Overview

Dataset statistics

Number of variables9
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory80.6 B

Variable types

Categorical7
Numeric2

Dataset

Description충청북도 옥천군의 연도별 지방세 과세 및 비과세에 대한 데이터로 세목별 과세건수, 과세금액, 비과세건수, 비과세금액 등 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15078503/fileData.do

Alerts

시도명 has constant value "충청북도"Constant
시군구명 has constant value "옥천군"Constant
자치단체코드 has constant value "43730"Constant
과세년도 has constant value "2020"Constant
비과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
과세건수 is highly overall correlated with 세목명 and 2 other fieldsHigh correlation
과세금액 is highly overall correlated with 세목명High correlation
세목명 has unique valuesUnique
과세건수 has unique valuesUnique
과세금액 has unique valuesUnique

Reproduction

Analysis started2023-08-05 01:34:23.605787
Analysis finished2023-08-05 01:34:25.447463
Duration1.84 second
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

시도명
Categorical

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size352.0 B
충청북도
28 

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 28
100.0%

Length

2023-08-05T10:34:25.551071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-05T10:34:25.844193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 28
100.0%

시군구명
Categorical

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size352.0 B
옥천군
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
옥천군 28
100.0%

Length

2023-08-05T10:34:26.061693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-05T10:34:26.308943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
옥천군 28
100.0%

자치단체코드
Categorical

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size352.0 B
43730
28 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43730
2nd row43730
3rd row43730
4th row43730
5th row43730

Common Values

ValueCountFrequency (%)
43730 28
100.0%

Length

2023-08-05T10:34:26.493604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-05T10:34:26.709679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
43730 28
100.0%

과세년도
Categorical

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size352.0 B
2020
28 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 28
100.0%

Length

2023-08-05T10:34:26.886506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-05T10:34:27.117324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2020 28
100.0%

세목명
Categorical

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size352.0 B
담배소비세
 
1
등록면허세(등록)
 
1
등록면허세(면허)
 
1
등록세(부동산)
 
1
자동차세(기계장비)
 
1
Other values (23)
23 

Length

Max length11
Median length10
Mean length8.5714286
Min length5

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row담배소비세
2nd row등록면허세(등록)
3rd row등록면허세(면허)
4th row등록세(부동산)
5th row자동차세(기계장비)

Common Values

ValueCountFrequency (%)
담배소비세 1
 
3.6%
등록면허세(등록) 1
 
3.6%
등록면허세(면허) 1
 
3.6%
등록세(부동산) 1
 
3.6%
자동차세(기계장비) 1
 
3.6%
자동차세(이륜차) 1
 
3.6%
자동차세(자동차) 1
 
3.6%
자동차세(주행) 1
 
3.6%
재산세(건축물) 1
 
3.6%
재산세(선박) 1
 
3.6%
Other values (18) 18
64.3%

Length

2023-08-05T10:34:27.301463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배소비세 1
 
3.6%
등록면허세(등록 1
 
3.6%
취득세(선박 1
 
3.6%
취득세(부동산 1
 
3.6%
취득세(기타 1
 
3.6%
취득세(기계장비 1
 
3.6%
지역자원시설세(특자 1
 
3.6%
지방소비세 1
 
3.6%
지방소득세(특별징수 1
 
3.6%
지방소득세(종합소득 1
 
3.6%
Other values (18) 18
64.3%

과세건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6419.6071
Minimum6
Maximum43691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2023-08-05T10:34:27.588850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.4
Q1249.5
median730.5
Q36476
95-th percentile31504.95
Maximum43691
Range43685
Interquartile range (IQR)6226.5

Descriptive statistics

Standard deviation11416.503
Coefficient of variation (CV)1.7783803
Kurtosis4.5137604
Mean6419.6071
Median Absolute Deviation (MAD)711
Skewness2.2221241
Sum179749
Variance1.3033653 × 108
MonotonicityNot monotonic
2023-08-05T10:34:28.173367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
269 1
 
