Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells6127
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory108.0 B

Variable types

Text2
Categorical3
Numeric4
DateTime3

Dataset

Description부산광역시_부산도시공간정보시스템_도로상하수도기반시설물_부과정보_20231017
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15084495

Alerts

부과번호 is highly overall correlated with 고지서번호 and 1 other fieldsHigh correlation
가산금 is highly overall correlated with 부과종류High correlation
고지서번호 is highly overall correlated with 부과번호 and 1 other fieldsHigh correlation
부과종류 is highly overall correlated with 부과번호 and 2 other fieldsHigh correlation
원인자부담금종류 is highly overall correlated with 고지서번호 and 1 other fieldsHigh correlation
부과종류 is highly imbalanced (99.9%)Imbalance
징수확인일 has 6039 (60.4%) missing valuesMissing
가산금 is highly skewed (γ1 = 30.20254045)Skewed
고지서번호 is highly skewed (γ1 = 99.4885098)Skewed
부과번호 has unique valuesUnique
가산금 has 4071 (40.7%) zerosZeros

Reproduction

Analysis started2023-12-10 19:47:21.899323
Analysis finished2023-12-10 19:47:27.593156
Duration5.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로굴착관리번호
Text

Distinct8577
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T04:47:27.868762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters180000
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7239 ?
Unique (%)72.4%

Sample

1st rowWRK001201906170002
2nd rowWRK002201707180001
3rd rowWRK001201903040024
4th rowWRK001201705110025
5th rowWRK001201801030047
ValueCountFrequency (%)
wrk001201702120002 5
 
< 0.1%
wrk002202303020001 4
 
< 0.1%
wrk001202207220026 3
 
< 0.1%
wrk001201909240038 3
 
< 0.1%
wrk001201906200048 3
 
< 0.1%
wrk001202006160008 3
 
< 0.1%
wrk001202004080022 3
 
< 0.1%
wrk001202203030017 3
 
< 0.1%
wrk001201808280017 3
 
< 0.1%
wrk001202110050011 3
 
< 0.1%
Other values (8567) 9967
99.7%
2023-12-11T04:47:28.451940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68152
37.9%
1 29604
16.4%
2 24412
 
13.6%
W 10000
 
5.6%
R 10000
 
5.6%
K 10000
 
5.6%
3 4919
 
2.7%
7 4817
 
2.7%
8 4550
 
2.5%
9 4429
 
2.5%
Other values (3) 9117
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150000
83.3%
Uppercase Letter 30000
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68152
45.4%
1 29604
19.7%
2 24412
 
16.3%
3 4919
 
3.3%
7 4817
 
3.2%
8 4550
 
3.0%
9 4429
 
3.0%
4 3323
 
2.2%
5 2951
 
2.0%
6 2843
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
W 10000
33.3%
R 10000
33.3%
K 10000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 150000
83.3%
Latin 30000
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68152
45.4%
1 29604
19.7%
2 24412
 
16.3%
3 4919
 
3.3%
7 4817
 
3.2%
8 4550
 
3.0%
9 4429
 
3.0%
4 3323
 
2.2%
5 2951
 
2.0%
6 2843
 
1.9%
Latin
ValueCountFrequency (%)
W 10000
33.3%
R 10000
33.3%
K 10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68152
37.9%
1 29604
16.4%
2 24412
 
13.6%
W 10000
 
5.6%
R 10000
 
5.6%
K 10000
 
5.6%
3 4919
 
2.7%
7 4817
 
2.7%
8 4550
 
2.5%
9 4429
 
2.5%
Other values (3) 9117
 
5.1%

부과종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
선납분
9999 
면제
 
1

Length

Max length3
Median length3
Mean length2.9999
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
선납분 9999
> 99.9%
면제 1
 
< 0.1%

Length

2023-12-11T04:47:28.723583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:47:28.908333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선납분 9999
> 99.9%
면제 1
 
