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
Categorical9
Unsupported2

Dataset

Description부산광역시상수도사업본부_수용가정보시스템_수납정보_당월및체납수납처리정보_20220518
Author부산광역시 상수도사업본부
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083422

Alerts

구분 has constant value ""Constant
기타금액 has constant value ""Constant
납기내후수납구분 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
납기내일자 is highly overall correlated with 상수도수납금액 and 2 other fieldsHigh correlation
구명 is highly overall correlated with 구코드 and 2 other fieldsHigh correlation
사업소명 is highly overall correlated with 구코드 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 납기내후수납구분High correlation
수납금액합계 is highly overall correlated with 상수도수납금액 and 2 other fieldsHigh correlation
상수도수납금액 is highly overall correlated with 수납금액합계 and 3 other fieldsHigh correlation
하수도수납금액 is highly overall correlated with 수납금액합계 and 2 other fieldsHigh correlation
물이용수납금액 is highly overall correlated with 수납금액합계 and 2 other fieldsHigh correlation
일련번호 is highly overall correlated with 구코드High correlation
구코드 is highly overall correlated with 일련번호 and 2 other fieldsHigh correlation
사업소코드 is highly overall correlated with 납기내일자 and 2 other fieldsHigh correlation
수납일자 is highly overall correlated with 납기내후수납구분High correlation
수납방법 is highly overall correlated with 은행명High correlation
은행명 is highly overall correlated with 수납방법High correlation
수납일자 is highly imbalanced (59.7%)Imbalance
수납방법 is highly imbalanced (57.1%)Imbalance
납기내일자 is highly imbalanced (88.2%)Imbalance
납기내후수납구분 is highly imbalanced (54.7%)Imbalance
체납시작년월 has 10000 (100.0%) missing valuesMissing
체납종료년월 has 10000 (100.0%) missing valuesMissing
수납금액합계 is highly skewed (γ1 = 33.6426905)Skewed
상수도수납금액 is highly skewed (γ1 = 44.61958722)Skewed
하수도수납금액 is highly skewed (γ1 = 48.46924811)Skewed
물이용수납금액 is highly skewed (γ1 = 46.38847294)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 972 (9.7%) zerosZeros
물이용수납금액 has 859 (8.6%) zerosZeros

Reproduction

Analysis started2023-12-10 16:39:51.860014
Analysis finished2023-12-10 16:40:03.572356
Duration11.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37103.117
Minimum5
Maximum73742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:03.645762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile3801.3
Q118145.75
median37058.5
Q356125
95-th percentile70107.05
Maximum73742
Range73737
Interquartile range (IQR)37979.25

Descriptive statistics

Standard deviation21504.579
Coefficient of variation (CV)0.57958956
Kurtosis-1.2347681
Mean37103.117
Median Absolute Deviation (MAD)18993
Skewness-0.013110079
Sum3.7103117 × 108
Variance4.6244694 × 108
MonotonicityNot monotonic
2023-12-11T01:40:03.788659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26303 1
 
< 0.1%
27984 1
 
< 0.1%
6737 1
 
< 0.1%
56245 1
 
< 0.1%
28132 1
 
< 0.1%
57306 1
 
< 0.1%
40690 1
 
< 0.1%
21450 1
 
< 0.1%
14264 1
 
< 0.1%
5825 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
15 1
< 0.1%
18 1
< 0.1%
30 1
< 0.1%
34 1
< 0.1%
48 1
< 0.1%
59 1
< 0.1%
71 1
< 0.1%
77 1
< 0.1%
106 1
< 0.1%
ValueCountFrequency (%)
73742 1
< 0.1%
73725 1
< 0.1%
73719 1
< 0.1%
73716 1
< 0.1%
73715 1
< 0.1%
73713 1
< 0.1%
73711 1
< 0.1%
73701 1
< 0.1%
73699 1
< 0.1%
73694 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

2023-12-11T01:40:03.942873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:40:04.032531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당월정상수납 10000
100.0%

