Overview

Dataset statistics

Number of variables11
Number of observations396
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.1 KiB
Average record size in memory93.3 B

Variable types

Numeric4
Categorical5
Boolean1
Text1

Dataset

Description지방세 신용카드,가상계좌 등 지방세 납부매체별 납부 현황을 제공하고 전자송달 시장 규모 및 편익 분석, 수수료 산정 시 기초자료로 활용하고자 함.
Author전라남도 영암군
URLhttps://www.data.go.kr/data/15078929/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
연번 is highly overall correlated with 납부년도High correlation
납부년도 is highly overall correlated with 연번High correlation
납부건수 is highly overall correlated with 납부매체비율High correlation
납부매체비율 is highly overall correlated with 납부건수High correlation
연번 has unique valuesUnique
납부금액 has unique valuesUnique
납부매체비율 has 15 (3.8%) zerosZeros

Reproduction

Analysis started2024-04-21 01:49:35.191870
Analysis finished2024-04-21 01:49:39.587408
Duration4.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct396
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.5
Minimum1
Maximum396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-21T10:49:39.665509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.75
Q199.75
median198.5
Q3297.25
95-th percentile376.25
Maximum396
Range395
Interquartile range (IQR)197.5

Descriptive statistics

Standard deviation114.4596
Coefficient of variation (CV)0.57662267
Kurtosis-1.2
Mean198.5
Median Absolute Deviation (MAD)99
Skewness0
Sum78606
Variance13101
MonotonicityStrictly increasing
2024-04-21T10:49:39.795866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
274 1
 
0.3%
272 1
 
0.3%
271 1
 
0.3%
270 1
 
0.3%
269 1
 
0.3%
268 1
 
0.3%
267 1
 
0.3%
266 1
 
0.3%
265 1
 
0.3%
Other values (386) 386
97.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
396 1
0.3%
395 1
0.3%
394 1
0.3%
393 1
0.3%
392 1
0.3%
391 1
0.3%
390 1
0.3%
389 1
0.3%
388 1
0.3%
387 1
0.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
전라남도
396 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 396
100.0%

Length

2024-04-21T10:49:39.957492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:40.053848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 396
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
영암군
396 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영암군 396
100.0%

Length

2024-04-21T10:49:40.163954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:40.269311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영암군 396
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
46830
396 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46830
2nd row46830
3rd row46830
4th row46830
5th row46830

Common Values

ValueCountFrequency (%)
46830 396
100.0%

Length

2024-04-21T10:49:40.350357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:40.442107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46830 396
100.0%

납부년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6237
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-21T10:49:40.527841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6770505
Coefficient of variation (CV)0.0008303777
Kurtosis-1.2179392
Mean2019.6237
Median Absolute Deviation (MAD)1
Skewness-0.094888406
Sum799771
Variance2.8124984
MonotonicityIncreasing
2024-04-21T10:49:40.623631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 72
18.2%
2020 70
17.7%
2022 69
17.4%
2019 67
16.9%
2018 62
15.7%
2017 56
14.1%
ValueCountFrequency (%)
2017 56
14.1%
2018 62
15.7%
2019 67
16.9%
2020 70
17.7%
2021 72
18.2%
2022 69
17.4%
ValueCountFrequency (%)
2022 69
17.4%
2021 72
18.2%
2020 70
17.7%
2019 67
16.9%
2018 62
15.7%
2017 56
14.1%

세목명
Categorical

Distinct13
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
자동차세
57 
재산세
57 
주민세
57 
등록면허세
55 
취득세
51 
Other values (8)
119 

Length

Max length7
Median length5
Mean length3.9217172
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 57
14.4%
재산세 57
14.4%
주민세 57
14.4%
등록면허세 55
13.9%
취득세 51
12.9%
지방소득세 50
12.6%
등록세 26
6.6%
담배소비세 14
 
3.5%
지역자원시설세 12
 
3.0%
종합토지세 7
 
1.8%
Other values (3) 10
 
2.5%

Length

2024-04-21T10:49:40.750268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 57
14.4%
재산세 57
14.4%
주민세 57
14.4%
등록면허세 55
13.9%
취득세 51
12.9%
지방소득세 50
12.6%
등록세 26
6.6%
담배소비세 14
 
3.5%
지역자원시설세 12
 
3.0%
종합토지세 7
 
1.8%
Other values (3) 10
 
2.5%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
가상계좌
56 
은행창구
53 
위택스
51 
기타
43 
자동화기기
43 
Other values (5)
150 

