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_20230501
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 상수도수납금액 and 3 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 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 납기내일자High correlation
구코드 is highly overall correlated with 구명 and 1 other fieldsHigh correlation
사업소코드 is highly overall correlated with 납기내일자 and 2 other fieldsHigh correlation
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 (86.6%)Imbalance
체납시작년월 has 10000 (100.0%) missing valuesMissing
체납종료년월 has 10000 (100.0%) missing valuesMissing
수납금액합계 is highly skewed (γ1 = 56.98385013)Skewed
상수도수납금액 is highly skewed (γ1 = 64.22335506)Skewed
하수도수납금액 is highly skewed (γ1 = 74.07731325)Skewed
물이용수납금액 is highly skewed (γ1 = 67.04236121)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 1425 (14.2%) zerosZeros
물이용수납금액 has 1010 (10.1%) zerosZeros

Reproduction

Analysis started2023-12-10 16:38:14.745292
Analysis finished2023-12-10 16:38:31.549486
Duration16.8 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%
Mean37610.798
Minimum7
Maximum74983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:31.656646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile3709.45
Q119098.5
median37966
Q355967.75
95-th percentile71254.5
Maximum74983
Range74976
Interquartile range (IQR)36869.25

Descriptive statistics

Standard deviation21616.622
Coefficient of variation (CV)0.5747451
Kurtosis-1.1833938
Mean37610.798
Median Absolute Deviation (MAD)18437
Skewness-0.009591242
Sum3.7610798 × 108
Variance4.6727833 × 108
MonotonicityNot monotonic
2023-12-11T01:38:31.867734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56446 1
 
< 0.1%
59504 1
 
< 0.1%
55529 1
 
< 0.1%
21988 1
 
< 0.1%
47672 1
 
< 0.1%
35481 1
 
< 0.1%
45481 1
 
< 0.1%
73642 1
 
< 0.1%
551 1
 
< 0.1%
62295 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
7 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
17 1
< 0.1%
26 1
< 0.1%
44 1
< 0.1%
50 1
< 0.1%
52 1
< 0.1%
53 1
< 0.1%
60 1
< 0.1%
ValueCountFrequency (%)
74983 1
< 0.1%
74979 1
< 0.1%
74977 1
< 0.1%
74952 1
< 0.1%
74946 1
< 0.1%
74945 1
< 0.1%
74942 1
< 0.1%
74934 1
< 0.1%
74932 1
< 0.1%
74930 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

2023-12-11T01:38:32.083283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:38:32.225147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당월정상수납 10000
100.0%

수납일자
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-02-28
2994 
2023-02-27
1407 
2023-02-24
1053 
2023-02-23
844 
2023-02-22
830 
Other values (25)
2872 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-28
2nd row2023-02-27
3rd row2023-02-28
4th row2023-02-05
5th row2023-02-28

Common Values

ValueCountFrequency (%)
2023-02-28 2994
29.9%
2023-02-27 1407
14.1%
2023-02-24 1053
 
10.5%
2023-02-23 844
 
8.4%
2023-02-22 830
 
8.3%
2023-02-21 681
 
6.8%
2023-02-20 462
 
4.6%
2023-02-26 229
 
2.3%
2023-02-01 216
 
2.2%
2023-02-25 173
 
1.7%
Other values (20) 1111
 
11.1%

Length

2023-12-11T01:38:32.362507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-02-28 2994
29.9%
2023-02-27 1407
14.1%
2023-02-24 1053
 
10.5%
2023-02-23 844
 
8.4%
2023-02-22 830
 
8.3%
2023-02-21 681
 
6.8%
2023-02-20 462
 
4.6%
2023-02-26 229
 
2.3%
2023-02-01 216
 
2.2%
2023-02-25 173
 
1.7%
Other values (20) 1111
 
11.1%

수납방법
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가상계좌
4624 
창구수납(간단E)
1500 
CD/ATM(간단E)
1330 
카드(자동납부)
876 
통장(자동납부)
 
432
Other values (9)
1238 

Length

Max length11
Median length10
Mean length6.7229
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 4624
46.2%
창구수납(간단E) 1500
 
