Overview

Dataset statistics

Number of variables6
Number of observations97
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)2.1%
Total size in memory4.9 KiB
Average record size in memory51.4 B

Variable types

Categorical5
Text1

Dataset

Description보령시에 설치되어 있는 현수막 지정 게시대의 정보(설치 읍면동, 위치, 현수막 규격, 상업용 게시면수, 행정용 게시면수)를 제공하는 파일데이터 입니다. (제공부서: 도시과 041-930-3881)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=419&beforeMenuCd=DOM_000000201001001000&publicdatapk=15031394

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 2 (2.1%) duplicate rowsDuplicates
게시면수(상업용) is highly overall correlated with 규격(m) and 1 other fieldsHigh correlation
게시면수(행정용) is highly overall correlated with 게시면수(상업용)High correlation
규격(m) is highly overall correlated with 게시면수(상업용)High correlation
규격(m) is highly imbalanced (66.5%)Imbalance

Reproduction

Analysis started2024-01-09 22:49:08.234648
Analysis finished2024-01-09 22:49:08.579920
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Categorical

Distinct17
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size908.0 B
대천4동
31 
남포면
8 
대천3동
8 
대천5동
7 
웅천읍
7 
Other values (12)
36 

Length

Max length5
Median length4
Mean length3.5670103
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
대천4동 31
32.0%
남포면 8
 
8.2%
대천3동 8
 
8.2%
대천5동 7
 
7.2%
웅천읍 7
 
7.2%
주교면 5
 
5.2%
성주면 5
 
5.2%
천북면 4
 
4.1%
대천2동 4
 
4.1%
대천1동 3
 
3.1%
Other values (7) 15
15.5%

Length

2024-01-10T07:49:08.641222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대천4동 31
32.0%
남포면 8
 
8.2%
대천3동 8
 
8.2%
대천5동 7
 
7.2%
웅천읍 7
 
7.2%
주교면 5
 
5.2%
성주면 5
 
5.2%
천북면 4
 
4.1%
대천2동 4
 
4.1%
대천1동 4
 
4.1%
Other values (6) 14
14.4%

위치
Text

Distinct80
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size908.0 B
2024-01-10T07:49:08.872577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length10.649485
Min length5

Characters and Unicode

Total characters1033
Distinct characters136
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)80.4%

Sample

1st row남포저수지 앞
2nd row남포중학교 입구
3rd row남포보건소 앞
4th row남포파출소 앞
5th row남포면사무소 앞
ValueCountFrequency (%)
앞 26
 
10.0%
터미널사거리 17
 
6.5%
신설사거리 17
 
6.5%
17
 
6.5%
삼거리 9
 
3.5%
입구 8
 
3.1%
1 6
 
2.3%
주차장 6
 
2.3%
2 6
 
2.3%
앞(1 5
 
1.9%
Other values (97) 143
55.0%
2024-01-10T07:49:09.244436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
172
 
16.7%
리 65
 
6.3%
거 47
 
4.5%
사 46
 
4.5%
앞 37
 
3.6%
( 33
 
3.2%
) 33
 
3.2%
터 26
 
2.5%
신 24
 
2.3%
주 22
 
2.1%
Other values (126) 528
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 739
71.5%
Space Separator 172
 
16.7%
Decimal Number 36
 
3.5%
Open Punctuation 33
 
3.2%
Close Punctuation 33
 
3.2%
Math Symbol 17
 
1.6%
Uppercase Letter 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 65
 
8.8%
거 47
 
6.4%
사 46
 
6.2%
앞 37
 
5.0%
터 26
 
3.5%
신 24
 
3.2%
주 22
 
3.0%
미 21
 
2.8%
널 19
 
2.6%
설 19
 
2.6%
Other values (115) 413
55.9%
Decimal Number
ValueCountFrequency (%)
1 17
47.2%
2 15
41.7%
3 3
 
8.3%
5 1
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
G 1
33.3%
L 1
33.3%
P 1
33.3%
Space Separator
ValueCountFrequency (%)
172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 739
71.5%
Common 291
 
28.2%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 65
 
8.8%
거 47
 
6.4%
사 46
 
6.2%
앞 37
 
5.0%
터 26
 
3.5%
신 24
 
3.2%
주 22
 
3.0%
미 21
 
2.8%
널 19
 
2.6%
설 19
 
2.6%
Other values (115) 413
55.9%
Common
ValueCountFrequency (%)
172
59.1%
( 33
 
11.3%
) 33
 
11.3%
~ 17
 
5.8%
1 17
 
5.8%
2 15
 
5.2%
3 3
 
1.0%
5 1
 
0.3%
Latin
ValueCountFrequency (%)
G 1
33.3%
L 1
33.3%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 739
71.5%
ASCII 294
 
