Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells20000
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory171.0 B

Variable types

Numeric8
Categorical8
DateTime1
Unsupported2

Dataset

Description부산광역시 상수도사업본부에서 상하수도 요금 계산 및 징수를 위해 운영하는 수용가정보시스템에 사용되는 수납정보(당월 및 체납 수납처리 정보) 자료입니다.
Author부산광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15083422/fileData.do

Alerts

구분 has constant value ""Constant
기타금액 has constant value ""Constant
구명 is highly overall correlated with 구코드 and 2 other fieldsHigh correlation
사업소명 is highly overall correlated with 구코드 and 2 other fieldsHigh correlation
수납금액합계 is highly overall correlated with 상수도수납금액 and 3 other fieldsHigh correlation
상수도수납금액 is highly overall correlated with 수납금액합계 and 3 other fieldsHigh correlation
하수도수납금액 is highly overall correlated with 수납금액합계 and 3 other fieldsHigh correlation
물이용수납금액 is highly overall correlated with 수납금액합계 and 2 other fieldsHigh correlation
일련번호 is highly overall correlated with 납기내일자High correlation
구코드 is highly overall correlated with 구명 and 1 other fieldsHigh correlation
사업소코드 is highly overall correlated with 납기내일자 and 2 other fieldsHigh correlation
수납방법 is highly overall correlated with 수납금액합계 and 2 other fieldsHigh correlation
납기내일자 is highly overall correlated with 하수도수납금액 and 3 other fieldsHigh correlation
납기내후수납구분 is highly overall correlated with 납기내일자High correlation
은행명 is highly overall correlated with 수납방법High correlation
납기내일자 is highly imbalanced (74.2%)Imbalance
체납시작년월 has 10000 (100.0%) missing valuesMissing
체납종료년월 has 10000 (100.0%) missing valuesMissing
수납금액합계 is highly skewed (γ1 = 75.40681392)Skewed
상수도수납금액 is highly skewed (γ1 = 76.15761927)Skewed
하수도수납금액 is highly skewed (γ1 = 74.03629059)Skewed
물이용수납금액 is highly skewed (γ1 = 44.77969814)Skewed
연번 has unique valuesUnique
체납시작년월 is an unsupported type, check if it needs cleaning or further analysisUnsupported
체납종료년월 is an unsupported type, check if it needs cleaning or further analysisUnsupported
하수도수납금액 has 1381 (13.8%) zerosZeros
물이용수납금액 has 1013 (10.1%) zerosZeros

Reproduction

Analysis started2024-03-14 18:22:11.059380
Analysis finished2024-03-14 18:22:32.178849
Duration21.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38294.393
Minimum8
Maximum76490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:32.410748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile3778.35
Q119180.25
median38696
Q356976.75
95-th percentile72500.1
Maximum76490
Range76482
Interquartile range (IQR)37796.5

Descriptive statistics

Standard deviation21981.363
Coefficient of variation (CV)0.57400995
Kurtosis-1.1847235
Mean38294.393
Median Absolute Deviation (MAD)18877.5
Skewness-0.013783429
Sum3.8294393 × 108
Variance4.831803 × 108
MonotonicityNot monotonic
2024-03-15T03:22:32.682788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40922 1
 
< 0.1%
39295 1
 
< 0.1%
65067 1
 
< 0.1%
50542 1
 
< 0.1%
18804 1
 
< 0.1%
69411 1
 
< 0.1%
6482 1
 
< 0.1%
52357 1
 
< 0.1%
8813 1
 
< 0.1%
61417 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
8 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
44 1
< 0.1%
65 1
< 0.1%
68 1
< 0.1%
ValueCountFrequency (%)
76490 1
< 0.1%
76466 1
< 0.1%
76456 1
< 0.1%
76453 1
< 0.1%
76447 1
< 0.1%
76444 1
< 0.1%
76437 1
< 0.1%
76430 1
< 0.1%
76427 1
< 0.1%
76423 1
< 0.1%

구분
Categorical

CONSTANT 

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

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
당월정상수납 10000
100.0%

Length

2024-03-15T03:22:33.116299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:22:33.411163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당월정상수납 10000
100.0%

수납일자
Date

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-11-01 00:00:00
Maximum2023-12-27 00:00:00
2024-03-15T03:22:33.829496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:34.207995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수납방법
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가상계좌
5311 
창구수납(간단E)
1651 
CD/ATM(간단E)
1345 
카드(간단E)
 
