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_20221107
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 수납일자 and 1 other fieldsHigh correlation
납기내일자 is highly overall correlated with 일련번호 and 1 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
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
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 구명 and 1 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
납기내일자 is highly imbalanced (88.5%)Imbalance
납기내후수납구분 is highly imbalanced (54.3%)Imbalance
체납시작년월 has 10000 (100.0%) missing valuesMissing
체납종료년월 has 10000 (100.0%) missing valuesMissing
수납금액합계 is highly skewed (γ1 = 58.2729838)Skewed
상수도수납금액 is highly skewed (γ1 = 67.58694303)Skewed
하수도수납금액 is highly skewed (γ1 = 46.31486481)Skewed
물이용수납금액 is highly skewed (γ1 = 74.99926973)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 1310 (13.1%) zerosZeros
물이용수납금액 has 875 (8.8%) zerosZeros

Reproduction

Analysis started2023-12-10 16:39:05.495277
Analysis finished2023-12-10 16:39:17.091486
Duration11.6 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%
Mean37198.686
Minimum1
Maximum74750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:17.163554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3655.35
Q118462.75
median37615
Q355667.75
95-th percentile70988.75
Maximum74750
Range74749
Interquartile range (IQR)37205

Descriptive statistics

Standard deviation21522.296
Coefficient of variation (CV)0.57857678
Kurtosis-1.1938432
Mean37198.686
Median Absolute Deviation (MAD)18607.5
Skewness-0.0016887637
Sum3.7198686 × 108
Variance4.6320922 × 108
MonotonicityNot monotonic
2023-12-11T01:39:17.332687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5427 1
 
< 0.1%
46421 1
 
< 0.1%
40992 1
 
< 0.1%
22256 1
 
< 0.1%
9026 1
 
< 0.1%
43767 1
 
< 0.1%
69606 1
 
< 0.1%
25949 1
 
< 0.1%
38344 1
 
< 0.1%
19428 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
9 1
< 0.1%
16 1
< 0.1%
23 1
< 0.1%
29 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
42 1
< 0.1%
72 1
< 0.1%
ValueCountFrequency (%)
74750 1
< 0.1%
74746 1
< 0.1%
74740 1
< 0.1%
74739 1
< 0.1%
74737 1
< 0.1%
74729 1
< 0.1%
74726 1
< 0.1%
74721 1
< 0.1%
74718 1
< 0.1%
74711 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

2023-12-11T01:39:17.477086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:17.585888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당월정상수납 10000
100.0%

수납일자
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-08-31
2069 
2022-08-30
1076 
2022-08-25
1002 
2022-08-29
933 
2022-08-23
755 
Other values (26)
4165 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-25
2nd row2022-08-26
3rd row2022-08-28
4th row2022-08-22
5th row2022-08-31

Common Values

ValueCountFrequency (%)
2022-08-31 2069
20.7%
2022-08-30 1076
10.8%
2022-08-25 1002
10.0%
2022-08-29 933
9.3%
2022-08-23 755
 
7.5%
2022-08-26 745
 
7.4%
2022-08-24 674
 
6.7%
2022-08-22 660
 
6.6%
2022-08-19 427
 
4.3%
2022-08-08 224
 
2.2%
Other values (21) 1435
14.3%

Length

2023-12-11T01:39:17.999309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-08-31 2069
20.7%
2022-08-30 1076
10.8%
2022-08-25 1002
10.0%
2022-08-29 933
9.3%
2022-08-23 755
 
7.5%
2022-08-26 745
 
7.4%
2022-08-24 674
 
6.7%
2022-08-22 660
 
6.6%
2022-08-19 427
 
4.3%
2022-08-08 224
 
2.2%
Other values (21) 1435
14.3%

수납방법
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가상계좌
4607 
창구수납(간단E)
1658 
CD/ATM(간단E)
1452 
카드(자동납부)
844 
인터넷뱅킹(간단E)
 
410
Other values (9)
1029 

Length

Max length11
Median length10
Mean length6.7792
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 4607
46.1%
창구수납(간단E) 1658
 
