Overview

Dataset statistics

Number of variables9
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory80.5 B

Variable types

Categorical5
Numeric4

Dataset

Description4년간 지방세 부과 과세 자료에 따른 지방세 세목별 통계자료를 근거로 연도별 지방세 과세 현황 자료 추출한 자료에 해당됩니다
Author충청북도 진천군
URLhttps://www.data.go.kr/data/15079476/fileData.do

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 1 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 비과세건수High correlation
세목명 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
과세건수 has 12 (23.1%) zerosZeros
과세금액 has 12 (23.1%) zerosZeros
비과세건수 has 20 (38.5%) zerosZeros
비과세금액 has 21 (40.4%) zerosZeros

Reproduction

Analysis started2024-03-23 04:55:24.449040
Analysis finished2024-03-23 04:55:32.553671
Duration8.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
충청북도
52 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 52
100.0%

Length

2024-03-23T04:55:32.870405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:55:33.160355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 52
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
진천군
52 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진천군 52
100.0%

Length

2024-03-23T04:55:33.451663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:55:33.830477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진천군 52
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
43750
52 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43750
2nd row43750
3rd row43750
4th row43750
5th row43750

Common Values

ValueCountFrequency (%)
43750 52
100.0%

Length

2024-03-23T04:55:34.243804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:55:34.589061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43750 52
100.0%

과세년도
Categorical

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
2019
13 
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 13
25.0%
2020 13
25.0%
2021 13
25.0%
2022 13
25.0%

Length

2024-03-23T04:55:34.977022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:55:35.308620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 13
25.0%
2020 13
25.0%
2021 13
25.0%
2022 13
25.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
취득세
4 
등록세
4 
주민세
4 
재산세
4 
자동차세
4 
Other values (8)
32 

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 4
 
7.7%
등록세 4
 
7.7%
주민세 4
 
7.7%
재산세 4
 
7.7%
자동차세 4
 
7.7%
레저세 4
 
7.7%
담배소비세 4
 
7.7%
지방소비세 4
 
7.7%
등록면허세 4
 
7.7%
도시계획세 4
 
7.7%
Other values (3) 12
23.1%

Length

2024-03-23T04:55:35.776619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 4
 
7.7%
등록세 4
 
7.7%
주민세 4
 
7.7%
재산세 4
 
7.7%
자동차세 4
 
7.7%
레저세 4
 
7.7%
담배소비세 4
 
7.7%
지방소비세 4
 
7.7%
등록면허세 4
 
7.7%
도시계획세 4
 
7.7%
Other values (3) 12
23.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40275.269
Minimum0
Maximum204184
Zeros12
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T04:55:36.260228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.75
median27449.5
Q346105.75
95-th percentile192600.65
Maximum204184
Range204184
Interquartile range (IQR)46099

Descriptive statistics

Standard deviation53516.688
Coefficient of variation (CV)1.3287729
Kurtosis3.7515243
Mean40275.269
Median Absolute Deviation (MAD)27441.5
Skewness1.9813682
Sum2094314
Variance2.8640359 × 109
MonotonicityNot monotonic
2024-03-23T04:55:36.933428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 12
 
23.1%
17758 1
 
1.9%
204184 1
 
1.9%
81628 1
 
1.9%
478 1
 
1.9%
7 1
 
1.9%
46135 1
 
1.9%
26690 1
 
1.9%
36893 1
 
1.9%
203251 1
 
1.9%
Other values (31) 31
59.6%
ValueCountFrequency (%)
0 12
23.1%
6 1
 
1.9%
7 1
 
1.9%
9 1
 
1.9%
45 1
 
1.9%
81 1
 
1.9%
278 1
 
1.9%
478 1
 
1.9%
637 1
 
1.9%
17500 1
 
1.9%
ValueCountFrequency (%)
204184 1
1.9%
203251 1
1.9%
194810 1
1.9%
190793 1
1.9%
83361 1
1.9%
81628 1
1.9%
77628 1
1.9%
75798 1
1.9%
75507 1
1.9%
74922 1
1.9%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4478292 × 1010
Minimum0
Maximum6.3676996 × 1010
Zeros12
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T04:55:37.381753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6170978 × 109
median9.1055395 × 109
Q31.7200863 × 1010
95-th percentile5.5057494 × 1010
Maximum6.3676996 × 1010
Range6.3676996 × 1010
Interquartile range (IQR)1.4583766 × 1010

Descriptive statistics

Standard deviation1.7060039 × 1010
Coefficient of variation (CV)1.1783185
Kurtosis1.6353904
Mean1.4478292 × 1010
Median Absolute Deviation (MAD)8.214436 × 109
Skewness1.5779878
Sum7.5287121 × 1011
Variance2.9104495 × 1020
MonotonicityNot monotonic
2024-03-23T04:55:38.022586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 12
 
23.1%
44309413000 1
 
1.9%
15549914000 1
 
1.9%
17081751000 1
 
1.9%
9134893000 1
 
1.9%
11374515000 1
 
1.9%
3894591000 1
 
1.9%
4812251000 1
 
1.9%
54334977000 1
 
1.9%
15191848000 1
 
1.9%
Other values (31) 31
59.6%
ValueCountFrequency (%)
0 12
23.1%
44969000 1
 
1.9%
3474474000 1
 
1.9%
3894591000 1
 
1.9%
4164039000 1
 
1.9%
4343770000 1
 
1.9%
4812251000 1
 
1.9%
5132218000 1
 
1.9%
5217281000 1
 
1.9%
5643144000 1
 
1.9%
ValueCountFrequency (%)
63676996000 1
1.9%
59183338000 1
1.9%
55940570000 1
1.9%
54334977000 1
1.9%
44309413000 1
1.9%
43343699000 1
1.9%
41548547000 1
1.9%
40959773000 1
1.9%
22480173000 1
1.9%
20515825000 1
1.9%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3433.6154
Minimum0
Maximum22233
Zeros20
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T04:55:38.694966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29.5
Q33392
95-th percentile20018.9
Maximum22233
Range22233
Interquartile range (IQR)3392

