Overview

Dataset statistics

Number of variables8
Number of observations72
Missing cells36
Missing cells (%)6.2%
Duplicate rows1
Duplicate rows (%)1.4%
Total size in memory4.9 KiB
Average record size in memory69.8 B

Variable types

Categorical2
Text1
Numeric4
DateTime1

Dataset

Description제주특별자치도 내 전기이륜차 보급대상 차종과 관련한 데이터로 구분, 제조수입사명, 차종, 가중연비(km/kWh), 배터리용량(kWh), 보조금지원금액 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15048679/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (1.4%) duplicate rowsDuplicates
가중연비(km_kWh) is highly overall correlated with 배터리용량(kWh) and 2 other fieldsHigh correlation
배터리용량(kWh) is highly overall correlated with 가중연비(km_kWh) and 1 other fieldsHigh correlation
보조금지원금액(만원) is highly overall correlated with 가중연비(km_kWh) and 2 other fieldsHigh correlation
구분 is highly overall correlated with 가중연비(km_kWh) and 1 other fieldsHigh correlation
차종 has 6 (8.3%) missing valuesMissing
가중연비(km_kWh) has 6 (8.3%) missing valuesMissing
배터리용량(kWh) has 6 (8.3%) missing valuesMissing
가중등판 has 6 (8.3%) missing valuesMissing
보조금지원금액(만원) has 6 (8.3%) missing valuesMissing
데이터기준일자 has 6 (8.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 05:30:10.745909
Analysis finished2023-12-12 05:30:13.274163
Duration2.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size708.0 B
소형
45 
경형
11 
기타형
10 
<NA>
6 

Length

Max length4
Median length2
Mean length2.3055556
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소형 45
62.5%
경형 11
 
15.3%
기타형 10
 
13.9%
<NA> 6
 
8.3%

Length

2023-12-12T14:30:13.362746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:30:13.523341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소형 45
62.5%
경형 11
 
15.3%
기타형 10
 
13.9%
na 6
 
8.3%

제조수입사
Categorical

Distinct28
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size708.0 B
디앤에이모터스
12 
㈜포도모빌리티
8 
<NA>
6 
케이알모터스
 
3
인에이블인터내셔널
 
3
Other values (23)
40 

Length

Max length9
Median length8
Mean length6.0277778
Min length2

Unique

Unique9 ?
Unique (%)12.5%

Sample

1st row디앤에이모터스
2nd row디앤에이모터스
3rd row㈜이누리
4th row인에이블인터내셔널
5th row지에스모터스

Common Values

ValueCountFrequency (%)
디앤에이모터스 12
16.7%
㈜포도모빌리티 8
 
11.1%
<NA> 6
 
8.3%
케이알모터스 3
 
4.2%
인에이블인터내셔널 3
 
4.2%
그린모빌리티 3
 
4.2%
㈜시엔케이 3
 
4.2%
㈜이누리 3
 
4.2%
㈜와코 2
 
2.8%
이쓰리모빌리티 2
 
2.8%
Other values (18) 27
37.5%

Length

2023-12-12T14:30:13.951875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
디앤에이모터스 12
16.4%
㈜포도모빌리티 8
 
11.0%
na 6
 
8.2%
케이알모터스 3
 
4.1%
인에이블인터내셔널 3
 
4.1%
그린모빌리티 3
 
4.1%
㈜시엔케이 3
 
4.1%
㈜이누리 3
 
4.1%
㈜블루샤크코리아 2
 
2.7%
㈜젠스테이션 2
 
2.7%
Other values (19) 28
38.4%

차종
Text

MISSING 

Distinct66
Distinct (%)100.0%
Missing6
Missing (%)8.3%
Memory size708.0 B
2023-12-12T14:30:14.241586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length7.1666667
Min length2

Characters and Unicode

Total characters473
Distinct characters66
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)100.0%

Sample

1st rowEG300
2nd rowEG300N
3rd rowV Basic(DJ1)
4th rowNIU-Npro
5th rowBONO
ValueCountFrequency (%)
plus 3
 
3.6%
e2 3
 
3.6%
gogoro2 2
 
2.4%
zentropy 2
 
2.4%
r1 2
 
2.4%
ecooter 2
 
2.4%
cargo 2
 
2.4%
e1s 2
 
2.4%
e5 1
 
1.2%
mv3 1
 
1.2%
Other values (63) 63
75.9%
2023-12-12T14:30:14.675234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 37
 
7.8%
- 23
 
4.9%
18
 
3.8%
1 17
 
3.6%
( 17
 
3.6%
D 17
 
3.6%
O 17
 
3.6%
) 17
 
3.6%
0 16
 
3.4%
2 16
 
3.4%
Other values (56) 278
58.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 220
46.5%
Lowercase Letter 72
 
