Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells3
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
DateTime1
Categorical4
Text1

Dataset

Description부산광역시상수도사업본부_수용가정보시스템_고지집계정보_고지전수집계_20230126
Author부산광역시 상수도사업본부
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083673

Alerts

구명 is highly overall correlated with 사업소코드 and 3 other fieldsHigh correlation
사업소명 is highly overall correlated with 사업소코드 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 상하수도구분High 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
상하수도구분 is highly overall correlated with 연번High correlation
상하수도구분 is highly imbalanced (50.4%)Imbalance
연번 has unique valuesUnique
구경 has 112 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-10 16:48:41.263681
Analysis finished2023-12-10 16:48:48.665233
Duration7.4 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%
Mean28910.247
Minimum5
Maximum58118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:48:48.738679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile2795.85
Q114459.5
median28710
Q343426.25
95-th percentile55155.6
Maximum58118
Range58113
Interquartile range (IQR)28966.75

Descriptive statistics

Standard deviation16822.908
Coefficient of variation (CV)0.58190121
Kurtosis-1.2043275
Mean28910.247
Median Absolute Deviation (MAD)14463.5
Skewness0.0065269022
Sum2.8910247 × 108
Variance2.8301022 × 108
MonotonicityNot monotonic
2023-12-11T01:48:48.909887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23155 1
 
< 0.1%
3634 1
 
< 0.1%
53033 1
 
< 0.1%
43999 1
 
< 0.1%
36023 1
 
< 0.1%
6644 1
 
< 0.1%
44068 1
 
< 0.1%
1127 1
 
< 0.1%
11824 1
 
< 0.1%
55638 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
9 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
28 1
< 0.1%
34 1
< 0.1%
42 1
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
58 1
< 0.1%
ValueCountFrequency (%)
58118 1
< 0.1%
58117 1
< 0.1%
58109 1
< 0.1%
58108 1
< 0.1%
58096 1
< 0.1%
58093 1
< 0.1%
58068 1
< 0.1%
58063 1
< 0.1%
58062 1
< 0.1%
58054 1
< 0.1%

고지년월
Date

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-11T01:48:49.026201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:49.124719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.1834
Minimum244
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:48:49.236352image/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 deviation25.043596
Coefficient of variation (CV)0.085419555
Kurtosis0.10555197
Mean293.1834
Median Absolute Deviation (MAD)3
Skewness-1.4283685
Sum2931834
Variance627.18168
MonotonicityNot monotonic
2023-12-11T01:48:49.339342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
244 2041
20.4%
306 1405
14.1%
307 1235
12.3%
304 1149
11.5%
309 852
8.5%
301 839
8.4%
308 832
8.3%
303 546
 
5.5%
302 522
 
5.2%
311 334
 
3.3%
ValueCountFrequency (%)
244 2041
20.4%
301 839
8.4%
302 522
 
5.2%
303 546
 
5.5%
304 1149
11.5%
306 1405
14.1%
307 1235
12.3%
308 832
8.3%
309 852
8.5%
311 334
 
3.3%
ValueCountFrequency (%)
312 245
 
2.5%
311 334
 
3.3%
309 852
8.5%
308 832
8.3%
307 1235
12.3%
306 1405
14.1%
304 1149
11.5%
303 546
 
5.5%
302 522
 
5.2%
301 839
8.4%

사업소명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
2041 
남부 사업소
1405 
북부 사업소
1235 
부산진 사업소
1149 
사하 사업소
852 
Other values (6)
3318 

Length

Max length9
Median length9
Mean length8.3098
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영도 사업소
2nd row남부 사업소
3rd row해운대 사업소
4th row사하 사업소
5th row동래통합사업소

Common Values

ValueCountFrequency (%)
동래통합사업소 2041
20.4%
남부 사업소 1405
14.1%
북부 사업소 1235
12.3%
부산진 사업소 1149
11.5%
사하 사업소 852
8.5%
중동부 사업소 839
8.4%
해운대 사업소 832
8.3%
영도 사업소 546
 
