Overview

Dataset statistics

Number of variables11
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory94.9 B

Variable types

Categorical7
Boolean1
Numeric3

Dataset

Description지방세납부현황 자료로, 납부년도, 세목명, 납부매체, 납부매체전자고지여부, 납부건수, 납부금액, 납부매체비율 등의 항목 데이터를 제공합니다.
Author경상북도 청송군
URLhttps://www.data.go.kr/data/15078278/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부년도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부건수 is highly overall correlated with 납부금액 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 납부건수 and 2 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 납부금액High correlation
납부금액 has unique valuesUnique
납부매체비율 has 2 (2.9%) zerosZeros

Reproduction

Analysis started2024-05-04 07:12:44.931775
Analysis finished2024-05-04 07:12:48.816107
Duration3.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
경상북도
68 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 68
100.0%

Length

2024-05-04T07:12:49.028734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:12:49.335507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 68
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
청송군
68 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청송군 68
100.0%

Length

2024-05-04T07:12:49.636490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:12:49.913722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청송군 68
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
47750
68 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47750
2nd row47750
3rd row47750
4th row47750
5th row47750

Common Values

ValueCountFrequency (%)
47750 68
100.0%

Length

2024-05-04T07:12:50.191537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:12:50.517201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47750 68
100.0%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
2022
68 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 68
100.0%

Length

2024-05-04T07:12:50.848915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:12:51.161338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 68
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size676.0 B
등록면허세
9 
자동차세
9 
재산세
9 
주민세
9 
지방소득세
8 
Other values (8)
24 

Length

Max length7
Median length5
Mean length4.0294118
Min length3

Unique

Unique3 ?
Unique (%)4.4%

Sample

1st row담배소비세
2nd row등록면허세
3rd row등록세
4th row자동차세
5th row재산세

Common Values

ValueCountFrequency (%)
등록면허세 9
13.2%
자동차세 9
13.2%
재산세 9
13.2%
주민세 9
13.2%
지방소득세 8
11.8%
취득세 8
11.8%
등록세 5
7.4%
지역자원시설세 4
5.9%
담배소비세 2
 
2.9%
종합토지세 2
 
2.9%
Other values (3) 3
 
4.4%

Length

2024-05-04T07:12:51.562926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 9
13.2%
자동차세 9
13.2%
재산세 9
13.2%
주민세 9
13.2%
지방소득세 8
11.8%
취득세 8
11.8%
등록세 5
7.4%
지역자원시설세 4
5.9%
담배소비세 2
 
2.9%
종합토지세 2
 
2.9%
Other values (3) 3
 
4.4%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size676.0 B
위택스
10 
가상계좌
9 
은행창구
9 
기타
8 
인터넷지로
8 
Other values (4)
24 

Length

Max length5
Median length4
Mean length4.0294118
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위택스 10
14.7%
가상계좌 9
13.2%
은행창구 9
13.2%
기타 8
11.8%
인터넷지로 8
11.8%
자동화기기 7
10.3%
지자체방문 7
10.3%
페이사납부 6
8.8%
자동이체 4
 
5.9%

Length

2024-05-04T07:12:51.984707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:12:52.351401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위택스 10
14.7%
가상계좌 9
13.2%
은행창구 9
13.2%
기타 8
11.8%
인터넷지로 8
11.8%
자동화기기 7
10.3%
지자체방문 7
10.3%
페이사납부 6
8.8%
자동이체 4
 
5.9%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size200.0 B
True
37 
False
31 
ValueCountFrequency (%)
True 37
54.4%
False 31
45.6%
2024-05-04T07:12:52.762824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1566.6471
Minimum1
Maximum17167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-04T07:12:53.110709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q128
median337
Q31792
95-th percentile6506.65
Maximum17167
Range17166
Interquartile range (IQR)1764

Descriptive statistics

Standard deviation2936.0869
Coefficient of variation (CV)1.8741215
Kurtosis12.940766
Mean1566.6471
Median Absolute Deviation (MAD)330
Skewness3.2886539
Sum106532
Variance8620606.2
MonotonicityNot monotonic
2024-05-04T07:12:53.626703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
5.9%
3 2
 
2.9%
25 2
 
2.9%
313 2
 
2.9%
158 2
 
2.9%
2087 1
 
1.5%
4720 1
 
1.5%
2331 1
 
1.5%
2362 1
 
1.5%
12 1
 
1.5%
Other values (51) 51
75.0%
ValueCountFrequency (%)
1 4
5.9%
3 2
2.9%
5 1
 
1.5%
6 1
 
1.5%
8 1
 
1.5%
9 1
 
1.5%
11 1
 
1.5%
12 1
 
1.5%
19 1
 
1.5%
23 1
 
1.5%
ValueCountFrequency (%)
17167 1
1.5%
10790 1
1.5%
9984 1
1.5%
7039 1
1.5%
5518 1
1.5%
5083 1
1.5%
4720 1
1.5%
4525 1
1.5%
4107 1
1.5%
3772 1
1.5%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3862406 × 108
Minimum4980
Maximum8.4913432 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-04T07:12:54.204421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4980
5-th percentile28006
Q15572960
median41941925
Q32.5726797 × 108
95-th percentile1.746076 × 109
Maximum8.4913432 × 109
Range8.4913382 × 109
Interquartile range (IQR)2.5169501 × 108

