Overview

Dataset statistics

Number of variables20
Number of observations440
Missing cells0
Missing cells (%)0.0%
Duplicate rows10
Duplicate rows (%)2.3%
Total size in memory72.3 KiB
Average record size in memory168.3 B

Variable types

Categorical11
DateTime2
Numeric7

Dataset

Description오산시 지방세 ARS카드납부시스템의 환경개선금에 대한 데이터로 차종, 적용기간, 적용일수, 고지금액, 납부 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15081651/fileData.do

Alerts

고지차수 has constant value ""Constant
수납여부 has constant value ""Constant
Dataset has 10 (2.3%) duplicate rowsDuplicates
적용기간 시작일 is highly overall correlated with 고지구분 and 1 other fieldsHigh correlation
과세년도 is highly overall correlated with 적용기간 시작일 and 1 other fieldsHigh correlation
납기후일자 is highly overall correlated with 수납일자 and 3 other fieldsHigh correlation
납기내일자 is highly overall correlated with 수납일자 and 3 other fieldsHigh correlation
은행명 is highly overall correlated with 수납방법High correlation
수납방법 is highly overall correlated with 은행명 and 1 other fieldsHigh correlation
이체일자 is highly overall correlated with 수납일자 and 4 other fieldsHigh correlation
적용일수 is highly overall correlated with 고지금액 and 1 other fieldsHigh correlation
고지금액 is highly overall correlated with 적용일수 and 2 other fieldsHigh correlation
납기후금액 is highly overall correlated with 고지금액 and 1 other fieldsHigh correlation
기존체납금 is highly overall correlated with 고지구분High correlation
수납일자 is highly overall correlated with 적용일수 and 4 other fieldsHigh correlation
수납금액 is highly overall correlated with 고지금액 and 1 other fieldsHigh correlation
고지구분 is highly overall correlated with 기존체납금 and 6 other fieldsHigh correlation
적용기간 시작일 is highly imbalanced (53.3%)Imbalance
과세년도 is highly imbalanced (51.9%)Imbalance
수납방법 is highly imbalanced (80.8%)Imbalance
가산금 has 71 (16.1%) zerosZeros
기존체납금 has 246 (55.9%) zerosZeros

Reproduction

Analysis started2023-12-12 14:57:55.054220
Analysis finished2023-12-12 14:58:01.990299
Duration6.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

차종
Categorical

Distinct16
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
승용차 다목적형 중형
161 
승용차 다목적형 대형
81 
승합차 일반형 중형
56 
화물차 일반형 소형
49 
화물차 일반형 중형
26 
Other values (11)
67 

Length

Max length12
Median length11
Mean length10.581818
Min length9

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row승용차 다목적형 대형
2nd row승용차 다목적형 중형
3rd row승용차 다목적형 중형
4th row승용차 다목적형 중형
5th row승용차 일반형 중형

Common Values

ValueCountFrequency (%)
승용차 다목적형 중형 161
36.6%
승용차 다목적형 대형 81
18.4%
승합차 일반형 중형 56
 
12.7%
화물차 일반형 소형 49
 
11.1%
화물차 일반형 중형 26
 
5.9%
승용차 일반형 중형 13
 
3.0%
승용차 기타형 대형 12
 
2.7%
화물차 밴형 소형 12
 
2.7%
화물차 일반형 대형 7
 
1.6%
화물차 특수용도형 소형 6
 
1.4%
Other values (6) 17
 
3.9%

Length

2023-12-12T23:58:02.073263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
승용차 275
20.8%
중형 266
20.2%
다목적형 242
18.3%
일반형 156
11.8%
화물차 107
 
8.1%
대형 101
 
7.7%
소형 73
 
5.5%
승합차 56
 
4.2%
기타형 15
 
1.1%
밴형 12
 
0.9%
Other values (4) 17
 
1.3%

적용기간 시작일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct40
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2021-07-01
218 
2022-01-01
117 
2021-01-01
30 
2020-07-01
 
16
2020-01-01
 
6
Other values (35)
53 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique27 ?
Unique (%)6.1%

Sample

1st row2020-07-01
2nd row2010-07-01
3rd row2021-07-01
4th row2021-07-01
5th row2021-07-01

