Overview

Dataset statistics

Number of variables5
Number of observations281
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory42.5 B

Variable types

Categorical3
Text1
Numeric1

Dataset

Description(101906) 전기이륜차 보조금 차량 보급현황 년도별_지역별 현황연: 연도별, 지역별 전기이륜차 보조금 지급현황은 정보공개 청구를 통해 작성양식을 첨부하여 요청부탁드리며, 해당 데이터는 연도별 전기이륜차 보조금 대상 차량 및 차량별 국비(만원)에 대한 데이터 입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15123150/fileData.do

Alerts

국비_만원 is highly overall correlated with 차종High correlation
차종 is highly overall correlated with 국비_만원 and 1 other fieldsHigh correlation
제조사 is highly overall correlated with 차종High correlation

Reproduction

Analysis started2023-12-12 15:11:16.296271
Analysis finished2023-12-12 15:11:16.805776
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2022
128 
2021
95 
2020
58 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 128
45.6%
2021 95
33.8%
2020 58
20.6%

Length

2023-12-13T00:11:16.903220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:11:17.016283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 128
45.6%
2021 95
33.8%
2020 58
20.6%

차종
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
이륜(소형)
115 
이륜(경형)
101 
이륜(대형·기타형)
65 

Length

Max length10
Median length6
Mean length6.9252669
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row이륜(경형)
2nd row이륜(경형)
3rd row이륜(경형)
4th row이륜(경형)
5th row이륜(경형)

Common Values

ValueCountFrequency (%)
이륜(소형) 115
40.9%
이륜(경형) 101
35.9%
이륜(대형·기타형) 65
23.1%

Length

2023-12-13T00:11:17.162255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:11:17.282327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이륜(소형 115
40.9%
이륜(경형 101
35.9%
이륜(대형·기타형 65
23.1%

제조사
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
그린모빌리티
56 
디앤에이모터스
21 
엠비아이
 
15
포도모빌리티
 
15
시엔케이
 
13
Other values (43)
161 

Length

Max length11
Median length9
Mean length5.3274021
Min length2

Unique

Unique8 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
그린모빌리티 56
19.9%
디앤에이모터스 21
 
7.5%
엠비아이 15
 
5.3%
포도모빌리티 15
 
5.3%
시엔케이 13
 
4.6%
와코 12
 
4.3%
케이알모터스 8
 
2.8%
대풍이브이자동차 8
 
2.8%
가브리엘 8
 
2.8%
성지모터스 8
 
2.8%
Other values (38) 117
41.6%

Length

2023-12-13T00:11:17.439819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
그린모빌리티 56
19.9%
디앤에이모터스 21
 
7.5%
엠비아이 15
 
5.3%
포도모빌리티 15
 
5.3%
시엔케이 13
 
4.6%
와코 12
 
4.3%
케이알모터스 8
 
2.8%
대풍이브이자동차 8
 
2.8%
가브리엘 8
 
2.8%
성지모터스 8
 
2.8%
Other values (38) 117
41.6%

모델
Text

Distinct130
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-13T00:11:17.782291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.2740214
Min length2

Characters and Unicode

Total characters2044
Distinct characters93
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)12.5%

Sample

1st rowVALENCIA
2nd rowSEBIA
3rd rowGXT-CITY
4th rowVALENCIA-Ⅱ
5th rowGXT-Ⅱ
ValueCountFrequency (%)
e2 8
 
2.4%
plus 8
 
2.4%
ecooter 8
 
2.4%
duo 7
 
2.1%
gogoro2 6
 
1.8%
e-deliroad 5
 
1.5%
e1s 5
 
1.5%
cargo 4
 
1.2%
pro 4
 
1.2%
플레타 3
 
0.9%
Other values (125) 273
82.5%
2023-12-13T00:11:18.264948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 153
 
