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
Categorical7
DateTime2
Unsupported2

Dataset

Description부산광역시상수도사업본부_수용가정보시스템_수납정보_당월및체납수납처리정보_20220803
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
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 2 other fieldsHigh correlation
상수도수납금액 is highly overall correlated with 수납금액합계 and 2 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
구코드 is highly overall correlated with 구명 and 1 other fieldsHigh correlation
사업소코드 is highly overall correlated with 구명 and 1 other fieldsHigh correlation
수납방법 is highly overall correlated with 은행명High correlation
은행명 is highly overall correlated with 수납방법High correlation
납기내후수납구분 is highly imbalanced (51.3%)Imbalance
체납시작년월 has 10000 (100.0%) missing valuesMissing
체납종료년월 has 10000 (100.0%) missing valuesMissing
수납금액합계 is highly skewed (γ1 = 27.40779522)Skewed
상수도수납금액 is highly skewed (γ1 = 41.49873123)Skewed
물이용수납금액 is highly skewed (γ1 = 69.37192475)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 1201 (12.0%) zerosZeros
물이용수납금액 has 899 (9.0%) zerosZeros

Reproduction

Analysis started2023-12-10 16:39:28.156970
Analysis finished2023-12-10 16:39:41.886316
Duration13.73 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%
Mean37194.921
Minimum12
Maximum74756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:41.989805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile3655.75
Q118396.75
median36910
Q355924.75
95-th percentile70954.25
Maximum74756
Range74744
Interquartile range (IQR)37528

Descriptive statistics

Standard deviation21608.969
Coefficient of variation (CV)0.58096559
Kurtosis-1.2034457
Mean37194.921
Median Absolute Deviation (MAD)18768.5
Skewness0.0082180269
Sum3.7194921 × 108
Variance4.6694755 × 108
MonotonicityNot monotonic
2023-12-11T01:39:42.156069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60669 1
 
< 0.1%
13449 1
 
< 0.1%
74756 1
 
< 0.1%
60539 1
 
< 0.1%
14697 1
 
< 0.1%
27485 1
 
< 0.1%
53003 1
 
< 0.1%
15940 1
 
< 0.1%
20674 1
 
< 0.1%
43815 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
12 1
< 0.1%
16 1
< 0.1%
26 1
< 0.1%
34 1
< 0.1%
44 1
< 0.1%
74 1
< 0.1%
76 1
< 0.1%
80 1
< 0.1%
123 1
< 0.1%
127 1
< 0.1%
ValueCountFrequency (%)
74756 1
< 0.1%
74745 1
< 0.1%
74742 1
< 0.1%
74740 1
< 0.1%
74737 1
< 0.1%
74733 1
< 0.1%
74728 1
< 0.1%
74717 1
< 0.1%
74714 1
< 0.1%
74707 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

2023-12-11T01:39:42.334984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:42.455685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당월정상수납 10000
100.0%

수납일자
Date

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-05-02 00:00:00
Maximum2022-06-01 00:00:00
2023-12-11T01:39:42.568800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:42.753192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

수납방법
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가상계좌
4466 
창구수납(간단E)
1844 
CD/ATM(간단E)
1447 
카드(자동납부)
842 
인터넷뱅킹(간단E)
 
355
Other values (11)
1046 

Length

Max length11
Median length10
Mean length6.8494
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row카드(간단E)
2nd row카드(자동납부)
3rd row창구수납(간단E)
4th row인터넷뱅킹(간단E)
5th row카드(자동납부)

Common Values

ValueCountFrequency (%)
가상계좌 4466
44.7%
창구수납(간단E) 1844
18.4%
CD/ATM(간단E) 1447
 
14.5%
카드(자동납부) 842
 
8.4%
인터넷뱅킹(간단E) 355
 
3.5%
카드(간단E) 264
 
2.6%
카드(ARS) 260
 
2.6%
통장(자동납부) 214
 
2.1%
카드(사이버) 120
 
1.2%
자동화기기(간단E) 64
 
0.6%
Other values (6) 124
 
1.2%

Length

2023-12-11T01:39:42.933040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가상계좌 4466
44.7%
창구수납(간단e 1844
18.4%
cd/atm(간단e 1447
 
