Overview

Dataset statistics

Number of variables16
Number of observations160
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory142.8 B

Variable types

Numeric12
DateTime1
Categorical3

Dataset

Description부산광역시상수도사업본부_수용가정보시스템_고지집계정보_당초고지집계_20210601
Author부산광역시 상수도사업본부
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083671

Alerts

업무용 has constant value ""Constant
타이틀 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
통계구분 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
연번 is highly overall correlated with 통계구분 and 1 other fieldsHigh correlation
사업소코드 is highly overall correlated with 공업용High correlation
생활용수계 is highly overall correlated with 가정용 and 4 other fieldsHigh correlation
가정용 is highly overall correlated with 생활용수계 and 4 other fieldsHigh correlation
영업용 is highly overall correlated with 생활용수계 and 4 other fieldsHigh correlation
욕탕용 is highly overall correlated with 생활용수계 and 4 other fieldsHigh correlation
공업용 is highly overall correlated with 사업소코드High correlation
온천수계 is highly overall correlated with 온천영업용 and 2 other fieldsHigh correlation
온천영업용 is highly overall correlated with 온천수계 and 2 other fieldsHigh correlation
온천일반 is highly overall correlated with 온천수계 and 2 other fieldsHigh correlation
온천목욕용 is highly overall correlated with 온천수계 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
생활용수계 has unique valuesUnique
가정용 has unique valuesUnique
영업용 has unique valuesUnique
공업용 has 130 (81.2%) zerosZeros
온천수계 has 140 (87.5%) zerosZeros
온천영업용 has 140 (87.5%) zerosZeros
온천일반 has 140 (87.5%) zerosZeros
온천목욕용 has 140 (87.5%) zerosZeros

Reproduction

Analysis started2023-12-10 17:28:57.106674
Analysis finished2023-12-10 17:29:33.617719
Duration36.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.5
Minimum1
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:33.780109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.95
Q140.75
median80.5
Q3120.25
95-th percentile152.05
Maximum160
Range159
Interquartile range (IQR)79.5

Descriptive statistics

Standard deviation46.332134
Coefficient of variation (CV)0.57555446
Kurtosis-1.2
Mean80.5
Median Absolute Deviation (MAD)40
Skewness0
Sum12880
Variance2146.6667
MonotonicityStrictly increasing
2023-12-11T02:29:34.142385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
82 1
 
0.6%
104 1
 
0.6%
105 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%
Other values (150) 150
93.8%
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 (%)
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%
153 1
0.6%
152 1
0.6%
151 1
0.6%
Distinct5
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum2021-01-01 00:00:00
Maximum2021-05-01 00:00:00
2023-12-11T02:29:34.432188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:35.504531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

통계구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
1
80 
2
80 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 80
50.0%
2 80
50.0%

Length

2023-12-11T02:29:36.270487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:29:36.821381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 80
50.0%
2 80
50.0%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.3125
Minimum244
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:37.047406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum244
5-th percentile244
Q1301
median305
Q3307.25
95-th percentile312
Maximum312
Range68
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation24.445341
Coefficient of variation (CV)0.083059132
Kurtosis0.52576322
Mean294.3125
Median Absolute Deviation (MAD)3.5
Skewness-1.5532923
Sum47090
Variance597.57469
MonotonicityNot monotonic
2023-12-11T02:29:37.302814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
244 30
18.8%
301 20
12.5%
306 20
12.5%
307 20
12.5%
302 10
 
6.2%
303 10
 
6.2%
304 10
 
6.2%
308 10
 
6.2%
309 10
 
6.2%
311 10
 
6.2%
ValueCountFrequency (%)
244 30
18.8%
301 20
12.5%
302 10
 
6.2%
303 10
 
6.2%
304 10
 
6.2%
306 20
12.5%
307 20
12.5%
308 10
 
6.2%
309 10
 
6.2%
311 10
 
6.2%
ValueCountFrequency (%)
312 10
6.2%
311 10
6.2%
309 10
6.2%
308 10
6.2%
307 20
12.5%
306 20
12.5%
304 10
6.2%
303 10
6.2%
302 10
6.2%
301 20
12.5%

