Overview

Dataset statistics

Number of variables5
Number of observations73
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory43.8 B

Variable types

Numeric2
Text1
DateTime2

Dataset

Description강원특별자치도 속초시 폐차 대수 정보입니다. 민원신청에 의한 데이터로 2023년도 폐차(등록말소) 대수 정보가 포함되어 있습니다. 말소차량은 화물카고형, 화물덤프형, 화물밴형 종류에 따라 구분되어 있습니다.
Author강원특별자치도 속초시
URLhttps://www.data.go.kr/data/15123804/fileData.do

Alerts

주행거리 has 1 (1.4%) missing valuesMissing
순번 has unique valuesUnique
최초등록일 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:29:00.239678
Analysis finished2023-12-12 05:29:01.157883
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T14:29:01.239888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.6
Q119
median37
Q355
95-th percentile69.4
Maximum73
Range72
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.217131
Coefficient of variation (CV)0.57343598
Kurtosis-1.2
Mean37
Median Absolute Deviation (MAD)18
Skewness0
Sum2701
Variance450.16667
MonotonicityStrictly increasing
2023-12-12T14:29:01.414021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
56 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
Other values (63) 63
86.3%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%

차명
Text

Distinct37
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-12T14:29:01.647486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length10.205479
Min length4

Characters and Unicode

Total characters745
Distinct characters86
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

Unique28 ?
Unique (%)38.4%

Sample

1st row포터초장축더블캡
2nd row현대4.5톤카고트럭-장축
3rd row포터Ⅱ(PORTERⅡ)
4th row호룡 엘리카 이삿짐사다리차
5th row포터Ⅱ초장축슈퍼캡냉동탑차(PORTERⅡ)
ValueCountFrequency (%)
포터ⅱ(porterⅱ 18
17.0%
봉고ⅲ 14
 
13.2%
1톤 10
 
9.4%
porterⅱ 7
 
6.6%
포터초장축슈퍼캡 5
 
4.7%
포터ⅱ 4
 
3.8%
프런티어 3
 
2.8%
봉고 3
 
2.8%
현대5톤트럭-장축 2
 
1.9%
포터ⅱ냉동탑차 2
 
1.9%
Other values (32) 38
35.8%
2023-12-12T14:29:02.094950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Ⅱ 56
 
7.5%
R 56
 
7.5%
포 36
 
4.8%
터 36
 
4.8%
33
 
4.4%
( 28
 
3.8%
P 28
 
3.8%
O 28
 
3.8%
T 28
 
3.8%
E 28
 
3.8%
Other values (76) 388
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 373
50.1%
Uppercase Letter 171
23.0%
Letter Number 70
 
9.4%
Space Separator 33
 
4.4%
Decimal Number 29
 
3.9%
Open Punctuation 28
 
3.8%
Close Punctuation 28
 
3.8%
Other Punctuation 7
 
0.9%
Dash Punctuation 6
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
포 36
 
9.7%
터 36
 
9.7%
고 21
 
5.6%
톤 19
 
5.1%
봉 19
 
5.1%
장 16
 
4.3%
이 15
 
4.0%
트 13
 
3.5%
축 13
 
3.5%
캡 11
 
2.9%
Other values (56) 174
46.6%
Uppercase Letter
ValueCountFrequency (%)
R 56
32.7%
P 28
16.4%
O 28
16.4%
T 28
16.4%
E 28
16.4%
G 1
 
0.6%
S 1
 
0.6%
L 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 13
44.8%
4 7
24.1%
5 6
20.7%
2 2
 
6.9%
3 1
 
3.4%
Letter Number
ValueCountFrequency (%)
Ⅱ 56
80.0%
Ⅲ 14
 
20.0%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 373
50.1%
Latin 241
32.3%
Common 131
 
17.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
포 36
 
9.7%
터 36
 
9.7%
고 21
 
5.6%
톤 19
 
5.1%
봉 19
 
5.1%
장 16
 
4.3%
이 15
 
4.0%
트 13
 
3.5%
축 13
 
3.5%
캡 11
 
2.9%
Other values (56) 174
46.6%
Latin
ValueCountFrequency (%)
Ⅱ 56
23.2%
R 56
23.2%
P 28
11.6%
O 28
11.6%
T 28
11.6%
E 28
11.6%
Ⅲ 14
 
5.8%
G 1
 
0.4%
S 1
 
0.4%
L 1
 
0.4%
Common
ValueCountFrequency (%)
33
25.2%
( 28
21.4%
) 28
21.4%
1 13
 
9.9%
4 7
 
5.3%
. 7
 
5.3%
5 6
 
4.6%
- 6
 
4.6%
2 2
 
1.5%
3 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 373
50.1%
ASCII 302
40.5%
Number Forms 70
 
