Overview

Dataset statistics

Number of variables5
Number of observations2136
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.7 KiB
Average record size in memory41.1 B

Variable types

Categorical3
Text1
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15976/S/1/datasetView.do

Alerts

기관 명 has constant value ""Constant
모델명 has constant value ""Constant

Reproduction

Analysis started2024-05-17 22:42:54.470230
Analysis finished2024-05-17 22:42:55.161533
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
양천구
2136 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양천구
2nd row양천구
3rd row양천구
4th row양천구
5th row양천구

Common Values

ValueCountFrequency (%)
양천구 2136
100.0%

Length

2024-05-18T07:42:55.365851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:42:55.743169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양천구 2136
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
G15v2
2136 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG15v2
2nd rowG15v2
3rd rowG15v2
4th rowG15v2
5th rowG15v2

Common Values

ValueCountFrequency (%)
G15v2 2136
100.0%

Length

2024-05-18T07:42:56.052752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:42:56.343881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g15v2 2136
100.0%

시리얼
Text

Distinct111
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
2024-05-18T07:42:56.830457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12816
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowyc0611
2nd rowyc0386
3rd rowyc0558
4th rowyc0558
5th rowyc0594
ValueCountFrequency (%)
yc0554 190
 
8.9%
yc0559 168
 
7.9%
yc0344 119
 
5.6%
yc0612 97
 
4.5%
yc0567 55
 
2.6%
yc0379 48
 
2.2%
yc0564 46
 
2.2%
yc0576 45
 
2.1%
yc0340 44
 
2.1%
yc0341 38
 
1.8%
Other values (101) 1286
60.2%
2024-05-18T07:42:57.767987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2515
19.6%
y 2136
16.7%
c 2136
16.7%
5 1603
12.5%
3 1241
9.7%
4 810
 
6.3%
6 602
 
4.7%
9 437
 
3.4%
1 416
 
3.2%
7 408
 
3.2%
Other values (2) 512
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8544
66.7%
Lowercase Letter 4272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2515
29.4%
5 1603
18.8%
3 1241
14.5%
4 810
 
9.5%
6 602
 
7.0%
9 437
 
5.1%
1 416
 
4.9%
7 408
 
4.8%
2 263
 
3.1%
8 249
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
y 2136
50.0%
c 2136
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8544
66.7%
Latin 4272
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2515
29.4%
5 1603
18.8%
3 1241
14.5%
4 810
 
9.5%
6 602
 
7.0%
9 437
 
5.1%
1 416
 
4.9%
7 408
 
4.8%
2 263
 
3.1%
8 249
 
2.9%
Latin
ValueCountFrequency (%)
y 2136
50.0%
c 2136
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2515
19.6%
y 2136
16.7%
c 2136
16.7%
5 1603
12.5%
3 1241
9.7%
4 810
 
6.3%
6 602
 
4.7%
9 437
 
3.4%
1 416
 
3.2%
7 408
 
3.2%
Other values (2) 512
 
4.0%

주차유무
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
0
1136 
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1136
53.2%
1 1000
46.8%

Length

2024-05-18T07:42:58.112318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:42:58.393668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1136
53.2%
1 1000
46.8%

등록일자
Date

Distinct2128
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
Minimum2024-01-29 00:03:58
Maximum2024-02-04 23:17:27
2024-05-18T07:42:58.655064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:42:59.054571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2024-05-18T07:42:54.694873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T07:42:55.036215image/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

기관 명모델명시리얼주차유무등록일자
0양천구G15v2yc061112024-01-29 00:03:58
1양천구G15v2yc038612024-01-29 00:35:36
2양천구G15v2yc055812024-01-29 01:58:29
3양천구G15v2yc055802024-01-29 01:58:49
4양천구G15v2yc059402024-01-29 04:34:37
5양천구G15v2yc034902024-01-29 05:33:18
6양천구G15v2yc059412024-01-29 05:43:16
7양천구G15v2yc059402024-01-29 05:46:28
8양천구G15v2yc037912024-01-29 05:53:21
9양천구G15v2yc037902024-01-29 05:53:45
기관 명모델명시리얼주차유무등록일자
2126양천구G15v2yc037212024-02-04 21:47:55
2127양천구G15v2yc059412024-02-04 21:57:44
2128양천구G15v2yc061212024-02-04 22:04:35
2129양천구G15v2yc061202024-02-04 22:04:56
2130양천구G15v2yc061212024-02-04 22:05:36
2131양천구G15v2yc061202024-02-04 22:05:56
2132양천구G15v2yc057402024-02-04 22:09:54
2133양천구G15v2yc037202024-02-04 22:11:11
2134양천구G15v2yc058812024-02-04 22:19:24
2135양천구G15v2yc038312024-02-04 23:17:27