< 0.1%

부과번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32337.725
Minimum0
Maximum59662
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T04:47:29.526284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8748.95
Q118624.75
median32721
Q345806.25
95-th percentile56192.3
Maximum59662
Range59662
Interquartile range (IQR)27181.5

Descriptive statistics

Standard deviation15420.406
Coefficient of variation (CV)0.47685499
Kurtosis-1.2351672
Mean32337.725
Median Absolute Deviation (MAD)13667.5
Skewness0.0070641422
Sum3.2337725 × 108
Variance2.3778891 × 108
MonotonicityNot monotonic
2023-12-11T04:47:29.778789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28345 1
 
< 0.1%
11526 1
 
< 0.1%
26476 1
 
< 0.1%
41508 1
 
< 0.1%
22209 1
 
< 0.1%
9638 1
 
< 0.1%
42068 1
 
< 0.1%
54541 1
 
< 0.1%
48924 1
 
< 0.1%
33769 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
5984 1
< 0.1%
5994 1
< 0.1%
5997 1
< 0.1%
6001 1
< 0.1%
6005 1
< 0.1%
6006 1
< 0.1%
6025 1
< 0.1%
6052 1
< 0.1%
6062 1
< 0.1%
ValueCountFrequency (%)
59662 1
< 0.1%
59661 1
< 0.1%
59642 1
< 0.1%
59629 1
< 0.1%
59580 1
< 0.1%
59578 1
< 0.1%
59571 1
< 0.1%
59568 1
< 0.1%
59567 1
< 0.1%
59564 1
< 0.1%

원인자부담금종류
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
군비 복구비_간접
4243 
군비 복구비_합계
3627 
시비 복구비_간접
2085 
시비 점용료
 
23
시비 복구비_합계
 
20

Length

Max length9
Median length9
Mean length8.9925
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시비 복구비_간접
2nd row군비 복구비_합계
3rd row군비 복구비_간접
4th row군비 복구비_간접
5th row군비 복구비_간접

Common Values

ValueCountFrequency (%)
군비 복구비_간접 4243
42.4%
군비 복구비_합계 3627
36.3%
시비 복구비_간접 2085
20.8%
시비 점용료 23
 
0.2%
시비 복구비_합계 20
 
0.2%
군비 점용료 2
 
< 0.1%

Length

2023-12-11T04:47:30.016532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:47:30.211009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군비 7872
39.4%
복구비_간접 6328
31.6%
복구비_합계 3647
18.2%
시비 2128
 
10.6%
점용료 25
 
0.1%

부과금액
Real number (ℝ)

Distinct3314
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean733369.49
Minimum0
Maximum2.001599 × 108
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T04:47:30.447466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile400
Q19300
median28700
Q394300
95-th percentile2055760
Maximum2.001599 × 108
Range2.001599 × 108
Interquartile range (IQR)85000

Descriptive statistics

Standard deviation5417369
Coefficient of variation (CV)7.3869571
Kurtosis465.54732
Mean733369.49
Median Absolute Deviation (MAD)26300
Skewness18.548816
Sum7.3336949 × 109
Variance2.9347887 × 1013
MonotonicityNot monotonic
2023-12-11T04:47:30.718682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 166
 
1.7%
200 140
 
1.4%
300 139
 
1.4%
400 125
 
1.2%
500 118
 
1.2%
11800 98
 
1.0%
600 90
 
0.9%
700 86
 
0.9%
19800 79
 
0.8%
800 74
 
0.7%
Other values (3304) 8885
88.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
100 166
1.7%
110 1
 
< 0.1%
200 140
1.4%
250 1
 
< 0.1%
300 139
1.4%
400 125
1.2%
500 118
1.2%
510 1
 
< 0.1%
600 90
0.9%
ValueCountFrequency (%)
200159900 1
< 0.1%
169043000 1
< 0.1%
139067300 1
< 0.1%
137301180 1
< 0.1%
131701900 1
< 0.1%
100359000 1
< 0.1%
96737000 1
< 0.1%
86210200 1
< 0.1%
81947700 1
< 0.1%
81140000 1
< 0.1%