구코드
Real number (ℝ)

Distinct16
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.375
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:37.666273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110
Q1222.5
median335
Q3447.5
95-th percentile710
Maximum710
Range600
Interquartile range (IQR)225

Descriptive statistics

Standard deviation157.53481
Coefficient of variation (CV)0.45745136
Kurtosis-0.26323719
Mean344.375
Median Absolute Deviation (MAD)120
Skewness0.49250642
Sum55100
Variance24817.217
MonotonicityNot monotonic
2023-12-11T02:29:37.978397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
260 10
 
6.2%
500 10
 
6.2%
710 10
 
6.2%
440 10
 
6.2%
380 10
 
6.2%
350 10
 
6.2%
530 10
 
6.2%
320 10
 
6.2%
290 10
 
6.2%
410 10
 
6.2%
Other values (6) 60
37.5%
ValueCountFrequency (%)
110 10
6.2%
140 10
6.2%
170 10
6.2%
200 10
6.2%
230 10
6.2%
260 10
6.2%
290 10
6.2%
320 10
6.2%
350 10
6.2%
380 10
6.2%
ValueCountFrequency (%)
710 10
6.2%
530 10
6.2%
500 10
6.2%
470 10
6.2%
440 10
6.2%
410 10
6.2%
380 10
6.2%
350 10
6.2%
320 10
6.2%
290 10
6.2%

생활용수계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.36169 × 108
Minimum18700050
Maximum2.7050372 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:38.293640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18700050
5-th percentile32447751
Q143965705
median2.411922 × 108
Q31.4292248 × 109
95-th percentile2.3611626 × 109
Maximum2.7050372 × 109
Range2.6863372 × 109
Interquartile range (IQR)1.3852591 × 109

Descriptive statistics

Standard deviation8.2511775 × 108
Coefficient of variation (CV)1.1208265
Kurtosis-0.66021191
Mean7.36169 × 108
Median Absolute Deviation (MAD)2.1581114 × 108
Skewness0.81076454
Sum1.1778704 × 1011
Variance6.8081929 × 1017
MonotonicityNot monotonic
2023-12-11T02:29:38.685642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49153760 1
 
0.6%
1189502440 1
 
0.6%
66750150 1
 
0.6%
47067730 1
 
0.6%
38528720 1
 
0.6%
34807680 1
 
0.6%
48028580 1
 
0.6%
64172500 1
 
0.6%
71440260 1
 
0.6%
59313120 1
 
0.6%
Other values (150) 150
93.8%
ValueCountFrequency (%)
18700050 1
0.6%
18740050 1
0.6%
18744690 1
0.6%
20353830 1
0.6%
20396780 1
0.6%
30365360 1
0.6%
30478880 1
0.6%
32442640 1
0.6%
32448020 1
0.6%
32506670 1
0.6%
ValueCountFrequency (%)
2705037230 1
0.6%
2675961430 1
0.6%
2629407900 1
0.6%
2583344060 1
0.6%
2489762610 1
0.6%
2454061990 1
0.6%
2412256770 1
0.6%
2367015160 1
0.6%
2360854620 1
0.6%
2359194160 1
0.6%

가정용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8713635 × 108
Minimum5955570
Maximum1.5831769 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:39.102074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5955570
5-th percentile14704298
Q123557300
median67996705
Q37.3657278 × 108
95-th percentile1.1909316 × 109
Maximum1.5831769 × 109
Range1.5772213 × 109
Interquartile range (IQR)7.1301548 × 108

Descriptive statistics

Standard deviation4.4784866 × 108
Coefficient of variation (CV)1.1568241
Kurtosis-0.44003681
Mean3.8713635 × 108
Median Absolute Deviation (MAD)57175795
Skewness0.90334091
Sum6.1941816 × 1010
Variance2.0056842 × 1017
MonotonicityNot monotonic
2023-12-11T02:29:39.476609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28907690 1
 
