Overview

Dataset statistics

Number of variables14
Number of observations161
Missing cells20
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.3 KiB
Average record size in memory122.8 B

Variable types

Categorical3
Text1
Numeric10

Dataset

Description지역별 전기 자동차 등록 및 보조금 신청 현황: 해당 데이터는 전국 전기차 구매보조금 지급현황(공고대수, 접수대수, 출고대수 등) 관련 정보입니다. ## HTML 미리보기 [![미리보기](http://curate.gimi9.com/linkview/www-data-go-kr-data-filedata-15123034?url=javascript%3Avoid%280%29&version=d7)](https://www.data.go.kr/data/15123034/fileData.do)
Author한국환경공단
URLhttps://www.data.go.kr/data/15123034/fileData.do

Alerts

시도 is highly overall correlated with 2022년 공고대수(전체) and 10 other fieldsHigh correlation
차종 is highly overall correlated with 2022년 공고대수(전체) and 11 other fieldsHigh correlation
접수방법 is highly overall correlated with 차종High correlation
2022년 공고대수(전체) is highly overall correlated with 2022년 공고대수(우선순위) and 10 other fieldsHigh correlation
2022년 공고대수(우선순위) is highly overall correlated with 2022년 공고대수(전체) and 10 other fieldsHigh correlation
2022년 공고대수(법인_기관) is highly overall correlated with 2022년 공고대수(전체) and 9 other fieldsHigh correlation
2022년 공고대수(택시) is highly overall correlated with 2022년 공고대수(전체) and 9 other fieldsHigh correlation
2022년 공고대수(일반) is highly overall correlated with 2022년 공고대수(전체) and 8 other fieldsHigh correlation
2022년 접수대수(우선순위) is highly overall correlated with 2022년 공고대수(전체) and 10 other fieldsHigh correlation
2022년 접수대수(법인_기관) is highly overall correlated with 2022년 공고대수(전체) and 10 other fieldsHigh correlation
2022년 접수대수(택시) is highly overall correlated with 2022년 공고대수(전체) and 10 other fieldsHigh correlation
2022년 접수대수(일반) is highly overall correlated with 2022년 공고대수(전체) and 10 other fieldsHigh correlation
2022년 출고대수 is highly overall correlated with 2022년 공고대수(전체) and 10 other fieldsHigh correlation
차종 is highly imbalanced (90.4%)Imbalance
접수방법 is highly imbalanced (76.7%)Imbalance
2022년 공고대수(전체) has 2 (1.2%) missing valuesMissing
2022년 공고대수(우선순위) has 2 (1.2%) missing valuesMissing
2022년 공고대수(법인_기관) has 2 (1.2%) missing valuesMissing
2022년 공고대수(택시) has 2 (1.2%) missing valuesMissing
2022년 공고대수(일반) has 2 (1.2%) missing valuesMissing
2022년 접수대수(우선순위) has 2 (1.2%) missing valuesMissing
2022년 접수대수(법인_기관) has 2 (1.2%) missing valuesMissing
2022년 접수대수(택시) has 2 (1.2%) missing valuesMissing
2022년 접수대수(일반) has 2 (1.2%) missing valuesMissing
2022년 출고대수 has 2 (1.2%) missing valuesMissing
2022년 공고대수(우선순위) has 27 (16.8%) zerosZeros
2022년 공고대수(법인_기관) has 42 (26.1%) zerosZeros
2022년 공고대수(택시) has 44 (27.3%) zerosZeros
2022년 접수대수(우선순위) has 14 (8.7%) zerosZeros
2022년 접수대수(법인_기관) has 3 (1.9%) zerosZeros
2022년 접수대수(택시) has 10 (6.2%) zerosZeros

Reproduction

Analysis started2023-12-12 10:28:49.953117
Analysis finished2023-12-12 10:29:01.248617
Duration11.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경기
31 
경북
23 
전남
22 
강원
18 
경남
18 
Other values (12)
49 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique9 ?
Unique (%)5.6%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주

Common Values

ValueCountFrequency (%)
경기 31
19.3%
경북 23
14.3%
전남 22
13.7%
강원 18
11.2%
경남 18
11.2%
충남 15
9.3%
전북 14
8.7%
충북 11
 
6.8%
대전 1
 
0.6%
대구 1
 
0.6%
Other values (7) 7
 
4.3%

Length

2023-12-12T19:29:01.316953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 31
19.3%
경북 23
14.3%
전남 22
13.7%
강원 18
11.2%
경남 18
11.2%
충남 15
9.3%
전북 14
8.7%
충북 11
 
