Overview

Dataset statistics

Number of variables11
Number of observations77
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory94.7 B

Variable types

Categorical7
Boolean1
Numeric3

Dataset

Description충청북도 증평군_지방세에 대한 자료입니다. 지방세에는 취득세, 재산세, 자동차세, 지방소득세, 등록면허세 등 다양한 자료가 있습니다.
URLhttps://www.data.go.kr/data/15080370/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 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 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부년도 is highly imbalanced (82.6%)Imbalance
납부금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:59:14.428586
Analysis finished2023-12-12 11:59:16.474127
Duration2.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
충청북도
77 

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 77
100.0%

Length

2023-12-12T20:59:16.553257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:16.675705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 77
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
증평군
77 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
증평군 77
100.0%

Length

2023-12-12T20:59:16.817036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:16.981484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
증평군 77
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
43745
77 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43745
2nd row43745
3rd row43745
4th row43745
5th row43745

Common Values

ValueCountFrequency (%)
43745 77
100.0%

Length

2023-12-12T20:59:17.114245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:17.239557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43745 77
100.0%

납부년도
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size748.0 B
2022
75 
2021
 
2

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 75
97.4%
2021 2
 
2.6%

Length

2023-12-12T20:59:17.370497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:17.505999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 75
97.4%
2021 2
 
2.6%

세목명
Categorical

Distinct12
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size748.0 B
재산세
11 
주민세
11 
등록면허세
10 
자동차세
10 
지방소득세
9 
Other values (7)
26 

Length

Max length7
Median length3
Mean length4.012987
Min length3

Unique

Unique2 ?
Unique (%)2.6%

Sample

1st row등록면허세
2nd row자동차세
3rd row재산세
4th row주민세
5th row지방소득세

Common Values

ValueCountFrequency (%)
재산세 11
14.3%
주민세 11
14.3%
등록면허세 10
13.0%
자동차세 10
13.0%
지방소득세 9
11.7%
취득세 9
11.7%
지역자원시설세 6
7.8%
등록세 5
6.5%
담배소비세 2
 
2.6%
면허세 2
 
2.6%
Other values (2) 2
 
2.6%

Length

2023-12-12T20:59:17.685746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
재산세 11
14.3%
주민세 11
14.3%
등록면허세 10
13.0%
자동차세 10
13.0%
지방소득세 9
11.7%
취득세 9
11.7%
지역자원시설세 6
7.8%
등록세 5
6.5%
담배소비세 2
 
2.6%
면허세 2
 
2.6%
Other values (2) 2
 
2.6%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size748.0 B
은행창구
11 
위택스
10 
가상계좌
9 
자동화기기
9 
기타
8 
Other values (5)
30 

Length

Max length5
Median length4
Mean length3.961039
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은행창구 11
14.3%
위택스 10
13.0%
가상계좌 9
11.7%
자동화기기 9
11.7%
기타 8
10.4%
지자체방문 8
10.4%
ARS 6
7.8%
인터넷지로 6
7.8%
페이사납부 6
7.8%
자동이체 4
 
5.2%

Length

2023-12-12T20:59:17.842512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:18.011837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은행창구 11
14.3%
위택스 10
13.0%
가상계좌 9
11.7%
자동화기기 9
11.7%
기타 8
10.4%
지자체방문 8
10.4%
ars 6
7.8%
인터넷지로 6
7.8%
페이사납부 6
7.8%
자동이체 4
 
5.2%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size209.0 B
False
42 
True
35 
ValueCountFrequency (%)
False 42
54.5%
True 35
45.5%
2023-12-12T20:59:18.173824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1428.8961
Minimum1
Maximum17367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T20:59:18.319643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q156
median468
Q31314
95-th percentile5935
Maximum17367
Range17366
Interquartile range (IQR)1258

Descriptive statistics

Standard deviation2998.3902
Coefficient of variation (CV)2.0983962
Kurtosis15.404739
Mean1428.8961
Median Absolute Deviation (MAD)436
Skewness3.7767382
Sum110025
Variance8990343.8
MonotonicityNot monotonic
2023-12-12T20:59:18.486723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 4
 
5.2%
1 2
 
2.6%
838 2
 
2.6%
291 2
 
2.6%
1889 2
 
2.6%
61 1
 
1.3%
556 1
 
1.3%
3058 1
 
1.3%
26 1
 
1.3%
979 1
 
1.3%
Other values (60) 60
77.9%
ValueCountFrequency (%)
1 2
2.6%
2 1
 
1.3%
3 4
5.2%
4 1
 
1.3%
9 1
 
1.3%
10 1
 
1.3%
11 1
 
1.3%
14 1
 
1.3%
15 1
 
1.3%
24 1
 
1.3%
ValueCountFrequency (%)
17367 1
1.3%
14265 1
1.3%
11893 1
1.3%
6075 1
1.3%
5900 1
1.3%
5569 1
1.3%
5225 1
1.3%
3058 1
1.3%
2572 1
1.3%
2213 1
1.3%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct77
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.613723 × 108
Minimum3150
Maximum1.0186609 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T20:59:18.705900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3150
5-th percentile44086
Q14882280
median86669130
Q35.0702704 × 108
95-th percentile3.8358049 × 109
Maximum1.0186609 × 1010
Range1.0186606 × 1010
Interquartile range (IQR)5.0214476 × 108