5.5%
서부 사업소 522
 
5.2%
강서 사업소 334
 
3.3%

Length

2023-12-11T01:48:49.467152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업소 7959
44.3%
동래통합사업소 2041
 
11.4%
남부 1405
 
7.8%
북부 1235
 
6.9%
부산진 1149
 
6.4%
사하 852
 
4.7%
중동부 839
 
4.7%
해운대 832
 
4.6%
영도 546
 
3.0%
서부 522
 
2.9%
Other values (2) 579
 
3.2%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.811
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:48:49.590584image/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 deviation131.34183
Coefficient of variation (CV)0.39583325
Kurtosis-0.0081768852
Mean331.811
Median Absolute Deviation (MAD)90
Skewness0.47083394
Sum3318110
Variance17250.675
MonotonicityNot monotonic
2023-12-11T01:48:49.931633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
230 1149
11.5%
290 899
 
9.0%
380 852
 
8.5%
350 832
 
8.3%
410 755
 
7.5%
260 668
 
6.7%
530 628
 
6.3%
470 618
 
6.2%
320 607
 
6.1%
200 546
 
5.5%
Other values (6) 2446
24.5%
ValueCountFrequency (%)
110 357
 
3.6%
140 522
5.2%
170 482
4.8%
200 546
5.5%
230 1149
11.5%
260 668
6.7%
290 899
9.0%
320 607
6.1%
350 832
8.3%
380 852
8.5%
ValueCountFrequency (%)
710 245
 
2.5%
530 628
6.3%
500 506
5.1%
470 618
6.2%
440 334
 
3.3%
410 755
7.5%
380 852
8.5%
350 832
8.3%
320 607
6.1%
290 899
9.0%

구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
1149 
남구
899 
사하구
852 
해운대구
832 
금정구
755 
Other values (11)
5513 

Length

Max length4
Median length3
Mean length2.9114
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영도구
2nd row수영구
3rd row해운대구
4th row사하구
5th row연제구

Common Values

ValueCountFrequency (%)
부산진구 1149
11.5%
남구 899
 
9.0%
사하구 852
 
8.5%
해운대구 832
 
8.3%
금정구 755
 
7.5%
동래구 668
 
6.7%
사상구 628
 
6.3%
연제구 618
 
6.2%
북구 607
 
6.1%
영도구 546
 
5.5%
Other values (6) 2446
24.5%

Length

2023-12-11T01:48:50.044727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 1149
11.5%
남구 899
 
9.0%
사하구 852
 
8.5%
해운대구 832
 
8.3%
금정구 755
 
7.5%
동래구 668
 
6.7%
사상구 628
 
6.3%
연제구 618
 
6.2%
북구 607
 
6.1%
영도구 546
 
5.5%
Other values (6) 2446
24.5%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean601.2479
Minimum9
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:48:50.164786image/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 deviation89.875153
Coefficient of variation (CV)0.14948103
Kurtosis3.3700586
Mean601.2479
Median Absolute Deviation (MAD)50
Skewness-0.74164591
Sum6012479
Variance8077.5432
MonotonicityNot monotonic
2023-12-11T01:48:50.301186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
530 567
 
5.7%
510 500
 
5.0%
560 470
 
4.7%
520 456
 
4.6%
590 422
 
4.2%
570 421
 
4.2%
550 419
 
4.2%
610 407
 
4.1%
580 378
 
3.8%
620 378
 
3.8%
Other values (53) 5582
55.8%
ValueCountFrequency (%)
9 3
 
< 0.1%
250 61
 
0.6%
253 55
 
0.5%
256 48
 
0.5%
310 39
 
0.4%
330 42
 
0.4%
510 500
5.0%
520 456
4.6%
521 43
 
0.4%
525 31
 
0.3%
ValueCountFrequency (%)
800 62
 
0.6%
790 97
1.0%
780 84
0.8%
770 107
1.1%
762 42
 
0.4%
761 51
 
0.5%
760 112
1.1%
750 120
1.2%
740 151
1.5%
730 155
1.6%

동명
Text

Distinct218
Distinct (%)2.2%
Missing3
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-11T01:48:50.605890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7871361
Min length3