수납일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-01-31
7376 
2022-01-20
 
283
2022-01-25
 
209
2022-01-07
 
203
2022-01-26
 
147
Other values (28)
1782 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2022-01-31
2nd row2022-01-31
3rd row2022-01-04
4th row2022-01-22
5th row2022-01-23

Common Values

ValueCountFrequency (%)
2022-01-31 7376
73.8%
2022-01-20 283
 
2.8%
2022-01-25 209
 
2.1%
2022-01-07 203
 
2.0%
2022-01-26 147
 
1.5%
2022-01-03 144
 
1.4%
2022-01-24 137
 
1.4%
2022-01-17 112
 
1.1%
2022-01-28 112
 
1.1%
2022-01-04 108
 
1.1%
Other values (23) 1169
 
11.7%

Length

2023-12-11T01:40:04.134384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-01-31 7376
73.8%
2022-01-20 283
 
2.8%
2022-01-25 209
 
2.1%
2022-01-07 203
 
2.0%
2022-01-26 147
 
1.5%
2022-01-03 144
 
1.4%
2022-01-24 137
 
1.4%
2022-01-17 112
 
1.1%
2022-01-28 112
 
1.1%
2022-01-04 108
 
1.1%
Other values (23) 1169
 
11.7%

수납방법
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
통장(자동납부)
6844 
가상계좌
1687 
창구수납(간단E)
 
360
카드(자동납부)
 
327
CD/ATM(간단E)
 
278
Other values (9)
 
504

Length

Max length11
Median length8
Mean length7.437
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row통장(자동납부)
2nd row통장(자동납부)
3rd row카드(ARS)
4th row카드(간단E)
5th row가상계좌

Common Values

ValueCountFrequency (%)
통장(자동납부) 6844
68.4%
가상계좌 1687
 
16.9%
창구수납(간단E) 360
 
3.6%
카드(자동납부) 327
 
3.3%
CD/ATM(간단E) 278
 
2.8%
카드(간단E) 130
 
1.3%
인터넷뱅킹(간단E) 127
 
1.3%
카드(ARS) 111
 
1.1%
카드(사이버) 53
 
0.5%
카드(카카오) 26
 
0.3%
Other values (4) 57
 
0.6%

Length

2023-12-11T01:40:04.282006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
통장(자동납부 6844
68.4%
가상계좌 1687
 
16.9%
창구수납(간단e 360
 
3.6%
카드(자동납부 327
 
3.3%
cd/atm(간단e 278
 
2.8%
카드(간단e 130
 
1.3%
인터넷뱅킹(간단e 127
 
1.3%
카드(ars 111
 
1.1%
카드(사이버 53
 
0.5%
카드(카카오 26
 
0.3%
Other values (4) 57
 
0.6%

수납금액합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3924
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238930.17
Minimum150
Maximum1.043341 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:04.466328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile2380
Q119420
median44625
Q3107905
95-th percentile580105.5
Maximum1.043341 × 108
Range1.0433395 × 108
Interquartile range (IQR)88485

Descriptive statistics

Standard deviation1934351.5
Coefficient of variation (CV)8.0958864
Kurtosis1489.1204
Mean238930.17
Median Absolute Deviation (MAD)32935
Skewness33.64269
Sum2.3893017 × 109
Variance3.7417157 × 1012
MonotonicityNot monotonic
2023-12-11T01:40:04.668442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2380 465
 