14.5%
카드(자동납부 842
 
8.4%
인터넷뱅킹(간단e 355
 
3.5%
카드(간단e 264
 
2.6%
카드(ars 260
 
2.6%
통장(자동납부 214
 
2.1%
카드(사이버 120
 
1.2%
자동화기기(간단e 64
 
0.6%
Other values (6) 124
 
1.2%

수납금액합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4162
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191977.69
Minimum40
Maximum63845080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:43.137243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile2400
Q119320
median48140
Q3109260
95-th percentile502559
Maximum63845080
Range63845040
Interquartile range (IQR)89940

Descriptive statistics

Standard deviation1182884
Coefficient of variation (CV)6.16157
Kurtosis1116.1678
Mean191977.69
Median Absolute Deviation (MAD)35180
Skewness27.407795
Sum1.9197769 × 109
Variance1.3992145 × 1012
MonotonicityNot monotonic
2023-12-11T01:39:43.333077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 564
 
5.6%
14260 80
 
0.8%
18240 77
 
0.8%
27480 74
 
0.7%
28800 72
 
0.7%
19560 70
 
0.7%
22200 70
 
0.7%
15600 64
 
0.6%
12960 62
 
0.6%
20880 61
 
0.6%
Other values (4152) 8806
88.1%
ValueCountFrequency (%)
40 1
< 0.1%
200 1
< 0.1%
230 1
< 0.1%
280 1
< 0.1%
360 1
< 0.1%
400 2
< 0.1%
420 1
< 0.1%
460 1
< 0.1%
490 1
< 0.1%
540 1
< 0.1%
ValueCountFrequency (%)
63845080 1
< 0.1%
42303380 1
< 0.1%
26887380 1
< 0.1%
24558580 1
< 0.1%
24312300 1
< 0.1%
23937600 1
< 0.1%
19833500 1
< 0.1%
18277080 1
< 0.1%
17986000 1
< 0.1%
17971560 1
< 0.1%

상수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3227
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109473.08
Minimum0
Maximum63844930
Zeros58
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:43.495213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2400
Q111040
median26880
Q358625
95-th percentile237360
Maximum63844930
Range63844930
Interquartile range (IQR)47585

Descriptive statistics

Standard deviation980782.86
Coefficient of variation (CV)8.9591239
Kurtosis2251.327
Mean109473.08
Median Absolute Deviation (MAD)18720
Skewness41.498731
Sum1.0947308 × 109
Variance9.6193502 × 1011
MonotonicityNot monotonic
2023-12-11T01:39:43.650649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 576
 
5.8%
9600 114
 
1.1%
6000 113
 
1.1%
16800 109
 
1.1%
13200 104
 
1.0%
8880 93
 
0.9%
11040 84
 
0.8%
16080 82
 
0.8%
11760 76
 
0.8%
8160 75
 
0.8%
Other values (3217) 8574
85.7%
ValueCountFrequency (%)
0 58
0.6%
40 1
 
< 0.1%
200 1
 
< 0.1%
230 1
 
< 0.1%
280 1
 
< 0.1%
320 1
 
< 0.1%
360 1
 
< 0.1%
400 2
 
< 0.1%
420 1
 
< 0.1%
460 1
 
< 0.1%
ValueCountFrequency (%)
63844930 1
< 0.1%
42303230 1
< 0.1%
26887230 1
< 0.1%
24558430 1
< 0.1%
18277080 1
< 0.1%
16715070 1
< 0.1%
12470400 1
< 0.1%
12389330 1
< 0.1%
10722650 1
< 0.1%
10603000 1
< 0.1%

