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/A/1/datasetView.do

Reproduction

Analysis started2024-05-03 21:46:47.556627
Analysis finished2024-05-03 21:46:48.806032
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct8015
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T21:46:49.502642image/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

Unique6402 ?
Unique (%)64.0%

Sample

1st rowSPB-63014
2nd rowSPB-36020
3rd rowSPB-47983
4th rowSPB-57182
5th rowSPB-32611
ValueCountFrequency (%)
spb-58572 6
 
0.1%
spb-52473 5
 
< 0.1%
spb-45722 5
 
< 0.1%
spb-53016 5
 
< 0.1%
spb-44200 5
 
< 0.1%
spb-53137 5
 
< 0.1%
spb-45403 5
 
< 0.1%
spb-38781 5
 
< 0.1%
spb-31055 5
 
< 0.1%
spb-33489 5
 
< 0.1%
Other values (8005) 9949
99.5%
2024-05-03T21:46:51.043682image/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%
4 7131
7.9%
3 7121
7.9%
5 7018
7.8%
6 4443
 
4.9%
0 4259
 
4.7%
1 4252
 
4.7%
Other values (4) 15776
17.5%

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 (%)
4 7131
14.3%
3 7121
14.2%
5 7018
14.0%
6 4443
8.9%
0 4259
8.5%
1 4252
8.5%
8 4236
8.5%
2 4183
8.4%
7 3904
7.8%
9 3453
6.9%
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%
4 7131
11.9%
3 7121
11.9%
5 7018
11.7%
6 4443
7.4%
0 4259
7.1%
1 4252
7.1%
8 4236
7.1%
2 4183
7.0%
7 3904
 
6.5%
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%
4 7131
7.9%
3 7121
7.9%
5 7018
7.8%
6 4443
 
4.9%
0 4259
 
4.7%
1 4252
 
4.7%
Other values (4) 15776
17.5%

등록일시
Date

Distinct9392
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 01:55:00
Maximum2022-06-29 22:51:00
2024-05-03T21:46:51.619699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:46:52.181151image/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
3337 
안장
1910 
체인
1847 
타이어
1534 
페달
857 

Length

Max length4
Median length3
Mean length2.692
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 3337
33.4%
안장 1910
19.1%
체인 1847
18.5%
타이어 1534
15.3%
페달 857
 
8.6%
단말기 515
 
5.1%

Length

2024-05-03T21:46:52.947111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:46:53.488479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 3337
33.4%
안장 1910
19.1%
체인 1847
18.5%
타이어 1534
15.3%
페달 857
 
8.6%
단말기 515
 
5.1%

Missing values

2024-05-03T21:46:48.260180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T21:46:48.635298image/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
71679SPB-630142022-06-22 17:26기타
3084SPB-360202022-01-13 8:53체인
70981SPB-479832022-06-21 19:08기타
36137SPB-571822022-04-29 20:45체인
42096SPB-326112022-05-10 16:29페달
71462SPB-343742022-06-22 9:14체인
19088SPB-445502022-03-26 17:15기타
50690SPB-398792022-05-22 19:59안장
65363SPB-569932022-06-13 12:58기타
32459SPB-409172022-04-23 9:03체인
자전거번호등록일시고장구분
44285SPB-397642022-05-13 13:31타이어
68714SPB-616672022-06-18 14:03안장
45109SPB-625642022-05-14 17:53타이어
57679SPB-468132022-06-01 20:38페달
67852SPB-451022022-06-17 8:30안장
24794SPB-579592022-04-08 18:58단말기
68673SPB-401762022-06-18 12:42안장
26161SPB-617872022-04-11 15:48기타
16834SPB-464632022-03-18 11:57체인
72622SPB-527542022-06-24 18:08기타