가산금
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct928
Distinct (%)9.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2863.8774
Minimum0
Maximum1348770
Zeros4071
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T04:47:31.003514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median50
Q3940
95-th percentile6830
Maximum1348770
Range1348770
Interquartile range (IQR)940

Descriptive statistics

Standard deviation26778.316
Coefficient of variation (CV)9.3503709
Kurtosis1171.4071
Mean2863.8774
Median Absolute Deviation (MAD)50
Skewness30.20254
Sum28635910
Variance7.1707819 × 108
MonotonicityNot monotonic
2023-12-11T04:47:31.255871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4071
40.7%
10 331
 
3.3%
20 209
 
2.1%
30 195
 
1.9%
40 123
 
1.2%
350 104
 
1.0%
60 89
 
0.9%
590 78
 
0.8%
50 76
 
0.8%
480 68
 
0.7%
Other values (918) 4655
46.6%
ValueCountFrequency (%)
0 4071
40.7%
10 331
 
3.3%
20 209
 
2.1%
30 195
 
1.9%
40 123
 
1.2%
50 76
 
0.8%
60 89
 
0.9%
70 51
 
0.5%
80 41
 
0.4%
90 48
 
0.5%
ValueCountFrequency (%)
1348770 1
< 0.1%
1086440 1
< 0.1%
895010 1
< 0.1%
693270 1
< 0.1%
672570 1
< 0.1%
578570 1
< 0.1%
546140 1
< 0.1%
390850 1
< 0.1%
370290 1
< 0.1%
363820 1
< 0.1%

고지서번호
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9816
Distinct (%)99.0%
Missing86
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2.0202598 × 1010
Minimum2.0170003 × 1010
Maximum1 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T04:47:31.523913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0170003 × 1010
5-th percentile2.0170028 × 1010
Q12.0180028 × 1010
median2.0190045 × 1010
Q32.0210027 × 1010
95-th percentile2.0230012 × 1010
Maximum1 × 1011
Range7.9829997 × 1010
Interquartile range (IQR)29999290

Descriptive statistics

Standard deviation8.017247 × 108
Coefficient of variation (CV)0.039684238
Kurtosis9903.3046
Mean2.0202598 × 1010
Median Absolute Deviation (MAD)19939408
Skewness99.48851
Sum2.0028855 × 1014
Variance6.427625 × 1017
MonotonicityNot monotonic
2023-12-11T04:47:31.795823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20220027373 2
 
< 0.1%
20190035282 2
 
< 0.1%
20190071357 2
 
< 0.1%
20180046983 2
 
< 0.1%
20190038279 2
 
< 0.1%
20170006179 2
 
< 0.1%
20220007701 2
 
< 0.1%
20170025120 2
 
< 0.1%
20210021018 2
 
< 0.1%
20210010981 2
 
< 0.1%
Other values (9806) 9894
98.9%
(Missing) 86
 
0.9%
ValueCountFrequency (%)
20170002506 1
< 0.1%
20170002707 1
< 0.1%
20170002717 1
< 0.1%
20170002801 1
< 0.1%
20170002823 1
< 0.1%
20170002936 1
< 0.1%
20170003156 1
< 0.1%
20170003202 1
< 0.1%
20170003209 1
< 0.1%
20170003221 1
< 0.1%
ValueCountFrequency (%)
99999999999 1
< 0.1%
20230093836 1
< 0.1%
20230093832 1
< 0.1%
20230091846 1
< 0.1%
20230091844 1
< 0.1%
20230091441 1
< 0.1%
20230091351 1
< 0.1%
20230090974 1
< 0.1%
20230090496 1
< 0.1%
20230090494 1
< 0.1%

