Overview

Dataset statistics

Number of variables18
Number of observations384
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.0 KiB
Average record size in memory157.3 B

Variable types

Numeric12
DateTime1
Categorical5

Dataset

Description부산광역시상수도사업본부_수용가정보시스템_고지집계정보_당초고지집계_20220131
Author부산광역시 상수도사업본부
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083671

Alerts

업무용 has constant value ""Constant
구명 is highly overall correlated with 사업소코드 and 2 other fieldsHigh correlation
타이틀 is highly overall correlated with 생활용수계 and 4 other fieldsHigh correlation
사업소명 is highly overall correlated with 사업소코드 and 2 other fieldsHigh correlation
통계구분 is highly overall correlated with 생활용수계 and 4 other fieldsHigh correlation
사업소코드 is highly overall correlated with 공업용 and 2 other fieldsHigh correlation
구코드 is highly overall correlated with 사업소명 and 1 other fieldsHigh 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 312 (81.2%) zerosZeros
온천수계 has 336 (87.5%) zerosZeros
온천영업용 has 336 (87.5%) zerosZeros
온천일반 has 336 (87.5%) zerosZeros
온천목욕용 has 336 (87.5%) zerosZeros

Reproduction

Analysis started2023-12-10 17:28:00.656549
Analysis finished2023-12-10 17:28:37.183739
Duration36.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct384
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.5
Minimum1
Maximum384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:37.369302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.15
Q196.75
median192.5
Q3288.25
95-th percentile364.85
Maximum384
Range383
Interquartile range (IQR)191.5

Descriptive statistics

Standard deviation110.9955
Coefficient of variation (CV)0.57659998
Kurtosis-1.2
Mean192.5
Median Absolute Deviation (MAD)96
Skewness0
Sum73920
Variance12320
MonotonicityStrictly increasing
2023-12-11T02:28:37.705851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
194 1
 
0.3%
264 1
 
0.3%
263 1
 
0.3%
262 1
 
0.3%
261 1
 
0.3%
260 1
 
0.3%
259 1
 
0.3%
258 1
 
0.3%
257 1
 
0.3%
Other values (374) 374
97.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
384 1
0.3%
383 1
0.3%
382 1
0.3%
381 1
0.3%
380 1
0.3%
379 1
0.3%
378 1
0.3%
377 1
0.3%
376 1
0.3%
375 1
0.3%
Distinct12
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2021-01-01 00:00:00
Maximum2021-12-01 00:00:00
2023-12-11T02:28:37.989365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:38.227800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

통계구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
기본요금
192 
사용료
192 

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기본요금 192
50.0%
사용료 192
50.0%

Length

2023-12-11T02:28:38.494220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:28:38.802570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본요금 192
50.0%
사용료 192
50.0%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.3125
Minimum244
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:39.193739image/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 deviation24.400622
Coefficient of variation (CV)0.082907188
Kurtosis0.49420513
Mean294.3125
Median Absolute Deviation (MAD)3.5
Skewness-1.5447328
Sum113016
Variance595.39034
MonotonicityNot monotonic
2023-12-11T02:28:39.495077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
244 72
18.8%
301 48
12.5%
306 48
12.5%
307 48
12.5%
302 24
 
6.2%
303 24
 
6.2%
304 24
 
6.2%
308 24
 
6.2%
309 24
 
6.2%
311 24
 
6.2%
ValueCountFrequency (%)
244 72
18.8%
301 48
12.5%
302 24
 
6.2%
303 24
 
6.2%
304 24
 
6.2%
306 48
12.5%
307 48
12.5%
308 24
 
6.2%
309 24
 
6.2%
311 24
 
6.2%
ValueCountFrequency (%)
312 24
6.2%
311 24
6.2%
309 24
6.2%
308 24
6.2%
307 48
12.5%
306 48
12.5%
304 24
6.2%
303 24
6.2%
302 24
6.2%
301 48
12.5%