15.0%
CD/ATM(간단E) 1330
 
13.3%
카드(자동납부) 876
 
8.8%
통장(자동납부) 432
 
4.3%
인터넷뱅킹(간단E) 402
 
4.0%
카드(간단E) 300
 
3.0%
카드(ARS) 254
 
2.5%
카드(사이버) 108
 
1.1%
자동화기기(간단E) 54
 
0.5%
Other values (4) 120
 
1.2%

Length

2023-12-11T01:38:32.560575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가상계좌 4624
46.2%
창구수납(간단e 1500
 
15.0%
cd/atm(간단e 1330
 
13.3%
카드(자동납부 876
 
8.8%
통장(자동납부 432
 
4.3%
인터넷뱅킹(간단e 402
 
4.0%
카드(간단e 300
 
3.0%
카드(ars 254
 
2.5%
카드(사이버 108
 
1.1%
자동화기기(간단e 54
 
0.5%
Other values (4) 120
 
1.2%

수납금액합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4600
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277756.24
Minimum70
Maximum3.7323104 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:32.769981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile2400
Q117600
median45105
Q3108920
95-th percentile558962.5
Maximum3.7323104 × 108
Range3.7323097 × 108
Interquartile range (IQR)91320

Descriptive statistics

Standard deviation5376612.7
Coefficient of variation (CV)19.357306
Kurtosis3459.9726
Mean277756.24
Median Absolute Deviation (MAD)34590
Skewness56.98385
Sum2.7775624 × 109
Variance2.8907964 × 1013
MonotonicityNot monotonic
2023-12-11T01:38:33.014674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 588
 
5.9%
2380 68
 
0.7%
15600 67
 
0.7%
24850 66
 
0.7%
14270 64
 
0.6%
22200 63
 
0.6%
28810 62
 
0.6%
19560 61
 
0.6%
3700 59
 
0.6%
10320 56
 
0.6%
Other values (4590) 8846
88.5%
ValueCountFrequency (%)
70 1
 
< 0.1%
110 1
 
< 0.1%
190 1
 
< 0.1%
250 1
 
< 0.1%
270 3
< 0.1%
340 3
< 0.1%
380 1
 
< 0.1%
420 1
 
< 0.1%
460 1
 
< 0.1%
580 1
 
< 0.1%
ValueCountFrequency (%)
373231040 1
< 0.1%
267740000 1
< 0.1%
258416500 1
< 0.1%
56033830 1
< 0.1%
39211460 1
< 0.1%
28729640 1
< 0.1%
23725650 1
< 0.1%
21914000 1
< 0.1%
20988810 1
< 0.1%
19222000 1
< 0.1%

상수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3265
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178633.4
Minimum0
Maximum3.7312184 × 108
Zeros49
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:33.241103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2400
Q110730
median25440
Q358937.5
95-th percentile266103
Maximum3.7312184 × 108
Range3.7312184 × 108
Interquartile range (IQR)48207.5

Descriptive statistics

Standard deviation4877964.1
Coefficient of variation (CV)27.307123
Kurtosis4412.2569
Mean178633.4
Median Absolute Deviation (MAD)18490
Skewness64.223355
Sum1.786334 × 109
Variance2.3794533 × 1013
MonotonicityNot monotonic
2023-12-11T01:38:33.437694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 597
 
6.0%
9600 110
 
1.1%
16800 106
 
1.1%
13200 93
 
0.9%
6000 89
 
0.9%
8880 83
 
0.8%
14640 80
 
0.8%
11760 78
 
0.8%
10320 75
 
0.8%
12480 70
 
0.7%
Other values (3255) 8619
86.2%
ValueCountFrequency (%)
0 49
0.5%
70 1
 
< 0.1%
110 1
 
< 0.1%
190 1
 
< 0.1%
250 1
 
< 0.1%
270 3
 
< 0.1%
340 3
 
< 0.1%
380 1
 
< 0.1%
410 1
 
< 0.1%
460 1
 
< 0.1%
ValueCountFrequency (%)
373121840 1
< 0.1%
267665000 1
< 0.1%
150714360 1
< 0.1%
35211840 1
< 0.1%
32337400 1
< 0.1%
21914000 1
< 0.1%
14607460 1
< 0.1%
12923680 1
< 0.1%
12330990 1
< 0.1%
11721260 1
< 0.1%

하수도수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2359
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83639.225
Minimum0
Maximum90519600
Zeros1425
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:33.648026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14110
median14220
Q337440
95-th percentile224631.5
Maximum90519600
Range90519600
Interquartile range (IQR)33330