0.6%
724888730 1
 
0.6%
39714480 1
 
0.6%
31858870 1
 
0.6%
23510330 1
 
0.6%
23527180 1
 
0.6%
23890990 1
 
0.6%
39683870 1
 
0.6%
41718510 1
 
0.6%
13413490 1
 
0.6%
Other values (150) 150
93.8%
ValueCountFrequency (%)
5955570 1
0.6%
5960500 1
0.6%
8205490 1
0.6%
8209180 1
0.6%
8233650 1
0.6%
13408170 1
0.6%
13413490 1
0.6%
14688860 1
0.6%
14705110 1
0.6%
14744960 1
0.6%
ValueCountFrequency (%)
1583176910 1
0.6%
1545380780 1
0.6%
1542807700 1
0.6%
1486574910 1
0.6%
1397904550 1
0.6%
1316155300 1
0.6%
1283199930 1
0.6%
1230353960 1
0.6%
1188856720 1
0.6%
1157431210 1
0.6%

업무용
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
160 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 160
100.0%

Length

2023-12-11T02:29:39.815724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:29:40.067675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 160
100.0%

영업용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.268258 × 108
Minimum8877640
Maximum1.3931744 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:40.331320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8877640
5-th percentile10838571
Q115011580
median1.4933109 × 108
Q35.0265956 × 108
95-th percentile1.0642496 × 109
Maximum1.3931744 × 109
Range1.3842967 × 109
Interquartile range (IQR)4.8764798 × 108

Descriptive statistics

Standard deviation3.7428299 × 108
Coefficient of variation (CV)1.1452063
Kurtosis-0.29882679
Mean3.268258 × 108
Median Absolute Deviation (MAD)1.3849278 × 108
Skewness0.93941015
Sum5.2292129 × 1010
Variance1.4008775 × 1017
MonotonicityNot monotonic
2023-12-11T02:29:40.662662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19608370 1
 
0.6%
457649590 1
 
0.6%
26295670 1
 
0.6%
14615360 1
 
0.6%
14590990 1
 
0.6%
10838600 1
 
0.6%
23258090 1
 
0.6%
23910030 1
 
0.6%
25261050 1
 
0.6%
40080750 1
 
0.6%
Other values (150) 150
93.8%
ValueCountFrequency (%)
8877640 1
0.6%
8882180 1
0.6%
9417730 1
0.6%
9436700 1
0.6%
10158400 1
0.6%
10171870 1
0.6%
10191200 1
0.6%
10838020 1
0.6%
10838600 1
0.6%
11309000 1
0.6%
ValueCountFrequency (%)
1393174370 1
0.6%
1253169210 1
0.6%
1173271940 1
0.6%
1122968760 1
0.6%
1107725070 1
0.6%
1100427810 1
0.6%
1100292900 1
0.6%
1098677790 1
0.6%
1062437570 1
0.6%
1058197180 1
0.6%

욕탕용
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13868853
Minimum81400
Maximum60699670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:40.968812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81400
5-th percentile132570
Q1470675
median2599075
Q331161215
95-th percentile44083537
Maximum60699670
Range60618270
Interquartile range (IQR)30690540

Descriptive statistics

Standard deviation16706624
Coefficient of variation (CV)1.2046147
Kurtosis-0.54690141
Mean13868853
Median Absolute Deviation (MAD)2515650
Skewness0.88929383
Sum2.2190164 × 109
Variance2.7911129 × 1014
MonotonicityNot monotonic
2023-12-11T02:29:41.276472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
637700 5
 
3.1%
348000 3
 
1.9%
379300 3
 
1.9%
578600 3
 
1.9%
421300 3
 
1.9%
427400 3
 
1.9%
392400 2
 
1.2%
319000 2
 
1.2%
81400 2
 
1.2%
85000 2
 
1.2%
Other values (122) 132
82.5%
ValueCountFrequency (%)
81400 2
1.2%
81850 1
 
0.6%
85000 2
1.2%
105600 1
 
0.6%
114900 2
1.2%
133500 2
1.2%
319000 2
1.2%
321300 2
1.2%
348000 3
1.9%
379300 3
1.9%
ValueCountFrequency (%)
60699670 1
0.6%
56643010 1
0.6%
56000760 1
0.6%
51642500 1
0.6%
48003100 1
0.6%
47299000 1
0.6%
45704640 1
0.6%
44781930 1
0.6%
44046780 1
0.6%
42069810 1
0.6%

