Overview

Dataset statistics

Number of variables10
Number of observations158
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 KiB
Average record size in memory85.8 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description부산광역시사상구_지방세납부현황_20211231
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079727

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부건수 is highly overall correlated with 납부금액 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부금액 has unique valuesUnique
납부매체비율 has 9 (5.7%) zerosZeros

Reproduction

Analysis started2023-12-10 17:34:08.305620
Analysis finished2023-12-10 17:34:12.379797
Duration4.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
부산광역시
158 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 158
100.0%

Length

2023-12-11T02:34:12.539201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:34:12.787240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 158
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
사상구
158 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사상구 158
100.0%

Length

2023-12-11T02:34:13.077350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:34:13.294108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사상구 158
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
26530
158 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26530
2nd row26530
3rd row26530
4th row26530
5th row26530

Common Values

ValueCountFrequency (%)
26530 158
100.0%

Length

2023-12-11T02:34:13.498751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:34:13.720433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26530 158
100.0%

납부년도
Categorical

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2020
79 
2021
79 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 79
50.0%
2021 79
50.0%

Length

2023-12-11T02:34:13.949246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:34:14.160731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 79
50.0%
2021 79
50.0%

세목명
Categorical

Distinct12
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
주민세
20 
등록면허세
20 
자동차세
20 
재산세
20 
지방소득세
18 
Other values (7)
60 

Length

Max length7
Median length5
Mean length4.0632911
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종합토지세
2nd row주민세
3rd row지방소득세
4th row지방소비세
5th row취득세

Common Values

ValueCountFrequency (%)
주민세 20
12.7%
등록면허세 20
12.7%
자동차세 20
12.7%
재산세 20
12.7%
지방소득세 18
11.4%
취득세 17
10.8%
지역자원시설세 13
8.2%
등록세 11
7.0%
면허세 8
 
5.1%
종합토지세 7
 
4.4%
Other values (2) 4
 
2.5%

Length

2023-12-11T02:34:14.416332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주민세 20
12.7%
등록면허세 20
12.7%
자동차세 20
12.7%
재산세 20
12.7%
지방소득세 18
11.4%
취득세 17
10.8%
지역자원시설세 13
8.2%
등록세 11
7.0%
면허세 8
 
5.1%
종합토지세 7
 
4.4%
Other values (2) 4
 
2.5%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
은행창구
21 
이택스
21 
기타
19 
위택스
16 
인터넷지로
16 
Other values (5)
65 

Length

Max length5
Median length4
Mean length3.8924051
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은행창구 21
13.3%
이택스 21
13.3%
기타 19
12.0%
위택스 16
10.1%
인터넷지로 16
10.1%
자동화기기 16
10.1%
지자체방문 15
9.5%
가상계좌 15
9.5%
페이사납부 11
7.0%
자동이체 8
 
5.1%

Length

2023-12-11T02:34:14.780564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:34:15.203067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은행창구 21
13.3%
이택스 21
13.3%
기타 19
12.0%
위택스 16
10.1%
인터넷지로 16
10.1%
자동화기기 16
10.1%
지자체방문 15
9.5%
가상계좌 15
9.5%
페이사납부 11
7.0%
자동이체 8
 
5.1%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size290.0 B
True
87 
False
71 
ValueCountFrequency (%)
True 87
55.1%
False 71
44.9%
2023-12-11T02:34:15.537713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7631.6899
Minimum1
Maximum94430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:34:16.420285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q145.5
median1500
Q39034.75
95-th percentile33746.35
Maximum94430
Range94429
Interquartile range (IQR)8989.25

Descriptive statistics

Standard deviation15577.275
Coefficient of variation (CV)2.0411305
Kurtosis13.641238
Mean7631.6899
Median Absolute Deviation (MAD)1492.5
Skewness3.4910671
Sum1205807
Variance2.426515 × 108
MonotonicityNot monotonic
2023-12-11T02:34:16.930479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
3.8%
7 5
 
3.2%
12 3
 
1.9%
13 3
 
1.9%
2 3
 
1.9%
16 2
 
1.3%
34 2
 
1.3%
4 2
 
1.3%
20 2
 
1.3%
73 2
 
1.3%
Other values (126) 128
81.0%
ValueCountFrequency (%)
1 6
3.8%
2 3
1.9%
3 1
 
0.6%
4 2
 
1.3%
5 1
 
0.6%
6 1
 
0.6%
7 5
3.2%
8 1
 
0.6%
9 1
 
0.6%
12 3
1.9%
ValueCountFrequency (%)
94430 1
0.6%
84637 1
0.6%
77704 1
0.6%
74318 1
0.6%
53939 1
0.6%
51610 1
0.6%
44714 1
0.6%
39279 1
0.6%
32770 1
0.6%
30203 1
0.6%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5753579 × 109
Minimum2650
Maximum3.8988888 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:34:17.241513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2650
5-th percentile173498
Q16254675
median3.6494281 × 108
Q32.2277419 × 109
95-th percentile1.1263586 × 1010
Maximum3.8988888 × 1010
Range3.8988885 × 1010
Interquartile range (IQR)2.2214872 × 109