6.8%
서울 1
 
0.6%
부산 1
 
0.6%
Other values (7) 7
 
4.3%
Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T19:29:01.631332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1552795
Min length3

Characters and Unicode

Total characters508
Distinct characters119
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)98.8%

Sample

1st row서울특별시
2nd row부산광역시
3rd row대구광역시
4th row인천광역시
5th row광주광역시
ValueCountFrequency (%)
고성군 2
 
1.2%
서울특별시 1
 
0.6%
강진군 1
 
0.6%
예산군 1
 
0.6%
곡성군 1
 
0.6%
구례군 1
 
0.6%
고흥군 1
 
0.6%
보성군 1
 
0.6%
화순군 1
 
0.6%
장흥군 1
 
0.6%
Other values (150) 150
93.2%
2023-12-12T19:29:02.120258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
 
16.5%
80
 
15.7%
21
 
4.1%
20
 
3.9%
16
 
3.1%
15
 
3.0%
13
 
2.6%
11
 
2.2%
11
 
2.2%
9
 
1.8%
Other values (109) 228
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 508
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
84
 
16.5%
80
 
15.7%
21
 
4.1%
20
 
3.9%
16
 
3.1%
15
 
3.0%
13
 
2.6%
11
 
2.2%
11
 
2.2%
9
 
1.8%
Other values (109) 228
44.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 508
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
84
 
16.5%
80
 
15.7%
21
 
4.1%
20
 
3.9%
16
 
3.1%
15
 
3.0%
13
 
2.6%
11
 
2.2%
11
 
2.2%
9
 
1.8%
Other values (109) 228
44.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 508
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
84
 
16.5%
80
 
15.7%
21
 
4.1%
20
 
3.9%
16
 
3.1%
15
 
3.0%
13
 
2.6%
11
 
2.2%
11
 
2.2%
9
 
1.8%
Other values (109) 228
44.9%

차종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
전기승용
159 
<NA>
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전기승용 159
98.8%
<NA> 2
 
1.2%

Length

2023-12-12T19:29:02.274155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:29:02.393779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전기승용 159
98.8%
na 2
 
1.2%

접수방법
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
*일반: 출고등록순*우선: 출고등록순
144 
*일반: 접수순*우선: 접수순
 
10
<NA>
 
2
*일반: 출고등록순*우선: 접수순
 
1
*일반: 출고등록순(보조금 지원가능 확인요청 제출순)*우선: 출고등록순(보조금 지원가능 확인요청 제출순)
 
1
Other values (3)
 
3

Length

Max length58
Median length20
Mean length19.993789
Min length4

Unique

Unique5 ?
Unique (%)3.1%

Sample

1st row*일반: 출고등록순*우선: 출고등록순
2nd row*일반: 출고등록순*우선: 출고등록순
3rd row*일반: 출고등록순*우선: 출고등록순
4th row*일반: 출고등록순*우선: 출고등록순
5th row*일반: 출고등록순*우선: 출고등록순

Common Values

ValueCountFrequency (%)
*일반: 출고등록순*우선: 출고등록순 144
89.4%
*일반: 접수순*우선: 접수순 10
 
6.2%
<NA> 2
 
1.2%
*일반: 출고등록순*우선: 접수순 1
 
0.6%
*일반: 출고등록순(보조금 지원가능 확인요청 제출순)*우선: 출고등록순(보조금 지원가능 확인요청 제출순) 1
 
0.6%
*일반: 보조금 지원확인요청서 순*우선: 보조금 지원확인요청서 순 1
 
0.6%
*일반: 갯벌세계유산 보존 등을 위해 도서지역 우선 배정*우선: - 1
 
0.6%
*일반: 접수+출고순*우선: 접수+출고순 1
 
0.6%

Length

2023-12-12T19:29:02.516557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:29:02.661503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 159
32.1%
출고등록순*우선 145
29.3%
출고등록순 144
29.1%
접수순 11
 
2.2%
접수순*우선 10
 
2.0%
확인요청 2
 
0.4%
보조금 2
 
0.4%
지원확인요청서 2
 
0.4%
지원가능 2
 
0.4%
출고등록순(보조금 2
 
0.4%
Other values (15) 16
 
3.2%

2022년 공고대수(전체)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct128
Distinct (%)80.5%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean684.4717
Minimum0
Maximum16300
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:02.833014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.9
Q178
median157
Q3533.5
95-th percentile2705.6
Maximum16300
Range16300
Interquartile range (IQR)455.5

