Overview

Dataset statistics

Number of variables9
Number of observations136
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory77.0 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://data.incheon.go.kr/findData/publicDataDetail?dataId=15079498&srcSe=7661IVAWM27C61E190

Alerts

시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
체납액구간 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 과세년도 and 1 other fieldsHigh correlation
과세년도 is highly overall correlated with 시도명High correlation
체납건수 is highly overall correlated with 체납금액High correlation
체납금액 is highly overall correlated with 체납건수High correlation
체납금액 has unique valuesUnique

Reproduction

Analysis started2024-01-28 06:30:54.211559
Analysis finished2024-01-28 06:30:55.232033
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
인천광역시
81 
인천광역시
55 

Length

Max length6
Median length5
Mean length5.4044118
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 81
59.6%
인천광역시 55
40.4%

Length

2024-01-28T15:30:55.279206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:30:55.346803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 136
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
남동구
136 

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 136
100.0%

Length

2024-01-28T15:30:55.420210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:30:55.486624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 136
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
28200
136 

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 136
100.0%

Length

2024-01-28T15:30:55.555051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:30:55.626369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28200 136
100.0%

과세년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.3162
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-28T15:30:55.686486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7541578
Coefficient of variation (CV)0.00086868904
Kurtosis-1.2429065
Mean2019.3162
Median Absolute Deviation (MAD)1.5
Skewness0.22379532
Sum274627
Variance3.0770697
MonotonicityIncreasing
2024-01-28T15:30:55.773266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 31
22.8%
2017 27
19.9%
2022 24
17.6%
2018 23
16.9%
2021 17
12.5%
2020 14
10.3%
ValueCountFrequency (%)
2017 27
19.9%
2018 23
16.9%
2019 31
22.8%
2020 14
10.3%
2021 17
12.5%
2022 24
17.6%
ValueCountFrequency (%)
2022 24
17.6%
2021 17
12.5%
2020 14
10.3%
2019 31
22.8%
2018 23
16.9%
2017 27
19.9%

세목명
Categorical

Distinct6
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
재산세
43 
지방소득세
29 
주민세
21 
자동차세
18 
취득세
13 

Length

Max length5
Median length3
Mean length3.7352941
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 43
31.6%
지방소득세 29
21.3%
주민세 21
15.4%
자동차세 18
13.2%
취득세 13
 
9.6%
등록면허세 12
 
8.8%

Length

2024-01-28T15:30:55.900915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:30:55.990765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 43
31.6%
지방소득세 29
21.3%
주민세 21
15.4%
자동차세 18
13.2%
취득세 13
 
9.6%
등록면허세 12
 
8.8%

체납액구간
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
50만원이하
17 
10만원 미만
16 
10만원~30만원미만
14 
50만원초과~100만원이하
14 
50만원~1백만원미만
12 
Other values (10)
63 

Length

Max length16
Median length14
Mean length10.676471
Min length6

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
50만원이하 17
12.5%
10만원 미만 16
11.8%
10만원~30만원미만 14
10.3%
50만원초과~100만원이하 14
10.3%
50만원~1백만원미만 12
8.8%
30만원~50만원미만 11
8.1%
100만원초과~300만원 이하 10
7.4%
1백만원~3백만원미만 9
6.6%
300만원초과~1000만원이하 7
 
5.1%
1000만원초과 7
 
5.1%
Other values (5) 19
14.0%

Length

2024-01-28T15:30:56.082511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50만원이하 17
10.5%
10만원 16
9.9%
미만 16
9.9%
10만원~30만원미만 14
8.6%
50만원초과~100만원이하 14
8.6%
50만원~1백만원미만 12
 
7.4%
30만원~50만원미만 11
 
6.8%
이하 10
 
6.2%
100만원초과~300만원 10
 
6.2%
1백만원~3백만원미만 9
 
5.6%
Other values (7) 33
20.4%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean754.03676
Minimum1
Maximum25425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-28T15:30:56.176352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median19
Q390
95-th percentile3475
Maximum25425
Range25424
Interquartile range (IQR)87

Descriptive statistics

Standard deviation2673.0778
Coefficient of variation (CV)3.5450231
Kurtosis57.998667
Mean754.03676
Median Absolute Deviation (MAD)18
Skewness6.981988
Sum102549
Variance7145344.7
MonotonicityNot monotonic
2024-01-28T15:30:56.278711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
13.2%
2 10
 
7.4%
3 9
 
6.6%
5 7
 
5.1%
4 5
 
3.7%
8 4
 
2.9%
6 3
 
2.2%
36 3
 
2.2%
40 2
 
1.5%
9 2
 
1.5%
Other values (64) 73
53.7%
ValueCountFrequency (%)
1 18
13.2%
2 10
7.4%
3 9
6.6%
4 5
 
