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 19:20:53.595005
Analysis finished2024-03-13 19:20:54.060057
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct8015
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T04:20:54.268619image/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

Unique6403 ?
Unique (%)64.0%

Sample

1st rowSPB-46239
2nd rowSPB-51099
3rd rowSPB-51301
4th rowSPB-35056
5th rowSPB-43780
ValueCountFrequency (%)
spb-35003 6
 
0.1%
spb-50744 6
 
0.1%
spb-45722 6
 
0.1%
spb-55846 5
 
< 0.1%
spb-37032 5
 
< 0.1%
spb-44393 5
 
< 0.1%
spb-50747 5
 
< 0.1%
spb-39866 5
 
< 0.1%
spb-38782 5
 
< 0.1%
spb-51516 5
 
< 0.1%
Other values (8005) 9947
99.5%
2024-03-14T04:20:54.604858image/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%
3 7269
8.1%
4 7100
7.9%
5 6915
7.7%
6 4526
 
5.0%
1 4293
 
4.8%
2 4292
 
4.8%
Other values (4) 15605
17.3%

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 (%)
3 7269
14.5%
4 7100
14.2%
5 6915
13.8%
6 4526
9.1%
1 4293
8.6%
2 4292
8.6%
8 4236
8.5%
0 4168
8.3%
7 3628
7.3%
9 3573
7.1%
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%
3 7269
12.1%
4 7100
11.8%
5 6915
11.5%
6 4526
7.5%
1 4293
7.2%
2 4292
7.2%
8 4236
7.1%
0 4168
6.9%
7 3628
 
6.0%
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%
3 7269
8.1%
4 7100
7.9%
5 6915
7.7%
6 4526
 
5.0%
1 4293
 
4.8%
2 4292
 
4.8%
Other values (4) 15605
17.3%

등록일시
Date

Distinct9403
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 02:25:00
Maximum2022-06-29 21:18:00
2024-03-14T04:20:54.711103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T04:20:54.814144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

고장구분
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3388 
안장
1944 
체인
1730 
타이어
1548 
페달
827 

Length

Max length4
Median length3
Mean length2.7047
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 3388
33.9%
안장 1944
19.4%
체인 1730
17.3%
타이어 1548
15.5%
페달 827
 
8.3%
단말기 563
 
5.6%

Length

2024-03-14T04:20:54.926642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T04:20:55.012266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 3388
33.9%
안장 1944
19.4%
체인 1730
17.3%
타이어 1548
15.5%
페달 827
 
8.3%
단말기 563
 
5.6%

Missing values

2024-03-14T04:20:53.952873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T04:20:54.015235image/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

자전거번호등록일시고장구분
34080SPB-462392022-04-26 13:32기타
14114SPB-510992022-03-09 10:09기타
15971SPB-513012022-03-15 22:13체인
46089SPB-350562022-05-16 13:17안장
36450SPB-437802022-04-30 17:08안장
20793SPB-337792022-03-30 22:58체인
56189SPB-539852022-05-30 18:46기타
38833SPB-508762022-05-04 20:03페달
66022SPB-327492022-06-14 8:07체인
42659SPB-388452022-05-11 8:51기타
자전거번호등록일시고장구분
50500SPB-525932022-05-22 16:03기타
44908SPB-560922022-05-14 12:35안장
32340SPB-369012022-04-22 20:36체인
44971SPB-518182022-05-14 14:32페달
25717SPB-434812022-04-10 18:15안장
24119SPB-439792022-04-07 15:16타이어
16938SPB-319462022-03-18 19:05체인
43587SPB-620332022-05-12 15:44체인
50228SPB-488182022-05-21 22:08페달
26377SPB-303092022-04-11 19:27타이어