Descriptive statistics

Standard deviation1008859.5
Coefficient of variation (CV)12.062038
Kurtosis6487.866
Mean83639.225
Median Absolute Deviation (MAD)12845
Skewness74.077313
Sum8.3639225 × 108
Variance1.0177975 × 1012
MonotonicityNot monotonic
2023-12-11T01:38:33.863875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1425
 
14.2%
7640 104
 
1.0%
4040 101
 
1.0%
7200 100
 
1.0%
4500 99
 
1.0%
9000 98
 
1.0%
5840 98
 
1.0%
3600 98
 
1.0%
8540 93
 
0.9%
6300 92
 
0.9%
Other values (2349) 7692
76.9%
ValueCountFrequency (%)
0 1425
14.2%
10 3
 
< 0.1%
20 2
 
< 0.1%
40 2
 
< 0.1%
60 1
 
< 0.1%
80 1
 
< 0.1%
250 1
 
< 0.1%
300 1
 
< 0.1%
310 1
 
< 0.1%
440 82
 
0.8%
ValueCountFrequency (%)
90519600 1
< 0.1%
19594800 1
< 0.1%
16860600 1
< 0.1%
13035600 1
< 0.1%
11990550 1
< 0.1%
9044260 1
< 0.1%
8742000 1
< 0.1%
7626880 1
< 0.1%
7454160 1
< 0.1%
6593280 1
< 0.1%

물이용수납금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1743
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15483.621
Minimum0
Maximum17182540
Zeros1010
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:34.059537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11500
median4255
Q39675
95-th percentile34496
Maximum17182540
Range17182540
Interquartile range (IQR)8175

Descriptive statistics

Standard deviation201684.36
Coefficient of variation (CV)13.025658
Kurtosis5381.7377
Mean15483.621
Median Absolute Deviation (MAD)3455
Skewness67.042361
Sum1.5483621 × 108
Variance4.067658 × 1010
MonotonicityNot monotonic
2023-12-11T01:38:34.282867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1010
 
10.1%
140 164
 
1.6%
300 146
 
1.5%
1500 145
 
1.5%
600 133
 
1.3%
1960 127
 
1.3%
900 125
 
1.2%
1050 121
 
1.2%
740 119
 
1.2%
1200 116
 
1.2%
Other values (1733) 7794
77.9%
ValueCountFrequency (%)
0 1010
10.1%
10 3
 
< 0.1%
20 4
 
< 0.1%
40 1
 
< 0.1%
80 1
 
< 0.1%
100 1
 
< 0.1%
140 164
 
1.6%
150 41
 
0.4%
160 13
 
0.1%
180 6
 
0.1%
ValueCountFrequency (%)
17182540 1
< 0.1%
6229990 1
< 0.1%
4101630 1
< 0.1%
3999620 1
< 0.1%
3258900 1
< 0.1%
1906720 1
< 0.1%
1664030 1
< 0.1%
1471850 1
< 0.1%
1429530 1
< 0.1%
1403390 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

2023-12-11T01:38:34.492965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:38:34.657440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

납기내일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-02-28
8807 
2023-01-31
1078 
2023-03-31
 
83
2023-03-13
 
3
2023-03-15
 
3
Other values (18)
 
26

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row2023-02-28
2nd row2023-02-28
3rd row2023-02-28
4th row2023-01-31
5th row2023-02-28

Common Values

ValueCountFrequency (%)
2023-02-28 8807
88.1%
2023-01-31 1078
 
10.8%
2023-03-31 83
 
0.8%
2023-03-13 3
 
< 0.1%
2023-03-15 3
 
< 0.1%
2023-02-24 3
 
< 0.1%
2023-03-01 2
 
< 0.1%
2023-03-09 2
 
< 0.1%
2023-03-20 2
 
< 0.1%
2023-02-17 2
 
< 0.1%
Other values (13) 15
 
0.1%

Length

2023-12-11T01:38:34.814478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-02-28 8807
88.1%
2023-01-31 1078
 
