Overview

Dataset statistics

Number of variables11
Number of observations134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory94.0 B

Variable types

Numeric3
Categorical6
Boolean1
Text1

Dataset

Description신용카드,가상계좌 등 지방세 납부매체별 납부 현황으로 전자송달 시장 규모 및 편익 분석, 수수료 산정시 기초자료 활용
URLhttps://www.data.go.kr/data/15079352/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
연번 is highly overall correlated with 납부년도High correlation
납부금액 is highly overall correlated with 납부매체비율 and 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부금액 High correlation
납부년도 is highly overall correlated with 연번High correlation
세목명 is highly overall correlated with 납부금액 High correlation
연번 has unique valuesUnique
납부금액 has unique valuesUnique
납부매체비율 has 10 (7.5%) zerosZeros

Reproduction

Analysis started2023-12-12 09:34:21.081190
Analysis finished2023-12-12 09:34:22.772784
Duration1.69 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.5
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T18:34:22.860100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.65
Q134.25
median67.5
Q3100.75
95-th percentile127.35
Maximum134
Range133
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation38.826537
Coefficient of variation (CV)0.57520796
Kurtosis-1.2
Mean67.5
Median Absolute Deviation (MAD)33.5
Skewness0
Sum9045
Variance1507.5
MonotonicityStrictly increasing
2023-12-12T18:34:23.066877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
86 1
 
0.7%
100 1
 
0.7%
99 1
 
0.7%
98 1
 
0.7%
97 1
 
0.7%
96 1
 
0.7%
95 1
 
0.7%
94 1
 
0.7%
93 1
 
0.7%
Other values (124) 124
92.5%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%
126 1
0.7%
125 1
0.7%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
대전광역시
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 134
100.0%

Length

2023-12-12T18:34:23.274688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:34:23.416236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 134
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
대전광역시
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 134
100.0%

Length

2023-12-12T18:34:23.850393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:34:23.984230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 134
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
30000
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30000
2nd row30000
3rd row30000
4th row30000
5th row30000

Common Values

ValueCountFrequency (%)
30000 134
100.0%

Length

2023-12-12T18:34:24.116451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:34:24.226271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30000 134
100.0%

납부년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2021
29 
2017
27 
2020
27 
2018
26 
2019
25 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2021 29
21.6%
2017 27
20.1%
2020 27
20.1%
2018 26
19.4%
2019 25
18.7%

Length

2023-12-12T18:34:24.348906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:34:24.498030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 29
21.6%
2017 27
20.1%
2020 27
20.1%
2018 26
19.4%
2019 25
18.7%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
취득세
43 
지방소득세
39 
주민세
18 
등록세
14 
담배소비세
10 
Other values (2)
10 

Length

Max length5
Median length3
Mean length3.8432836
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방소득세
2nd row취득세
3rd row등록세
4th row주민세
5th row지방소득세

Common Values

ValueCountFrequency (%)
취득세 43
32.1%
지방소득세 39
29.1%
주민세 18
13.4%
등록세 14
 
10.4%
담배소비세 10
 
7.5%
자동차세 5
 
3.7%
지방소비세 5
 
3.7%

Length

2023-12-12T18:34:24.680938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:34:24.837541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취득세 43
32.1%
지방소득세 39
29.1%
주민세 18
13.4%
등록세 14
 
10.4%
담배소비세 10
 
7.5%
자동차세 5
 
3.7%
지방소비세 5
 
3.7%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
기타
32 
가상계좌
21 
은행창구
16 
자동화기기
16 
지자체방문
14 
Other values (4)
35 

Length

Max length5
Median length4
Mean length3.6791045
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 32
23.9%
가상계좌 21
15.7%
은행창구 16
11.9%
자동화기기 16
11.9%
지자체방문 14
10.4%
위택스 13
9.7%
인터넷지로 10
 
7.5%
ARS 9
 
6.7%
페이사납부 3
 
2.2%

Length

2023-12-12T18:34:25.001984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:34:25.170144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 32
23.9%
가상계좌 21
15.7%
은행창구 16
11.9%
자동화기기 16
11.9%
지자체방문 14
10.4%
위택스 13
9.7%
인터넷지로 10
 