공업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8337994.5
Minimum0
Maximum1.7944481 × 108
Zeros130
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:41.647524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile92580560
Maximum1.7944481 × 108
Range1.7944481 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31782159
Coefficient of variation (CV)3.811727
Kurtosis15.698698
Mean8337994.5
Median Absolute Deviation (MAD)0
Skewness4.0553257
Sum1.3340791 × 109
Variance1.0101056 × 1015
MonotonicityNot monotonic
2023-12-11T02:29:41.969513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 130
81.2%
3418600 2
 
1.2%
3039200 2
 
1.2%
454600 2
 
1.2%
3695200 2
 
1.2%
335800 2
 
1.2%
136172820 1
 
0.6%
151026870 1
 
0.6%
106188150 1
 
0.6%
5069830 1
 
0.6%
Other values (16) 16
 
10.0%
ValueCountFrequency (%)
0 130
81.2%
330400 1
 
0.6%
335800 2
 
1.2%
454600 2
 
1.2%
3024700 1
 
0.6%
3039200 2
 
1.2%
3384200 1
 
0.6%
3418600 2
 
1.2%
3695200 2
 
1.2%
5069830 1
 
0.6%
ValueCountFrequency (%)
179444810 1
0.6%
169154810 1
0.6%
151026870 1
0.6%
150022170 1
0.6%
136172820 1
0.6%
106188150 1
0.6%
104789820 1
0.6%
98185760 1
0.6%
92285550 1
0.6%
79626000 1
0.6%

온천수계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625405.94
Minimum0
Maximum11125250
Zeros140
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:42.269405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9325775
Maximum11125250
Range11125250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2409271.7
Coefficient of variation (CV)3.8523326
Kurtosis11.887297
Mean625405.94
Median Absolute Deviation (MAD)0
Skewness3.6906638
Sum1.0006495 × 108
Variance5.80459 × 1012
MonotonicityNot monotonic
2023-12-11T02:29:42.597775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 140
87.5%
96600 5
 
3.1%
117800 3
 
1.9%
140000 2
 
1.2%
11125250 1
 
0.6%
9399400 1
 
0.6%
9905750 1
 
0.6%
10647550 1
 
0.6%
10115700 1
 
0.6%
9571400 1
 
0.6%
Other values (4) 4
 
2.5%
ValueCountFrequency (%)
0 140
87.5%
96600 5
 
3.1%
117800 3
 
1.9%
140000 2
 
1.2%
8400300 1
 
0.6%
9321900 1
 
0.6%
9399400 1
 
0.6%
9511250 1
 
0.6%
9571400 1
 
0.6%
9905750 1
 
0.6%
ValueCountFrequency (%)
11125250 1
0.6%
10950050 1
0.6%
10647550 1
0.6%
10115700 1
0.6%
9905750 1
0.6%
9571400 1
0.6%
9511250 1
0.6%
9399400 1
0.6%
9321900 1
0.6%
8400300 1
0.6%

온천영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226358.12
Minimum0
Maximum6144200
Zeros140
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:42.836953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1667755
Maximum6144200
Range6144200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation990534.36
Coefficient of variation (CV)4.3759612
Kurtosis23.695522
Mean226358.12
Median Absolute Deviation (MAD)0
Skewness4.89463
Sum36217300
Variance9.8115832 × 1011
MonotonicityNot monotonic
2023-12-11T02:29:43.123318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 140
87.5%
35600 5
 
