Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory74.7 B

Variable types

Text3
Numeric4
Categorical1

Dataset

Description전라남도 함평군 관내 배수펌프장현황에 관한 자료입니다. 이 자료에는 전라남도 함평군 관내 시설명, 위치, 용량 등을 나태냅니다.
Author전라남도 함평군
URLhttps://www.data.go.kr/data/3073937/fileData.do

Alerts

계약용량(KW) is highly overall correlated with 처리능력(m3_분) and 2 other fieldsHigh correlation
처리능력(m3_분) is highly overall correlated with 계약용량(KW) and 2 other fieldsHigh correlation
유수지용량(m3) is highly overall correlated with 계약용량(KW) and 1 other fieldsHigh correlation
수문(기) is highly overall correlated with 계약용량(KW) and 1 other fieldsHigh correlation
시설명 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2024-03-23 05:32:54.473272
Analysis finished2024-03-23 05:32:59.277409
Duration4.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-23T14:32:59.492899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.8695652
Min length5

Characters and Unicode

Total characters158
Distinct characters46
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

Unique23 ?
Unique (%)100.0%

Sample

1st row우시장 배수펌프장
2nd row5일시장 배수펌프장
3rd row원고막 배수펌프장
4th row쌍교 배수펌프장
5th row복천 배수펌프장
ValueCountFrequency (%)
배수펌프장 10
30.3%
우시장 1
 
3.0%
영흥1배수장 1
 
3.0%
영천배수장 1
 
3.0%
송암배수장 1
 
3.0%
삼정배수장 1
 
3.0%
곡창배수장 1
 
3.0%
삼축배수장 1
 
3.0%
진례배수장 1
 
3.0%
학교배수장 1
 
3.0%
Other values (14) 14
42.4%
2024-03-23T14:33:00.278441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 25
15.8%
배 23
14.6%
수 23
14.6%
펌 13
 
8.2%
프 13
 
8.2%
10
 
6.3%
삼 3
 
1.9%
영 3
 
1.9%
시 2
 
1.3%
진 2
 
1.3%
Other values (36) 41
25.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 145
91.8%
Space Separator 10
 
6.3%
Decimal Number 3
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 25
17.2%
배 23
15.9%
수 23
15.9%
펌 13
 
9.0%
프 13
 
9.0%
삼 3
 
2.1%
영 3
 
2.1%
시 2
 
1.4%
진 2
 
1.4%
정 2
 
1.4%
Other values (32) 36
24.8%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 145
91.8%
Common 13
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 25
17.2%
배 23
15.9%
수 23
15.9%
펌 13
 
9.0%
프 13
 
9.0%
삼 3
 
2.1%
영 3
 
2.1%
시 2
 
1.4%
진 2
 
1.4%
정 2
 
1.4%
Other values (32) 36
24.8%
Common
ValueCountFrequency (%)
10
76.9%
1 1
 
7.7%
5 1
 
7.7%
2 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 145
91.8%
ASCII 13
 
8.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 25
17.2%
배 23
15.9%
수 23
15.9%
펌 13
 
9.0%
프 13
 
9.0%
삼 3
 
2.1%
영 3
 
2.1%
시 2
 
1.4%
진 2
 
1.4%
정 2
 
1.4%
Other values (32) 36
24.8%
ASCII
ValueCountFrequency (%)
10
76.9%
1 1
 
7.7%
5 1
 
7.7%
2 1
 
7.7%

위치
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-23T14:33:00.831081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length22.217391
Min length20

Characters and Unicode

Total characters511
Distinct characters48
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

Unique23 ?
Unique (%)100.0%

Sample

1st row전라남도 함평군 함평읍 기각리 194-13
2nd row전라남도 함평군 함평읍 기각리 262
3rd row전라남도 함평군 학교면 고막리 630
4th row전라남도 함평군 학교면 학교리 445-10
5th row전라남도 함평군 학교면 복천리 1055-7
ValueCountFrequency (%)
전라남도 23
20.0%
함평군 23
20.0%
학교면 11
 
9.6%
엄다면 8
 
7.0%
엄다리 3
 
2.6%
고막리 3
 
2.6%
석정리 2
 
1.7%
학야리 2
 
1.7%
학교리 2
 
1.7%
영흥리 2
 
1.7%
Other values (33) 36
31.3%
2024-03-23T14:33:01.679429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
92
18.0%
함 25
 
4.9%
평 25
 
4.9%
전 23
 
4.5%
남 23
 
4.5%
도 23
 
4.5%
군 23
 
4.5%
라 23
 
4.5%
리 23
 
4.5%
1 23
 
4.5%
Other values (38) 208
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
58.5%
Decimal Number 102
 
20.0%
Space Separator 92
 
18.0%
Dash Punctuation 18
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
함 25
 
