Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory312.5 KiB
Average record size in memory32.0 B

Variable types

Text1
DateTime1
Categorical1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15644/F/1/datasetView.do

Reproduction

Analysis started2024-03-13 19:21:08.692964
Analysis finished2024-03-13 19:21:08.926277
Duration0.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct8257
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T04:21:09.106290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters14
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

Unique6799 ?
Unique (%)68.0%

Sample

1st rowSPB-43981
2nd rowSPB-66056
3rd rowSPB-30135
4th rowSPB-66140
5th rowSPB-52504
ValueCountFrequency (%)
spb-32433 6
 
0.1%
spb-33499 5
 
< 0.1%
spb-64432 5
 
< 0.1%
spb-66726 5
 
< 0.1%
spb-32139 5
 
< 0.1%
spb-47011 5
 
< 0.1%
spb-32952 5
 
< 0.1%
spb-31386 4
 
< 0.1%
spb-54658 4
 
< 0.1%
spb-31346 4
 
< 0.1%
Other values (8247) 9952
99.5%
2024-03-14T04:21:09.753185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
5 6904
7.7%
4 6562
7.3%
3 6523
7.2%
6 6282
7.0%
8 4124
 
4.6%
0 4098
 
4.6%
Other values (4) 15507
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
55.6%
Uppercase Letter 30000
33.3%
Dash Punctuation 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 6904
13.8%
4 6562
13.1%
3 6523
13.0%
6 6282
12.6%
8 4124
8.2%
0 4098
8.2%
2 4035
8.1%
1 4018
8.0%
7 3844
7.7%
9 3610
7.2%
Uppercase Letter
ValueCountFrequency (%)
S 10000
33.3%
P 10000
33.3%
B 10000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
66.7%
Latin 30000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 10000
16.7%
5 6904
11.5%
4 6562
10.9%
3 6523
10.9%
6 6282
10.5%
8 4124
6.9%
0 4098
6.8%
2 4035
6.7%
1 4018
6.7%
7 3844
 
6.4%
Latin
ValueCountFrequency (%)
S 10000
33.3%
P 10000
33.3%
B 10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
5 6904
7.7%
4 6562
7.3%
3 6523
7.2%
6 6282
7.0%
8 4124
 
4.6%
0 4098
 
4.6%
Other values (4) 15507
17.2%

등록일시
Date

Distinct9423
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-01 01:49:00
Maximum2023-06-30 22:31:00
2024-03-14T04:21:09.857906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:21:09.963373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

구분
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3106 
체인
1912 
안장
1897 
타이어
1695 
페달
897 

Length

Max length4
Median length3
Mean length2.6989
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row안장
3rd row기타
4th row안장
5th row기타

Common Values

ValueCountFrequency (%)
기타 3106
31.1%
체인 1912
19.1%
안장 1897
19.0%
타이어 1695
17.0%
페달 897
 
9.0%
단말기 493
 
4.9%

Length

2024-03-14T04:21:10.070728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T04:21:10.163427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 3106
31.1%
체인 1912
19.1%
안장 1897
19.0%
타이어 1695
17.0%
페달 897
 
9.0%
단말기 493
 
4.9%

Missing values

2024-03-14T04:21:08.839892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T04:21:08.899565image/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

자전거번호등록일시구분
39042SPB-439812023-04-17 16:34기타
45230SPB-660562023-04-28 8:21안장
6566SPB-301352023-01-21 15:33기타
47550SPB-661402023-05-02 20:11안장
4717SPB-525042023-01-17 8:24기타
72969SPB-412102023-06-12 19:02기타
70287SPB-803872023-06-08 8:51기타
55564SPB-638932023-05-16 8:58안장
60884SPB-454962023-05-23 20:55체인
46092SPB-335112023-04-30 15:21페달
자전거번호등록일시구분
27900SPB-563152023-03-27 0:06타이어
28269SPB-422192023-03-27 18:17안장
35893SPB-556612023-04-11 6:50타이어
82615SPB-628322023-06-28 9:17체인
37705SPB-627182023-04-14 14:52기타
76847SPB-662872023-06-18 1:18단말기
41211SPB-494272023-04-21 8:32타이어
70991SPB-664022023-06-09 12:42기타
15456SPB-447062023-02-24 5:13체인
21445SPB-466252023-03-13 7:55기타