10.8%
2023-03-31 83
 
0.8%
2023-03-13 3
 
< 0.1%
2023-03-15 3
 
< 0.1%
2023-02-24 3
 
< 0.1%
2023-02-17 2
 
< 0.1%
2023-03-22 2
 
< 0.1%
2023-03-05 2
 
< 0.1%
2023-03-20 2
 
< 0.1%
Other values (13) 15
 
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 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
납기내
8815 
납기후
1185 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
납기내 8815
88.1%
납기후 1185
 
11.8%

Length

2023-12-11T01:38:35.009035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:38:35.146217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
납기내 8815
88.1%
납기후 1185
 
11.8%

은행명
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산은행
3016 
농협은행
1278 
국민은행
1098 
새마을금고중앙회
912 
우체국
456 
Other values (30)
3240 

Length

Max length8
Median length4
Mean length4.5204
Min length2

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row부산은행
2nd row부산은행
3rd row새마을금고중앙회
4th row하나SK카드
5th row수협중앙회

Common Values

ValueCountFrequency (%)
부산은행 3016
30.2%
농협은행 1278
12.8%
국민은행 1098
 
11.0%
새마을금고중앙회 912
 
9.1%
우체국 456
 
4.6%
지역농축협 365
 
3.6%
우리은행 354
 
3.5%
신한은행 314
 
3.1%
BC카드 277
 
2.8%
KB국민카드 248
 
2.5%
Other values (25) 1682
16.8%

Length

2023-12-11T01:38:35.322029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산은행 3016
30.2%
농협은행 1278
12.8%
국민은행 1098
 
11.0%
새마을금고중앙회 912
 
9.1%
우체국 456
 
4.6%
지역농축협 365
 
3.6%
우리은행 354
 
3.5%
신한은행 314
 
3.1%
bc카드 277
 
2.8%
kb국민카드 248
 
2.5%
Other values (25) 1682
16.8%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8890
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46469091
Minimum1
Maximum3.0295393 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:35.528848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile261
Q12569.75
median682695
Q35264200
95-th percentile9033455
Maximum3.0295393 × 109
Range3.0295393 × 109
Interquartile range (IQR)5261630.2

Descriptive statistics

Standard deviation2.8619223 × 108
Coefficient of variation (CV)6.1587654
Kurtosis66.097849
Mean46469091
Median Absolute Deviation (MAD)682241.5
Skewness7.7611327
Sum4.6469091 × 1011
Variance8.1905993 × 1016
MonotonicityNot monotonic
2023-12-11T01:38:35.750636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 6
 
0.1%
37 5
 
0.1%
169 5
 
0.1%
1495 5
 
0.1%
11 5
 
0.1%
94 5
 
0.1%
271 5
 
0.1%
47 5
 
0.1%
30 5
 
0.1%
65 5
 
0.1%
Other values (8880) 9949
99.5%
ValueCountFrequency (%)
1 3
< 0.1%
2 4
< 0.1%
3 3
< 0.1%
4 4
< 0.1%
5 3
< 0.1%
6 4
< 0.1%
7 4
< 0.1%
8 1
 
< 0.1%
9 3
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
3029539300 1
< 0.1%
3029539200 1
< 0.1%
3029537500 1
< 0.1%
3029534500 1
< 0.1%
3029533000 1
< 0.1%
3029532600 1
< 0.1%
3029532100 1
< 0.1%
3029530600 1
< 0.1%
3029529100 1
< 0.1%
3029526200 1
< 0.1%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351.3119
Minimum0
Maximum710
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:35.945488image/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 deviation146.41539
Coefficient of variation (CV)0.41676752
Kurtosis-0.14406386
Mean351.3119
Median Absolute Deviation (MAD)120
Skewness0.40927362
Sum3513119
Variance21437.466
MonotonicityNot monotonic
2023-12-11T01:38:36.441909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
230 871
 
8.7%
410 847
 
8.5%
350 816
 
8.2%
380 798
 
8.0%
260 683
 
6.8%
440 681
 
6.8%
530 670
 
6.7%
290 622
 
6.2%
500 605
 
6.0%
470 597
 
6.0%
Other values (10) 2810
28.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
110 422
4.2%
140 577
5.8%
170 442
4.4%
200 463
4.6%
201 1
 
< 0.1%
203 1
 
< 0.1%
205 1
 
< 0.1%
230 871
8.7%
260 683
6.8%
ValueCountFrequency (%)
710 506
5.1%
530 670
6.7%
500 605
6.0%
470 597
6.0%
440 681
6.8%
410 847
8.5%
380 798
8.0%
350 816
8.2%
320 394
3.9%
290 622
6.2%

