Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

Numeric6
Categorical5
Text1

Dataset

Description부산광역시 상수도사업본부에서 상하수도 요금 계산 및 징수를 위해 운영하는 수용가정보시스템에 사용되는 고지집계(고지 전수집계) 자료입니다.
Author부산광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15083673/fileData.do

Alerts

상하수도구분 has constant value ""Constant
사업소명 is highly overall correlated with 사업소코드 and 2 other fieldsHigh correlation
구명 is highly overall correlated with 사업소코드 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 고지년월High correlation
사업소코드 is highly overall correlated with 사업소명 and 1 other fieldsHigh correlation
구코드 is highly overall correlated with 사업소명 and 1 other fieldsHigh correlation
동코드 is highly overall correlated with 구명High correlation
구경 is highly overall correlated with 전수High correlation
전수 is highly overall correlated with 구경High correlation
고지년월 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 09:32:59.984667
Analysis finished2024-03-14 09:33:11.629462
Duration11.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25923.824
Minimum6
Maximum51705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T18:33:11.760506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile2581.95
Q113219.75
median25835
Q338827
95-th percentile49072.1
Maximum51705
Range51699
Interquartile range (IQR)25607.25

Descriptive statistics

Standard deviation14891.912
Coefficient of variation (CV)0.57444891
Kurtosis-1.1888225
Mean25923.824
Median Absolute Deviation (MAD)12818.5
Skewness-0.0041606647
Sum2.5923824 × 108
Variance2.2176905 × 108
MonotonicityNot monotonic
2024-03-14T18:33:12.014416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18096 1
 
< 0.1%
10194 1
 
< 0.1%
19189 1
 
< 0.1%
5550 1
 
< 0.1%
13591 1
 
< 0.1%
51619 1
 
< 0.1%
45652 1
 
< 0.1%
34834 1
 
< 0.1%
1835 1
 
< 0.1%
9951 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
6 1
< 0.1%
16 1
< 0.1%
22 1
< 0.1%
28 1
< 0.1%
38 1
< 0.1%
44 1
< 0.1%
46 1
< 0.1%
50 1
< 0.1%
57 1
< 0.1%
60 1
< 0.1%
ValueCountFrequency (%)
51705 1
< 0.1%
51693 1
< 0.1%
51688 1
< 0.1%
51686 1
< 0.1%
51673 1
< 0.1%
51672 1
< 0.1%
51671 1
< 0.1%
51670 1
< 0.1%
51669 1
< 0.1%
51661 1
< 0.1%

고지년월
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-05
870 
2023-06
867 
2023-01
846 
2023-11
840 
2023-10
836 
Other values (7)
5741 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05
2nd row2023-08
3rd row2023-10
4th row2023-12
5th row2023-05

Common Values

ValueCountFrequency (%)
2023-05 870
8.7%
2023-06 867
8.7%
2023-01 846
8.5%
2023-11 840
8.4%
2023-10 836
8.4%
2023-08 834
8.3%
2023-03 834
8.3%
2023-12 829
8.3%
2023-07 828
8.3%
2023-09 826
8.3%
Other values (2) 1590
15.9%

Length

2024-03-14T18:33:12.240253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-05 870
8.7%
2023-06 867
8.7%
2023-01 846
8.5%
2023-11 840
8.4%
2023-10 836
8.4%
2023-08 834
8.3%
2023-03 834
8.3%
2023-12 829
8.3%
2023-07 828
8.3%
2023-09 826
8.3%
Other values (2) 1590
15.9%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.2711
Minimum244
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T18:33:12.414735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum244
5-th percentile244
Q1301
median304
Q3307
95-th percentile311
Maximum312
Range68
Interquartile range (IQR)6

Descriptive statistics

Standard deviation24.945309
Coefficient of variation (CV)0.085058872
Kurtosis0.14604987
Mean293.2711
Median Absolute Deviation (MAD)3
Skewness-1.441674
Sum2932711
Variance622.26843
MonotonicityNot monotonic
2024-03-14T18:33:12.667449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
244 2022
20.2%
306 1363
13.6%
307 1274
12.7%
304 1117
11.2%
301 885
8.8%
309 855
8.6%
308 822
8.2%
302 564
 