Common Values

ValueCountFrequency (%)
2021-07-01 218
49.5%
2022-01-01 117
26.6%
2021-01-01 30
 
6.8%
2020-07-01 16
 
3.6%
2020-01-01 6
 
1.4%
2019-07-01 5
 
1.1%
2017-01-01 4
 
0.9%
2019-01-01 4
 
0.9%
2015-01-01 4
 
0.9%
2016-07-01 3
 
0.7%
Other values (30) 33
 
7.5%

Length

2023-12-12T23:58:02.234022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-07-01 218
49.5%
2022-01-01 117
26.6%
2021-01-01 30
 
6.8%
2020-07-01 16
 
3.6%
2020-01-01 6
 
1.4%
2019-07-01 5
 
1.1%
2017-01-01 4
 
0.9%
2019-01-01 4
 
0.9%
2015-01-01 4
 
0.9%
2016-07-01 3
 
0.7%
Other values (30) 33
 
7.5%

적용기간 종료일
Date

Distinct66
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Minimum2009-05-12 00:00:00
Maximum2022-06-30 00:00:00
2023-12-12T23:58:02.360644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:02.486708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

적용일수
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.29318
Minimum4
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T23:58:02.596766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile80.8
Q1181
median184
Q3184
95-th percentile365
Maximum365
Range361
Interquartile range (IQR)3

Descriptive statistics

Standard deviation78.951864
Coefficient of variation (CV)0.39615938
Kurtosis0.9981506
Mean199.29318
Median Absolute Deviation (MAD)3
Skewness0.97064489
Sum87689
Variance6233.3968
MonotonicityNot monotonic
2023-12-12T23:58:02.730850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 156
35.5%
181 141
32.0%
365 69
15.7%
182 8
 
1.8%
94 3
 
0.7%
132 3
 
0.7%
75 2
 
0.5%
93 2
 
0.5%
138 2
 
0.5%
150 2
 
0.5%
Other values (47) 52
 
11.8%
ValueCountFrequency (%)
4 1
0.2%
13 1
0.2%
15 1
0.2%
16 2
0.5%
17 1
0.2%
18 1
0.2%
40 1
0.2%
43 1
0.2%
45 1
0.2%
52 1
0.2%
ValueCountFrequency (%)
365 69
15.7%
184 156
35.5%
183 1
 
0.2%
182 8
 
1.8%
181 141
32.0%
178 1
 
0.2%
177 1
 
0.2%
164 1
 
0.2%
162 1
 
0.2%
161 1
 
0.2%

고지차수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0
440 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 440
100.0%

Length

2023-12-12T23:58:02.842216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:58:02.921394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 440
100.0%

고지구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
부과
253 
체납
187 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row체납
2nd row체납
3rd row부과
4th row부과
5th row부과

Common Values

ValueCountFrequency (%)
부과 253
57.5%
체납 187
42.5%

Length

2023-12-12T23:58:03.001355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:58:03.083906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부과 253
57.5%
체납 187
42.5%

과세년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2022-03-10
223 
2022-09-10
125 
2021-09-10
30 
2021-03-10
 
17
2020-09-10
 
6
Other values (19)
39 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique9 ?
Unique (%)2.0%

Sample

1st row2021-03-10
2nd row2011-03-10
3rd row2022-03-10
4th row2022-03-10
5th row2022-03-10

Common Values

ValueCountFrequency (%)
2022-03-10 223
50.7%
2022-09-10 125
28.4%
2021-09-10 30
 
6.8%
2021-03-10 17
 
3.9%
2020-09-10 6
 
1.4%
2020-03-10 5
 
1.1%
2015-09-10 4
 
0.9%
2017-09-10 4
 
0.9%
2019-09-10 4
 
0.9%
2017-03-10 3
 
0.7%
Other values (14) 19
 
4.3%

Length

2023-12-12T23:58:03.171666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-03-10 223
50.7%
2022-09-10 125
28.4%
2021-09-10 30
 
6.8%
2021-03-10 17
 
3.9%
2020-09-10 6
 
1.4%
2020-03-10 5
 
1.1%
2015-09-10 4
 
0.9%
2017-09-10 4
 
0.9%
2019-09-10 4
 
0.9%
2017-03-10 3
 
0.7%
Other values (14) 19
 
4.3%

고지금액
Real number (ℝ)

HIGH CORRELATION 

Distinct156
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57686.159
Minimum3560
Maximum334520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T23:58:03.276128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3560
5-th percentile20250
Q141580
median45815
Q372860
95-th percentile110920.5
Maximum334520
Range330960
Interquartile range (IQR)31280

