Overview

Dataset statistics

Number of variables5
Number of observations164
Missing cells12
Missing cells (%)1.5%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory6.5 KiB
Average record size in memory40.8 B

Variable types

Categorical1
Text3
DateTime1

Dataset

Description충청남도 공주시 결식아동 지정 급식소에 대한 데이터로 (읍면동, 가맹점명, 전화번호, 주소) 등의 항목을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=442&beforeMenuCd=DOM_000000201001001000&publicdatapk=3084483

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.6%) duplicate rowsDuplicates
전화번호 has 12 (7.3%) missing valuesMissing

Reproduction

Analysis started2024-01-09 22:38:38.886291
Analysis finished2024-01-09 22:38:39.458537
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Categorical

Distinct17
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
신관동
42 
웅진동
31 
중학동
15 
유구읍
10 
옥룡동
Other values (12)
57 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신관동 42
25.6%
웅진동 31
18.9%
중학동 15
 
9.1%
유구읍 10
 
6.1%
옥룡동 9
 
5.5%
의당면 8
 
4.9%
금학동 7
 
4.3%
반포면 7
 
4.3%
계룡면 6
 
3.7%
사곡면 5
 
3.0%
Other values (7) 24
14.6%

Length

2024-01-10T07:38:39.512955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신관동 42
25.6%
웅진동 31
18.9%
중학동 15
 
9.1%
유구읍 10
 
6.1%
옥룡동 9
 
5.5%
의당면 8
 
4.9%
금학동 7
 
4.3%
반포면 7
 
4.3%
계룡면 6
 
3.7%
사곡면 5
 
3.0%
Other values (7) 24
14.6%
Distinct156
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-01-10T07:38:39.719917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.4268293
Min length2

Characters and Unicode

Total characters1218
Distinct characters211
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

Unique148 ?
Unique (%)90.2%

Sample

1st row주식회사 계룡산오마트
2nd row백화마트
3rd row경천할인마트
4th row장순루
5th row계룡농협하나로마트
ValueCountFrequency (%)
공주마트 3
 
1.7%
주식회사 3
 
1.7%
충남슈퍼 2
 
1.1%
드림마트 2
 
1.1%
세븐일레븐공주여고점 2
 
1.1%
사곡농협하나로마트 2
 
1.1%
공주농협 2
 
1.1%
김밥천국 2
 
1.1%
주공할인마트 2
 
1.1%
소비자마트 2
 
1.1%
Other values (156) 157
87.7%
2024-01-10T07:38:40.051946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
 
5.7%
67
 
5.5%
60
 
4.9%
47
 
3.9%
46
 
3.8%
38
 
3.1%
2 28
 
2.3%
G 27
 
2.2%
S 26
 
2.1%
5 26
 
2.1%
Other values (201) 783
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1069
87.8%
Uppercase Letter 63
 
5.2%
Decimal Number 56
 
4.6%
Space Separator 15
 
1.2%
Close Punctuation 7
 
0.6%
Open Punctuation 7
 
0.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
70
 
6.5%
67
 
6.3%
60
 
5.6%
47
 
4.4%
46
 
4.3%
38
 
3.6%
25
 
2.3%
25
 
2.3%
23
 
2.2%
23
 
2.2%
Other values (189) 645
60.3%
Decimal Number
ValueCountFrequency (%)
2 28
50.0%
5 26
46.4%
4 1
 
1.8%
1 1
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
G 27
42.9%
S 26
41.3%
U 5
 
7.9%
C 5
 
7.9%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1069
87.8%
Common 86
 
7.1%
Latin 63
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
70
 
6.5%
67
 
6.3%
60
 
5.6%
47
 
4.4%
46
 
4.3%
38
 
3.6%
25
 
2.3%
25
 
2.3%
23
 
2.2%
23
 
2.2%
Other values (189) 645
60.3%
Common
ValueCountFrequency (%)
2 28
32.6%
5 26
30.2%
15
17.4%
) 7
 
8.1%
( 7
 
8.1%
4 1
 
1.2%
, 1
 
1.2%
1 1
 
1.2%
Latin
ValueCountFrequency (%)
G 27
42.9%
S 26
41.3%
U 5
 
7.9%
C 5
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1069
87.8%
ASCII 149
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
70
 
6.5%
67
 
6.3%
60
 
5.6%
47
 
4.4%
46
 
4.3%
38
 
3.6%
25
 
2.3%
25
 
2.3%
23
 
2.2%
23
 
2.2%
Other values (189) 645
60.3%
ASCII
ValueCountFrequency (%)
2 28
18.8%
G 27
18.1%
S 26
17.4%
5 26
17.4%
15
10.1%
) 7
 
4.7%
( 7
 
4.7%
U 5
 
3.4%
C 5
 
3.4%
4 1
 
0.7%
Other values (2) 2
 
1.3%

전화번호
Text

MISSING 

Distinct145
Distinct (%)95.4%
Missing12
Missing (%)7.3%
Memory size1.4 KiB
2024-01-10T07:38:40.292820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1824
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique138 ?
Unique (%)90.8%

Sample

1st row041-853-6677
2nd row041-857-8465
3rd row041-853-7663
4th row041-857-3498
5th row041-857-5035
ValueCountFrequency (%)
041-857-9999 2
 
1.3%
041-854-7741 2
 
1.3%
041-881-9511 2
 
1.3%
041-855-6413 2
 
1.3%
041-856-1017 2
 
1.3%
041-841-0624 2
 
1.3%
041-857-6885 2
 
1.3%
041-857-0037 1
 
0.7%
041-856-4097 1
 
0.7%
041-857-0011 1
 
0.7%
Other values (135) 135
88.8%
2024-01-10T07:38:40.680251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 304
16.7%
0 256
14.0%
4 232
12.7%
1 232
12.7%
8 228
12.5%
5 203
11.1%
6 84
 
4.6%
3 78
 
4.3%
2 73
 
4.0%
7 71
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1520
83.3%
Dash Punctuation 304
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 256
16.8%
4 232
15.3%
1 232
15.3%
8 228
15.0%
5 203
13.4%
6 84
 
5.5%
3 78
 
5.1%
2 73
 
4.8%
7 71
 
4.7%
9 63
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1824
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 304
16.7%
0 256
14.0%
4 232
12.7%
1 232
12.7%
8 228
12.5%
5 203
11.1%
6 84
 
4.6%
3 78
 
4.3%
2 73
 
4.0%
7 71
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 304
16.7%
0 256
14.0%
4 232
12.7%
1 232
12.7%
8 228
12.5%
5 203
11.1%
6 84
 
4.6%
3 78
 
4.3%
2 73
 
4.0%
7 71
 
3.9%

주소
Text

Distinct161
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-01-10T07:38:40.906891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.29878
Min length16

Characters and Unicode

Total characters3821
Distinct characters169
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)96.3%

Sample

1st row충청남도 공주시 계룡면 갑사로 5
2nd row충청남도 공주시 계룡면 영규대사로 478(월암리54-1)
3rd row충청남도 공주시 계룡면 신원사로 117
4th row충청남도 공주시 계룡면 월암리 23-5
5th row충청남도 공주시 계룡면 월암리 241
ValueCountFrequency (%)
공주시 161
19.9%
충청남도 129
 
15.9%
충남 32
 
4.0%
신관동 30
 
3.7%
산성동 15
 
1.9%
중동 14
 
1.7%
의당면 9
 
1.1%
웅진로 7
 
0.9%
유구읍 7
 
0.9%
반포면 7
 
0.9%
Other values (305) 399
49.3%
2024-01-10T07:38:41.240705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/