Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells4
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

Description부산광역시상수도사업본부_수용가정보시스템_고지집계정보_고지전수집계_20220131
Author부산광역시 상수도사업본부
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083673

Alerts

상하수도구분 has constant value ""Constant
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 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 started2023-12-10 16:48:56.079705
Analysis finished2023-12-10 16:49:07.361882
Duration11.28 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%
Mean25822.294
Minimum1
Maximum51644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:07.479219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2583.9
Q113101.75
median25895.5
Q338492.5
95-th percentile49060.45
Maximum51644
Range51643
Interquartile range (IQR)25390.75

Descriptive statistics

Standard deviation14803.793
Coefficient of variation (CV)0.57329501
Kurtosis-1.1787245
Mean25822.294
Median Absolute Deviation (MAD)12700
Skewness-0.0028036605
Sum2.5822294 × 108
Variance2.1915227 × 108
MonotonicityNot monotonic
2023-12-11T01:49:07.701359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39875 1
 
< 0.1%
14742 1
 
< 0.1%
47033 1
 
< 0.1%
34975 1
 
< 0.1%
13102 1
 
< 0.1%
7214 1
 
< 0.1%
23665 1
 
< 0.1%
45932 1
 
< 0.1%
39755 1
 
< 0.1%
38139 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
11 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
24 1
< 0.1%
32 1
< 0.1%
40 1
< 0.1%
48 1
< 0.1%
ValueCountFrequency (%)
51644 1
< 0.1%
51631 1
< 0.1%
51625 1
< 0.1%
51624 1
< 0.1%
51623 1
< 0.1%
51619 1
< 0.1%
51618 1
< 0.1%
51609 1
< 0.1%
51602 1
< 0.1%
51598 1
< 0.1%

고지년월
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-07
885 
2021-06
864 
2021-04
848 
2021-02
841 
2021-08
838 
Other values (7)
5724 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10
2nd row2021-04
3rd row2021-11
4th row2021-04
5th row2021-09

Common Values

ValueCountFrequency (%)
2021-07 885
8.8%
2021-06 864
8.6%
2021-04 848
8.5%
2021-02 841
8.4%
2021-08 838
8.4%
2021-10 835
8.3%
2021-09 835
8.3%
2021-01 820
8.2%
2021-12 814
8.1%
2021-11 810
8.1%
Other values (2) 1610
16.1%

Length

2023-12-11T01:49:07.918611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-07 885
8.8%
2021-06 864
8.6%
2021-04 848
8.5%
2021-02 841
8.4%
2021-08 838
8.4%
2021-10 835
8.3%
2021-09 835
8.3%
2021-01 820
8.2%
2021-12 814
8.1%
2021-11 810
8.1%
Other values (2) 1610
16.1%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.8032
Minimum244
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:08.075068image/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 deviation24.547015
Coefficient of variation (CV)0.083549174
Kurtosis0.34434927
Mean293.8032
Median Absolute Deviation (MAD)3
Skewness-1.5071041
Sum2938032
Variance602.55593
MonotonicityNot monotonic
2023-12-11T01:49:08.267767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
244 1936
19.4%
306 1336
13.4%
307 1226
12.3%
304 1201
12.0%
301 902
9.0%
309 890
8.9%
308 856
8.6%
302 540
 
5.4%
303 535
 
5.3%
311 335
 
3.4%
ValueCountFrequency (%)
244 1936
19.4%
301 902
9.0%
302 540
 
5.4%
303 535
 
5.3%
304 1201
12.0%
306 1336
13.4%
307 1226
12.3%
308 856
8.6%
309 890
8.9%
311 335
 
3.4%
ValueCountFrequency (%)
312 243
 
2.4%
311 335
 
3.4%
309 890
8.9%
308 856
8.6%
307 1226
12.3%
306 1336
13.4%
304 1201
12.0%
303 535
5.3%
302 540
5.4%
301 902
9.0%