Descriptive statistics

Standard deviation33916.336
Coefficient of variation (CV)0.58794582
Kurtosis13.279081
Mean57686.159
Median Absolute Deviation (MAD)8425
Skewness2.7162701
Sum25381910
Variance1.1503179 × 109
MonotonicityNot monotonic
2023-12-12T23:58:03.435511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41580 60
 
13.6%
42950 50
 
11.4%
76080 33
 
7.5%
51980 19
 
4.3%
72770 19
 
4.3%
53690 17
 
3.9%
75170 14
 
3.2%
95100 8
 
1.8%
45480 7
 
1.6%
133150 6
 
1.4%
Other values (146) 207
47.0%
ValueCountFrequency (%)
3560 1
0.2%
4520 1
0.2%
4740 1
0.2%
5040 1
0.2%
5140 1
0.2%
5330 1
0.2%
5930 1
0.2%
6430 1
0.2%
7810 1
0.2%
8000 2
0.5%
ValueCountFrequency (%)
334520 1
 
0.2%
196250 3
0.7%
188860 2
 
0.5%
187140 2
 
0.5%
182840 2
 
0.5%
163740 1
 
0.2%
140420 1
 
0.2%
139000 1
 
0.2%
133150 6
1.4%
113400 3
0.7%

가산금
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1277.8864
Minimum0
Maximum5660
Zeros71
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T23:58:03.570331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1867.5
median1245
Q31562.5
95-th percentile2492
Maximum5660
Range5660
Interquartile range (IQR)695

Descriptive statistics

Standard deviation934.72023
Coefficient of variation (CV)0.73145802
Kurtosis5.4109547
Mean1277.8864
Median Absolute Deviation (MAD)335
Skewness1.4973042
Sum562270
Variance873701.9
MonotonicityNot monotonic
2023-12-12T23:58:03.705121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
16.1%
1240 60
 
13.6%
1280 52
 
11.8%
1550 20
 
4.5%
2180 19
 
4.3%
1610 17
 
3.9%
2250 15
 
3.4%
1360 9
 
2.0%
1250 6
 
1.4%
1020 5
 
1.1%
Other values (90) 166
37.7%
ValueCountFrequency (%)
0 71
16.1%
100 1
 
0.2%
130 1
 
0.2%
140 1
 
0.2%
150 3
 
0.7%
170 1
 
0.2%
190 1
 
0.2%
230 1
 
0.2%
240 2
 
0.5%
290 1
 
0.2%
ValueCountFrequency (%)
5660 2
 
0.5%
5610 2
 
0.5%
5480 2
 
0.5%
4910 1
 
0.2%
4170 1
 
0.2%
3400 3
0.7%
3320 1
 
0.2%
3290 5
1.1%
3230 1
 
0.2%
3210 2
 
0.5%

납기내일자
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2022-03-31
124 
2022-09-30
95 
2022-02-03
63 
2022-10-31
27 
2022-11-30
27 
Other values (9)
104 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row2022-01-31
2nd row2022-01-31
3rd row2022-02-03
4th row2022-02-03
5th row2022-02-03

Common Values

ValueCountFrequency (%)
2022-03-31 124
28.2%
2022-09-30 95
21.6%
2022-02-03 63
14.3%
2022-10-31 27
 
6.1%
2022-11-30 27
 
6.1%
2022-04-30 19
 
4.3%
2022-05-31 18
 
4.1%
2022-06-30 15
 
3.4%
2022-07-31 12
 
2.7%
2022-08-31 12
 
2.7%
Other values (4) 28
 
6.4%

Length

2023-12-12T23:58:03.871195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-03-31 124
28.2%
2022-09-30 95
21.6%
2022-02-03 63
14.3%
2022-10-31 27
 
6.1%
2022-11-30 27
 
6.1%
2022-04-30 19
 
4.3%
2022-05-31 18
 
4.1%
2022-06-30 15
 
3.4%
2022-07-31 12
 
2.7%
2022-08-31 12
 
2.7%
Other values (4) 28
 
6.4%

납기후금액
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58964.045
Minimum3660
Maximum334520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T23:58:04.287844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3660
5-th percentile20849
Q142820
median47640
Q374950
95-th percentile114244.5
Maximum334520
Range330860
Interquartile range (IQR)32130

Descriptive statistics

Standard deviation34202.505
Coefficient of variation (CV)0.58005696
Kurtosis12.935327
Mean58964.045
Median Absolute Deviation (MAD)8445
Skewness2.6928083
Sum25944180
Variance1.1698113 × 109
MonotonicityNot monotonic
2023-12-12T23:58:04.416707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42820 60
 
