Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory81.5 B

Variable types

Categorical5
Numeric4

Dataset

Description2017년부터 2022년까지 서천군 지방세 세목별 과세현황, 과세건수, 과세금액 및 비과세건수, 비과세금액에 대한 지방세 과세현황 자료입니다
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=347&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080474

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 과세금액 and 3 other fieldsHigh correlation
과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
과세건수 has 11 (28.9%) zerosZeros
과세금액 has 11 (28.9%) zerosZeros
비과세건수 has 15 (39.5%) zerosZeros
비과세금액 has 15 (39.5%) zerosZeros

Reproduction

Analysis started2024-01-09 22:47:38.750140
Analysis finished2024-01-09 22:47:40.422527
Duration1.67 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
충청남도
38 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 38
100.0%

Length

2024-01-10T07:47:40.485095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:47:40.571445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 38
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
서천군
38 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 38
100.0%

Length

2024-01-10T07:47:40.672654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:47:40.754298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 38
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
44770
38 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44770
2nd row44770
3rd row44770
4th row44770
5th row44770

Common Values

ValueCountFrequency (%)
44770 38
100.0%

Length

2024-01-10T07:47:40.851507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:47:40.949787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 38
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
2018
13 
2017
13 
2019
12 

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 (%)
2018 13
34.2%
2017 13
34.2%
2019 12
31.6%

Length

2024-01-10T07:47:41.041611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:47:41.148926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 13
34.2%
2017 13
34.2%
2019 12
31.6%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size436.0 B
취득세
3 
주민세
3 
재산세
3 
자동차세
3 
레저세
3 
Other values (8)
23 

Length

Max length7
Median length5
Mean length4.1842105
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 3
 
7.9%
주민세 3
 
7.9%
재산세 3
 
7.9%
자동차세 3
 
7.9%
레저세 3
 
7.9%
담배소비세 3
 
7.9%
지방소비세 3
 
7.9%
등록면허세 3
 
7.9%
도시계획세 3
 
7.9%
지역자원시설세 3
 
7.9%
Other values (3) 8
21.1%

Length

2024-01-10T07:47:41.504851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 3
 
7.9%
주민세 3
 
7.9%
재산세 3
 
7.9%
자동차세 3
 
7.9%
레저세 3
 
7.9%
담배소비세 3
 
7.9%
지방소비세 3
 
7.9%
등록면허세 3
 
7.9%
도시계획세 3
 
7.9%
지역자원시설세 3
 
7.9%
Other values (3) 8
21.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28940.658
Minimum0
Maximum145355
Zeros11
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T07:47:41.612310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12249.5
Q327688.5
95-th percentile144710.2
Maximum145355
Range145355
Interquartile range (IQR)27688.5

Descriptive statistics

Standard deviation42290.408
Coefficient of variation (CV)1.4612801
Kurtosis2.6539129
Mean28940.658
Median Absolute Deviation (MAD)12249.5
Skewness1.8775867
Sum1099745
Variance1.7884786 × 109
MonotonicityNot monotonic
2024-01-10T07:47:41.724477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 11
28.9%
10833 1
 
2.6%
27555 1
 
2.6%
145355 1
 
2.6%
10684 1
 
2.6%
13461 1
 
2.6%
27200 1
 
2.6%
107 1
 
2.6%
40991 1
 
2.6%
88458 1
 
2.6%
Other values (18) 18
47.4%
ValueCountFrequency (%)
0 11
28.9%
81 1
 
2.6%
87 1
 
2.6%
107 1
 
2.6%
10684 1
 
2.6%
10833 1
 
2.6%
10911 1
 
2.6%
11471 1
 
2.6%
11930 1
 
2.6%
12569 1
 
2.6%
ValueCountFrequency (%)
145355 1
2.6%
144887 1
2.6%
144679 1
2.6%
90175 1
2.6%
90072 1
2.6%
88458 1
2.6%
41050 1
2.6%
40991 1
2.6%
40914 1
2.6%
27706 1
2.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8721844 × 109
Minimum0
Maximum1.5665717 × 1010
Zeros11
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T07:47:41.829600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.8789345 × 109
Q35.4755135 × 109
95-th percentile1.2954433 × 1010
Maximum1.5665717 × 1010
Range1.5665717 × 1010
Interquartile range (IQR)5.4755135 × 109

Descriptive statistics

Standard deviation4.3058065 × 109
Coefficient of variation (CV)1.1119839
Kurtosis0.54585365
Mean3.8721844 × 109
Median Absolute Deviation (MAD)1.8789345 × 109
Skewness1.1356055
Sum1.4714301 × 1011
Variance1.853997 × 1019
MonotonicityNot monotonic
2024-01-10T07:47:41.936329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 11
28.9%
12887476000 1
 
2.6%
1228950000 1
 
2.6%
4775902000 1
 
2.6%
8381626000 1
 
2.6%
1175249000 1
 
2.6%
1077374000 1
 
2.6%
4124495000 1
 
2.6%
9240270000 1
 
2.6%
4952086000 1
 
2.6%
Other values (18) 18
47.4%
ValueCountFrequency (%)
0 11
28.9%
814323000 1
 
2.6%
869240000 1
 
2.6%
1028149000 1
 
2.6%
1077374000 1
 
2.6%
1175249000 1
 
2.6%
1228950000 1
 
2.6%
1615267000 1
 
2.6%
1758263000 1
 
2.6%
1999606000 1
 
2.6%
ValueCountFrequency (%)
15665717000 1
2.6%
13333858000 1
2.6%
12887476000 1
2.6%
10784848000 1
2.6%
9240270000 1
2.6%
8501343000 1
2.6%
8381626000 1
2.6%
7325261000 1
2.6%
7104039000 1
2.6%
5551277000 1
2.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4069.5
Minimum0
Maximum28567
Zeros15
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T07:47:42.043937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median224
Q33666.5
95-th percentile26885.15
Maximum28567
Range28567
Interquartile range (IQR)3666.5

