Overview

Dataset statistics

Number of variables11
Number of observations87
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory94.5 B

Variable types

Categorical7
Boolean1
Numeric3

Dataset

Description신용카드, 가상계좌 등 기방세 납부매체별 납부현황에 관한 데이터로 전자송달 시장 규모 및 편의 분석, 수수료 산정 시 기초 자료로 활용됩니다.
Author경상남도 거제시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079867

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부년도 has constant value ""Constant
기준일자 has constant value ""Constant
납부건수 is highly overall correlated with 납부금액(원) and 1 other fieldsHigh correlation
납부금액(원) 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(원) has unique valuesUnique
납부매체비율 has 6 (6.9%) zerosZeros

Reproduction

Analysis started2023-12-11 00:42:47.273829
Analysis finished2023-12-11 00:42:48.721474
Duration1.45 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
경상남도
87 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 87
100.0%

Length

2023-12-11T09:42:48.788719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:48.871342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 87
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거제시 87
100.0%

Length

2023-12-11T09:42:48.967625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:49.063411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거제시 87
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
48310
87 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48310
2nd row48310
3rd row48310
4th row48310
5th row48310

Common Values

ValueCountFrequency (%)
48310 87
100.0%

Length

2023-12-11T09:42:49.157509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:49.246394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48310 87
100.0%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
2021
87 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 87
100.0%

Length

2023-12-11T09:42:49.367039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:49.469038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 87
100.0%

세목명
Categorical

Distinct12
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size828.0 B
등록면허세
11 
자동차세
11 
재산세
11 
주민세
11 
지방소득세
10 
Other values (7)
33 

Length

Max length7
Median length5
Mean length4.091954
Min length3

Unique

Unique2 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
등록면허세 11
12.6%
자동차세 11
12.6%
재산세 11
12.6%
주민세 11
12.6%
지방소득세 10
11.5%
취득세 9
10.3%
지역자원시설세 8
9.2%
등록세 7
8.0%
면허세 4
 
4.6%
담배소비세 3
 
3.4%
Other values (2) 2
 
2.3%

Length

2023-12-11T09:42:49.583651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 11
12.6%
자동차세 11
12.6%
재산세 11
12.6%
주민세 11
12.6%
지방소득세 10
11.5%
취득세 9
10.3%
지역자원시설세 8
9.2%
등록세 7
8.0%
면허세 4
 
4.6%
담배소비세 3
 
3.4%
Other values (2) 2
 
2.3%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size828.0 B
ARS
13 
가상계좌
11 
기타
10 
위택스
9 
은행창구
9 
Other values (5)
35 

Length

Max length5
Median length4
Mean length3.8735632
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ARS 13
14.9%
가상계좌 11
12.6%
기타 10
11.5%
위택스 9
10.3%
은행창구 9
10.3%
지자체방문 9
10.3%
인터넷지로 8
9.2%
자동화기기 8
9.2%
페이사납부 6
6.9%
자동이체 4
 
4.6%

Length

2023-12-11T09:42:49.711444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:49.851105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ars 13
14.9%
가상계좌 11
12.6%
기타 10
11.5%
위택스 9
10.3%
은행창구 9
10.3%
지자체방문 9
10.3%
인터넷지로 8
9.2%
자동화기기 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%
2023-12-11T09:42:50.008011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7727.2989
Minimum1
Maximum106044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T09:42:50.112448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q129
median1861
Q37236.5
95-th percentile30062.2
Maximum106044
Range106043
Interquartile range (IQR)7207.5

Descriptive statistics

Standard deviation16619.324
Coefficient of variation (CV)2.1507287
Kurtosis19.778435
Mean7727.2989
Median Absolute Deviation (MAD)1857
Skewness4.145448
Sum672275
Variance2.7620192 × 108
MonotonicityNot monotonic
2023-12-11T09:42:50.244995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4
 
4.6%
1 3
 
3.4%
4 3
 
3.4%
3 2
 
2.3%
5 2
 
2.3%
32 2
 
2.3%
874 1
 
1.1%
25431 1
 
1.1%
10317 1
 
1.1%
2233 1
 
1.1%
Other values (67) 67
77.0%
ValueCountFrequency (%)
1 3
3.4%
2 4
4.6%
3 2
2.3%
4 3
3.4%
5 2
2.3%
7 1
 
1.1%
8 1
 
1.1%
16 1
 
1.1%
19 1
 
1.1%
20 1
 
1.1%
ValueCountFrequency (%)
106044 1
1.1%
86780 1
1.1%
58126 1
1.1%
32135 1
1.1%
32047 1
1.1%
25431 1
1.1%
21847 1
1.1%
21587 1
1.1%
20253 1
1.1%
18140 1
1.1%

납부금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4092081 × 109
Minimum6300
Maximum4.3645442 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T09:42:50.378157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6300
5-th percentile25173
Q111203365
median3.9610418 × 108
Q33.2225953 × 109
95-th percentile1.7095487 × 1010
Maximum4.3645442 × 1010
Range4.3645435 × 1010
Interquartile range (IQR)3.211392 × 109

Descriptive statistics

Standard deviation7.2800972 × 109
Coefficient of variation (CV)2.1354218
Kurtosis13.886825
Mean3.4092081 × 109
Median Absolute Deviation (MAD)3.9588245 × 108
Skewness3.4922634
Sum2.9660111 × 1011
Variance5.2999816 × 1019
MonotonicityNot monotonic
2023-12-11T09:42:50.517774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4521800 1
 