Characters and Unicode

Total characters37860
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row청학2동
2nd row광안4동
3rd row반송2동
4th row장림2동
5th row거제1동
ValueCountFrequency (%)
녹산동 90
 
0.9%
반송2동 71
 
0.7%
학장동 71
 
0.7%
신평2동 71
 
0.7%
초읍동 69
 
0.7%
온천1동 68
 
0.7%
용호1동 68
 
0.7%
다대1동 68
 
0.7%
연산4동 67
 
0.7%
구서2동 67
 
0.7%
Other values (208) 9287
92.9%
2023-12-11T01:48:51.187888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10095
26.7%
1 2889
 
7.6%
2 2545
 
6.7%
3 1380
 
3.6%
대 744
 
2.0%
연 743
 
2.0%
천 656
 
1.7%
4 642
 
1.7%
산 609
 
1.6%
장 565
 
1.5%
Other values (97) 16992
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29998
79.2%
Decimal Number 7862
 
20.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10095
33.7%
대 744
 
2.5%
연 743
 
2.5%
천 656
 
2.2%
산 609
 
2.0%
장 565
 
1.9%
부 532
 
1.8%
정 531
 
1.8%
포 478
 
1.6%
전 471
 
1.6%
Other values (89) 14574
48.6%
Decimal Number
ValueCountFrequency (%)
1 2889
36.7%
2 2545
32.4%
3 1380
17.6%
4 642
 
8.2%
5 162
 
2.1%
6 129
 
1.6%
9 66
 
0.8%
8 49
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29998
79.2%
Common 7862
 
20.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10095
33.7%
대 744
 
2.5%
연 743
 
2.5%
천 656
 
2.2%
산 609
 
2.0%
장 565
 
1.9%
부 532
 
1.8%
정 531
 
1.8%
포 478
 
1.6%
전 471
 
1.6%
Other values (89) 14574
48.6%
Common
ValueCountFrequency (%)
1 2889
36.7%
2 2545
32.4%
3 1380
17.6%
4 642
 
8.2%
5 162
 
2.1%
6 129
 
1.6%
9 66
 
0.8%
8 49
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29998
79.2%
ASCII 7862
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10095
33.7%
대 744
 
2.5%
연 743
 
2.5%
천 656
 
2.2%
산 609
 
2.0%
장 565
 
1.9%
부 532
 
1.8%
정 531
 
1.8%
포 478
 
1.6%
전 471
 
1.6%
Other values (89) 14574
48.6%
ASCII
ValueCountFrequency (%)
1 2889
36.7%
2 2545
32.4%
3 1380
17.6%
4 642
 
8.2%
5 162
 
2.1%
6 129
 
1.6%
9 66
 
0.8%
8 49
 
0.6%

상하수도구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
상수도
8912 
하수도
1088 

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 8912
89.1%
하수도 1088
 
10.9%

Length

2023-12-11T01:48:51.354989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:48:51.469816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도 8912
89.1%
하수도 1088
 
10.9%

상수도업종
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가정용
3894 
일반용
3567 
사회복지
1207 
욕탕용
867 
<NA>
 
222
Other values (6)
 
243

Length

Max length8
Median length3
Mean length3.1879
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반용
2nd row욕탕용
3rd row가정용
4th row가정용
5th row가정용

Common Values

ValueCountFrequency (%)
가정용 3894
38.9%
일반용 3567
35.7%
사회복지 1207
 
12.1%
욕탕용 867
 
8.7%
<NA> 222
 
2.2%
공업용수 101
 
1.0%
공동수도 56
 
0.6%
국가유공단체 56
 
0.6%
영업용(온천) 15
 
0.1%
일반용(온천) 10
 
0.1%

Length

2023-12-11T01:48:51.695024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가정용 3894
38.9%
일반용 3567
35.7%
사회복지 1207
 