7.5%
- 115
 
5.6%
I 113
 
5.5%
O 92
 
4.5%
2 86
 
4.2%
R 68
 
3.3%
T 67
 
3.3%
A 66
 
3.2%
S 59
 
2.9%
D 58
 
2.8%
Other values (83) 1167
57.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1217
59.5%
Decimal Number 282
 
13.8%
Lowercase Letter 151
 
7.4%
Dash Punctuation 115
 
5.6%
Other Letter 102
 
5.0%
Space Separator 50
 
2.4%
Close Punctuation 46
 
2.3%
Open Punctuation 46
 
2.3%
Other Punctuation 19
 
0.9%
Letter Number 16
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 9
 
8.8%
바 7
 
6.9%
크 7
 
6.9%
썬 6
 
5.9%
로 5
 
4.9%
레 3
 
2.9%
타 3
 
2.9%
리 3
 
2.9%
플 3
 
2.9%
주 3
 
2.9%
Other values (23) 53
52.0%
Uppercase Letter
ValueCountFrequency (%)
E 153
 
12.6%
I 113
 
9.3%
O 92
 
7.6%
R 68
 
5.6%
T 67
 
5.5%
A 66
 
5.4%
S 59
 
4.8%
D 58
 
4.8%
N 56
 
4.6%
M 55
 
4.5%
Other values (16) 430
35.3%
Lowercase Letter
ValueCountFrequency (%)
h 22
14.6%
o 19
12.6%
k 19
12.6%
r 15
9.9%
e 15
9.9%
l 10
6.6%
i 9
 
6.0%
a 8
 
5.3%
d 5
 
3.3%
u 5
 
3.3%
Other values (7) 24
15.9%
Decimal Number
ValueCountFrequency (%)
2 86
30.5%
0 48
17.0%
1 42
14.9%
4 23
 
8.2%
6 22
 
7.8%
3 20
 
7.1%
5 15
 
5.3%
9 12
 
4.3%
7 10
 
3.5%
8 4
 
1.4%
Letter Number
ValueCountFrequency (%)
Ⅱ 13
81.2%
Ⅲ 3
 
18.8%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1384
67.7%
Common 558
27.3%
Hangul 102
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 153
 
11.1%
I 113
 
8.2%
O 92
 
6.6%
R 68
 
4.9%
T 67
 
4.8%
A 66
 
4.8%
S 59
 
4.3%
D 58
 
4.2%
N 56
 
4.0%
M 55
 
4.0%
Other values (35) 597
43.1%
Hangul
ValueCountFrequency (%)
이 9
 
8.8%
바 7
 
6.9%
크 7
 
6.9%
썬 6
 
5.9%
로 5
 
4.9%
레 3
 
2.9%
타 3
 
2.9%
리 3
 
2.9%
플 3
 
2.9%
주 3
 
2.9%
Other values (23) 53
52.0%
Common
ValueCountFrequency (%)
- 115
20.6%
2 86
15.4%
50
9.0%
0 48
8.6%
) 46
 
8.2%
( 46
 
8.2%
1 42
 
7.5%
4 23
 
4.1%
6 22
 
3.9%
3 20
 
3.6%
Other values (5) 60
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1926
94.2%
Hangul 102
 
5.0%
Number Forms 16
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 153
 
7.9%
- 115
 
6.0%
I 113
 
5.9%
O 92
 
4.8%
2 86
 
4.5%
R 68
 
3.5%
T 67
 
3.5%
A 66
 
3.4%
S 59
 
3.1%
D 58
 
3.0%
Other values (48) 1049
54.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 13
81.2%
Ⅲ 3
 
18.8%
Hangul
ValueCountFrequency (%)
이 9
 
8.8%
바 7
 
6.9%
크 7
 
6.9%
썬 6
 
5.9%
로 5
 
4.9%
레 3
 
2.9%
타 3
 
2.9%
리 3
 
2.9%
플 3
 
2.9%
주 3
 
2.9%
Other values (23) 53
52.0%

국비_만원
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.85765
Minimum42.5
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-13T00:11:18.416688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.5
5-th percentile52
Q175
median106
Q3130
95-th percentile165
Maximum165
Range122.5
Interquartile range (IQR)55