Length

Max length5
Median length4
Mean length3.9343434
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 56
14.1%
은행창구 53
13.4%
위택스 51
12.9%
기타 43
10.9%
자동화기기 43
10.9%
인터넷지로 38
9.6%
지자체방문 37
9.3%
ARS 29
7.3%
자동이체 24
6.1%
페이사납부 22
 
5.6%

Length

2024-04-21T10:49:40.881202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:41.013495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 56
14.1%
은행창구 53
13.4%
위택스 51
12.9%
기타 43
10.9%
자동화기기 43
10.9%
인터넷지로 38
9.6%
지자체방문 37
9.3%
ars 29
7.3%
자동이체 24
6.1%
페이사납부 22
 
5.6%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size528.0 B
False
205 
True
191 
ValueCountFrequency (%)
False 205
51.8%
True 191
48.2%
2024-04-21T10:49:41.142282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct327
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3411.6111
Minimum1
Maximum37143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-21T10:49:41.251754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q147.5
median543
Q33655.25
95-th percentile15804.75
Maximum37143
Range37142
Interquartile range (IQR)3607.75

Descriptive statistics

Standard deviation5972.9475
Coefficient of variation (CV)1.7507703
Kurtosis7.8183469
Mean3411.6111
Median Absolute Deviation (MAD)538
Skewness2.6304803
Sum1350998
Variance35676102
MonotonicityNot monotonic
2024-04-21T10:49:41.427558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16
 
4.0%
2 8
 
2.0%
12 6
 
1.5%
10 5
 
1.3%
4 5
 
1.3%
5 4
 
1.0%
7 4
 
1.0%
8 4
 
1.0%
14 4
 
1.0%
21 3
 
0.8%
Other values (317) 337
85.1%
ValueCountFrequency (%)
1 16
4.0%
2 8
2.0%
3 2
 
0.5%
4 5
 
1.3%
5 4
 
1.0%
6 3
 
0.8%
7 4
 
1.0%
8 4
 
1.0%
9 1
 
0.3%
10 5
 
1.3%
ValueCountFrequency (%)
37143 1
0.3%
34832 1
0.3%
31560 1
0.3%
28026 1
0.3%
26738 1
0.3%
26002 1
0.3%
25014 1
0.3%
23945 1
0.3%
22886 1
0.3%
22034 1
0.3%

납부금액
Text

UNIQUE 

Distinct396
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-21T10:49:41.717401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.570707
Min length7

Characters and Unicode

Total characters4978
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique396 ?
Unique (%)100.0%

Sample

1st row 608,990
2nd row 144,973,620
3rd row 5,900
4th row 2,949,256,390
5th row 2,921,806,760
ValueCountFrequency (%)
608,990 1
 
0.3%
1,813,002,150 1
 
0.3%
4,684,532,770 1
 
0.3%
1,431,818,640 1
 
0.3%
228,660 1
 
0.3%
5,230,381,400 1
 
0.3%
4,965,062,310 1
 
0.3%
33,280 1
 
0.3%
12,330,870 1
 
0.3%
228,224,170 1
 
0.3%
Other values (386) 386
97.5%
2024-04-21T10:49:42.110096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 859
17.3%
792
15.9%
0 650
13.1%
1 362
7.3%
2 329
 
6.6%
3 309
 
6.2%
5 299
 
6.0%
9 290
 
5.8%
6 282
 
5.7%
4 278
 
5.6%
Other values (2) 528
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3327
66.8%
Other Punctuation 859
 
17.3%
Space Separator 792
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 650
19.5%
1 362
10.9%
2 329
9.9%
3 309
9.3%
5 299
9.0%
9 290
8.7%
6 282
8.5%
4 278
8.4%
7 277
8.3%
8 251
 
7.5%
Other Punctuation
ValueCountFrequency (%)
, 859
100.0%
Space Separator
ValueCountFrequency (%)
792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4978
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 859
17.3%
792
15.9%
0 650
13.1%
1 362
7.3%
2 329
 
6.6%
3 309
 
6.2%
5 299
 
6.0%
9 290
 
5.8%
6 282
 
5.7%
4 278
 
5.6%
Other values (2) 528
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 859
17.3%
792
15.9%
0 650
13.1%
1 362
7.3%
2 329
 
6.6%
3 309
 
6.2%
5 299
 
6.0%
9 290
 
5.8%
6 282
 
5.7%
4 278
 
5.6%
Other values (2) 528
10.6%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct343
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.393813
Minimum0
Maximum82.7
Zeros15
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-21T10:49:42.454332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q11.8625
median10.105
Q320.525
95-th percentile46.4
Maximum82.7
Range82.7
Interquartile range (IQR)18.6625