16.6%
CD/ATM(간단E) 1452
 
14.5%
카드(자동납부) 844
 
8.4%
인터넷뱅킹(간단E) 410
 
4.1%
카드(간단E) 360
 
3.6%
카드(ARS) 213
 
2.1%
통장(자동납부) 158
 
1.6%
카드(사이버) 136
 
1.4%
자동화기기(간단E) 62
 
0.6%
Other values (4) 100
 
1.0%

Length

2023-12-11T01:39:18.141578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가상계좌 4607
46.1%
창구수납(간단e 1658
 
16.6%
cd/atm(간단e 1452
 
14.5%
카드(자동납부 844
 
8.4%
인터넷뱅킹(간단e 410
 
4.1%
카드(간단e 360
 
3.6%
카드(ars 213
 
2.1%
통장(자동납부 158
 
1.6%
카드(사이버 136
 
1.4%
자동화기기(간단e 62
 
0.6%
Other values (4) 100
 
1.0%

수납금액합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4402
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299158.92
Minimum70
Maximum3.257835 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:18.300278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile2400
Q121022.5
median52580
Q3122622.5
95-th percentile631301
Maximum3.257835 × 108
Range3.2578343 × 108
Interquartile range (IQR)101600

Descriptive statistics

Standard deviation4663087.3
Coefficient of variation (CV)15.587325
Kurtosis3766.0009
Mean299158.92
Median Absolute Deviation (MAD)39360
Skewness58.272984
Sum2.9915892 × 109
Variance2.1744383 × 1013
MonotonicityNot monotonic
2023-12-11T01:39:18.512866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 516
 
5.2%
26160 64
 
0.6%
28800 63
 
0.6%
22200 62
 
0.6%
15600 60
 
0.6%
24840 59
 
0.6%
14260 57
 
0.6%
20880 56
 
0.6%
27480 52
 
0.5%
23520 52
 
0.5%
Other values (4392) 8959
89.6%
ValueCountFrequency (%)
70 2
< 0.1%
140 1
< 0.1%
200 1
< 0.1%
230 1
< 0.1%
240 1
< 0.1%
280 1
< 0.1%
310 2
< 0.1%
420 2
< 0.1%
540 2
< 0.1%
560 1
< 0.1%
ValueCountFrequency (%)
325783500 1
< 0.1%
283238380 1
< 0.1%
111790640 1
< 0.1%
56099600 1
< 0.1%
47500880 1
< 0.1%
36000000 1
< 0.1%
31670510 1
< 0.1%
27486000 1
< 0.1%
27176100 1
< 0.1%
27031140 1
< 0.1%

상수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3339
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181863.96
Minimum0
Maximum3.257835 × 108
Zeros47
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:18.713822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2400
Q112480
median29760
Q366300
95-th percentile303195
Maximum3.257835 × 108
Range3.257835 × 108
Interquartile range (IQR)53820

Descriptive statistics

Standard deviation4230561.6
Coefficient of variation (CV)23.262232
Kurtosis4815.4801
Mean181863.96
Median Absolute Deviation (MAD)21300
Skewness67.586943
Sum1.8186396 × 109
Variance1.7897652 × 1013
MonotonicityNot monotonic
2023-12-11T01:39:18.968921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 526
 
5.3%
9600 121
 
1.2%
16800 99
 
1.0%
13200 94
 
0.9%
6000 89
 
0.9%
12480 76
 
0.8%
15360 71
 
0.7%
16080 66
 
0.7%
8880 66
 
0.7%
14640 66
 
0.7%
Other values (3329) 8726
87.3%
ValueCountFrequency (%)
0 47
0.5%
70 2
 
< 0.1%
140 1
 
< 0.1%
200 1
 
< 0.1%
230 1
 
< 0.1%
240 1
 
< 0.1%
280 1
 
< 0.1%
310 2
 
< 0.1%
420 2
 
< 0.1%
540 2
 
< 0.1%
ValueCountFrequency (%)
325783500 1
< 0.1%
254417330 1
< 0.1%
43384670 1
< 0.1%
36000000 1
< 0.1%
27471000 1
< 0.1%
24352930 1
< 0.1%
21992510 1
< 0.1%
20845780 1
< 0.1%
18461800 1
< 0.1%
15869910 1
< 0.1%

하수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2554
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97579.149
Minimum0
Maximum63490050
Zeros1310
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:19.198554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15340
median17100
Q342460
95-th percentile251684
Maximum63490050
Range63490050
Interquartile range (IQR)37120

