Overview

Dataset statistics

Number of variables4
Number of observations178
Missing cells117
Missing cells (%)16.4%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory5.7 KiB
Average record size in memory32.7 B

Variable types

Text2
Categorical1
DateTime1

Dataset

Description전라남도 여수시 공영자전거 운영 고장현황(수리현황 아이디, 고장구분, 처리 내용, 등록일자 등)등에 대한 제공합니다.
Author전라남도 여수시
URLhttps://www.data.go.kr/data/15049728/fileData.do

Alerts

Dataset has 1 (0.6%) duplicate rowsDuplicates
자전거 아이디 has 39 (21.9%) missing valuesMissing
처리 내용 has 39 (21.9%) missing valuesMissing
등록일자 has 39 (21.9%) missing valuesMissing

Reproduction

Analysis started2024-03-14 12:20:46.100173
Analysis finished2024-03-14 12:20:47.366627
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거 아이디
Text

MISSING 

Distinct116
Distinct (%)83.5%
Missing39
Missing (%)21.9%
Memory size1.5 KiB
2024-03-14T21:20:48.503647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1251
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)68.3%

Sample

1st rowYS_000253
2nd rowYS_000102
3rd rowYS_000171
4th rowYS_000130
5th rowYS_000095
ValueCountFrequency (%)
ys_000201 3
 
2.2%
ys_000258 3
 
2.2%
ys_000157 2
 
1.4%
ys_000087 2
 
1.4%
ys_000273 2
 
1.4%
ys_000246 2
 
1.4%
ys_000196 2
 
1.4%
ys_000166 2
 
1.4%
ys_000170 2
 
1.4%
ys_000031 2
 
1.4%
Other values (106) 117
84.2%
2024-03-14T21:20:49.987890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 486
38.8%
Y 139
 
11.1%
S 139
 
11.1%
_ 139
 
11.1%
1 77
 
6.2%
2 72
 
5.8%
7 32
 
2.6%
5 30
 
2.4%
3 29
 
2.3%
8 28
 
2.2%
Other values (3) 80
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 834
66.7%
Uppercase Letter 278
 
22.2%
Connector Punctuation 139
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 486
58.3%
1 77
 
9.2%
2 72
 
8.6%
7 32
 
3.8%
5 30
 
3.6%
3 29
 
3.5%
8 28
 
3.4%
4 27
 
3.2%
6 27
 
3.2%
9 26
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
Y 139
50.0%
S 139
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 973
77.8%
Latin 278
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 486
49.9%
_ 139
 
14.3%
1 77
 
7.9%
2 72
 
7.4%
7 32
 
3.3%
5 30
 
3.1%
3 29
 
3.0%
8 28
 
2.9%
4 27
 
2.8%
6 27
 
2.8%
Latin
ValueCountFrequency (%)
Y 139
50.0%
S 139
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 486
38.8%
Y 139
 
11.1%
S 139
 
11.1%
_ 139
 
11.1%
1 77
 
6.2%
2 72
 
5.8%
7 32
 
2.6%
5 30
 
2.4%
3 29
 
2.3%
8 28
 
2.2%
Other values (3) 80
 
6.4%

구분
Categorical

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
교체
105 
<NA>
39 
수리
33 
교체
 
1

Length

Max length4
Median length2
Mean length2.4438202
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row수리
2nd row수리
3rd row수리
4th row수리
5th row교체

Common Values

ValueCountFrequency (%)
교체 105
59.0%
<NA> 39
 
21.9%
수리 33
 
18.5%
교체 1
 
0.6%

Length

2024-03-14T21:20:50.446166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:20:50.812385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교체 106
59.6%
na 39
 
21.9%
수리 33
 
18.5%

처리 내용
Text

MISSING 

Distinct95
Distinct (%)68.3%
Missing39
Missing (%)21.9%
Memory size1.5 KiB
2024-03-14T21:20:51.534077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length10.47482
Min length2

Characters and Unicode

Total characters1456
Distinct characters71
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)52.5%

