Overview

Dataset statistics

Number of variables9
Number of observations76
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory79.7 B

Variable types

Categorical4
Numeric5

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

자치단체코드 has constant value ""Constant
시도명 is highly overall correlated with 과세년도 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 과세년도 and 2 other fieldsHigh correlation
과세년도 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
과세건수 is highly overall correlated with 과세금액 and 3 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 과세건수 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 4 other fieldsHigh correlation
과세건수 has 18 (23.7%) zerosZeros
과세금액 has 18 (23.7%) zerosZeros
비과세건수 has 29 (38.2%) zerosZeros
비과세금액 has 29 (38.2%) zerosZeros

Reproduction

Analysis started2024-01-09 22:47:43.098012
Analysis finished2024-01-09 22:47:45.554438
Duration2.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size740.0 B
충청남도
64 
충청남도
12 

Length

Max length6
Median length4
Mean length4.3157895
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 64
84.2%
충청남도 12
 
15.8%

Length

2024-01-10T07:47:45.625980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:47:45.723506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 76
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size740.0 B
서천군
64 
서천군
12 

Length

Max length5
Median length3
Mean length3.3157895
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 64
84.2%
서천군 12
 
15.8%

Length

2024-01-10T07:47:45.828116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:47:45.929366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 76
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
44770
76 

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 76
100.0%

Length

2024-01-10T07:47:46.031710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:47:46.121595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 76
100.0%

과세년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-01-10T07:47:46.206280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1.7397318
Coefficient of variation (CV)0.00086146659
Kurtosis-1.3125663
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum153482
Variance3.0266667
MonotonicityIncreasing
2024-01-10T07:47:46.315041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 13
17.1%
2018 13
17.1%
2021 13
17.1%
2022 13
17.1%
2019 12
15.8%
2020 12
15.8%
ValueCountFrequency (%)
2017 13
17.1%
2018 13
17.1%
2019 12
15.8%
2020 12
15.8%
2021 13
17.1%
2022 13
17.1%
ValueCountFrequency (%)
2022 13
17.1%
2021 13
17.1%
2020 12
15.8%
2019 12
15.8%
2018 13
17.1%
2017 13
17.1%

세목명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size740.0 B
취득세
 
5
지방소비세
 
5
등록세
 
5
지방소득세
 
5
지역자원시설세
 
5
Other values (20)
51 

Length

Max length9
Median length7
Mean length4.4736842
Min length3

Unique

Unique12 ?
Unique (%)15.8%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 5
 
6.6%
지방소비세 5
 
6.6%
등록세 5
 
6.6%
지방소득세 5
 
6.6%
지역자원시설세 5
 
6.6%
등록면허세 5
 
6.6%
교육세 5
 
6.6%
담배소비세 5
 
6.6%
자동차세 5
 
6.6%
재산세 5
 
6.6%
Other values (15) 26
34.2%

Length

2024-01-10T07:47:46.434986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 6
 
7.9%
지방소비세 6
 
7.9%
지방소득세 6
 
7.9%
지역자원시설세 6
 
7.9%
등록면허세 6
 
7.9%
교육세 6
 
7.9%
담배소비세 6
 
7.9%
자동차세 6
 
7.9%
재산세 6
 
7.9%
주민세 6
 
7.9%
Other values (3) 16
21.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29535.408
Minimum0
Maximum149650
Zeros18
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-01-10T07:47:46.551254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.75
median13017
Q330866.5
95-th percentile145004
Maximum149650
Range149650
Interquartile range (IQR)30859.75

Descriptive statistics

Standard deviation42492.844
Coefficient of variation (CV)1.4387086
Kurtosis2.4028229
Mean29535.408
Median Absolute Deviation (MAD)13017
Skewness1.8369462
Sum2244691
Variance1.8056418 × 109
MonotonicityNot monotonic
2024-01-10T07:47:46.961567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
23.7%
11471 1
 