4.7%
2400 154
 
1.5%
18120 98
 
1.0%
24640 95
 
0.9%
20720 90
 
0.9%
25980 85
 
0.9%
16800 83
 
0.8%
23360 83
 
0.8%
19420 81
 
0.8%
28600 81
 
0.8%
Other values (3914) 8685
86.9%
ValueCountFrequency (%)
150 1
 
< 0.1%
200 1
 
< 0.1%
420 1
 
< 0.1%
580 1
 
< 0.1%
610 1
 
< 0.1%
730 2
 
< 0.1%
920 9
0.1%
960 1
 
< 0.1%
1050 1
 
< 0.1%
1060 1
 
< 0.1%
ValueCountFrequency (%)
104334100 1
< 0.1%
85457810 1
< 0.1%
75768000 1
< 0.1%
31604950 1
< 0.1%
30219080 1
< 0.1%
28671940 1
< 0.1%
28208980 1
< 0.1%
28000190 1
< 0.1%
27108270 1
< 0.1%
21932260 1
< 0.1%

상수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3304
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129031.56
Minimum0
Maximum85457810
Zeros44
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:04.992966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2380
Q111545
median24960
Q358152.5
95-th percentile280866
Maximum85457810
Range85457810
Interquartile range (IQR)46607.5

Descriptive statistics

Standard deviation1204972.3
Coefficient of variation (CV)9.3385859
Kurtosis2739.32
Mean129031.56
Median Absolute Deviation (MAD)17580
Skewness44.619587
Sum1.2903156 × 109
Variance1.4519584 × 1012
MonotonicityNot monotonic
2023-12-11T01:40:05.238234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2380 470
 
4.7%
2400 154
 
1.5%
10940 98
 
1.0%
14500 98
 
1.0%
11660 93
 
0.9%
9520 92
 
0.9%
12360 91
 
0.9%
16640 89
 
0.9%
10220 85
 
0.9%
5940 84
 
0.8%
Other values (3294) 8646
86.5%
ValueCountFrequency (%)
0 44
0.4%
150 1
 
< 0.1%
200 1
 
< 0.1%
420 1
 
< 0.1%
580 1
 
< 0.1%
610 1
 
< 0.1%
730 2
 
< 0.1%
920 9
 
0.1%
960 1
 
< 0.1%
1080 1
 
< 0.1%
ValueCountFrequency (%)
85457810 1
< 0.1%
40702040 1
< 0.1%
30218930 1
< 0.1%
28208830 1
< 0.1%
20651330 1
< 0.1%
16942700 1
< 0.1%
15662500 1
< 0.1%
15417930 1
< 0.1%
14964740 1
< 0.1%
14553200 1
< 0.1%

하수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2431
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91064.725
Minimum0
Maximum59039980
Zeros972
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:05.460291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15400
median14800
Q338020
95-th percentile226765.5
Maximum59039980
Range59039980
Interquartile range (IQR)32620

Descriptive statistics

Standard deviation900851.92
Coefficient of variation (CV)9.8924356
Kurtosis2902.158
Mean91064.725
Median Absolute Deviation (MAD)11660
Skewness48.469248
Sum9.1064725 × 108
Variance8.1153419 × 1011
MonotonicityNot monotonic
2023-12-11T01:40:05.642657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 972
 
9.7%
5840 138
 
1.4%
5400 133
 
1.3%
7200 126
 
1.3%
7640 124
 
1.2%
4940 121
 
1.2%
8100 120
 
1.2%
9000 117
 
1.2%
6300 116
 
1.2%
8540 113
 
1.1%
Other values (2421) 7920
79.2%
ValueCountFrequency (%)
0 972
9.7%
30 2
 
< 0.1%
60 2
 
< 0.1%
440 81
 
0.8%
450 8
 
0.1%
490 1
 
< 0.1%
580 11
 
0.1%
620 1
 
< 0.1%
680 1
 
< 0.1%
900 78
 
0.8%
ValueCountFrequency (%)
59039980 1
< 0.1%
51360400 1
< 0.1%
13469200 1
< 0.1%
11502880 1
< 0.1%
11310200 1
< 0.1%
10078840 1
< 0.1%
9849600 1
< 0.1%
9679400 1
< 0.1%
9403720 1
< 0.1%
7877480 1
< 0.1%

물이용수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1407
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18833.88
Minimum0
Maximum12835100
Zeros859
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:05.838039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11640
median4280
Q39600
95-th percentile41582
Maximum12835100
Range12835100
Interquartile range (IQR)7960

