Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory654.3 KiB
Average record size in memory67.0 B

Variable types

Categorical4
Numeric3

Dataset

Description부산광역시_부산시인터넷지방세청(사이버지방세청)_지방세등납부현황_20231231
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15061359

Alerts

건수 is highly overall correlated with 금액High correlation
금액 is highly overall correlated with 건수High correlation
금액 is highly skewed (γ1 = 20.78611483)Skewed

Reproduction

Analysis started2024-03-13 13:19:22.778250
Analysis finished2024-03-13 13:19:24.905543
Duration2.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구청명
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
 
628
금정구
 
614
연제구
 
610
부산진구
 
602
남구
 
594
Other values (13)
6952 

Length

Max length4
Median length3
Mean length2.7673
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기장군
2nd row해운대구
3rd row남구
4th row시청
5th row북구

Common Values

ValueCountFrequency (%)
해운대구 628
 
6.3%
금정구 614
 
6.1%
연제구 610
 
6.1%
부산진구 602
 
6.0%
남구 594
 
5.9%
시청 589
 
5.9%
사하구 589
 
5.9%
수영구 588
 
5.9%
사상구 583
 
5.8%
북구 578
 
5.8%
Other values (8) 4025
40.2%

Length

2024-03-13T22:19:25.005107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 628
 
6.3%
금정구 614
 
6.1%
연제구 610
 
6.1%
부산진구 602
 
6.0%
남구 594
 
5.9%
시청 589
 
5.9%
사하구 589
 
5.9%
수영구 588
 
5.9%
사상구 583
 
5.8%
북구 578
 
5.8%
Other values (8) 4025
40.2%

납부연도
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.1737
Minimum2017
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:19:25.123693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12019
median2020
Q32022
95-th percentile2023
Maximum2023
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.937138
Coefficient of variation (CV)0.00095889674
Kurtosis-1.1655048
Mean2020.1737
Median Absolute Deviation (MAD)2
Skewness-0.10815766
Sum20201737
Variance3.7525036
MonotonicityNot monotonic
2024-03-13T22:19:25.243136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 1593
15.9%
2022 1555
15.6%
2020 1503
15.0%
2019 1482
14.8%
2023 1477
14.8%
2018 1255
12.6%
2017 1135
11.3%
ValueCountFrequency (%)
2017 1135
11.3%
2018 1255
12.6%
2019 1482
14.8%
2020 1503
15.0%
2021 1593
15.9%
2022 1555
15.6%
2023 1477
14.8%
ValueCountFrequency (%)
2023 1477
14.8%
2022 1555
15.6%
2021 1593
15.9%
2020 1503
15.0%
2019 1482
14.8%
2018 1255
12.6%
2017 1135
11.3%

구분
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지방세
3863 
표준세외수입
1760 
환경개선부담금
1668 
주정차위반과태료
1288 
교통유발부담금
623 
Other values (3)
798 

Length

Max length11
Median length9
Mean length5.4935
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방세
2nd row교통유발부담금
3rd row지방세
4th row상하수도요금
5th row주정차위반과태료

Common Values

ValueCountFrequency (%)
지방세 3863
38.6%
표준세외수입 1760
17.6%
환경개선부담금 1668
16.7%
주정차위반과태료 1288
 
12.9%
교통유발부담금 623
 
6.2%
주거지전용주차요금 419
 
4.2%
상하수도요금 299
 
3.0%
버스전용차로위반과태료 80
 
0.8%

Length

2024-03-13T22:19:25.403241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:19:25.565200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방세 3863
38.6%
표준세외수입 1760
17.6%
환경개선부담금 1668
16.7%
주정차위반과태료 1288
 
12.9%
교통유발부담금 623
 
6.2%
주거지전용주차요금 419
 
4.2%
상하수도요금 299
 
3.0%
버스전용차로위반과태료 80
 
0.8%

기분
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기분
4232 
수시분
3925 
자납분
1742 
6
 
27
4
 
26
Other values (3)
 
48

Length

Max length3
Median length3
Mean length2.9798
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수시분
2nd row정기분
3rd row수시분
4th row정기분
5th row정기분

