Overview

Dataset statistics

Number of variables9
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory84.0 B

Variable types

Categorical4
Text1
Numeric4

Dataset

Description충청남도 공주시 지방세 과세현황 데이터를 공공데이터포털에 개방하였습니다. 각 세목명과 과세년도, 과세건수, 과세금액, 비과세금액 등을 확인하여 주시기 바랍니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=35&beforeMenuCd=DOM_000000201001001000&publicdatapk=15117531

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 비과세건수 High correlation
비과세건수 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
비과세금액 is highly overall correlated with 비과세건수 High correlation
과세건수 has unique valuesUnique
과세금액 has unique valuesUnique
비과세건수 has 6 (27.3%) zerosZeros
비과세금액 has 6 (27.3%) zerosZeros

Reproduction

Analysis started2024-01-09 22:31:41.080497
Analysis finished2024-01-09 22:31:42.691466
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
충청남도
22 

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 22
100.0%

Length

2024-01-10T07:31:42.742276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:31:42.822351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 22
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
공주시
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공주시 22
100.0%

Length

2024-01-10T07:31:42.902985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:31:42.982066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공주시 22
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
44150
22 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44150
2nd row44150
3rd row44150
4th row44150
5th row44150

Common Values

ValueCountFrequency (%)
44150 22
100.0%

Length

2024-01-10T07:31:43.064452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:31:43.143130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44150 22
100.0%

과세년도
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
2020
11 
2021
11 

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 11
50.0%
2021 11
50.0%

Length

2024-01-10T07:31:43.240597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:31:43.332208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 11
50.0%
2021 11
50.0%

세목명
Text

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-01-10T07:31:43.462599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.1818182
Min length3

Characters and Unicode

Total characters92
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취득세
2nd row등록세
3rd row주민세
4th row재산세
5th row자동차세
ValueCountFrequency (%)
취득세 2
9.1%
등록세 2
9.1%
주민세 2
9.1%
재산세 2
9.1%
자동차세 2
9.1%
담배소비세 2
9.1%
지방소비세 2
9.1%
등록면허세 2
9.1%
지역자원시설세 2
9.1%
지방소득세 2
9.1%
2024-01-10T07:31:43.733689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 22
23.9%
지 6
 
6.5%
소 6
 
6.5%
등 4
 
4.3%
록 4
 
4.3%
비 4
 
4.3%
득 4
 
4.3%
방 4
 
4.3%
자 4
 
4.3%
허 2
 
2.2%
Other values (16) 32
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 22
23.9%
지 6
 
6.5%
소 6
 
6.5%
등 4
 
4.3%
록 4
 
4.3%
비 4
 
4.3%
득 4
 
4.3%
방 4
 
4.3%
자 4
 
4.3%
허 2
 
2.2%
Other values (16) 32
34.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 22
23.9%
지 6
 
6.5%
소 6
 
6.5%
등 4
 
4.3%
록 4
 
4.3%
비 4
 
4.3%
득 4
 
4.3%
방 4
 
4.3%
자 4
 
4.3%
허 2
 
2.2%
Other values (16) 32
34.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 22
23.9%
지 6
 
6.5%
소 6
 
6.5%
등 4
 
4.3%
록 4
 
4.3%
비 4
 
4.3%
득 4
 
4.3%
방 4
 
4.3%
자 4
 
4.3%
허 2
 
2.2%
Other values (16) 32
34.8%

과세건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60182.727
Minimum6
Maximum265933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:31:43.838483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20.35
Q15969.25
median36710
Q369432.25
95-th percentile252162.45
Maximum265933
Range265927
Interquartile range (IQR)63463

Descriptive statistics

Standard deviation74810.579
Coefficient of variation (CV)1.2430573
Kurtosis3.5369773
Mean60182.727
Median Absolute Deviation (MAD)36081.5
Skewness1.9538994
Sum1324020
Variance5.5966227 × 109
MonotonicityNot monotonic
2024-01-10T07:31:43.942364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21744 1
 
4.5%
711 1
 
4.5%
265933 1
 
4.5%
38913 1
 
4.5%
33638 1
 
4.5%
53512 1
 
4.5%
7 1
 
4.5%
479 1
 
4.5%
79210 1
 
4.5%
123921 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
6 1
4.5%
7 1
4.5%
274 1
4.5%
479 1
4.5%
546 1
4.5%
711 1
4.5%
21744 1
4.5%
22861 1
4.5%
33638 1
4.5%
33951 1
4.5%
ValueCountFrequency (%)
265933 1
4.5%
258912 1
4.5%
123921 1
4.5%
123198 1
4.5%
79210 1
4.5%
74739 1
4.5%
53512 1
4.5%
52874 1
4.5%
52549 1
4.5%
51535 1
4.5%

과세금액
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4560513 × 1010
Minimum1.1570383 × 108
Maximum5.7265589 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:31:44.277580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1570383 × 108
5-th percentile2.5123407 × 108
Q13.3733636 × 109
median8.92685 × 109
Q31.8111553 × 1010
95-th percentile4.2840599 × 1010
Maximum5.7265589 × 1010
Range5.7149886 × 1010
Interquartile range (IQR)1.473819 × 1010

Descriptive statistics

Standard deviation1.5167465 × 1010
Coefficient of variation (CV)1.0416847
Kurtosis1.9443892
Mean1.4560513 × 1010
Median Absolute Deviation (MAD)6.7218957 × 109
Skewness1.5250557
Sum3.2033129 × 1011
Variance2.3005199 × 1020
MonotonicityNot monotonic
2024-01-10T07:31:44.369389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
43150661460 1
 