13.6%
44230 50
 
11.4%
76080 33
 
7.5%
53530 19
 
4.3%
74950 19
 
4.3%
55300 17
 
3.9%
77420 14
 
3.2%
95100 8
 
1.8%
46840 7
 
1.6%
133150 6
 
1.4%
Other values (147) 207
47.0%
ValueCountFrequency (%)
3660 1
0.2%
4650 1
0.2%
4880 1
0.2%
5190 1
0.2%
5290 1
0.2%
5480 1
0.2%
6100 1
0.2%
6620 1
0.2%
8040 1
0.2%
8240 2
0.5%
ValueCountFrequency (%)
334520 1
 
0.2%
196250 3
0.7%
194520 2
 
0.5%
192750 2
 
0.5%
188320 2
 
0.5%
168650 1
 
0.2%
143170 1
 
0.2%
140420 1
 
0.2%
133150 6
1.4%
116800 3
0.7%

납기후일자
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2022-04-30
95 
2022-10-31
95 
2022-02-03
63 
2022-03-31
40 
2022-11-30
27 
Other values (9)
120 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row2022-02-28
2nd row2022-02-28
3rd row2022-02-03
4th row2022-02-03
5th row2022-02-03

Common Values

ValueCountFrequency (%)
2022-04-30 95
21.6%
2022-10-31 95
21.6%
2022-02-03 63
14.3%
2022-03-31 40
9.1%
2022-11-30 27
 
6.1%
2022-12-31 27
 
6.1%
2022-05-31 19
 
4.3%
2022-06-30 18
 
4.1%
2022-07-31 16
 
3.6%
2022-09-30 12
 
2.7%
Other values (4) 28
 
6.4%

Length

2023-12-12T23:58:04.543661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-04-30 95
21.6%
2022-10-31 95
21.6%
2022-02-03 63
14.3%
2022-03-31 40
9.1%
2022-11-30 27
 
6.1%
2022-12-31 27
 
6.1%
2022-05-31 19
 
4.3%
2022-06-30 18
 
4.1%
2022-07-31 16
 
3.6%
2022-09-30 12
 
2.7%
Other values (4) 28
 
6.4%

기존체납금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52940.955
Minimum0
Maximum407000
Zeros246
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T23:58:04.678039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q354017.5
95-th percentile300540
Maximum407000
Range407000
Interquartile range (IQR)54017.5

Descriptive statistics

Standard deviation95398.36
Coefficient of variation (CV)1.8019766
Kurtosis5.3215382
Mean52940.955
Median Absolute Deviation (MAD)0
Skewness2.3988939
Sum23294020
Variance9.100847 × 109
MonotonicityNot monotonic
2023-12-12T23:58:04.847594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 246
55.9%
42820 28
 
6.4%
407000 11
 
2.5%
44230 9
 
2.0%
77420 7
 
1.6%
53530 6
 
1.4%
370380 5
 
1.1%
236820 5
 
1.1%
209760 4
 
0.9%
85640 4
 
0.9%
Other values (70) 115
26.1%
ValueCountFrequency (%)
0 246
55.9%
4880 1
 
0.2%
5480 1
 
0.2%
6620 1
 
0.2%
10990 1
 
0.2%
18840 1
 
0.2%
19300 1
 
0.2%
25650 1
 
0.2%
26060 1
 
0.2%
27160 1
 
0.2%
ValueCountFrequency (%)
407000 11
2.5%
370380 5
1.1%
369480 4
 
0.9%
300540 4
 
0.9%
273390 2
 
0.5%
236820 5
1.1%
226160 3
 
0.7%
209760 4
 
0.9%
192750 1
 
0.2%
188320 1
 
0.2%

은행명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
삼성
91 
신한
72 
현대
67 
국민
60 
비씨
58 
Other values (5)
92 

Length

Max length3
Median length2
Mean length2.0295455
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신한
2nd row현대
3rd row비씨
4th row신한
5th row농협

Common Values

ValueCountFrequency (%)
삼성 91
20.7%
신한 72
16.4%
현대 67
15.2%
국민 60
13.6%
비씨 58
13.2%
롯데 39
8.9%
농협 26
 
5.9%
외환 14
 
3.2%
LGT 9
 
2.0%
SKT 4
 
0.9%

Length

2023-12-12T23:58:05.031145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:58:05.146817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
삼성 91
20.7%
신한 72
16.4%
현대 67
15.2%
국민 60
13.6%
비씨 58
13.2%
롯데 39
8.9%
농협 26
 