하수도수납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2335
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69145.13
Minimum0
Maximum12209400
Zeros1201
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:43.858851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14940
median15380
Q338242.5
95-th percentile196548
Maximum12209400
Range12209400
Interquartile range (IQR)33302.5

Descriptive statistics

Standard deviation364290.81
Coefficient of variation (CV)5.2684956
Kurtosis365.46504
Mean69145.13
Median Absolute Deviation (MAD)12780
Skewness16.618477
Sum6.914513 × 108
Variance1.327078 × 1011
MonotonicityNot monotonic
2023-12-11T01:39:44.054804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1201
 
12.0%
9000 113
 
1.1%
4040 111
 
1.1%
8540 111
 
1.1%
4500 102
 
1.0%
5400 96
 
1.0%
6300 94
 
0.9%
6740 94
 
0.9%
3600 88
 
0.9%
5840 86
 
0.9%
Other values (2325) 7904
79.0%
ValueCountFrequency (%)
0 1201
12.0%
10 1
 
< 0.1%
20 2
 
< 0.1%
40 1
 
< 0.1%
120 1
 
< 0.1%
130 1
 
< 0.1%
140 1
 
< 0.1%
190 1
 
< 0.1%
240 1
 
< 0.1%
400 1
 
< 0.1%
ValueCountFrequency (%)
12209400 1
< 0.1%
10167110 1
< 0.1%
9238480 1
< 0.1%
8552700 1
< 0.1%
7759540 1
< 0.1%
7087400 1
< 0.1%
7084920 1
< 0.1%
6708700 1
< 0.1%
5789550 1
< 0.1%
5721300 1
< 0.1%

물이용수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1453
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13359.485
Minimum0
Maximum11871270
Zeros899
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:44.299235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11500
median4365
Q39500
95-th percentile31968
Maximum11871270
Range11871270
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation135978.56
Coefficient of variation (CV)10.178428
Kurtosis5833.9777
Mean13359.485
Median Absolute Deviation (MAD)3465
Skewness69.371925
Sum1.3359485 × 108
Variance1.8490169 × 1010
MonotonicityNot monotonic
2023-12-11T01:39:44.511163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 899
 
9.0%
600 163
 
1.6%
300 152
 
1.5%
1340 143
 
1.4%
140 142
 
1.4%
1500 134
 
1.3%
1800 131
 
1.3%
900 130
 
1.3%
740 128
 
1.3%
1200 125
 
1.2%
Other values (1443) 7853
78.5%
ValueCountFrequency (%)
0 899
9.0%
10 2
 
< 0.1%
20 4
 
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
50 1
 
< 0.1%
60 1
 
< 0.1%
80 1
 
< 0.1%
90 1
 
< 0.1%
140 142
 
1.4%
ValueCountFrequency (%)
11871270 1
< 0.1%
3052350 1
< 0.1%
2309620 1
< 0.1%
2143100 1
< 0.1%
1881830 1
< 0.1%
1771230 1
< 0.1%
1492460 1
< 0.1%
1199270 1
< 0.1%
1164840 1
< 0.1%
1155730 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

2023-12-11T01:39:44.740557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:44.865891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

납기내일자
Date

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-11-30 00:00:00
Maximum2022-06-30 00:00:00
2023-12-11T01:39:44.962008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:45.105584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

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

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
납기내
8943 
납기후
1057 

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 8943
89.4%
납기후 1057
 
10.6%

Length

2023-12-11T01:39:45.271454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:45.382533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
납기내 8943
89.4%
납기후 1057
 
10.6%

은행명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산은행
2944 
농협은행
1287 
새마을금고중앙회
1091 
국민은행
1029 
우체국
460 
Other values (26)
3189 

Length

Max length8
Median length4
Mean length4.5838
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row롯데카드
2nd rowBC카드
3rd row우체국
4th row기업은행
5th row롯데카드