7.5%
ars 9
 
6.7%
페이사납부 3
 
2.2%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size266.0 B
False
87 
True
47 
ValueCountFrequency (%)
False 87
64.9%
True 47
35.1%
2023-12-12T18:34:25.301744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Text

Distinct78
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T18:34:25.514216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.5820896
Min length1

Characters and Unicode

Total characters346
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)43.3%

Sample

1st row1
2nd row3
3rd row3
4th row94
5th row444
ValueCountFrequency (%)
3 13
 
9.7%
6 8
 
6.0%
1 7
 
5.2%
12 7
 
5.2%
2 6
 
4.5%
5 4
 
3.0%
9 4
 
3.0%
8 3
 
2.2%
474 2
 
1.5%
18 2
 
1.5%
Other values (68) 78
58.2%
2023-12-12T18:34:25.942129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 57
16.5%
3 43
12.4%
4 37
10.7%
2 35
10.1%
8 34
9.8%
, 31
9.0%
6 23
6.6%
0 23
6.6%
9 22
 
6.4%
5 21
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315
91.0%
Other Punctuation 31
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 57
18.1%
3 43
13.7%
4 37
11.7%
2 35
11.1%
8 34
10.8%
6 23
7.3%
0 23
7.3%
9 22
 
7.0%
5 21
 
6.7%
7 20
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 57
16.5%
3 43
12.4%
4 37
10.7%
2 35
10.1%
8 34
9.8%
, 31
9.0%
6 23
6.6%
0 23
6.6%
9 22
 
6.4%
5 21
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 57
16.5%
3 43
12.4%
4 37
10.7%
2 35
10.1%
8 34
9.8%
, 31
9.0%
6 23
6.6%
0 23
6.6%
9 22
 
6.4%
5 21
 
6.1%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6555643 × 1010
Minimum3710
Maximum4.7441066 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T18:34:26.105332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3710
5-th percentile259441
Q18952145
median54860335
Q34.5981364 × 109
95-th percentile1.3185624 × 1011
Maximum4.7441066 × 1011
Range4.7441066 × 1011
Interquartile range (IQR)4.5891843 × 109

Descriptive statistics

Standard deviation7.4541055 × 1010
Coefficient of variation (CV)2.8069761
Kurtosis18.518718
Mean2.6555643 × 1010
Median Absolute Deviation (MAD)54508880
Skewness4.0463841
Sum3.5584561 × 1012
Variance5.5563689 × 1021
MonotonicityNot monotonic
2023-12-12T18:34:26.255898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1367770 1
 
0.7%
26407560 1
 
0.7%
20515660 1
 
0.7%
2099420 1
 
0.7%
10106317370 1
 
0.7%
15905560 1
 
0.7%
12649655350 1
 
0.7%
20161620 1
 
0.7%
3710 1
 
0.7%
3881693290 1
 
0.7%
Other values (124) 124
92.5%
ValueCountFrequency (%)
3710 1
0.7%
12170 1
0.7%
19730 1
0.7%
22750 1
0.7%
32750 1
0.7%
106270 1
0.7%
148590 1
0.7%
319130 1
0.7%
383780 1
0.7%
567680 1
0.7%
ValueCountFrequency (%)
474410660520 1
0.7%
432862495100 1
0.7%
362665600330 1
0.7%
232491213830 1
0.7%
232474288080 1
0.7%
138630303410 1
0.7%
133009974760 1
0.7%
131234990550 1
0.7%
129914372660 1
0.7%
110663079310 1
0.7%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.089328
Minimum0
Maximum100
Zeros10
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T18:34:26.428246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.125
median4.035
Q393.0175
95-th percentile99.99
Maximum100
Range100
Interquartile range (IQR)92.8925

Descriptive statistics

Standard deviation43.203856
Coefficient of variation (CV)1.3463621
Kurtosis-1.2569864
Mean32.089328
Median Absolute Deviation (MAD)4.025
Skewness0.81877573
Sum4299.97
Variance1866.5732
MonotonicityNot monotonic
2023-12-12T18:34:26.599233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 11
 
