Overview

Dataset statistics

Number of variables2
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)14.5%
Total size in memory1012.0 B
Average record size in memory18.4 B

Variable types

Text1
Categorical1

Dataset

Description전라북도 임실군 공간정보시스템의 테이블중 하나인 가구획지 테이블 정보로써 블록명, 블록타입 지구코드가 포함된 자료입니다.
Author전라북도 임실군
URLhttps://www.data.go.kr/data/15122651/fileData.do

Alerts

Dataset has 8 (14.5%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 22:27:13.512076
Analysis finished2023-12-12 22:27:13.686928
Duration0.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

블록명(BLOCKNAME)
Text

Distinct30
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-13T07:27:13.762276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length1.8363636
Min length1

Characters and Unicode

Total characters101
Distinct characters43
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)40.0%

Sample

1st row녹
2nd row치즈피아
3rd row치즈캐슬
4th row광1
5th row주민1
ValueCountFrequency (%)
주거 16
29.1%
완 4
 
7.3%
도 3
 
5.5%
ⅱ 2
 
3.6%
상 2
 
3.6%
주1 2
 
3.6%
경 2
 
3.6%
주2 2
 
3.6%
근공1 1
 
1.8%
완1 1
 
1.8%
Other values (20) 20
36.4%
2023-12-13T07:27:13.999295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 21
20.8%
거 16
15.8%
1 10
 
9.9%
완 5
 
5.0%
2 3
 
3.0%
도 3
 
3.0%
즈 2
 
2.0%
치 2
 
2.0%
근 2
 
2.0%
경 2
 
2.0%
Other values (33) 35
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
76.2%
Decimal Number 13
 
12.9%
Letter Number 10
 
9.9%
Other Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 21
27.3%
거 16
20.8%
완 5
 
6.5%
도 3
 
3.9%
즈 2
 
2.6%
치 2
 
2.6%
근 2
 
2.6%
경 2
 
2.6%
상 2
 
2.6%
공 1
 
1.3%
Other values (21) 21
27.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
20.0%
Ⅲ 1
10.0%
Ⅷ 1
10.0%
Ⅳ 1
10.0%
Ⅵ 1
10.0%
Ⅴ 1
10.0%
Ⅶ 1
10.0%
Ⅰ 1
10.0%
Ⅸ 1
10.0%
Decimal Number
ValueCountFrequency (%)
1 10
76.9%
2 3
 
23.1%
Other Punctuation
ValueCountFrequency (%)
＆ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
76.2%
Common 14
 
13.9%
Latin 10
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 21
27.3%
거 16
20.8%
완 5
 
6.5%
도 3
 
3.9%
즈 2
 
2.6%
치 2
 
2.6%
근 2
 
2.6%
경 2
 
2.6%
상 2
 
2.6%
공 1
 
1.3%
Other values (21) 21
27.3%
Latin
ValueCountFrequency (%)
Ⅱ 2
20.0%
Ⅲ 1
10.0%
Ⅷ 1
10.0%
Ⅳ 1
10.0%
Ⅵ 1
10.0%
Ⅴ 1
10.0%
Ⅶ 1
10.0%
Ⅰ 1
10.0%
Ⅸ 1
10.0%
Common
ValueCountFrequency (%)
1 10
71.4%
2 3
 
21.4%
＆ 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
76.2%
ASCII 13
 
12.9%
Number Forms 10
 
9.9%
None 1
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 21
27.3%
거 16
20.8%
완 5
 
6.5%
도 3
 
3.9%
즈 2
 
2.6%
치 2
 
2.6%
근 2
 
2.6%
경 2
 
2.6%
상 2
 
2.6%
공 1
 
1.3%
Other values (21) 21
27.3%
ASCII
ValueCountFrequency (%)
1 10
76.9%
2 3
 
23.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
20.0%
Ⅲ 1
10.0%
Ⅷ 1
10.0%
Ⅳ 1
10.0%
Ⅵ 1
10.0%
Ⅴ 1
10.0%
Ⅶ 1
10.0%
Ⅰ 1
10.0%
Ⅸ 1
10.0%
None
ValueCountFrequency (%)
＆ 1
100.0%

블록타입(BLOCKTYPE)
Categorical

Distinct18
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Memory size572.0 B
주거용지
16 
단독주택
10 
완충녹지
5 
주차장
4 
도로
3 
Other values (13)
17 

Length

Max length8
Median length4
Mean length3.9272727
Min length2

Unique

Unique10 ?
Unique (%)18.2%

Sample

1st row녹지
2nd row관광레저
3rd row관광레저
4th row광장기타
5th row주민센터

Common Values

ValueCountFrequency (%)
주거용지 16
29.1%
단독주택 10
18.2%
완충녹지 5
 
9.1%
주차장 4
 
7.3%
도로 3
 
5.5%
관광레저 3
 
5.5%
상업용지 2
 
3.6%
경관녹지 2
 
3.6%
근린공원 1
 
1.8%
광장기타 1
 
1.8%
Other values (8) 8
14.5%

Length

2023-12-13T07:27:14.119704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주거용지 16
29.1%
단독주택 10
18.2%
완충녹지 5
 
9.1%
주차장 4
 
7.3%
도로 3
 
5.5%
관광레저 3
 
5.5%
상업용지 2
 
3.6%
경관녹지 2
 
3.6%
학교 1
 
1.8%
근린공공시설 1
 
1.8%
Other values (8) 8
14.5%

Correlations

2023-12-13T07:27:14.186159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
블록명(BLOCKNAME)블록타입(BLOCKTYPE)
블록명(BLOCKNAME)1.0001.000
블록타입(BLOCKTYPE)1.0001.000

Missing values

2023-12-13T07:27:13.606205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:27:13.661572image/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

블록명(BLOCKNAME)블록타입(BLOCKTYPE)
0녹녹지
1치즈피아관광레저
2치즈캐슬관광레저
3광1광장기타
4주민1주민센터
5소2소공원
6근1근린공원
7학1학교
8주2주차장
9종말1하수종말처리시설
블록명(BLOCKNAME)블록타입(BLOCKTYPE)
45주1주차장
46상상업용지
47경경관녹지
48주거주거용지
49주거주거용지
50주거주거용지
51주거주거용지
52주거주거용지
53주거주거용지
54주거주거용지

Duplicate rows

Most frequently occurring

블록명(BLOCKNAME)블록타입(BLOCKTYPE)# duplicates
7주거주거용지16
4완완충녹지4
2도도로3
0Ⅱ단독주택2
1경경관녹지2
3상상업용지2
5주1주차장2
6주2주차장2