Overview

Dataset statistics

Number of variables10
Number of observations87
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory86.5 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description제주특별자치도 제주시 지방세 신용카드,가상계좌 등 지방세 납부매체*별 납부 현황 (*가상계좌,신용카드,지로,신용카드포인트등)을 제공합니다. 전자송달 시장 규모 및 편익 분석, 수수료 산정시 기초자료로 활용할 수 있습니다.
Author제주특별자치도 제주시
URLhttps://www.data.go.kr/data/15078987/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부년도 has constant value ""Constant
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
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부금액 has unique valuesUnique
납부매체비율 has 13 (14.9%) zerosZeros

Reproduction

Analysis started2023-12-12 06:53:36.966943
Analysis finished2023-12-12 06:53:38.808185
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
제주특별자치도
87 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 87
100.0%

Length

2023-12-12T15:53:38.880906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:53:38.983073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 87
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
제주시
87 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 87
100.0%

Length

2023-12-12T15:53:39.089168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:53:39.194455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 87
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
50110
87 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50110
2nd row50110
3rd row50110
4th row50110
5th row50110

Common Values

ValueCountFrequency (%)
50110 87
100.0%

Length

2023-12-12T15:53:39.315087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:53:39.432357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 87
100.0%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
2020
87 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 87
100.0%

Length

2023-12-12T15:53:39.565165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:53:39.673415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 87
100.0%

세목명
Categorical

Distinct11
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size828.0 B
취득세
11 
등록면허세
11 
자동차세
11 
재산세
11 
주민세
11 
Other values (6)
32 

Length

Max length7
Median length3
Mean length3.862069
Min length3

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 11
12.6%
등록면허세 11
12.6%
자동차세 11
12.6%
재산세 11
12.6%
주민세 11
12.6%
지방소득세 10
11.5%
등록세 8
9.2%
면허세 5
5.7%
종합토지세 5
5.7%
지역자원시설세 3
 
3.4%

Length

2023-12-12T15:53:39.820190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 11
12.6%
등록면허세 11
12.6%
자동차세 11
12.6%
재산세 11
12.6%
주민세 11
12.6%
지방소득세 10
11.5%
등록세 8
9.2%
면허세 5
5.7%
종합토지세 5
5.7%
지역자원시설세 3
 
3.4%

납부매체
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size828.0 B
ARS
13 
가상계좌
10 
은행창구
10 
자동화기기
9 
지자체방문
9 
Other values (6)
36 

Length

Max length5
Median length4
Mean length3.908046
Min length2

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row인터넷지로
2nd row인터넷지로
3rd row자동이체
4th row자동이체
5th row자동이체

Common Values

ValueCountFrequency (%)
ARS 13
14.9%
가상계좌 10
11.5%
은행창구 10
11.5%
자동화기기 9
10.3%
지자체방문 9
10.3%
기타 9
10.3%
인터넷지로 8
9.2%
위택스 8
9.2%
페이사납부 6
6.9%
자동이체 4
 
4.6%

Length

2023-12-12T15:53:39.995094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ars 13
14.9%
가상계좌 10
11.5%
은행창구 10
11.5%
자동화기기 9
10.3%
지자체방문 9
10.3%
기타 9
10.3%
인터넷지로 8
9.2%
위택스 8
9.2%
페이사납부 6
6.9%
자동이체 4
 
4.6%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size219.0 B
False
44 
True
43 
ValueCountFrequency (%)
False 44
50.6%
True 43
49.4%
2023-12-12T15:53:40.130437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21341.701
Minimum1
Maximum292560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-12T15:53:40.270193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q140.5
median4484
Q320514.5
95-th percentile97031.4
Maximum292560
Range292559
Interquartile range (IQR)20474

Descriptive statistics

Standard deviation45929.697
Coefficient of variation (CV)2.1521104
Kurtosis17.345379
Mean21341.701
Median Absolute Deviation (MAD)4481
Skewness3.861713
Sum1856728
Variance2.1095371 × 109
MonotonicityNot monotonic
2023-12-12T15:53:40.440564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6
 
6.9%
11 3
 
3.4%
1 3
 
3.4%
458 2
 
2.3%
7 2
 
2.3%
2363 1
 
1.1%
292560 1
 
1.1%
21 1
 
1.1%
559 1
 
1.1%
71114 1
 
1.1%
Other values (66) 66
75.9%
ValueCountFrequency (%)
1 3
3.4%
2 6
6.9%
3 1
 
1.1%
4 1
 
1.1%
6 1
 
1.1%
7 2
 
2.3%
10 1
 
1.1%
11 3
3.4%
12 1
 
1.1%
21 1
 
1.1%
ValueCountFrequency (%)
292560 1
1.1%
202249 1
1.1%
182532 1
1.1%
108626 1
1.1%
106374 1
1.1%
75232 1
1.1%
71114 1
1.1%
64970 1
1.1%
59896 1
1.1%
59186 1
1.1%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.516276 × 109
Minimum5080
Maximum8.5987646 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-12T15:53:40.603227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5080
5-th percentile59005
Q17156275
median5.8294818 × 108
Q36.3822789 × 109
95-th percentile4.4464151 × 1010
Maximum8.5987646 × 1010
Range8.5987641 × 1010
Interquartile range (IQR)6.3751226 × 109