사업소명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
동래통합사업소
72 
중동부 사업소
48 
남부 사업소
48 
북부 사업소
48 
서부 사업소
24 
Other values (6)
144 

Length

Max length9
Median length9
Mean length8.375
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동래통합사업소 72
18.8%
중동부 사업소 48
12.5%
남부 사업소 48
12.5%
북부 사업소 48
12.5%
서부 사업소 24
 
6.2%
영도 사업소 24
 
6.2%
부산진 사업소 24
 
6.2%
해운대 사업소 24
 
6.2%
사하 사업소 24
 
6.2%
강서 사업소 24
 
6.2%

Length

2023-12-11T02:28:39.826183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업소 312
44.8%
동래통합사업소 72
 
10.3%
중동부 48
 
6.9%
남부 48
 
6.9%
북부 48
 
6.9%
서부 24
 
3.4%
영도 24
 
3.4%
부산진 24
 
3.4%
해운대 24
 
3.4%
사하 24
 
3.4%
Other values (2) 48
 
6.9%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.375
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:40.130476image/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 deviation157.24663
Coefficient of variation (CV)0.45661452
Kurtosis-0.28040315
Mean344.375
Median Absolute Deviation (MAD)120
Skewness0.48979243
Sum132240
Variance24726.501
MonotonicityNot monotonic
2023-12-11T02:28:40.438637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
260 24
 
6.2%
500 24
 
6.2%
710 24
 
6.2%
440 24
 
6.2%
380 24
 
6.2%
350 24
 
6.2%
530 24
 
6.2%
320 24
 
6.2%
290 24
 
6.2%
410 24
 
6.2%
Other values (6) 144
37.5%
ValueCountFrequency (%)
110 24
6.2%
140 24
6.2%
170 24
6.2%
200 24
6.2%
230 24
6.2%
260 24
6.2%
290 24
6.2%
320 24
6.2%
350 24
6.2%
380 24
6.2%
ValueCountFrequency (%)
710 24
6.2%
530 24
6.2%
500 24
6.2%
470 24
6.2%
440 24
6.2%
410 24
6.2%
380 24
6.2%
350 24
6.2%
320 24
6.2%
290 24
6.2%

구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
동래구
 
24
금정구
 
24
연제구
 
24
중구
 
24
동구
 
24
Other values (11)
264 

Length

Max length4
Median length3
Mean length2.8125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동래구
2nd row금정구
3rd row연제구
4th row중구
5th row동구

Common Values

ValueCountFrequency (%)
동래구 24
 
6.2%
금정구 24
 
6.2%
연제구 24
 
6.2%
중구 24
 
6.2%
동구 24
 
6.2%
서구 24
 
6.2%
영도구 24
 
6.2%
부산진구 24
 
6.2%
남구 24
 
6.2%
수영구 24
 
6.2%
Other values (6) 144
37.5%

Length

2023-12-11T02:28:40.724369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동래구 24
 
6.2%
금정구 24
 
6.2%
연제구 24
 
6.2%
중구 24
 
6.2%
동구 24
 
6.2%
서구 24
 
6.2%
영도구 24
 
6.2%
부산진구 24
 
6.2%
남구 24
 
6.2%
수영구 24
 
6.2%
Other values (6) 144
37.5%

생활용수계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct384
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6943817 × 108
Minimum18650410
Maximum3.1088824 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:41.056520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18650410
5-th percentile32385376
Q144140362
median2.4122958 × 108
Q31.4720982 × 109
95-th percentile2.427223 × 109
Maximum3.1088824 × 109
Range3.090232 × 109
Interquartile range (IQR)1.4279578 × 109

Descriptive statistics

Standard deviation8.6121664 × 108
Coefficient of variation (CV)1.1192799
Kurtosis-0.65267308
Mean7.6943817 × 108
Median Absolute Deviation (MAD)2.2082266 × 108
Skewness0.80201346
Sum2.9546426 × 1011
Variance7.416941 × 1017
MonotonicityNot monotonic
2023-12-11T02:28:41.402626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49153760 1
 