450
인터넷뱅킹(간단E)
 
378
Other values (10)
865 

Length

Max length11
Median length4
Mean length6.4319
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row창구수납(간단E)
2nd rowCD/ATM(간단E)
3rd row창구수납(간단E)
4th row가상계좌
5th row창구수납(간단E)

Common Values

ValueCountFrequency (%)
가상계좌 5311
53.1%
창구수납(간단E) 1651
 
16.5%
CD/ATM(간단E) 1345
 
13.5%
카드(간단E) 450
 
4.5%
인터넷뱅킹(간단E) 378
 
3.8%
카드(ARS) 230
 
2.3%
통장(자동납부) 226
 
2.3%
카드(사이버) 125
 
1.2%
카드(카카오) 105
 
1.1%
모바일뱅킹(간단E) 61
 
0.6%
Other values (5) 118
 
1.2%

Length

2024-03-15T03:22:34.612162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가상계좌 5311
53.1%
창구수납(간단e 1651
 
16.5%
cd/atm(간단e 1345
 
13.5%
카드(간단e 450
 
4.5%
인터넷뱅킹(간단e 378
 
3.8%
카드(ars 230
 
2.3%
통장(자동납부 226
 
2.3%
카드(사이버 125
 
1.2%
카드(카카오 105
 
1.1%
모바일뱅킹(간단e 61
 
0.6%
Other values (5) 118
 
1.2%

수납금액합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3898
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347238.94
Minimum10
Maximum1.0094767 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:34.963284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile2400
Q116245
median42060
Q397600
95-th percentile440595
Maximum1.0094767 × 109
Range1.0094767 × 109
Interquartile range (IQR)81355

Descriptive statistics

Standard deviation11626962
Coefficient of variation (CV)33.484038
Kurtosis6123.1912
Mean347238.94
Median Absolute Deviation (MAD)31740
Skewness75.406814
Sum3.4723894 × 109
Variance1.3518624 × 1014
MonotonicityNot monotonic
2024-03-15T03:22:35.228709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 640
 
6.4%
16920 102
 
1.0%
24860 90
 
0.9%
14280 90
 
0.9%
22200 83
 
0.8%
15600 83
 
0.8%
11640 82
 
0.8%
20900 81
 
0.8%
23540 78
 
0.8%
18260 77
 
0.8%
Other values (3888) 8594
85.9%
ValueCountFrequency (%)
10 1
< 0.1%
30 2
< 0.1%
50 1
< 0.1%
110 2
< 0.1%
150 1
< 0.1%
190 1
< 0.1%
230 1
< 0.1%
270 1
< 0.1%
290 1
< 0.1%
310 2
< 0.1%
ValueCountFrequency (%)
1009476700 1
< 0.1%
533938480 1
< 0.1%
155278800 1
< 0.1%
99901480 1
< 0.1%
70414490 1
< 0.1%
58432000 1
< 0.1%
28097400 1
< 0.1%
24715800 1
< 0.1%
18866000 1
< 0.1%
17039460 1
< 0.1%

상수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2975
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273761.96
Minimum0
Maximum1.0094767 × 109
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:35.492869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2400
Q19625
median23820
Q353240
95-th percentile223889
Maximum1.0094767 × 109
Range1.0094767 × 109
Interquartile range (IQR)43615

Descriptive statistics

Standard deviation11587878
Coefficient of variation (CV)42.328297
Kurtosis6208.001
Mean273761.96
Median Absolute Deviation (MAD)17100
Skewness76.157619
Sum2.7376196 × 109
Variance1.3427891 × 1014
MonotonicityNot monotonic
2024-03-15T03:22:35.754134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 649
 
6.5%
9600 136
 
1.4%
6000 122
 
1.2%
13200 122
 
1.2%
16800 108
 
1.1%
8880 104
 
1.0%
14640 97
 
1.0%
13920 95
 
0.9%
11760 94
 
0.9%
12480 93
 
0.9%
Other values (2965) 8380
83.8%
ValueCountFrequency (%)
0 41
0.4%
10 2
 
< 0.1%
30 1
 
< 0.1%
50 1
 
< 0.1%
110 2
 
< 0.1%
150 1
 
< 0.1%
190 1
 
< 0.1%
230 1
 
< 0.1%
270 1
 
< 0.1%
290 1
 
< 0.1%
ValueCountFrequency (%)
1009476700 1
< 0.1%
533938330 1
< 0.1%
155278800 1
< 0.1%
99901330 1
< 0.1%
33735230 1
< 0.1%
28096650 1
< 0.1%
24715050 1
< 0.1%
18858500 1
< 0.1%
16849980 1
< 0.1%
12328330 1
< 0.1%