5.9%
외환 14
 
3.2%
lgt 9
 
2.0%
skt 4
 
0.9%

이체일자
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2022-04-13
116 
2022-10-13
101 
2022-02-14
65 
2022-12-13
25 
2022-05-13
23 
Other values (14)
110 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique5 ?
Unique (%)1.1%

Sample

1st row2022-02-14
2nd row2022-02-14
3rd row2022-02-14
4th row2022-02-14
5th row2022-02-14

Common Values

ValueCountFrequency (%)
2022-04-13 116
26.4%
2022-10-13 101
23.0%
2022-02-14 65
14.8%
2022-12-13 25
 
5.7%
2022-05-13 23
 
5.2%
2022-07-13 20
 
4.5%
2022-03-14 16
 
3.6%
2022-11-14 16
 
3.6%
2022-06-13 15
 
3.4%
2023-01-13 13
 
3.0%
Other values (9) 30
 
6.8%

Length

2023-12-12T23:58:05.273214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-04-13 116
26.4%
2022-10-13 101
23.0%
2022-02-14 65
14.8%
2022-12-13 25
 
5.7%
2022-05-13 23
 
5.2%
2022-07-13 20
 
4.5%
2022-03-14 16
 
3.6%
2022-11-14 16
 
3.6%
2022-06-13 15
 
3.4%
2023-01-13 13
 
3.0%
Other values (9) 30
 
6.8%

회계일자
Date

Distinct118
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Minimum2022-01-04 00:00:00
Maximum2022-12-28 00:00:00
2023-12-12T23:58:05.384839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:05.528362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수납방법
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
카드
427 
소액결제
 
13

Length

Max length4
Median length2
Mean length2.0590909
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
카드 427
97.0%
소액결제 13
 
3.0%

Length

2023-12-12T23:58:05.667973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:58:05.798797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
카드 427
97.0%
소액결제 13
 
3.0%

수납여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
수납
440 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수납 440
100.0%

Length

2023-12-12T23:58:05.901457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:58:05.988879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수납 440
100.0%

수납일자
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220581
Minimum20220104
Maximum20221228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T23:58:06.103351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220104
5-th percentile20220120
Q120220315
median20220426
Q320220926
95-th percentile20221117
Maximum20221228
Range1124
Interquartile range (IQR)611.25

Descriptive statistics

Standard deviation345.97434
Coefficient of variation (CV)1.711001 × 10-5
Kurtosis-1.4534205
Mean20220581
Median Absolute Deviation (MAD)295.5
Skewness0.25466624
Sum8.8970557 × 109
Variance119698.24
MonotonicityIncreasing
2023-12-12T23:58:06.228518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20220930 25
 
5.7%
20220331 16
 
3.6%
20220315 15
 
3.4%
20220928 15
 
3.4%
20220203 13
 
3.0%
20220314 12
 
2.7%
20221116 11
 
2.5%
20220927 10
 
2.3%
20220316 10
 
2.3%
20220630 10
 
2.3%
Other values (123) 303
68.9%
ValueCountFrequency (%)
20220104 1
 
0.2%
20220105 1
 
0.2%
20220112 2
 
0.5%
20220114 2
 
0.5%
20220115 2
 
0.5%
20220116 2
 
0.5%
20220117 3
0.7%
20220118 1
 
0.2%
20220119 4
0.9%
20220120 5
1.1%
ValueCountFrequency (%)
20221228 3
0.7%
20221226 1
 
0.2%
20221221 2
0.5%
20221220 4
0.9%
20221212 1
 
0.2%
20221208 1
 
0.2%
20221201 1
 
0.2%
20221128 1
 
0.2%
20221126 1
 
0.2%
20221125 1
 
0.2%

수납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58322.773
Minimum3560
Maximum334520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T23:58:06.349370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3560
5-th percentile20250
Q141580
median47640
Q374950
95-th percentile113096
Maximum334520
Range330960
Interquartile range (IQR)33370

Descriptive statistics

Standard deviation34002.743
Coefficient of variation (CV)0.58300971
Kurtosis13.106822
Mean58322.773
Median Absolute Deviation (MAD)8650
Skewness2.6968163
Sum25662020
Variance1.1561865 × 109
MonotonicityNot monotonic
2023-12-12T23:58:06.483591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42950 41
 