0.3%
46215440 1
 
0.3%
78431570 1
 
0.3%
33954670 1
 
0.3%
33457640 1
 
0.3%
32369290 1
 
0.3%
18758850 1
 
0.3%
36158330 1
 
0.3%
46167500 1
 
0.3%
50117100 1
 
0.3%
Other values (374) 374
97.4%
ValueCountFrequency (%)
18650410 1
0.3%
18700050 1
0.3%
18740050 1
0.3%
18744690 1
0.3%
18758850 1
0.3%
18775440 1
0.3%
20353830 1
0.3%
20396780 1
0.3%
20417050 1
0.3%
20439750 1
0.3%
ValueCountFrequency (%)
3108882390 1
0.3%
2947759980 1
0.3%
2852803370 1
0.3%
2827768080 1
0.3%
2793467910 1
0.3%
2781206660 1
0.3%
2771494660 1
0.3%
2712523410 1
0.3%
2705037230 1
0.3%
2675961430 1
0.3%

가정용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct384
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0047039 × 108
Minimum5955570
Maximum1.7238164 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:41.814109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5955570
5-th percentile14711088
Q124023490
median67913315
Q37.5326926 × 108
95-th percentile1.2454763 × 109
Maximum1.7238164 × 109
Range1.7178608 × 109
Interquartile range (IQR)7.2924577 × 108

Descriptive statistics

Standard deviation4.6220039 × 108
Coefficient of variation (CV)1.1541437
Kurtosis-0.4762143
Mean4.0047039 × 108
Median Absolute Deviation (MAD)59691900
Skewness0.88621428
Sum1.5378063 × 1011
Variance2.136292 × 1017
MonotonicityNot monotonic
2023-12-11T02:28:42.277062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28907690 1
 
0.3%
31119750 1
 
0.3%
44587610 1
 
0.3%
21225780 1
 
0.3%
21246580 1
 
0.3%
17146230 1
 
0.3%
8160670 1
 
0.3%
20367860 1
 
0.3%
31130800 1
 
0.3%
29383270 1
 
0.3%
Other values (374) 374
97.4%
ValueCountFrequency (%)
5955570 1
0.3%
5960500 1
0.3%
6046330 1
0.3%
6077120 1
0.3%
6105840 1
0.3%
6120500 1
0.3%
8160670 1
0.3%
8160830 1
0.3%
8177410 1
0.3%
8205490 1
0.3%
ValueCountFrequency (%)
1723816400 1
0.3%
1654894510 1
0.3%
1621447130 1
0.3%
1583176910 1
0.3%
1567834140 1
0.3%
1545380780 1
0.3%
1543722980 1
0.3%
1542807700 1
0.3%
1515358650 1
0.3%
1508397600 1
0.3%

업무용
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0
384 

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 (%)
0 384
100.0%

Length

2023-12-11T02:28:42.645656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:28:42.930625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 384
100.0%

영업용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct384
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4758182 × 108
Minimum8877640
Maximum1.43911 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:43.251176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8877640
5-th percentile10716436
Q115054162
median1.4967791 × 108
Q35.5062123 × 108
95-th percentile1.1250564 × 109
Maximum1.43911 × 109
Range1.4302323 × 109
Interquartile range (IQR)5.3556707 × 108

Descriptive statistics

Standard deviation3.9884433 × 108
Coefficient of variation (CV)1.1474833
Kurtosis-0.29523684
Mean3.4758182 × 108
Median Absolute Deviation (MAD)1.4025053 × 108
Skewness0.9390676
Sum1.3347142 × 1011
Variance1.590768 × 1017
MonotonicityNot monotonic
2023-12-11T02:28:44.329533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19608370 1
 