4.5%
149384790 1
 
4.5%
13829012250 1
 
4.5%
36949408700 1
 
4.5%
2223538330 1
 
4.5%
3806797640 1
 
4.5%
8928000000 1
 
4.5%
8570869240 1
 
4.5%
18420265110 1
 
4.5%
17185418130 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
115703830 1
4.5%
149384790 1
4.5%
2186370310 1
4.5%
2223538330 1
4.5%
3248286160 1
4.5%
3336028780 1
4.5%
3485367860 1
4.5%
3806797640 1
4.5%
8570869240 1
4.5%
8818886620 1
4.5%
ValueCountFrequency (%)
57265589420 1
4.5%
43150661460 1
4.5%
36949408700 1
4.5%
31600833950 1
4.5%
19193862200 1
4.5%
18420265110 1
4.5%
17185418130 1
4.5%
16507898500 1
4.5%
13829012250 1
4.5%
12433411630 1
4.5%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5888
Minimum0
Maximum35887
Zeros6
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:31:44.482809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.25
median1949
Q35877.75
95-th percentile34284.2
Maximum35887
Range35887
Interquartile range (IQR)5871.5

Descriptive statistics

Standard deviation10252.858
Coefficient of variation (CV)1.7413143
Kurtosis5.6400285
Mean5888
Median Absolute Deviation (MAD)1949
Skewness2.4798318
Sum129536
Variance1.051211 × 108
MonotonicityNot monotonic
2024-01-10T07:31:44.580963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 6
27.3%
4610 1
 
4.5%
60 1
 
4.5%
62 1
 
4.5%
1951 1
 
4.5%
5676 1
 
4.5%
11735 1
 
4.5%
35471 1
 
4.5%
5945 1
 
4.5%
4585 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
0 6
27.3%
25 1
 
4.5%
60 1
 
4.5%
62 1
 
4.5%
68 1
 
4.5%
1947 1
 
4.5%
1951 1
 
4.5%
4585 1
 
4.5%
4610 1
 
4.5%
5214 1
 
4.5%
ValueCountFrequency (%)
35887 1
4.5%
35471 1
4.5%
11735 1
4.5%
10271 1
4.5%
6029 1
4.5%
5945 1
4.5%
5676 1
4.5%
5214 1
4.5%
4610 1
4.5%
4585 1
4.5%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5940758 × 109
Minimum0
Maximum1.5210316 × 1010
Zeros6
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:31:44.680643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11242.5
median2.0543646 × 108
Q31.4514513 × 109
95-th percentile1.2809644 × 1010
Maximum1.5210316 × 1010
Range1.5210316 × 1010
Interquartile range (IQR)1.4514501 × 109

Descriptive statistics

Standard deviation4.9670992 × 109
Coefficient of variation (CV)1.9147857
Kurtosis1.6899556
Mean2.5940758 × 109
Median Absolute Deviation (MAD)2.0543646 × 108
Skewness1.8189526
Sum5.7069669 × 1010
Variance2.4672075 × 1019
MonotonicityNot monotonic
2024-01-10T07:31:44.808611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 6
27.3%
15210315630 1
 
4.5%
13678970 1
 
4.5%
4970 1
 
4.5%
497130240 1
 
4.5%
232475340 1
 
4.5%
598704520 1
 
4.5%
12843781480 1
 
4.5%
1730104670 1
 
4.5%
10696191150 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
0 6
27.3%
4970 1
 
4.5%
6440 1
 
4.5%
8624500 1
 
4.5%
13678970 1
 
4.5%
178397590 1
 
4.5%
232475340 1
 
4.5%
482523470 1
 
4.5%
497130240 1
 
4.5%
598704520 1
 
4.5%
ValueCountFrequency (%)
15210315630 1
4.5%
12843781480 1
4.5%
12161032460 1
4.5%
10696191150 1
4.5%
1801205730 1
4.5%
1730104670 1
4.5%
615491370 1
4.5%
598704520 1
4.5%
497130240 1
4.5%
482523470 1
4.5%

Interactions

2024-01-10T07:31:42.186668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.302605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.588842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.875714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:42.256406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.371458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.659200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.946440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:42.335078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.441857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.728834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:42.019482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:42.419521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.515973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:41.802427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:31:42.091752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:31:44.913240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9740.8760.8820.767
과세건수0.0000.9741.0000.7790.8950.673
과세금액0.0000.8760.7791.0000.6640.675
비과세건수0.0000.8820.8950.6641.0000.778
비과세금액0.0000.7670.6730.6750.7781.000
2024-01-10T07:31:45.001232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도
과세건수1.0000.2550.6810.4330.000
과세금액0.2551.0000.1050.1560.000
비과세건수0.6810.1051.0000.8880.000
비과세금액0.4330.1560.8881.0000.000
과세년도0.0000.0000.0000.0001.000

Missing values

2024-01-10T07:31:42.531126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:31:42.647613image/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
0충청남도공주시441502020취득세2174443150661460461015210315630
1충청남도공주시441502020등록세546115703830258624500
2충청남도공주시441502020주민세52874333602878060291801205730
3충청남도공주시441502020재산세123198165078985003588712161032460
4충청남도공주시441502020자동차세747391919386220010271615491370
5충청남도공주시441502020담배소비세274881888662000
6충청남도공주시441502020지방소비세6892570000000
7충청남도공주시441502020등록면허세5153532482861605214178397590
8충청남도공주시441502020지역자원시설세3395121863703101947482523470
9충청남도공주시441502020지방소득세345073160083395000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
12충청남도공주시441502021등록세7111493847906013678970
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