Overview

Dataset statistics

Number of variables10
Number of observations169
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 KiB
Average record size in memory85.8 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description경기도 안산시 지방세 납부매체별 납부 현황을 세목별, 납부매체 전자고지 여부, 납부금액, 납부매체 비율로 구분하여 자료제공 합니다.
URLhttps://www.data.go.kr/data/15080182/fileData.do

Alerts

시도명 has constant value ""Constant
납부년도 has constant value ""Constant
자치단체코드 is highly overall correlated with 시군구명 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 자치단체코드 and 1 other fieldsHigh 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
세목명 is highly overall correlated with 시군구명 and 1 other fieldsHigh correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부금액 has unique valuesUnique
납부매체비율 has 21 (12.4%) zerosZeros

Reproduction

Analysis started2023-12-11 23:06:44.290260
Analysis finished2023-12-11 23:06:45.866443
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경기도
169 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 169
100.0%

Length

2023-12-12T08:06:45.927555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:06:46.020275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 169
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
안산시 단원구
83 
안산시 상록구
82 
안산시
 
4

Length

Max length7
Median length7
Mean length6.9053254
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안산시 단원구
2nd row안산시 단원구
3rd row안산시 단원구
4th row안산시 단원구
5th row안산시 단원구

Common Values

ValueCountFrequency (%)
안산시 단원구 83
49.1%
안산시 상록구 82
48.5%
안산시 4
 
2.4%

Length

2023-12-12T08:06:46.130366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:06:46.230271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안산시 169
50.6%
단원구 83
24.9%
상록구 82
24.6%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
41273
83 
41271
82 
41270
 
4

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41273
2nd row41273
3rd row41273
4th row41273
5th row41273

Common Values

ValueCountFrequency (%)
41273 83
49.1%
41271 82
48.5%
41270 4
 
2.4%

Length

2023-12-12T08:06:46.329023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:06:46.436732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41273 83
49.1%
41271 82
48.5%
41270 4
 
2.4%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2022
169 

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 169
100.0%

Length

2023-12-12T08:06:46.544733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:06:46.643785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 169
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
재산세
23 
자동차세
23 
등록면허세
22 
주민세
22 
지방소득세
20 
Other values (9)
59 

Length

Max length7
Median length5
Mean length3.9881657
Min length3

Unique

Unique2 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 23
13.6%
자동차세 23
13.6%
등록면허세 22
13.0%
주민세 22
13.0%
지방소득세 20
11.8%
취득세 18
10.7%
면허세 11
6.5%
지역자원시설세 10
5.9%
등록세 8
 
4.7%
종합토지세 6
 
3.6%
Other values (4) 6
 
3.6%

Length

2023-12-12T08:06:46.742733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
재산세 23
13.6%
자동차세 23
13.6%
등록면허세 22
13.0%
주민세 22
13.0%
지방소득세 20
11.8%
취득세 18
10.7%
면허세 11
6.5%
지역자원시설세 10
5.9%
등록세 8
 
4.7%
종합토지세 6
 
3.6%
Other values (4) 6
 
3.6%

납부매체
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
ARS
26 
가상계좌
22 
은행창구
19 
위택스
18 
기타
17 
Other values (6)
67 

Length

Max length5
Median length4
Mean length3.8816568
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd rowARS
3rd rowARS
4th rowARS
5th rowARS

Common Values

ValueCountFrequency (%)
ARS 26
15.4%
가상계좌 22
13.0%
은행창구 19
11.2%
위택스 18
10.7%
기타 17
10.1%
자동화기기 17
10.1%
인터넷지로 16
9.5%
지자체방문 13
7.7%
페이사납부 12
7.1%
자동이체 8
 
4.7%

Length

2023-12-12T08:06:46.854152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ars 26
15.4%
가상계좌 22
13.0%
은행창구 19
11.2%
위택스 18
10.7%
기타 17
10.1%
자동화기기 17
10.1%
인터넷지로 16
9.5%
지자체방문 13
7.7%
페이사납부 12
7.1%
자동이체 8
 