구명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
871 
금정구
847 
해운대구
816 
사하구
798 
동래구
683 
Other values (15)
5985 

Length

Max length4
Median length3
Mean length2.923
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row해운대구
2nd row금정구
3rd row서구
4th row강서구
5th row중구

Common Values

ValueCountFrequency (%)
부산진구 871
 
8.7%
금정구 847
 
8.5%
해운대구 816
 
8.2%
사하구 798
 
8.0%
동래구 683
 
6.8%
강서구 681
 
6.8%
사상구 670
 
6.7%
남구 622
 
6.2%
수영구 605
 
6.0%
연제구 597
 
6.0%
Other values (10) 2810
28.1%

Length

2023-12-11T01:38:36.640622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산진구 871
 
8.7%
금정구 847
 
8.5%
해운대구 816
 
8.2%
사하구 798
 
8.0%
동래구 683
 
6.8%
강서구 681
 
6.8%
사상구 670
 
6.7%
남구 622
 
6.2%
수영구 605
 
6.0%
연제구 597
 
6.0%
Other values (10) 2810
28.1%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.8974
Minimum101
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:38:36.823075image/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 deviation25.890579
Coefficient of variation (CV)0.088394706
Kurtosis0.73268471
Mean292.8974
Median Absolute Deviation (MAD)3
Skewness-1.4411312
Sum2928974
Variance670.32211
MonotonicityNot monotonic
2023-12-11T01:38:36.965678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
244 2127
21.3%
306 1227
12.3%
307 1064
10.6%
304 871
8.7%
301 864
8.6%
308 816
 
8.2%
309 798
 
8.0%
311 681
 
6.8%
302 577
 
5.8%
312 506
 
5.1%
Other values (5) 469
 
4.7%
ValueCountFrequency (%)
101 3
 
< 0.1%
201 1
 
< 0.1%
203 1
 
< 0.1%
205 1
 
< 0.1%
244 2127
21.3%
301 864
8.6%
302 577
 
5.8%
303 463
 
4.6%
304 871
8.7%
306 1227
12.3%
ValueCountFrequency (%)
312 506
5.1%
311 681
6.8%
309 798
8.0%
308 816
8.2%
307 1064
10.6%
306 1227
12.3%
304 871
8.7%
303 463
 
4.6%
302 577
5.8%
301 864
8.6%

사업소명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동래통합사업소
2127 
남부 사업소
1227 
북부 사업소
1064 
부산진 사업소
871 
중동부 사업소
864 
Other values (10)
3847 

Length

Max length9
Median length9
Mean length8.3177
Min length5

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
동래통합사업소 2127
21.3%
남부 사업소 1227
12.3%
북부 사업소 1064
10.6%
부산진 사업소 871
8.7%
중동부 사업소 864
8.6%
해운대 사업소 816
 
8.2%
사하 사업소 798
 
8.0%
강서 사업소 681
 
6.8%
서부 사업소 577
 
5.8%
기장 사업소 506
 
5.1%
Other values (5) 469
 
4.7%

Length

2023-12-11T01:38:37.177655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업소 7867
44.0%
동래통합사업소 2127
 