하수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2232
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63106.122
Minimum0
Maximum70414490
Zeros1381
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:36.092560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14040
median13505
Q333160
95-th percentile164240
Maximum70414490
Range70414490
Interquartile range (IQR)29120

Descriptive statistics

Standard deviation789358.85
Coefficient of variation (CV)12.508435
Kurtosis6380.7289
Mean63106.122
Median Absolute Deviation (MAD)11705
Skewness74.036291
Sum6.3106122 × 108
Variance6.2308739 × 1011
MonotonicityNot monotonic
2024-03-15T03:22:36.480948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1381
 
13.8%
4040 114
 
1.1%
7200 113
 
1.1%
2700 111
 
1.1%
6740 107
 
1.1%
4500 107
 
1.1%
9000 104
 
1.0%
7640 101
 
1.0%
3140 101
 
1.0%
6300 100
 
1.0%
Other values (2222) 7661
76.6%
ValueCountFrequency (%)
0 1381
13.8%
10 2
 
< 0.1%
20 2
 
< 0.1%
40 1
 
< 0.1%
50 3
 
< 0.1%
60 1
 
< 0.1%
220 3
 
< 0.1%
340 1
 
< 0.1%
370 1
 
< 0.1%
440 87
 
0.9%
ValueCountFrequency (%)
70414490 1
< 0.1%
20435600 1
< 0.1%
17039460 1
< 0.1%
9584250 1
< 0.1%
6318800 1
< 0.1%
6181500 1
< 0.1%
5524350 1
< 0.1%
5183100 1
< 0.1%
4735540 1
< 0.1%
4498000 1
< 0.1%

물이용수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1369
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10370.856
Minimum0
Maximum4261170
Zeros1013
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:36.916001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11360
median3940
Q38820
95-th percentile30414.5
Maximum4261170
Range4261170
Interquartile range (IQR)7460

Descriptive statistics

Standard deviation62036.977
Coefficient of variation (CV)5.981857
Kurtosis2640.0711
Mean10370.856
Median Absolute Deviation (MAD)3180
Skewness44.779698
Sum1.0370856 × 108
Variance3.8485865 × 109
MonotonicityNot monotonic
2024-03-15T03:22:37.360311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1013
 
10.1%
1060 169
 
1.7%
140 152
 
1.5%
1360 149
 
1.5%
300 148
 
1.5%
900 138
 
1.4%
2120 137
 
1.4%
1500 134
 
1.3%
1200 133
 
1.3%
2420 128
 
1.3%
Other values (1359) 7699
77.0%
ValueCountFrequency (%)
0 1013
10.1%
10 1
 
< 0.1%
20 8
 
0.1%
40 2
 
< 0.1%
70 4
 
< 0.1%
80 1
 
< 0.1%
100 1
 
< 0.1%
140 152
 
1.5%
150 57
 
0.6%
160 23
 
0.2%
ValueCountFrequency (%)
4261170 1
< 0.1%
2714380 1
< 0.1%
1494920 1
< 0.1%
1260850 1
< 0.1%
1093260 1
< 0.1%
901340 1
< 0.1%
859940 1
< 0.1%
781610 1
< 0.1%
752160 1
< 0.1%
733490 1
< 0.1%

기타금액
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

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 10000
100.0%

Length

2024-03-15T03:22:37.772330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:22:38.142408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

납기내일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-11-30
6315 
2023-12-31
2624 
2023-10-31
984 
2024-01-31
 
15
2023-12-29
 
8
Other values (32)
 
54

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st row2023-11-30
2nd row2023-11-30
3rd row2023-11-30
4th row2023-11-30
5th row2023-11-30

Common Values

ValueCountFrequency (%)
2023-11-30 6315
63.1%
2023-12-31 2624
26.2%
2023-10-31 984
 
9.8%
2024-01-31 15
 
0.1%
2023-12-29 8
 
0.1%
2023-12-15 6
 
0.1%
2023-12-28 5
 
0.1%
2023-12-07 3
 
< 0.1%
2023-12-12 3
 
< 0.1%
2023-12-30 3
 
< 0.1%
Other values (27) 34
 
0.3%

Length

2024-03-15T03:22:38.597262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-11-30 6315
63.1%
2023-12-31 2624
26.2%
2023-10-31 984
 