28.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
58.5%
( 33
 
11.2%
) 33
 
11.2%
~ 17
 
5.8%
1 17
 
5.8%
2 15
 
5.1%
3 3
 
1.0%
G 1
 
0.3%
L 1
 
0.3%
P 1
 
0.3%
Hangul
ValueCountFrequency (%)
리 65
 
8.8%
거 47
 
6.4%
사 46
 
6.2%
앞 37
 
5.0%
터 26
 
3.5%
신 24
 
3.2%
주 22
 
3.0%
미 21
 
2.8%
널 19
 
2.6%
설 19
 
2.6%
Other values (115) 413
55.9%

규격(m)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
5.6 x 0.7
91 
2.4×1.7
 
6

Length

Max length9
Median length9
Mean length8.8762887
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.6 x 0.7
2nd row5.6 x 0.7
3rd row5.6 x 0.7
4th row5.6 x 0.7
5th row5.6 x 0.7

Common Values

ValueCountFrequency (%)
5.6 x 0.7 91
93.8%
2.4×1.7 6
 
6.2%

Length

2024-01-10T07:49:09.377203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:09.465766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5.6 91
32.6%
x 91
32.6%
0.7 91
32.6%
2.4×1.7 6
 
2.2%

게시면수(상업용)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
5
48 
6
26 
0
19 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 48
49.5%
6 26
26.8%
0 19
 
19.6%
2 4
 
4.1%

Length

2024-01-10T07:49:09.551662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:09.631219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 48
49.5%
6 26
26.8%
0 19
 
19.6%
2 4
 
4.1%

게시면수(행정용)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
0
78 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 78
80.4%
1 19
 
19.6%

Length

2024-01-10T07:49:09.714819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:09.799034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 78
80.4%
1 19
 
19.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2020-09-04
97 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-04
2nd row2020-09-04
3rd row2020-09-04
4th row2020-09-04
5th row2020-09-04

Common Values

ValueCountFrequency (%)
2020-09-04 97
100.0%

Length

2024-01-10T07:49:09.880475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:49:09.960378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-04 97
100.0%

Correlations

2024-01-10T07:49:10.006530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동위치규격(m)게시면수(상업용)게시면수(행정용)
읍면동1.0001.0000.5420.6930.585
위치1.0001.0001.0001.0001.000
규격(m)0.5421.0001.0000.9600.000
게시면수(상업용)0.6931.0000.9601.0001.000
게시면수(행정용)0.5851.0000.0001.0001.000
2024-01-10T07:49:10.099268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동규격(m)게시면수(상업용)게시면수(행정용)
읍면동1.0000.4480.4300.485
규격(m)0.4481.0000.8110.000
게시면수(상업용)0.4300.8111.0000.989
게시면수(행정용)0.4850.0000.9891.000
2024-01-10T07:49:10.178113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동규격(m)게시면수(상업용)게시면수(행정용)
읍면동1.0000.4480.4300.485
규격(m)0.4481.0000.8110.000
게시면수(상업용)0.4300.8111.0000.989
게시면수(행정용)0.4850.0000.9891.000

Missing values

2024-01-10T07:49:08.457141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:49:08.545484image/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

읍면동위치규격(m)게시면수(상업용)게시면수(행정용)데이터기준일자
0남포면남포저수지 앞5.6 x 0.7502020-09-04
1남포면남포중학교 입구5.6 x 0.7502020-09-04
2남포면남포보건소 앞5.6 x 0.7502020-09-04
3남포면남포파출소 앞5.6 x 0.7502020-09-04
4남포면남포면사무소 앞5.6 x 0.7502020-09-04
5남포면욕장 흑포 삼거리 (1)5.6 x 0.7602020-09-04
6남포면욕장 흑포 삼거리 (2)5.6 x 0.7602020-09-04
7남포면죽도 입구 앞2.4×1.7202020-09-04
8대천1동대천 체육관 앞(2)5.6 x 0.7602020-09-04
9대천1동대천노인복지관 앞5.6 x 0.7502020-09-04
읍면동위치규격(m)게시면수(상업용)게시면수(행정용)데이터기준일자
87대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
88대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
89대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
90대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
91대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
92대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
93대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
94대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
95대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04
96대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-04

Duplicate rows

Most frequently occurring

읍면동위치규격(m)게시면수(상업용)게시면수(행정용)데이터기준일자# duplicates
1대천4동터미널사거리 ~ 신설사거리5.6 x 0.7012020-09-0417
0대천3동한내로터리2.4×1.7012020-09-042