3.1%
79500 3
 
1.9%
72700 2
 
1.2%
1580800 1
 
0.6%
4964400 1
 
0.6%
1699600 1
 
0.6%
5298300 1
 
0.6%
1707300 1
 
0.6%
5744300 1
 
0.6%
Other values (4) 4
 
2.5%
ValueCountFrequency (%)
0 140
87.5%
35600 5
 
3.1%
72700 2
 
1.2%
79500 3
 
1.9%
1580800 1
 
0.6%
1667700 1
 
0.6%
1668800 1
 
0.6%
1699600 1
 
0.6%
1707300 1
 
0.6%
4964400 1
 
0.6%
ValueCountFrequency (%)
6144200 1
0.6%
5744300 1
0.6%
5298300 1
0.6%
5180000 1
0.6%
4964400 1
0.6%
1707300 1
0.6%
1699600 1
0.6%
1668800 1
0.6%
1667700 1
0.6%
1580800 1
0.6%

온천일반
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40509.375
Minimum0
Maximum833500
Zeros140
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:43.434356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile403325
Maximum833500
Range833500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation160704.09
Coefficient of variation (CV)3.967084
Kurtosis16.257066
Mean40509.375
Median Absolute Deviation (MAD)0
Skewness4.1509436
Sum6481500
Variance2.5825805 × 1010
MonotonicityNot monotonic
2023-12-11T02:29:43.728773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 140
87.5%
18600 5
 
3.1%
9300 3
 
1.9%
809500 2
 
1.2%
38300 2
 
1.2%
588000 2
 
1.2%
239000 1
 
0.6%
833500 1
 
0.6%
812500 1
 
0.6%
402500 1
 
0.6%
Other values (2) 2
 
1.2%
ValueCountFrequency (%)
0 140
87.5%
9300 3
 
1.9%
18600 5
 
3.1%
38300 2
 
1.2%
239000 1
 
0.6%
402500 1
 
0.6%
419000 1
 
0.6%
588000 2
 
1.2%
782500 1
 
0.6%
809500 2
 
1.2%
ValueCountFrequency (%)
833500 1
 
0.6%
812500 1
 
0.6%
809500 2
 
1.2%
782500 1
 
0.6%
588000 2
 
1.2%
419000 1
 
0.6%
402500 1
 
0.6%
239000 1
 
0.6%
38300 2
 
1.2%
18600 5
3.1%

온천목욕용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean358538.44
Minimum0
Maximum8734950
Zeros140
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:29:44.093173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3746555
Maximum8734950
Range8734950
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1454511.1
Coefficient of variation (CV)4.0567787
Kurtosis17.512684
Mean358538.44
Median Absolute Deviation (MAD)0
Skewness4.2318686
Sum57366150
Variance2.1156025 × 1012
MonotonicityNot monotonic
2023-12-11T02:29:45.966707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 140
87.5%
42400 5
 
3.1%
29000 5
 
3.1%
8734950 1
 
0.6%
4196000 1
 
0.6%
7372650 1
 
0.6%
4761250 1
 
0.6%
7595900 1
 
0.6%
3424600 1
 
0.6%
7061050 1
 
0.6%
Other values (3) 3
 
1.9%
ValueCountFrequency (%)
0 140
87.5%
29000 5
 
3.1%
42400 5
 
3.1%
3424600 1
 
0.6%
3722900 1
 
0.6%
4196000 1
 
0.6%
4217850 1
 
0.6%
4761250 1
 
0.6%
5922000 1
 
0.6%
7061050 1
 
0.6%
ValueCountFrequency (%)
8734950 1
0.6%
7595900 1
0.6%
7372650 1
0.6%
7061050 1
0.6%
5922000 1
0.6%
4761250 1
0.6%
4217850 1
0.6%
4196000 1
0.6%
3722900 1
0.6%
3424600 1
0.6%

타이틀
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
기본요금집계(고지)
80 
사용료집계(고지)
80 

Length

Max length10
Median length9.5
Mean length9.5
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기본요금집계(고지)
2nd row기본요금집계(고지)
3rd row기본요금집계(고지)
4th row기본요금집계(고지)
5th row기본요금집계(고지)