Common Values

ValueCountFrequency (%)
부산은행 2944
29.4%
농협은행 1287
12.9%
새마을금고중앙회 1091
 
10.9%
국민은행 1029
 
10.3%
우체국 460
 
4.6%
우리은행 389
 
3.9%
지역농축협 382
 
3.8%
신한은행 288
 
2.9%
BC카드 286
 
2.9%
신한카드 242
 
2.4%
Other values (21) 1602
16.0%

Length

2023-12-11T01:39:45.516352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산은행 2944
29.4%
농협은행 1287
12.9%
새마을금고중앙회 1091
 
10.9%
국민은행 1029
 
10.3%
우체국 460
 
4.6%
우리은행 389
 
3.9%
지역농축협 382
 
3.8%
신한은행 288
 
2.9%
bc카드 286
 
2.9%
신한카드 242
 
2.4%
Other values (21) 1602
16.0%

일련번호
Real number (ℝ)

Distinct8440
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57620207
Minimum1
Maximum3.029007 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:45.652911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile191
Q11886
median576826
Q34851625
95-th percentile9201975
Maximum3.029007 × 109
Range3.029007 × 109
Interquartile range (IQR)4849739

Descriptive statistics

Standard deviation3.3433853 × 108
Coefficient of variation (CV)5.8024527
Kurtosis52.693949
Mean57620207
Median Absolute Deviation (MAD)576452
Skewness7.0332573
Sum5.7620207 × 1011
Variance1.1178225 × 1017
MonotonicityNot monotonic
2023-12-11T01:39:45.816098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 8
 
0.1%
45 7
 
0.1%
1026 6
 
0.1%
72 6
 
0.1%
117 6
 
0.1%
52 6
 
0.1%
1251 6
 
0.1%
372 5
 
0.1%
1236 5
 
0.1%
27 5
 
0.1%
Other values (8430) 9940
99.4%
ValueCountFrequency (%)
1 4
< 0.1%
2 8
0.1%
3 1
 
< 0.1%
4 4
< 0.1%
5 4
< 0.1%
6 5
0.1%
7 2
 
< 0.1%
8 5
0.1%
9 3
 
< 0.1%
10 4
< 0.1%
ValueCountFrequency (%)
3029007000 1
< 0.1%
3029005900 1
< 0.1%
3029004200 1
< 0.1%
3029000600 1
< 0.1%
3029000300 1
< 0.1%
3028998700 1
< 0.1%
3028998500 1
< 0.1%
3028998100 1
< 0.1%
3028997800 1
< 0.1%
3028995200 1
< 0.1%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.1625
Minimum0
Maximum710
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:46.237946image/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 deviation140.8707
Coefficient of variation (CV)0.40931449
Kurtosis0.11801524
Mean344.1625
Median Absolute Deviation (MAD)120
Skewness0.57769432
Sum3441625
Variance19844.553
MonotonicityNot monotonic
2023-12-11T01:39:46.378915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
230 1193
11.9%
380 874
 
8.7%
290 820
 
8.2%
410 740
 
7.4%
260 734
 
7.3%
350 722
 
7.2%
530 624
 
6.2%
500 548
 
5.5%
440 542
 
5.4%
320 541
 
5.4%
Other values (11) 2662
26.6%
ValueCountFrequency (%)
0 5
 
0.1%
110 331
 
3.3%
140 454
 
4.5%
170 444
 
4.4%
200 453
 
4.5%
201 3
 
< 0.1%
203 1
 
< 0.1%
204 1
 
< 0.1%
205 1
 
< 0.1%
230 1193
11.9%
ValueCountFrequency (%)
710 464
4.6%
530 624
6.2%
500 548
5.5%
470 505
5.1%
440 542
5.4%
410 740
7.4%
380 874
8.7%
350 722
7.2%
320 541
5.4%
290 820
8.2%

구명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
1193 
사하구
874 
남구
820 
금정구
740 
동래구
734 
Other values (16)
5639 