9.3%
42820 34
 
7.7%
76080 33
 
7.5%
41580 26
 
5.9%
53690 14
 
3.2%
51980 11
 
2.5%
74950 10
 
2.3%
44230 9
 
2.0%
72770 9
 
2.0%
53530 8
 
1.8%
Other values (169) 245
55.7%
ValueCountFrequency (%)
3560 1
0.2%
4650 1
0.2%
4880 1
0.2%
5040 1
0.2%
5290 1
0.2%
5480 1
0.2%
6100 1
0.2%
6620 1
0.2%
8000 2
0.5%
8040 1
0.2%
ValueCountFrequency (%)
334520 1
 
0.2%
196250 3
0.7%
192750 1
 
0.2%
188860 2
0.5%
188320 1
 
0.2%
187140 1
 
0.2%
182840 1
 
0.2%
163740 1
 
0.2%
143170 1
 
0.2%
140420 1
 
0.2%

Interactions

2023-12-12T23:58:00.780358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:56.515150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.238358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.992285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.801586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.379456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.991293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:00.898067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:56.621473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.336942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.082079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.879049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.462581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:00.101879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:01.024314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:56.722542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.430725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.166620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.957063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.550920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:00.193596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:01.130716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:56.832964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.556349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.246050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.027808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.631719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:00.305133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:01.235914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:56.927031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.659680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.325419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.107385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.723467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:00.453498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:01.368840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.034760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.763089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.407213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.189147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.802300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:00.573066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:01.462074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.129441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:57.876472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:58.481834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.282097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:57:59.881817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:00.677505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:58:06.581236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차종적용기간 시작일적용기간 종료일적용일수고지구분과세년도고지금액가산금납기내일자납기후금액납기후일자기존체납금은행명이체일자수납방법수납일자수납금액