1.1%
15450 1
 
1.1%
166311730 1
 
1.1%
76403500 1
 
1.1%
3053333130 1
 
1.1%
1037098680 1
 
1.1%
67110950 1
 
1.1%
1956469150 1
 
1.1%
2063920 1
 
1.1%
5903101520 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
6300 1
1.1%
12360 1
1.1%
15450 1
1.1%
21150 1
1.1%
21630 1
1.1%
33440 1
1.1%
45360 1
1.1%
221730 1
1.1%
221830 1
1.1%
222180 1
1.1%
ValueCountFrequency (%)
43645441670 1
1.1%
31901066980 1
1.1%
27930249810 1
1.1%
21349732480 1
1.1%
18696439930 1
1.1%
13359930940 1
1.1%
11638390170 1
1.1%
11630272000 1
1.1%
10055480600 1
1.1%
8928355200 1
1.1%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.494253
Minimum0
Maximum53.08
Zeros6
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T09:42:50.648974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.125
median4.64
Q319.01
95-th percentile40.103
Maximum53.08
Range53.08
Interquartile range (IQR)18.885

Descriptive statistics

Standard deviation14.193764
Coefficient of variation (CV)1.2348575
Kurtosis0.60782964
Mean11.494253
Median Absolute Deviation (MAD)4.63
Skewness1.2330166
Sum1000
Variance201.46294
MonotonicityNot monotonic
2023-12-11T09:42:50.782007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.9%
0.03 5
 
5.7%
0.01 4
 
4.6%
0.12 2
 
2.3%
0.02 2
 
2.3%
1.81 1
 
1.1%
21.63 1
 
1.1%
50.13 1
 
1.1%
20.34 1
 
1.1%
0.06 1
 
1.1%
Other values (63) 63
72.4%
ValueCountFrequency (%)
0.0 6
6.9%
0.01 4
4.6%
0.02 2
 
2.3%
0.03 5
5.7%
0.04 1
 
1.1%
0.06 1
 
1.1%
0.1 1
 
1.1%
0.12 2
 
2.3%
0.13 1
 
1.1%
0.14 1
 
1.1%
ValueCountFrequency (%)
53.08 1
1.1%
50.13 1
1.1%
48.15 1
1.1%
44.07 1
1.1%
40.28 1
1.1%
39.69 1
1.1%
38.16 1
1.1%
36.01 1
1.1%
35.12 1
1.1%
34.72 1
1.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
2022-08-30
87 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-30
2nd row2022-08-30
3rd row2022-08-30
4th row2022-08-30
5th row2022-08-30

Common Values

ValueCountFrequency (%)
2022-08-30 87
100.0%

Length

2023-12-11T09:42:50.907129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:51.314476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-30 87
100.0%

Interactions

2023-12-11T09:42:48.132051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:47.588826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:47.864962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:48.241981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:47.678035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:47.950970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:48.340532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:47.764549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:48.041042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:42:51.376447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부납부건수납부금액(원)납부매체비율
세목명1.0000.0000.0000.0000.4870.467
납부매체0.0001.0000.9910.0000.1900.000
납부매체전자고지여부0.0000.9911.0000.0000.1600.301
납부건수0.0000.0000.0001.0000.6330.682
납부금액(원)0.4870.1900.1600.6331.0000.548
납부매체비율0.4670.0000.3010.6820.5481.000
2023-12-11T09:42:51.516760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체납부매체전자고지여부세목명
납부매체1.0000.8730.000
납부매체전자고지여부0.8731.0000.000
세목명0.0000.0001.000
2023-12-11T09:42:51.625605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액(원)납부매체비율세목명납부매체납부매체전자고지여부
납부건수1.0000.7860.9070.0000.0000.000
납부금액(원)0.7861.0000.6760.2250.0930.115
납부매체비율0.9070.6761.0000.2100.0000.217
세목명0.0000.2250.2101.0000.0000.000
납부매체0.0000.0930.0000.0001.0000.873
납부매체전자고지여부0.0000.1150.2170.0000.8731.000

Missing values

2023-12-11T09:42:48.493202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:42:48.648656image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액(원)납부매체비율기준일자
0경상남도거제시483102021등록면허세ARSN29045218001.812022-08-30
1경상남도거제시483102021등록면허세ARSY1154500.012022-08-30
2경상남도거제시483102021면허세ARSN163000.012022-08-30
3경상남도거제시483102021자동차세ARSN7076147263241044.072022-08-30
4경상남도거제시483102021자동차세ARSY1921571800.122022-08-30
5경상남도거제시483102021재산세ARSN646795002695040.282022-08-30
6경상남도거제시483102021재산세ARSY42217300.022022-08-30
7경상남도거제시483102021주민세ARSN19264118207012.02022-08-30
8경상남도거제시483102021주민세ARSY162218300.12022-08-30
9경상남도거제시483102021지방소득세ARSN215664332301.342022-08-30
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액(원)납부매체비율기준일자
77경상남도거제시483102021주민세지자체방문N45671649011806.482022-08-30
78경상남도거제시483102021지방소득세지자체방문N13654337328801.942022-08-30
79경상남도거제시483102021지역자원시설세지자체방문N3011499000.042022-08-30
80경상남도거제시483102021취득세지자체방문N178244364544167025.282022-08-30
81경상남도거제시483102021등록면허세페이사납부Y27946516102.712022-08-30
82경상남도거제시483102021자동차세페이사납부Y371066726457036.012022-08-30
83경상남도거제시483102021재산세페이사납부Y393242020492038.162022-08-30
84경상남도거제시483102021주민세페이사납부Y23162646098022.482022-08-30
85경상남도거제시483102021지방소득세페이사납부Y2637458600.252022-08-30
86경상남도거제시483102021취득세페이사납부Y40565934600.392022-08-30