11.9%
남부 1227
 
6.9%
북부 1064
 
6.0%
부산진 871
 
4.9%
중동부 864
 
4.8%
해운대 816
 
4.6%
사하 798
 
4.5%
강서 681
 
3.8%
서부 577
 
3.2%
Other values (6) 975
 
5.5%

Interactions

2023-12-11T01:38:29.574212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:20.269025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:21.679882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:23.122844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:24.566567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:25.766291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:26.888883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.998451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:29.730016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:20.438910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:21.856818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:23.324270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:24.704458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:25.927629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.024662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:28.156129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:29.885855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:20.621287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:22.033863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:23.495438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:24.873391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:26.071767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.156363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:28.308365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:30.055817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:20.807147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:22.208957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:23.680668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:25.032773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:26.198504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.319791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:28.448660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:30.198170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:20.972862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:22.365803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:23.860401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:25.179968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:26.322790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.458594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:28.578543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:30.359828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:21.181850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:22.573429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:24.062207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:25.341106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:26.461222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.595812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:28.705677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:30.514441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:21.369543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:22.730621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:24.228129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:25.481035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:26.639715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.732402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:29.264581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:30.756354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:21.537384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:22.978456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:24.420468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:25.632057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:26.760311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:27.875952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:29.428744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:38:37.331323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액납기내일자납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
연번1.0000.6450.3780.0000.0000.0000.0000.2360.3970.1450.0900.1640.1950.0670.168
수납일자0.6451.0000.6020.0000.0000.0000.0000.5980.9200.3520.3810.1770.2180.1100.235
수납방법0.3780.6021.0000.0000.0000.0000.0360.1610.2830.9170.1790.3970.4110.0940.349
수납금액합계0.0000.0000.0001.0001.0000.7590.9570.7570.0000.0000.0000.0230.0000.0000.035
상수도수납금액0.0000.0000.0001.0001.0000.8950.6470.8040.0000.0000.0000.0150.0110.0000.028
하수도수납금액0.0000.0000.0000.7590.8951.0000.8410.0000.0000.0000.0000.0000.0000.0000.000
물이용수납금액0.0000.0000.0360.9570.6470.8411.0000.0000.0000.0000.0000.0000.0000.0000.000
납기내일자0.2360.5980.1610.7570.8040.0000.0001.0000.9670.0640.7980.4770.6960.7490.757
납기내후수납구분0.3970.9200.2830.0000.0000.0000.0000.9671.0000.0960.2180.0650.0860.0570.071
은행명0.1450.3520.9170.0000.0000.0000.0000.0640.0961.0000.1130.2420.2900.0000.257
일련번호0.0900.3810.1790.0000.0000.0000.0000.7980.2180.1131.0000.0520.2840.3660.267
구코드0.1640.1770.3970.0230.0150.0000.0000.4770.0650.2420.0521.0001.0000.8040.969
구명0.1950.2180.4110.0000.0110.0000.0000.6960.0860.2900.2841.0001.0001.0001.000
사업소코드0.0670.1100.0940.0000.0000.0000.0000.7490.0570.0000.3660.8041.0001.0001.000
사업소명0.1680.2350.3490.0350.0280.0000.0000.7570.0710.2570.2670.9691.0001.0001.000
2023-12-11T01:38:37.536919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납기내후수납구분은행명수납방법납기내일자수납일자구명사업소명
납기내후수납구분1.0000.0810.2210.9470.7950.0760.064
은행명0.0811.0000.5610.0160.0830.0800.076
수납방법0.2210.5611.0000.0520.2270.1490.128
납기내일자0.9470.0160.0521.0000.1870.2730.345
수납일자0.7950.0830.2270.1871.0000.0600.063
구명0.0760.0800.1490.2730.0601.0001.000
사업소명0.0640.0760.1280.3450.0631.0001.000
2023-12-11T01:38:37.706666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수납금액합계상수도수납금액하수도수납금액물이용수납금액일련번호구코드사업소코드수납일자수납방법납기내일자납기내후수납구분은행명구명사업소명
연번1.0000.0100.0100.0130.0110.020-0.061-0.0080.2610.1620.0890.3050.0500.0740.063
수납금액합계0.0101.0000.9810.9190.947-0.0000.0520.0350.0000.0000.4980.0000.0000.0000.015
상수도수납금액0.0100.9811.0000.8730.9640.0040.0640.0550.0000.0000.5760.0000.0000.0060.016
하수도수납금액0.0130.9190.8731.0000.884-0.033-0.017-0.0450.0000.0000.0000.0000.0000.0000.000
물이용수납금액0.0110.9470.9640.8841.000-0.0230.0520.0390.0000.0190.0000.0000.0000.0000.000
일련번호0.020-0.0000.004-0.033-0.0231.0000.3290.1550.2060.1020.5670.1450.0580.1580.154
구코드-0.0610.0520.064-0.0170.0520.3291.0000.4110.0660.1790.2050.0650.0900.9990.856
사업소코드-0.0080.0350.055-0.0450.0390.1550.4111.0000.0570.0530.5080.0370.0000.9990.999
수납일자0.2610.0000.0000.0000.0000.2060.0660.0571.0000.2270.1870.7950.0830.0600.063
수납방법0.1620.0000.0000.0000.0190.1020.1790.0530.2271.0000.0520.2210.5610.1490.128
납기내일자0.0890.4980.5760.0000.0000.5670.2050.5080.1870.0521.0000.9470.0160.2730.345
납기내후수납구분0.3050.0000.0000.0000.0000.1450.0650.0370.7950.2210.9471.0000.0810.0760.064
은행명0.0500.0000.0000.0000.0000.0580.0900.0000.0830.5610.0160.0811.0000.0800.076
구명0.0740.0000.0060.0000.0000.1580.9990.9990.0600.1490.2730.0760.0801.0001.000
사업소명0.0630.0150.0160.0000.0000.1540.8560.9990.0630.1280.3450.0640.0761.0001.000

