Overview

Dataset statistics

Number of variables9
Number of observations209
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory76.6 B

Variable types

Categorical5
Numeric3
DateTime1

Dataset

Description인천광역시 남동구 지방세 체납 현황에 대한 데이터로(시군구명, 자치단체코드, 과세년도, 세목명, 체납액구간, 체납건수, 체납금액, 데이터생성일, 데이터기준일)등을 제공합니다.
Author인천광역시 남동구
URLhttps://www.data.go.kr/data/15079498/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
체납건수 is highly overall correlated with 체납금액High correlation
체납금액 is highly overall correlated with 체납건수High correlation

Reproduction

Analysis started2024-03-14 10:38:58.327096
Analysis finished2024-03-14 10:39:01.761368
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
인천광역시
209 

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 209
100.0%

Length

2024-03-14T19:39:01.953870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:39:02.269480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 209
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
남동구
209 

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 209
100.0%

Length

2024-03-14T19:39:02.590369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:39:02.886258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 209
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
28200
209 

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 209
100.0%

Length

2024-03-14T19:39:03.224885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:39:03.549150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28200 209
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.8708
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T19:39:03.838392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12019
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.669166
Coefficient of variation (CV)0.00082637266
Kurtosis-1.0736853
Mean2019.8708
Median Absolute Deviation (MAD)1
Skewness-0.34440322
Sum422153
Variance2.7861152
MonotonicityIncreasing
2024-03-14T19:39:04.191103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 45
21.5%
2022 43
20.6%
2020 40
19.1%
2019 31
14.8%
2017 27
12.9%
2018 23
11.0%
ValueCountFrequency (%)
2017 27
12.9%
2018 23
11.0%
2019 31
14.8%
2020 40
19.1%
2021 45
21.5%
2022 43
20.6%
ValueCountFrequency (%)
2022 43
20.6%
2021 45
21.5%
2020 40
19.1%
2019 31
14.8%
2018 23
11.0%
2017 27
12.9%

세목명
Categorical

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
재산세
57 
지방소득세
50 
주민세
31 
취득세
27 
자동차세
24 
Other values (2)
20 

Length

Max length7
Median length3
Mean length3.8133971
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록면허세
2nd row등록면허세
3rd row자동차세
4th row자동차세
5th row자동차세

Common Values

ValueCountFrequency (%)
재산세 57
27.3%
지방소득세 50
23.9%
주민세 31
14.8%
취득세 27
12.9%
자동차세 24
11.5%
등록면허세 17
 
8.1%
지역자원시설세 3
 
1.4%

Length

2024-03-14T19:39:04.608347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:39:04.837584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 57
27.3%
지방소득세 50
23.9%
주민세 31
14.8%
취득세 27
12.9%
자동차세 24
11.5%
등록면허세 17
 
8.1%
지역자원시설세 3
 
1.4%

체납액구간
Categorical

Distinct12
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
10만원 미만
37 
10만원~30만원미만
31 
50만원~1백만원미만
28 
30만원~50만원미만
27 
1백만원~3백만원미만
23 
Other values (7)
63 

Length

Max length11
Median length11
Mean length10.22488
Min length7

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row10만원 미만
2nd row10만원~30만원미만
3rd row10만원 미만
4th row10만원~30만원미만
5th row30만원~50만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 37
17.7%
10만원~30만원미만 31
14.8%
50만원~1백만원미만 28
13.4%
30만원~50만원미만 27
12.9%
1백만원~3백만원미만 23
11.0%
3백만원~5백만원미만 17
8.1%
5백만원~1천만원미만 15
7.2%
1천만원~3천만원미만 12
 
5.7%
3천만원~5천만원미만 9
 
4.3%
5천만원~1억원미만 6
 
2.9%
Other values (2) 4
 
1.9%

Length

2024-03-14T19:39:05.093564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 37
15.0%
미만 37
15.0%
10만원~30만원미만 31
12.6%
50만원~1백만원미만 28
11.4%
30만원~50만원미만 27
11.0%
1백만원~3백만원미만 23
9.3%
3백만원~5백만원미만 17
6.9%
5백만원~1천만원미만 15
6.1%
1천만원~3천만원미만 12
 
4.9%
3천만원~5천만원미만 9
 
3.7%
Other values (3) 10
 
4.1%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean795.29187
Minimum1
Maximum22933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T19:39:05.330215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median21
Q3197
95-th percentile5111.2
Maximum22933
Range22932
Interquartile range (IQR)194

Descriptive statistics

Standard deviation2310.8934
Coefficient of variation (CV)2.9057173
Kurtosis42.576152
Mean795.29187
Median Absolute Deviation (MAD)20
Skewness5.5120826
Sum166216
Variance5340228.2
MonotonicityNot monotonic
2024-03-14T19:39:05.573622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 32
 