15.2%
Decimal Number 64
 
13.5%
Other Letter 41
 
8.7%
Dash Punctuation 23
 
4.9%
Space Separator 18
 
3.8%
Open Punctuation 17
 
3.6%
Close Punctuation 17
 
3.6%
Letter Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 37
16.8%
D 17
 
7.7%
O 17
 
7.7%
M 13
 
5.9%
T 13
 
5.9%
I 12
 
5.5%
S 12
 
5.5%
R 10
 
4.5%
N 10
 
4.5%
A 9
 
4.1%
Other values (14) 70
31.8%
Other Letter
ValueCountFrequency (%)
유 12
29.3%
공 12
29.3%
타 2
 
4.9%
이 2
 
4.9%
레 2
 
4.9%
오 2
 
4.9%
로 2
 
4.9%
드 1
 
2.4%
프 1
 
2.4%
리 1
 
2.4%
Other values (4) 4
 
9.8%
Lowercase Letter
ValueCountFrequency (%)
o 12
16.7%
i 7
9.7%
r 7
9.7%
g 6
8.3%
t 6
8.3%
s 5
6.9%
u 5
6.9%
l 5
6.9%
e 5
6.9%
p 4
 
5.6%
Other values (4) 10
13.9%
Decimal Number
ValueCountFrequency (%)
1 17
26.6%
0 16
25.0%
2 16
25.0%
3 4
 
6.2%
4 4
 
6.2%
7 3
 
4.7%
6 2
 
3.1%
8 1
 
1.6%
5 1
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 293
61.9%
Common 139
29.4%
Hangul 41
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 37
 
12.6%
D 17
 
5.8%
O 17
 
5.8%
M 13
 
4.4%
T 13
 
4.4%
I 12
 
4.1%
o 12
 
4.1%
S 12
 
4.1%
R 10
 
3.4%
N 10
 
3.4%
Other values (29) 140
47.8%
Hangul
ValueCountFrequency (%)
유 12
29.3%
공 12
29.3%
타 2
 
4.9%
이 2
 
4.9%
레 2
 
4.9%
오 2
 
4.9%
로 2
 
4.9%
드 1
 
2.4%
프 1
 
2.4%
리 1
 
2.4%
Other values (4) 4
 
9.8%
Common
ValueCountFrequency (%)
- 23
16.5%
18
12.9%
1 17
12.2%
( 17
12.2%
) 17
12.2%
0 16
11.5%
2 16
11.5%
3 4
 
2.9%
4 4
 
2.9%
7 3
 
2.2%
Other values (3) 4
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431
91.1%
Hangul 41
 
8.7%
Number Forms 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 37
 
8.6%
- 23
 
5.3%
18
 
4.2%
1 17
 
3.9%
( 17
 
3.9%
D 17
 
3.9%
O 17
 
3.9%
) 17
 
3.9%
0 16
 
3.7%
2 16
 
3.7%
Other values (41) 236
54.8%
Hangul
ValueCountFrequency (%)
유 12
29.3%
공 12
29.3%
타 2
 
4.9%
이 2
 
4.9%
레 2
 
4.9%
오 2
 
4.9%
로 2
 
4.9%
드 1
 
2.4%
프 1
 
2.4%
리 1
 
2.4%
Other values (4) 4
 
9.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

가중연비(km_kWh)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)83.3%
Missing6
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean22.129091
Minimum10.32
Maximum30.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T14:30:14.825994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.32
5-th percentile13.08
Q120.2125
median23.28
Q325.25
95-th percentile27.8375
Maximum30.01
Range19.69
Interquartile range (IQR)5.0375

Descriptive statistics

Standard deviation4.5614437
Coefficient of variation (CV)0.20612883
Kurtosis0.39905598
Mean22.129091
Median Absolute Deviation (MAD)2.375
Skewness-0.94610774
Sum1460.52
Variance20.806768
MonotonicityNot monotonic
2023-12-12T14:30:14.975671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.99 2
 