9.4%

Most frequent character per block

Number Forms
ValueCountFrequency (%)
Ⅱ 56
80.0%
Ⅲ 14
 
20.0%
ASCII
ValueCountFrequency (%)
R 56
18.5%
33
10.9%
( 28
9.3%
P 28
9.3%
O 28
9.3%
T 28
9.3%
E 28
9.3%
) 28
9.3%
1 13
 
4.3%
4 7
 
2.3%
Other values (8) 25
8.3%
Hangul
ValueCountFrequency (%)
포 36
 
9.7%
터 36
 
9.7%
고 21
 
5.6%
톤 19
 
5.1%
봉 19
 
5.1%
장 16
 
4.3%
이 15
 
4.0%
트 13
 
3.5%
축 13
 
3.5%
캡 11
 
2.9%
Other values (56) 174
46.6%

최초등록일
Date

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
Minimum1994-01-04 00:00:00
Maximum2023-03-07 00:00:00
2023-12-12T14:29:02.564735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:02.684519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

주행거리
Real number (ℝ)

MISSING 

Distinct72
Distinct (%)100.0%
Missing1
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean195774.33
Minimum25032
Maximum878975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T14:29:02.835241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25032
5-th percentile68429
Q1151311.75
median182289
Q3228199.5
95-th percentile305476
Maximum878975
Range853943
Interquartile range (IQR)76887.75

Descriptive statistics

Standard deviation106439.5
Coefficient of variation (CV)0.54368465
Kurtosis23.451059
Mean195774.33
Median Absolute Deviation (MAD)37888
Skewness3.7540885
Sum14095752
Variance1.1329367 × 1010
MonotonicityNot monotonic
2023-12-12T14:29:02.998428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181247 1
 
1.4%
153416 1
 
1.4%
228984 1
 
1.4%
297727 1
 
1.4%
159192 1
 
1.4%
147816 1
 
1.4%
878975 1
 
1.4%
86197 1
 
1.4%
184149 1
 
1.4%
178382 1
 
1.4%
Other values (62) 62
84.9%
ValueCountFrequency (%)
25032 1
1.4%
53996 1
1.4%
55652 1
1.4%
61312 1
1.4%
74252 1
1.4%
85811 1
1.4%
86197 1
1.4%
99051 1
1.4%
108035 1
1.4%
108923 1
1.4%
ValueCountFrequency (%)
878975 1
1.4%
360354 1
1.4%
340810 1
1.4%
314947 1
1.4%
297727 1
1.4%
295674 1
1.4%
292409 1
1.4%
291984 1
1.4%
291499 1
1.4%
269386 1
1.4%

말소일자
Date

Distinct44
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Memory size716.0 B
Minimum2022-12-23 00:00:00
Maximum2023-08-16 00:00:00
2023-12-12T14:29:03.124010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:03.253195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

Interactions

2023-12-12T14:29:00.772554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:00.529255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:00.894162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:00.659546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:29:03.360426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번차명최초등록일주행거리말소일자
순번1.0000.4841.0000.1600.922
차명0.4841.0001.0000.8150.739
최초등록일1.0001.0001.0001.0001.000
주행거리0.1600.8151.0001.0000.776
말소일자0.9220.7391.0000.7761.000
2023-12-12T14:29:03.457722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주행거리
순번1.000-0.179
주행거리-0.1791.000

Missing values

2023-12-12T14:29:01.004589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:29:01.110005image/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

순번차명최초등록일주행거리말소일자
01포터초장축더블캡2001-05-241812472023-08-11
12현대4.5톤카고트럭-장축1994-01-04613122023-07-26
23포터Ⅱ(PORTERⅡ)2004-08-261742912023-08-03
34호룡 엘리카 이삿짐사다리차2007-02-021343742023-07-31
45포터Ⅱ초장축슈퍼캡냉동탑차(PORTERⅡ)2005-06-202608072023-08-09
56봉고Ⅲ 1톤2010-05-272956742023-07-25
67봉고Ⅲ 1톤2006-02-221756852023-08-16
78봉고Ⅲ 1톤2006-03-163603542023-08-09
89트레이드홈로리1994-10-282473592023-07-03
910포터Ⅱ(PORTERⅡ)2007-05-212914992023-06-30
순번차명최초등록일주행거리말소일자
6364포터Ⅱ냉동탑차 (PORTERⅡ)2008-06-231511222023-03-15
6465포터Ⅱ(PORTERⅡ)2011-01-201872072023-03-14
6566봉고Ⅲ 파워게이트2005-05-311149042023-03-15
6667이-마이티2005-10-311833312023-01-12
6768프런티어 1.3톤2003-12-161441272023-01-10
6869메가트럭2006-10-231089232023-01-12
6970현대5톤트럭-장축1997-07-28556522023-01-10
7071포터Ⅱ(PORTERⅡ)2006-01-021610722023-01-10
7172포터Ⅱ(PORTERⅡ)2004-09-081922422022-12-23
7273포터초장축슈퍼캡1996-07-302053042023-01-11