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 19:21:10.690065
Analysis finished2024-03-13 19:21:10.932904
Duration0.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct7452
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T04:21:11.112731image/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

Unique5619 ?
Unique (%)56.2%

Sample

1st rowSPB-55008
2nd rowSPB-36458
3rd rowSPB-59939
4th rowSPB-65744
5th rowSPB-64627
ValueCountFrequency (%)
spb-67698 9
 
0.1%
spb-63734 9
 
0.1%
spb-65600 7
 
0.1%
spb-82541 7
 
0.1%
spb-44469 7
 
0.1%
spb-41514 7
 
0.1%
spb-65267 7
 
0.1%
spb-64074 6
 
0.1%
spb-43150 6
 
0.1%
spb-66505 6
 
0.1%
Other values (7442) 9929
99.3%
2024-03-14T04:21:11.437780image/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%
6 7206
8.0%
5 7096
7.9%
4 6093
6.8%
3 6091
6.8%
8 4239
 
4.7%
7 4190
 
4.7%
Other values (4) 15085
16.8%

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 (%)
6 7206
14.4%
5 7096
14.2%
4 6093
12.2%
3 6091
12.2%
8 4239
8.5%
7 4190
8.4%
1 3944
7.9%
2 3904
7.8%
0 3851
7.7%
9 3386
6.8%
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%
6 7206
12.0%
5 7096
11.8%
4 6093
10.2%
3 6091
10.2%
8 4239
7.1%
7 4190
7.0%
1 3944
 
6.6%
2 3904
 
6.5%
0 3851
 
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%
6 7206
8.0%
5 7096
7.9%
4 6093
6.8%
3 6091
6.8%
8 4239
 
4.7%
7 4190
 
4.7%
Other values (4) 15085
16.8%

등록일시
Date

Distinct9189
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-07-01 00:02:28
Maximum2023-07-31 23:59:19
2024-03-14T04:21:11.568771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:21:11.704741image/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
2725 
타이어
2313 
체인
1783 
안장
1521 
페달
1047 

Length

Max length4
Median length3
Mean length2.7962
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 2725
27.3%
타이어 2313
23.1%
체인 1783
17.8%
안장 1521
15.2%
페달 1047
 
10.5%
단말기 611
 
6.1%

Length

2024-03-14T04:21:11.806591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T04:21:11.891248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 2725
27.3%
타이어 2313
23.1%
체인 1783
17.8%
안장 1521
15.2%
페달 1047
 
10.5%
단말기 611
 
6.1%

Missing values

2024-03-14T04:21:10.837299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T04:21:10.902182image/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
3827SPB-550082023-07-07 18:13:34타이어
6726SPB-364582023-07-15 12:43:37페달
2658SPB-599392023-07-05 22:51:04기타
7212SPB-657442023-07-17 00:12:56타이어
2981SPB-646272023-07-06 13:32:22안장
8491SPB-643832023-07-19 09:04:35기타
7630SPB-609532023-07-17 19:19:22타이어
7633SPB-490182023-07-17 19:21:35단말기
12531SPB-564372023-07-26 15:11:49페달
11302SPB-482602023-07-24 18:50:40체인
자전거번호등록일시구분
8340SPB-628752023-07-19 03:16:23기타
4740SPB-306312023-07-09 18:27:54기타
470SPB-352862023-07-02 00:48:37기타
7426SPB-504492023-07-17 14:21:55안장
8255SPB-650772023-07-18 21:52:31체인
515SPB-647652023-07-02 08:53:05기타
6023SPB-814922023-07-12 16:58:48안장
15045SPB-512972023-07-31 17:23:03단말기
14313SPB-602462023-07-29 17:10:32타이어
8043SPB-644032023-07-18 18:29:40타이어