2.8%
24.98 2
 
2.8%
21.76 2
 
2.8%
20.67 2
 
2.8%
22.51 2
 
2.8%
26.09 2
 
2.8%
18.32 2
 
2.8%
23.81 2
 
2.8%
25.34 2
 
2.8%
26.16 2
 
2.8%
Other values (45) 46
63.9%
(Missing) 6
 
8.3%
ValueCountFrequency (%)
10.32 1
1.4%
10.49 1
1.4%
11.51 1
1.4%
12.89 1
1.4%
13.65 1
1.4%
13.85 1
1.4%
14.76 1
1.4%
14.79 1
1.4%
15.48 1
1.4%
15.87 1
1.4%
ValueCountFrequency (%)
30.01 1
1.4%
28.99 1
1.4%
28.62 1
1.4%
28.02 1
1.4%
27.29 1
1.4%
26.92 1
1.4%
26.56 1
1.4%
26.16 2
2.8%
26.09 2
2.8%
26.04 1
1.4%

배터리용량(kWh)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)45.5%
Missing6
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean3.2618182
Minimum1.8
Maximum5.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T14:30:15.136060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.2425
Q12.8575
median2.94
Q33.6
95-th percentile4.88
Maximum5.92
Range4.12
Interquartile range (IQR)0.7425

Descriptive statistics

Standard deviation0.83264344
Coefficient of variation (CV)0.25526973
Kurtosis2.134317
Mean3.2618182
Median Absolute Deviation (MAD)0.3
Skewness1.3323287
Sum215.28
Variance0.6932951
MonotonicityNot monotonic
2023-12-12T14:30:15.260584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.88 12
16.7%
2.94 6
 
8.3%
3.6 6
 
8.3%
2.69 5
 
6.9%
3.24 4
 
5.6%
2.49 3
 
4.2%
4.2 2
 
2.8%
3.39 2
 
2.8%
1.8 2
 
2.8%
4.88 2
 
2.8%
Other values (20) 22
30.6%
(Missing) 6
 
8.3%
ValueCountFrequency (%)
1.8 2
 
2.8%
2.1 1
 
1.4%
2.16 1
 
1.4%
2.49 3
 
4.2%
2.68 1
 
1.4%
2.69 5
6.9%
2.7 1
 
1.4%
2.77 1
 
1.4%
2.85 2
 
2.8%
2.88 12
16.7%
ValueCountFrequency (%)
5.92 1
1.4%
5.76 1
1.4%
5.47 1
1.4%
4.88 2
2.8%
4.8 1
1.4%
4.32 1
1.4%
4.2 2
2.8%
4.1 1
1.4%
3.96 1
1.4%
3.75 1
1.4%

가중등판
Real number (ℝ)

MISSING 

Distinct54
Distinct (%)81.8%
Missing6
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean47.549545
Minimum22.78
Maximum112.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T14:30:15.386153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.78
5-th percentile26.5425
Q137.8725
median46.1
Q355.95
95-th percentile70.05
Maximum112.2
Range89.42
Interquartile range (IQR)18.0775

Descriptive statistics

Standard deviation15.830008
Coefficient of variation (CV)0.33291608
Kurtosis4.0927877
Mean47.549545
Median Absolute Deviation (MAD)9.85
Skewness1.4581833
Sum3138.27
Variance250.58916
MonotonicityNot monotonic
2023-12-12T14:30:15.520724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.8 3
 
4.2%
55.95 2
 
2.8%
62.4 2
 
2.8%
31.75 2
 
2.8%
47.48 2
 
2.8%
26.23 2
 
2.8%
47.6 2
 
2.8%
70.05 2
 
2.8%
35.95 2
 
2.8%
41.95 2
 
2.8%
Other values (44) 45
62.5%
(Missing) 6
 
8.3%
ValueCountFrequency (%)
22.78 1
1.4%
25.65 1
1.4%
26.23 2
2.8%
27.48 1
1.4%
27.68 1
1.4%
29.0 1
1.4%
30.15 1
1.4%
31.75 2
2.8%
32.65 1
1.4%
32.73 1
1.4%
ValueCountFrequency (%)
112.2 1
1.4%
97.48 1
1.4%
71.0 1
1.4%
70.05 2
2.8%
66.68 1
1.4%
65.08 1
1.4%
64.6 1
1.4%
62.4 2
2.8%
61.35 1
1.4%
59.8 1
1.4%

보조금지원금액(만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)78.8%
Missing6
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean165.34848
Minimum79
Maximum267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T14:30:15.660800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile95.25
Q1126.5
median159.5
Q3210.75
95-th percentile241.5
Maximum267
Range188
Interquartile range (IQR)84.25

Descriptive statistics

Standard deviation49.869828
Coefficient of variation (CV)0.30160439
Kurtosis-0.99975771
Mean165.34848
Median Absolute Deviation (MAD)41
Skewness0.18786693
Sum10913
Variance2486.9998
MonotonicityNot monotonic
2023-12-12T14:30:15.804753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 4
 