Descriptive statistics

Standard deviation6007.2338
Coefficient of variation (CV)1.749536
Kurtosis3.6307379
Mean3433.6154
Median Absolute Deviation (MAD)29.5
Skewness2.1071136
Sum178548
Variance36086858
MonotonicityNot monotonic
2024-03-23T04:55:39.293895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 20
38.5%
2640 1
 
1.9%
3356 1
 
1.9%
20284 1
 
1.9%
11501 1
 
1.9%
2977 1
 
1.9%
1085 1
 
1.9%
25 1
 
1.9%
35 1
 
1.9%
20 1
 
1.9%
Other values (23) 23
44.2%
ValueCountFrequency (%)
0 20
38.5%
4 1
 
1.9%
13 1
 
1.9%
15 1
 
1.9%
20 1
 
1.9%
25 1
 
1.9%
29 1
 
1.9%
30 1
 
1.9%
35 1
 
1.9%
615 1
 
1.9%
ValueCountFrequency (%)
22233 1
1.9%
21481 1
1.9%
20284 1
1.9%
19802 1
1.9%
12429 1
1.9%
11501 1
1.9%
10759 1
1.9%
9351 1
1.9%
5956 1
1.9%
5949 1
1.9%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9877073 × 109
Minimum0
Maximum1.7390126 × 1010
Zeros21
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T04:55:39.810950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10720500
Q33.0111 × 108
95-th percentile1.2566651 × 1010
Maximum1.7390126 × 1010
Range1.7390126 × 1010
Interquartile range (IQR)3.0111 × 108

Descriptive statistics

Standard deviation4.6198397 × 109
Coefficient of variation (CV)2.3242052
Kurtosis3.3020133
Mean1.9877073 × 109
Median Absolute Deviation (MAD)10720500
Skewness2.1777912
Sum1.0336078 × 1011
Variance2.1342919 × 1019
MonotonicityNot monotonic
2024-03-23T04:55:40.428672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 21
40.4%
3000 2
 
3.8%
13605197000 1
 
1.9%
39461000 1
 
1.9%
310824000 1
 
1.9%
104495000 1
 
1.9%
52460000 1
 
1.9%
417943000 1
 
1.9%
11716931000 1
 
1.9%
2238000 1
 
1.9%
Other values (21) 21
40.4%
ValueCountFrequency (%)
0 21
40.4%
1000 1
 
1.9%
3000 2
 
3.8%
815000 1
 
1.9%
2238000 1
 
1.9%
19203000 1
 
1.9%
28152000 1
 
1.9%
39461000 1
 
1.9%
52460000 1
 
1.9%
54293000 1
 
1.9%
ValueCountFrequency (%)
17390126000 1
1.9%
14913402000 1
1.9%
13605197000 1
1.9%
11716931000 1
1.9%
11142174000 1
1.9%
11121388000 1
1.9%
9958537000 1
1.9%
9931587000 1
1.9%
417943000 1
1.9%
410524000 1
1.9%

Interactions

2024-03-23T04:55:30.201718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:25.221926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:27.154711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:28.892789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:30.635397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:25.690705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:27.604549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:29.263045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:30.990447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:26.065387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:28.235931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:29.595764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:31.359936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:26.504271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:28.590922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:29.823554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T04:55:40.778450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9520.9350.9200.619
과세건수0.0000.9521.0000.6540.7550.179
과세금액0.0000.9350.6541.0000.8810.744
비과세건수0.0000.9200.7550.8811.0000.643
비과세금액0.0000.6190.1790.7440.6431.000
2024-03-23T04:55:41.071434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-03-23T04:55:41.576251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.6130.6610.4690.0000.785
과세금액0.6131.0000.3480.3670.0000.736
비과세건수0.6610.3481.0000.8970.0000.699
비과세금액0.4690.3670.8971.0000.0000.330
과세년도0.0000.0000.0000.0001.0000.000
세목명0.7850.7360.6990.3300.0001.000

Missing values

2024-03-23T04:55:31.904147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:55:32.346514image/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
0충청북도진천군437502019취득세1775844309413000264013605197000
1충청북도진천군437502019등록세002966961000
2충청북도진천군437502019주민세417848008166000594928152000
3충청북도진천군437502019재산세7408319300353000198029931587000
4충청북도진천군437502019자동차세73827150432730009351398710000
5충청북도진천군437502019레저세0000
6충청북도진천군437502019담배소비세81851792600000
7충청북도진천군437502019지방소비세0000
8충청북도진천군437502019등록면허세4381756431440002912133026000
9충청북도진천군437502019도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
42충청북도진천군437502022취득세1859359183338000335611121388000
43충청북도진천군437502022등록세00202238000
44충청북도진천군437502022레저세454496900000
45충청북도진천군437502022재산세75798224801730002148111716931000
46충청북도진천군437502022자동차세833611529245200012429417943000
47충청북도진천군437502022주민세460969458244000595652460000
48충청북도진천군437502022등록면허세4409952172810003500104495000
49충청북도진천군437502022지역자원시설세2688551322180001122310824000
50충청북도진천군437502022지방소득세445725594057000000
51충청북도진천군437502022담배소비세637963707400000