1.3%
278 1
 
1.3%
6 1
 
1.3%
30521 1
 
1.3%
13747 1
 
1.3%
13938 1
 
1.3%
149650 1
 
1.3%
11963 1
 
1.3%
27236 1
 
1.3%
Other values (49) 49
64.5%
ValueCountFrequency (%)
0 18
23.7%
6 1
 
1.3%
7 1
 
1.3%
9 1
 
1.3%
45 1
 
1.3%
81 1
 
1.3%
87 1
 
1.3%
107 1
 
1.3%
278 1
 
1.3%
476 1
 
1.3%
ValueCountFrequency (%)
149650 1
1.3%
148416 1
1.3%
148379 1
1.3%
145355 1
1.3%
144887 1
1.3%
144679 1
1.3%
92541 1
1.3%
91824 1
1.3%
91586 1
1.3%
90175 1
1.3%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7742834 × 109
Minimum0
Maximum3.7975004 × 1010
Zeros18
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-01-10T07:47:47.132081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.1805475 × 108
median3.986392 × 109
Q37.429732 × 109
95-th percentile1.3734403 × 1010
Maximum3.7975004 × 1010
Range3.7975004 × 1010
Interquartile range (IQR)6.8116772 × 109

Descriptive statistics

Standard deviation5.8769978 × 109
Coefficient of variation (CV)1.2309696
Kurtosis13.031
Mean4.7742834 × 109
Median Absolute Deviation (MAD)3.455074 × 109
Skewness2.8740307
Sum3.6284554 × 1011
Variance3.4539103 × 1019
MonotonicityNot monotonic
2024-01-10T07:47:47.265298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
23.7%
13333858000 1
 
1.3%
4605933000 1
 
1.3%
7557100000 1
 
1.3%
1584474000 1
 
1.3%
972504000 1
 
1.3%
7676860000 1
 
1.3%
5604928000 1
 
1.3%
37975004000 1
 
1.3%
1843328000 1
 
1.3%
Other values (49) 49
64.5%
ValueCountFrequency (%)
0 18
23.7%
29250000 1
 
1.3%
814323000 1
 
1.3%
869240000 1
 
1.3%
972504000 1
 
1.3%
1028149000 1
 
1.3%
1077374000 1
 
1.3%
1175249000 1
 
1.3%
1228950000 1
 
1.3%
1311099000 1
 
1.3%
ValueCountFrequency (%)
37975004000 1
1.3%
20889475000 1
1.3%
15665717000 1
1.3%
14936037000 1
1.3%
13333858000 1
1.3%
12887476000 1
1.3%
11516755000 1
1.3%
10784848000 1
1.3%
9240270000 1
1.3%
8665054000 1
1.3%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4117.5132
Minimum0
Maximum29527
Zeros29
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-01-10T07:47:47.405441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median236.5
Q34596
95-th percentile28303.5
Maximum29527
Range29527
Interquartile range (IQR)4596

Descriptive statistics

Standard deviation7631.5121
Coefficient of variation (CV)1.8534275
Kurtosis5.8332056
Mean4117.5132
Median Absolute Deviation (MAD)236.5
Skewness2.5730638
Sum312931
Variance58239977
MonotonicityNot monotonic
2024-01-10T07:47:47.535630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 29
38.2%
1940 1
 
1.3%
4150 1
 
1.3%
29527 1
 
1.3%
7844 1
 
1.3%
4062 1
 
1.3%
2890 1
 
1.3%
93 1
 
1.3%
1916 1
 
1.3%
7 1
 
1.3%
Other values (38) 38
50.0%
ValueCountFrequency (%)
0 29
38.2%
2 1
 
1.3%
3 1
 
1.3%
5 1
 
1.3%
6 1
 
1.3%
7 1
 
1.3%
92 1
 
1.3%
93 1
 
1.3%
98 1
 
1.3%
123 1
 
1.3%
ValueCountFrequency (%)
29527 1
1.3%
28815 1
1.3%
28567 1
1.3%
28374 1
1.3%
28280 1
1.3%
26639 1
1.3%
8000 1
1.3%
7872 1
1.3%
7844 1
1.3%
7766 1
1.3%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3056095 × 108
Minimum0
Maximum5.931871 × 109
Zeros29
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-01-10T07:47:47.653623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2055000
Q31.96379 × 108
95-th percentile4.6792848 × 109
Maximum5.931871 × 109
Range5.931871 × 109
Interquartile range (IQR)1.96379 × 108