Descriptive statistics

Standard deviation2.1098238 × 109
Coefficient of variation (CV)2.4493751
Kurtosis12.524298
Mean8.613723 × 108
Median Absolute Deviation (MAD)86527900
Skewness3.5190711
Sum6.6325667 × 1010
Variance4.4513567 × 1018
MonotonicityNot monotonic
2023-12-12T20:59:19.298864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
637630 1
 
1.3%
67572680 1
 
1.3%
961347550 1
 
1.3%
480646970 1
 
1.3%
3150 1
 
1.3%
6743720 1
 
1.3%
82813940 1
 
1.3%
10229000 1
 
1.3%
250003710 1
 
1.3%
5838600 1
 
1.3%
Other values (67) 67
87.0%
ValueCountFrequency (%)
3150 1
1.3%
5720 1
1.3%
9330 1
1.3%
19830 1
1.3%
50150 1
1.3%
82950 1
1.3%
141230 1
1.3%
153920 1
1.3%
485820 1
1.3%
589650 1
1.3%
ValueCountFrequency (%)
10186608860 1
1.3%
9969532820 1
1.3%
9878163020 1
1.3%
5728841770 1
1.3%
3362545700 1
1.3%
3201509560 1
1.3%
3154146210 1
1.3%
2868595670 1
1.3%
2845942560 1
1.3%
2401844290 1
1.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.381429
Minimum0.01
Maximum61.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T20:59:19.483163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.87
median11.16
Q320.28
95-th percentile38.68
Maximum61.46
Range61.45
Interquartile range (IQR)19.41

Descriptive statistics

Standard deviation13.43498
Coefficient of variation (CV)1.0040019
Kurtosis1.6589657
Mean13.381429
Median Absolute Deviation (MAD)9.56
Skewness1.2876892
Sum1030.37
Variance180.49868
MonotonicityNot monotonic
2023-12-12T20:59:19.647477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.18 2
 
2.6%
0.04 2
 
2.6%
0.01 2
 
2.6%
23.06 2
 
2.6%
2.85 1
 
1.3%
14.74 1
 
1.3%
34.97 1
 
1.3%
19.2 1
 
1.3%
0.16 1
 
1.3%
11.86 1
 
1.3%
Other values (63) 63
81.8%
ValueCountFrequency (%)
0.01 2
2.6%
0.02 1
1.3%
0.04 2
2.6%
0.06 1
1.3%
0.08 1
1.3%
0.12 1
1.3%
0.16 1
1.3%
0.17 1
1.3%
0.2 1
1.3%
0.32 1
1.3%
ValueCountFrequency (%)
61.46 1
1.3%
48.49 1
1.3%
46.16 1
1.3%
44.92 1
1.3%
37.12 1
1.3%
36.56 1
1.3%
34.97 1
1.3%
31.7 1
1.3%
30.64 1
1.3%
29.86 1
1.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
2022-12-31
77 

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 77
100.0%

Length

2023-12-12T20:59:19.796347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:19.888443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 77
100.0%

Interactions

2023-12-12T20:59:15.691272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:14.886315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:15.320970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:15.819577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:15.020679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:15.440466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:15.987951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:15.171467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:15.563500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:59:19.970199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.2880.0000.0000.0000.000
세목명0.0001.0000.0000.0000.0000.6610.311
납부매체0.2880.0001.0001.0000.2900.1670.257
납부매체전자고지여부0.0000.0001.0001.0000.1160.0000.000
납부건수0.0000.0000.2900.1161.0000.7330.617
납부금액0.0000.6610.1670.0000.7331.0000.225
납부매체비율0.0000.3110.2570.0000.6170.2251.000
2023-12-12T20:59:20.121683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부년도납부매체납부매체전자고지여부
세목명1.0000.0000.0000.000
납부년도0.0001.0000.2050.000
납부매체0.0000.2051.0000.945
납부매체전자고지여부0.0000.0000.9451.000
2023-12-12T20:59:20.238082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.7660.7660.0000.0000.1470.076
납부금액0.7661.0000.5580.0000.3070.0820.000
납부매체비율0.7660.5581.0000.0000.1240.1160.080
납부년도0.0000.0000.0001.0000.0000.2050.000
세목명0.0000.3070.1240.0001.0000.0000.000
납부매체0.1470.0820.1160.2050.0001.0000.945
납부매체전자고지여부0.0760.0000.0800.0000.0000.9451.000

Missing values

2023-12-12T20:59:16.154038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:59:16.385927image/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
0충청북도증평군437452022등록면허세ARSN616376302.852022-12-31
1충청북도증평군437452022자동차세ARSN131427705087061.462022-12-31
2충청북도증평군437452022재산세ARSN4438666913020.722022-12-31
3충청북도증평군437452022주민세ARSN263370931012.32022-12-31
4충청북도증평군437452022지방소득세ARSN4388731002.012022-12-31
5충청북도증평군437452022취득세ARSN14390301700.652022-12-31
6충청북도증평군437452022담배소비세가상계좌Y3198300.012022-12-31
7충청북도증평군437452022등록면허세가상계좌Y590017973874010.412022-12-31
8충청북도증평군437452022등록세가상계좌Y3270803000.062022-12-31
9충청북도증평군437452022자동차세가상계좌Y17367286859567030.642022-12-31
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