3.6%
1647 1
 
3.6%
332 1
 
3.6%
11213 1
 
3.6%
310 1
 
3.6%
363 1
 
3.6%
191 1
 
3.6%
36574 1
 
3.6%
12 1
 
3.6%
5197 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
6 1
3.6%
8 1
3.6%
12 1
3.6%
27 1
3.6%
99 1
3.6%
135 1
3.6%
191 1
3.6%
269 1
3.6%
310 1
3.6%
332 1
3.6%
ValueCountFrequency (%)
43691 1
3.6%
36574 1
3.6%
22091 1
3.6%
21119 1
3.6%
14544 1
3.6%
11213 1
3.6%
8900 1
3.6%
5668 1
3.6%
5197 1
3.6%
3205 1
3.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8256765 × 109
Minimum1272190
Maximum9.4949196 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2023-08-05T10:34:28.425478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1272190
5-th percentile2503142.5
Q139801570
median5.4859215 × 108
Q32.8346296 × 109
95-th percentile7.6099544 × 109
Maximum9.4949196 × 109
Range9.4936474 × 109
Interquartile range (IQR)2.794828 × 109

Descriptive statistics

Standard deviation2.6830713 × 109
Coefficient of variation (CV)1.4696313
Kurtosis1.9375755
Mean1.8256765 × 109
Median Absolute Deviation (MAD)5.4544292 × 108
Skewness1.6794076
Sum5.1118943 × 1010
Variance7.1988718 × 1018
MonotonicityNot monotonic
2023-08-05T10:34:28.667324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3740876770 1
 
3.6%
82752600 1
 
3.6%
31758660 1
 
3.6%
189764930 1
 
3.6%
42482540 1
 
3.6%
17540240 1
 
3.6%
2260470 1
 
3.6%
4143750150 1
 
3.6%
7007412490 1
 
3.6%
1421215760 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1272190 1
3.6%
2260470 1
3.6%
2953820 1
3.6%
3344640 1
3.6%
6946120 1
3.6%
17540240 1
3.6%
31758660 1
3.6%
42482540 1
3.6%
68175600 1
3.6%
82752600 1
3.6%
ValueCountFrequency (%)
9494919590 1
3.6%
7934400000 1
3.6%
7007412490 1
3.6%
5530117220 1
3.6%
4143750150 1
3.6%
3740876770 1
3.6%
2963043070 1
3.6%
2791825060 1
3.6%
1421215760 1
3.6%
1359581870 1
3.6%

비과세건수
Categorical

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size352.0 B
12 
6
2 
2808
 
1
924
 
1
18
 
1
Other values (11)
11 

Length

Max length5
Median length2
Mean length2.3928571
Min length1

Unique

Unique14 ?
Unique (%)50.0%

Sample

1st row
2nd row2808
3rd row924
4th row18
5th row8

Common Values

ValueCountFrequency (%)
12
42.9%
6 2
 
7.1%
2808 1
 
3.6%
924 1
 
3.6%
18 1
 
3.6%
8 1
 
3.6%
2 1
 
3.6%
5402 1
 
3.6%
5673 1
 
3.6%
24 1
 
3.6%
Other values (6) 6
21.4%

Length

2023-08-05T10:34:28.933491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6 2
 
12.5%
2808 1
 
6.2%
924 1
 
6.2%
18 1
 
6.2%
8 1
 
6.2%
2 1
 
6.2%
5402 1
 
6.2%
5673 1
 
6.2%
24 1
 
6.2%
2970 1
 
6.2%
Other values (5) 5
31.2%

비과세금액
Categorical

Distinct17
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size352.0 B
12 
326436270
 
1
9123000
 
1
16496670
 
1
750000
 
1
Other values (12)
12 

Length

Max length10
Median length9
Mean length5.3571429
Min length2

Unique

Unique16 ?
Unique (%)57.1%

Sample

1st row
2nd row326436270
3rd row9123000
4th row16496670
5th row750000

Common Values

ValueCountFrequency (%)
12
42.9%
326436270 1
 
3.6%
9123000 1
 
3.6%
16496670 1
 
3.6%
750000 1
 
3.6%
34200 1
 
3.6%
496424100 1
 
3.6%
1583508990 1
 
3.6%
573030 1
 
3.6%
638241024 1
 
3.6%
Other values (7) 7
25.0%

Length

2023-08-05T10:34:29.196279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
326436270 1
 