5.6%
100 3
 
4.2%
141 2
 
2.8%
150 2
 
2.8%
173 2
 
2.8%
184 2
 
2.8%
166 2
 
2.8%
115 2
 
2.8%
153 2
 
2.8%
227 2
 
2.8%
Other values (42) 43
59.7%
(Missing) 6
 
8.3%
ValueCountFrequency (%)
79 1
 
1.4%
80 1
 
1.4%
92 1
 
1.4%
95 1
 
1.4%
96 1
 
1.4%
100 3
4.2%
103 1
 
1.4%
104 1
 
1.4%
112 1
 
1.4%
114 1
 
1.4%
ValueCountFrequency (%)
267 1
 
1.4%
262 1
 
1.4%
251 1
 
1.4%
244 1
 
1.4%
234 1
 
1.4%
230 4
5.6%
228 1
 
1.4%
227 2
2.8%
225 1
 
1.4%
222 1
 
1.4%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)1.5%
Missing6
Missing (%)8.3%
Memory size708.0 B
Minimum2023-08-07 00:00:00
Maximum2023-08-07 00:00:00
2023-12-12T14:30:15.923968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:16.004610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T14:30:12.304670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.040800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.444633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.904607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:12.404552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.145554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.630083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:12.016278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:12.545801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.243086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.706791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:12.114540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:12.655287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.321741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:11.788491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:30:12.197547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:30:16.072248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분제조수입사차종가중연비(km_kWh)배터리용량(kWh)가중등판보조금지원금액(만원)
구분1.0000.7111.0000.7320.5780.0000.687
제조수입사0.7111.0001.0000.8530.8330.7220.671
차종1.0001.0001.0001.0001.0001.0001.000
가중연비(km_kWh)0.7320.8531.0001.0000.8050.3440.724
배터리용량(kWh)0.5780.8331.0000.8051.0000.2010.709
가중등판0.0000.7221.0000.3440.2011.0000.460
보조금지원금액(만원)0.6870.6711.0000.7240.7090.4601.000
2023-12-12T14:30:16.174742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제조수입사구분
제조수입사1.0000.343
구분0.3431.000
2023-12-12T14:30:16.256174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가중연비(km_kWh)배터리용량(kWh)가중등판보조금지원금액(만원)구분제조수입사
가중연비(km_kWh)1.000-0.501-0.294-0.5310.5620.427
배터리용량(kWh)-0.5011.0000.3420.6150.3960.401
가중등판-0.2940.3421.0000.4690.0000.310
보조금지원금액(만원)-0.5310.6150.4691.0000.5090.251
구분0.5620.3960.0000.5091.0000.343
제조수입사0.4270.4010.3100.2510.3431.000

Missing values

2023-12-12T14:30:12.864963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:30:13.027411image/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.
2023-12-12T14:30:13.165910image/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

구분제조수입사차종가중연비(km_kWh)배터리용량(kWh)가중등판보조금지원금액(만원)데이터기준일자
0경형디앤에이모터스EG30024.62.1637.681142023-08-07
1경형디앤에이모터스EG300N24.072.4941.01252023-08-07
2경형㈜이누리V Basic(DJ1)28.993.0238.451402023-08-07
3경형인에이블인터내셔널NIU-Npro26.562.127.481002023-08-07
4경형지에스모터스BONO28.622.8846.231392023-08-07
5경형착한바이크아울렛PH-DA2(2000)22.674.152.01402023-08-07
6경형㈜코리아하이테크H-one24.532.7745.531312023-08-07
7경형㈜포도모빌리티ECOOTER E2 Cargo26.042.6927.681152023-08-07
8경형㈜포도모빌리티ECOOTER E1S27.292.6930.151202023-08-07
9경형㈜포도모빌리티E2K25.582.8839.81332023-08-07
구분제조수입사차종가중연비(km_kWh)배터리용량(kWh)가중등판보조금지원금액(만원)데이터기준일자
62기타형그린모빌리티DELI-D4E10.324.3225.652192023-08-07
63기타형(주)에이치비타고타10.494.232.732252023-08-07
64기타형(주)에이치비HB 20011.514.232.652282023-08-07
65기타형케이알모터스E-SKO TRI14.793.649.232342023-08-07
66<NA><NA><NA><NA><NA><NA><NA><NA>
67<NA><NA><NA><NA><NA><NA><NA><NA>
68<NA><NA><NA><NA><NA><NA><NA><NA>
69<NA><NA><NA><NA><NA><NA><NA><NA>
70<NA><NA><NA><NA><NA><NA><NA><NA>
71<NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분제조수입사차종가중연비(km_kWh)배터리용량(kWh)가중등판보조금지원금액(만원)데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>6