사업소명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
1936 
남부 사업소
1336 
북부 사업소
1226 
부산진 사업소
1201 
중동부 사업소
902 
Other values (6)
3399 

Length

Max length9
Median length9
Mean length8.3169
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중동부 사업소
2nd row중동부 사업소
3rd row북부 사업소
4th row동래통합사업소
5th row사하 사업소

Common Values

ValueCountFrequency (%)
동래통합사업소 1936
19.4%
남부 사업소 1336
13.4%
북부 사업소 1226
12.3%
부산진 사업소 1201
12.0%
중동부 사업소 902
9.0%
사하 사업소 890
8.9%
해운대 사업소 856
8.6%
서부 사업소 540
 
5.4%
영도 사업소 535
 
5.3%
강서 사업소 335
 
3.4%

Length

2023-12-11T01:49:08.502829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업소 8064
44.6%
동래통합사업소 1936
 
10.7%
남부 1336
 
7.4%
북부 1226
 
6.8%
부산진 1201
 
6.6%
중동부 902
 
5.0%
사하 890
 
4.9%
해운대 856
 
4.7%
서부 540
 
3.0%
영도 535
 
3.0%
Other values (2) 578
 
3.2%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.797
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:08.700049image/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 deviation131.26043
Coefficient of variation (CV)0.40043206
Kurtosis0.053013418
Mean327.797
Median Absolute Deviation (MAD)90
Skewness0.50970903
Sum3277970
Variance17229.3
MonotonicityNot monotonic
2023-12-11T01:49:08.882548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
230 1201
12.0%
380 890
 
8.9%
350 856
 
8.6%
290 854
 
8.5%
260 691
 
6.9%
410 664
 
6.6%
320 630
 
6.3%
530 596
 
6.0%
470 581
 
5.8%
140 540
 
5.4%
Other values (6) 2497
25.0%
ValueCountFrequency (%)
110 384
 
3.8%
140 540
5.4%
170 518
5.2%
200 535
5.3%
230 1201
12.0%
260 691
6.9%
290 854
8.5%
320 630
6.3%
350 856
8.6%
380 890
8.9%
ValueCountFrequency (%)
710 243
 
2.4%
530 596
6.0%
500 482
4.8%
470 581
5.8%
440 335
 
3.4%
410 664
6.6%
380 890
8.9%
350 856
8.6%
320 630
6.3%
290 854
8.5%

구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
1201 
사하구
890 
해운대구
856 
남구
854 
동래구
691 
Other values (11)
5508 

Length

Max length4
Median length3
Mean length2.9131
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동구
2nd row중구
3rd row사상구
4th row금정구
5th row사하구

Common Values

ValueCountFrequency (%)
부산진구 1201
12.0%
사하구 890
 
8.9%
해운대구 856
 
8.6%
남구 854
 
8.5%
동래구 691
 
6.9%
금정구 664
 
6.6%
북구 630
 
6.3%
사상구 596
 
6.0%
연제구 581
 
5.8%
서구 540
 
5.4%
Other values (6) 2497
25.0%

Length

2023-12-11T01:49:09.112407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 1201
12.0%
사하구 890
 
8.9%
해운대구 856
 
8.6%
남구 854
 
8.5%
동래구 691
 
6.9%
금정구 664
 
6.6%
북구 630
 
6.3%
사상구 596
 
6.0%
연제구 581
 
5.8%
서구 540
 
5.4%
Other values (6) 2497
25.0%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.5439
Minimum9
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:09.349191image/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 deviation90.014993
Coefficient of variation (CV)0.14988911
Kurtosis3.4833083
Mean600.5439
Median Absolute Deviation (MAD)50
Skewness-0.73644211
Sum6005439
Variance8102.6989
MonotonicityNot monotonic
2023-12-11T01:49:09.642802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
530 549
 