5.6%
303 531
 
5.3%
311 302
 
3.0%
ValueCountFrequency (%)
244 2022
20.2%
301 885
8.8%
302 564
 
5.6%
303 531
 
5.3%
304 1117
11.2%
306 1363
13.6%
307 1274
12.7%
308 822
8.2%
309 855
8.6%
311 302
 
3.0%
ValueCountFrequency (%)
312 265
 
2.6%
311 302
 
3.0%
309 855
8.6%
308 822
8.2%
307 1274
12.7%
306 1363
13.6%
304 1117
11.2%
303 531
 
5.3%
302 564
5.6%
301 885
8.8%

사업소명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
2022 
남부사업소
1363 
북부사업소
1274 
부산진 사업소
1117 
중동부사업소
885 
Other values (6)
3339 

Length

Max length9
Median length8
Mean length6.1358
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동래통합사업소
2nd row북부사업소
3rd row북부사업소
4th row해운대사업소
5th row사하사업소

Common Values

ValueCountFrequency (%)
동래통합사업소 2022
20.2%
남부사업소 1363
13.6%
북부사업소 1274
12.7%
부산진 사업소 1117
11.2%
중동부사업소 885
8.8%
사하사업소 855
8.6%
해운대사업소 822
8.2%
서부 사업소 564
 
5.6%
영도사업소 531
 
5.3%
강서사업소 302
 
3.0%

Length

2024-03-14T18:33:13.122974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동래통합사업소 2022
17.3%
사업소 1681
14.4%
남부사업소 1363
11.7%
북부사업소 1274
10.9%
부산진 1117
9.6%
중동부사업소 885
7.6%
사하사업소 855
7.3%
해운대사업소 822
7.0%
서부 564
 
4.8%
영도사업소 531
 
4.5%
Other values (2) 567
 
4.9%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.544
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T18:33:13.320564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1230
median320
Q3410
95-th percentile530
Maximum710
Range600
Interquartile range (IQR)180

Descriptive statistics

Standard deviation133.37592
Coefficient of variation (CV)0.40228723
Kurtosis-0.014768029
Mean331.544
Median Absolute Deviation (MAD)90
Skewness0.48374643
Sum3315440
Variance17789.135
MonotonicityNot monotonic
2024-03-14T18:33:13.516785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
230 1117
11.2%
290 858
 
8.6%
380 855
 
8.6%
350 822
 
8.2%
410 746
 
7.5%
260 661
 
6.6%
530 646
 
6.5%
320 628
 
6.3%
470 615
 
6.2%
140 564
 
5.6%
Other values (6) 2488
24.9%
ValueCountFrequency (%)
110 369
 
3.7%
140 564
5.6%
170 516
5.2%
200 531
5.3%
230 1117
11.2%
260 661
6.6%
290 858
8.6%
320 628
6.3%
350 822
8.2%
380 855
8.6%
ValueCountFrequency (%)
710 265
 
2.6%
530 646
6.5%
500 505
5.1%
470 615
6.2%
440 302
 
3.0%
410 746
7.5%
380 855
8.6%
350 822
8.2%
320 628
6.3%
290 858
8.6%

구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
1117 
남구
858 
사하구
855 
해운대구
822 
금정구
746 
Other values (11)
5602 

Length

Max length4
Median length3
Mean length2.9004
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연제구
2nd row사상구
3rd row북구
4th row해운대구
5th row사하구

Common Values

ValueCountFrequency (%)
부산진구 1117
11.2%
남구 858
 
8.6%
사하구 855
 
8.6%
해운대구 822
 
8.2%
금정구 746
 
7.5%
동래구 661
 
6.6%
사상구 646
 
6.5%
북구 628
 
6.3%
연제구 615
 
6.2%
서구 564
 
5.6%
Other values (6) 2488
24.9%

Length

2024-03-14T18:33:13.791794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 1117
11.2%
남구 858
 
8.6%
사하구 855
 
8.6%
해운대구 822
 
8.2%
금정구 746
 
7.5%
동래구 661
 
6.6%
사상구 646
 
6.5%
북구 628
 
6.3%
연제구 615
 
6.2%
서구 564
 
5.6%
Other values (6) 2488
24.9%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.9912
Minimum9
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T18:33:14.105524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile510
Q1550
median590
Q3660
95-th percentile760
Maximum800
Range791
Interquartile range (IQR)110