8.4%
평 25
 
8.4%
전 23
 
7.7%
남 23
 
7.7%
도 23
 
7.7%
군 23
 
7.7%
라 23
 
7.7%
리 23
 
7.7%
면 21
 
7.0%
학 15
 
5.0%
Other values (26) 75
25.1%
Decimal Number
ValueCountFrequency (%)
1 23
22.5%
9 14
13.7%
2 13
12.7%
4 10
9.8%
0 9
 
8.8%
3 9
 
8.8%
6 8
 
7.8%
5 8
 
7.8%
7 5
 
4.9%
8 3
 
2.9%
Space Separator
ValueCountFrequency (%)
92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
58.5%
Common 212
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
함 25
 
8.4%
평 25
 
8.4%
전 23
 
7.7%
남 23
 
7.7%
도 23
 
7.7%
군 23
 
7.7%
라 23
 
7.7%
리 23
 
7.7%
면 21
 
7.0%
학 15
 
5.0%
Other values (26) 75
25.1%
Common
ValueCountFrequency (%)
92
43.4%
1 23
 
10.8%
- 18
 
8.5%
9 14
 
6.6%
2 13
 
6.1%
4 10
 
4.7%
0 9
 
4.2%
3 9
 
4.2%
6 8
 
3.8%
5 8
 
3.8%
Other values (2) 8
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
58.5%
ASCII 212
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
92
43.4%
1 23
 
10.8%
- 18
 
8.5%
9 14
 
6.6%
2 13
 
6.1%
4 10
 
4.7%
0 9
 
4.2%
3 9
 
4.2%
6 8
 
3.8%
5 8
 
3.8%
Other values (2) 8
 
3.8%
Hangul
ValueCountFrequency (%)
함 25
 
8.4%
평 25
 
8.4%
전 23
 
7.7%
남 23
 
7.7%
도 23
 
7.7%
군 23
 
7.7%
라 23
 
7.7%
리 23
 
7.7%
면 21
 
7.0%
학 15
 
5.0%
Other values (26) 75
25.1%

준공년도
Real number (ℝ)

Distinct14
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5652
Minimum1985
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:33:01.900622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1992.5
Q11997
median2001
Q32004
95-th percentile2013.9
Maximum2018
Range33
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.3967197
Coefficient of variation (CV)0.0036954677
Kurtosis0.78283405
Mean2001.5652
Median Absolute Deviation (MAD)4
Skewness0.36854587
Sum46036
Variance54.711462
MonotonicityNot monotonic
2024-03-23T14:33:02.166317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1997 6
26.1%
2004 4
17.4%
2001 2
 
8.7%
1992 1
 
4.3%
1985 1
 
4.3%
1999 1
 
4.3%
2000 1
 
4.3%
2003 1
 
4.3%
2002 1
 
4.3%
2012 1
 
4.3%
Other values (4) 4
17.4%
ValueCountFrequency (%)
1985 1
 
4.3%
1992 1
 
4.3%
1997 6
26.1%
1998 1
 
4.3%
1999 1
 
4.3%
2000 1
 
4.3%
2001 2
 
8.7%
2002 1
 
4.3%
2003 1
 
4.3%
2004 4
17.4%
ValueCountFrequency (%)
2018 1
 
4.3%
2014 1
 
4.3%
2013 1
 
4.3%
2012 1
 
4.3%
2004 4
17.4%
2003 1
 
4.3%
2002 1
 
4.3%
2001 2
8.7%
2000 1
 
4.3%
1999 1
 
4.3%

펌프(HP 기)
Text

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-23T14:33:02.427270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.3913043
Min length7

Characters and Unicode

Total characters170
Distinct characters12
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)43.5%

Sample

1st row15HP 2대
2nd row15HP 4대
3rd row50HP 2대
4th row20HP 2대
5th row200HP 4대
ValueCountFrequency (%)
4대 11
23.9%
2대 9
19.6%
130hp 6
13.0%
20hp 5
10.9%
75hp 4
 
8.7%
15hp 2
 
4.3%
200hp 2
 
4.3%
5대 2
 
4.3%
50hp 1
 
2.2%
3대 1
 
2.2%
Other values (3) 3
 
6.5%
2024-03-23T14:33:02.927098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
H 23
13.5%
P 23
13.5%
23
13.5%
대 23
13.5%
0 18
10.6%
2 17
10.0%
4 11
6.5%
5 11
6.5%
1 8
 
4.7%
3 8
 
4.7%
Other values (2) 5
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78
45.9%
Uppercase Letter 46
27.1%
Space Separator 23
 
13.5%
Other Letter 23
 
13.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18
23.1%
2 17
21.8%
4 11
14.1%
5 11
14.1%
1 8
10.3%
3 8
10.3%
7 4
 