6.2%
9123000 1
 
6.2%
16496670 1
 
6.2%
750000 1
 
6.2%
34200 1
 
6.2%
496424100 1
 
6.2%
1583508990 1
 
6.2%
573030 1
 
6.2%
638241024 1
 
6.2%
5618838742 1
 
6.2%
Other values (6) 6
37.5%

Interactions

2023-08-05T10:34:24.517591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-05T10:34:24.184926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-05T10:34:24.700765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-05T10:34:24.350693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-05T10:34:29.378423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
자치단체코드과세년도과세건수과세금액
자치단체코드NaNNaNNaNNaN
과세년도NaNNaNNaNNaN
과세건수NaNNaN1.0000.256
과세금액NaNNaN0.2561.000
2023-08-05T10:34:29.592197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
자치단체코드과세년도과세건수과세금액
자치단체코드NaNNaNNaNNaN
과세년도NaNNaNNaNNaN
과세건수NaNNaN1.0000.391
과세금액NaNNaN0.3911.000
2023-08-05T10:34:29.804893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
자치단체코드과세년도과세건수과세금액
자치단체코드1.000NaNNaNNaN
과세년도NaN1.000NaNNaN
과세건수NaNNaN1.0000.333
과세금액NaNNaN0.3331.000
2023-08-05T10:34:29.998728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
세목명과세건수과세금액비과세건수비과세금액
세목명1.0001.0001.0001.0001.000
과세건수1.0001.0000.8270.9720.921
과세금액1.0000.8271.0000.3490.000
비과세건수1.0000.9720.3491.0001.000
비과세금액1.0000.9210.0001.0001.000
2023-08-05T10:34:30.198479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
비과세금액세목명비과세건수
비과세금액1.0001.0000.968
세목명1.0001.0001.000
비과세건수0.9681.0001.000
2023-08-05T10:34:30.376492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
과세건수과세금액세목명비과세건수비과세금액
과세건수1.0000.3911.0000.5640.518
과세금액0.3911.0001.0000.0000.000
세목명1.0001.0001.0001.0001.000
비과세건수0.5640.0001.0001.0000.968
비과세금액0.5180.0001.0000.9681.000

Missing values

2023-08-05T10:34:24.958683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-05T10:34:25.300227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
0충청북도옥천군437302020담배소비세2693740876770
1충청북도옥천군437302020등록면허세(등록)1454411576288102808326436270
2충청북도옥천군437302020등록면허세(면허)112131897649309249123000
3충청북도옥천군437302020등록세(부동산)310424825401816496670
4충청북도옥천군437302020자동차세(기계장비)363175402408750000
5충청북도옥천군437302020자동차세(이륜차)1912260470234200
6충청북도옥천군437302020자동차세(자동차)3657441437501505402496424100
7충청북도옥천군437302020자동차세(주행)127007412490
8충청북도옥천군437302020재산세(건축물)5197142121576056731583508990
9충청북도옥천군437302020재산세(선박)99127219024573030
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
18충청북도옥천군437302020지방소득세(양도소득)860683237250
19충청북도옥천군437302020지방소득세(종합소득)3205821804650
20충청북도옥천군437302020지방소득세(특별징수)89002791825060
21충청북도옥천군437302020지방소비세67934400000
22충청북도옥천군437302020지역자원시설세(특자)1353344640
23충청북도옥천군437302020취득세(기계장비)3494481076608731592610
24충청북도옥천군437302020취득세(기타)86946120
25충청북도옥천군437302020취득세(부동산)5668949491959048459433842540
26충청북도옥천군437302020취득세(선박)27295382061274940
27충청북도옥천군437302020취득세(이륜차량)33231758660331471800