Length

Max length4
Median length3
Mean length2.9324
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row사하구
2nd row서구
3rd row남구
4th row강서구
5th row부산진구

Common Values

ValueCountFrequency (%)
부산진구 1193
11.9%
사하구 874
 
8.7%
남구 820
 
8.2%
금정구 740
 
7.4%
동래구 734
 
7.3%
해운대구 722
 
7.2%
사상구 624
 
6.2%
수영구 548
 
5.5%
강서구 542
 
5.4%
북구 541
 
5.4%
Other values (11) 2662
26.6%

Length

2023-12-11T01:39:46.534286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 1193
11.9%
사하구 874
 
8.7%
남구 820
 
8.2%
금정구 740
 
7.4%
동래구 734
 
7.3%
해운대구 722
 
7.2%
사상구 624
 
6.2%
수영구 548
 
5.5%
강서구 542
 
5.4%
북구 541
 
5.4%
Other values (11) 2662
26.6%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.6945
Minimum101
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:46.666479image/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 deviation25.388466
Coefficient of variation (CV)0.086445154
Kurtosis1.7311084
Mean293.6945
Median Absolute Deviation (MAD)3
Skewness-1.6184077
Sum2936945
Variance644.57423
MonotonicityNot monotonic
2023-12-11T01:39:46.790796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
244 1979
19.8%
306 1368
13.7%
304 1193
11.9%
307 1165
11.7%
309 874
8.7%
301 775
 
7.8%
308 722
 
7.2%
311 542
 
5.4%
312 464
 
4.6%
302 454
 
4.5%
Other values (6) 464
 
4.6%
ValueCountFrequency (%)
101 5
 
0.1%
201 3
 
< 0.1%
203 1
 
< 0.1%
204 1
 
< 0.1%
205 1
 
< 0.1%
244 1979
19.8%
301 775
 
7.8%
302 454
 
4.5%
303 453
 
4.5%
304 1193
11.9%
ValueCountFrequency (%)
312 464
 
4.6%
311 542
 
5.4%
309 874
8.7%
308 722
7.2%
307 1165
11.7%
306 1368
13.7%
304 1193
11.9%
303 453
 
4.5%
302 454
 
4.5%
301 775
7.8%

사업소명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
1979 
남부 사업소
1368 
부산진 사업소
1193 
북부 사업소
1165 
사하 사업소
874 
Other values (11)
3421 

Length

Max length9
Median length9
Mean length8.332
Min length5

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row사하 사업소
2nd row서부 사업소
3rd row남부 사업소
4th row강서 사업소
5th row부산진 사업소

Common Values

ValueCountFrequency (%)
동래통합사업소 1979
19.8%
남부 사업소 1368
13.7%
부산진 사업소 1193
11.9%
북부 사업소 1165
11.7%
사하 사업소 874
8.7%
중동부 사업소 775
 
7.8%
해운대 사업소 722
 
7.2%
강서 사업소 542
 
5.4%
기장 사업소 464
 
4.6%
서부 사업소 454
 
4.5%
Other values (6) 464
 
4.6%

Length

2023-12-11T01:39:46.943409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업소 8010
44.5%
동래통합사업소 1979
 