Descriptive statistics

Standard deviation1004266.3
Coefficient of variation (CV)10.291813
Kurtosis2626.0426
Mean97579.149
Median Absolute Deviation (MAD)14780
Skewness46.314865
Sum9.7579149 × 108
Variance1.0085509 × 1012
MonotonicityNot monotonic
2023-12-11T01:39:19.418193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1310
 
13.1%
3600 90
 
0.9%
6740 88
 
0.9%
7200 87
 
0.9%
4500 86
 
0.9%
9000 83
 
0.8%
4940 81
 
0.8%
8100 80
 
0.8%
6300 80
 
0.8%
8540 80
 
0.8%
Other values (2544) 7935
79.3%
ValueCountFrequency (%)
0 1310
13.1%
20 2
 
< 0.1%
40 2
 
< 0.1%
90 1
 
< 0.1%
100 1
 
< 0.1%
180 1
 
< 0.1%
320 1
 
< 0.1%
440 65
 
0.7%
450 7
 
0.1%
460 1
 
< 0.1%
ValueCountFrequency (%)
63490050 1
< 0.1%
56099600 1
< 0.1%
26949000 1
< 0.1%
14886150 1
< 0.1%
14812200 1
< 0.1%
14084840 1
< 0.1%
13095440 1
< 0.1%
9507600 1
< 0.1%
9430800 1
< 0.1%
8888100 1
< 0.1%

물이용수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1542
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19715.813
Minimum0
Maximum28821050
Zeros875
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:19.595502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11800
median4840
Q310840
95-th percentile38141
Maximum28821050
Range28821050
Interquartile range (IQR)9040

Descriptive statistics

Standard deviation323177.6
Coefficient of variation (CV)16.391797
Kurtosis6426.6693
Mean19715.813
Median Absolute Deviation (MAD)3800
Skewness74.99927
Sum1.9715813 × 108
Variance1.0444376 × 1011
MonotonicityNot monotonic
2023-12-11T01:39:19.818713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 875
 
8.8%
900 134
 
1.3%
140 133
 
1.3%
600 126
 
1.3%
1200 124
 
1.2%
300 124
 
1.2%
1500 123
 
1.2%
1040 120
 
1.2%
2560 111
 
1.1%
1340 109
 
1.1%
Other values (1532) 8021
80.2%
ValueCountFrequency (%)
0 875
8.8%
10 1
 
< 0.1%
20 6
 
0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
60 1
 
< 0.1%
140 133
 
1.3%
150 50
 
0.5%
160 19
 
0.2%
170 1
 
< 0.1%
ValueCountFrequency (%)
28821050 1
< 0.1%
10438170 1
< 0.1%
4915920 1
< 0.1%
3521210 1
< 0.1%
3273860 1
< 0.1%
2480200 1
< 0.1%
2361980 1
< 0.1%
2209830 1
< 0.1%
2090080 1
< 0.1%
2084600 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

2023-12-11T01:39:20.043035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:20.169478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

납기내일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-08-31
8919 
2022-07-31
960 
2022-09-30
 
69
2022-09-09
 
6
2022-08-26
 
3
Other values (29)
 
43

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row2022-08-31
2nd row2022-08-31
3rd row2022-08-31
4th row2022-08-31
5th row2022-08-31

Common Values

ValueCountFrequency (%)
2022-08-31 8919
89.2%
2022-07-31 960
 
9.6%
2022-09-30 69
 
0.7%
2022-09-09 6
 
0.1%
2022-08-26 3
 
< 0.1%
2022-09-08 3
 
< 0.1%
2022-09-16 3
 
< 0.1%
2022-08-22 2
 
< 0.1%
2022-09-19 2
 
< 0.1%
2022-08-30 2
 
< 0.1%
Other values (24) 31
 
0.3%

Length

2023-12-11T01:39:20.309643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-08-31 8919
89.2%
2022-07-31 960
 
9.6%
2022-09-30 69
 
0.7%
2022-09-09 6
 
0.1%
2022-08-26 3
 
< 0.1%
2022-09-08 3
 
< 0.1%
2022-09-16 3
 
< 0.1%
2022-09-02 2
 
< 0.1%
2022-09-04 2
 
< 0.1%
2022-09-29 2
 
< 0.1%
Other values (24) 31
 
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  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
납기내
9038 
납기후
962 