8.2%
0.0 10
 
7.5%
99.99 6
 
4.5%
100.0 4
 
3.0%
0.02 4
 
3.0%
99.98 3
 
2.2%
0.23 3
 
2.2%
4.57 2
 
1.5%
0.03 2
 
1.5%
99.96 2
 
1.5%
Other values (80) 87
64.9%
ValueCountFrequency (%)
0.0 10
7.5%
0.01 11
8.2%
0.02 4
 
3.0%
0.03 2
 
1.5%
0.04 2
 
1.5%
0.06 2
 
1.5%
0.09 1
 
0.7%
0.12 2
 
1.5%
0.14 1
 
0.7%
0.15 1
 
0.7%
ValueCountFrequency (%)
100.0 4
3.0%
99.99 6
4.5%
99.98 3
2.2%
99.97 2
 
1.5%
99.96 2
 
1.5%
99.94 1
 
0.7%
99.88 2
 
1.5%
99.84 1
 
0.7%
99.7 1
 
0.7%
99.08 1
 
0.7%

Interactions

2023-12-12T18:34:22.145986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:21.529832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:21.850906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:22.264852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:21.640888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:21.947212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:22.354647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:21.748422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:34:22.039238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:34:26.706430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
연번1.0000.9990.0000.4790.0000.0000.0000.158
납부년도0.9991.0000.0000.0000.0000.0000.0000.000
세목명0.0000.0001.0000.3290.0770.7070.7430.614
납부매체0.4790.0000.3291.0001.0000.3260.3310.183
납부매체전자고지여부0.0000.0000.0771.0001.0000.4420.1980.206
납부건수0.0000.0000.7070.3260.4421.0000.6810.887
납부금액0.0000.0000.7430.3310.1980.6811.0000.234
납부매체비율0.1580.0000.6140.1830.2060.8870.2341.000
2023-12-12T18:34:26.849844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체전자고지여부납부매체
납부년도1.0000.0000.0000.000
세목명0.0001.0000.0780.177
납부매체전자고지여부0.0000.0781.0000.973
납부매체0.0000.1770.9731.000
2023-12-12T18:34:26.975417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
연번1.0000.0970.0330.9370.0000.2390.000
납부금액0.0971.0000.6580.0000.5590.1700.135
납부매체비율0.0330.6581.0000.0000.3860.0880.150
납부년도0.9370.0000.0001.0000.0000.0000.000
세목명0.0000.5590.3860.0001.0000.1770.078
납부매체0.2390.1700.0880.0000.1771.0000.973
납부매체전자고지여부0.0000.1350.1500.0000.0780.9731.000

Missing values

2023-12-12T18:34:22.501037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:34:22.702539image/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
01대전광역시대전광역시300002017지방소득세ARSN1136777025.0
12대전광역시대전광역시300002017취득세ARSN310627075.0
23대전광역시대전광역시300002017등록세가상계좌Y3121700.06
34대전광역시대전광역시300002017주민세가상계좌Y94510407001.73
45대전광역시대전광역시300002017지방소득세가상계좌Y4449677810808.19
56대전광역시대전광역시300002017취득세가상계좌Y4,880216973754090.02
67대전광역시대전광역시300002017담배소비세기타N22613863030341076.61
78대전광역시대전광역시300002017등록세기타N3184149401.02
89대전광역시대전광역시300002017자동차세기타N121092844318304.07
910대전광역시대전광역시300002017주민세기타N3537945101.02
연번시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
124125대전광역시대전광역시300002021취득세은행창구N59,4331056657851099.99
125126대전광역시대전광역시300002021지방소득세인터넷지로Y6110370400.02
126127대전광역시대전광역시300002021취득세인터넷지로Y29,4991100284659099.98
127128대전광역시대전광역시300002021주민세자동화기기N113757800.0
128129대전광역시대전광역시300002021지방소득세자동화기기N13257583500.01
129130대전광역시대전광역시300002021취득세자동화기기N190,6319139217279099.99
130131대전광역시대전광역시300002021주민세지자체방문N245092600.15
131132대전광역시대전광역시300002021지방소득세지자체방문N15315769901.1
132133대전광역시대전광역시300002021취득세지자체방문N1,34457816336098.75
133134대전광역시대전광역시300002021취득세페이사납부Y7,1884863088950100.0