Descriptive statistics

Standard deviation1771.6167
Coefficient of variation (CV)2.5882979
Kurtosis43.567095
Mean684.4717
Median Absolute Deviation (MAD)101
Skewness5.9577684
Sum108831
Variance3138625.6
MonotonicityNot monotonic
2023-12-12T19:29:02.997484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 4
 
2.5%
50 4
 
2.5%
125 3
 
1.9%
78 3
 
1.9%
53 3
 
1.9%
170 2
 
1.2%
90 2
 
1.2%
106 2
 
1.2%
58 2
 
1.2%
93 2
 
1.2%
Other values (118) 132
82.0%
ValueCountFrequency (%)
0 1
0.6%
8 1
0.6%
17 1
0.6%
28 1
0.6%
30 1
0.6%
37 1
0.6%
40 1
0.6%
42 1
0.6%
43 1
0.6%
45 1
0.6%
ValueCountFrequency (%)
16300 1
0.6%
10030 1
0.6%
6410 1
0.6%
5926 1
0.6%
5648 1
0.6%
4500 1
0.6%
3320 1
0.6%
2900 1
0.6%
2684 1
0.6%
2594 1
0.6%

2022년 공고대수(우선순위)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct63
Distinct (%)39.6%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean55.031447
Minimum0
Maximum1650
Zeros27
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:03.182933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median11
Q342
95-th percentile216.5
Maximum1650
Range1650
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation168.92004
Coefficient of variation (CV)3.0695184
Kurtosis56.840483
Mean55.031447
Median Absolute Deviation (MAD)11
Skewness6.9328962
Sum8750
Variance28533.98
MonotonicityNot monotonic
2023-12-12T19:29:03.346290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
16.8%
5 11
 
6.8%
13 7
 
4.3%
6 7
 
4.3%
11 6
 
3.7%
9 5
 
3.1%
7 5
 
3.1%
4 5
 
3.1%
10 4
 
2.5%
8 4
 
2.5%
Other values (53) 78
48.4%
ValueCountFrequency (%)
0 27
16.8%
1 2
 
1.2%
2 3
 
1.9%
3 3
 
1.9%
4 5
 
3.1%
5 11
6.8%
6 7
 
4.3%
7 5
 
3.1%
8 4
 
2.5%
9 5
 
3.1%
ValueCountFrequency (%)
1650 1
0.6%
1005 1
0.6%
530 1
0.6%
500 1
0.6%
485 1
0.6%
300 1
0.6%
269 1
0.6%
230 1
0.6%
215 1
0.6%
138 1
0.6%

2022년 공고대수(법인_기관)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct83
Distinct (%)52.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean167.86792
Minimum0
Maximum2955
Zeros42
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:03.526591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q381.5
95-th percentile1036.6
Maximum2955
Range2955
Interquartile range (IQR)81.5

Descriptive statistics

Standard deviation463.17128
Coefficient of variation (CV)2.759141
Kurtosis16.881998
Mean167.86792
Median Absolute Deviation (MAD)17
Skewness4.0484475
Sum26691
Variance214527.63
MonotonicityNot monotonic
2023-12-12T19:29:03.689900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
26.1%
15 6
 
3.7%
10 4
 
2.5%
17 4
 
2.5%
6 3
 
1.9%
30 3
 
1.9%
16 3
 
1.9%
1 3
 
1.9%
5 2
 
1.2%
32 2
 
1.2%
Other values (73) 87
54.0%
ValueCountFrequency (%)
0 42
26.1%
1 3
 
1.9%
2 2
 
1.2%
3 1
 
0.6%
4 2
 
1.2%
5 2
 
1.2%
6 3
 
1.9%
7 1
 
0.6%
8 2
 
1.2%
9 2
 
1.2%
ValueCountFrequency (%)
2955 1
0.6%
2500 1
0.6%
2125 1
0.6%
2044 1
0.6%
2000 1
0.6%
1622 1
0.6%
1587 1
0.6%
1366 1
0.6%
1000 1
0.6%
806 1
0.6%

