts

연번 is highly overall correlated with 자치구 and 2 other fieldsHigh correlation
설치연도 is highly overall correlated with 용량(톤)High correlation
용량(톤) is highly overall correlated with 설치연도High correlation
자치구 is highly overall correlated with 연번High correlation
구분 is highly overall correlated with 연번High correlation
수질종류 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:25:45.442789
Analysis finished2023-12-12 07:25:47.394018
Duration1.95 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct163
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T16:25:47.478770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.1
Q141.5
median82
Q3122.5
95-th percentile154.9
Maximum163
Range162
Interquartile range (IQR)81

Descriptive statistics

Standard deviation47.198164
Coefficient of variation (CV)0.57558736
Kurtosis-1.2
Mean82
Median Absolute Deviation (MAD)41
Skewness0
Sum13366
Variance2227.6667
MonotonicityStrictly increasing
2023-12-12T16:25:47.647574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
104 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
108 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
Other values (153) 153
93.9%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
163 1
0.6%
162 1
0.6%
161 1
0.6%
160 1
0.6%
159 1
0.6%
158 1
0.6%
157 1
0.6%
156 1
0.6%
155 1
0.6%
154 1
0.6%

자치구
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
광산구
64 
북구
31 
서구
29 
남구
27 
동구
12 

Length

Max length3
Median length2
Mean length2.392638
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광산구 64
39.3%
북구 31
19.0%
서구 29
17.8%
남구 27
16.6%
동구 12
 
7.4%

Length

2023-12-12T16:25:47.785392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:25:47.908072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구 64
39.3%
북구 31
19.0%
서구 29
17.8%
남구 27
16.6%
동구 12
 
7.4%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공공용
101 
정부지원
57 
지자체
 
5

Length

Max length4
Median length3
Mean length3.3496933
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공용 101
62.0%
정부지원 57
35.0%
지자체 5
 
3.1%

Length

2023-12-12T16:25:48.041947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:25:48.144561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공용 101
62.0%
정부지원 57
35.0%
지자체 5
 
3.1%

급수시설명
Text

Distinct162
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T16:25:48.379339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length6.4662577
Min length3

Characters and Unicode

Total characters1054
Distinct characters226
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)98.8%

Sample

1st row충장동주민센터
2nd row동구청
3rd row삼익세라믹아파트
4th row남초등학교
5th row장원초등학교
ValueCountFrequency (%)
옆 4
 
2.2%
한두레농산물센터 2
 
1.1%
내 2
 
1.1%
고려고등학교 1
 
0.6%
동신대학교광주한방병원 1
 
0.6%
각화초등학교 1
 
0.6%
씨너스사우나 1
 
0.6%
호남대학교 1
 
0.6%
국제온천 1
 
0.6%
남흥대중사우나 1
 
0.6%
Other values (165) 165
91.7%
2023-12-12T16:25:48.833174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 69
 
6.5%
교 68
 
6.5%
등 47
 
4.5%
초 35
 
3.3%
동 28
 
2.7%
원 27
 
2.6%
중 24
 
2.3%
공 23
 
2.2%
19
 
1.8%
아 19
 
1.8%
Other values (216) 695
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1014
96.2%
Space Separator 19
 
1.8%
Decimal Number 7
 
0.7%
Uppercase Letter 6
 
0.6%
Close Punctuation 4
 
0.4%
Open Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 69
 
6.8%
교 68
 
6.7%
등 47
 
4.6%
초 35
 
3.5%
동 28
 
2.8%
원 27
 
2.7%
중 24
 
2.4%
공 23
 
2.3%
아 19
 
1.9%
대 19
 
1.9%
Other values (206) 655
64.6%
Decimal Number
ValueCountFrequency (%)
2 3
42.9%
1 2
28.6%
5 1
 
14.3%
3 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
P 2
33.3%
T 2
33.3%
Space Separator
ValueCountFrequency (%)
19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1014
96.2%
Common 34
 
3.2%
Latin 6
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 69
 
6.8%
교 68
 
6.7%
등 47
 
4.6%
초 35
 
3.5%
동 28
 
2.8%
원 27
 
2.7%
중 24
 
2.4%
공 23
 
2.3%
아 19
 
1.9%
대 19
 
1.9%
Other values (206) 655
64.6%
Common
ValueCountFrequency (%)
19
55.9%
) 4
 
11.8%
( 4
 
11.8%
2 3
 
8.8%
1 2
 
5.9%
5 1
 
2.9%
3 1
 
2.9%
Latin
ValueCountFrequency (%)
A 2
33.3%
P 2
33.3%
T 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1014
96.2%
ASCII 40
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 69
 