11.0%
남부 1368
 
7.6%
부산진 1193
 
6.6%
북부 1165
 
6.5%
사하 874
 
4.9%
중동부 775
 
4.3%
해운대 722
 
4.0%
강서 542
 
3.0%
기장 464
 
2.6%
Other values (7) 918
 
5.1%

Interactions

2023-12-11T01:39:39.848129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:32.056887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:33.564947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:34.817263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:35.902325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.033664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.902136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.816683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:39.965290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:32.213994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:33.727457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:34.971306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:36.037984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.126223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.010770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.952859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:40.079764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:32.355233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:33.894821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:35.099303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:36.180324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.246165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.141675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:39.093027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:40.201407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:32.510708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:34.023992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:35.216278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:36.341772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.365771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.243087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:39.220226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:40.320857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:32.685062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:34.200605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:35.353169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:36.501343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.483048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.346139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:39.334836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:40.908368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:32.833651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:34.366075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:35.490678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:36.659274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.594470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.440501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:39.445475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:41.052784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:33.267286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:34.507499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:35.606851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:36.808746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.695934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.546887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:39.563944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:41.199802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:33.417887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:34.689162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:35.757656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:36.945777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:37.814749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:38.679393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:39.710276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:39:47.045364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액납기내일자납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
연번1.0000.6790.3300.0340.0470.0510.0000.3390.4900.1880.1350.0400.0740.0000.027
수납일자0.6791.0000.6960.0830.1520.0000.0000.5950.8350.4900.3870.1240.2010.1010.192
수납방법0.3300.6961.0000.0000.0000.0390.0000.5130.4430.9010.2370.3930.2440.5040.462
수납금액합계0.0340.0830.0001.0000.9830.6820.6460.8060.0000.0000.2260.0000.0000.0000.000
상수도수납금액0.0470.1520.0000.9831.0000.3370.3650.8650.0000.0000.2910.0000.0000.0000.000
하수도수납금액0.0510.0000.0390.6820.3371.0000.8500.0000.0000.0640.0000.0000.0000.0000.000
물이용수납금액0.0000.0000.0000.6460.3650.8501.0000.0000.0000.0000.0000.0000.0000.0000.000
납기내일자0.3390.5950.5130.8060.8650.0000.0001.0000.9920.0000.8200.6220.5920.7840.731
납기내후수납구분0.4900.8350.4430.0000.0000.0000.0000.9921.0000.0780.2090.0470.0620.0000.039
은행명0.1880.4900.9010.0000.0000.0640.0000.0000.0781.0000.1300.2160.2690.0090.251
일련번호0.1350.3870.2370.2260.2910.0000.0000.8200.2090.1301.0000.1070.2730.3060.295
구코드0.0400.1240.3930.0000.0000.0000.0000.6220.0470.2160.1071.0001.0000.7980.964
구명0.0740.2010.2440.0000.0000.0000.0000.5920.0620.2690.2731.0001.0001.0001.000
사업소코드0.0000.1010.5040.0000.0000.0000.0000.7840.0000.0090.3060.7981.0001.0001.000
사업소명0.0270.1920.4620.0000.0000.0000.0000.7310.0390.2510.2950.9641.0001.0001.000
2023-12-11T01:39:47.195815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납기내후수납구분은행명수납방법구명사업소명
납기내후수납구분1.0000.0660.3480.0490.031
은행명0.0661.0000.5170.0740.074
수납방법0.3480.5171.0000.0780.129
구명0.0490.0740.0781.0001.000
사업소명0.0310.0740.1291.0001.000
2023-12-11T01:39:47.339169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납금액합계상수도수납금액하수도수납금액물이용수납금액일련번호구코드사업소코드수납방법납기내후수납구분은행명구명사업소명
연번1.0000.0150.0180.0050.0090.0860.0270.0210.1360.3770.0670.0230.010
수납금액합계0.0151.0000.9810.9210.933-0.0070.039-0.0050.0000.0000.0000.0000.000
상수도수납금액0.0180.9811.0000.8780.950-0.0060.0460.0110.0000.0000.0000.0000.000
하수도수납금액0.0050.9210.8781.0000.899-0.048-0.013-0.0700.0150.0000.0220.0000.000
물이용수납금액0.0090.9330.9500.8991.000-0.0490.031-0.0080.0000.0000.0000.0000.000
일련번호0.086-0.007-0.006-0.048-0.0491.0000.2550.1470.1130.1390.0680.1320.142
구코드0.0270.0390.046-0.0130.0310.2551.0000.4480.1730.0470.0820.9990.854
사업소코드0.021-0.0050.011-0.070-0.0080.1470.4481.0000.2570.0000.0050.9990.999
수납방법0.1360.0000.0000.0150.0000.1130.1730.2571.0000.3480.5170.0780.129
납기내후수납구분0.3770.0000.0000.0000.0000.1390.0470.0000.3481.0000.0660.0490.031
은행명0.0670.0000.0000.0220.0000.0680.0820.0050.5170.0661.0000.0740.074
구명0.0230.0000.0000.0000.0000.1320.9990.9990.0780.0490.0741.0001.000
사업소명0.0100.0000.0000.0000.0000.1420.8540.9990.1290.0310.0741.0001.000