Descriptive statistics

Standard deviation174220.1
Coefficient of variation (CV)9.2503564
Kurtosis3044.9453
Mean18833.88
Median Absolute Deviation (MAD)3240
Skewness46.388473
Sum1.883388 × 108
Variance3.0352644 × 1010
MonotonicityNot monotonic
2023-12-11T01:40:06.527570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 859
 
8.6%
1780 162
 
1.6%
1640 159
 
1.6%
1040 156
 
1.6%
440 150
 
1.5%
1920 147
 
1.5%
880 146
 
1.5%
140 143
 
1.4%
1180 139
 
1.4%
1480 134
 
1.3%
Other values (1397) 7805
78.0%
ValueCountFrequency (%)
0 859
8.6%
10 3
 
< 0.1%
20 1
 
< 0.1%
60 1
 
< 0.1%
80 1
 
< 0.1%
90 1
 
< 0.1%
120 1
 
< 0.1%
140 143
 
1.4%
150 25
 
0.2%
280 102
 
1.0%
ValueCountFrequency (%)
12835100 1
< 0.1%
4592080 1
< 0.1%
3352050 1
< 0.1%
3159840 1
< 0.1%
2956610 1
< 0.1%
2875670 1
< 0.1%
2870720 1
< 0.1%
2345930 1
< 0.1%
2344720 1
< 0.1%
2305480 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

2023-12-11T01:40:06.719005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:40:06.859145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

납기내일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-01-31
9015 
2021-12-31
944 
2022-02-28
 
16
2022-02-04
 
4
2022-02-03
 
3
Other values (14)
 
18

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row2022-01-31
2nd row2022-01-31
3rd row2021-12-31
4th row2022-01-31
5th row2022-01-31

Common Values

ValueCountFrequency (%)
2022-01-31 9015
90.1%
2021-12-31 944
 
9.4%
2022-02-28 16
 
0.2%
2022-02-04 4
 
< 0.1%
2022-02-03 3
 
< 0.1%
2022-01-21 2
 
< 0.1%
2022-02-07 2
 
< 0.1%
2022-01-04 2
 
< 0.1%
2022-02-12 2
 
< 0.1%
2022-01-17 1
 
< 0.1%
Other values (9) 9
 
0.1%

Length

2023-12-11T01:40:07.010672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-01-31 9015
90.1%
2021-12-31 944
 
9.4%
2022-02-28 16
 
0.2%
2022-02-04 4
 
< 0.1%
2022-02-03 3
 
< 0.1%
2022-01-21 2
 
< 0.1%
2022-02-07 2
 
< 0.1%
2022-01-04 2
 
< 0.1%
2022-02-12 2
 
< 0.1%
2022-02-18 1
 
< 0.1%
Other values (9) 9
 
0.1%

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  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
납기내
9050 
납기후
950 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
납기내 9050
90.5%
납기후 950
 
9.5%

Length

2023-12-11T01:40:07.173284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:40:07.315338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
납기내 9050
90.5%
납기후 950
 
9.5%

은행명
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산은행
3352 
새마을금고중앙회
1416 
농협은행
1035 
국민은행
1015 
지역농축협
571 
Other values (28)
2611 

Length

Max length8
Median length4
Mean length4.7389
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row지역농축협
2nd row새마을금고중앙회
3rd row신한카드
4th row롯데카드
5th row농협은행

Common Values

ValueCountFrequency (%)
부산은행 3352
33.5%
새마을금고중앙회 1416
14.2%
농협은행 1035
 
10.3%
국민은행 1015
 
10.2%
지역농축협 571
 
5.7%
우리은행 406
 
4.1%
신한은행 314
 
3.1%
우체국 306
 
3.1%
KEB하나은행 252
 
2.5%
기업은행 251
 
2.5%
Other values (23) 1082
 
10.8%

Length

2023-12-11T01:40:07.489561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산은행 3352
33.5%
새마을금고중앙회 1416
14.2%
농협은행 1035
 