2022년 공고대수(택시)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct59
Distinct (%)37.1%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean71.761006
Minimum0
Maximum3000
Zeros44
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:03.865170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q342.5
95-th percentile269.5
Maximum3000
Range3000
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation276.97309
Coefficient of variation (CV)3.8596601
Kurtosis81.343265
Mean71.761006
Median Absolute Deviation (MAD)7
Skewness8.2803977
Sum11410
Variance76714.094
MonotonicityNot monotonic
2023-12-12T19:29:04.034474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
27.3%
5 9
 
5.6%
10 7
 
4.3%
7 7
 
4.3%
3 6
 
3.7%
11 5
 
3.1%
6 5
 
3.1%
4 5
 
3.1%
8 4
 
2.5%
45 3
 
1.9%
Other values (49) 64
39.8%
ValueCountFrequency (%)
0 44
27.3%
1 1
 
0.6%
2 3
 
1.9%
3 6
 
3.7%
4 5
 
3.1%
5 9
 
5.6%
6 5
 
3.1%
7 7
 
4.3%
8 4
 
2.5%
9 2
 
1.2%
ValueCountFrequency (%)
3000 1
0.6%
1033 1
0.6%
985 1
0.6%
860 1
0.6%
530 1
0.6%
500 1
0.6%
400 1
0.6%
274 1
0.6%
269 1
0.6%
236 1
0.6%

2022년 공고대수(일반)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct128
Distinct (%)80.5%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean389.81132
Minimum0
Maximum9150
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:04.186885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q151.5
median102
Q3369.5
95-th percentile1356
Maximum9150
Range9150
Interquartile range (IQR)318

Descriptive statistics

Standard deviation940.24936
Coefficient of variation (CV)2.4120627
Kurtosis51.68515
Mean389.81132
Median Absolute Deviation (MAD)68
Skewness6.4082698
Sum61980
Variance884068.85
MonotonicityNot monotonic
2023-12-12T19:29:04.355624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 4
 
2.5%
25 4
 
2.5%
40 3
 
1.9%
54 3
 
1.9%
222 3
 
1.9%
50 3
 
1.9%
52 2
 
1.2%
55 2
 
1.2%
88 2
 
1.2%
33 2
 
1.2%
Other values (118) 131
81.4%
ValueCountFrequency (%)
0 1
 
0.6%
8 1
 
0.6%
15 1
 
0.6%
16 1
 
0.6%
17 1
 
0.6%
19 1
 
0.6%
22 1
 
0.6%
25 4
2.5%
26 2
1.2%
30 2
1.2%
ValueCountFrequency (%)
9150 1
0.6%
5085 1
0.6%
3001 1
0.6%
2940 1
0.6%
2743 1
0.6%
1864 1
0.6%
1630 1
0.6%
1500 1
0.6%
1340 1
0.6%
1339 1
0.6%

2022년 접수대수(우선순위)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct53
Distinct (%)33.3%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean22.937107
Minimum0
Maximum511
Zeros14
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:04.518466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q322
95-th percentile91.4
Maximum511
Range511
Interquartile range (IQR)19

Descriptive statistics

Standard deviation50.191997
Coefficient of variation (CV)2.1882444
Kurtosis57.375221
Mean22.937107
Median Absolute Deviation (MAD)5
Skewness6.4978441
Sum3647
Variance2519.2365
MonotonicityNot monotonic
2023-12-12T19:29:04.695934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 15
 
9.3%
1 14
 
8.7%
0 14
 
8.7%
4 11
 
6.8%
5 11
 
6.8%
2 9
 
5.6%
8 8
 
5.0%
6 6
 
3.7%
9 6
 
3.7%
10 5
 
3.1%
Other values (43) 60
37.3%
ValueCountFrequency (%)
0 14
8.7%
1 14
8.7%
2 9
5.6%
3 15
9.3%
4 11
6.8%
5 11
6.8%
6 6
 
3.7%
7 1
 
0.6%
8 8
5.0%
9 6
 
3.7%
ValueCountFrequency (%)
511 1
0.6%
182 1
0.6%
177 1
0.6%
127 1
0.6%
110 1
0.6%
102 1
0.6%
98 1
0.6%
95 1
0.6%
91 1
0.6%
90 1
0.6%

2022년 접수대수(법인_기관)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct81
Distinct (%)50.9%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean210.42767
Minimum0
Maximum6216
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:04.856072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median12
Q369.5
95-th percentile1051.7
Maximum6216
Range6216
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation708.45062
Coefficient of variation (CV)3.3667179
Kurtosis39.051775
Mean210.42767
Median Absolute Deviation (MAD)10
Skewness5.75563
Sum33458
Variance501902.28
MonotonicityNot monotonic
2023-12-12T19:29:05.029781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 12
 