Descriptive statistics

Standard deviation90.846558
Coefficient of variation (CV)0.15116121
Kurtosis3.1931321
Mean600.9912
Median Absolute Deviation (MAD)50
Skewness-0.78921242
Sum6009912
Variance8253.097
MonotonicityNot monotonic
2024-03-14T18:33:14.556865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
530 592
 
5.9%
510 486
 
4.9%
560 468
 
4.7%
520 429
 
4.3%
550 414
 
4.1%
580 394
 
3.9%
540 389
 
3.9%
590 381
 
3.8%
620 381
 
3.8%
610 380
 
3.8%
Other values (53) 5686
56.9%
ValueCountFrequency (%)
9 1
 
< 0.1%
250 66
 
0.7%
253 70
 
0.7%
256 52
 
0.5%
310 41
 
0.4%
330 36
 
0.4%
510 486
4.9%
520 429
4.3%
521 52
 
0.5%
525 38
 
0.4%
ValueCountFrequency (%)
800 47
 
0.5%
790 95
0.9%
780 82
0.8%
770 89
0.9%
762 45
 
0.4%
761 53
 
0.5%
760 105
1.1%
750 152
1.5%
740 171
1.7%
730 165
1.7%

동명
Text

Distinct218
Distinct (%)2.2%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-14T18:33:16.024600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7859786
Min length3

Characters and Unicode

Total characters37856
Distinct characters107
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

Unique0 ?
Unique (%)0.0%

Sample

1st row연산9동
2nd row엄궁동
3rd row만덕1동
4th row우2동
5th row하단1동
ValueCountFrequency (%)
온천1동 74
 
0.7%
학장동 72
 
0.7%
신평2동 71
 
0.7%
녹산동 70
 
0.7%
장안읍 70
 
0.7%
연산9동 69
 
0.7%
대연3동 69
 
0.7%
망미1동 68
 
0.7%
감전1동 67
 
0.7%
우1동 67
 
0.7%
Other values (208) 9302
93.0%
2024-03-14T18:33:17.764673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10053
26.6%
1 2845
 
7.5%
2 2490
 
6.6%
3 1418
 
3.7%
대 737
 
1.9%
연 731
 
1.9%
4 688
 
1.8%
천 668
 
1.8%
장 583
 
1.5%
산 581
 
1.5%
Other values (97) 17062
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30007
79.3%
Decimal Number 7849
 
20.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10053
33.5%
대 737
 
2.5%
연 731
 
2.4%
천 668
 
2.2%
장 583
 
1.9%
산 581
 
1.9%
정 549
 
1.8%
부 547
 
1.8%
전 478
 
1.6%
신 477
 
1.6%
Other values (89) 14603
48.7%
Decimal Number
ValueCountFrequency (%)
1 2845
36.2%
2 2490
31.7%
3 1418
18.1%
4 688
 
8.8%
5 177
 
2.3%
6 127
 
1.6%
9 69
 
0.9%
8 35
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30007
79.3%
Common 7849
 
20.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10053
33.5%
대 737
 
2.5%
연 731
 
2.4%
천 668
 
2.2%
장 583
 
1.9%
산 581
 
1.9%
정 549
 
1.8%
부 547
 
1.8%
전 478
 
1.6%
신 477
 
1.6%
Other values (89) 14603
48.7%
Common
ValueCountFrequency (%)
1 2845
36.2%
2 2490
31.7%
3 1418
18.1%
4 688
 
8.8%
5 177
 
2.3%
6 127
 
1.6%
9 69
 
0.9%
8 35
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30007
79.3%
ASCII 7849
 
20.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10053
33.5%
대 737
 
2.5%
연 731
 
2.4%
천 668
 
2.2%
장 583
 
1.9%
산 581
 
1.9%
정 549
 
1.8%
부 547
 
1.8%
전 478
 
1.6%
신 477
 
1.6%
Other values (89) 14603
48.7%
ASCII
ValueCountFrequency (%)
1 2845
36.2%
2 2490
31.7%
3 1418
18.1%
4 688
 
8.8%
5 177
 
2.3%
6 127
 
1.6%
9 69
 
0.9%
8 35
 
0.4%

상하수도구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
상수도
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도 10000
100.0%

Length

2024-03-14T18:33:18.170590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:33:18.471437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도 10000
100.0%

상수도업종
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가정용
3906 
일반용
3807 
사회복지
1242 
욕탕용
834 
공업용수
 