4.7%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size301.0 B
True
86 
False
83 
ValueCountFrequency (%)
True 86
50.9%
False 83
49.1%
2023-12-12T08:06:46.943978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12593.627
Minimum1
Maximum181395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T08:06:47.041460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q119
median2063
Q39047
95-th percentile67183.4
Maximum181395
Range181394
Interquartile range (IQR)9028

Descriptive statistics

Standard deviation30287.763
Coefficient of variation (CV)2.4050071
Kurtosis15.093782
Mean12593.627
Median Absolute Deviation (MAD)2059
Skewness3.8281914
Sum2128323
Variance9.1734859 × 108
MonotonicityNot monotonic
2023-12-12T08:06:47.151477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
3.6%
9 5
 
3.0%
18 4
 
2.4%
8 3
 
1.8%
14 3
 
1.8%
4 3
 
1.8%
7 3
 
1.8%
15 3
 
1.8%
3 3
 
1.8%
43 2
 
1.2%
Other values (130) 134
79.3%
ValueCountFrequency (%)
1 6
3.6%
2 2
 
1.2%
3 3
1.8%
4 3
1.8%
5 2
 
1.2%
7 3
1.8%
8 3
1.8%
9 5
3.0%
10 1
 
0.6%
12 1
 
0.6%
ValueCountFrequency (%)
181395 1
0.6%
157698 1
0.6%
152388 1
0.6%
152229 1
0.6%
129887 1
0.6%
121962 1
0.6%
110149 1
0.6%
70721 1
0.6%
68739 1
0.6%
64850 1
0.6%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct169
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8594762 × 109
Minimum10580
Maximum9.4093177 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T08:06:47.269325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10580
5-th percentile97936
Q12714590
median2.6917252 × 108
Q33.7270729 × 109
95-th percentile3.0545372 × 1010
Maximum9.4093177 × 1010
Range9.4093166 × 1010
Interquartile range (IQR)3.7243583 × 109

Descriptive statistics

Standard deviation1.4471492 × 1010
Coefficient of variation (CV)2.4697587
Kurtosis16.812254
Mean5.8594762 × 109
Median Absolute Deviation (MAD)2.6906943 × 108
Skewness3.8707866
Sum9.9025148 × 1011
Variance2.0942409 × 1020
MonotonicityNot monotonic
2023-12-12T08:06:47.385850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10580 1
 
0.6%
1509241100 1
 
0.6%
245060 1
 
0.6%
6691573550 1
 
0.6%
130234430 1
 
0.6%
1959930 1
 
0.6%
18850 1
 
0.6%
1287186740 1
 
0.6%
2243959960 1
 
0.6%
269172520 1
 
0.6%
Other values (159) 159
94.1%
ValueCountFrequency (%)
10580 1
0.6%
12250 1
0.6%
18850 1
0.6%
33200 1
0.6%
37800 1
0.6%
62450 1
0.6%
64380 1
0.6%
83770 1
0.6%
94500 1
0.6%
103090 1
0.6%
ValueCountFrequency (%)
94093176620 1
0.6%
83910695590 1
0.6%
74181915810 1
0.6%
63799696610 1
0.6%
52992395050 1
0.6%
42371784270 1
0.6%
40782074380 1
0.6%
34667133380 1
0.6%
31483739060 1
0.6%
29137822630 1
0.6%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct117
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5086391
Minimum0
Maximum100
Zeros21
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T08:06:47.507207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median4.22
Q310.17
95-th percentile20.194
Maximum100
Range100
Interquartile range (IQR)10.15