0.3%
14703290 1
 
0.3%
33123660 1
 
0.3%
12230390 1
 
0.3%
11831760 1
 
0.3%
14760160 1
 
0.3%
10279180 1
 
0.3%
15322470 1
 
0.3%
14644300 1
 
0.3%
20105430 1
 
0.3%
Other values (374) 374
97.4%
ValueCountFrequency (%)
8877640 1
0.3%
8882180 1
0.3%
8934800 1
0.3%
8984860 1
0.3%
8984940 1
0.3%
9027180 1
0.3%
9417730 1
0.3%
9417900 1
0.3%
9425900 1
0.3%
9428860 1
0.3%
ValueCountFrequency (%)
1439109980 1
0.3%
1394016540 1
0.3%
1393174370 1
0.3%
1362759320 1
0.3%
1351224370 1
0.3%
1320079430 1
0.3%
1278369930 1
0.3%
1276909750 1
0.3%
1267315280 1
0.3%
1253338800 1
0.3%

욕탕용
Real number (ℝ)

HIGH CORRELATION 

Distinct299
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12973753
Minimum81400
Maximum60699670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:44.755474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81400
5-th percentile133500
Q1457267.5
median1937580
Q327540370
95-th percentile41995611
Maximum60699670
Range60618270
Interquartile range (IQR)27083102

Descriptive statistics

Standard deviation15757244
Coefficient of variation (CV)1.2145478
Kurtosis-0.48836633
Mean12973753
Median Absolute Deviation (MAD)1856180
Skewness0.92186348
Sum4.9819211 × 109
Variance2.4829073 × 1014
MonotonicityNot monotonic
2023-12-11T02:28:45.209544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
319000 9
 
2.3%
637700 6
 
1.6%
578600 6
 
1.6%
421300 6
 
1.6%
85000 6
 
1.6%
379300 5
 
1.3%
133500 5
 
1.3%
321300 5
 
1.3%
81400 5
 
1.3%
427400 5
 
1.3%
Other values (289) 326
84.9%
ValueCountFrequency (%)
81400 5
1.3%
81850 1
 
0.3%
85000 6
1.6%
85900 2
 
0.5%
105600 1
 
0.3%
114900 3
 
0.8%
133500 5
1.3%
134430 1
 
0.3%
306800 1
 
0.3%
319000 9
2.3%
ValueCountFrequency (%)
60699670 1
0.3%
56643010 1
0.3%
56000760 1
0.3%
55511720 1
0.3%
51642500 1
0.3%
50566470 1
0.3%
48997220 1
0.3%
48261510 1
0.3%
48003100 1
0.3%
47862750 1
0.3%

공업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8412211.7
Minimum0
Maximum1.7944481 × 108
Zeros312
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:45.531760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile96428555
Maximum1.7944481 × 108
Range1.7944481 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32014469
Coefficient of variation (CV)3.8057137
Kurtosis15.393826
Mean8412211.7
Median Absolute Deviation (MAD)0
Skewness4.0318319
Sum3.2302893 × 109
Variance1.0249262 × 1015
MonotonicityNot monotonic
2023-12-11T02:28:45.935869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 312
81.2%
454600 2
 
0.5%
379000 2
 
0.5%
497800 2
 
0.5%
3695200 2
 
0.5%
3753800 2
 
0.5%
335800 2
 
0.5%
3353200 2
 
0.5%
3039200 2
 
0.5%
3418600 2
 
0.5%
Other values (54) 54
 
14.1%
ValueCountFrequency (%)
0 312
81.2%
330400 1
 
0.3%
335800 2
 
0.5%
365060 1
 
0.3%
379000 2
 
0.5%
449200 1
 
0.3%
454600 2
 
0.5%
497800 2
 
0.5%
501200 1
 
0.3%
2931100 1
 
0.3%
ValueCountFrequency (%)
179444810 1
0.3%
177777340 1
0.3%
176894730 1
0.3%
172982260 1
0.3%
169154810 1
0.3%
157811100 1
0.3%
154511700 1
0.3%
151026870 1
0.3%
150349600 1
0.3%
150022170 1
0.3%