Common Values

ValueCountFrequency (%)
기본요금집계(고지) 80
50.0%
사용료집계(고지) 80
50.0%

Length

2023-12-11T02:29:46.690646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:29:47.274495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본요금집계(고지 80
50.0%
사용료집계(고지 80
50.0%

Interactions

2023-12-11T02:29:29.808280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:58.197847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:00.838444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:03.444553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:06.036519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:08.907180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:11.397227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:14.185334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:17.560504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:20.820541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:24.061821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:26.677180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:30.012762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:58.407270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:01.032650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:03.708267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:06.260768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:09.117610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:11.628671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:14.963426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:17.800980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:21.095977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:24.373631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:26.873525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:30.214516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:58.617505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:01.201166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:03.920644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:06.464936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:09.313282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:11.897324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:15.191039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:18.066849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:21.384348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:24.600686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:27.063888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:30.435721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:58.831084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:01.363424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:04.123765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:06.781353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:09.512147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:12.175051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:15.429606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:18.377598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:21.733722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:24.809895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:27.277033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:30.677258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:59.038571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:01.533893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:04.345111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:07.054153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:09.683484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:12.430329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:15.640127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:18.648530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:21.963080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:25.053642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:27.461266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:30.925228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:59.273182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:01.712048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:04.576502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:07.369790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:09.879277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:12.636894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:15.916863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:18.907511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:22.187611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:25.293734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:27.672327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:31.197814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:59.525221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:02.339912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:04.770027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:07.650212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:10.091076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:12.868873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:16.204776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:19.167285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:22.452723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:25.506022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:27.894316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:31.433260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:59.781291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:02.515935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:04.955560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:07.856839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:10.289104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:13.091605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:16.418928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:19.441754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:22.711835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:25.713468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:28.123658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:31.663156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:00.015764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:02.709235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:05.187788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:08.090893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:10.497259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:13.334735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:16.626662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:19.750334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:22.997323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:25.936117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:28.348213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:31.894777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:00.228061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:02.905736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:05.388107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:08.297819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:10.679051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:13.544892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:16.903655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:19.979618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:23.245336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:26.126342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:28.577787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:32.224817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:00.451161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:03.094513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:05.639908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:08.497172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:10.902653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:13.763943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:17.137996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:20.253690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:23.555078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:26.319277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:28.780944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:32.453584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:00.650222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:03.266849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:05.833740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:08.705607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:11.120772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:13.969797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:17.324459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:20.483634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:23.814024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:26.487851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:29:28.959224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:29:48.033012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번고지년월통계구분사업소코드구코드생활용수계가정용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용타이틀
연번1.0001.0001.0000.0000.0000.7030.6500.7220.6660.0000.0000.0000.0000.0001.000
고지년월1.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
통계구분1.0000.0001.0000.0000.0001.0000.9951.0000.9860.2660.3310.3310.2660.1631.000
사업소코드0.0000.0000.0001.0001.0000.7000.7320.4390.2510.1650.0600.0600.0000.0670.000
구코드0.0000.0000.0001.0001.0000.5930.6090.3710.4670.3770.3960.6450.3770.3260.000
생활용수계0.7030.0001.0000.7000.5931.0000.9550.9400.8950.6620.4870.7030.6380.5951.000
가정용0.6500.0000.9950.7320.6090.9551.0000.8800.8550.5450.6200.8030.7520.7360.995
영업용0.7220.0001.0000.4390.3710.9400.8801.0000.8510.7740.5350.6340.5110.4731.000
욕탕용0.6660.0000.9860.2510.4670.8950.8550.8511.0000.6770.4820.5050.3610.2670.986
공업용0.0000.0000.2660.1650.3770.6620.5450.7740.6771.0000.0000.0000.0000.0000.266
온천수계0.0000.0000.3310.0600.3960.4870.6200.5350.4820.0001.0000.9100.8650.9180.331
온천영업용0.0000.0000.3310.0600.6450.7030.8030.6340.5050.0000.9101.0000.9870.9230.331
온천일반0.0000.0000.2660.0000.3770.6380.7520.5110.3610.0000.8650.9871.0000.9060.266
온천목욕용0.0000.0000.1630.0670.3260.5950.7360.4730.2670.0000.9180.9230.9061.0000.163
타이틀1.0000.0001.0000.0000.0001.0000.9951.0000.9860.2660.3310.3310.2660.1631.000
2023-12-11T02:29:48.902657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
타이틀통계구분
타이틀1.0000.987
통계구분0.9871.000
2023-12-11T02:29:49.374671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드구코드생활용수계가정용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용통계구분타이틀
연번1.0000.0990.0510.1710.1500.1890.1260.059-0.011-0.006-0.009-0.0120.9740.974
사업소코드0.0991.0000.4700.1760.0800.220-0.0540.528-0.075-0.068-0.091-0.0960.0000.000
구코드0.0510.4701.0000.1430.0660.168-0.1000.388-0.080-0.078-0.084-0.0860.0000.000
생활용수계0.1710.1760.1431.0000.9660.9750.8610.2180.2040.2060.2010.2010.9740.974
가정용0.1500.0800.0660.9661.0000.9060.8830.0580.2080.2090.2050.2040.9120.912
영업용0.1890.2200.1680.9750.9061.0000.8230.2880.1580.1600.1550.1540.9740.974
욕탕용0.126-0.054-0.1000.8610.8830.8231.0000.0090.1470.1470.1470.1480.8750.875
공업용0.0590.5280.3880.2180.0580.2880.0091.000-0.180-0.180-0.180-0.1800.1880.188
온천수계-0.011-0.075-0.0800.2040.2080.1580.147-0.1801.0000.9990.9990.9980.2190.219
온천영업용-0.006-0.068-0.0780.2060.2090.1600.147-0.1800.9991.0000.9980.9980.2190.219
온천일반-0.009-0.091-0.0840.2010.2050.1550.147-0.1800.9990.9981.0000.9990.1880.188
온천목욕용-0.012-0.096-0.0860.2010.2040.1540.148-0.1800.9980.9980.9991.0000.1710.171
통계구분0.9740.0000.0000.9740.9120.9740.8750.1880.2190.2190.1880.1711.0000.987
타이틀0.9740.0000.0000.9740.9120.9740.8750.1880.2190.2190.1880.1710.9871.000

