Overview

Dataset statistics

Number of variables2
Number of observations382
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory16.3 B

Variable types

Text2

Dataset

Description샘플 데이터
Author롯데멤버스
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=56

Alerts

지역(AREA) has unique valuesUnique
코드(CODE) has unique valuesUnique

Reproduction

Analysis started2024-04-17 23:18:18.340202
Analysis finished2024-04-17 23:18:19.213334
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역(AREA)
Text

UNIQUE 

Distinct382
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-04-18T08:18:19.540561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.0078534
Min length5

Characters and Unicode

Total characters2677
Distinct characters212
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

Unique382 ?
Unique (%)100.0%

Sample

1st row강남구 개포동
2nd row강남구 논현동
3rd row강남구 대치동
4th row강남구 도곡동
5th row강남구 삼성동
ValueCountFrequency (%)
종로구 72
 
9.4%
중구 44
 
5.8%
마포구 26
 
3.4%
용산구 24
 
3.1%
관악구 22
 
2.9%
서대문구 19
 
2.5%
강남구 15
 
2.0%
송파구 15
 
2.0%
성북구 13
 
1.7%
은평구 12
 
1.6%
Other values (391) 502
65.7%
2024-04-18T08:18:20.030321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 420
 
15.7%
구 400
 
14.9%
382
 
14.3%
로 96
 
3.6%
종 75
 
2.8%
중 59
 
2.2%
서 51
 
1.9%
강 49
 
1.8%
대 41
 
1.5%
문 41
 
1.5%
Other values (202) 1063
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2295
85.7%
Space Separator 382
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 420
18.3%
구 400
 
17.4%
로 96
 
4.2%
종 75
 
3.3%
중 59
 
2.6%
서 51
 
2.2%
강 49
 
2.1%
대 41
 
1.8%
문 41
 
1.8%
포 40
 
1.7%
Other values (201) 1023
44.6%
Space Separator
ValueCountFrequency (%)
382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2295
85.7%
Common 382
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 420
18.3%
구 400
 
17.4%
로 96
 
4.2%
종 75
 
3.3%
중 59
 
2.6%
서 51
 
2.2%
강 49
 
2.1%
대 41
 
1.8%
문 41
 
1.8%
포 40
 
1.7%
Other values (201) 1023
44.6%
Common
ValueCountFrequency (%)
382
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2295
85.7%
ASCII 382
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 420
18.3%
구 400
 
17.4%
로 96
 
4.2%
종 75
 
3.3%
중 59
 
2.6%
서 51
 
2.2%
강 49
 
2.1%
대 41
 
1.8%
문 41
 
1.8%
포 40
 
1.7%
Other values (201) 1023
44.6%
ASCII
ValueCountFrequency (%)
382
100.0%

코드(CODE)
Text

UNIQUE 

Distinct382
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-04-18T08:18:20.475367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1146
Distinct characters35
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

Unique382 ?
Unique (%)100.0%

Sample

1st rowA01
2nd rowA02
3rd rowA03
4th rowA04
5th rowA05
ValueCountFrequency (%)
a01 1
 
0.3%
v03 1
 
0.3%
v12 1
 
0.3%
v11 1
 
0.3%
v10 1
 
0.3%
v09 1
 
0.3%
v08 1
 
0.3%
v07 1
 
0.3%
v06 1
 
0.3%
v05 1
 
0.3%
Other values (372) 372
97.4%
2024-04-18T08:18:21.002114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 226
19.7%
1 142
12.4%
2 84
 
7.3%
W 72
 
6.3%
3 64
 
5.6%
4 56
 
4.9%
5 48
 
4.2%
6 45
 
3.9%
X 44
 
3.8%
7 36
 
3.1%
Other values (25) 329
28.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 764
66.7%
Uppercase Letter 382
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 72
18.8%
X 44
 
11.5%
M 26
 
6.8%
U 24
 
6.3%
E 22
 
5.8%
N 19
 
5.0%
A 15
 
3.9%
R 15
 
3.9%
Q 13
 
3.4%
V 12
 
3.1%
Other values (15) 120
31.4%
Decimal Number
ValueCountFrequency (%)
0 226
29.6%
1 142
18.6%
2 84
 
11.0%
3 64
 
8.4%
4 56
 
7.3%
5 48
 
6.3%
6 45
 
5.9%
7 36
 
4.7%
8 32
 
4.2%
9 31
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 764
66.7%
Latin 382
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 72
18.8%
X 44
 
11.5%
M 26
 
6.8%
U 24
 
6.3%
E 22
 
5.8%
N 19
 
5.0%
A 15
 
3.9%
R 15
 
3.9%
Q 13
 
3.4%
V 12
 
3.1%
Other values (15) 120
31.4%
Common
ValueCountFrequency (%)
0 226
29.6%
1 142
18.6%
2 84
 
11.0%
3 64
 
8.4%
4 56
 
7.3%
5 48
 
6.3%
6 45
 
5.9%
7 36
 
4.7%
8 32
 
4.2%
9 31
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 226
19.7%
1 142
12.4%
2 84
 
7.3%
W 72
 
6.3%
3 64
 
5.6%
4 56
 
4.9%
5 48
 
4.2%
6 45
 
3.9%
X 44
 
3.8%
7 36
 
3.1%
Other values (25) 329
28.7%

Missing values

2024-04-18T08:18:19.181004image/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

지역(AREA)코드(CODE)
0강남구 개포동A01
1강남구 논현동A02
2강남구 대치동A03
3강남구 도곡동A04
4강남구 삼성동A05
5강남구 세곡동A06
6강남구 수서동A07
7강남구 신사동A08
8강남구 압구정동A09
9강남구 역삼동A10
지역(AREA)코드(CODE)
372중구 필동X41
373중구 황학동X42
374중구 회현동X43
375중구 흥인동X44
376중랑구 망우동Y01
377중랑구 면목동Y02
378중랑구 묵동Y03
379중랑구 상봉동Y04
380중랑구 신내동Y05
381중랑구 중화동Y06