Overview

Dataset statistics

Number of variables9
Number of observations65
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory80.0 B

Variable types

Categorical5
Numeric4

Dataset

Description인천광역시 서구의 2017년도부터 2021년도까지 세목별 과세건수, 과세금액, 비과세건수, 비과세금액을 포함하고 있습니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15078572&srcSe=7661IVAWM27C61E190

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 과세금액 and 2 other fieldsHigh correlation
과세금액 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
과세건수 has 23 (35.4%) zerosZeros
과세금액 has 24 (36.9%) zerosZeros
비과세건수 has 26 (40.0%) zerosZeros
비과세금액 has 29 (44.6%) zerosZeros

Reproduction

Analysis started2024-01-28 11:21:03.425801
Analysis finished2024-01-28 11:21:05.115302
Duration1.69 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
인천광역시
65 

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 65
100.0%

Length

2024-01-28T20:21:05.164784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:21:05.234486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 65
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
서구
65 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 65
100.0%

Length

2024-01-28T20:21:05.315313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:21:05.385019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 65
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
28260
65 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28260
2nd row28260
3rd row28260
4th row28260
5th row28260

Common Values

ValueCountFrequency (%)
28260 65
100.0%

Length

2024-01-28T20:21:05.458662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:21:05.529237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28260 65
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size652.0 B
2017
13 
2018
13 
2019
13 
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 13
20.0%
2018 13
20.0%
2019 13
20.0%
2020 13
20.0%
2021 13
20.0%

Length

2024-01-28T20:21:05.599117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:21:05.677835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
20.0%
2018 13
20.0%
2019 13
20.0%
2020 13
20.0%
2021 13
20.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size652.0 B
취득세
5 
등록세
5 
주민세
5 
재산세
5 
자동차세
5 
Other values (8)
40 

Length

Max length7
Median length5
Mean length4.1538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 5
 
7.7%
등록세 5
 
7.7%
주민세 5
 
7.7%
재산세 5
 
7.7%
자동차세 5
 
7.7%
레저세 5
 
7.7%
담배소비세 5
 
7.7%
지방소비세 5
 
7.7%
등록면허세 5
 
7.7%
도시계획세 5
 
7.7%
Other values (3) 15
23.1%

Length

2024-01-28T20:21:05.772274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 5
 
7.7%
등록세 5
 
7.7%
주민세 5
 
7.7%
재산세 5
 
7.7%
자동차세 5
 
7.7%
레저세 5
 
7.7%
담배소비세 5
 
7.7%
지방소비세 5
 
7.7%
등록면허세 5
 
7.7%
도시계획세 5
 
7.7%
Other values (3) 15
23.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213900.2
Minimum0
Maximum1114310
Zeros23
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-01-28T20:21:05.873570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median206247
Q3279845
95-th percentile1005568.2
Maximum1114310
Range1114310
Interquartile range (IQR)279845

Descriptive statistics

Standard deviation278423.66
Coefficient of variation (CV)1.3016522
Kurtosis4.15209
Mean213900.2
Median Absolute Deviation (MAD)159487
Skewness2.0337755
Sum13903513
Variance7.7519735 × 1010
MonotonicityNot monotonic
2024-01-28T20:21:05.976027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 23
35.4%
67915 1
 
1.5%
1067201 1
 
1.5%
79530 1
 
1.5%
232897 1
 
1.5%
279845 1
 
1.5%
373051 1
 
1.5%
6 1
 
1.5%
263757 1
 
1.5%
363081 1
 
1.5%
Other values (33) 33
50.8%
ValueCountFrequency (%)
0 23
35.4%
6 1
 
1.5%
7 1
 
1.5%
67915 1
 
1.5%
77744 1
 
1.5%
79530 1
 
1.5%
81721 1
 
1.5%
89636 1
 
1.5%
156134 1
 
1.5%
183997 1
 
1.5%
ValueCountFrequency (%)
1114310 1
1.5%
1092552 1
1.5%
1067201 1
1.5%
1019677 1
1.5%
949133 1
1.5%
384406 1
1.5%
373051 1
1.5%
365734 1
1.5%
364181 1
1.5%
363081 1
1.5%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5847833 × 1010
Minimum0
Maximum5.2762215 × 1011
Zeros24
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-01-28T20:21:06.074775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.2881422 × 1010
Q36.9194284 × 1010
95-th percentile3.49359 × 1011
Maximum5.2762215 × 1011
Range5.2762215 × 1011
Interquartile range (IQR)6.9194284 × 1010

Descriptive statistics

Standard deviation1.08615 × 1011
Coefficient of variation (CV)1.6494847
Kurtosis6.1344572
Mean6.5847833 × 1010
Median Absolute Deviation (MAD)2.2881422 × 1010
Skewness2.4415275
Sum4.2801091 × 1012
Variance1.1797217 × 1022
MonotonicityNot monotonic
2024-01-28T20:21:06.181276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 24
36.9%
24311584000 1
 
1.5%
110386000000 1
 
1.5%
67565436000 1
 
1.5%
358680000000 1
 
1.5%
18585272000 1
 
1.5%
179960000000 1
 
1.5%
47111591000 1
 
1.5%
25096039000 1
 
1.5%
25632152000 1
 
1.5%
Other values (32) 32
49.2%
ValueCountFrequency (%)
0 24
36.9%
2483933000 1
 
1.5%
15966307000 1
 
1.5%
17004873000 1
 
1.5%
18402181000 1
 
1.5%
18585272000 1
 
1.5%
19005735000 1
 
1.5%
20274375000 1
 
1.5%
20944896000 1
 
1.5%
22881422000 1
 
1.5%
ValueCountFrequency (%)
527622151000 1
1.5%
383220000000 1
1.5%
358883000000 1
1.5%
358680000000 1
1.5%
312075000000 1
1.5%
189585026000 1
1.5%
179960000000 1
1.5%
177304000000 1
1.5%
155649000000 1
1.5%
146109000000 1
1.5%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20897.4
Minimum0
Maximum121810
Zeros26
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-01-28T20:21:06.277254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median279
Q326062
95-th percentile109715.8
Maximum121810
Range121810
Interquartile range (IQR)26062