온천수계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean563998.05
Minimum0
Maximum11125250
Zeros336
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:46.274928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7804802.5
Maximum11125250
Range11125250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2181337.9
Coefficient of variation (CV)3.8676338
Kurtosis12.464069
Mean563998.05
Median Absolute Deviation (MAD)0
Skewness3.7529315
Sum2.1657525 × 108
Variance4.7582351 × 1012
MonotonicityNot monotonic
2023-12-11T02:28:46.582420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 336
87.5%
96600 12
 
3.1%
117800 6
 
1.6%
140000 6
 
1.6%
8508300 1
 
0.3%
9904950 1
 
0.3%
9326150 1
 
0.3%
8417350 1
 
0.3%
7020550 1
 
0.3%
9031350 1
 
0.3%
Other values (18) 18
 
4.7%
ValueCountFrequency (%)
0 336
87.5%
96600 12
 
3.1%
117800 6
 
1.6%
140000 6
 
1.6%
6372550 1
 
0.3%
6710750 1
 
0.3%
6736000 1
 
0.3%
7020550 1
 
0.3%
7943200 1
 
0.3%
8400300 1
 
0.3%
ValueCountFrequency (%)
11125250 1
0.3%
10950050 1
0.3%
10647550 1
0.3%
10115700 1
0.3%
9905750 1
0.3%
9904950 1
0.3%
9571400 1
0.3%
9511250 1
0.3%
9399400 1
0.3%
9326150 1
0.3%

온천영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220008.59
Minimum0
Maximum6144200
Zeros336
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:46.862400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1668635
Maximum6144200
Range6144200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation946653.25
Coefficient of variation (CV)4.3028013
Kurtosis22.591637
Mean220008.59
Median Absolute Deviation (MAD)0
Skewness4.7849101
Sum84483300
Variance8.9615238 × 1011
MonotonicityNot monotonic
2023-12-11T02:28:47.198313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 336
87.5%
35600 12
 
3.1%
79500 6
 
1.6%
72700 6
 
1.6%
2104400 1
 
0.3%
4927600 1
 
0.3%
2026300 1
 
0.3%
4698200 1
 
0.3%
1786500 1
 
0.3%
5142100 1
 
0.3%
Other values (18) 18
 
4.7%
ValueCountFrequency (%)
0 336
87.5%
35600 12
 
3.1%
72700 6
 
1.6%
79500 6
 
1.6%
1482900 1
 
0.3%
1580800 1
 
0.3%
1633600 1
 
0.3%
1667700 1
 
0.3%
1668800 1
 
0.3%
1699600 1
 
0.3%
ValueCountFrequency (%)
6144200 1
0.3%
5744300 1
0.3%
5596900 1
0.3%
5499600 1
0.3%
5298300 1
0.3%
5180000 1
0.3%
5142100 1
0.3%
4964400 1
0.3%
4927600 1
0.3%
4698200 1
0.3%

온천일반
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33430.469
Minimum0
Maximum833500
Zeros336
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:47.549028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile216000
Maximum833500
Range833500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation139326.13
Coefficient of variation (CV)4.1676391
Kurtosis19.540092
Mean33430.469
Median Absolute Deviation (MAD)0
Skewness4.5142468
Sum12837300
Variance1.941177 × 1010
MonotonicityNot monotonic
2023-12-11T02:28:47.845479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 336
87.5%
18600 12
 
3.1%
9300 6
 
1.6%
38300 6
 
1.6%
809500 2
 
0.5%
588000 2
 
0.5%
643000 1
 
0.3%
237000 1
 
0.3%
748000 1
 
0.3%
105500 1
 
0.3%
Other values (16) 16
 
4.2%
ValueCountFrequency (%)
0 336
87.5%
9300 6
 
1.6%
18600 12
 
3.1%
38300 6
 
1.6%
62000 1
 
0.3%
92000 1
 
0.3%
105500 1
 
0.3%
190500 1
 
0.3%
220500 1
 
0.3%
237000 1
 
0.3%
ValueCountFrequency (%)
833500 1
0.3%
812500 1
0.3%
809500 2
0.5%
782500 1
0.3%
748000 1
0.3%
671500 1
0.3%
670000 1
0.3%
652000 1
0.3%
643000 1
0.3%
596500 1
0.3%

