Overview

Dataset statistics

Number of variables3
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory27.1 B

Variable types

Categorical2
Text1

Dataset

Description파일 다운로드
Author서울 교통공사
URLhttps://data.seoul.go.kr/dataList/OA-13192/F/1/datasetView.do

Alerts

설치현황 is highly imbalanced (55.8%)Imbalance

Reproduction

Analysis started2023-12-11 06:11:18.763931
Analysis finished2023-12-11 06:11:19.397611
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호선
Categorical

Distinct4
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
50 
3
34 
4
26 
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 50
41.7%
3 34
28.3%
4 26
21.7%
1 10
 
8.3%

Length

2023-12-11T15:11:19.487047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:11:19.613362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 50
41.7%
3 34
28.3%
4 26
21.7%
1 10
 
8.3%

역명
Text

Distinct110
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T15:11:19.950892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.9416667
Min length2

Characters and Unicode

Total characters353
Distinct characters141
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)83.3%

Sample

1st row서울
2nd row시청
3rd row종각
4th row종로3가
5th row종로5가
ValueCountFrequency (%)
서울 2
 
1.7%
충무로 2
 
1.7%
동대문역사문화공원 2
 
1.7%
을지로3가 2
 
1.7%
교대 2
 
1.7%
시청 2
 
1.7%
동대문 2
 
1.7%
종로3가 2
 
1.7%
신설동 2
 
1.7%
사당 2
 
1.7%
Other values (100) 100
83.3%
2023-12-11T15:11:20.520283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 21
 
5.9%
신 15
 
4.2%
구 15
 
4.2%
동 13
 
3.7%
로 11
 
3.1%
입 9
 
2.5%
문 9
 
2.5%
지 8
 
2.3%
가 7
 
2.0%
청 6
 
1.7%
Other values (131) 239
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 347
98.3%
Decimal Number 6
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 21
 
6.1%
신 15
 
4.3%
구 15
 
4.3%
동 13
 
3.7%
로 11
 
3.2%
입 9
 
2.6%
문 9
 
2.6%
지 8
 
2.3%
가 7
 
2.0%
청 6
 
1.7%
Other values (128) 233
67.1%
Decimal Number
ValueCountFrequency (%)
3 4
66.7%
5 1
 
16.7%
4 1
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 347
98.3%
Common 6
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 21
 
6.1%
신 15
 
4.3%
구 15
 
4.3%
동 13
 
3.7%
로 11
 
3.2%
입 9
 
2.6%
문 9
 
2.6%
지 8
 
2.3%
가 7
 
2.0%
청 6
 
1.7%
Other values (128) 233
67.1%
Common
ValueCountFrequency (%)
3 4
66.7%
5 1
 
16.7%
4 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 347
98.3%
ASCII 6
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 21
 
6.1%
신 15
 
4.3%
구 15
 
4.3%
동 13
 
3.7%
로 11
 
3.2%
입 9
 
2.6%
문 9
 
2.6%
지 8
 
2.3%
가 7
 
2.0%
청 6
 
1.7%
Other values (128) 233
67.1%
ASCII
ValueCountFrequency (%)
3 4
66.7%
5 1
 
16.7%
4 1
 
16.7%

설치현황
Categorical

IMBALANCE 

Distinct6
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
92 
1
18 
2
 
5
6
 
3
5
 
1

Length

Max length4
Median length4
Mean length3.3
Min length1

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st row2
2nd row<NA>
3rd row<NA>
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 92
76.7%
1 18
 
15.0%
2 5
 
4.2%
6 3
 
2.5%
5 1
 
0.8%
3 1
 
0.8%

Length

2023-12-11T15:11:20.738369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:11:20.900138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 92
76.7%
1 18
 
15.0%
2 5
 
4.2%
6 3
 
2.5%
5 1
 
0.8%
3 1
 
0.8%

Correlations

2023-12-11T15:11:21.024916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선설치현황
호선1.0000.000
설치현황0.0001.000
2023-12-11T15:11:21.118580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선설치현황
호선1.0000.000
설치현황0.0001.000
2023-12-11T15:11:21.217443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선설치현황
호선1.0000.000
설치현황0.0001.000

Missing values

2023-12-11T15:11:19.252694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:11:19.357743image/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

호선역명설치현황
01서울2
11시청<NA>
21종각<NA>
31종로3가1
41종로5가<NA>
51동대문<NA>
61동묘앞<NA>
71신설동6
81제기동<NA>
91청량리1
호선역명설치현황
1104회현1
1114서울2
1124숙대입구<NA>
1134삼각지<NA>
1144신용산1
1154이촌1
1164동작<NA>
1174총신대입구<NA>
1184사당1
1194남태령<NA>