Overview

Dataset statistics

Number of variables10
Number of observations478
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.8 KiB
Average record size in memory85.3 B

Variable types

Categorical5
Numeric4
Boolean1

Dataset

Description신용카드 가상계좌 등 지방세 납부매체별 납부현황(가상계좌,신용카드, 지로, 신용카드 포인드 등)전자송달 시장 규모 및 편익분석, 수수료 산정시 기초자료로 활용
Author경상북도 경산시
URLhttps://www.data.go.kr/data/15079696/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
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
납부금액 has unique valuesUnique
납부매체비율 has 32 (6.7%) zerosZeros

Reproduction

Analysis started2024-03-16 06:43:51.170237
Analysis finished2024-03-16 06:44:03.499530
Duration12.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
경상북도
478 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 478
100.0%

Length

2024-03-16T06:44:03.855356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:44:04.144946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 478
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
경산시
478 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경산시 478
100.0%

Length

2024-03-16T06:44:04.533144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:44:05.063364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경산시 478
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
47290
478 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47290
2nd row47290
3rd row47290
4th row47290
5th row47290

Common Values

ValueCountFrequency (%)
47290 478
100.0%

Length

2024-03-16T06:44:05.495281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:44:05.928216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47290 478
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.659
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-03-16T06:44:06.205983image/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 deviation1.6869311
Coefficient of variation (CV)0.00083525541
Kurtosis-1.2168717
Mean2019.659
Median Absolute Deviation (MAD)1
Skewness-0.1251561
Sum965397
Variance2.8457365
MonotonicityIncreasing
2024-03-16T06:44:06.788020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 88
18.4%
2020 86
18.0%
2021 86
18.0%
2019 79
16.5%
2018 71
14.9%
2017 68
14.2%
ValueCountFrequency (%)
2017 68
14.2%
2018 71
14.9%
2019 79
16.5%
2020 86
18.0%
2021 86
18.0%
2022 88
18.4%
ValueCountFrequency (%)
2022 88
18.4%
2021 86
18.0%
2020 86
18.0%
2019 79
16.5%
2018 71
14.9%
2017 68
14.2%

세목명
Categorical

Distinct14
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
자동차세
60 
재산세
60 
주민세
60 
등록면허세
58 
지방소득세
51 
Other values (9)
189 

Length

Max length7
Median length5
Mean length4.041841
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 60
12.6%
재산세 60
12.6%
주민세 60
12.6%
등록면허세 58
12.1%
지방소득세 51
10.7%
취득세 50
10.5%
지역자원시설세 34
7.1%
등록세 32
6.7%
면허세 25
5.2%
종합토지세 19
 
4.0%
Other values (4) 29
6.1%

Length

2024-03-16T06:44:07.448012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 60
12.6%
재산세 60
12.6%
주민세 60
12.6%
등록면허세 58
12.1%
지방소득세 51
10.7%
취득세 50
10.5%
지역자원시설세 34
7.1%
등록세 32
6.7%
면허세 25
5.2%
종합토지세 19
 
4.0%
Other values (4) 29
6.1%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
가상계좌
69 
자동화기기
61 
기타
58 
은행창구
58 
위택스
55 
Other values (5)
177 

Length

Max length5
Median length4
Mean length3.8995816
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 69
14.4%
자동화기기 61
12.8%
기타 58
12.1%
은행창구 58
12.1%
위택스 55
11.5%
인터넷지로 49
10.3%
ARS 46
9.6%
지자체방문 36
7.5%
자동이체 23
 
4.8%
페이사납부 23
 
4.8%

Length

2024-03-16T06:44:08.043236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:44:08.570341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 69
14.4%
자동화기기 61
12.8%
기타 58
12.1%
은행창구 58
12.1%
위택스 55
11.5%
인터넷지로 49
10.3%
ars 46
9.6%
지자체방문 36
7.5%
자동이체 23
 
4.8%
페이사납부 23
 
4.8%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size610.0 B
False
245 
True
233 
ValueCountFrequency (%)
False 245
51.3%
True 233
48.7%
2024-03-16T06:44:09.067871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9278.546
Minimum1
Maximum128800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-03-16T06:44:09.532829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q125
median606.5
Q38553
95-th percentile53989.25
Maximum128800
Range128799
Interquartile range (IQR)8528

Descriptive statistics

Standard deviation20198.355
Coefficient of variation (CV)2.1768879
Kurtosis13.689792
Mean9278.546
Median Absolute Deviation (MAD)605
Skewness3.517701
Sum4435145
Variance4.0797354 × 108
MonotonicityNot monotonic
2024-03-16T06:44:10.272085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22
 
4.6%
6 13
 
2.7%
2 11
 
2.3%
3 10
 
2.1%
5 8
 
1.7%
4 7
 
1.5%
7 7
 
1.5%
10 5
 
1.0%
17 5
 
1.0%
11 5
 
1.0%
Other values (343) 385
80.5%
ValueCountFrequency (%)
1 22
4.6%
2 11
2.3%
3 10
2.1%
4 7
 