온천목욕용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310558.98
Minimum0
Maximum8734950
Zeros336
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T02:28:48.104311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3585292.5
Maximum8734950
Range8734950
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1255767
Coefficient of variation (CV)4.04357
Kurtosis18.331931
Mean310558.98
Median Absolute Deviation (MAD)0
Skewness4.2740141
Sum1.1925465 × 108
Variance1.5769507 × 1012
MonotonicityNot monotonic
2023-12-11T02:28:48.424027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 336
87.5%
42400 12
 
3.1%
29000 12
 
3.1%
5733900 1
 
0.3%
4740350 1
 
0.3%
6551850 1
 
0.3%
3613650 1
 
0.3%
4562550 1
 
0.3%
3668750 1
 
0.3%
4032450 1
 
0.3%
Other values (17) 17
 
4.4%
ValueCountFrequency (%)
0 336
87.5%
29000 12
 
3.1%
42400 12
 
3.1%
2939150 1
 
0.3%
3241250 1
 
0.3%
3312500 1
 
0.3%
3424600 1
 
0.3%
3613650 1
 
0.3%
3668750 1
 
0.3%
3722900 1
 
0.3%
ValueCountFrequency (%)
8734950 1
0.3%
7595900 1
0.3%
7372650 1
0.3%
7061050 1
0.3%
6551850 1
0.3%
5922000 1
0.3%
5874500 1
0.3%
5733900 1
0.3%
4761250 1
0.3%
4740350 1
0.3%

타이틀
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
기본요금집계(고지)
192 
사용료집계(고지)
192 

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) 192
50.0%
사용료집계(고지) 192
50.0%

Length

2023-12-11T02:28:48.771148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:28:49.012994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본요금집계(고지 192
50.0%
사용료집계(고지 192
50.0%