5.1%
6 1
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
H 23
50.0%
P 23
50.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Other Letter
ValueCountFrequency (%)
대 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101
59.4%
Latin 46
27.1%
Hangul 23
 
13.5%

Most frequent character per script

Common
ValueCountFrequency (%)
23
22.8%
0 18
17.8%
2 17
16.8%
4 11
10.9%
5 11
10.9%
1 8
 
7.9%
3 8
 
7.9%
7 4
 
4.0%
6 1
 
1.0%
Latin
ValueCountFrequency (%)
H 23
50.0%
P 23
50.0%
Hangul
ValueCountFrequency (%)
대 23
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147
86.5%
Hangul 23
 
13.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 23
15.6%
P 23
15.6%
23
15.6%
0 18
12.2%
2 17
11.6%
4 11
7.5%
5 11
7.5%
1 8
 
5.4%
3 8
 
5.4%
7 4
 
2.7%
Hangul
ValueCountFrequency (%)
대 23
100.0%

계약용량(KW)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.86957
Minimum10
Maximum3570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:33:03.129450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median120
Q3300
95-th percentile1526.3
Maximum3570
Range3560
Interquartile range (IQR)290

Descriptive statistics

Standard deviation800.88941
Coefficient of variation (CV)1.973268
Kurtosis11.445693
Mean405.86957
Median Absolute Deviation (MAD)110
Skewness3.1949902
Sum9335
Variance641423.85
MonotonicityNot monotonic
2024-03-23T14:33:03.313185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10 7
30.4%
11 2
 
8.7%
150 2
 
8.7%
200 2
 
8.7%
37 1
 
4.3%
149 1
 
4.3%
120 1
 
4.3%
1088 1
 
4.3%
3570 1
 
4.3%
660 1
 
4.3%
Other values (4) 4
17.4%
ValueCountFrequency (%)
10 7
30.4%
11 2
 
8.7%
37 1
 
4.3%
94 1
 
4.3%
120 1
 
4.3%
149 1
 
4.3%
150 2
 
8.7%
200 2
 
8.7%
400 1
 
4.3%
660 1
 
4.3%
ValueCountFrequency (%)
3570 1
4.3%
1575 1
4.3%
1088 1
4.3%
850 1
4.3%
660 1
4.3%
400 1
4.3%
200 2
8.7%
150 2
8.7%
149 1
4.3%
120 1
4.3%

처리능력(m3_분)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.50435
Minimum7
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:33:03.572998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17
median30
Q3143
95-th percentile824.58
Maximum2040
Range2033
Interquartile range (IQR)136

Descriptive statistics

Standard deviation456.50667
Coefficient of variation (CV)2.138161
Kurtosis12.145019
Mean213.50435
Median Absolute Deviation (MAD)23
Skewness3.2989305
Sum4910.6
Variance208398.34
MonotonicityNot monotonic
2024-03-23T14:33:03.843080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.0 10
43.5%
45.0 3
 
13.0%
30.0 2
 
8.7%
136.0 1
 
4.3%
852.0 1
 
4.3%
2040.0 1
 
4.3%
360.0 1
 
4.3%
150.0 1
 
4.3%
480.0 1
 
4.3%
577.8 1
 
4.3%
ValueCountFrequency (%)
7.0 10
43.5%
30.0 2
 
8.7%
45.0 3
 
13.0%
49.8 1
 
4.3%
136.0 1
 
4.3%
150.0 1
 
4.3%
360.0 1
 
4.3%
480.0 1
 
4.3%
577.8 1
 
4.3%
852.0 1
 
4.3%
ValueCountFrequency (%)
2040.0 1
 
4.3%
852.0 1
 
4.3%
577.8 1
 
4.3%
480.0 1
 
4.3%
360.0 1
 
4.3%
150.0 1
 
4.3%
136.0 1
 
4.3%
49.8 1
 
4.3%
45.0 3
13.0%
30.0 2
8.7%

유수지용량(m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.6957
Minimum1035
Maximum6530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:33:04.053987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1035
5-th percentile1351
Q11545
median1640
Q32515
95-th percentile4588.2
Maximum6530
Range5495
Interquartile range (IQR)970

Descriptive statistics

Standard deviation1339.9845
Coefficient of variation (CV)0.57815378
Kurtosis3.5173091
Mean2317.6957
Median Absolute Deviation (MAD)190
Skewness1.9158757
Sum53307
Variance1795558.5
MonotonicityNot monotonic
2024-03-23T14:33:04.256098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1560 3
 