1.5%
5 8
 
1.7%
6 13
2.7%
7 7
 
1.5%
8 2
 
0.4%
9 4
 
0.8%
10 5
 
1.0%
ValueCountFrequency (%)
128800 1
0.2%
126301 1
0.2%
119153 1
0.2%
118065 1
0.2%
115604 1
0.2%
108317 1
0.2%
103087 1
0.2%
93530 1
0.2%
91538 1
0.2%
89670 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct478
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5317095 × 109
Minimum10
Maximum9.2655464 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-03-16T06:44:10.813864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile43716
Q13545450
median1.7102646 × 108
Q34.7501222 × 109
95-th percentile2.5433796 × 1010
Maximum9.2655464 × 1010
Range9.2655464 × 1010
Interquartile range (IQR)4.7465767 × 109

Descriptive statistics

Standard deviation9.4898696 × 109
Coefficient of variation (CV)2.0941037
Kurtosis20.281991
Mean4.5317095 × 109
Median Absolute Deviation (MAD)1.7099967 × 108
Skewness3.6868229
Sum2.1661572 × 1012
Variance9.0057625 × 1019
MonotonicityNot monotonic
2024-03-16T06:44:11.301344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
756609470 1
 
0.2%
7390928520 1
 
0.2%
1138826560 1
 
0.2%
88200 1
 
0.2%
42675240 1
 
0.2%
926183820 1
 
0.2%
92655463870 1
 
0.2%
268520 1
 
0.2%
14246294580 1
 
0.2%
2767266710 1
 
0.2%
Other values (468) 468
97.9%
ValueCountFrequency (%)
10 1
0.2%
370 1
0.2%
1270 1
0.2%
4140 1
0.2%
6270 1
0.2%
6300 1
0.2%
6410 1
0.2%
8240 1
0.2%
10300 1
0.2%
10500 1
0.2%
ValueCountFrequency (%)
92655463870 1
0.2%
53728613390 1
0.2%
50944771300 1
0.2%
43064135220 1
0.2%
42870944660 1
0.2%
42141923210 1
0.2%
40138218620 1
0.2%
38139073560 1
0.2%
37295593970 1
0.2%
36489688880 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct331
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.924686
Minimum0
Maximum87.14
Zeros32
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-03-16T06:44:11.758756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.07
median5.925
Q319.255
95-th percentile40.4175
Maximum87.14
Range87.14
Interquartile range (IQR)19.185

Descriptive statistics

Standard deviation15.349147
Coefficient of variation (CV)1.2871741
Kurtosis3.6173185
Mean11.924686
Median Absolute Deviation (MAD)5.905
Skewness1.7290234
Sum5700
Variance235.59632
MonotonicityNot monotonic
2024-03-16T06:44:12.259480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 32
 
6.7%
0.01 30
 
6.3%
0.03 17
 
3.6%
0.04 17
 
3.6%
0.02 13
 
2.7%
0.08 7
 
1.5%
0.05 5
 
1.0%
0.07 4
 
0.8%
0.22 4
 
0.8%
0.13 3
 
0.6%
Other values (321) 346
72.4%
ValueCountFrequency (%)
0.0 32
6.7%
0.01 30
6.3%
0.02 13
2.7%
0.03 17
3.6%
0.04 17
3.6%
0.05 5
 
1.0%
0.06 3
 
0.6%
0.07 4
 
0.8%
0.08 7
 
1.5%
0.09 2
 
0.4%
ValueCountFrequency (%)
87.14 1
0.2%
87.07 1
0.2%
79.09 1
0.2%
74.3 1
0.2%
66.94 1
0.2%
66.9 1
0.2%
61.04 1
0.2%
59.05 1
0.2%
55.37 1
0.2%
54.26 1
0.2%

Interactions

2024-03-16T06:44:01.147291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:56.996249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:58.465051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:44:00.070353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:44:01.458060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:57.353393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:58.852179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:44:00.340810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:44:01.753728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:57.731556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:59.270272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:44:00.719486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:44:02.104894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:58.079374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:43:59.709993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:44:00.924171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T06:44:12.609094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.2290.0000.2740.5240.580
납부매체0.0000.2291.0000.9980.4830.2820.521
납부매체전자고지여부0.0000.0000.9981.0000.1560.0000.000
납부건수0.0000.2740.4830.1561.0000.4070.611
납부금액0.0000.5240.2820.0000.4071.0000.222
납부매체비율0.0000.5800.5210.0000.6110.2221.000
2024-03-16T06:44:12.938657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체전자고지여부납부매체
세목명1.0000.0000.093
납부매체전자고지여부0.0001.0000.949
납부매체0.0930.9491.000
2024-03-16T06:44:13.296734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.000-0.061-0.048-0.0050.0000.0000.000
납부건수-0.0611.0000.8310.8040.1130.1660.118
납부금액-0.0480.8311.0000.6360.2190.1460.000
납부매체비율-0.0050.8040.6361.0000.2770.1830.000
세목명0.0000.1130.2190.2771.0000.0930.000
납부매체0.0000.1660.1460.1830.0931.0000.949
납부매체전자고지여부0.0000.1180.0000.0000.0000.9491.000

Missing values

2024-03-16T06:44:02.580502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T06:44:03.200278image/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
0경상북도경산시472902017등록면허세가상계좌Y198627566094708.22
1경상북도경산시472902017등록세가상계좌Y42109641000.02
2경상북도경산시472902017면허세가상계좌Y585690500.02
3경상북도경산시472902017사업소세가상계좌Y425917100.0
4경상북도경산시472902017자동차세가상계좌Y915381327354546037.87
5경상북도경산시472902017재산세가상계좌Y692471520354737028.65
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