Interactions

2023-12-11T02:28:33.463065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:02.416467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:05.299632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:07.334191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:09.370999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:11.616611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:14.951334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:17.480680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:20.176550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:23.205016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:26.606305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:30.288801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:33.749932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:02.623757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:05.481419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:07.526124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:09.541455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:11.845206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:15.223145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:17.720154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:20.438711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:23.490131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:26.897425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:30.535524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:34.110181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:02.818200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:05.660220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:07.678864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:09.702193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:12.030757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:15.451151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:17.955802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:20.688266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:23.765602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:27.187068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:30.763093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:34.374016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:03.543208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:05.823920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:07.837451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:09.878265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:12.249761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:15.646687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:18.170947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:20.916684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:24.159499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:27.493368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:31.013411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:34.612019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:03.777772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:05.990408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:08.001217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:10.046845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:12.446719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:15.856253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:18.383528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:21.164349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:24.397995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:27.753865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:31.367238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:34.863742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:04.009135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:06.158015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:08.190667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:10.201167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:12.687140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:16.058342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:18.589369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:21.429871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:24.671380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:28.071022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:31.628482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:35.094033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:04.206762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:06.330601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:08.363018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:10.355635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:12.936375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:16.258717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:18.817776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:21.672200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:25.006862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:28.316195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:31.917383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:35.338139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:04.394709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:06.498040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:08.556201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:10.514427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:13.162152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:16.458334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:19.039914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:21.911288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:25.279499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:28.546278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:32.283001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:35.556249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:04.613683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:06.678846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:08.760400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:10.731011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:13.389128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:16.682893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:19.304694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:22.172139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:25.597706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:28.807348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:32.548968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:35.738019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:04.785183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:06.843443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:08.910537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:10.951884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:13.612916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:16.885425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:19.533177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:22.446497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:25.871464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:29.577922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:32.757409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:35.945222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:04.964128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:07.017481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:09.077218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:11.178588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:13.902570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:17.104035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:19.764863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:22.683445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:26.134607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:29.820631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:33.021094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:36.150872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:05.118751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:07.186629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:09.218043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:11.399065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:14.122764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:17.285699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:19.962559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:22.932638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:26.332938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:30.061373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:28:33.284965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:28:49.204772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번고지년월통계구분사업소코드사업소명구코드구명생활용수계가정용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용타이틀
연번1.0000.9570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
고지년월0.9571.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
통계구분0.0000.0001.0000.0000.0000.0000.0001.0000.9951.0000.9850.2130.3240.3240.2090.2961.000
사업소코드0.0000.0000.0001.0001.0001.0001.0000.7410.7240.5060.2400.1710.1880.1880.0570.1880.000
사업소명0.0000.0000.0001.0001.0000.9461.0000.7170.7270.6660.5690.6270.3990.5460.4040.4290.000
구코드0.0000.0000.0001.0000.9461.0001.0000.5930.6580.5100.4010.4200.3170.4510.3560.4970.000
구명0.0000.0000.0001.0001.0001.0001.0000.7690.8060.7250.6530.6370.5200.6700.5380.6400.000
생활용수계0.0000.0001.0000.7410.7170.5930.7691.0000.9490.9410.8350.6280.5940.6410.5880.6011.000
가정용0.0000.0000.9950.7240.7270.6580.8060.9491.0000.8860.8200.5340.5940.7220.6850.6310.995
영업용0.0000.0001.0000.5060.6660.5100.7250.9410.8861.0000.8340.7270.4550.5300.4910.4641.000
욕탕용0.0000.0000.9850.2400.5690.4010.6530.8350.8200.8341.0000.6430.4440.4190.3460.3360.985
공업용0.0000.0000.2130.1710.6270.4200.6370.6280.5340.7270.6431.0000.0000.0000.0000.0000.213
온천수계0.0000.0000.3240.1880.3990.3170.5200.5940.5940.4550.4440.0001.0000.9030.8490.8580.324
온천영업용0.0000.0000.3240.1880.5460.4510.6700.6410.7220.5300.4190.0000.9031.0000.9610.8600.324
온천일반0.0000.0000.2090.0570.4040.3560.5380.5880.6850.4910.3460.0000.8490.9611.0000.8440.209
온천목욕용0.0000.0000.2960.1880.4290.4970.6400.6010.6310.4640.3360.0000.8580.8600.8441.0000.296
타이틀0.0000.0001.0000.0000.0000.0000.0001.0000.9951.0000.9850.2130.3240.3240.2090.2961.000
2023-12-11T02:28:49.576993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명타이틀사업소명통계구분
구명1.0000.0000.9930.000
타이틀0.0001.0000.0000.995
사업소명0.9930.0001.0000.000
통계구분0.0000.9950.0001.000
2023-12-11T02:28:49.844393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드구코드생활용수계가정용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용통계구분사업소명구명타이틀
연번1.0000.0410.0210.0990.0810.1110.0180.026-0.007-0.003-0.005-0.0060.0000.0000.0000.000
사업소코드0.0411.0000.4700.1850.0760.227-0.0540.529-0.073-0.068-0.089-0.0950.0000.9890.9830.000
구코드0.0210.4701.0000.1510.0690.183-0.0960.388-0.079-0.078-0.084-0.0850.0000.8370.9890.000
생활용수계0.0990.1850.1511.0000.9630.9750.8630.2210.2000.2020.1970.1970.9890.4050.4300.989
가정용0.0810.0760.0690.9631.0000.8980.8890.0510.2140.2150.2110.2100.9280.4160.4770.928
영업용0.1110.2270.1830.9750.8981.0000.8220.2890.1540.1560.1510.1500.9890.3580.3840.989
욕탕용0.018-0.054-0.0960.8630.8890.8221.0000.0130.1690.1680.1690.1690.8840.2860.3230.884
공업용0.0260.5290.3880.2210.0510.2890.0131.000-0.180-0.180-0.180-0.1800.2260.3700.3520.226
온천수계-0.007-0.073-0.0790.2000.2140.1540.169-0.1801.0000.9990.9990.9980.2320.2160.2770.232
온천영업용-0.003-0.068-0.0780.2020.2150.1560.168-0.1800.9991.0000.9980.9980.2320.3160.3950.232
온천일반-0.005-0.089-0.0840.1970.2110.1510.169-0.1800.9990.9981.0000.9990.2070.1950.2530.207
온천목욕용-0.006-0.095-0.0850.1970.2100.1500.169-0.1800.9980.9980.9991.0000.2200.2180.2750.220
통계구분0.0000.0000.0000.9890.9280.9890.8840.2260.2320.2320.2070.2201.0000.0000.0000.995
사업소명0.0000.9890.8370.4050.4160.3580.2860.3700.2160.3160.1950.2180.0001.0000.9930.000
구명0.0000.9830.9890.4300.4770.3840.3230.3520.2770.3950.2530.2750.0000.9931.0000.000
타이틀0.0000.0000.0000.9890.9280.9890.8840.2260.2320.2320.2070.2200.9950.0000.0001.000