Missing values

2023-12-11T01:38:30.991464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:38:31.394096image/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
5644556446당월정상수납2023-02-28가상계좌2316401148201043801244002023-02-28<NA><NA>납기내부산은행4699300350해운대구308해운대 사업소
5810958110당월정상수납2023-02-27CD/ATM(간단E)30330175809580317002023-02-28<NA><NA>납기내부산은행5362410금정구244동래통합사업소
7182371824당월정상수납2023-02-28통장(자동납부)376702128012480391002023-02-28<NA><NA>납기내새마을금고중앙회5129140서구302서부 사업소
48254826당월정상수납2023-02-05카드(간단E)3465001652401610602020002023-01-31<NA><NA>납기후하나SK카드3440강서구311강서 사업소
7152171522당월정상수납2023-02-28통장(자동납부)264601426010700150002023-02-28<NA><NA>납기내수협중앙회1329110중구301중동부 사업소
4550245503당월정상수납2023-02-27CD/ATM(간단E)24850146407640257002023-02-28<NA><NA>납기내농협은행2066470연제구244동래통합사업소
6386963870당월정상수납2023-02-27카드(ARS)380502184012140407002023-02-28<NA><NA>납기내삼성카드678585350해운대구308해운대 사업소
4221942220당월정상수납2023-02-28카드(자동납부)706603946023400780002023-02-28<NA><NA>납기내현대카드679347530사상구307북부 사업소
22932294당월정상수납2023-02-24가상계좌414902337013830429002023-02-28<NA><NA>납기내농협은행3604700290남구306남부 사업소
2786527866당월정상수납2023-02-28CD/ATM(간단E)661203640023080664002023-02-28<NA><NA>납기내지역농축협1104530사상구307북부 사업소
연번구분수납일자수납방법수납금액합계상수도수납금액하수도수납금액물이용수납금액기타금액납기내일자체납시작년월체납종료년월납기내후수납구분은행명일련번호구코드구명사업소코드사업소명
1127711278당월정상수납2023-02-10가상계좌789604021034010474002023-01-31<NA><NA>납기후부산은행2006401400380사하구309사하 사업소
66596660당월정상수납2023-02-28카드(자동납부)2520251001002023-02-28<NA><NA>납기내신한카드685669710기장군312기장 사업소
57595760당월정상수납2023-02-24창구수납(간단E)598003354019740652002023-02-28<NA><NA>납기내새마을금고중앙회4158170동구301중동부 사업소
4287842879당월정상수납2023-02-28가상계좌220022000002023-02-28<NA><NA>납기내우체국4527700350해운대구308해운대 사업소
4128141282당월정상수납2023-02-21카드(간단E)240024000002023-02-28<NA><NA>납기내NH카드2064530사상구307북부 사업소
6905869059당월정상수납2023-02-25모바일뱅킹(간단E)3920318058016002023-02-28<NA><NA>납기내부산은행251140서구302서부 사업소
1553515536당월정상수납2023-02-21창구수납(간단E)240024000002023-02-28<NA><NA>납기내부산은행1725140서구302서부 사업소
2508525086당월정상수납2023-02-23창구수납(간단E)484802694016540500002023-02-28<NA><NA>납기내새마을금고중앙회2134350해운대구308해운대 사업소
3377533776당월정상수납2023-02-21가상계좌24850146407640257002023-02-28<NA><NA>납기내우리은행8043700500수영구306남부 사업소
3903039031당월정상수납2023-02-28CD/ATM(간단E)9094050640301401016002023-02-28<NA><NA>납기내부산은행3055410금정구244동래통합사업소