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 9038
90.4%
납기후 962
 
9.6%

Length

2023-12-11T01:39:20.447306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:20.565200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
납기내 9038
90.4%
납기후 962
 
9.6%

은행명
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산은행
2956 
농협은행
1288 
국민은행
1073 
새마을금고중앙회
957 
우체국
482 
Other values (29)
3244 

Length

Max length8
Median length4
Mean length4.5348
Min length2

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row부산은행
2nd row롯데카드
3rd row국민은행
4th row신한은행
5th rowBC카드

Common Values

ValueCountFrequency (%)
부산은행 2956
29.6%
농협은행 1288
12.9%
국민은행 1073
 
10.7%
새마을금고중앙회 957
 
9.6%
우체국 482
 
4.8%
우리은행 401
 
4.0%
지역농축협 358
 
3.6%
BC카드 281
 
2.8%
신한은행 270
 
2.7%
KEB하나은행 245
 
2.5%
Other values (24) 1689
16.9%

Length

2023-12-11T01:39:20.703296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산은행 2956
29.6%
농협은행 1288
12.9%
국민은행 1073
 
10.7%
새마을금고중앙회 957
 
9.6%
우체국 482
 
4.8%
우리은행 401
 
4.0%
지역농축협 358
 
3.6%
bc카드 281
 
2.8%
신한은행 270
 
2.7%
keb하나은행 245
 
2.5%
Other values (24) 1689
16.9%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8542
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58926103
Minimum1
Maximum3.0292195 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:20.902541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile204.95
Q12052
median612353.5
Q35266925
95-th percentile9152460
Maximum3.0292195 × 109
Range3.0292195 × 109
Interquartile range (IQR)5264873

Descriptive statistics

Standard deviation3.354511 × 108
Coefficient of variation (CV)5.6927419
Kurtosis51.521771
Mean58926103
Median Absolute Deviation (MAD)612032
Skewness6.9421085
Sum5.8926103 × 1011
Variance1.1252744 × 1017
MonotonicityNot monotonic
2023-12-11T01:39:21.109872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
0.1%
67 8
 
0.1%
63 7
 
0.1%
104 7
 
0.1%
48 7
 
0.1%
51 7
 
0.1%
102 6
 
0.1%
45 6
 
0.1%
8 6
 
0.1%
66 6
 
0.1%
Other values (8532) 9930
99.3%
ValueCountFrequency (%)
1 10
0.1%
2 2
 
< 0.1%
3 5
0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 6
0.1%
9 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
3029219500 1
< 0.1%
3029218900 1
< 0.1%
3029218000 1
< 0.1%
3029217300 1
< 0.1%
3029215500 1
< 0.1%
3029215400 1
< 0.1%
3029213600 1
< 0.1%
3029212800 1
< 0.1%
3029212200 1
< 0.1%
3029211200 1
< 0.1%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.3619
Minimum0
Maximum710
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:21.294978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140
Q1230
median350
Q3440
95-th percentile710
Maximum710
Range710
Interquartile range (IQR)210

Descriptive statistics

Standard deviation142.19927
Coefficient of variation (CV)0.40015341
Kurtosis-0.039618342
Mean355.3619
Median Absolute Deviation (MAD)120
Skewness0.46064388
Sum3553619
Variance20220.634
MonotonicityNot monotonic
2023-12-11T01:39:21.448196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
230 1002
 
10.0%
410 865
 
8.6%
350 817
 
8.2%
260 802
 
8.0%
380 752
 
7.5%
440 675
 
6.8%
290 660
 
6.6%
500 644
 
6.4%
530 639
 
6.4%
470 637
 
6.4%
Other values (10) 2507
25.1%
ValueCountFrequency (%)
0 6
 
0.1%
110 271
 
2.7%
140 439
4.4%
170 436
4.4%
200 452
4.5%
201 4
 
< 0.1%
202 1
 
< 0.1%
203 1
 
< 0.1%
230 1002
10.0%
260 802
8.0%
ValueCountFrequency (%)
710 502
5.0%
530 639
6.4%
500 644
6.4%
470 637
6.4%
440 675
6.8%
410 865
8.6%
380 752
7.5%
350 817
8.2%
320 395
4.0%
290 660
6.6%