10.3%
국민은행 1015
 
10.2%
지역농축협 571
 
5.7%
우리은행 406
 
4.1%
신한은행 314
 
3.1%
우체국 306
 
3.1%
keb하나은행 252
 
2.5%
기업은행 251
 
2.5%
Other values (23) 1082
 
10.8%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9778
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25402074
Minimum1
Maximum3.0287484 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:07.721961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1073.35
Q119850.5
median40603.5
Q362269.5
95-th percentile7727920
Maximum3.0287484 × 109
Range3.0287484 × 109
Interquartile range (IQR)42419

Descriptive statistics

Standard deviation2.1600574 × 108
Coefficient of variation (CV)8.5034687
Kurtosis130.69624
Mean25402074
Median Absolute Deviation (MAD)21158
Skewness10.837971
Sum2.5402074 × 1011
Variance4.6658481 × 1016
MonotonicityNot monotonic
2023-12-11T01:40:07.995424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 6
 
0.1%
7 6
 
0.1%
70 5
 
0.1%
45 5
 
0.1%
52 5
 
0.1%
18 5
 
0.1%
40 5
 
0.1%
63 4
 
< 0.1%
100 4
 
< 0.1%
44 4
 
< 0.1%
Other values (9768) 9951
99.5%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 3
< 0.1%
3 3
< 0.1%
4 6
0.1%
5 3
< 0.1%
6 2
 
< 0.1%
7 6
0.1%
8 3
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
3028748400 1
< 0.1%
3028740100 1
< 0.1%
3028735300 1
< 0.1%
3028730200 1
< 0.1%
3028729600 1
< 0.1%
3028726300 1
< 0.1%
3028721300 1
< 0.1%
3028717700 1
< 0.1%
3028714600 1
< 0.1%
3028714200 1
< 0.1%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.302
Minimum0
Maximum710
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:08.177189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile170
Q1230
median290
Q3380
95-th percentile500
Maximum710
Range710
Interquartile range (IQR)150

Descriptive statistics

Standard deviation107.53882
Coefficient of variation (CV)0.35573307
Kurtosis1.7681379
Mean302.302
Median Absolute Deviation (MAD)60
Skewness0.97037637
Sum3023020
Variance11564.597
MonotonicityNot monotonic
2023-12-11T01:40:08.360703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
230 1693
16.9%
380 1206
12.1%
290 1098
11.0%
260 1031
10.3%
350 785
7.8%
170 762
7.6%
200 747
7.5%
410 684
6.8%
320 656
 
6.6%
530 221
 
2.2%
Other values (7) 1117
11.2%
ValueCountFrequency (%)
0 5
 
0.1%
110 171
 
1.7%
140 216
 
2.2%
170 762
7.6%
200 747
7.5%
230 1693
16.9%
260 1031
10.3%
290 1098
11.0%
320 656
 
6.6%
350 785
7.8%
ValueCountFrequency (%)
710 157
 
1.6%
530 221
 
2.2%
500 194
 
1.9%
470 187
 
1.9%
440 187
 
1.9%
410 684
6.8%
380 1206
12.1%
350 785
7.8%
320 656
6.6%
290 1098
11.0%

구명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
1693 
사하구
1206 
남구
1098 
동래구
1031 
해운대구
785 
Other values (12)
4187 

Length

Max length4
Median length3
Mean length2.958
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남구
2nd row사하구
3rd row사상구
4th row기장군
5th row동구

Common Values

ValueCountFrequency (%)
부산진구 1693
16.9%
사하구 1206
12.1%
남구 1098
11.0%
동래구 1031
10.3%
해운대구 785
7.8%
동구 762
7.6%
영도구 747
7.5%
금정구 684
6.8%
북구 656
 
