Overview

Dataset statistics

Number of variables5
Number of observations114
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory44.2 B

Variable types

Text1
Numeric3
DateTime1

Dataset

Descriptionㅇ 2018년도 장성군 차종별 등록현황
Author전라남도 장성군
URLhttps://www.data.go.kr/data/15074001/fileData.do

Alerts

데이터기준일 has constant value ""Constant
관용 is highly overall correlated with 자가용High correlation
자가용 is highly overall correlated with 관용High correlation
차종 has unique valuesUnique
관용 has 85 (74.6%) zerosZeros
자가용 has 34 (29.8%) zerosZeros
영업용 has 74 (64.9%) zerosZeros

Reproduction

Analysis started2023-12-12 21:13:35.179102
Analysis finished2023-12-12 21:13:36.506801
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

차종
Text

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-13T06:13:36.665863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length11.921053
Min length3

Characters and Unicode

Total characters1359
Distinct characters115
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)100.0%

Sample

1st row승용일반형 국산 800CC미만
2nd row승용일반형 국산 1000CC미만
3rd row승용일반형 국산 1500CC미만
4th row승용일반형 국산 2000CC미만
5th row승용일반형 국산 2500CC미만
ValueCountFrequency (%)
승용일반형 26
 
10.2%
외산 13
 
5.1%
국산 13
 
5.1%
이하 10
 
3.9%
미만 9
 
3.5%
화물 9
 
3.5%
승용겸 8
 
3.1%
승용다목적형 8
 
3.1%
승용기타형 8
 
3.1%
화물카고형 7
 
2.8%
Other values (75) 143
56.3%
2023-12-13T06:13:37.070750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
140
 
10.3%
0 104
 
7.7%
C 81
 
6.0%
형 75
 
5.5%
용 63
 
4.6%
승 57
 
4.2%
미 44
 
3.2%
만 44
 
3.2%
5 35
 
2.6%
반 32
 
2.4%
Other values (105) 684
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 883
65.0%
Decimal Number 197
 
14.5%
Space Separator 140
 
10.3%
Uppercase Letter 81
 
6.0%
Close Punctuation 26
 
1.9%
Open Punctuation 26
 
1.9%
Other Punctuation 6
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
형 75
 
8.5%
용 63
 
7.1%
승 57
 
6.5%
미 44
 
5.0%
만 44
 
5.0%
반 32
 
3.6%
물 31
 
3.5%
화 31
 
3.5%
일 31
 
3.5%
산 26
 
2.9%
Other values (93) 449
50.8%
Decimal Number
ValueCountFrequency (%)
0 104
52.8%
5 35
 
17.8%
1 21
 
10.7%
2 15
 
7.6%
3 15
 
7.6%
4 4
 
2.0%
8 3
 
1.5%
Space Separator
ValueCountFrequency (%)
140
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 81
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 883
65.0%
Common 395
29.1%
Latin 81
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
형 75
 
8.5%
용 63
 
7.1%
승 57
 
6.5%
미 44
 
5.0%
만 44
 
5.0%
반 32
 
3.6%
물 31
 
3.5%
화 31
 
3.5%
일 31
 
3.5%
산 26
 
2.9%
Other values (93) 449
50.8%
Common
ValueCountFrequency (%)
140
35.4%
0 104
26.3%
5 35
 
8.9%
) 26
 
6.6%
( 26
 
6.6%
1 21
 
5.3%
2 15
 
3.8%
3 15
 
3.8%
, 6
 
1.5%
4 4
 
1.0%
Latin
ValueCountFrequency (%)
C 81
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 883
65.0%
ASCII 476
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
140
29.4%
0 104
21.8%
C 81
17.0%
5 35
 
7.4%
) 26
 
5.5%
( 26
 
5.5%
1 21
 
4.4%
2 15
 
3.2%
3 15
 
3.2%
, 6
 
1.3%
Other values (2) 7
 
1.5%
Hangul
ValueCountFrequency (%)
형 75
 
8.5%
용 63
 
7.1%
승 57
 
6.5%
미 44
 
5.0%
만 44
 
5.0%
반 32
 
3.6%
물 31
 
3.5%
화 31
 
3.5%
일 31
 
3.5%
산 26
 
2.9%
Other values (93) 449
50.8%

관용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.377193
Minimum0
Maximum30
Zeros85
Zeros (%)74.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:13:37.189245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.75
95-th percentile7.35
Maximum30
Range30
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation4.2890896
Coefficient of variation (CV)3.1143708
Kurtosis29.848974
Mean1.377193
Median Absolute Deviation (MAD)0
Skewness5.1125965
Sum157
Variance18.396289
MonotonicityNot monotonic
2023-12-13T06:13:37.305645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 85
74.6%
1 11
 