6.8%
교 68
 
6.7%
등 47
 
4.6%
초 35
 
3.5%
동 28
 
2.8%
원 27
 
2.7%
중 24
 
2.4%
공 23
 
2.3%
아 19
 
1.9%
대 19
 
1.9%
Other values (206) 655
64.6%
ASCII
ValueCountFrequency (%)
19
47.5%
) 4
 
10.0%
( 4
 
10.0%
2 3
 
7.5%
A 2
 
5.0%
1 2
 
5.0%
P 2
 
5.0%
T 2
 
5.0%
5 1
 
2.5%
3 1
 
2.5%

소재지
Text

UNIQUE 

Distinct163
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T16:25:49.142701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length14.447853
Min length6

Characters and Unicode

Total characters2355
Distinct characters146
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

Unique163 ?
Unique (%)100.0%

Sample

1st row금남로 231(금남로2가)
2nd row서남로 1(서석동)
3rd row남문로 676(학동)
4th row지원로5번길 20(소태동)
5th row무등로 533(산수동)
ValueCountFrequency (%)
월곡산정로 5
 
1.3%
월산동 4
 
1.0%
운암동 4
 
1.0%
두암동 3
 
0.8%
왕버들로 3
 
0.8%
백운동 3
 
0.8%
봉선동 3
 
0.8%
주월동 3
 
0.8%
3 3
 
0.8%
10 3
 
0.8%
Other values (313) 347
91.1%
2023-12-12T16:25:49.583960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
246
 
10.4%
동 163
 
6.9%
( 155
 
6.6%
) 155
 
6.6%
로 149
 
6.3%
1 110
 
4.7%
2 92
 
3.9%
길 83
 
3.5%
번 71
 
3.0%
0 62
 
2.6%
Other values (136) 1069
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1184
50.3%
Decimal Number 583
24.8%
Space Separator 246
 
10.4%
Open Punctuation 155
 
6.6%
Close Punctuation 155
 
6.6%
Dash Punctuation 29
 
1.2%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 163
 
13.8%
로 149
 
12.6%
길 83
 
7.0%
번 71
 
6.0%
월 38
 
3.2%
산 34
 
2.9%
대 33
 
2.8%
암 22
 
1.9%
남 20
 
1.7%
운 20
 
1.7%
Other values (121) 551
46.5%
Decimal Number
ValueCountFrequency (%)
1 110
18.9%
2 92
15.8%
0 62
10.6%
3 56
9.6%
4 52
8.9%
7 50
8.6%
5 48
8.2%
6 42
 
7.2%
8 41
 
7.0%
9 30
 
5.1%
Space Separator
ValueCountFrequency (%)
246
100.0%
Open Punctuation
ValueCountFrequency (%)
( 155
100.0%
Close Punctuation
ValueCountFrequency (%)
) 155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1184
50.3%
Common 1171
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 163
 
13.8%
로 149
 
12.6%
길 83
 
7.0%
번 71
 
6.0%
월 38
 
3.2%
산 34
 
2.9%
대 33
 
2.8%
암 22
 
1.9%
남 20
 
1.7%
운 20
 
1.7%
Other values (121) 551
46.5%
Common
ValueCountFrequency (%)
246
21.0%
( 155
13.2%
) 155
13.2%
1 110
9.4%
2 92
 
7.9%
0 62
 
5.3%
3 56
 
4.8%
4 52
 
4.4%
7 50
 
4.3%
5 48
 
4.1%
Other values (5) 145
12.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1184
50.3%
ASCII 1171
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246
21.0%
( 155
13.2%
) 155
13.2%
1 110
9.4%
2 92
 
7.9%
0 62
 
5.3%
3 56
 
4.8%
4 52
 
4.4%
7 50
 
4.3%
5 48
 
4.1%
Other values (5) 145
12.4%
Hangul
ValueCountFrequency (%)
동 163
 
13.8%
로 149
 
12.6%
길 83
 
7.0%
번 71
 
6.0%
월 38
 
3.2%
산 34
 
2.9%
대 33
 
2.8%
암 22
 
1.9%
남 20
 
1.7%
운 20
 
1.7%
Other values (121) 551
46.5%

설치연도
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.7055
Minimum1977
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T16:25:49.745377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1977
5-th percentile1982
Q11991
median1997
Q32003
95-th percentile2012
Maximum2019
Range42
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1664529
Coefficient of variation (CV)0.0045907886
Kurtosis-0.47623467
Mean1996.7055
Median Absolute Deviation (MAD)6
Skewness0.13704279
Sum325463
Variance84.023858
MonotonicityNot monotonic
2023-12-12T16:25:49.883611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1997 15
 