6.6%
사상구 221
 
2.2%
Other values (7) 1117
11.2%

Length

2023-12-11T01:40:08.571221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 1693
16.9%
사하구 1206
12.1%
남구 1098
11.0%
동래구 1031
10.3%
해운대구 785
7.8%
동구 762
7.6%
영도구 747
7.5%
금정구 684
6.8%
북구 656
 
6.6%
사상구 221
 
2.2%
Other values (7) 1117
11.2%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.7837
Minimum101
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:40:08.764677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile244
Q1301
median304
Q3307
95-th percentile309
Maximum312
Range211
Interquartile range (IQR)6

Descriptive statistics

Standard deviation24.68829
Coefficient of variation (CV)0.084035602
Kurtosis2.0639114
Mean293.7837
Median Absolute Deviation (MAD)3
Skewness-1.6941397
Sum2937837
Variance609.51167
MonotonicityNot monotonic
2023-12-11T01:40:08.917440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
244 1902
19.0%
304 1693
16.9%
306 1292
12.9%
309 1206
12.1%
301 933
9.3%
307 877
8.8%
308 785
7.8%
303 747
 
7.5%
302 216
 
2.2%
311 187
 
1.9%
Other values (2) 162
 
1.6%
ValueCountFrequency (%)
101 5
 
0.1%
244 1902
19.0%
301 933
9.3%
302 216
 
2.2%
303 747
 
7.5%
304 1693
16.9%
306 1292
12.9%
307 877
8.8%
308 785
7.8%
309 1206
12.1%
ValueCountFrequency (%)
312 157
 
1.6%
311 187
 
1.9%
309 1206
12.1%
308 785
7.8%
307 877
8.8%
306 1292
12.9%
304 1693
16.9%
303 747
7.5%
302 216
 
2.2%
301 933
9.3%

사업소명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
1902 
부산진 사업소
1693 
남부 사업소
1292 
사하 사업소
1206 
중동부 사업소
933 
Other values (7)
2974 

Length

Max length9
Median length8
Mean length8.2765
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동래통합사업소 1902
19.0%
부산진 사업소 1693
16.9%
남부 사업소 1292
12.9%
사하 사업소 1206
12.1%
중동부 사업소 933
9.3%
북부 사업소 877
8.8%
해운대 사업소 785
7.8%
영도 사업소 747
 
7.5%
서부 사업소 216
 
2.2%
강서 사업소 187
 
1.9%
Other values (2) 162
 
1.6%

Length

2023-12-11T01:40:09.109063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업소 8093
44.7%
동래통합사업소 1902
 