9.6%
4 3
 
2.6%
3 3
 
2.6%
9 2
 
1.8%
7 2
 
1.8%
6 2
 
1.8%
2 2
 
1.8%
28 1
 
0.9%
11 1
 
0.9%
Other values (2) 2
 
1.8%
ValueCountFrequency (%)
0 85
74.6%
1 11
 
9.6%
2 2
 
1.8%
3 3
 
2.6%
4 3
 
2.6%
6 2
 
1.8%
7 2
 
1.8%
8 1
 
0.9%
9 2
 
1.8%
11 1
 
0.9%
ValueCountFrequency (%)
30 1
 
0.9%
28 1
 
0.9%
11 1
 
0.9%
9 2
1.8%
8 1
 
0.9%
7 2
1.8%
6 2
1.8%
4 3
2.6%
3 3
2.6%
2 2
1.8%

자가용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.73684
Minimum0
Maximum5411
Zeros34
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:13:37.478720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q377.5
95-th percentile839.85
Maximum5411
Range5411
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation741.68623
Coefficient of variation (CV)3.3298767
Kurtosis32.242951
Mean222.73684
Median Absolute Deviation (MAD)7.5
Skewness5.4508149
Sum25392
Variance550098.46
MonotonicityNot monotonic
2023-12-13T06:13:37.631234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
29.8%
1 7
 
6.1%
3 4
 
3.5%
5 4
 
3.5%
153 2
 
1.8%
13 2
 
1.8%
18 2
 
1.8%
17 2
 
1.8%
7 2
 
1.8%
6 2
 
1.8%
Other values (46) 53
46.5%
ValueCountFrequency (%)
0 34
29.8%
1 7
 
6.1%
2 2
 
1.8%
3 4
 
3.5%
4 2
 
1.8%
5 4
 
3.5%
6 2
 
1.8%
7 2
 
1.8%
8 2
 
1.8%
10 1
 
0.9%
ValueCountFrequency (%)
5411 1
0.9%
4544 1
0.9%
3097 1
0.9%
1518 1
0.9%
984 1
0.9%
925 1
0.9%
794 1
0.9%
753 1
0.9%
739 1
0.9%
607 1
0.9%

영업용
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5263158
Minimum0
Maximum154
Zeros74
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:13:37.756480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile74.1
Maximum154
Range154
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.758577
Coefficient of variation (CV)2.8089114
Kurtosis12.564946
Mean9.5263158
Median Absolute Deviation (MAD)0
Skewness3.4814997
Sum1086
Variance716.02143
MonotonicityNot monotonic
2023-12-13T06:13:37.913765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 74
64.9%
1 7
 
6.1%
2 6
 
5.3%
4 5
 
4.4%
3 3
 
2.6%
33 2
 
1.8%
9 1
 
0.9%
78 1
 
0.9%
7 1
 
0.9%
154 1
 
0.9%
Other values (13) 13
 
11.4%
ValueCountFrequency (%)
0 74
64.9%
1 7
 
6.1%
2 6
 
5.3%
3 3
 
2.6%
4 5
 
4.4%
5 1
 
0.9%
7 1
 
0.9%
9 1
 
0.9%
11 1
 
0.9%
15 1
 
0.9%
ValueCountFrequency (%)
154 1
0.9%
132 1
0.9%
106 1
0.9%
80 1
0.9%
79 1
0.9%
78 1
0.9%
72 1
0.9%
70 1
0.9%
68 1
0.9%
37 1
0.9%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2018-12-31 00:00:00
Maximum2018-12-31 00:00:00
2023-12-13T06:13:38.035014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.130846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T06:13:35.966855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.337021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.625178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.082425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.453081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.722990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.195395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.531942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.836438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:13:38.206163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관용자가용영업용
관용1.0000.7610.443
자가용0.7611.0000.476
영업용0.4430.4761.000
2023-12-13T06:13:38.290200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관용자가용영업용
관용1.0000.6090.301
자가용0.6091.0000.438
영업용0.3010.4381.000

Missing values

2023-12-13T06:13:36.346487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:13:36.461370image/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

차종관용자가용영업용데이터기준일
0승용일반형 국산 800CC미만023802018-12-31
1승용일반형 국산 1000CC미만9151812018-12-31
2승용일반형 국산 1500CC미만775302018-12-31
3승용일반형 국산 2000CC미만285411792018-12-31
4승용일반형 국산 2500CC미만173912018-12-31
5승용일반형 국산 3000CC미만1984682018-12-31
6승용일반형 국산 3500CC미만144012018-12-31
7승용일반형 국산 4000CC미만017512018-12-31
8승용일반형 국산 4500CC미만0402018-12-31
9승용일반형 국산 5000CC미만0202018-12-31
차종관용자가용영업용데이터기준일
104구난차 10톤 미만0002018-12-31
105구난차 10톤 이상0002018-12-31
106견인차 5톤 이하0122018-12-31
107견인차 10톤 미만0002018-12-31
108견인차 10톤 이상010782018-12-31
109특수용도형(고소작업차)02522018-12-31
110특수용도형(고가사다리소방차)1012018-12-31
111특수용도형(오가크레인)0002018-12-31
112특수용도형(피견인형)0002018-12-31
113특수용도형(기타)0702018-12-31