Common Values

ValueCountFrequency (%)
정기분 4232
42.3%
수시분 3925
39.2%
자납분 1742
17.4%
6 27
 
0.3%
4 26
 
0.3%
A 22
 
0.2%
5 20
 
0.2%
0 6
 
0.1%

Length

2024-03-13T22:19:25.709490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:19:25.833087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기분 4232
42.3%
수시분 3925
39.2%
자납분 1742
17.4%
6 27
 
0.3%
4 26
 
0.3%
a 22
 
0.2%
5 20
 
0.2%
0 6
 
0.1%

수납매체
Categorical

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
금융결제원
868 
부산은행가상계좌
868 
사이버세청신용카드
856 
ARS
828 
앱카드
818 
Other values (23)
5762 

Length

Max length9
Median length8
Mean length6.2303
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNAVERPAY
2nd rowSSGPAY
3rd row국민은행가상계좌
4th row금융결제원
5th row페이코

Common Values

ValueCountFrequency (%)
금융결제원 868
 
8.7%
부산은행가상계좌 868
 
8.7%
사이버세청신용카드 856
 
8.6%
ARS 828
 
8.3%
앱카드 818
 
8.2%
장애인키오스크 816
 
8.2%
사이버세청이지로 761
 
7.6%
페이코 478
 
4.8%
카카오페이 467
 
4.7%
NAVERPAY 336
 
3.4%
Other values (18) 2904
29.0%

Length

2024-03-13T22:19:25.954502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금융결제원 868
 
8.7%
부산은행가상계좌 868
 
8.7%
사이버세청신용카드 856
 
8.6%
ars 828
 
8.3%
앱카드 818
 
8.2%
장애인키오스크 816
 
8.2%
사이버세청이지로 761
 
7.6%
페이코 478
 
4.8%
카카오페이 467
 
4.7%
naverpay 336
 
3.4%
Other values (18) 2904
29.0%

건수
Real number (ℝ)

HIGH CORRELATION 

Distinct3215
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4851.4802
Minimum1
Maximum404717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:19:26.099244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q115
median136.5
Q31193.75
95-th percentile16248.4
Maximum404717
Range404716
Interquartile range (IQR)1178.75

Descriptive statistics

Standard deviation23903.959
Coefficient of variation (CV)4.9271476
Kurtosis109.95149
Mean4851.4802
Median Absolute Deviation (MAD)134.5
Skewness9.5353777
Sum48514802
Variance5.7139926 × 108
MonotonicityNot monotonic
2024-03-13T22:19:26.244608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 568
 
5.7%
2 343
 
3.4%
3 254
 
2.5%
4 215
 
2.1%
5 173
 
1.7%
6 153
 
1.5%
7 122
 
1.2%
8 121
 
1.2%
9 102
 
1.0%
10 94
 
0.9%
Other values (3205) 7855
78.5%
ValueCountFrequency (%)
1 568
5.7%
2 343
3.4%
3 254
2.5%
4 215
 
2.1%
5 173
 
1.7%
6 153
 
1.5%
7 122
 
1.2%
8 121
 
1.2%
9 102
 
1.0%
10 94
 
0.9%
ValueCountFrequency (%)
404717 1
< 0.1%
400250 1
< 0.1%
398091 1
< 0.1%
394360 1
< 0.1%
378503 1
< 0.1%
371091 1
< 0.1%
369426 1
< 0.1%
366420 1
< 0.1%
351033 1
< 0.1%
346621 1
< 0.1%

금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9518
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3009377 × 109
Minimum1210
Maximum6.8923318 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:19:26.391331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1210
5-th percentile96000
Q11876440
median20922080
Q32.7097572 × 108
95-th percentile4.7175799 × 109
Maximum6.8923318 × 1011
Range6.8923318 × 1011
Interquartile range (IQR)2.6909928 × 108

Descriptive statistics

Standard deviation2.04537 × 1010
Coefficient of variation (CV)8.889289
Kurtosis543.09061
Mean2.3009377 × 109
Median Absolute Deviation (MAD)20767930
Skewness20.786115
Sum2.3009377 × 1013
Variance4.1835383 × 1020
MonotonicityNot monotonic
2024-03-13T22:19:26.589951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32000 37
 