10.5%
부산진 1693
 
9.4%
남부 1292
 
7.1%
사하 1206
 
6.7%
중동부 933
 
5.2%
북부 877
 
4.8%
해운대 785
 
4.3%
영도 747
 
4.1%
서부 216
 
1.2%
Other values (3) 349
 
1.9%

Interactions

2023-12-11T01:40:02.385718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:54.756980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:55.783061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:57.225769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.356498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:59.407264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:00.801995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.639383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.489553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:54.863603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:55.977913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:57.378655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.478664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:59.572587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:00.903703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.738647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.601399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:54.986686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:56.123563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:57.538023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.586962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:59.704557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:00.995892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.836822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.705462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:55.104488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:56.284447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:57.670504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.712053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:59.839102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.093146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.918192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.801062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:55.231583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:56.484312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:57.809108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.836528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:59.998366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.188272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.004371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.911154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:55.359037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:56.700527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:57.971593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.974981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:00.142699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.324758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.111211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:03.001012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:55.516683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:56.914158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.091145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:59.114237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:00.282383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.445976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.201784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:03.093651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:55.647266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:57.098033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:58.231705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:59.254035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:00.681743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:01.546043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:02.295789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:40:09.271244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액납기내일자납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
연번1.0000.7800.5250.0000.0190.0300.0140.5960.9440.3030.2630.3590.4400.1620.339
수납일자0.7801.0000.7300.0380.2060.0000.0000.7180.9200.4800.4390.4050.3930.1960.310
수납방법0.5250.7301.0000.0470.0970.0000.0000.3370.4940.8820.2840.3850.3740.1200.271
수납금액합계0.0000.0380.0471.0000.8820.8800.8950.6020.0240.0000.0950.3310.0000.4240.357
상수도수납금액0.0190.2060.0970.8821.0000.7490.7690.8200.0350.0000.2260.3810.0360.6170.466
하수도수납금액0.0300.0000.0000.8800.7491.0000.9830.0000.0230.0000.0000.0000.0000.0000.000
물이용수납금액0.0140.0000.0000.8950.7690.9831.0000.0000.0220.0000.0000.0000.0000.0000.000
납기내일자0.5960.7180.3370.6020.8200.0000.0001.0000.9910.0780.6810.6100.2400.7430.577
납기내후수납구분0.9440.9200.4940.0240.0350.0230.0220.9911.0000.1410.1790.2230.3000.0240.227
은행명0.3030.4800.8820.0000.0000.0000.0000.0780.1411.0000.0790.2820.3240.1030.272
일련번호0.2630.4390.2840.0950.2260.0000.0000.6810.1790.0791.0000.2170.2310.1210.289
구코드0.3590.4050.3850.3310.3810.0000.0000.6100.2230.2820.2171.0001.0000.9680.965
구명0.4400.3930.3740.0000.0360.0000.0000.2400.3000.3240.2311.0001.0001.0001.000
사업소코드0.1620.1960.1200.4240.6170.0000.0000.7430.0240.1030.1210.9681.0001.0001.000
사업소명0.3390.3100.2710.3570.4660.0000.0000.5770.2270.2720.2890.9651.0001.0001.000
2023-12-11T01:40:09.532540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납기내후수납구분은행명수납방법납기내일자수납일자구명사업소명
납기내후수납구분1.0000.1190.3880.9960.8490.2350.176
은행명0.1191.0000.5030.0210.0960.0970.091
수납방법0.3880.5031.0000.1180.3140.1380.105
납기내일자0.9960.0210.1181.0000.2690.0790.244
수납일자0.8490.0960.3140.2691.0000.1210.105
구명0.2350.0970.1380.0790.1211.0001.000
사업소명0.1760.0910.1050.2440.1051.0001.000
2023-12-11T01:40:09.721183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납금액합계상수도수납금액하수도수납금액물이용수납금액일련번호구코드사업소코드수납일자수납방법납기내일자납기내후수납구분은행명구명사업소명
연번1.0000.0210.0180.0430.032-0.048-0.007-0.0340.4100.2430.2700.7990.1110.1890.149
수납금액합계0.0211.0000.9870.9620.9590.0150.0590.0170.0150.0170.3180.0260.0000.0000.182
상수도수납금액0.0180.9871.0000.9380.9710.0200.0630.0230.0900.0470.5590.0250.0000.0180.201
하수도수납금액0.0430.9620.9381.0000.944-0.0050.030-0.0080.0000.0000.0000.0280.0000.0000.000
물이용수납금액0.0320.9590.9710.9441.0000.0070.0530.0150.0000.0000.0000.0270.0000.0000.000
일련번호-0.0480.0150.020-0.0050.0071.0000.5430.2880.2400.1640.4460.1190.0410.1100.137
구코드-0.0070.0590.0630.0300.0530.5431.0000.4730.1600.1730.2910.2230.1071.0000.849
사업소코드-0.0340.0170.023-0.0080.0150.2880.4731.0000.0910.0660.5480.0390.0470.9991.000
수납일자0.4100.0150.0900.0000.0000.2400.1600.0911.0000.3140.2690.8490.0960.1210.105
수납방법0.2430.0170.0470.0000.0000.1640.1730.0660.3141.0000.1180.3880.5030.1380.105
납기내일자0.2700.3180.5590.0000.0000.4460.2910.5480.2690.1181.0000.9960.0210.0790.244
납기내후수납구분0.7990.0260.0250.0280.0270.1190.2230.0390.8490.3880.9961.0000.1190.2350.176
은행명0.1110.0000.0000.0000.0000.0410.1070.0470.0960.5030.0210.1191.0000.0970.091
구명0.1890.0000.0180.0000.0000.1101.0000.9990.1210.1380.0790.2350.0971.0001.000
사업소명0.1490.1820.2010.0000.0000.1370.8491.0000.1050.1050.2440.1760.0911.0001.000