Descriptive statistics

Standard deviation7561.6437
Coefficient of variation (CV)1.858126
Kurtosis6.167679
Mean4069.5
Median Absolute Deviation (MAD)224
Skewness2.592843
Sum154641
Variance57178455
MonotonicityNot monotonic
2024-01-10T07:47:42.139214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 15
39.5%
1860 1
 
2.6%
3584 1
 
2.6%
92 1
 
2.6%
2887 1
 
2.6%
3352 1
 
2.6%
7211 1
 
2.6%
26639 1
 
2.6%
7379 1
 
2.6%
3 1
 
2.6%
Other values (14) 14
36.8%
ValueCountFrequency (%)
0 15
39.5%
3 1
 
2.6%
6 1
 
2.6%
92 1
 
2.6%
98 1
 
2.6%
350 1
 
2.6%
1860 1
 
2.6%
1940 1
 
2.6%
1988 1
 
2.6%
2876 1
 
2.6%
ValueCountFrequency (%)
28567 1
2.6%
28280 1
2.6%
26639 1
2.6%
8000 1
2.6%
7766 1
2.6%
7621 1
2.6%
7502 1
2.6%
7379 1
2.6%
7211 1
2.6%
3694 1
2.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3663568 × 108
Minimum0
Maximum4.319655 × 109
Zeros15
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T07:47:42.242464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1554000
Q31.858515 × 108
95-th percentile4.0308004 × 109
Maximum4.319655 × 109
Range4.319655 × 109
Interquartile range (IQR)1.858515 × 108

Descriptive statistics

Standard deviation1.3825673 × 109
Coefficient of variation (CV)2.1716774
Kurtosis2.4276911
Mean6.3663568 × 108
Median Absolute Deviation (MAD)1554000
Skewness2.0321745
Sum2.4192156 × 1010
Variance1.9114924 × 1018
MonotonicityNot monotonic
2024-01-10T07:47:42.342031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 15
39.5%
3388856000 1
 
2.6%
93226000 1
 
2.6%
7000 1
 
2.6%
186794000 1
 
2.6%
111636000 1
 
2.6%
294442000 1
 
2.6%
3823535000 1
 
2.6%
18084000 1
 
2.6%
1161000 1
 
2.6%
Other values (14) 14
36.8%
ValueCountFrequency (%)
0 15
39.5%
7000 1
 
2.6%
8000 1
 
2.6%
31000 1
 
2.6%
1161000 1
 
2.6%
1947000 1
 
2.6%
18084000 1
 
2.6%
18351000 1
 
2.6%
18871000 1
 
2.6%
77966000 1
 
2.6%
ValueCountFrequency (%)
4319655000 1
2.6%
4173076000 1
2.6%
4005693000 1
2.6%
3823535000 1
2.6%
3388856000 1
2.6%
2744921000 1
2.6%
294442000 1
2.6%
282947000 1
2.6%
274816000 1
2.6%
186794000 1
2.6%

Interactions

2024-01-10T07:47:39.918787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:38.966587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.307026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.617712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:40.011014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.040995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.389552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.691245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:40.110404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.138996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.472856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.772624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:40.177224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.214869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.544792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:39.838408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:47:42.410524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0001.0000.9091.0000.536
과세건수0.0001.0001.0000.8120.8160.693
과세금액0.0000.9090.8121.0000.7820.734
비과세건수0.0001.0000.8160.7821.0000.495
비과세금액0.0000.5360.6930.7340.4951.000
2024-01-10T07:47:42.496682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T07:47:42.570380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.6360.7750.6250.0000.870
과세금액0.6361.0000.4320.5450.0000.651
비과세건수0.7750.4321.0000.8850.0000.857
비과세금액0.6250.5450.8851.0000.0000.269
과세년도0.0000.0000.0000.0001.0000.000
세목명0.8700.6510.8570.2690.0001.000

Missing values

2024-01-10T07:47:40.267777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:47:40.377365image/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
0충청남도서천군447702018취득세108331288747600018603388856000
1충청남도서천군447702018등록세0061947000
2충청남도서천군447702018주민세275271758263000750218351000
3충청남도서천군447702018재산세900725248223000282804005693000
4충청남도서천군447702018자동차세40914107848480007621282947000
5충청남도서천군447702018레저세0000
6충청남도서천군447702018담배소비세87390566100000
7충청남도서천군447702018지방소비세0000
8충청남도서천군447702018등록면허세276361028149000369477966000
9충청남도서천군447702018도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
28충청남도서천군447702017재산세884584952086000266393823535000
29충청남도서천군447702017자동차세4099192402700007211294442000
30충청남도서천군447702017레저세0000
31충청남도서천군447702017담배소비세107412449500000
32충청남도서천군447702017지방소비세0000
33충청남도서천군447702017등록면허세2720010773740003352111636000
34충청남도서천군447702017도시계획세0000
35충청남도서천군447702017지역자원시설세1346111752490002887186794000
36충청남도서천군447702017지방소득세10684838162600000
37충청남도서천군447702017교육세1453554775902000927000