구명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
1002 
금정구
865 
해운대구
817 
동래구
802 
사하구
752 
Other values (15)
5762 

Length

Max length4
Median length3
Mean length2.9618
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row영도구
2nd row금정구
3rd row해운대구
4th row동래구
5th row사상구

Common Values

ValueCountFrequency (%)
부산진구 1002
 
10.0%
금정구 865
 
8.6%
해운대구 817
 
8.2%
동래구 802
 
8.0%
사하구 752
 
7.5%
강서구 675
 
6.8%
남구 660
 
6.6%
수영구 644
 
6.4%
사상구 639
 
6.4%
연제구 637
 
6.4%
Other values (10) 2507
25.1%

Length

2023-12-11T01:39:21.653197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 1002
 
10.0%
금정구 865
 
8.6%
해운대구 817
 
8.2%
동래구 802
 
8.0%
사하구 752
 
7.5%
강서구 675
 
6.8%
남구 660
 
6.6%
수영구 644
 
6.4%
사상구 639
 
6.4%
연제구 637
 
6.4%
Other values (10) 2507
25.1%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.7955
Minimum101
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:39:21.840614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile244
Q1301
median306
Q3308
95-th percentile312
Maximum312
Range211
Interquartile range (IQR)7

Descriptive statistics

Standard deviation26.899978
Coefficient of variation (CV)0.092187777
Kurtosis0.97789413
Mean291.7955
Median Absolute Deviation (MAD)3
Skewness-1.3904884
Sum2917955
Variance723.60884
MonotonicityNot monotonic
2023-12-11T01:39:21.989640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
244 2304
23.0%
306 1304
13.0%
307 1034
10.3%
304 1002
10.0%
308 817
 
8.2%
309 752
 
7.5%
301 707
 
7.1%
311 675
 
6.8%
312 502
 
5.0%
303 452
 
4.5%
Other values (5) 451
 
4.5%
ValueCountFrequency (%)
101 6
 
0.1%
201 4
 
< 0.1%
202 1
 
< 0.1%
203 1
 
< 0.1%
244 2304
23.0%
301 707
 
7.1%
302 439
 
4.4%
303 452
 
4.5%
304 1002
10.0%
306 1304
13.0%
ValueCountFrequency (%)
312 502
 
5.0%
311 675
6.8%
309 752
7.5%
308 817
8.2%
307 1034
10.3%
306 1304
13.0%
304 1002
10.0%
303 452
 
4.5%
302 439
 
4.4%
301 707
7.1%

사업소명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
2304 
남부 사업소
1304 
북부 사업소
1034 
부산진 사업소
1002 
해운대 사업소
817 
Other values (10)
3539 

Length

Max length9
Median length9
Mean length8.2828
Min length5

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row영도 사업소
2nd row동래통합사업소
3rd row해운대 사업소
4th row동래통합사업소
5th row북부 사업소

Common Values

ValueCountFrequency (%)
동래통합사업소 2304
23.0%
남부 사업소 1304
13.0%
북부 사업소 1034
10.3%
부산진 사업소 1002
10.0%
해운대 사업소 817
 
8.2%
사하 사업소 752
 
7.5%
중동부 사업소 707
 
7.1%
강서 사업소 675
 
6.8%
기장 사업소 502
 
5.0%
영도 사업소 452
 
4.5%
Other values (5) 451
 
4.5%

Length

2023-12-11T01:39:22.151646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업소 7684
43.5%
동래통합사업소 2304
 