차종1.0000.8220.7680.3310.2430.6800.6030.7080.4210.6040.3580.5840.3430.3540.0000.2790.603
적용기간 시작일0.8221.0000.9920.7900.7141.0000.0000.7320.7270.0000.7640.7400.3830.7220.0000.6950.000
적용기간 종료일0.7680.9921.0000.9600.7531.0000.0000.6870.8570.0000.8760.7720.0000.4080.0000.7090.000
적용일수0.3310.7900.9601.0000.5220.4580.5790.6810.7100.6080.7110.2310.0390.7180.2260.6390.592
고지구분0.2430.7140.7530.5221.0000.7020.1730.3860.9160.1780.9640.8350.1560.8290.0000.8730.168
과세년도0.6801.0001.0000.4580.7021.0000.0000.6980.6910.0000.7300.7400.0000.4850.0000.6420.000
고지금액0.6030.0000.0000.5790.1730.0001.0000.8980.5331.0000.5360.3120.0000.4060.0000.4051.000
가산금0.7080.7320.6870.6810.3860.6980.8981.0000.5770.8890.5870.5020.2360.5450.0000.6530.893
납기내일자0.4210.7270.8570.7100.9160.6910.5330.5771.0000.5311.0000.7500.2730.9640.0000.9600.520
납기후금액0.6040.0000.0000.6080.1780.0001.0000.8890.5311.0000.5340.2950.0000.4090.0000.4031.000
납기후일자0.3580.7640.8760.7110.9640.7300.5360.5871.0000.5341.0000.7570.2840.9580.0000.9570.523
기존체납금0.5840.7400.7720.2310.8350.7400.3120.5020.7500.2950.7571.0000.3060.7730.0000.6640.299
은행명0.3430.3830.0000.0390.1560.0000.0000.2360.2730.0000.2840.3061.0000.7861.0000.3270.000
이체일자0.3540.7220.4080.7180.8290.4850.4060.5450.9640.4090.9580.7730.7861.0001.0000.9820.391
수납방법0.0000.0000.0000.2260.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.000
수납일자0.2790.6950.7090.6390.8730.6420.4050.6530.9600.4030.9570.6640.3270.9820.0001.0000.394
수납금액0.6030.0000.0000.5920.1680.0001.0000.8930.5201.0000.5230.2990.0000.3910.0000.3941.000
2023-12-12T23:58:06.749736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적용기간 시작일차종과세년도납기후일자납기내일자은행명고지구분수납방법이체일자
적용기간 시작일1.0000.3610.9810.3270.2950.1260.5560.0000.258
차종0.3611.0000.2570.1300.1580.1400.1870.0000.118
과세년도0.9810.2571.0000.3220.2910.0000.5540.0000.154
납기후일자0.3270.1300.3221.0000.9620.1180.8320.0000.753
납기내일자0.2950.1580.2910.9621.0000.1130.7610.0000.780
은행명0.1260.1400.0000.1180.1131.0000.1180.9910.433
고지구분0.5560.1870.5540.8320.7610.1181.0000.0000.754
수납방법0.0000.0000.0000.0000.0000.9910.0001.0000.980
이체일자0.2580.1180.1540.7530.7800.4330.7540.9801.000
2023-12-12T23:58:06.894827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적용일수고지금액가산금납기후금액기존체납금수납일자수납금액차종적용기간 시작일고지구분과세년도납기내일자납기후일자은행명이체일자수납방법
적용일수1.0000.514-0.2840.490-0.197-0.6050.4990.1630.4680.3750.1970.4440.4450.0190.4280.162
고지금액0.5141.0000.2680.998-0.135-0.3210.9950.3260.0000.1810.0000.2230.2250.0000.1890.000
가산금-0.2840.2681.0000.3010.2860.3650.2840.3690.3150.2940.3350.2740.2820.0740.2350.000
납기후금액0.4900.9980.3011.000-0.123-0.2970.9950.3270.0000.1850.0000.2220.2240.0000.1910.000
기존체납금-0.197-0.1350.286-0.1231.0000.256-0.0750.2840.3620.8570.3840.4370.4450.1440.4330.000
수납일자-0.605-0.3210.365-0.2970.2561.000-0.3020.1120.2860.6960.2910.8260.8190.1050.8840.000
수납금액0.4990.9950.2840.995-0.075-0.3021.0000.3250.0000.1770.0000.2160.2180.0000.1810.000
차종0.1630.3260.3690.3270.2840.1120.3251.0000.3610.1870.2570.1580.1300.1400.1180.000
적용기간 시작일0.4680.0000.3150.0000.3620.2860.0000.3611.0000.5560.9810.2950.3270.1260.2580.000
고지구분0.3750.1810.2940.1850.8570.6960.1770.1870.5561.0000.5540.7610.8320.1180.7540.000
과세년도0.1970.0000.3350.0000.3840.2910.0000.2570.9810.5541.0000.2910.3220.0000.1540.000
납기내일자0.4440.2230.2740.2220.4370.8260.2160.1580.2950.7610.2911.0000.9620.1130.7800.000
납기후일자0.4450.2250.2820.2240.4450.8190.2180.1300.3270.8320.3220.9621.0000.1180.7530.000
은행명0.0190.0000.0740.0000.1440.1050.0000.1400.1260.1180.0000.1130.1181.0000.4330.991
이체일자0.4280.1890.2350.1910.4330.8840.1810.1180.2580.7540.1540.7800.7530.4331.0000.980
수납방법0.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9910.9801.000