Missing values

2023-12-11T02:29:32.798048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:29:33.412082image/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

연번고지년월통계구분사업소코드구코드생활용수계가정용업무용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용타이틀
012021-0112442604915376028907690019608370637700096600356001860042400기본요금집계(고지)
122021-011244410461283303116308001456995039530000000기본요금집계(고지)
232021-011244470357593702037649001489218049070000000기본요금집계(고지)
342021-01130111018740050823365001015840034800000000기본요금집계(고지)
452021-011301170324426401714338001479606050320000000기본요금집계(고지)
562021-011302140336602602152531001175565037930000000기본요금집계(고지)
672021-011303200341853002148107001218193052230000000기본요금집계(고지)
782021-011304230783297704495060003261007076910000000기본요금집계(고지)
892021-011306290578476603618972002104474061320000000기본요금집계(고지)
9102021-011306500348041102102256001330655047500000000기본요금집계(고지)
연번고지년월통계구분사업소코드구코드생활용수계가정용업무용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용타이틀
1501512021-05230320063269454033613274002807772601578454000000사용료집계(고지)
1511522021-05230423024897626101283199930011732719403329074000000사용료집계(고지)
1521532021-05230629020957977201033113380010305513203213302000000사용료집계(고지)
1531542021-05230650099917301056034132003997868903904480000000사용료집계(고지)
1541552021-0523073201590681450109187247004804495801835940000000사용료집계(고지)
1551562021-052307530161823787074606170008542062201290012050698300000사용료집계(고지)
1561572021-0523083502629407900148657491001107725070351079200932190051800004190003722900사용료집계(고지)
1571582021-0523093802317946260111519567001050857800457046401061881500000사용료집계(고지)
1581592021-05231144014258814005116289000747887800153378301510268700000사용료집계(고지)
1591602021-05231271011945320106470806700542854550459679000000사용료집계(고지)