12.1%
욕탕용 867
 
8.7%
na 222
 
2.2%
공업용수 101
 
1.0%
공동수도 56
 
0.6%
국가유공단체 56
 
0.6%
영업용(온천 15
 
0.1%
일반용(온천 10
 
0.1%

구경
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5119
Minimum0
Maximum300
Zeros112
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:48:51.828110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q120
median32
Q350
95-th percentile150
Maximum300
Range300
Interquartile range (IQR)30

Descriptive statistics

Standard deviation48.375639
Coefficient of variation (CV)0.95770777
Kurtosis6.46893
Mean50.5119
Median Absolute Deviation (MAD)17
Skewness2.3920718
Sum505119
Variance2340.2025
MonotonicityNot monotonic
2023-12-11T01:48:52.008565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
15 1492
14.9%
40 1428
14.3%
25 1420
14.2%
20 1306
13.1%
50 1284
12.8%
80 769
7.7%
32 716
7.2%
100 663
6.6%
150 437
 
4.4%
200 197
 
2.0%
Other values (4) 288
 
2.9%
ValueCountFrequency (%)
0 112
 
1.1%
13 9
 
0.1%
15 1492
14.9%
20 1306
13.1%
25 1420
14.2%
32 716
7.2%
40 1428
14.3%
50 1284
12.8%
80 769
7.7%
100 663
6.6%
ValueCountFrequency (%)
300 45
 
0.4%
250 122
 
1.2%
200 197
 
2.0%
150 437
 
4.4%
100 663
6.6%
80 769
7.7%
50 1284
12.8%
40 1428
14.3%
32 716
7.2%
25 1420
14.2%

전수
Real number (ℝ)

HIGH CORRELATION 

Distinct749
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.1813
Minimum1
Maximum4559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:48:52.197678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q321
95-th percentile562
Maximum4559
Range4558
Interquartile range (IQR)19

Descriptive statistics

Standard deviation280.22572
Coefficient of variation (CV)3.4098478
Kurtosis51.244608
Mean82.1813
Median Absolute Deviation (MAD)3
Skewness6.0172704
Sum821813
Variance78526.455
MonotonicityNot monotonic
2023-12-11T01:48:52.360385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2431
24.3%
2 1265
 
12.7%
3 776
 
7.8%
4 536
 
5.4%
5 398
 
4.0%
6 396
 
4.0%
8 229
 
2.3%
7 226
 
2.3%
9 207
 
2.1%
11 193
 
1.9%
Other values (739) 3343
33.4%
ValueCountFrequency (%)
1 2431
24.3%
2 1265
12.7%
3 776
 
7.8%
4 536
 
5.4%
5 398
 
4.0%
6 396
 
4.0%
7 226
 
2.3%
8 229
 
2.3%
9 207
 
2.1%
10 120
 
1.2%
ValueCountFrequency (%)
4559 1
< 0.1%
4557 2
< 0.1%
4549 1
< 0.1%
2898 1
< 0.1%
2889 1
< 0.1%
2882 1
< 0.1%
2741 1
< 0.1%
2714 1
< 0.1%
2713 1
< 0.1%
2712 1
< 0.1%