Descriptive statistics

Standard deviation5.2798091 × 109
Coefficient of variation (CV)2.0501264
Kurtosis18.649264
Mean2.5753579 × 109
Median Absolute Deviation (MAD)3.6447484 × 108
Skewness3.7664551
Sum4.0690654 × 1011
Variance2.7876384 × 1019
MonotonicityNot monotonic
2023-12-11T02:34:17.629804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
718700 1
 
0.6%
11857190250 1
 
0.6%
11302807470 1
 
0.6%
262170 1
 
0.6%
38988887500 1
 
0.6%
595928560 1
 
0.6%
25343210 1
 
0.6%
460310 1
 
0.6%
1372971510 1
 
0.6%
1641330 1
 
0.6%
Other values (148) 148
93.7%
ValueCountFrequency (%)
2650 1
0.6%
37800 1
0.6%
46350 1
0.6%
50000 1
0.6%
118440 1
0.6%
125620 1
0.6%
141750 1
0.6%
157450 1
0.6%
176330 1
0.6%
229950 1
0.6%
ValueCountFrequency (%)
38988887500 1
0.6%
29000514650 1
0.6%
21785534840 1
0.6%
20254695130 1
0.6%
13132707650 1
0.6%
11857190250 1
0.6%
11595191460 1
0.6%
11302807470 1
0.6%
11256664680 1
0.6%
11230569850 1
0.6%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.658038
Minimum0
Maximum80.28
Zeros9
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T02:34:18.016650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1225
median7.03
Q319.9825
95-th percentile38.0235
Maximum80.28
Range80.28
Interquartile range (IQR)19.86

Descriptive statistics

Standard deviation15.022131
Coefficient of variation (CV)1.1867661
Kurtosis3.351445
Mean12.658038
Median Absolute Deviation (MAD)7.02
Skewness1.6347352
Sum1999.97
Variance225.66441
MonotonicityNot monotonic
2023-12-11T02:34:18.365380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
5.7%
0.01 8
 
5.1%
0.05 4
 
2.5%
0.02 4
 
2.5%
0.03 4
 
2.5%
0.07 3
 
1.9%
0.13 3
 
1.9%
0.04 3
 
1.9%
3.11 2
 
1.3%
0.1 2
 
1.3%
Other values (113) 116
73.4%
ValueCountFrequency (%)
0.0 9
5.7%
0.01 8
5.1%
0.02 4
2.5%
0.03 4
2.5%
0.04 3
 
1.9%
0.05 4
2.5%
0.06 1
 
0.6%
0.07 3
 
1.9%
0.08 1
 
0.6%
0.1 2
 
1.3%
ValueCountFrequency (%)
80.28 1
0.6%
67.4 1
0.6%
60.56 1
0.6%
59.16 1
0.6%
50.75 1
0.6%
49.48 1
0.6%
38.63 1
0.6%
38.61 1
0.6%
37.92 1
0.6%
36.55 1
0.6%

Interactions

2023-12-11T02:34:10.827888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:09.137720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:09.927779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:11.063111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:09.367137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:10.257579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:11.366939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:09.673815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:34:10.548376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:34:18.595186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.0960.4050.599
납부매체0.0000.0001.0001.0000.3670.3220.479
납부매체전자고지여부0.0000.0001.0001.0000.0770.1570.214
납부건수0.0000.0960.3670.0771.0000.7570.848
납부금액0.0000.4050.3220.1570.7571.0000.468
납부매체비율0.0000.5990.4790.2140.8480.4681.000
2023-12-11T02:34:18.846811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부년도납부매체전자고지여부
세목명1.0000.0000.0000.000
납부매체0.0001.0000.0000.974
납부년도0.0000.0001.0000.000
납부매체전자고지여부0.0000.9740.0001.000
2023-12-11T02:34:19.095938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.8300.8220.0000.0340.1750.073
납부금액0.8301.0000.6410.0000.2070.1660.164
납부매체비율0.8220.6411.0000.0000.3010.2400.208
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.0340.2070.3010.0001.0000.0000.000
납부매체0.1750.1660.2400.0000.0001.0000.974
납부매체전자고지여부0.0730.1640.2080.0000.0000.9741.000

Missing values

2023-12-11T02:34:11.752472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:34:12.218879image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
0부산광역시사상구265302020종합토지세기타N127187000.07
1부산광역시사상구265302020주민세기타N13413902527307.82
2부산광역시사상구265302020지방소득세기타N13763705033448080.28
3부산광역시사상구265302020지방소비세기타N642770000000.03
4부산광역시사상구265302020취득세기타N712722700.04
5부산광역시사상구265302020등록면허세위택스Y26177518301979035.61
6부산광역시사상구265302020등록세위택스Y3497098300.05
7부산광역시사상구265302020자동차세위택스Y11983240080627016.3
8부산광역시사상구265302020재산세위택스Y13636714939196018.55
9부산광역시사상구265302020주민세위택스Y612016846736108.32
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
148부산광역시사상구265302021재산세지자체방문N32710765385030.88
149부산광역시사상구265302021주민세지자체방문N198622975018.7
150부산광역시사상구265302021지방소득세지자체방문N52184731504.91
151부산광역시사상구265302021취득세지자체방문N8731184919808.22
152부산광역시사상구265302021등록면허세페이사납부Y380206348503.47
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