Sample

1st row안장, 바퀴(림)
2nd row체인, 후미등
3rd row앞 브레이크, 후미등
4th row체인
5th row뒷물받이, 후미등
ValueCountFrequency (%)
브레이크 54
 
12.4%
뒤 44
 
10.1%
타이어 38
 
8.8%
패드 35
 
8.1%
스포크 25
 
5.8%
바퀴 25
 
5.8%
앞 21
 
4.8%
체인 21
 
4.8%
물받이 18
 
4.1%
후미등 17
 
3.9%
Other values (34) 136
31.3%
2024-03-14T21:20:52.702988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
295
20.3%
이 125
 
8.6%
, 116
 
8.0%
크 103
 
7.1%
브 69
 
4.7%
레 62
 
4.3%
뒤 44
 
3.0%
어 43
 
3.0%
드 43
 
3.0%
타 38
 
2.6%
Other values (61) 518
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1043
71.6%
Space Separator 295
 
20.3%
Other Punctuation 116
 
8.0%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 125
 
12.0%
크 103
 
9.9%
브 69
 
6.6%
레 62
 
5.9%
뒤 44
 
4.2%
어 43
 
4.1%
드 43
 
4.1%
타 38
 
3.6%
패 38
 
3.6%
포 30
 
2.9%
Other values (57) 448
43.0%
Space Separator
ValueCountFrequency (%)
295
100.0%
Other Punctuation
ValueCountFrequency (%)
, 116
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1043
71.6%
Common 413
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 125
 
12.0%
크 103
 
9.9%
브 69
 
6.6%
레 62
 
5.9%
뒤 44
 
4.2%
어 43
 
4.1%
드 43
 
4.1%
타 38
 
3.6%
패 38
 
3.6%
포 30
 
2.9%
Other values (57) 448
43.0%
Common
ValueCountFrequency (%)
295
71.4%
, 116
 
28.1%
( 1
 
0.2%
) 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1043
71.6%
ASCII 413
 
28.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295
71.4%
, 116
 
28.1%
( 1
 
0.2%
) 1
 
0.2%
Hangul
ValueCountFrequency (%)
이 125
 
12.0%
크 103
 
9.9%
브 69
 
6.6%
레 62
 
5.9%
뒤 44
 
4.2%
어 43
 
4.1%
드 43
 
4.1%
타 38
 
3.6%
패 38
 
3.6%
포 30
 
2.9%
Other values (57) 448
43.0%

등록일자
Date

MISSING 

Distinct63
Distinct (%)45.3%
Missing39
Missing (%)21.9%
Memory size1.5 KiB
Minimum2023-08-10 00:00:00
Maximum2024-02-06 00:00:00
2024-03-14T21:20:53.086923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:20:53.722273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Correlations

2024-03-14T21:20:53.987300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분처리 내용등록일자
구분1.0000.9880.939
처리 내용0.9881.0000.952
등록일자0.9390.9521.000

Missing values

2024-03-14T21:20:46.633043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:20:46.924295image/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.
2024-03-14T21:20:47.210309image/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

자전거 아이디구분처리 내용등록일자
0YS_000253수리안장, 바퀴(림)2023-08-10
1YS_000102수리체인, 후미등2023-08-10
2YS_000171수리앞 브레이크, 후미등2023-08-14
3YS_000130수리체인2023-08-14
4YS_000095교체뒷물받이, 후미등2023-08-14
5YS_000128교체크랭크2023-08-14
6YS_000116수리밴드 브레이크 수리2023-08-14
7YS_000258교체바구니, 태양광 패널2023-08-15
8YS_000192교체물받이, 후미등, 브레이크 패드2023-08-15
9YS_000007교체후미등, 물받이2023-08-15
자전거 아이디구분처리 내용등록일자
168<NA><NA><NA><NA>
169<NA><NA><NA><NA>
170<NA><NA><NA><NA>
171<NA><NA><NA><NA>
172<NA><NA><NA><NA>
173<NA><NA><NA><NA>
174<NA><NA><NA><NA>
175<NA><NA><NA><NA>
176<NA><NA><NA><NA>
177<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

자전거 아이디구분처리 내용등록일자# duplicates
0<NA><NA><NA><NA>39