5.5%
510 508
 
5.1%
520 476
 
4.8%
560 466
 
4.7%
550 426
 
4.3%
590 409
 
4.1%
620 403
 
4.0%
580 389
 
3.9%
610 387
 
3.9%
570 382
 
3.8%
Other values (53) 5605
56.0%
ValueCountFrequency (%)
9 4
 
< 0.1%
250 67
 
0.7%
253 50
 
0.5%
256 44
 
0.4%
310 49
 
0.5%
330 33
 
0.3%
510 508
5.1%
520 476
4.8%
521 50
 
0.5%
525 40
 
0.4%
ValueCountFrequency (%)
800 64
0.6%
790 91
0.9%
780 89
0.9%
770 101
1.0%
762 62
0.6%
761 49
 
0.5%
760 114
1.1%
750 130
1.3%
740 152
1.5%
730 146
1.5%

동명
Text

Distinct219
Distinct (%)2.2%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-11T01:49:10.223922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7956182
Min length3

Characters and Unicode

Total characters37941
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 row수정4동
2nd row영주1동
3rd row엄궁동
4th row금성동
5th row신평2동
ValueCountFrequency (%)
녹산동 83
 
0.8%
신평2동 79
 
0.8%
신평1동 76
 
0.8%
온천1동 75
 
0.8%
학장동 71
 
0.7%
양정1동 70
 
0.7%
감천1동 68
 
0.7%
기장읍 67
 
0.7%
감전1동 66
 
0.7%
장림1동 66
 
0.7%
Other values (209) 9275
92.8%
2023-12-11T01:49:10.986735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10104
26.6%
1 2916
 
7.7%
2 2542
 
6.7%
3 1382
 
3.6%
대 760
 
2.0%
천 695
 
1.8%
연 694
 
1.8%
4 666
 
1.8%
산 570
 
1.5%
장 552
 
1.5%
Other values (97) 17060
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30016
79.1%
Decimal Number 7925
 
20.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10104
33.7%
대 760
 
2.5%
천 695
 
2.3%
연 694
 
2.3%
산 570
 
1.9%
장 552
 
1.8%
정 550
 
1.8%
신 514
 
1.7%
부 502
 
1.7%
포 495
 
1.6%
Other values (89) 14580
48.6%
Decimal Number
ValueCountFrequency (%)
1 2916
36.8%
2 2542
32.1%
3 1382
17.4%
4 666
 
8.4%
5 186
 
2.3%
6 123
 
1.6%
9 60
 
0.8%
8 50
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30016
79.1%
Common 7925
 
20.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10104
33.7%
대 760
 
2.5%
천 695
 
2.3%
연 694
 
2.3%
산 570
 
1.9%
장 552
 
1.8%
정 550
 
1.8%
신 514
 
1.7%
부 502
 
1.7%
포 495
 
1.6%
Other values (89) 14580
48.6%
Common
ValueCountFrequency (%)
1 2916
36.8%
2 2542
32.1%
3 1382
17.4%
4 666
 
8.4%
5 186
 
2.3%
6 123
 
1.6%
9 60
 
0.8%
8 50
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30016
79.1%
ASCII 7925
 
20.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10104
33.7%
대 760
 
2.5%
천 695
 
2.3%
연 694
 
2.3%
산 570
 
1.9%
장 552
 
1.8%
정 550
 
1.8%
신 514
 
1.7%
부 502
 
1.7%
포 495
 
1.6%
Other values (89) 14580
48.6%
ASCII
ValueCountFrequency (%)
1 2916
36.8%
2 2542
32.1%
3 1382
17.4%
4 666
 
8.4%
5 186
 
2.3%
6 123
 
1.6%
9 60
 
0.8%
8 50
 
0.6%

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

2023-12-11T01:49:11.229291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:49:11.387773image/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
4003 
일반용
3646 
사회복지
1211 
욕탕용
901 
공업용수
 
129
Other values (5)
 
110

Length

Max length8
Median length3
Mean length3.1618
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가정용 4003
40.0%
일반용 3646
36.5%
사회복지 1211
 