Descriptive statistics

Standard deviation1.609372 × 109
Coefficient of variation (CV)2.2029264
Kurtosis2.5900212
Mean7.3056095 × 108
Median Absolute Deviation (MAD)2055000
Skewness2.0575252
Sum5.5522632 × 1010
Variance2.5900781 × 1018
MonotonicityNot monotonic
2024-01-10T07:47:47.814579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 29
38.2%
2744921000 1
 
1.3%
83794000 1
 
1.3%
4492685000 1
 
1.3%
259460000 1
 
1.3%
101123000 1
 
1.3%
186701000 1
 
1.3%
9000 1
 
1.3%
5931871000 1
 
1.3%
13160000 1
 
1.3%
Other values (38) 38
50.0%
ValueCountFrequency (%)
0 29
38.2%
7000 1
 
1.3%
8000 1
 
1.3%
9000 1
 
1.3%
11000 1
 
1.3%
31000 1
 
1.3%
113000 1
 
1.3%
553000 1
 
1.3%
1161000 1
 
1.3%
1947000 1
 
1.3%
ValueCountFrequency (%)
5931871000 1
1.3%
5055652000 1
1.3%
4852304000 1
1.3%
4695253000 1
1.3%
4673962000 1
1.3%
4492685000 1
1.3%
4319655000 1
1.3%
4173076000 1
1.3%
4005693000 1
1.3%
3823535000 1
1.3%

Interactions

2024-01-10T07:47:44.969933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.364996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.738156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.191426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.597494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:45.049785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.434675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.812973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.289036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.667871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:45.130931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.512751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.909266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.366359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.746444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:45.209526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.589412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.005822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.446406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.822406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:45.286126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:43.661981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.090785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.520557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:47:44.894633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:47:47.909340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명과세년도세목명과세건수과세금액비과세건수비과세금액
시도명1.0000.9971.0001.0000.0000.1010.0000.000
시군구명0.9971.0001.0001.0000.0000.1010.0000.000
과세년도1.0001.0001.0000.0000.0000.0000.0000.000
세목명1.0001.0000.0001.0000.9430.8610.9740.000
과세건수0.0000.0000.0000.9431.0000.3350.8800.570
과세금액0.1010.1010.0000.8610.3351.0000.2410.924
비과세건수0.0000.0000.0000.9740.8800.2411.0000.557
비과세금액0.0000.0000.0000.0000.5700.9240.5571.000
2024-01-10T07:47:48.016969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명세목명시군구명
시도명1.0000.8300.950
세목명0.8301.0000.830
시군구명0.9500.8301.000
2024-01-10T07:47:48.102315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도과세건수과세금액비과세건수비과세금액시도명시군구명세목명
과세년도1.0000.0850.1510.0200.0250.9730.9730.000
과세건수0.0851.0000.5350.7470.5700.0000.0000.665
과세금액0.1510.5351.0000.2940.4150.0990.0990.508
비과세건수0.0200.7470.2941.0000.8810.0000.0000.761
비과세금액0.0250.5700.4150.8811.0000.0000.0000.000
시도명0.9730.0000.0990.0000.0001.0000.9500.830
시군구명0.9730.0000.0990.0000.0000.9501.0000.830
세목명0.0000.6650.5080.7610.0000.8300.8301.000

Missing values

2024-01-10T07:47:45.392484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:47:45.508708image/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
0충청남도서천군447702017취득세114711333385800019402744921000
1충청남도서천군447702017등록세0031161000
2충청남도서천군447702017주민세273721615267000737918084000
3충청남도서천군447702017재산세884584952086000266393823535000
4충청남도서천군447702017자동차세4099192402700007211294442000
5충청남도서천군447702017레저세0000
6충청남도서천군447702017담배소비세107412449500000
7충청남도서천군447702017지방소비세0000
8충청남도서천군447702017등록면허세2720010773740003352111636000
9충청남도서천군447702017도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
66충청남도서천군447702022재산세925417909033000288155055652000
67충청남도서천군447702022자동차세4108074179980007496236230000
68충청남도서천군447702022레저세452925000000
69충청남도서천군447702022담배소비세636417516400000
70충청남도서천군447702022지방소비세91151675500000
71충청남도서천군447702022등록면허세319031351525000593469676000
72충청남도서천군447702022도시계획세0000
73충청남도서천군447702022지역자원시설세1372532595550002974207380000
74충청남도서천군447702022지방소득세16778847785200000
75충청남도서천군447702022교육세1484165460796000461113000