Descriptive statistics

Standard deviation9.9224547
Coefficient of variation (CV)1.5245053
Kurtosis46.268944
Mean6.5086391
Median Absolute Deviation (MAD)4.21
Skewness5.3181054
Sum1099.96
Variance98.455108
MonotonicityNot monotonic
2023-12-12T08:06:47.633851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
12.4%
0.01 20
 
11.8%
0.02 5
 
3.0%
0.03 4
 
2.4%
0.05 3
 
1.8%
9.76 2
 
1.2%
2.6 2
 
1.2%
5.44 2
 
1.2%
18.8 2
 
1.2%
7.85 1
 
0.6%
Other values (107) 107
63.3%
ValueCountFrequency (%)
0.0 21
12.4%
0.01 20
11.8%
0.02 5
 
3.0%
0.03 4
 
2.4%
0.04 1
 
0.6%
0.05 3
 
1.8%
0.1 1
 
0.6%
0.11 1
 
0.6%
0.15 1
 
0.6%
0.21 1
 
0.6%
ValueCountFrequency (%)
100.0 1
0.6%
28.47 1
0.6%
24.49 1
0.6%
24.14 1
0.6%
23.4 1
0.6%
23.18 1
0.6%
22.4 1
0.6%
20.71 1
0.6%
20.25 1
0.6%
20.11 1
0.6%

Interactions

2023-12-12T08:06:45.136223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:44.631995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:44.879272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:45.234818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:44.710418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:44.977127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:45.566320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:44.787110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:45.053181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:06:47.750994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
시군구명1.0001.0000.7500.0000.0000.0000.5420.000
자치단체코드1.0001.0000.7500.0000.0000.0000.5420.000
세목명0.7500.7501.0000.0000.0000.0000.5170.436
납부매체0.0000.0000.0001.0000.9920.3400.2070.320
납부매체전자고지여부0.0000.0000.0000.9921.0000.1900.0000.077
납부건수0.0000.0000.0000.3400.1901.0000.7830.604
납부금액0.5420.5420.5170.2070.0000.7831.0000.280
납부매체비율0.0000.0000.4360.3200.0770.6040.2801.000
2023-12-12T08:06:47.866595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치단체코드납부매체전자고지여부세목명납부매체시군구명
자치단체코드1.0000.0000.5620.0001.000
납부매체전자고지여부0.0001.0000.0000.8970.000
세목명0.5620.0001.0000.0000.562
납부매체0.0000.8970.0001.0000.000
시군구명1.0000.0000.5620.0001.000
2023-12-12T08:06:47.954355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율시군구명자치단체코드세목명납부매체납부매체전자고지여부
납부건수1.0000.8380.8060.0000.0000.0000.1680.139
납부금액0.8381.0000.6480.3770.3770.2330.0620.000
납부매체비율0.8060.6481.0000.0000.0000.2510.1970.050
시군구명0.0000.3770.0001.0001.0000.5620.0000.000
자치단체코드0.0000.3770.0001.0001.0000.5620.0000.000
세목명0.0000.2330.2510.5620.5621.0000.0000.000
납부매체0.1680.0620.1970.0000.0000.0001.0000.897
납부매체전자고지여부0.1390.0000.0500.0000.0000.0000.8971.000

Missing values

2023-12-12T08:06:45.691425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:06:45.818130image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
0경기도안산시 단원구412732022재산세<NA>N110580100.0
1경기도안산시 단원구412732022등록면허세ARSN905286227301.01
2경기도안산시 단원구412732022등록면허세ARSY144231600.02
3경기도안산시 단원구412732022면허세ARSN41134000.0
4경기도안산시 단원구412732022자동차세ARSN17245336655943019.21
5경기도안산시 단원구412732022자동차세ARSY92299900.01
6경기도안산시 단원구412732022재산세ARSN18589372707289020.71
7경기도안산시 단원구412732022재산세ARSY72646300.01
8경기도안산시 단원구412732022종합토지세ARSN2837700.0
9경기도안산시 단원구412732022주민세ARSN51881126381005.78
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