0.4%
40000 21
 
0.2%
160000 18
 
0.2%
128000 17
 
0.2%
256000 15
 
0.1%
96000 14
 
0.1%
192000 13
 
0.1%
80000 11
 
0.1%
64000 11
 
0.1%
224000 11
 
0.1%
Other values (9508) 9832
98.3%
ValueCountFrequency (%)
1210 1
< 0.1%
1810 1
< 0.1%
2970 1
< 0.1%
3200 1
< 0.1%
4260 1
< 0.1%
4650 1
< 0.1%
4860 1
< 0.1%
6400 1
< 0.1%
6540 1
< 0.1%
7000 1
< 0.1%
ValueCountFrequency (%)
689233184360 1
< 0.1%
661691702670 1
< 0.1%
644769192160 1
< 0.1%
543007553800 1
< 0.1%
530493219700 1
< 0.1%
514190516060 1
< 0.1%
488040907590 1
< 0.1%
347242316370 1
< 0.1%
320276305330 1
< 0.1%
312922055670 1
< 0.1%

Interactions

2024-03-13T22:19:24.228408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:23.499446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:23.840312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:24.376222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:23.605144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:23.959166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:24.515996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:23.712407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:19:24.085004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:19:26.698389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구청명납부연도구분기분수납매체건수금액
구청명1.0000.1720.6090.3560.4450.1260.080
납부연도0.1721.0000.0580.0770.3180.0000.000
구분0.6090.0581.0000.6740.4880.1250.085
기분0.3560.0770.6741.0000.2010.0850.144
수납매체0.4450.3180.4880.2011.0000.2870.220
건수0.1260.0000.1250.0850.2871.0000.549
금액0.0800.0000.0850.1440.2200.5491.000
2024-03-13T22:19:26.810211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기분수납매체구청명구분
기분1.0000.0790.1580.282
수납매체0.0791.0000.1380.211
구청명0.1580.1381.0000.309
구분0.2820.2110.3091.000
2024-03-13T22:19:26.901795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부연도건수금액구청명구분기분수납매체
납부연도1.000-0.055-0.0370.0620.0310.0390.149
건수-0.0551.0000.9300.0480.0600.0410.106
금액-0.0370.9301.0000.0340.0280.0490.086
구청명0.0620.0480.0341.0000.3090.1580.138
구분0.0310.0600.0280.3091.0000.2820.211
기분0.0390.0410.0490.1580.2821.0000.079
수납매체0.1490.1060.0860.1380.2110.0791.000

Missing values

2024-03-13T22:19:24.660959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:19:24.805333image/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

구청명납부연도구분기분수납매체건수금액
16066기장군2023지방세수시분NAVERPAY277910
9592해운대구2019교통유발부담금정기분SSGPAY2177260
6896남구2022지방세수시분국민은행가상계좌1456179366280
579시청2021상하수도요금정기분금융결제원24402272174659550
8205북구2018주정차위반과태료정기분페이코140000
10908금정구2018지방세정기분부산은행가상계좌8305910175872470
9137해운대구2023지방세수시분카카오페이151376520
3796영도구2021지방세정기분사이버세청신용카드3566523983220
4806부산진구2017지방세수시분하나은행가상계좌40262565150
10150사하구2023지방세수시분NAVERPAY81046240
구청명납부연도구분기분수납매체건수금액
5623부산진구2023환경개선부담금수시분앱카드563174130
16274기장군2022표준세외수입정기분카카오페이112182460
7152남구2019표준세외수입수시분국민은행가상계좌638327505200
12896연제구2021지방세정기분금융결제원18811735343274870
9336해운대구2023표준세외수입정기분NAVERPAY71704270
10509사하구2022주정차위반과태료정기분ARS97049332120
8117북구2023표준세외수입정기분앱카드122806730
3621동구2017환경개선부담금수시분사이버세청신용카드1429167380
3407동구2020주정차위반과태료수시분ARS32811132000
16054기장군2022지방세수시분ARS42974758360