Overview

Dataset statistics

Number of variables6
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory58.3 B

Variable types

Text1
Categorical1
Numeric4

Dataset

Description경주시에서 관리하는 연도별, 연료별, 차종별 등록대수 현황입니다. 차량등록소 차량등록팀에서 관리 하는 사항으로 매년 1번 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15006289/fileData.do

Alerts

승합 is highly overall correlated with 화물 and 1 other fieldsHigh correlation
화물 is highly overall correlated with 승합 and 1 other fieldsHigh correlation
특수 is highly overall correlated with 승합 and 1 other fieldsHigh correlation
승용 has 4 (19.0%) zerosZeros
승합 has 8 (38.1%) zerosZeros
화물 has 10 (47.6%) zerosZeros
특수 has 16 (76.2%) zerosZeros

Reproduction

Analysis started2023-12-12 17:46:53.130822
Analysis finished2023-12-12 17:46:55.202160
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연료별
Text

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:46:55.322488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.7619048
Min length2

Characters and Unicode

Total characters121
Distinct characters28
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)23.8%

Sample

1st rowCNG
2nd rowCNG
3rd row경유
4th row경유
5th row기타연료
ValueCountFrequency (%)
cng 2
9.5%
경유 2
9.5%
기타연료 2
9.5%
엘피지 2
9.5%
전기 2
9.5%
하이브리드(휘발유+전기 2
9.5%
휘발유 2
9.5%
휘발유(무연 2
9.5%
수소 1
 
4.8%
하이브리드(cng+전기 1
 
4.8%
Other values (3) 3
14.3%
2023-12-13T02:46:55.671924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 11
 
9.1%
기 9
 
7.4%
) 8
 
6.6%
( 8
 
6.6%
발 7
 
5.8%
휘 7
 
5.8%
전 7
 
5.8%
이 5
 
4.1%
브 5
 
4.1%
+ 5
 
4.1%
Other values (18) 49
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
72.7%
Uppercase Letter 12
 
9.9%
Close Punctuation 8
 
6.6%
Open Punctuation 8
 
6.6%
Math Symbol 5
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 11
12.5%
기 9
10.2%
발 7
 
8.0%
휘 7
 
8.0%
전 7
 
8.0%
이 5
 
5.7%
브 5
 
5.7%
연 5
 
5.7%
드 5
 
5.7%
하 5
 
5.7%
Other values (10) 22
25.0%
Uppercase Letter
ValueCountFrequency (%)
G 4
33.3%
C 3
25.0%
N 3
25.0%
L 1
 
8.3%
P 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
72.7%
Common 21
 
17.4%
Latin 12
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 11
12.5%
기 9
10.2%
발 7
 
8.0%
휘 7
 
8.0%
전 7
 
8.0%
이 5
 
5.7%
브 5
 
5.7%
연 5
 
5.7%
드 5
 
5.7%
하 5
 
5.7%
Other values (10) 22
25.0%
Latin
ValueCountFrequency (%)
G 4
33.3%
C 3
25.0%
N 3
25.0%
L 1
 
8.3%
P 1
 
8.3%
Common
ValueCountFrequency (%)
) 8
38.1%
( 8
38.1%
+ 5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
72.7%
ASCII 33
 
27.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 11
12.5%
기 9
10.2%
발 7
 
8.0%
휘 7
 
8.0%
전 7
 
8.0%
이 5
 
5.7%
브 5
 
5.7%
연 5
 
5.7%
드 5
 
5.7%
하 5
 
5.7%
Other values (10) 22
25.0%
ASCII
ValueCountFrequency (%)
) 8
24.2%
( 8
24.2%
+ 5
15.2%
G 4
12.1%
C 3
 
9.1%
N 3
 
9.1%
L 1
 
3.0%
P 1
 
3.0%

용도별
Categorical

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
비사업용
12 
사업용
9 

Length

Max length4
Median length4
Mean length3.5714286
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비사업용
2nd row사업용
3rd row비사업용
4th row사업용
5th row비사업용

Common Values

ValueCountFrequency (%)
비사업용 12
57.1%
사업용 9
42.9%

Length

2023-12-13T02:46:55.822416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:46:55.933286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 12
57.1%
사업용 9
42.9%

승용
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5625.0952
Minimum0
Maximum47465
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:46:56.049389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median62
Q31404
95-th percentile31442
Maximum47465
Range47465
Interquartile range (IQR)1387

Descriptive statistics

Standard deviation12555.158
Coefficient of variation (CV)2.2319904
Kurtosis6.2363701
Mean5625.0952
Median Absolute Deviation (MAD)62
Skewness2.5645334
Sum118127
Variance1.57632 × 108
MonotonicityNot monotonic
2023-12-13T02:46:56.175363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 4
19.0%
27 1
 
4.8%
62 1
 
4.8%
39 1
 
4.8%
241 1
 
4.8%
47465 1
 
4.8%
17 1
 
4.8%
21395 1
 
4.8%
7 1
 
4.8%
5108 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
0 4
19.0%
7 1
 
4.8%
17 1
 
4.8%
27 1
 
4.8%
39 1
 
4.8%
49 1
 
4.8%
57 1
 
4.8%
62 1
 
4.8%
104 1
 
4.8%
190 1
 
4.8%
ValueCountFrequency (%)
47465 1
4.8%
31442 1
4.8%
21395 1
4.8%
9403 1
4.8%
5108 1
4.8%
1404 1
4.8%
1117 1
4.8%
241 1
4.8%
190 1
4.8%
104 1
4.8%