Descriptive statistics

Standard deviation1.167859 × 109
Coefficient of variation (CV)2.6625512
Kurtosis34.577498
Mean4.3862406 × 108
Median Absolute Deviation (MAD)41913565
Skewness5.3844931
Sum2.9826436 × 1010
Variance1.3638947 × 1018
MonotonicityNot monotonic
2024-05-04T07:12:54.634728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11180 1
 
1.5%
28775550 1
 
1.5%
169857510 1
 
1.5%
3654530 1
 
1.5%
24901800 1
 
1.5%
22316100 1
 
1.5%
288252760 1
 
1.5%
259434430 1
 
1.5%
127773720 1
 
1.5%
2639116330 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
4980 1
1.5%
11180 1
1.5%
26110 1
1.5%
27180 1
1.5%
29540 1
1.5%
83390 1
1.5%
184820 1
1.5%
256820 1
1.5%
301320 1
1.5%
487050 1
1.5%
ValueCountFrequency (%)
8491343230 1
1.5%
3233691780 1
1.5%
2639116330 1
1.5%
1747768590 1
1.5%
1742932590 1
1.5%
1394759260 1
1.5%
1099875640 1
1.5%
1061275200 1
1.5%
994205850 1
1.5%
909776280 1
1.5%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.234706
Minimum0
Maximum79.67
Zeros2
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-05-04T07:12:55.079778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.044
Q11.2775
median8.975
Q318.305
95-th percentile46.4025
Maximum79.67
Range79.67
Interquartile range (IQR)17.0275

Descriptive statistics

Standard deviation15.878577
Coefficient of variation (CV)1.1997681
Kurtosis4.3809172
Mean13.234706
Median Absolute Deviation (MAD)8.385
Skewness1.9444919
Sum899.96
Variance252.12922
MonotonicityNot monotonic
2024-05-04T07:12:55.560207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.06 2
 
2.9%
0.1 2
 
2.9%
3.5 2
 
2.9%
0.0 2
 
2.9%
41.8 1
 
1.5%
0.14 1
 
1.5%
9.75 1
 
1.5%
10.26 1
 
1.5%
0.56 1
 
1.5%
14.52 1
 
1.5%
Other values (54) 54
79.4%
ValueCountFrequency (%)
0.0 2
2.9%
0.01 1
1.5%
0.03 1
1.5%
0.07 1
1.5%
0.1 2
2.9%
0.14 1
1.5%
0.28 1
1.5%
0.29 1
1.5%
0.35 1
1.5%
0.41 1
1.5%
ValueCountFrequency (%)
79.67 1
1.5%
55.05 1
1.5%
52.3 1
1.5%
47.68 1
1.5%
44.03 1
1.5%
41.8 1
1.5%
41.04 1
1.5%
32.74 1
1.5%
26.5 1
1.5%
25.61 1
1.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
2022-12-31
68 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 68
100.0%

Length

2024-05-04T07:12:56.001693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:12:56.333711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 68
100.0%

Interactions

2024-05-04T07:12:47.204867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:45.425283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:46.437071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:47.490252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:45.889510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:46.705247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:47.756718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:46.142636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:12:46.942193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:12:56.569002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
세목명1.0000.0000.0000.0000.7670.170
납부매체0.0001.0001.0000.3420.0000.139
납부매체전자고지여부0.0001.0001.0000.0000.0000.000
납부건수0.0000.3420.0001.0000.3590.875
납부금액0.7670.0000.0000.3591.0000.000
납부매체비율0.1700.1390.0000.8750.0001.000
2024-05-04T07:12:56.871423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부
세목명1.0000.0000.000
납부매체0.0001.0000.945
납부매체전자고지여부0.0000.9451.000
2024-05-04T07:12:57.133541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부건수1.0000.7550.8020.0000.1680.000
납부금액0.7551.0000.5740.5150.0000.000
납부매체비율0.8020.5741.0000.0510.0510.000
세목명0.0000.5150.0511.0000.0000.000
납부매체0.1680.0000.0510.0001.0000.945
납부매체전자고지여부0.0000.0000.0000.0000.9451.000

Missing values

2024-05-04T07:12:48.156220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:12:48.641254image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율데이터기준일자
0경상북도청송군477502022담배소비세가상계좌Y3111800.012022-12-31
1경상북도청송군477502022등록면허세가상계좌Y3772608511509.672022-12-31
2경상북도청송군477502022등록세가상계좌Y1295400.02022-12-31
3경상북도청송군477502022자동차세가상계좌Y9984139475926025.612022-12-31
4경상북도청송군477502022재산세가상계좌Y17167106127520044.032022-12-31
5경상북도청송군477502022주민세가상계좌Y508316650955013.042022-12-31
6경상북도청송군477502022지방소득세가상계좌Y236610998756406.072022-12-31
7경상북도청송군477502022지역자원시설세가상계좌Y1157013800.032022-12-31
8경상북도청송군477502022취득세가상계좌Y6035801399801.552022-12-31
9경상북도청송군477502022등록면허세기타N192568201.312022-12-31
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