부과일
Date

Distinct1663
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-01-16 00:00:00
Maximum2023-10-17 00:00:00
2023-12-11T04:47:32.055027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:32.293180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

징수확인일
Date

MISSING 

Distinct1217
Distinct (%)30.7%
Missing6039
Missing (%)60.4%
Memory size156.2 KiB
Minimum2017-01-16 00:00:00
Maximum2023-10-12 00:00:00
2023-12-11T04:47:32.569840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:32.801439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

납기내납부기한
Text

Distinct1664
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T04:47:33.279244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)1.9%

Sample

1st row2019-07-03
2nd row2017-08-04
3rd row2019-03-20
4th row2017-05-31
5th row2018-01-24
ValueCountFrequency (%)
2017-09-28 33
 
0.3%
2021-03-25 28
 
0.3%
2020-10-23 27
 
0.3%
2019-08-02 26
 
0.3%
2019-09-26 25
 
0.2%
2018-11-02 24
 
0.2%
2019-12-04 23
 
0.2%
2019-11-06 22
 
0.2%
2017-10-27 22
 
0.2%
2022-11-05 22
 
0.2%
Other values (1654) 9748
97.5%
2023-12-11T04:47:33.936809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23565
23.6%
2 21489
21.5%
- 20000
20.0%
1 15269
15.3%
7 3740
 
3.7%
9 3655
 
3.7%
8 3542
 
3.5%
3 3006
 
3.0%
4 1946
 
1.9%
5 1925
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
80.0%
Dash Punctuation 20000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23565
29.5%
2 21489
26.9%
1 15269
19.1%
7 3740
 
4.7%
9 3655
 
4.6%
8 3542
 
4.4%
3 3006
 
3.8%
4 1946
 
2.4%
5 1925
 
2.4%
6 1863
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23565
23.6%
2 21489
21.5%
- 20000
20.0%
1 15269
15.3%
7 3740
 
3.7%
9 3655
 
3.7%
8 3542
 
3.5%
3 3006
 
3.0%
4 1946
 
1.9%
5 1925
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23565
23.6%
2 21489
21.5%
- 20000
20.0%
1 15269
15.3%
7 3740
 
3.7%
9 3655
 
3.7%
8 3542
 
3.5%
3 3006
 
3.0%
4 1946
 
1.9%
5 1925
 
1.9%

납부후납부기한
Date

Distinct1636
Distinct (%)16.4%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
Minimum2017-02-16 00:00:00
Maximum2023-11-17 00:00:00
2023-12-11T04:47:34.181416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:34.457531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

부과상태
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부과
5840 
완납
3961 
납부확인요청
 
199

Length

Max length6
Median length2
Mean length2.0796
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부과
2nd row부과
3rd row완납
4th row완납
5th row부과

Common Values

ValueCountFrequency (%)
부과 5840
58.4%
완납 3961
39.6%
납부확인요청 199
 
2.0%

Length

2023-12-11T04:47:34.731045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:47:34.942577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부과 5840
58.4%
완납 3961
39.6%
납부확인요청 199
 
2.0%

Interactions

2023-12-11T04:47:26.071981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:23.737692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:24.545328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:25.319517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:26.258702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:23.899881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:24.730464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:25.486132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:26.456961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:24.105881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:24.928146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:25.684264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:26.655144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:24.295452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:25.123979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:47:25.866380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T04:47:35.072336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과종류부과번호원인자부담금종류부과금액가산금고지서번호부과상태
부과종류1.0001.0000.8910.000NaN0.7070.000
부과번호1.0001.0000.5350.0000.0041.0000.187
원인자부담금종류0.8910.5351.0000.0760.0170.8910.199
부과금액0.0000.0000.0761.0000.2690.0000.000
가산금NaN0.0040.0170.2691.000NaN0.000
고지서번호0.7071.0000.8910.000NaN1.0000.000
부과상태0.0000.1870.1990.0000.0000.0001.000
2023-12-11T04:47:35.278664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원인자부담금종류부과상태부과종류
원인자부담금종류1.0000.0830.707
부과상태0.0831.0000.000
부과종류0.7070.0001.000
2023-12-11T04:47:35.438535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과번호부과금액가산금고지서번호부과종류원인자부담금종류부과상태
부과번호1.0000.1020.0500.9931.0000.3170.113
부과금액0.1021.0000.1770.1000.0000.0380.000
가산금0.0500.1771.0000.0401.0000.0090.000
고지서번호0.9930.1000.0401.0000.5000.7070.000
부과종류1.0000.0001.0000.5001.0000.7070.000
원인자부담금종류0.3170.0380.0090.7070.7071.0000.083
부과상태0.1130.0000.0000.0000.0000.0831.000