12.1%
욕탕용 901
 
9.0%
공업용수 129
 
1.3%
공동수도 55
 
0.5%
영업용(온천) 23
 
0.2%
일반용(온천) 19
 
0.2%
목욕장용(온천) 8
 
0.1%
국가유공단체 5
 
0.1%

Length

2023-12-11T01:49:11.572209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:49:11.775734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가정용 4003
40.0%
일반용 3646
36.5%
사회복지 1211
 
12.1%
욕탕용 901
 
9.0%
공업용수 129
 
1.3%
공동수도 55
 
0.5%
영업용(온천 23
 
0.2%
일반용(온천 19
 
0.2%
목욕장용(온천 8
 
0.1%
국가유공단체 5
 
< 0.1%

구경
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.2109
Minimum13
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:11.964350image/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 deviation50.776702
Coefficient of variation (CV)0.97253068
Kurtosis6.4419762
Mean52.2109
Median Absolute Deviation (MAD)17
Skewness2.4211201
Sum522109
Variance2578.2734
MonotonicityNot monotonic
2023-12-11T01:49:12.168368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 1503
15.0%
40 1448
14.5%
25 1427
14.3%
20 1325
13.2%
50 1244
12.4%
80 773
7.7%
32 745
7.4%
100 664
6.6%
150 446
 
4.5%
200 216
 
2.2%
Other values (3) 209
 
2.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
15 1503
15.0%
20 1325
13.2%
25 1427
14.3%
32 745
7.4%
40 1448
14.5%
50 1244
12.4%
80 773
7.7%
100 664
6.6%
150 446
 
4.5%
ValueCountFrequency (%)
300 71
 
0.7%
250 135
 
1.4%
200 216
 
2.2%
150 446
 
4.5%
100 664
6.6%
80 773
7.7%
50 1244
12.4%
40 1448
14.5%
32 745
7.4%
25 1427
14.3%

전수
Real number (ℝ)

HIGH CORRELATION 

Distinct750
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.5713
Minimum1
Maximum4572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:12.352830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q321
95-th percentile560.2
Maximum4572
Range4571
Interquartile range (IQR)19

Descriptive statistics

Standard deviation279.69139
Coefficient of variation (CV)3.3467397
Kurtosis41.389116
Mean83.5713
Median Absolute Deviation (MAD)4
Skewness5.5748636
Sum835713
Variance78227.273
MonotonicityNot monotonic
2023-12-11T01:49:12.495422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2449
24.5%
2 1223
 
12.2%
3 778
 
7.8%
4 519
 
5.2%
5 401
 
4.0%
6 351
 
3.5%
8 242
 
2.4%
7 241
 
2.4%
9 201
 
2.0%
11 173
 
1.7%
Other values (740) 3422
34.2%
ValueCountFrequency (%)
1 2449
24.5%
2 1223
12.2%
3 778
 