7.5%
3 10
 
6.2%
4 10
 
6.2%
8 9
 
5.6%
6 9
 
5.6%
1 8
 
5.0%
5 5
 
3.1%
12 4
 
2.5%
9 4
 
2.5%
32 4
 
2.5%
Other values (71) 84
52.2%
ValueCountFrequency (%)
0 3
 
1.9%
1 8
5.0%
2 12
7.5%
3 10
6.2%
4 10
6.2%
5 5
3.1%
6 9
5.6%
7 3
 
1.9%
8 9
5.6%
9 4
 
2.5%
ValueCountFrequency (%)
6216 1
0.6%
3929 1
0.6%
3095 1
0.6%
2302 1
0.6%
2192 1
0.6%
2039 1
0.6%
1634 1
0.6%
1364 1
0.6%
1017 1
0.6%
603 1
0.6%

2022년 접수대수(택시)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct75
Distinct (%)47.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean97.440252
Minimum0
Maximum3323
Zeros10
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:05.194204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11
Q354.5
95-th percentile406.3
Maximum3323
Range3323
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation325.63211
Coefficient of variation (CV)3.3418645
Kurtosis63.648854
Mean97.440252
Median Absolute Deviation (MAD)10
Skewness7.2145883
Sum15493
Variance106036.27
MonotonicityNot monotonic
2023-12-12T19:29:05.367645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
9.3%
2 13
 
8.1%
0 10
 
6.2%
7 10
 
6.2%
4 9
 
5.6%
10 6
 
3.7%
11 6
 
3.7%
6 6
 
3.7%
3 4
 
2.5%
5 3
 
1.9%
Other values (65) 77
47.8%
ValueCountFrequency (%)
0 10
6.2%
1 15
9.3%
2 13
8.1%
3 4
 
2.5%
4 9
5.6%
5 3
 
1.9%
6 6
 
3.7%
7 10
6.2%
9 1
 
0.6%
10 6
 
3.7%
ValueCountFrequency (%)
3323 1
0.6%
1343 1
0.6%
1216 1
0.6%
1151 1
0.6%
951 1
0.6%
582 1
0.6%
432 1
0.6%
427 1
0.6%
404 1
0.6%
351 1
0.6%

2022년 접수대수(일반)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct135
Distinct (%)84.9%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean399.87421
Minimum21
Maximum8277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:05.521426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile30.9
Q162
median125
Q3433.5
95-th percentile1503.1
Maximum8277
Range8256
Interquartile range (IQR)371.5

Descriptive statistics

Standard deviation857.65011
Coefficient of variation (CV)2.1447997
Kurtosis47.051941
Mean399.87421
Median Absolute Deviation (MAD)77
Skewness5.9727403
Sum63580
Variance735563.71
MonotonicityNot monotonic
2023-12-12T19:29:05.672423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 4
 
2.5%
58 3
 
1.9%
56 3
 
1.9%
54 2
 
1.2%
38 2
 
1.2%
64 2
 
1.2%
50 2
 
1.2%
168 2
 
1.2%
135 2
 
1.2%
68 2
 
1.2%
Other values (125) 135
83.9%
ValueCountFrequency (%)
21 1
0.6%
24 1
0.6%
25 1
0.6%
26 1
0.6%
27 2
1.2%
29 1
0.6%
30 1
0.6%
31 1
0.6%
32 1
0.6%
33 1
0.6%
ValueCountFrequency (%)
8277 1
0.6%
3771 1
0.6%
2976 1
0.6%
2968 1
0.6%
2817 1
0.6%
2169 1
0.6%
1872 1
0.6%
1738 1
0.6%
1477 1
0.6%
1458 1
0.6%

2022년 출고대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct141
Distinct (%)88.7%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean686.27673
Minimum20
Maximum15217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T19:29:05.834036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile37.9
Q174
median144
Q3580.5
95-th percentile2696.3
Maximum15217
Range15197
Interquartile range (IQR)506.5

Descriptive statistics

Standard deviation1739.9413
Coefficient of variation (CV)2.5353349
Kurtosis36.939144
Mean686.27673
Median Absolute Deviation (MAD)95
Skewness5.5229782
Sum109118
Variance3027395.9
MonotonicityNot monotonic
2023-12-12T19:29:06.295804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 3
 