3.7%
5 7
 
5.1%
6 3
 
2.2%
7 1
 
0.7%
8 4
 
2.9%
9 2
 
1.5%
10 1
 
0.7%
ValueCountFrequency (%)
25425 1
0.7%
13908 1
0.7%
6453 1
0.7%
5547 1
0.7%
4752 1
0.7%
4003 1
0.7%
3583 1
0.7%
3439 1
0.7%
3352 1
0.7%
3026 1
0.7%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2131019 × 108
Minimum67260
Maximum3.7063609 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-28T15:30:56.376369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67260
5-th percentile709770
Q13004722.5
median23957370
Q398908298
95-th percentile4.056078 × 108
Maximum3.7063609 × 109
Range3.7062936 × 109
Interquartile range (IQR)95903575

Descriptive statistics

Standard deviation3.669077 × 108
Coefficient of variation (CV)3.0245414
Kurtosis70.014687
Mean1.2131019 × 108
Median Absolute Deviation (MAD)22844020
Skewness7.6735559
Sum1.6498186 × 1010
Variance1.3462126 × 1017
MonotonicityNot monotonic
2024-01-28T15:30:56.480266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19390030 1
 
0.7%
5085670 1
 
0.7%
103306110 1
 
0.7%
370838810 1
 
0.7%
1564420 1
 
0.7%
26981710 1
 
0.7%
5318610 1
 
0.7%
5337290 1
 
0.7%
3034500 1
 
0.7%
737900 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
67260 1
0.7%
111240 1
0.7%
125140 1
0.7%
192350 1
0.7%
464200 1
0.7%
567970 1
0.7%
632010 1
0.7%
735690 1
0.7%
737900 1
0.7%
802800 1
0.7%
ValueCountFrequency (%)
3706360910 1
0.7%
1636000620 1
0.7%
820512810 1
0.7%
731187510 1
0.7%
690760040 1
0.7%
588460170 1
0.7%
460764810 1
0.7%
387222130 1
0.7%
372154550 1
0.7%
370838810 1
0.7%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum2023-02-17 00:00:00
Maximum2023-02-17 00:00:00
2024-01-28T15:30:56.567008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:56.631510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-28T15:30:54.848834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.463455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.653334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.916649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.523356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.714524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.988208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.583378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:30:54.774543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T15:30:56.682122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명과세년도세목명체납액구간체납건수체납금액
시도명1.0001.0000.0341.0000.1040.175
과세년도1.0001.0000.0000.6500.2930.000
세목명0.0340.0001.0000.0000.1670.123
체납액구간1.0000.6500.0001.0000.0000.000
체납건수0.1040.2930.1670.0001.0000.974
체납금액0.1750.0000.1230.0000.9741.000
2024-01-28T15:30:56.757012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간시도명
세목명1.0000.0000.017
체납액구간0.0001.0000.950
시도명0.0170.9501.000
2024-01-28T15:30:56.826428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액시도명세목명체납액구간
과세년도1.0000.1390.2820.9850.0000.353
체납건수0.1391.0000.6710.1250.1120.000
체납금액0.2820.6711.0000.2120.0840.000
시도명0.9850.1250.2121.0000.0170.950
세목명0.0000.1120.0840.0171.0000.000
체납액구간0.3530.0000.0000.9500.0001.000

Missing values

2024-01-28T15:30:55.095295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T15:30:55.194831image/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 미만611193900302023-02-17
1인천광역시남동구282002017등록면허세10만원~30만원미만11112402023-02-17
2인천광역시남동구282002017자동차세10만원 미만1806728000302023-02-17
3인천광역시남동구282002017자동차세10만원~30만원미만13772230661602023-02-17
4인천광역시남동구282002017자동차세30만원~50만원미만32107554702023-02-17
5인천광역시남동구282002017자동차세50만원~1백만원미만528827702023-02-17
6인천광역시남동구282002017재산세10만원 미만1632904371102023-02-17
7인천광역시남동구282002017재산세10만원~30만원미만572923742402023-02-17
8인천광역시남동구282002017재산세1백만원~3백만원미만14229743702023-02-17
9인천광역시남동구282002017재산세1천만원~3천만원미만1165008702023-02-17
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액데이터기준일
126인천광역시남동구282002022자동차세50만원초과~100만원이하49259798002023-02-17
127인천광역시남동구282002022등록면허세50만원이하2448835522802023-02-17
128인천광역시남동구282002022등록면허세100만원초과~300만원 이하115412202023-02-17
129인천광역시남동구282002022등록면허세300만원초과~1000만원이하131889002023-02-17
130인천광역시남동구282002022등록면허세1000만원초과2248479302023-02-17
131인천광역시남동구282002022지방소득세50만원이하1059666824402023-02-17
132인천광역시남동구282002022지방소득세50만원초과~100만원이하40284959502023-02-17
133인천광역시남동구282002022지방소득세100만원초과~300만원 이하571046015502023-02-17
134인천광역시남동구282002022지방소득세300만원초과~1000만원이하331741973402023-02-17
135인천광역시남동구282002022지방소득세1000만원초과194607648102023-02-17