Missing values

2023-12-12T23:58:01.632083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:58:01.889433image/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

차종적용기간 시작일적용기간 종료일적용일수고지차수고지구분과세년도고지금액가산금납기내일자납기후금액납기후일자기존체납금은행명이체일자회계일자수납방법수납여부수납일자수납금액
0승용차 다목적형 대형2020-07-012020-11-261490체납2021-03-105922017702022-01-31609902022-02-2860990신한2022-02-142022-01-04카드수납2022010460990
1승용차 다목적형 중형2010-07-012010-12-311840체납2011-03-104597022902022-01-31482602022-02-2848260현대2022-02-142022-01-05카드수납2022010548260
2승용차 다목적형 중형2021-07-012022-06-303650부과2022-03-107608002022-02-03760802022-02-030비씨2022-02-142022-01-12카드수납2022011276080
3승용차 다목적형 중형2021-07-012022-06-303650부과2022-03-107608002022-02-03760802022-02-030신한2022-02-142022-01-12카드수납2022011276080
4승용차 일반형 중형2021-07-012022-06-303650부과2022-03-107608002022-02-03760802022-02-030농협2022-02-142022-01-14카드수납2022011476080
5화물차 특수용도형 소형2021-07-012022-06-303650부과2022-03-109292002022-02-03929202022-02-030국민2022-02-142022-01-14카드수납2022011492920
6승용차 다목적형 중형2021-07-012022-06-303650부과2022-03-107608002022-02-03760802022-02-030삼성2022-02-142022-01-17카드수납2022011576080
7승용차 다목적형 중형2021-07-012022-06-303650부과2022-03-107608002022-02-03760802022-02-030현대2022-02-142022-01-17카드수납2022011576080
8승용차 다목적형 중형2021-07-012022-06-303650부과2022-03-107608002022-02-03760802022-02-030롯데2022-02-142022-01-17카드수납2022011676080
9승용차 다목적형 대형2021-07-012022-06-303650부과2022-03-105780002022-02-03578002022-02-030외환2022-02-142022-01-17카드수납2022011657800
차종적용기간 시작일적용기간 종료일적용일수고지차수고지구분과세년도고지금액가산금납기내일자납기후금액납기후일자기존체납금은행명이체일자회계일자수납방법수납여부수납일자수납금액
430승용차 다목적형 중형2021-01-012021-06-301810체납2021-09-104158012402022-12-05428202023-01-0242820신한2023-01-132022-12-20카드수납2022122042820
431화물차 일반형 소형2022-01-012022-06-301810체납2022-09-105639016902022-12-05580802023-01-0258080신한2023-01-132022-12-20카드수납2022122058080
432승합차 일반형 중형2022-03-112022-03-28180체납2022-09-1053301502022-12-0554802023-01-025480신한2023-01-132022-12-20카드수납202212205480
433승용차 다목적형 중형2022-01-012022-06-301810체납2022-09-104295012802022-12-05442302023-01-0244230현대2023-01-132022-12-20카드수납2022122044230
434승용차 다목적형 대형2017-01-012017-06-301810체납2017-09-105108015302022-12-05526102023-01-0294420신한2023-01-132022-12-21카드수납2022122152610
435승용차 다목적형 대형2017-07-012017-11-271500체납2018-03-104060012102022-12-05418102023-01-0294420신한2023-01-132022-12-21카드수납2022122141810
436승용차 다목적형 대형2022-01-012022-06-301810체납2022-09-103619010802022-12-05372702023-01-0237270비씨2023-01-132022-12-26카드수납2022122637270
437승용차 다목적형 대형2022-01-012022-06-301810체납2022-09-107517022502022-12-05774202023-01-0277420국민2023-01-132022-12-28카드수납2022122877420
438승용차 다목적형 대형2022-01-012022-06-301810체납2022-09-107517022502022-12-05774202023-01-0277420롯데2023-01-132022-12-28카드수납2022122877420
439승용차 다목적형 중형2022-01-012022-06-301810체납2022-09-104295012802022-12-05442302023-01-0244230롯데2023-01-132022-12-28카드수납2022122844230

Duplicate rows

Most frequently occurring

차종적용기간 시작일적용기간 종료일적용일수고지차수고지구분과세년도고지금액가산금납기내일자납기후금액납기후일자기존체납금은행명이체일자회계일자수납방법수납여부수납일자수납금액# duplicates
7승용차 다목적형 중형2022-01-012022-06-301810부과2022-09-104295012802022-09-30442302022-10-310삼성2022-10-132022-09-29카드수납20220929429503
0승용차 다목적형 대형2022-01-012022-06-301810부과2022-09-105369016102022-09-30553002022-10-310현대2022-10-132022-09-26카드수납20220925536902
1승용차 다목적형 중형2021-07-012021-12-311840부과2022-03-104158012402022-03-31428202022-04-300국민2022-04-132022-03-18카드수납20220318415802
2승용차 다목적형 중형2021-07-012021-12-311840부과2022-03-104158012402022-03-31428202022-04-300국민2022-04-132022-03-31카드수납20220331415802
3승용차 다목적형 중형2021-07-012022-06-303650부과2022-03-107608002022-02-03760802022-02-030비씨2022-02-142022-02-03카드수납20220203760802
4승용차 다목적형 중형2022-01-012022-06-301810부과2022-09-104295012802022-09-30442302022-10-310국민2022-10-132022-09-26카드수납20220924429502
5승용차 다목적형 중형2022-01-012022-06-301810부과2022-09-104295012802022-09-30442302022-10-310롯데2022-10-132022-09-27카드수납20220927429502
6승용차 다목적형 중형2022-01-012022-06-301810부과2022-09-104295012802022-09-30442302022-10-310비씨2022-10-132022-09-23카드수납20220923429502
8승용차 다목적형 중형2022-01-012022-06-301810부과2022-09-104295012802022-10-31442302022-11-300외환2022-10-132022-09-30카드수납20220930429502
9승용차 다목적형 중형2022-01-012022-06-301810체납2022-09-104295012802022-10-31442302022-11-3044230신한2022-11-142022-10-12카드수납20221012442302