13.0%
1520 1
 
4.3%
4610 1
 
4.3%
1035 1
 
4.3%
4392 1
 
4.3%
2430 1
 
4.3%
2120 1
 
4.3%
1340 1
 
4.3%
1640 1
 
4.3%
4000 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
1035 1
 
4.3%
1340 1
 
4.3%
1450 1
 
4.3%
1460 1
 
4.3%
1520 1
 
4.3%
1530 1
 
4.3%
1560 3
13.0%
1570 1
 
4.3%
1620 1
 
4.3%
1640 1
 
4.3%
ValueCountFrequency (%)
6530 1
4.3%
4610 1
4.3%
4392 1
4.3%
4000 1
4.3%
2720 1
4.3%
2530 1
4.3%
2500 1
4.3%
2430 1
4.3%
2120 1
4.3%
1820 1
4.3%

수문(기)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
20
13 
10
9 
5
 
1

Length

Max length2
Median length2
Mean length1.9565217
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row20
2nd row20
3rd row10
4th row20
5th row10

Common Values

ValueCountFrequency (%)
20 13
56.5%
10 9
39.1%
5 1
 
4.3%

Length

2024-03-23T14:33:04.540960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T14:33:04.848947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 13
56.5%
10 9
39.1%
5 1
 
4.3%

Interactions

2024-03-23T14:32:57.940878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:54.984529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:55.781947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:56.640763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:58.139213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:55.168543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:56.057056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:57.293512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:58.324249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:55.360651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:56.245076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:57.537709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:58.535903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:55.574379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:56.462660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:32:57.754136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:33:04.954254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명위치준공년도펌프(HP 기)계약용량(KW)처리능력(m3_분)유수지용량(m3)수문(기)
시설명1.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.000
준공년도1.0001.0001.0000.9980.0000.0000.0000.810
펌프(HP 기)1.0001.0000.9981.0000.0000.6400.5070.953
계약용량(KW)1.0001.0000.0000.0001.0000.9380.7510.939
처리능력(m3_분)1.0001.0000.0000.6400.9381.0000.7290.712
유수지용량(m3)1.0001.0000.0000.5070.7510.7291.0000.000
수문(기)1.0001.0000.8100.9530.9390.7120.0001.000
2024-03-23T14:33:05.163070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공년도계약용량(KW)처리능력(m3_분)유수지용량(m3)수문(기)
준공년도1.000-0.240-0.192-0.1680.421
계약용량(KW)-0.2401.0000.9500.5510.642
처리능력(m3_분)-0.1920.9501.0000.5570.659
유수지용량(m3)-0.1680.5510.5571.0000.000
수문(기)0.4210.6420.6590.0001.000

Missing values

2024-03-23T14:32:58.877106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:32:59.152767image/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

시설명위치준공년도펌프(HP 기)계약용량(KW)처리능력(m3_분)유수지용량(m3)수문(기)
0우시장 배수펌프장전라남도 함평군 함평읍 기각리 194-13199215HP 2대117.0152020
15일시장 배수펌프장전라남도 함평군 함평읍 기각리 262200415HP 4대117.0145020
2원고막 배수펌프장전라남도 함평군 학교면 고막리 630198550HP 2대377.0182010
3쌍교 배수펌프장전라남도 함평군 학교면 학교리 445-10199920HP 2대107.0146020
4복천 배수펌프장전라남도 함평군 학교면 복천리 1055-72000200HP 4대149136.0272010
5광진 배수펌프장전라남도 함평군 학교면 고막리 279200320HP 2대107.0162010
6야리 배수펌프장전라남도 함평군 엄다면 학야리 945-3200220HP 3대107.0181010
7원삼정 배수펌프장전라남도 함평군 엄다면 삼정리 499-16200120HP 2대107.0153010
8제동 배수펌프장전라남도 함평군 엄다면 엄다리 1125-1200120HP 2대107.0156010
9월호 배수펌프장전라남도 함평군 학교면 월호리 1021-3201230HP 2대107.0156010
시설명위치준공년도펌프(HP 기)계약용량(KW)처리능력(m3_분)유수지용량(m3)수문(기)
13엄다배수장전라남도 함평군 학교면 학교리 102-91997650HP 5대35702040.0653020
14학교배수장전라남도 함평군 엄다면 학야리 9-221998130HP 4대660360.0461020
15진례배수장전라남도 함평군 학교면 석정리 954-41997130HP 4대400150.0253020
16삼축배수장전라남도 함평군 나산면 월봉리 96-221997130HP 4대850480.0400020
17곡창배수장전라남도 함평군 학교면 곡창리 4471997130HP 4대1575577.8164020
18삼정배수장전라남도 함평군 학교면 삼정리 499-131997130HP 4대9445.0134020
19송암배수장전라남도 함평군 엄다면 엄다리 1351-151997130HP 4대15045.0212020
20영천배수장전라남도 함평군 엄다면 엄다리 1266-2200475HP 4대20049.8243020
21영흥1배수장전라남도 함평군 엄다면 영흥리 1168-16200475HP 4대15030.0439220
22영흥2배수장전라남도 함평군 엄다면 영흥리 1078-19200475HP 4대20045.0103520