Descriptive statistics

Standard deviation34.306148
Coefficient of variation (CV)0.32407812
Kurtosis-0.93905728
Mean105.85765
Median Absolute Deviation (MAD)24
Skewness0.021339101
Sum29746
Variance1176.9118
MonotonicityNot monotonic
2023-12-13T00:11:18.619977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.0 28
 
10.0%
165.0 25
 
8.9%
120.0 14
 
5.0%
105.0 10
 
3.6%
60.0 10
 
3.6%
150.0 7
 
2.5%
75.0 6
 
2.1%
132.0 3
 
1.1%
83.5 3
 
1.1%
95.0 3
 
1.1%
Other values (111) 172
61.2%
ValueCountFrequency (%)
42.5 2
0.7%
45.5 1
 
0.4%
46.0 1
 
0.4%
47.0 2
0.7%
47.5 1
 
0.4%
48.0 1
 
0.4%
50.0 1
 
0.4%
50.5 3
1.1%
51.0 1
 
0.4%
52.0 3
1.1%
ValueCountFrequency (%)
165.0 25
8.9%
162.0 1
 
0.4%
160.5 1
 
0.4%
159.5 1
 
0.4%
159.0 2
 
0.7%
158.5 1
 
0.4%
151.5 1
 
0.4%
150.5 1
 
0.4%
150.0 7
 
2.5%
145.5 1
 
0.4%

Interactions

2023-12-13T00:11:16.545156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:11:18.739269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도차종제조사국비_만원
연도1.0000.3420.0000.584
차종0.3421.0000.8580.859
제조사0.0000.8581.0000.532
국비_만원0.5840.8590.5321.000
2023-12-13T00:11:18.839134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차종연도제조사
차종1.0000.1190.570
연도0.1191.0000.000
제조사0.5700.0001.000
2023-12-13T00:11:18.917117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국비_만원연도차종제조사
국비_만원1.0000.4220.7720.194
연도0.4221.0000.1190.000
차종0.7720.1191.0000.570
제조사0.1940.0000.5701.000

Missing values

2023-12-13T00:11:16.668723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:11:16.760754image/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

연도차종제조사모델국비_만원
02022이륜(경형)그린모빌리티VALENCIA47.5
12022이륜(경형)그린모빌리티SEBIA55.0
22022이륜(경형)그린모빌리티GXT-CITY47.0
32022이륜(경형)그린모빌리티VALENCIA-Ⅱ62.0
42022이륜(경형)그린모빌리티GXT-Ⅱ52.0
52022이륜(경형)그린모빌리티SEBIA-Ⅱ70.0
62022이륜(경형)에코카LUCE42.5
72022이륜(경형)와코2K2(E5)51.0
82022이륜(경형)와코2K2(E6)50.5
92022이륜(경형)비엠모터스코알라45.5
연도차종제조사모델국비_만원
2712020이륜(대형·기타형)그린모빌리티DELI-D4165.0
2722020이륜(대형·기타형)그린모빌리티DELI-D5165.0
2732020이륜(대형·기타형)그린모빌리티DELI-Q5165.0
2742020이륜(대형·기타형)그린모빌리티JANGBORI150.5
2752020이륜(대형·기타형)대풍이브이자동차Echo-ev2159.5
2762020이륜(대형·기타형)대풍이브이자동차NICE EV3L165.0
2772020이륜(대형·기타형)스마트솔루션즈R3G165.0
2782020이륜(대형·기타형)시엔케이TRIO1165.0
2792020이륜(대형·기타형)성지모터스WIND-K2165.0
2802020이륜(대형·기타형)리스타트S-V28(2.96kWh)165.0