Descriptive statistics

Standard deviation1.6772464 × 1010
Coefficient of variation (CV)1.9694599
Kurtosis7.7282895
Mean8.516276 × 109
Median Absolute Deviation (MAD)5.8289123 × 108
Skewness2.6963553
Sum7.4091602 × 1011
Variance2.8131555 × 1020
MonotonicityNot monotonic
2023-12-12T15:53:40.775788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267376240 1
 
1.1%
15738484710 1
 
1.1%
14504572200 1
 
1.1%
156992460 1
 
1.1%
17480932650 1
 
1.1%
301455230 1
 
1.1%
21663830600 1
 
1.1%
647214320 1
 
1.1%
34580 1
 
1.1%
158996860 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
5080 1
1.1%
31500 1
1.1%
34580 1
1.1%
51450 1
1.1%
56950 1
1.1%
63800 1
1.1%
91850 1
1.1%
99450 1
1.1%
111970 1
1.1%
132370 1
1.1%
ValueCountFrequency (%)
85987646250 1
1.1%
74158227850 1
1.1%
57319469140 1
1.1%
52790313450 1
1.1%
47034756090 1
1.1%
38466073210 1
1.1%
35028316380 1
1.1%
33502096530 1
1.1%
29069968470 1
1.1%
28678215330 1
1.1%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.643333
Minimum0
Maximum100
Zeros13
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-12T15:53:40.957118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.055
median4.96
Q318.36
95-th percentile48.728
Maximum100
Range100
Interquartile range (IQR)18.305

Descriptive statistics

Standard deviation18.377821
Coefficient of variation (CV)1.4535582
Kurtosis6.6432133
Mean12.643333
Median Absolute Deviation (MAD)4.96
Skewness2.3249781
Sum1099.97
Variance337.74432
MonotonicityNot monotonic
2023-12-12T15:53:41.129462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
14.9%
0.01 5
 
5.7%
0.11 2
 
2.3%
0.02 1
 
1.1%
5.5 1
 
1.1%
0.13 1
 
1.1%
16.02 1
 
1.1%
0.19 1
 
1.1%
74.28 1
 
1.1%
8.67 1
 
1.1%
Other values (60) 60
69.0%
ValueCountFrequency (%)
0.0 13
14.9%
0.01 5
 
5.7%
0.02 1
 
1.1%
0.03 1
 
1.1%
0.04 1
 
1.1%
0.05 1
 
1.1%
0.06 1
 
1.1%
0.11 2
 
2.3%
0.13 1
 
1.1%
0.16 1
 
1.1%
ValueCountFrequency (%)
100.0 1
1.1%
74.28 1
1.1%
65.91 1
1.1%
52.91 1
1.1%
50.27 1
1.1%
45.13 1
1.1%
43.1 1
1.1%
35.41 1
1.1%
33.65 1
1.1%
30.37 1
1.1%

Interactions

2023-12-12T15:53:37.898922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:37.283767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:37.575715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:38.002300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:37.365767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:37.684948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:38.102454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:37.450961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:37.794160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:53:41.258763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
세목명1.0000.0000.0000.0000.2010.433
납부매체0.0001.0000.8990.0000.0000.642
납부매체전자고지여부0.0000.8991.0000.0000.0000.000
납부건수0.0000.0000.0001.0000.6910.820
납부금액0.2010.0000.0000.6911.0000.584
납부매체비율0.4330.6420.0000.8200.5841.000
2023-12-12T15:53:41.393939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체세목명납부매체전자고지여부
납부매체1.0000.0000.862
세목명0.0001.0000.000
납부매체전자고지여부0.8620.0001.000
2023-12-12T15:53:41.516441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부건수1.0000.8480.8340.0000.0000.000
납부금액0.8481.0000.6800.0920.0000.000
납부매체비율0.8340.6801.0000.2060.3510.000
세목명0.0000.0920.2061.0000.0000.000
납부매체0.0000.0000.3510.0001.0000.862
납부매체전자고지여부0.0000.0000.0000.0000.8621.000

Missing values

2023-12-12T15:53:38.570374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:53:38.746412image/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
0제주특별자치도제주시501102020지역자원시설세인터넷지로Y102673762400.02
1제주특별자치도제주시501102020취득세인터넷지로Y2110157384847104.34
2제주특별자치도제주시501102020등록면허세자동이체Y2033323685004.1
3제주특별자치도제주시501102020자동차세자동이체Y12080152230682024.34
4제주특별자치도제주시501102020재산세자동이체Y26257557919325052.91
5제주특별자치도제주시501102020주민세자동이체Y925310788030018.65
6제주특별자치도제주시501102020등록면허세자동화기기N59454411668804.99
7제주특별자치도제주시501102020등록세자동화기기N59273881400.05
8제주특별자치도제주시501102020면허세자동화기기N2315000.0
9제주특별자치도제주시501102020자동차세자동화기기N29193417949726024.5
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
77제주특별자치도제주시501102020지방소득세은행창구N17876335020965305.95
78제주특별자치도제주시501102020지역자원시설세은행창구N375829481800.01
79제주특별자치도제주시501102020취득세은행창구N14821384660732104.93
80제주특별자치도제주시501102020취득세이택스Y22385040100.0
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