105
Other values (5)
 
106

Length

Max length8
Median length3
Mean length3.1595
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반용
2nd row사회복지
3rd row사회복지
4th row공동수도
5th row일반용

Common Values

ValueCountFrequency (%)
가정용 3906
39.1%
일반용 3807
38.1%
사회복지 1242
 
12.4%
욕탕용 834
 
8.3%
공업용수 105
 
1.1%
공동수도 59
 
0.6%
영업용(온천) 22
 
0.2%
일반용(온천) 10
 
0.1%
목욕장용(온천) 8
 
0.1%
국가유공단체 7
 
0.1%

Length

2024-03-14T18:33:18.791674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:33:19.145255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가정용 3906
39.1%
일반용 3807
38.1%
사회복지 1242
 
12.4%
욕탕용 834
 
8.3%
공업용수 105
 
1.1%
공동수도 59
 
0.6%
영업용(온천 22
 
0.2%
일반용(온천 10
 
0.1%
목욕장용(온천 8
 
0.1%
국가유공단체 7
 
0.1%

구경
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.974
Minimum13
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T18:33:19.472416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q120
median32
Q350
95-th percentile150
Maximum300
Range287
Interquartile range (IQR)30

Descriptive statistics

Standard deviation48.049843
Coefficient of variation (CV)0.94263434
Kurtosis6.4856028
Mean50.974
Median Absolute Deviation (MAD)17
Skewness2.3947637
Sum509740
Variance2308.7874
MonotonicityNot monotonic
2024-03-14T18:33:19.809429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 1496
15.0%
25 1448
14.5%
40 1428
14.3%
20 1334
13.3%
50 1267
12.7%
80 801
8.0%
32 749
7.5%
100 668
6.7%
150 442
 
4.4%
200 207
 
2.1%
Other values (3) 160
 
1.6%
ValueCountFrequency (%)
13 4
 
< 0.1%
15 1496
15.0%
20 1334
13.3%
25 1448
14.5%
32 749
7.5%
40 1428
14.3%
50 1267
12.7%
80 801
8.0%
100 668
6.7%
150 442
 
4.4%
ValueCountFrequency (%)
300 47
 
0.5%
250 109
 
1.1%
200 207
 
2.1%
150 442
 
4.4%
100 668
6.7%
80 801
8.0%
50 1267
12.7%
40 1428
14.3%
32 749
7.5%
25 1448
14.5%

전수
Real number (ℝ)

HIGH CORRELATION 

Distinct735
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.0162
Minimum1
Maximum4647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T18:33:20.078640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q324
95-th percentile608.1
Maximum4647
Range4646
Interquartile range (IQR)22

Descriptive statistics

Standard deviation290.3918
Coefficient of variation (CV)3.2992995
Kurtosis50.630331
Mean88.0162
Median Absolute Deviation (MAD)4
Skewness5.9133559
Sum880162
Variance84327.399
MonotonicityNot monotonic
2024-03-14T18:33:20.319706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2388
23.9%
2 1237
 
12.4%
3 726
 
7.3%
4 541
 
5.4%
5 378
 
3.8%
6 329
 
3.3%
8 235
 
2.4%
7 232
 
2.3%
9 208
 
2.1%
11 170
 
1.7%
Other values (725) 3556
35.6%
ValueCountFrequency (%)
1 2388
23.9%
2 1237
12.4%
3 726
 
7.3%
4 541
 
5.4%
5 378
 
3.8%
6 329
 
3.3%
7 232
 
2.3%
8 235
 
2.4%
9 208
 
2.1%
10 146
 
1.5%
ValueCountFrequency (%)
4647 1
< 0.1%
4622 1
< 0.1%
4617 1
< 0.1%
4570 1
< 0.1%
4566 1
< 0.1%
2827 1
< 0.1%
2704 1
< 0.1%
2686 1
< 0.1%
2685 1
< 0.1%
2576 1
< 0.1%