Descriptive statistics

Standard deviation15.47266
Coefficient of variation (CV)1.0749521
Kurtosis2.2613525
Mean14.393813
Median Absolute Deviation (MAD)8.805
Skewness1.4868266
Sum5699.95
Variance239.40321
MonotonicityNot monotonic
2024-04-21T10:49:42.594149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
3.8%
0.02 7
 
1.8%
0.01 6
 
1.5%
0.04 3
 
0.8%
0.14 3
 
0.8%
0.13 3
 
0.8%
0.15 3
 
0.8%
0.18 3
 
0.8%
3.52 2
 
0.5%
0.4 2
 
0.5%
Other values (333) 349
88.1%
ValueCountFrequency (%)
0.0 15
3.8%
0.01 6
 
1.5%
0.02 7
1.8%
0.03 1
 
0.3%
0.04 3
 
0.8%
0.05 1
 
0.3%
0.06 2
 
0.5%
0.07 2
 
0.5%
0.1 2
 
0.5%
0.11 2
 
0.5%
ValueCountFrequency (%)
82.7 1
0.3%
78.86 1
0.3%
74.82 1
0.3%
65.69 1
0.3%
63.81 1
0.3%
61.81 1
0.3%
60.13 1
0.3%
55.91 1
0.3%
52.12 1
0.3%
51.83 1
0.3%

Interactions

2024-04-21T10:49:38.980718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:37.563561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.115963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.569860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:39.072753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:37.786519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.231741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.690632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:39.151948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:37.902019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.345665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.798583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:39.237824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.021544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.469008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:38.885596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:49:42.679106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번납부년도세목명납부매체납부매체전자고지여부납부건수납부매체비율
연번1.0000.9490.0000.6370.0000.0000.000
납부년도0.9491.0000.0000.0000.0000.0000.000
세목명0.0000.0001.0000.1670.0000.1190.629
납부매체0.6370.0000.1671.0001.0000.5240.470
납부매체전자고지여부0.0000.0000.0001.0001.0000.1330.113
납부건수0.0000.0000.1190.5240.1331.0000.601
납부매체비율0.0000.0000.6290.4700.1130.6011.000
2024-04-21T10:49:42.777690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부
세목명1.0000.0690.000
납부매체0.0691.0000.990
납부매체전자고지여부0.0000.9901.000
2024-04-21T10:49:42.861902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번납부년도납부건수납부매체비율세목명납부매체납부매체전자고지여부
연번1.0000.986-0.0150.0600.0000.2420.000
납부년도0.9861.000-0.0170.0400.0000.0000.000
납부건수-0.015-0.0171.0000.6760.0480.1840.101
납부매체비율0.0600.0400.6761.0000.3310.2360.119
세목명0.0000.0000.0480.3311.0000.0690.000
납부매체0.2420.0000.1840.2360.0691.0000.990
납부매체전자고지여부0.0000.0000.1010.1190.0000.9901.000

Missing values

2024-04-21T10:49:39.360947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:49:39.521082image/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

연번시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
01전라남도영암군468302017담배소비세가상계좌Y1608,9900.0
12전라남도영암군468302017등록면허세가상계좌Y6225144,973,62010.09
23전라남도영암군468302017등록세가상계좌Y15,9000.0
34전라남도영암군468302017자동차세가상계좌Y187092,949,256,39030.33
45전라남도영암군468302017재산세가상계좌Y228862,921,806,76037.11
56전라남도영암군468302017주민세가상계좌Y10635712,108,04017.24
67전라남도영암군468302017지방소득세가상계좌Y26782,916,346,6004.34
78전라남도영암군468302017지역자원시설세가상계좌Y123,433,6500.02
89전라남도영암군468302017취득세가상계좌Y529695,216,2100.86
910전라남도영암군468302017등록면허세기타N81967,7400.9
연번시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
386387전라남도영암군468302022주민세위택스Y12632,395,444,2205.11
387388전라남도영암군468302022지방소득세위택스Y37408,515,196,50015.13
388389전라남도영암군468302022지역자원시설세위택스Y184,424,2600.07
389390전라남도영암군468302022취득세위택스Y31366,751,324,82012.69
390391전라남도영암군468302022등록면허세은행창구N9692468,309,19021.43
391392전라남도영암군468302022등록세은행창구N1012116,523,3802.24
392393전라남도영암군468302022자동차세은행창구N67625,055,491,47014.95
393394전라남도영암군468302022재산세은행창구N141102,367,622,22031.2
394395전라남도영암군468302022주민세은행창구N6650397,704,95014.7
395396전라남도영암군468302022지방소득세은행창구N22542,142,436,6904.98