9.8%
2024-01-31 15
 
0.1%
2023-12-29 8
 
0.1%
2023-12-15 6
 
0.1%
2023-12-28 5
 
< 0.1%
2023-12-07 3
 
< 0.1%
2023-12-12 3
 
< 0.1%
2023-12-30 3
 
< 0.1%
Other values (27) 34
 
0.3%

체납시작년월
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

체납종료년월
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

납기내후수납구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
납기내
8431 
납기후
1569 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
납기내 8431
84.3%
납기후 1569
 
15.7%

Length

2024-03-15T03:22:38.993984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:22:39.323762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
납기내 8431
84.3%
납기후 1569
 
15.7%

은행명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산은행
3170 
농협은행
1488 
국민은행
1239 
새마을금고중앙회
1023 
우체국
432 
Other values (25)
2648 

Length

Max length8
Median length4
Mean length4.5421
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row부산은행
2nd row부산은행
3rd row새마을금고중앙회
4th row국민은행
5th row새마을금고중앙회

Common Values

ValueCountFrequency (%)
부산은행 3170
31.7%
농협은행 1488
14.9%
국민은행 1239
 
12.4%
새마을금고중앙회 1023
 
10.2%
우체국 432
 
4.3%
우리은행 423
 
4.2%
지역농축협 421
 
4.2%
신한은행 292
 
2.9%
KEB하나은행 211
 
2.1%
BC카드 207
 
2.1%
Other values (20) 1094
 
10.9%

Length

2024-03-15T03:22:39.957259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산은행 3170
31.7%
농협은행 1488
14.9%
국민은행 1239
 
12.4%
새마을금고중앙회 1023
 
10.2%
우체국 432
 
4.3%
우리은행 423
 
4.2%
지역농축협 421
 
4.2%
신한은행 292
 
2.9%
keb하나은행 211
 
2.1%
bc카드 207
 
2.1%
Other values (20) 1094
 
10.9%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8279
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70877034
Minimum1
Maximum3.0300705 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:40.380301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile125.95
Q11539.75
median1277400
Q35839225
95-th percentile9179260
Maximum3.0300705 × 109
Range3.0300705 × 109
Interquartile range (IQR)5837685.2

Descriptive statistics

Standard deviation3.5400796 × 108
Coefficient of variation (CV)4.994678
Kurtosis41.248815
Mean70877034
Median Absolute Deviation (MAD)1277213.5
Skewness6.1667107
Sum7.0877034 × 1011
Variance1.2532164 × 1017
MonotonicityNot monotonic
2024-03-15T03:22:40.860583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 11
 
0.1%
7 9
 
0.1%
10 9
 
0.1%
69 9
 
0.1%
5 8
 
0.1%
1 8
 
0.1%
129 7
 
0.1%
53 7
 
0.1%
105 7
 
0.1%
25 7
 
0.1%
Other values (8269) 9918
99.2%
ValueCountFrequency (%)
1 8
0.1%
2 4
< 0.1%
3 1
 
< 0.1%
4 6
0.1%
5 8
0.1%
6 4
< 0.1%
7 9
0.1%
8 6
0.1%
9 6
0.1%
10 9
0.1%
ValueCountFrequency (%)
3030070500 1
< 0.1%
3030068600 1
< 0.1%
3030066400 1
< 0.1%
3030065600 1
< 0.1%
3030061500 1
< 0.1%
3030060500 1
< 0.1%
3030056000 1
< 0.1%
3030054600 1
< 0.1%
3030045800 1
< 0.1%
3030044800 1
< 0.1%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.8172
Minimum0
Maximum710
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:41.296494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140
Q1230
median350
Q3440
95-th percentile530
Maximum710
Range710
Interquartile range (IQR)210

Descriptive statistics

Standard deviation141.34768
Coefficient of variation (CV)0.40290979
Kurtosis0.034486895
Mean350.8172
Median Absolute Deviation (MAD)120
Skewness0.5206802
Sum3508172
Variance19979.168
MonotonicityNot monotonic
2024-03-15T03:22:41.706270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
230 1072
10.7%
410 837
 
8.4%
350 811
 
8.1%
380 793
 
7.9%
290 756
 
7.6%
260 735
 
7.3%
530 647
 
6.5%
470 606
 
6.1%
500 576
 
5.8%
440 557
 
5.6%
Other values (11) 2610
26.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
110 282
 