9.2%
1994 12
 
7.4%
2003 10
 
6.1%
1985 7
 
4.3%
1995 7
 
4.3%
2001 7
 
4.3%
1983 7
 
4.3%
1982 7
 
4.3%
1992 6
 
3.7%
2007 5
 
3.1%
Other values (29) 80
49.1%
ValueCountFrequency (%)
1977 1
 
0.6%
1978 1
 
0.6%
1980 1
 
0.6%
1982 7
4.3%
1983 7
4.3%
1984 1
 
0.6%
1985 7
4.3%
1986 3
1.8%
1987 3
1.8%
1988 2
 
1.2%
ValueCountFrequency (%)
2019 2
1.2%
2018 1
 
0.6%
2015 1
 
0.6%
2014 1
 
0.6%
2013 2
1.2%
2012 4
2.5%
2011 1
 
0.6%
2010 4
2.5%
2009 2
1.2%
2008 4
2.5%

수질종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
생활용수
91 
음용수
72 

Length

Max length4
Median length4
Mean length3.5582822
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row음용수
2nd row음용수
3rd row음용수
4th row음용수
5th row생활용수

Common Values

ValueCountFrequency (%)
생활용수 91
55.8%
음용수 72
44.2%

Length

2023-12-12T16:25:50.039215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:25:50.179438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활용수 91
55.8%
음용수 72
44.2%

용량(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.88344
Minimum50
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T16:25:50.294555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile100
Q1100
median150
Q3300
95-th percentile600
Maximum990
Range940
Interquartile range (IQR)200

Descriptive statistics

Standard deviation172.80283
Coefficient of variation (CV)0.74201428
Kurtosis2.2870394
Mean232.88344
Median Absolute Deviation (MAD)50
Skewness1.5942146
Sum37960
Variance29860.82
MonotonicityNot monotonic
2023-12-12T16:25:50.437556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
100 43
26.4%
150 13
 
8.0%
200 13
 
8.0%
120 11
 
6.7%
300 10
 
6.1%
600 7
 
4.3%
500 6
 
3.7%
250 6
 
3.7%
350 6
 
3.7%
180 5
 
3.1%
Other values (30) 43
26.4%
ValueCountFrequency (%)
50 2
 
1.2%
71 1
 
0.6%
80 1
 
0.6%
100 43
26.4%
110 2
 
1.2%
115 2
 
1.2%
120 11
 
6.7%
123 1
 
0.6%
130 3
 
1.8%
134 1
 
0.6%
ValueCountFrequency (%)
990 1
 
0.6%
700 3
1.8%
650 2
 
1.2%
600 7
4.3%
550 2
 
1.2%
505 1
 
0.6%
500 6
3.7%
450 1
 
0.6%
400 4
2.5%
390 1
 
0.6%

관할동
Text

Distinct84
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T16:25:50.759871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0184049
Min length2

Characters and Unicode

Total characters492
Distinct characters81
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

Unique44 ?
Unique (%)27.0%

Sample

1st row충장동
2nd row서남동
3rd row학운동
4th row지원1
5th row산수2동
ValueCountFrequency (%)
운남 6
 
3.7%
어룡 6
 
3.7%
첨단1 5
 
3.1%
첨단2 5
 
3.1%
월곡2 5
 
3.1%
우산 5
 
3.1%
상무2동 5
 
3.1%
수완 5
 
3.1%
월곡1 4
 
2.5%
신창 4
 
2.5%
Other values (74) 113
69.3%
2023-12-12T16:25:51.255056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 101
20.5%
2 41
 
8.3%
1 24
 
4.9%
월 19
 
3.9%
산 19
 
3.9%
곡 15
 
3.0%
운 14
 
2.8%
암 13
 
2.6%
첨 10
 
2.0%
단 10
 
2.0%
Other values (71) 226
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 420
85.4%
Decimal Number 72
 
14.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
24.0%
월 19
 
4.5%
산 19
 
4.5%
곡 15
 
3.6%
운 14
 
3.3%
암 13
 
3.1%
첨 10
 
2.4%
단 10
 
2.4%
남 10
 
2.4%
정 9
 
2.1%
Other values (66) 200
47.6%
Decimal Number
ValueCountFrequency (%)
2 41
56.9%
1 24
33.3%
3 4
 