승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.71429
Minimum0
Maximum3548
Zeros8
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:46:56.298921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q329
95-th percentile421
Maximum3548
Range3548
Interquartile range (IQR)29

Descriptive statistics

Standard deviation771.35388
Coefficient of variation (CV)3.4790446
Kurtosis19.854945
Mean221.71429
Median Absolute Deviation (MAD)4
Skewness4.4131888
Sum4656
Variance594986.81
MonotonicityNot monotonic
2023-12-13T02:46:56.429818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8
38.1%
9 1
 
4.8%
97 1
 
4.8%
3548 1
 
4.8%
421 1
 
4.8%
110 1
 
4.8%
378 1
 
4.8%
2 1
 
4.8%
12 1
 
4.8%
4 1
 
4.8%
Other values (4) 4
19.0%
ValueCountFrequency (%)
0 8
38.1%
1 1
 
4.8%
2 1
 
4.8%
4 1
 
4.8%
9 1
 
4.8%
12 1
 
4.8%
22 1
 
4.8%
23 1
 
4.8%
29 1
 
4.8%
97 1
 
4.8%
ValueCountFrequency (%)
3548 1
4.8%
421 1
4.8%
378 1
4.8%
110 1
4.8%
97 1
4.8%
29 1
4.8%
23 1
4.8%
22 1
4.8%
12 1
4.8%
9 1
4.8%

화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1335.4762
Minimum0
Maximum24002
Zeros10
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:46:56.572764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3300
95-th percentile1608
Maximum24002
Range24002
Interquartile range (IQR)300

Descriptive statistics

Standard deviation5208.799
Coefficient of variation (CV)3.9003308
Kurtosis20.715671
Mean1335.4762
Median Absolute Deviation (MAD)4
Skewness4.5394268
Sum28045
Variance27131587
MonotonicityNot monotonic
2023-12-13T02:46:56.692642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10
47.6%
15 1
 
4.8%
24002 1
 
4.8%
1608 1
 
4.8%
379 1
 
4.8%
300 1
 
4.8%
861 1
 
4.8%
4 1
 
4.8%
628 1
 
4.8%
30 1
 
4.8%
Other values (2) 2
 
9.5%
ValueCountFrequency (%)
0 10
47.6%
4 1
 
4.8%
15 1
 
4.8%
30 1
 
4.8%
41 1
 
4.8%
177 1
 
4.8%
300 1
 
4.8%
379 1
 
4.8%
628 1
 
4.8%
861 1
 
4.8%
ValueCountFrequency (%)
24002 1
4.8%
1608 1
4.8%
861 1
4.8%
628 1
4.8%
379 1
4.8%
300 1
4.8%
177 1
4.8%
41 1
4.8%
30 1
4.8%
15 1
4.8%

특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.285714
Minimum0
Maximum433
Zeros16
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:46:56.819530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile376
Maximum433
Range433
Interquartile range (IQR)0

Descriptive statistics

Standard deviation121.82042
Coefficient of variation (CV)2.8808883
Kurtosis7.4597636
Mean42.285714
Median Absolute Deviation (MAD)0
Skewness2.9194782
Sum888
Variance14840.214
MonotonicityNot monotonic
2023-12-13T02:46:56.938146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 16
76.2%
433 1
 
4.8%
376 1
 
4.8%
74 1
 
4.8%
3 1
 
4.8%
2 1
 
4.8%
ValueCountFrequency (%)
0 16
76.2%
2 1
 
4.8%
3 1
 
4.8%
74 1
 
4.8%
376 1
 
4.8%
433 1
 
4.8%
ValueCountFrequency (%)
433 1
 
4.8%
376 1
 
4.8%
74 1
 
4.8%
3 1
 
4.8%
2 1
 
4.8%
0 16
76.2%

Interactions

2023-12-13T02:46:54.689714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:53.380081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:53.828730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.314474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.762707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:53.474641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:53.949649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.400722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.861642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:53.595077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.084018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.503696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.935300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:53.707117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.195022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:46:54.603685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:46:57.038250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료별용도별승용승합화물특수
연료별1.0000.0000.0000.0000.0000.000
용도별0.0001.0000.1900.0000.0000.000
승용0.0000.1901.0000.7221.0000.505
승합0.0000.0000.7221.0001.0000.777
화물0.0000.0001.0001.0001.0001.000
특수0.0000.0000.5050.7771.0001.000
2023-12-13T02:46:57.150005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용승합화물특수용도별
승용1.0000.2360.3680.2110.190
승합0.2361.0000.7110.7060.000
화물0.3680.7111.0000.7850.000
특수0.2110.7060.7851.0000.000
용도별0.1900.0000.0000.0001.000

Missing values

2023-12-13T02:46:55.047478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:46:55.160513image/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

연료별용도별승용승합화물특수
0CNG비사업용279150
1CNG사업용09700
2경유비사업용31442354824002433
3경유사업용574211608376
4기타연료비사업용011037974
5기타연료사업용003000
6수소비사업용49000
7엘피지비사업용94033788613
8엘피지사업용1117240
9전기비사업용1404126282
연료별용도별승용승합화물특수
11하이브리드(CNG+전기)사업용02300
12하이브리드(LPG+전기)비사업용104000
13하이브리드(경유+전기)비사업용62000
14하이브리드(휘발유+전기)비사업용5108000
15하이브리드(휘발유+전기)사업용7000
16휘발유비사업용21395221770
17휘발유사업용17000
18휘발유(무연)비사업용4746529410
19휘발유(무연)사업용241100
20휘발유(유연)비사업용39000