2.8%
140 460
4.6%
170 441
4.4%
200 443
4.4%
201 4
 
< 0.1%
202 2
 
< 0.1%
204 1
 
< 0.1%
205 2
 
< 0.1%
230 1072
10.7%
ValueCountFrequency (%)
710 487
4.9%
530 647
6.5%
500 576
5.8%
470 606
6.1%
440 557
5.6%
410 837
8.4%
380 793
7.9%
350 811
8.1%
320 486
4.9%
290 756
7.6%

구명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
1072 
금정구
837 
해운대구
811 
사하구
793 
남구
756 
Other values (16)
5731 

Length

Max length4
Median length3
Mean length2.9451
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row해운대구
2nd row해운대구
3rd row연제구
4th row기장군
5th row남구

Common Values

ValueCountFrequency (%)
부산진구 1072
10.7%
금정구 837
 
8.4%
해운대구 811
 
8.1%
사하구 793
 
7.9%
남구 756
 
7.6%
동래구 735
 
7.3%
사상구 647
 
6.5%
연제구 606
 
6.1%
수영구 576
 
5.8%
강서구 557
 
5.6%
Other values (11) 2610
26.1%

Length

2024-03-15T03:22:42.212796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 1072
10.7%
금정구 837
 
8.4%
해운대구 811
 
8.1%
사하구 793
 
7.9%
남구 756
 
7.6%
동래구 735
 
7.3%
사상구 647
 
6.5%
연제구 606
 
6.1%
수영구 576
 
5.8%
강서구 557
 
5.6%
Other values (11) 2610
26.1%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.5535
Minimum101
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T03:22:42.599441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile244
Q1301
median306
Q3308
95-th percentile311
Maximum312
Range211
Interquartile range (IQR)7

Descriptive statistics

Standard deviation26.119149
Coefficient of variation (CV)0.089279907
Kurtosis0.38639538
Mean292.5535
Median Absolute Deviation (MAD)3
Skewness-1.3852618
Sum2925535
Variance682.20996
MonotonicityNot monotonic
2024-03-15T03:22:42.961240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
244 2178
21.8%
306 1332
13.3%
307 1133
11.3%
304 1072
10.7%
308 811
 
8.1%
309 793
 
7.9%
301 723
 
7.2%
311 557
 
5.6%
312 487
 
4.9%
302 460
 
4.6%
Other values (6) 454
 
4.5%
ValueCountFrequency (%)
101 2
 
< 0.1%
201 4
 
< 0.1%
202 2
 
< 0.1%
204 1
 
< 0.1%
205 2
 
< 0.1%
244 2178
21.8%
301 723
 
7.2%
302 460
 
4.6%
303 443
 
4.4%
304 1072
10.7%
ValueCountFrequency (%)
312 487
 
4.9%
311 557
5.6%
309 793
7.9%
308 811
8.1%
307 1133
11.3%
306 1332
13.3%
304 1072
10.7%
303 443
 
4.4%
302 460
 
4.6%
301 723
7.2%

사업소명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
2178 
남부사업소
1332 
북부사업소
1133 
부산진 사업소
1072 
해운대사업소
811 
Other values (11)
3474 

Length

Max length9
Median length8
Mean length6.096
Min length5

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row해운대사업소
2nd row해운대사업소
3rd row동래통합사업소
4th row기장사업소
5th row남부사업소

Common Values

ValueCountFrequency (%)
동래통합사업소 2178
21.8%
남부사업소 1332
13.3%
북부사업소 1133
11.3%
부산진 사업소 1072
10.7%
해운대사업소 811
 
8.1%
사하사업소 793
 
7.9%
중동부사업소 723
 
7.2%
강서사업소 557
 
5.6%
기장사업소 487
 
4.9%
서부 사업소 460
 
4.6%
Other values (6) 454
 
4.5%

Length

2024-03-15T03:22:43.274578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동래통합사업소 2178
18.9%
사업소 1532
13.3%
남부사업소 1332
11.6%
북부사업소 1133
9.8%
부산진 1072
9.3%
해운대사업소 811
 