13.0%
남부 1304
 
7.4%
북부 1034
 
5.8%
부산진 1002
 
5.7%
해운대 817
 
4.6%
사하 752
 
4.3%
중동부 707
 
4.0%
강서 675
 
3.8%
기장 502
 
2.8%
Other values (6) 903
 
5.1%

Interactions

2023-12-11T01:39:15.469156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:08.657912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.464106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:10.324229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.443510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.357787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:13.395475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.328114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:15.643136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:08.765045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.562744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:10.466127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.579318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.476013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:13.537066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.424094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:15.858214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:08.869199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.712870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:10.576367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.685643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.604046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:13.644948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.551415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:16.069920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:08.973258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.821345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:10.675059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.811454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.699594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:13.774227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.659924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:16.198188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.067825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.943108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.029304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.916855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.851208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:13.889405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.758960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:16.296231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.163509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:10.042120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.148023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.046595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.993226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.004104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.910484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:16.405347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.259604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:10.136325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.247242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.152086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:13.166633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.109067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:15.114231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:16.512558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:09.365644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:10.233771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:11.345497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:12.265150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:13.286489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:14.226342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:15.315258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:39:22.296495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액납기내일자납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
연번1.0000.6620.2280.0000.0000.0090.0000.1890.2440.1500.0680.0330.0630.0370.062
수납일자0.6621.0000.7140.0000.0000.0730.0000.6700.8350.3940.4230.1030.2390.2040.239
수납방법0.2280.7141.0000.0000.0000.0000.0000.3350.5290.9090.2010.1360.1870.0230.156
수납금액합계0.0000.0000.0001.0000.9270.7670.7260.3940.0000.0000.1240.3400.2950.3260.521
상수도수납금액0.0000.0000.0000.9271.0000.5740.9190.4230.0000.0000.2030.3570.4230.6270.505
하수도수납금액0.0090.0730.0000.7670.5741.0000.6420.0000.0000.0000.0990.0100.0000.0000.000
물이용수납금액0.0000.0000.0000.7260.9190.6421.0000.0000.0000.0000.0000.0000.0000.0000.000
납기내일자0.1890.6700.3350.3940.4230.0000.0001.0001.0000.0000.8050.5830.5310.7110.686
납기내후수납구분0.2440.8350.5290.0000.0000.0000.0001.0001.0000.1000.2430.0490.0680.0430.042
은행명0.1500.3940.9090.0000.0000.0000.0000.0000.1001.0000.1280.2430.2940.0590.265
일련번호0.0680.4230.2010.1240.2030.0990.0000.8050.2430.1281.0000.1350.1970.2870.232
구코드0.0330.1030.1360.3400.3570.0100.0000.5830.0490.2430.1351.0001.0000.8050.969
구명0.0630.2390.1870.2950.4230.0000.0000.5310.0680.2940.1971.0001.0001.0001.000
사업소코드0.0370.2040.0230.3260.6270.0000.0000.7110.0430.0590.2870.8051.0001.0001.000
사업소명0.0620.2390.1560.5210.5050.0000.0000.6860.0420.2650.2320.9691.0001.0001.000
2023-12-11T01:39:22.541934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납기내후수납구분은행명수납방법납기내일자수납일자구명사업소명
납기내후수납구분1.0000.0790.4160.9980.7450.0610.039
은행명0.0791.0000.5590.0000.0950.0820.080
수납방법0.4160.5591.0000.1070.3020.0630.055
납기내일자0.9980.0000.1071.0000.1990.1670.270
수납일자0.7450.0950.3020.1991.0000.0660.072
구명0.0610.0820.0630.1670.0661.0001.000
사업소명0.0390.0800.0550.2700.0721.0001.000
2023-12-11T01:39:22.704253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납금액합계상수도수납금액하수도수납금액물이용수납금액일련번호구코드사업소코드수납일자수납방법납기내일자납기내후수납구분은행명구명사업소명
연번1.000-0.003-0.002-0.005-0.004-0.055-0.0150.0190.2990.0940.0670.1870.0530.0230.023
수납금액합계-0.0031.0000.9830.9140.939-0.0120.0610.0280.0000.0000.1960.0000.0000.1650.249
상수도수납금액-0.0020.9831.0000.8680.953-0.0080.0770.0510.0000.0000.2290.0000.0000.2490.310
하수도수납금액-0.0050.9140.8681.0000.887-0.051-0.007-0.0560.0320.0000.0000.0000.0000.0000.000
물이용수납금액-0.0040.9390.9530.8871.000-0.0520.0630.0290.0000.0000.0000.0000.0000.0000.000
일련번호-0.055-0.012-0.008-0.051-0.0521.0000.2850.1340.2310.1150.5570.1620.0660.1080.133
구코드-0.0150.0610.077-0.0070.0630.2851.0000.4000.0390.0570.2580.0490.0920.9990.855
사업소코드0.0190.0280.051-0.0560.0290.1340.4001.0000.1070.0130.4490.0280.0300.9990.999
수납일자0.2990.0000.0000.0320.0000.2310.0390.1071.0000.3020.1990.7450.0950.0660.072
수납방법0.0940.0000.0000.0000.0000.1150.0570.0130.3021.0000.1070.4160.5590.0630.055
납기내일자0.0670.1960.2290.0000.0000.5570.2580.4490.1990.1071.0000.9980.0000.1670.270
납기내후수납구분0.1870.0000.0000.0000.0000.1620.0490.0280.7450.4160.9981.0000.0790.0610.039
은행명0.0530.0000.0000.0000.0000.0660.0920.0300.0950.5590.0000.0791.0000.0820.080
구명0.0230.1650.2490.0000.0000.1080.9990.9990.0660.0630.1670.0610.0821.0001.000
사업소명0.0230.2490.3100.0000.0000.1330.8550.9990.0720.0550.2700.0390.0801.0001.000