Missing values

2023-12-11T01:39:41.382724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:39:41.732856image/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
6066860669당월정상수납2022-05-31카드(간단E)697403912022940768002022-05-31<NA><NA>납기내롯데카드1944380사하구309사하 사업소
6806368064당월정상수납2022-05-31카드(자동납부)556603054019440568002022-05-31<NA><NA>납기내BC카드579487140서구302서부 사업소
87298730당월정상수납2022-05-25창구수납(간단E)240024000002022-05-31<NA><NA>납기내우체국3901290남구306남부 사업소
4345843459당월정상수납2022-05-25인터넷뱅킹(간단E)6717604067402169204810002022-05-31<NA><NA>납기내기업은행4058440강서구311강서 사업소
7285972860당월정상수납2022-05-31카드(자동납부)16620100204940166002022-05-31<NA><NA>납기내롯데카드580639230부산진구304부산진 사업소
6582665827당월정상수납2022-05-31가상계좌2831201454001185801914002022-05-31<NA><NA>납기내국민은행1168900200영도구303영도 사업소
4609546096당월정상수납2022-05-26카드(사이버)671003768022040738002022-05-31<NA><NA>납기내현대카드165600110중구301중동부 사업소
4048840489당월정상수납2022-05-30CD/ATM(간단E)2387801149001080501583002022-05-31<NA><NA>납기내농협은행2600230부산진구304부산진 사업소
5402754028당월정상수납2022-05-24창구수납(간단E)30120175209440316002022-05-31<NA><NA>납기내우체국1932500수영구306남부 사업소
26852686당월정상수납2022-05-30CD/ATM(간단E)4210202013001933402638002022-05-31<NA><NA>납기내지역농축협2776440강서구311강서 사업소
연번구분수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액기타금액납기내일자체납시작년월체납종료년월납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
4569345694당월정상수납2022-05-31카드(자동납부)218580111280941601314002022-05-31<NA><NA>납기내BC카드575627470연제구244동래통합사업소
5873058731당월정상수납2022-05-18가상계좌1651011630428060002022-05-31<NA><NA>납기내국민은행4976000350해운대구308해운대 사업소
487488당월정상수납2022-05-28가상계좌314401680012840180002022-05-31<NA><NA>납기내농협은행6021100380사하구309사하 사업소
1094310944당월정상수납2022-05-19가상계좌688203822023220738002022-05-31<NA><NA>납기내농협은행8697400530사상구307북부 사업소
5756557566당월정상수납2022-05-29카드(사이버)11560063900389001280002022-05-31<NA><NA>납기내BC카드3354000260동래구244동래통합사업소
4726647267당월정상수납2022-05-26창구수납(간단E)17078091620612201794002022-05-31<NA><NA>납기내새마을금고중앙회2025530사상구307북부 사업소
6788767888당월정상수납2022-05-18가상계좌240024000002022-05-31<NA><NA>납기내우리은행4916300350해운대구308해운대 사업소
1831418315당월정상수납2022-05-19가상계좌523902956017100573002022-05-31<NA><NA>납기내국민은행1251000200영도구303영도 사업소
6522665227당월정상수납2022-05-31카드(자동납부)23230145906500214002022-05-31<NA><NA>납기내현대카드578786200영도구303영도 사업소
16651666당월정상수납2022-05-24가상계좌534002900020780362002022-05-31<NA><NA>납기내부산은행460800140서구302서부 사업소