7.0%
사하사업소 793
 
6.9%
중동부사업소 723
 
6.3%
강서사업소 557
 
4.8%
기장사업소 487
 
4.2%
Other values (7) 914
7.9%

Interactions

2024-03-15T03:22:29.479635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:13.819385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:16.547281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:19.389874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:21.532063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:23.835647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:25.378134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:27.371438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:29.655201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:14.077881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:16.951276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:19.685813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:21.829968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:24.076163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:25.566044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:27.696574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:29.823273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:14.253776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:17.345042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:20.072359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:22.102184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:24.261554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:25.733017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:27.922382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:30.087044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:14.514382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:17.756062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:20.255011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:22.375173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:24.426571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:26.004854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:28.121564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:30.262894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:14.887933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:18.197059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:20.509262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:22.659326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:24.601743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:26.286480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:28.411679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:30.420556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:15.384947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:18.531991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:20.730371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:22.937161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:24.789518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:26.556119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:28.674968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:30.624729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:15.739886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:18.813921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:20.993937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:23.214761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:24.964201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:26.829737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:28.939341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:30.906366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:16.204360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:19.125564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:21.269045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:23.606076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:25.146079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:27.110807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:22:29.316535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:22:43.485807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액납기내일자납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
연번1.0000.7710.1380.0000.0000.0000.0000.5390.2780.1090.0730.0000.0530.0170.037
수납일자0.7711.0000.7410.1620.1620.0210.0000.7730.8990.3830.4120.1950.2310.1150.220
수납방법0.1380.7411.0000.7820.7820.0000.0000.2850.4260.9300.1950.5430.1780.6260.511
수납금액합계0.0000.1620.7821.0001.0000.0000.0000.4600.0000.0000.2400.5750.5550.8090.799
상수도수납금액0.0000.1620.7821.0001.0000.0000.0000.4600.0000.0000.2400.5750.5550.8090.799
하수도수납금액0.0000.0210.0000.0000.0001.0000.6260.8200.0260.0000.1140.0150.0280.0000.051
물이용수납금액0.0000.0000.0000.0000.0000.6261.0000.0000.0000.1920.0000.0190.0000.0000.013
납기내일자0.5390.7730.2850.4600.4600.8200.0001.0000.8650.0000.7630.5830.8010.7850.844
납기내후수납구분0.2780.8990.4260.0000.0000.0260.0000.8651.0000.0960.2310.0850.1130.0000.095
은행명0.1090.3830.9300.0000.0000.0000.1920.0000.0961.0000.1490.2240.2850.0450.244
일련번호0.0730.4120.1950.2400.2400.1140.0000.7630.2310.1491.0000.0540.3930.4420.389
구코드0.0000.1950.5430.5750.5750.0150.0190.5830.0850.2240.0541.0001.0000.8050.964
구명0.0530.2310.1780.5550.5550.0280.0000.8010.1130.2850.3931.0001.0001.0001.000
사업소코드0.0170.1150.6260.8090.8090.0000.0000.7850.0000.0450.4420.8051.0001.0001.000
사업소명0.0370.2200.5110.7990.7990.0510.0130.8440.0950.2440.3890.9641.0001.0001.000
2024-03-15T03:22:43.920180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납기내일자구명사업소명수납방법납기내후수납구분은행명
납기내일자1.0000.3300.4130.0860.7720.000
구명0.3301.0001.0000.0590.0890.075
사업소명0.4131.0001.0000.1960.0740.073
수납방법0.0860.0590.1961.0000.3890.534
납기내후수납구분0.7720.0890.0740.3891.0000.076
은행명0.0000.0750.0730.5340.0761.000
2024-03-15T03:22:44.189875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납금액합계상수도수납금액하수도수납금액물이용수납금액일련번호구코드사업소코드수납방법납기내일자납기내후수납구분은행명구명사업소명
연번1.0000.0290.0270.0280.028-0.0380.007-0.0150.0520.2190.2130.0350.0170.015
수납금액합계0.0291.0000.9820.9210.943-0.0170.0210.0120.5760.2510.0000.0000.3520.499
상수도수납금액0.0270.9821.0000.8820.958-0.0160.0320.0270.5760.2510.0000.0000.3520.499
하수도수납금액0.0280.9210.8821.0000.899-0.051-0.029-0.0500.0000.5740.0170.0000.0130.024
물이용수납금액0.0280.9430.9580.8991.000-0.0570.0250.0170.0000.0000.0000.0780.0000.006
일련번호-0.038-0.017-0.016-0.051-0.0571.0000.3150.1530.1120.5040.1540.0770.1940.191
구코드0.0070.0210.032-0.0290.0250.3151.0000.4040.2550.2540.0840.0840.9990.853
사업소코드-0.0150.0120.027-0.0500.0170.1530.4041.0000.4080.5300.0000.0230.9990.999
수납방법0.0520.5760.5760.0000.0000.1120.2550.4081.0000.0860.3890.5340.0590.196
납기내일자0.2190.2510.2510.5740.0000.5040.2540.5300.0861.0000.7720.0000.3300.413
납기내후수납구분0.2130.0000.0000.0170.0000.1540.0840.0000.3890.7721.0000.0760.0890.074
은행명0.0350.0000.0000.0000.0780.0770.0840.0230.5340.0000.0761.0000.0750.073
구명0.0170.3520.3520.0130.0000.1940.9990.9990.0590.3300.0890.0751.0001.000
사업소명0.0150.4990.4990.0240.0060.1910.8530.9990.1960.4130.0740.0731.0001.000