Missing values

2023-12-11T01:39:16.675886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:39:16.980260image/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
54265427당월정상수납2022-08-25CD/ATM(간단E)1162074403140104002022-08-31<NA><NA>납기내부산은행4739200영도구303영도 사업소
5440754408당월정상수납2022-08-26카드(사이버)944405240033000904002022-08-31<NA><NA>납기내롯데카드6438900410금정구244동래통합사업소
4571545716당월정상수납2022-08-28가상계좌17302093540609401854002022-08-31<NA><NA>납기내국민은행4661600350해운대구308해운대 사업소
2198421985당월정상수납2022-08-22가상계좌329401914010340346002022-08-31<NA><NA>납기내신한은행3218300260동래구244동래통합사업소
4894248943당월정상수납2022-08-31카드(자동납부)421402360014220432002022-08-31<NA><NA>납기내BC카드611673530사상구307북부 사업소
5003050031당월정상수납2022-08-30창구수납(간단E)11112060060394601160002022-08-31<NA><NA>납기내우체국1366440강서구311강서 사업소
4580545806당월정상수납2022-08-29CD/ATM(간단E)424002382014220436002022-08-31<NA><NA>납기내우리은행1902410금정구244동래통합사업소
27872788당월정상수납2022-08-04가상계좌251025100002022-08-31<NA><NA>납기내국민은행909505700230부산진구304부산진 사업소
6115961160당월정상수납2022-08-24가상계좌17058096080558001870002022-08-31<NA><NA>납기내부산은행5904000410금정구244동래통합사업소
5306953070당월정상수납2022-08-29가상계좌338601800013900196002022-08-31<NA><NA>납기내부산은행5685900410금정구244동래통합사업소
연번구분수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액기타금액납기내일자체납시작년월체납종료년월납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
5187551876당월정상수납2022-08-31가상계좌2802301312001324501658002022-08-31<NA><NA>납기내농협은행4332300350해운대구308해운대 사업소
4138641387당월정상수납2022-08-20가상계좌718403978024380768002022-08-31<NA><NA>납기내부산은행598300140서구302서부 사업소
5453054531당월정상수납2022-08-31CD/ATM(간단E)263300144920891202926002022-08-31<NA><NA>납기내부산은행3113710기장군312기장 사업소
5582055821당월정상수납2022-08-30인터넷뱅킹(간단E)1105807040031900828002022-08-31<NA><NA>납기내KEB하나은행1086710기장군312기장 사업소
4873648737당월정상수납2022-08-26CD/ATM(간단E)469402616015960482002022-08-31<NA><NA>납기내지역농축협2278320북구307북부 사업소
5014850149당월정상수납2022-08-30CD/ATM(간단E)638003472023360572002022-08-31<NA><NA>납기내부산은행2114410금정구244동래통합사업소
6059060591당월정상수납2022-08-23카드(ARS)264960146180899802880002022-08-31<NA><NA>납기내BC카드607066380사하구309사하 사업소
1286112862당월정상수납2022-08-31카드(자동납부)55100461400896002022-08-31<NA><NA>납기내롯데카드609754440강서구311강서 사업소
6355563556당월정상수납2022-08-10인터넷뱅킹(간단E)72404800214030002022-07-31<NA><NA>납기후경남은행43440강서구311강서 사업소
1489214893당월정상수납2022-08-31창구수납(간단E)62436102477280347890028743002022-08-31<NA><NA>납기내부산은행867500수영구306남부 사업소