Overview

Dataset statistics

Number of variables10
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory89.6 B

Variable types

Categorical5
Numeric4
DateTime1

Dataset

Description인천광역시 남동구 지방세 비과/감면율 현황에 대한 데이터로(세목명, 과세년도, 비과세금액, 감면금액, 부과금액, 비과세감면율, 데이터기준일) 등을 제공합니다.
Author인천광역시 남동구
URLhttps://www.data.go.kr/data/15079464/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 2 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 감면금액 and 1 other fieldsHigh correlation
감면금액 has unique valuesUnique
비과세금액 has 6 (16.7%) zerosZeros
부과금액 has 3 (8.3%) zerosZeros
비과세감면율 has 5 (13.9%) zerosZeros

Reproduction

Analysis started2024-03-14 14:43:58.074635
Analysis finished2024-03-14 14:44:02.926390
Duration4.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size416.0 B
인천광역시
36 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 36
100.0%

Length

2024-03-14T23:44:03.042120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:44:03.211714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 36
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size416.0 B
남동구
36 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남동구 36
100.0%

Length

2024-03-14T23:44:03.389633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:44:03.618041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 36
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size416.0 B
28200
36 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28200
2nd row28200
3rd row28200
4th row28200
5th row28200

Common Values

ValueCountFrequency (%)
28200 36
100.0%

Length

2024-03-14T23:44:03.948889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:44:04.260160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28200 36
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size416.0 B
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
등록면허세
5 
Other values (3)
11 

Length

Max length7
Median length3
Mean length3.9722222
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록세
2nd row재산세
3rd row주민세
4th row취득세
5th row자동차세

Common Values

ValueCountFrequency (%)
재산세 5
13.9%
주민세 5
13.9%
취득세 5
13.9%
자동차세 5
13.9%
등록면허세 5
13.9%
지역자원시설세 5
13.9%
등록세 4
11.1%
교육세 2
 
5.6%

Length

2024-03-14T23:44:04.616827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:44:04.997600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 5
13.9%
주민세 5
13.9%
취득세 5
13.9%
자동차세 5
13.9%
등록면허세 5
13.9%
지역자원시설세 5
13.9%
등록세 4
11.1%
교육세 2
 
5.6%

과세년도
Categorical

Distinct5
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size416.0 B
2019
8 
2022
8 
2018
7 
2020
7 
2021
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2019 8
22.2%
2022 8
22.2%
2018 7
19.4%
2020 7
19.4%
2021 6
16.7%

Length

2024-03-14T23:44:05.426125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:44:05.762749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 8
22.2%
2022 8
22.2%
2018 7
19.4%
2020 7
19.4%
2021 6
16.7%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1968552 × 109
Minimum0
Maximum4.6864751 × 1010
Zeros6
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:44:06.134999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126502500
median1.66017 × 108
Q33.7336335 × 109
95-th percentile3.7661438 × 1010
Maximum4.6864751 × 1010
Range4.6864751 × 1010
Interquartile range (IQR)3.707131 × 109

Descriptive statistics

Standard deviation1.3281275 × 1010
Coefficient of variation (CV)2.1432283
Kurtosis3.2772721
Mean6.1968552 × 109
Median Absolute Deviation (MAD)1.66017 × 108
Skewness2.1774204
Sum2.2308679 × 1011
Variance1.7639227 × 1020
MonotonicityNot monotonic
2024-03-14T23:44:06.527943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 6
 
16.7%
35689832000 1
 
2.8%
689424000 1
 
2.8%
28789000 1
 
2.8%
191894000 1
 
2.8%
4148673000 1
 
2.8%
19493000 1
 
2.8%
46864751000 1
 
2.8%
666976000 1
 
2.8%
41516000 1
 
2.8%
Other values (21) 21
58.3%
ValueCountFrequency (%)
0 6
16.7%
13350000 1
 
2.8%
19493000 1
 
2.8%
19643000 1
 
2.8%
28789000 1
 
2.8%
41516000 1
 
2.8%
117915000 1
 
2.8%
144975000 1
 
2.8%
153849000 1
 
2.8%
156319000 1
 
2.8%
ValueCountFrequency (%)
46864751000 1
2.8%
38654979000 1
2.8%
37330258000 1
2.8%
35689832000 1
2.8%
31881818000 1
2.8%
10046072000 1
2.8%
5746379000 1
2.8%
4148673000 1
2.8%
3805971000 1
2.8%
3709521000 1
2.8%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9667302 × 109
Minimum1000
Maximum3.4963432 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:44:06.909271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile16398000
Q12.3269725 × 108
median1.0834015 × 109
Q37.7969548 × 109
95-th percentile2.9470028 × 1010
Maximum3.4963432 × 1010
Range3.4963431 × 1010
Interquartile range (IQR)7.5642575 × 109

Descriptive statistics

Standard deviation1.0154553 × 1010
Coefficient of variation (CV)1.7018623
Kurtosis2.3967067
Mean5.9667302 × 109
Median Absolute Deviation (MAD)1.0181995 × 109
Skewness1.9199983
Sum2.1480229 × 1011
Variance1.0311495 × 1020
MonotonicityNot monotonic
2024-03-14T23:44:07.334261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
54278000 1
 