Interactions

2024-03-14T18:33:10.023802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:01.439346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:03.034697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:04.661801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:06.267167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:08.354954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:10.198033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:01.699106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:03.300996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:04.924930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:06.539155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:08.639588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:10.422104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:01.971065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:03.572086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:05.194964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:06.816264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:09.006720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:10.584213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:02.238602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:03.847047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:05.467052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:07.101853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:09.380123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:10.755362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:02.514809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:04.130394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:05.744111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:07.547622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:09.706448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:10.916016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:02.782163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:04.407561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:06.012790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:08.007849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:33:09.869303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:33:20.526058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번고지년월사업소코드사업소명구코드구명동코드상수도업종구경전수
연번1.0000.9590.1920.2300.1880.2600.0670.0000.0000.000
고지년월0.9591.0000.0000.0000.0000.0000.0110.0000.0000.000
사업소코드0.1920.0001.0001.0001.0001.0000.1770.1350.1260.069
사업소명0.2300.0001.0001.0000.9461.0000.7180.1960.1050.143
구코드0.1880.0001.0000.9461.0001.0000.6940.1410.1260.184
구명0.2600.0001.0001.0001.0001.0000.8070.2430.1460.204
동코드0.0670.0110.1770.7180.6940.8071.0000.0850.0390.125
상수도업종0.0000.0000.1350.1960.1410.2430.0851.0000.3070.149
구경0.0000.0000.1260.1050.1260.1460.0390.3071.0000.195
전수0.0000.0000.0690.1430.1840.2040.1250.1490.1951.000
2024-03-14T18:33:20.794712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고지년월사업소명구명상수도업종
고지년월1.0000.0000.0000.000
사업소명0.0001.0001.0000.084
구명0.0001.0001.0000.097
상수도업종0.0000.0840.0971.000
2024-03-14T18:33:21.065277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드구코드동코드구경전수고지년월사업소명구명상수도업종
연번1.0000.0900.029-0.023-0.0100.0120.8410.1000.1040.000
사업소코드0.0901.0000.371-0.2700.0480.0260.0001.0000.9990.089
구코드0.0290.3711.0000.1070.0310.0370.0000.8421.0000.068
동코드-0.023-0.2700.1071.000-0.004-0.0200.0050.4610.5410.043
구경-0.0100.0480.031-0.0041.000-0.5590.0000.0500.0520.151
전수0.0120.0260.037-0.020-0.5591.0000.0000.0670.0720.071
고지년월0.8410.0000.0000.0050.0000.0001.0000.0000.0000.000
사업소명0.1001.0000.8420.4610.0500.0670.0001.0001.0000.084
구명0.1040.9991.0000.5410.0520.0720.0001.0001.0000.097
상수도업종0.0000.0890.0680.0430.1510.0710.0000.0840.0971.000

Missing values

2024-03-14T18:33:11.127605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:33:11.493240image/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

연번고지년월사업소코드사업소명구코드구명동코드동명상하수도구분상수도업종구경전수
18095180962023-05244동래통합사업소470연제구730연산9동상수도일반용20132
33503335042023-08307북부사업소530사상구680엄궁동상수도사회복지157
41808418092023-10307북부사업소320북구571만덕1동상수도사회복지501
50776507772023-12308해운대사업소350해운대구520우2동상수도공동수도201
21050210512023-05309사하사업소380사하구561하단1동상수도일반용20101
33901339022023-08309사하사업소380사하구520괴정2동상수도일반용2036
27528275292023-07303영도사업소200영도구650동삼1동상수도욕탕용503
39283392842023-10244동래통합사업소410금정구610장전2동상수도일반용2027
30984309852023-08244동래통합사업소470연제구700연산6동상수도가정용152236
699369942023-02306남부사업소500수영구760광안1동상수도가정용803
연번고지년월사업소코드사업소명구코드구명동코드동명상하수도구분상수도업종구경전수
887588762023-03244동래통합사업소260동래구750안락2동상수도일반용509
33913339142023-08309사하사업소380사하구530괴정3동상수도가정용321
258125822023-01306남부사업소500수영구660남천1동상수도일반용15613
25315253162023-06309사하사업소380사하구540괴정4동상수도일반용2522
48545485462023-12301중동부사업소170동구590수정4동상수도가정용15758
14135141362023-04301중동부사업소170동구660범일2동상수도가정용209
687268732023-02306남부사업소290남구710문현4동상수도일반용15133
10470104712023-03304부산진 사업소230부산진구570양정2동상수도사회복지155
44580445812023-11302서부 사업소140서구680암남동상수도사회복지401
35238352392023-09244동래통합사업소470연제구670연산3동상수도가정용1002