15.3%
3 17
 
8.1%
2 12
 
5.7%
5 7
 
3.3%
8 6
 
2.9%
6 5
 
2.4%
31 4
 
1.9%
4 4
 
1.9%
14 4
 
1.9%
12 3
 
1.4%
Other values (100) 115
55.0%
ValueCountFrequency (%)
1 32
15.3%
2 12
 
5.7%
3 17
8.1%
4 4
 
1.9%
5 7
 
3.3%
6 5
 
2.4%
8 6
 
2.9%
9 3
 
1.4%
10 3
 
1.4%
11 2
 
1.0%
ValueCountFrequency (%)
22933 1
0.5%
10260 1
0.5%
9203 1
0.5%
7935 1
0.5%
7482 1
0.5%
6280 1
0.5%
5819 1
0.5%
5651 1
0.5%
5591 1
0.5%
5502 1
0.5%

체납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2108542 × 108
Minimum17340
Maximum9.8583489 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T19:39:05.907466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17340
5-th percentile337050
Q14806480
median44315010
Q31.6403267 × 108
95-th percentile5.037068 × 108
Maximum9.8583489 × 108
Range9.8581755 × 108
Interquartile range (IQR)1.5922619 × 108

Descriptive statistics

Standard deviation1.8954085 × 108
Coefficient of variation (CV)1.5653482
Kurtosis7.1118302
Mean1.2108542 × 108
Median Absolute Deviation (MAD)43107670
Skewness2.557914
Sum2.5306853 × 1010
Variance3.5925734 × 1016
MonotonicityNot monotonic
2024-03-14T19:39:06.166756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125140 2
 
1.0%
19390030 1
 
0.5%
16925550 1
 
0.5%
30768160 1
 
0.5%
164032670 1
 
0.5%
235169930 1
 
0.5%
80416300 1
 
0.5%
347615990 1
 
0.5%
50080840 1
 
0.5%
16260560 1
 
0.5%
Other values (198) 198
94.7%
ValueCountFrequency (%)
17340 1
0.5%
23330 1
0.5%
67260 1
0.5%
69620 1
0.5%
111240 1
0.5%
116900 1
0.5%
125140 2
1.0%
287560 1
0.5%
307340 1
0.5%
311270 1
0.5%
ValueCountFrequency (%)
985834890 1
0.5%
934365730 1
0.5%
889026620 1
0.5%
881394060 1
0.5%
875501630 1
0.5%
820512810 1
0.5%
742990780 1
0.5%
588440010 1
0.5%
580828920 1
0.5%
569500600 1
0.5%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum2024-01-08 00:00:00
Maximum2024-01-08 00:00:00
2024-03-14T19:39:06.361357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:39:06.519578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T19:39:00.294421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:38:58.648842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:38:59.576368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:39:00.541774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:38:59.093071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:38:59.820970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:39:00.777305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:38:59.328890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:39:00.053609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:39:06.642530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액
과세년도1.0000.0000.0000.0000.163
세목명0.0001.0000.1570.2840.320
체납액구간0.0000.1571.0000.3470.369
체납건수0.0000.2840.3471.0000.771
체납금액0.1630.3200.3690.7711.000
2024-03-14T19:39:06.817354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.074
세목명0.0741.000
2024-03-14T19:39:07.028605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액세목명체납액구간
과세년도1.0000.1000.3060.0000.000
체납건수0.1001.0000.6250.1720.140
체납금액0.3060.6251.0000.1750.164
세목명0.0000.1720.1751.0000.074
체납액구간0.0000.1400.1640.0741.000

Missing values

2024-03-14T19:39:01.125988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:39:01.580026image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액데이터기준일
0인천광역시남동구282002017등록면허세10만원 미만611193900302024-01-08
1인천광역시남동구282002017등록면허세10만원~30만원미만11112402024-01-08
2인천광역시남동구282002017자동차세10만원 미만1806728000302024-01-08
3인천광역시남동구282002017자동차세10만원~30만원미만13772230661602024-01-08
4인천광역시남동구282002017자동차세30만원~50만원미만32107554702024-01-08
5인천광역시남동구282002017자동차세50만원~1백만원미만528827702024-01-08
6인천광역시남동구282002017재산세10만원 미만1632904371102024-01-08
7인천광역시남동구282002017재산세10만원~30만원미만572923742402024-01-08
8인천광역시남동구282002017재산세1백만원~3백만원미만14229743702024-01-08
9인천광역시남동구282002017재산세1천만원~3천만원미만1165008702024-01-08
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액데이터기준일
199인천광역시남동구282002022지방소득세5백만원~1천만원미만372600672302024-01-08
200인천광역시남동구282002022지방소득세5천만원~1억원미만31917753102024-01-08
201인천광역시남동구282002022지역자원시설세10만원 미만2233302024-01-08
202인천광역시남동구282002022취득세10만원 미만146427502024-01-08
203인천광역시남동구282002022취득세10만원~30만원미만58913602024-01-08
204인천광역시남동구282002022취득세1백만원~3백만원미만26411555602024-01-08
205인천광역시남동구282002022취득세1천만원~3천만원미만3508818602024-01-08
206인천광역시남동구282002022취득세30만원~50만원미만415402602024-01-08
207인천광역시남동구282002022취득세3백만원~5백만원미만3130498402024-01-08
208인천광역시남동구282002022취득세50만원~1백만원미만1175381202024-01-08