5.6%
4 2
 
2.8%
5 1
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 420
85.4%
Common 72
 
14.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 101
24.0%
월 19
 
4.5%
산 19
 
4.5%
곡 15
 
3.6%
운 14
 
3.3%
암 13
 
3.1%
첨 10
 
2.4%
단 10
 
2.4%
남 10
 
2.4%
정 9
 
2.1%
Other values (66) 200
47.6%
Common
ValueCountFrequency (%)
2 41
56.9%
1 24
33.3%
3 4
 
5.6%
4 2
 
2.8%
5 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 420
85.4%
ASCII 72
 
14.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 101
24.0%
월 19
 
4.5%
산 19
 
4.5%
곡 15
 
3.6%
운 14
 
3.3%
암 13
 
3.1%
첨 10
 
2.4%
단 10
 
2.4%
남 10
 
2.4%
정 9
 
2.1%
Other values (66) 200
47.6%
ASCII
ValueCountFrequency (%)
2 41
56.9%
1 24
33.3%
3 4
 
5.6%
4 2
 
2.8%
5 1
 
1.4%

Interactions

2023-12-12T16:25:46.916763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:46.032301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:46.639439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:47.010968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:46.160477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:46.733797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:47.083140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:46.544999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:25:46.810920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:25:51.366954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번자치구구분설치연도수질종류용량(톤)관할동
연번1.0000.9780.8420.5770.6600.5410.897
자치구0.9781.0000.3790.5670.0740.4351.000
구분0.8420.3791.0000.6140.1020.4240.384
설치연도0.5770.5670.6141.0000.1930.5900.697
수질종류0.6600.0740.1020.1931.0000.0000.419
용량(톤)0.5410.4350.4240.5900.0001.0000.738
관할동0.8971.0000.3840.6970.4190.7381.000
2023-12-12T16:25:51.475167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수질종류구분자치구
수질종류1.0000.1680.089
구분0.1681.0000.308
자치구0.0890.3081.000
2023-12-12T16:25:51.562571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치연도용량(톤)자치구구분수질종류
연번1.0000.131-0.3010.7720.7370.511
설치연도0.1311.000-0.5770.2560.4530.106
용량(톤)-0.301-0.5771.0000.2820.2930.000
자치구0.7720.2560.2821.0000.3080.089
구분0.7370.4530.2930.3081.0000.168
수질종류0.5110.1060.0000.0890.1681.000

Missing values

2023-12-12T16:25:47.185926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:25:47.333313image/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

연번자치구구분급수시설명소재지설치연도수질종류용량(톤)관할동
01동구정부지원충장동주민센터금남로 231(금남로2가)1983음용수700충장동
12동구정부지원동구청서남로 1(서석동)1996음용수180서남동
23동구정부지원삼익세라믹아파트남문로 676(학동)1992음용수500학운동
34동구정부지원남초등학교지원로5번길 20(소태동)1997음용수100지원1
45동구정부지원장원초등학교무등로 533(산수동)1997생활용수100산수2동
56동구정부지원고등검찰청준법로 7-12(지산동)1978생활용수260지산2동
67동구정부지원당산나무앞밤실로4번길 16(지산동)1982생활용수450지산2동
78동구정부지원동진맨션무등로 417-9(산수동)1982생활용수300산수1동
89서구정부지원금호중앙공원마재로 72(금호동)2007음용수120금호2동
910서구정부지원서석고 내화정로253번길 27(화정동)1985생활용수350화정2동
연번자치구구분급수시설명소재지설치연도수질종류용량(톤)관할동
153154광산구공공용하남초등학교하남대로54번길 10(하남동)1994생활용수100하남
154155광산구공공용돈보스코학교고봉로 136(하남동)1994생활용수100하남
155156광산구공공용임곡초등학교하림길 26(임곡동)1994생활용수100임곡
156157광산구공공용더하기센터(본량중)용진로 303(남산동)1991생활용수100본량
157158광산구공공용평동초등학교평동로741 (옥동)1993생활용수120평동
158159광산구공공용하남금호타운월곡산정로 80(월곡동)1992생활용수390월곡1
159160광산구공공용한성2차아파트월곡산정로 96-21(월곡동)1991생활용수190월곡1
160161광산구공공용한두레농산물센터왕버들로 207(수완동) 1호공2007생활용수150비아
161162광산구공공용한두레농산물센터왕버들로 207(수완동) 2호공2007생활용수150비아
162163광산구공공용건영APT첨단중앙로181번길 104(월계)1999생활용수150첨단1