2.8%
2090677000 1
 
2.8%
266182000 1
 
2.8%
8548674000 1
 
2.8%
235258000 1
 
2.8%
27224960000 1
 
2.8%
1984759000 1
 
2.8%
78784000 1
 
2.8%
270594000 1
 
2.8%
9000 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1000 1
2.8%
9000 1
2.8%
21861000 1
2.8%
37602000 1
2.8%
39225000 1
2.8%
54278000 1
2.8%
78784000 1
2.8%
222936000 1
2.8%
231324000 1
2.8%
233155000 1
2.8%
ValueCountFrequency (%)
34963432000 1
2.8%
31000612000 1
2.8%
28959834000 1
2.8%
27224960000 1
2.8%
26345791000 1
2.8%
9434877000 1
2.8%
8548674000 1
2.8%
7986834000 1
2.8%
7895903000 1
2.8%
7763972000 1
2.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1478002 × 1010
Minimum0
Maximum3.27 × 1011
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:44:07.715292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.565472 × 1010
median1.9128163 × 1010
Q39.9006088 × 1010
95-th percentile2.755 × 1011
Maximum3.27 × 1011
Range3.27 × 1011
Interquartile range (IQR)8.3351369 × 1010

Descriptive statistics

Standard deviation9.1991773 × 1010
Coefficient of variation (CV)1.2869942
Kurtosis1.9109173
Mean7.1478002 × 1010
Median Absolute Deviation (MAD)1.9128163 × 1010
Skewness1.727157
Sum2.5732081 × 1012
Variance8.4624863 × 1021
MonotonicityNot monotonic
2024-03-14T23:44:07.948551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 3
 
8.3%
15906060000 1
 
2.8%
15674354000 1
 
2.8%
110000000000 1
 
2.8%
18619930000 1
 
2.8%
327000000000 1
 
2.8%
61690180000 1
 
2.8%
15626804000 1
 
2.8%
43764074000 1
 
2.8%
63046303000 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
0 3
8.3%
65645000 1
 
2.8%
13792053000 1
 
2.8%
15162670000 1
 
2.8%
15336788000 1
 
2.8%
15362471000 1
 
2.8%
15626804000 1
 
2.8%
15664025000 1
 
2.8%
15674354000 1
 
2.8%
15906060000 1
 
2.8%
ValueCountFrequency (%)
327000000000 1
2.8%
304000000000 1
2.8%
266000000000 1
2.8%
253000000000 1
2.8%
249000000000 1
2.8%
119000000000 1
2.8%
110000000000 1
2.8%
107000000000 1
2.8%
102000000000 1
2.8%
98008118000 1
2.8%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.138889
Minimum0
Maximum59.75
Zeros5
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:44:08.175079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.82
median5.56
Q314.4825
95-th percentile46.0325
Maximum59.75
Range59.75
Interquartile range (IQR)12.6625

Descriptive statistics

Standard deviation17.162523
Coefficient of variation (CV)1.3062385
Kurtosis0.85657232
Mean13.138889
Median Absolute Deviation (MAD)4.905
Skewness1.4936674
Sum473
Variance294.55218
MonotonicityNot monotonic
2024-03-14T23:44:08.545730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 5
 
13.9%
1.37 2
 
5.6%
3.58 1
 
2.8%
5.94 1
 
2.8%
14.45 1
 
2.8%
3.65 1
 
2.8%
12.03 1
 
2.8%
1.46 1
 
2.8%
47.45 1
 
2.8%
5.89 1
 
2.8%
Other values (21) 21
58.3%
ValueCountFrequency (%)
0.0 5
13.9%
0.77 1
 
2.8%
1.37 2
 
5.6%
1.46 1
 
2.8%
1.94 1
 
2.8%
2.03 1
 
2.8%
3.49 1
 
2.8%
3.58 1
 
2.8%
3.65 1
 
2.8%
3.8 1
 
2.8%
ValueCountFrequency (%)
59.75 1
2.8%
47.45 1
2.8%
45.56 1
2.8%
44.34 1
2.8%
44.29 1
2.8%
43.39 1
2.8%
36.76 1
2.8%
16.5 1
2.8%
14.58 1
2.8%
14.45 1
2.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size416.0 B
Minimum2024-01-08 00:00:00
Maximum2024-01-08 00:00:00
2024-03-14T23:44:08.885648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:09.188825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T23:44:01.377980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:58.394502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:59.366633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:00.378188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:01.615784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:58.623681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:59.610153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:00.618243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:01.872104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:58.874732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:59.868045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:00.874692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:02.124286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:59.125204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:00.125188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:44:01.125605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:44:09.406225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.3560.7330.8160.669
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.3560.0001.0000.7260.8890.756
감면금액0.7330.0000.7261.0000.8740.703
부과금액0.8160.0000.8890.8741.0000.735
비과세감면율0.6690.0000.7560.7030.7351.000
2024-03-14T23:44:09.674621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-03-14T23:44:09.920362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.8310.6680.7260.1790.000
감면금액0.8311.0000.7680.7360.5400.000
부과금액0.6680.7681.0000.4050.6020.000
비과세감면율0.7260.7360.4051.0000.4320.000
세목명0.1790.5400.6020.4321.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2024-03-14T23:44:02.479941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:44:02.828415image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율데이터기준일자
0인천광역시남동구28200등록세201805427800000.02024-01-08
1인천광역시남동구28200재산세20183568983200077639720009800811800044.342024-01-08
2인천광역시남동구28200주민세2018117915000222936000176121320001.942024-01-08
3인천광역시남동구28200취득세201838059710003496343200026600000000014.582024-01-08
4인천광역시남동구28200자동차세20185511510001900688000574554300004.272024-01-08
5인천광역시남동구28200등록면허세2018166027000410894000151626700003.82024-01-08
6인천광역시남동구28200지역자원시설세2018543828000699847000156640250007.942024-01-08
7인천광역시남동구28200교육세201901000442006390000.02024-01-08
8인천광역시남동구28200등록세201903760200000.02024-01-08
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