Missing values

2023-12-11T02:28:36.499932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:28:36.993758image/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

연번고지년월통계구분사업소코드사업소명구코드구명생활용수계가정용업무용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용타이틀
012021-01기본요금244동래통합사업소260동래구4915376028907690019608370637700096600356001860042400기본요금집계(고지)
122021-01기본요금244동래통합사업소410금정구461283303116308001456995039530000000기본요금집계(고지)
232021-01기본요금244동래통합사업소470연제구357593702037649001489218049070000000기본요금집계(고지)
342021-01기본요금301중동부 사업소110중구18740050823365001015840034800000000기본요금집계(고지)
452021-01기본요금301중동부 사업소170동구324426401714338001479606050320000000기본요금집계(고지)
562021-01기본요금302서부 사업소140서구336602602152531001175565037930000000기본요금집계(고지)
672021-01기본요금303영도 사업소200영도구341853002148107001218193052230000000기본요금집계(고지)
782021-01기본요금304부산진 사업소230부산진구783297704495060003261007076910000000기본요금집계(고지)
892021-01기본요금306남부 사업소290남구578476603618972002104474061320000000기본요금집계(고지)
9102021-01기본요금306남부 사업소500수영구348041102102256001330655047500000000기본요금집계(고지)
연번고지년월통계구분사업소코드사업소명구코드구명생활용수계가정용업무용영업용욕탕용공업용온천수계온천영업용온천일반온천목욕용타이틀
3743752021-12사용료303영도 사업소200영도구72715309041595363002915029701969649000000사용료집계(고지)
3753762021-12사용료304부산진 사업소230부산진구21741252901127925680010040444004215521000000사용료집계(고지)
3763772021-12사용료306남부 사업소290남구184305862089655216008986437104786275000000사용료집계(고지)
3773782021-12사용료306남부 사업소500수영구120041899064644871005227356903123459000000사용료집계(고지)
3783792021-12사용료307북부 사업소320북구1471920150104594380004058597702011658000000사용료집계(고지)
3793802021-12사용료307북부 사업소530사상구166827712074490042009014295501669820052489500000사용료집계(고지)
3803812021-12사용료308해운대 사업소350해운대구2771494660150839760001226783530363135300990495049276002370004740350사용료집계(고지)
3813822021-12사용료309사하 사업소380사하구232890374010633945500111210930055511720978881700000사용료집계(고지)
3823832021-12사용료311강서 사업소440강서구211079365055477226001362759320154847301777773400000사용료집계(고지)
3833842021-12사용료312기장 사업소710기장군13814663406650699800708853880754248000000사용료집계(고지)