Missing values

2023-12-11T01:40:03.231338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:40:03.455677image/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
2630226303당월정상수납2022-01-31통장(자동납부)652803660021600708002022-01-31<NA><NA>납기내지역농축협39353290남구306남부 사업소
3791137912당월정상수납2022-01-31통장(자동납부)253300154400783802052002022-01-31<NA><NA>납기내새마을금고중앙회54565380사하구309사하 사업소
62056206당월정상수납2022-01-04카드(ARS)450702571014550481002021-12-31<NA><NA>납기후신한카드525515530사상구307북부 사업소
2426024261당월정상수납2022-01-22카드(간단E)22560191400342002022-01-31<NA><NA>납기내롯데카드214710기장군312기장 사업소
2505825059당월정상수납2022-01-23가상계좌240024000002022-01-31<NA><NA>납기내농협은행938000170동구301중동부 사업소
6490664907당월정상수납2022-01-27인터넷뱅킹(간단E)200760100800877401222002022-01-31<NA><NA>납기내국민은행2284290남구306남부 사업소
3912939130당월정상수납2022-01-31통장(자동납부)788004326027280826002022-01-31<NA><NA>납기내신협중앙회16520200영도구303영도 사업소
2304123042당월정상수납2022-01-31통장(자동납부)1156073803140104002022-01-31<NA><NA>납기내새마을금고중앙회43054290남구306남부 사업소
1547215473당월정상수납2022-01-31통장(자동납부)522002948017100562002022-01-31<NA><NA>납기내농협은행35949290남구306남부 사업소
28082809당월정상수납2022-01-10가상계좌103006720270088002021-12-31<NA><NA>납기후부산은행8863800530사상구307북부 사업소
연번구분수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액기타금액납기내일자체납시작년월체납종료년월납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
5849958500당월정상수납2022-01-31통장(자동납부)24640145007640250002022-01-31<NA><NA>납기내농협은행60636410금정구244동래통합사업소
6584065841당월정상수납2022-01-31가상계좌27440160808540282002022-01-31<NA><NA>납기내우리은행2063800230부산진구304부산진 사업소
6389763898당월정상수납2022-01-31통장(자동납부)18050007286609896808666002022-01-31<NA><NA>납기내기업은행19640230부산진구304부산진 사업소
4639246393당월정상수납2022-01-31통장(자동납부)18120109405400178002022-01-31<NA><NA>납기내부산은행20326230부산진구304부산진 사업소
6266362664당월정상수납2022-01-31통장(자동납부)10460057980351001152002022-01-31<NA><NA>납기내부산은행52405380사하구309사하 사업소
6458764588당월정상수납2022-01-28가상계좌2467170981350137475011107002022-01-31<NA><NA>납기내KEB하나은행5682700380사하구309사하 사업소
31963197당월정상수납2022-01-13가상계좌22160132006740222002021-12-31<NA><NA>납기후부산은행6255700410금정구244동래통합사업소
5135051351당월정상수납2022-01-31통장(자동납부)5204002834401857405122002022-01-31<NA><NA>납기내부산은행48509350해운대구308해운대 사업소
2764527646당월정상수납2022-01-23가상계좌1022088800134002022-01-31<NA><NA>납기내부산은행7524300440강서구311강서 사업소
6135061351당월정상수납2022-01-31통장(자동납부)4191002290601431004694002022-01-31<NA><NA>납기내국민은행35802290남구306남부 사업소