Missing values

2023-12-11T04:47:26.932272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T04:47:27.229889image/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.
2023-12-11T04:47:27.468245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

도로굴착관리번호부과종류부과번호원인자부담금종류부과금액가산금고지서번호부과일징수확인일납기내납부기한납부후납부기한부과상태
16128WRK001201906170002선납분28345시비 복구비_간접453001350201900708492019-06-18<NA>2019-07-032019-07-18부과
3529WRK002201707180001선납분10713군비 복구비_합계4945250201700344182017-07-20<NA>2017-08-042017-08-20부과
12764WRK001201903040024선납분26065군비 복구비_간접462001380201900609602019-03-052019-03-212019-03-202019-04-05완납
1818WRK001201705110025선납분8782군비 복구비_간접10000300201700574332017-05-162017-05-162017-05-312017-06-16완납
9300WRK001201801030047선납분15937군비 복구비_간접1402004200201800013612018-01-09<NA>2018-01-242018-02-09부과
25542WRK001202105040016선납분44769시비 복구비_간접90020202100102902021-05-17<NA>2021-06-012021-06-17부과
30857WRK001202204060005선납분50629군비 복구비_간접2033006090202200090562022-04-14<NA>2022-04-292022-05-14부과
13424WRK001201904120026선납분26955군비 복구비_합계50000201900166932019-04-172019-04-172019-05-022019-05-17완납
33749WRK001202206230026선납분51905시비 복구비_간접24500730202200189972022-06-29<NA>2022-07-142022-07-29부과
17592WRK001201907020007선납분28770시비 복구비_간접40010201901167202019-07-032019-07-052019-07-182019-08-03완납
도로굴착관리번호부과종류부과번호원인자부담금종류부과금액가산금고지서번호부과일징수확인일납기내납부기한납부후납부기한부과상태
27747WRK001202105050001선납분44619군비 복구비_합계94000202100184012021-05-112021-05-122021-05-262021-06-11완납
28876WRK001202105310035선납분45192군비 복구비_간접10000300202100247652021-06-07<NA>2021-06-222021-07-07부과
31020WRK001202201100015선납분49491군비 복구비_합계701500202200025222022-01-18<NA>2022-02-022022-02-18부과
13916WRK001201903110015선납분27248군비 복구비_합계483000201900174702019-04-302020-10-142019-05-152019-05-30완납
15631WRK001201901240029선납분27733군비 복구비_간접893002670201900288192019-05-272019-05-272019-06-112019-06-27완납
34974WRK001202208050032선납분52655군비 복구비_합계17000202200222132022-08-09<NA>2022-08-242022-09-09부과
18155WRK001201907110021선납분30437시비 복구비_간접160040201901201202019-07-182019-07-182019-08-022019-08-18완납
21427WRK001201910150037선납분33089군비 복구비_간접562001680201901375482019-10-212019-10-232019-11-052019-11-21완납
531WRK001201702160027선납분6569군비 복구비_간접21000630201700049232017-02-22<NA>2017-03-092017-03-22부과
35471WRK001202303130003선납분56012군비 복구비_간접14500430202300121532023-03-24<NA>2023-04-082023-04-24납부확인요청