7.8%
4 519
 
5.2%
5 401
 
4.0%
6 351
 
3.5%
7 241
 
2.4%
8 242
 
2.4%
9 201
 
2.0%
10 157
 
1.6%
ValueCountFrequency (%)
4572 1
 
< 0.1%
4561 1
 
< 0.1%
2900 1
 
< 0.1%
2898 1
 
< 0.1%
2897 4
< 0.1%
2719 2
< 0.1%
2632 1
 
< 0.1%
2542 1
 
< 0.1%
2539 1
 
< 0.1%
2537 1
 
< 0.1%

Interactions

2023-12-11T01:49:05.496657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:59.726591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:00.922889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:02.248891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:03.453207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:04.578429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:05.639205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:59.894573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:01.101882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:02.461943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:03.639216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:04.727632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:05.799454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:00.112408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:01.290421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:02.711298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:03.863242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:04.883064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:06.303045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:00.285857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:01.474357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:02.918599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:04.060373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:05.040146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:06.473912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:00.525341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:01.715012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:03.105920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:04.237764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:05.212691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:06.635433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:00.730557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:02.062393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:03.291553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:04.405180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:05.373228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:49:12.629681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번고지년월사업소코드사업소명구코드구명동코드상수도업종구경전수
연번1.0000.9600.1810.2120.1710.2400.0510.0000.0000.000
고지년월0.9601.0000.0000.0000.0000.0000.0000.0000.0000.000
사업소코드0.1810.0001.0001.0001.0001.0000.1910.1460.1240.071
사업소명0.2120.0001.0001.0000.9481.0000.7190.2100.1170.154
구코드0.1710.0001.0000.9481.0001.0000.6940.1600.1210.176
구명0.2400.0001.0001.0001.0001.0000.8040.2670.1530.205
동코드0.0510.0000.1910.7190.6940.8041.0000.0990.0000.115
상수도업종0.0000.0000.1460.2100.1600.2670.0991.0000.3010.143
구경0.0000.0000.1240.1170.1210.1530.0000.3011.0000.192
전수0.0000.0000.0710.1540.1760.2050.1150.1430.1921.000
2023-12-11T01:49:12.808097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명사업소명고지년월상수도업종
구명1.0001.0000.0000.108
사업소명1.0001.0000.0000.091
고지년월0.0000.0001.0000.000
상수도업종0.1080.0910.0001.000
2023-12-11T01:49:12.972708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드구코드동코드구경전수고지년월사업소명구명상수도업종
연번1.0000.0770.039-0.0200.0010.0060.8430.0910.0960.000
사업소코드0.0771.0000.396-0.2540.0460.0040.0001.0000.9990.101
구코드0.0390.3961.0000.1010.0350.0380.0000.8461.0000.077
동코드-0.020-0.2540.1011.0000.011-0.0160.0000.4630.5370.050
구경0.0010.0460.0350.0111.000-0.5590.0000.0550.0540.149
전수0.0060.0040.038-0.016-0.5591.0000.0000.0730.0730.068
고지년월0.8430.0000.0000.0000.0000.0001.0000.0000.0000.000
사업소명0.0911.0000.8460.4630.0550.0730.0001.0001.0000.091
구명0.0960.9991.0000.5370.0540.0730.0001.0001.0000.108
상수도업종0.0000.1010.0770.0500.1490.0680.0000.0910.1081.000

Missing values

2023-12-11T01:49:06.930463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:49:07.229337image/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
39874398752021-10301중동부 사업소170동구590수정4동상수도일반용401
13902139032021-04301중동부 사업소110중구590영주1동상수도가정용15559
46352463532021-11307북부 사업소530사상구680엄궁동상수도욕탕용252
13522135232021-04244동래통합사업소410금정구700금성동상수도가정용15190
38331383322021-09309사하 사업소380사하구572신평2동상수도공업용수2501
34436344372021-09244동래통합사업소260동래구510수민동상수도가정용2017
29539295402021-07309사하 사업소380사하구510괴정1동상수도사회복지321
26550265512021-07244동래통합사업소470연제구660연산2동상수도가정용2016
32728327292021-08306남부 사업소500수영구670남천2동상수도가정용252
19340193412021-05304부산진 사업소230부산진구750개금2동상수도사회복지155
연번고지년월사업소코드사업소명구코드구명동코드동명상하수도구분상수도업종구경전수
21739217402021-06244동래통합사업소260동래구600사직3동상수도일반용328
14798147992021-04304부산진 사업소230부산진구610전포2동상수도일반용2567
12360123612021-03309사하 사업소380사하구530괴정3동상수도욕탕용502
27930279312021-07304부산진 사업소230부산진구740개금1동상수도사회복지251
17786177872021-05244동래통합사업소410금정구680구서1동상수도가정용2542
20030200312021-05307북부 사업소320북구520구포2동상수도가정용151507
709870992021-02307북부 사업소320북구510구포1동상수도가정용409
33128331292021-08307북부 사업소320북구571만덕1동상수도가정용2015
51004510052021-12308해운대 사업소350해운대구660재송2동상수도가정용2566
19109191102021-05304부산진 사업소230부산진구610전포2동상수도욕탕용251