Missing values

2024-03-15T03:22:31.296708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:22:32.000528image/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

연번구분수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액기타금액납기내일자체납시작년월체납종료년월납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
4092140922당월정상수납2023-11-24창구수납(간단E)685003840022500760002023-11-30<NA><NA>납기내부산은행210350해운대구308해운대사업소
1522915230당월정상수납2023-11-21CD/ATM(간단E)2344401196001005801426002023-11-30<NA><NA>납기내부산은행397350해운대구308해운대사업소
3290332904당월정상수납2023-11-27창구수납(간단E)823004200035300500002023-11-30<NA><NA>납기내새마을금고중앙회1303470연제구244동래통합사업소
2815428155당월정상수납2023-11-26가상계좌379002148012480394002023-11-30<NA><NA>납기내국민은행8953800710기장군312기장사업소
2467724678당월정상수납2023-11-22창구수납(간단E)48103610106014002023-11-30<NA><NA>납기내새마을금고중앙회1057290남구306남부사업소
5672356724당월정상수납2023-12-19CD/ATM(간단E)608803300022280560002023-12-31<NA><NA>납기내수협중앙회794140서구302서부 사업소
5184451845당월정상수납2023-12-05카드(간단E)187740102960635802120002023-11-30<NA><NA>납기후KB국민카드190230부산진구304부산진 사업소
5534355344당월정상수납2023-12-19가상계좌439402460014800454002023-12-31<NA><NA>납기내농협은행5025400380사하구309사하사업소
1877618777당월정상수납2023-11-10가상계좌639403554021480692002023-10-31<NA><NA>납기후신한은행2006068600380사하구309사하사업소
47724773당월정상수납2023-11-06가상계좌489502721017310443002023-10-31<NA><NA>납기후부산은행1356700200영도구303영도사업소
연번구분수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액기타금액납기내일자체납시작년월체납종료년월납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
6237562376당월정상수납2023-11-29가상계좌20920124806300214002023-11-30<NA><NA>납기내부산은행5125200350해운대구308해운대사업소
3893438935당월정상수납2023-11-20가상계좌1428088804040136002023-11-30<NA><NA>납기내부산은행2145200230부산진구304부산진 사업소
7109171092당월정상수납2023-12-25가상계좌24860146407640258002023-12-31<NA><NA>납기내부산은행7669800500수영구306남부사업소
6369763698당월정상수납2023-12-15가상계좌240024000002023-12-31<NA><NA>납기내부산은행2421100230부산진구304부산진 사업소
4085640857당월정상수납2023-11-24CD/ATM(간단E)344601860013280258002023-11-30<NA><NA>납기내새마을금고중앙회135380사하구309사하사업소
5068650687당월정상수납2023-12-18가상계좌681603660025500606002023-12-31<NA><NA>납기내국민은행1959700230부산진구304부산진 사업소
3169531696당월정상수납2023-11-24창구수납(간단E)240024000002023-11-30<NA><NA>납기내새마을금고중앙회2572350해운대구308해운대사업소
4844948450당월정상수납2023-12-05가상계좌52703460136045002023-12-31<NA><NA>납기내신한은행912090900350해운대구308해운대사업소
1064110642당월정상수납2023-11-02가상계좌9760053040342401032002023-10-31<NA><NA>납기후신한은행4503900350해운대구308해운대사업소
6024960250당월정상수납2023-12-21CD/ATM(간단E)186580100400667601942002023-12-31<NA><NA>납기내국민은행123230부산진구304부산진 사업소