1.9%
92 3
 
1.9%
70 3
 
1.9%
74 3
 
1.9%
57 3
 
1.9%
65 2
 
1.2%
178 2
 
1.2%
38 2
 
1.2%
105 2
 
1.2%
49 2
 
1.2%
Other values (131) 134
83.2%
ValueCountFrequency (%)
20 1
0.6%
26 1
0.6%
27 1
0.6%
28 1
0.6%
30 1
0.6%
32 1
0.6%
34 1
0.6%
37 1
0.6%
38 2
1.2%
39 1
0.6%
ValueCountFrequency (%)
15217 1
0.6%
10030 1
0.6%
6694 1
0.6%
6500 1
0.6%
5724 1
0.6%
4958 1
0.6%
3365 1
0.6%
2915 1
0.6%
2672 1
0.6%
2595 1
0.6%

Interactions

2023-12-12T19:28:59.425504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:50.578938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.631460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.601527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.575871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.822665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.623759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.508123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.490681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.413232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.535581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:50.695378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.726647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.694907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.677129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.925352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.709382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.609686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.592673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.503945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.624928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:50.806428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.813584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.784330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.783297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.009000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.790486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.703975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.677948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.603168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.711403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:50.935033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.904172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.898332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.877145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.089102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.876301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.792416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.763536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.705017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.795936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.050354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.983989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.012965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.004198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.172534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.957062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.907468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.875175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.799851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.888260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.148942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.072351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.103927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.122772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.239240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.036761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.021154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.988276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.877026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.973907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.255089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.175185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.199684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.205970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.314052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.124561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.110313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.076384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.967352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:29:00.069619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.351433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.293677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.298933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.298816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.387984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.223578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.208136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.162997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.051675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:29:00.165773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.459100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.396102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.385882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.393582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.469875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.313996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.310562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.242856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.196979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:29:00.269934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:51.539483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:52.484244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:53.473303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:54.480801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:55.543890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:56.400356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:57.394124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:58.322303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:28:59.306124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:29:06.411764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도접수방법2022년 공고대수(전체)2022년 공고대수(우선순위)2022년 공고대수(법인_기관)2022년 공고대수(택시)2022년 공고대수(일반)2022년 접수대수(우선순위)2022년 접수대수(법인_기관)2022년 접수대수(택시)2022년 접수대수(일반)2022년 출고대수
시도1.0000.6340.9570.9780.9450.9830.9600.8540.9440.9820.9620.947
접수방법0.6341.0000.4070.4050.3280.3290.0000.0000.3700.0830.2900.553
2022년 공고대수(전체)0.9570.4071.0000.9790.9440.8450.9870.7190.9130.9810.9910.961
2022년 공고대수(우선순위)0.9780.4050.9791.0000.9630.9300.9750.6870.9400.9830.9760.932
2022년 공고대수(법인_기관)0.9450.3280.9440.9631.0000.9970.9280.9550.9830.9500.9320.934
2022년 공고대수(택시)0.9830.3290.8450.9300.9971.0000.8670.9150.9430.8970.8370.904
2022년 공고대수(일반)0.9600.0000.9870.9750.9280.8671.0000.7410.8910.9830.9880.906
2022년 접수대수(우선순위)0.8540.0000.7190.6870.9550.9150.7411.0000.7820.7730.7640.756
2022년 접수대수(법인_기관)0.9440.3700.9130.9400.9830.9430.8910.7821.0000.9010.8980.985
2022년 접수대수(택시)0.9820.0830.9810.9830.9500.8970.9830.7730.9011.0000.9840.928
2022년 접수대수(일반)0.9620.2900.9910.9760.9320.8370.9880.7640.8980.9841.0000.923
2022년 출고대수0.9470.5530.9610.9320.9340.9040.9060.7560.9850.9280.9231.000
2023-12-12T19:29:06.569918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도차종접수방법
시도1.0001.0000.337
차종1.0001.0001.000
접수방법0.3371.0001.000
2023-12-12T19:29:06.667854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2022년 공고대수(전체)2022년 공고대수(우선순위)2022년 공고대수(법인_기관)2022년 공고대수(택시)2022년 공고대수(일반)2022년 접수대수(우선순위)2022년 접수대수(법인_기관)2022년 접수대수(택시)2022년 접수대수(일반)2022년 출고대수시도차종접수방법
2022년 공고대수(전체)1.0000.7240.5940.6290.9600.7860.8550.8050.9240.9410.8151.0000.255
2022년 공고대수(우선순위)0.7241.0000.8040.8090.6090.6380.6440.6250.6850.6860.8841.0000.254
2022년 공고대수(법인_기관)0.5940.8041.0000.9040.4170.5380.5490.5500.5400.5630.7501.0000.170
2022년 공고대수(택시)0.6290.8090.9041.0000.4740.5880.5540.6490.5550.5840.9181.0000.215
2022년 공고대수(일반)0.9600.6090.4170.4741.0000.7520.8190.7450.8940.9000.8251.0000.000
2022년 접수대수(우선순위)0.7860.6380.5380.5880.7521.0000.8020.7270.8570.8440.6291.0000.000
2022년 접수대수(법인_기관)0.8550.6440.5490.5540.8190.8021.0000.7600.8800.9110.7821.0000.136
2022년 접수대수(택시)0.8050.6250.5500.6490.7450.7270.7601.0000.8070.8400.8991.0000.046
2022년 접수대수(일반)0.9240.6850.5400.5550.8940.8570.8800.8071.0000.9820.8311.0000.176
2022년 출고대수0.9410.6860.5630.5840.9000.8440.9110.8400.9821.0000.7901.0000.220
시도0.8150.8840.7500.9180.8250.6290.7820.8990.8310.7901.0001.0000.337
차종1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
접수방법0.2550.2540.1700.2150.0000.0000.1360.0460.1760.2200.3371.0001.000