Interactions

2023-12-11T01:48:47.671511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:43.095854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:43.952076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:44.914026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:45.825092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:46.785322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:47.796947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:43.221154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:44.089639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:45.070530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:45.996605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:46.948812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:47.907846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:43.332286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:44.265634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:45.223475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:46.180542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:47.100905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:48.006517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:43.480534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:44.440328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:45.377620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:46.349769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:47.263583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:48.133891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:43.621035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:44.617045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:45.549008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:46.511037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:47.400274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:48.233097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:43.821007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:44.768975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:45.716083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:46.668770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:47.528087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:48:52.483535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번고지년월사업소코드사업소명구코드구명동코드상하수도구분상수도업종구경전수
연번1.0000.9550.2230.3510.2770.3780.0990.7730.1360.0200.000
고지년월0.9551.0000.0000.0000.0000.0000.0000.8910.1270.0380.000
사업소코드0.2230.0001.0001.0001.0001.0000.1850.0000.1430.1320.078
사업소명0.3510.0001.0001.0000.9471.0000.7190.0000.2130.1110.159
구코드0.2770.0001.0000.9471.0001.0000.6930.0000.1420.1310.202
구명0.3780.0001.0001.0001.0001.0000.8080.0000.2450.1520.220
동코드0.0990.0000.1850.7190.6930.8081.0000.0000.0820.0370.145
상하수도구분0.7730.8910.0000.0000.0000.0000.0001.0000.3160.0870.015
상수도업종0.1360.1270.1430.2130.1420.2450.0820.3161.0000.3580.141
구경0.0200.0380.1320.1110.1310.1520.0370.0870.3581.0000.300
전수0.0000.0000.0780.1590.2020.2200.1450.0150.1410.3001.000
2023-12-11T01:48:52.649715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명사업소명상수도업종상하수도구분
구명1.0001.0000.0980.000
사업소명1.0001.0000.0920.000
상수도업종0.0980.0921.0000.243
상하수도구분0.0000.0000.2431.000
2023-12-11T01:48:52.750334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드구코드동코드구경전수사업소명구명상하수도구분상수도업종
연번1.0000.0980.041-0.025-0.0080.0100.1570.1580.6090.042
사업소코드0.0981.0000.366-0.2650.0480.0211.0000.9990.0000.088
구코드0.0410.3661.0000.1200.0340.0260.8441.0000.0000.068
동코드-0.025-0.2650.1201.0000.006-0.0280.4630.5420.0000.041
구경-0.0080.0480.0340.0061.000-0.5230.0520.0540.0650.179
전수0.0100.0210.026-0.028-0.5231.0000.0750.0790.0120.067
사업소명0.1571.0000.8440.4630.0520.0751.0001.0000.0000.092
구명0.1580.9991.0000.5420.0540.0791.0001.0000.0000.098
상하수도구분0.6090.0000.0000.0000.0650.0120.0000.0001.0000.243
상수도업종0.0420.0880.0680.0410.1790.0670.0920.0980.2431.000

Missing values

2023-12-11T01:48:48.388942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:48:48.573297image/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
23154231552022-06303영도 사업소200영도구640청학2동상수도일반용2066
56567565682022-12306남부 사업소500수영구790광안4동상수도욕탕용501
25144251452022-06308해운대 사업소350해운대구620반송2동상수도가정용2519
823582362022-02309사하 사업소380사하구590장림2동상수도가정용506
50121501222022-11244동래통합사업소470연제구610거제1동상수도가정용15505
16063160642022-04307북부 사업소530사상구610덕포2동상수도욕탕용402
42732427332022-09304부산진 사업소230부산진구570양정2동상수도가정용321
17458174592022-05244동래통합사업소260동래구750안락2동상수도가정용151154
17937179382022-05244동래통합사업소470연제구660연산2동상수도가정용503
22376223772022-06244동래통합사업소470연제구730연산9동상수도가정용1502
연번고지년월사업소코드사업소명구코드구명동코드동명상하수도구분상수도업종구경전수
52854528552022-11308해운대 사업소350해운대구510우1동상수도일반용(온천)501
342034212022-01308해운대 사업소350해운대구540중2동상수도가정용802
829983002022-02309사하 사업소380사하구602다대2동상수도사회복지153
52363523642022-11307북부 사업소320북구530금곡동상수도가정용802
28862288632022-07301중동부 사업소170동구680범일5동하수도일반용503
51670516712022-11304부산진 사업소230부산진구780범천2동상수도가정용1002
56295562962022-12306남부 사업소290남구650우암2동상수도가정용252
48064480652022-10307북부 사업소320북구530금곡동상수도일반용326
339733982022-01308해운대 사업소350해운대구530중1동상수도일반용25117
13996139972022-04301중동부 사업소170동구550초량6동상수도일반용255