Descriptive statistics

Standard deviation36029.9
Coefficient of variation (CV)1.7241332
Kurtosis1.2066757
Mean20897.4
Median Absolute Deviation (MAD)279
Skewness1.6008831
Sum1358331
Variance1.2981537 × 109
MonotonicityNot monotonic
2024-01-28T20:21:06.381861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 26
40.0%
8 2
 
3.1%
2341 1
 
1.5%
7 1
 
1.5%
75683 1
 
1.5%
116 1
 
1.5%
26062 1
 
1.5%
72641 1
 
1.5%
121810 1
 
1.5%
3463 1
 
1.5%
Other values (29) 29
44.6%
ValueCountFrequency (%)
0 26
40.0%
4 1
 
1.5%
7 1
 
1.5%
8 2
 
3.1%
116 1
 
1.5%
191 1
 
1.5%
279 1
 
1.5%
443 1
 
1.5%
444 1
 
1.5%
518 1
 
1.5%
ValueCountFrequency (%)
121810 1
1.5%
113407 1
1.5%
112073 1
1.5%
111223 1
1.5%
103687 1
1.5%
79428 1
1.5%
76635 1
1.5%
75683 1
1.5%
73364 1
1.5%
72641 1
1.5%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6281938 × 1010
Minimum0
Maximum1.25767 × 1011
Zeros29
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-01-28T20:21:06.504822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1847 × 108
Q31.161887 × 109
95-th percentile1.0252338 × 1011
Maximum1.25767 × 1011
Range1.25767 × 1011
Interquartile range (IQR)1.161887 × 109

Descriptive statistics

Standard deviation3.7431509 × 1010
Coefficient of variation (CV)2.2989591
Kurtosis2.3868199
Mean1.6281938 × 1010
Median Absolute Deviation (MAD)1.1847 × 108
Skewness2.0378805
Sum1.058326 × 1012
Variance1.4011179 × 1021
MonotonicityNot monotonic
2024-01-28T20:21:06.614279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 29
44.6%
82156739000 1
 
1.5%
1000 1
 
1.5%
125767000000 1
 
1.5%
150660000 1
 
1.5%
135906000 1
 
1.5%
94465912000 1
 
1.5%
4673096000 1
 
1.5%
118470000 1
 
1.5%
1041265000 1
 
1.5%
Other values (27) 27
41.5%
ValueCountFrequency (%)
0 29
44.6%
1000 1
 
1.5%
113324000 1
 
1.5%
113571000 1
 
1.5%
118470000 1
 
1.5%
129594000 1
 
1.5%
135906000 1
 
1.5%
140045000 1
 
1.5%
150660000 1
 
1.5%
163189000 1
 
1.5%
ValueCountFrequency (%)
125767000000 1
1.5%
121327497000 1
1.5%
110751000000 1
1.5%
102729000000 1
1.5%
101700886000 1
1.5%
97215042000 1
1.5%
97015269000 1
1.5%
94465912000 1
1.5%
92472488000 1
1.5%
82156739000 1
1.5%

Interactions

2024-01-28T20:21:04.683336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:03.639853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.138807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.415960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.748291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:03.708005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.213899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.488890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.824145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.010174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.286784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.552421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.887864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.072433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.348755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:21:04.615535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:21:06.686223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8970.8260.7700.687
과세건수0.0000.8971.0000.6250.5120.000
과세금액0.0000.8260.6251.0000.7180.886
비과세건수0.0000.7700.5120.7181.0000.778
비과세금액0.0000.6870.0000.8860.7781.000
2024-01-28T20:21:06.762908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-01-28T20:21:06.825530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.7000.5660.3950.0000.678
과세금액0.7001.0000.5940.5570.0000.540
비과세건수0.5660.5941.0000.9210.0000.450
비과세금액0.3950.5570.9211.0000.0000.427
과세년도0.0000.0000.0000.0001.0000.000
세목명0.6780.5400.4500.4270.0001.000

Missing values

2024-01-28T20:21:04.975648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:21:05.073169image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
0인천광역시서구282602017취득세679153120750000007152182156739000
1인천광역시서구282602017등록세001911553186000
2인천광역시서구282602017주민세220823159663070009749163189000
3인천광역시서구282602017재산세2316701461090000002600397015269000
4인천광역시서구282602017자동차세339176408585570001036874448073000
5인천광역시서구282602017레저세0000
6인천광역시서구282602017담배소비세0000
7인천광역시서구282602017지방소비세0000
8인천광역시서구282602017등록면허세21838919005735000518208525000
9인천광역시서구282602017도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
55인천광역시서구282602021재산세28601518958502600079428101700886000
56인천광역시서구282602021자동차세384406500351970001120734237394000
57인천광역시서구282602021레저세0000
58인천광역시서구282602021담배소비세0000
59인천광역시서구282602021지방소비세7248393300000
60인천광역시서구282602021등록면허세250902287440300005352113571000
61인천광역시서구282602021도시계획세0000
62인천광역시서구282602021지역자원시설세3641812847097000023921036333000
63인천광역시서구282602021지방소득세27197514444820800000
64인천광역시서구282602021교육세11143108525049800080