Missing values

2023-12-12T19:29:00.720803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:29:00.898488image/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.
2023-12-12T19:29:01.079432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도지역구분차종접수방법2022년 공고대수(전체)2022년 공고대수(우선순위)2022년 공고대수(법인_기관)2022년 공고대수(택시)2022년 공고대수(일반)2022년 접수대수(우선순위)2022년 접수대수(법인_기관)2022년 접수대수(택시)2022년 접수대수(일반)2022년 출고대수
0서울서울특별시전기승용*일반: 출고등록순*우선: 출고등록순16300165025003000915051139293323827715217
1부산부산광역시전기승용*일반: 출고등록순*우선: 출고등록순6410485212586029401023095115129686694
2대구대구광역시전기승용*일반: 출고등록순*우선: 출고등록순59261062044103327431102302121629766500
3인천인천광역시전기승용*일반: 출고등록순*우선: 출고등록순100301005295598550859162161343377110030
4광주광주광역시전기승용*일반: 출고등록순*우선: 출고등록순268426980626913405250342717382672
5대전대전광역시전기승용*일반: 출고등록순*우선: 출고등록순56485301587530300195203995128175724
6울산울산광역시전기승용*일반: 출고등록순*우선: 출고등록순145196230236889982352368971449
7세종세종특별자치시전기승용*일반: 출고등록순*우선: 출고등록순74947141475147111138646784
8경기수원시전기승용*일반: 출고등록순*우선: 출고등록순186400018647222429012991885
9경기성남시전기승용*일반: 출고등록순*우선: 출고등록순3320851622274133988163432814583365
시도지역구분차종접수방법2022년 공고대수(전체)2022년 공고대수(우선순위)2022년 공고대수(법인_기관)2022년 공고대수(택시)2022년 공고대수(일반)2022년 접수대수(우선순위)2022년 접수대수(법인_기관)2022년 접수대수(택시)2022년 접수대수(일반)2022년 출고대수
151경남함안군전기승용*일반: 출고등록순*우선: 출고등록순144480753480753144
152경남창녕군전기승용*일반: 출고등록순*우선: 출고등록순106161666821128499
153경남고성군전기승용*일반: 출고등록순*우선: 출고등록순849179495276175
154경남남해군전기승용*일반: 출고등록순*우선: 출고등록순8571585510346776
155경남하동군전기승용*일반: 출고등록순*우선: 출고등록순705910461635765
156경남산청군전기승용*일반: 출고등록순*우선: 출고등록순777237401207174
157경남함양군전기승용*일반: 출고등록순*우선: 출고등록순60600540115557
158경남거창군전기승용*일반: 출고등록순*우선: 출고등록순931043760638488
159경남합천군전기승용*일반: 출고등록순*우선: 출고등록순123125115427106113
160제주제주특별자치도전기승용*일반: 접수+출고순*우선: 접수+출고순45005002000500150015219258221694958