Overview

Dataset statistics

Number of variables5
Number of observations55
Missing cells55
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory43.3 B

Variable types

Numeric1
Text4

Dataset

Description대구광역시 서구 관내의 안경업소 현황입니다. 2024. 2. 2. 기준 자료이며, 안경업소 명칭, 사업장 소재지(도로명), 전화번호를 포함하는 데이터입니다.
Author대구광역시 서구
URLhttps://www.data.go.kr/data/15103327/fileData.do

Alerts

기타유의사항 has constant value ""Constant
사업장전화번호 has 1 (1.8%) missing valuesMissing
기타유의사항 has 54 (98.2%) missing valuesMissing
순번 has unique valuesUnique
안경업소명칭 has unique valuesUnique
사업장소재지 has unique valuesUnique

Reproduction

Analysis started2024-03-15 02:13:51.670245
Analysis finished2024-03-15 02:13:53.132121
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-15T11:13:53.346479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q114.5
median28
Q341.5
95-th percentile52.3
Maximum55
Range54
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.02082
Coefficient of variation (CV)0.57217214
Kurtosis-1.2
Mean28
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance256.66667
MonotonicityStrictly increasing
2024-03-15T11:13:53.714184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
31 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%
46 1
1.8%

안경업소명칭
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size568.0 B
2024-03-15T11:13:54.725641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.4727273
Min length2

Characters and Unicode

Total characters356
Distinct characters120
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

Unique55 ?
Unique (%)100.0%

Sample

1st row아이데코 안경
2nd row렌즈미(팔달시장점)
3rd row눈사랑안경원(평리점)
4th row안경하우스안경점
5th row글라스스토리대구평리점
ValueCountFrequency (%)
안경원 2
 
3.2%
아이데코 1
 
1.6%
서남안경(see 1
 
1.6%
아이월드 1
 
1.6%
제일 1
 
1.6%
동산 1
 
1.6%
룩스안경(팔달점 1
 
1.6%
롯데마트 1
 
1.6%
진안경콘텍트렌즈 1
 
1.6%
건영 1
 
1.6%
Other values (52) 52
82.5%
2024-03-15T11:13:56.134959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 36
 
10.1%
경 35
 
9.8%
원 15
 
4.2%
점 13
 
3.7%
이 12
 
3.4%
트 12
 
3.4%
스 12
 
3.4%
콘 10
 
2.8%
아 10
 
2.8%
리 8
 
2.2%
Other values (110) 193
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 321
90.2%
Lowercase Letter 9
 
2.5%
Space Separator 8
 
2.2%
Open Punctuation 7
 
2.0%
Close Punctuation 7
 
2.0%
Uppercase Letter 3
 
0.8%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 36
 
11.2%
경 35
 
10.9%
원 15
 
4.7%
점 13
 
4.0%
이 12
 
3.7%
트 12
 
3.7%
스 12
 
3.7%
콘 10
 
3.1%
아 10
 
3.1%
리 8
 
2.5%
Other values (97) 158
49.2%
Lowercase Letter
ValueCountFrequency (%)
e 3
33.3%
n 2
22.2%
l 1
 
11.1%
a 1
 
11.1%
h 1
 
11.1%
c 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
K 1
33.3%
O 1
33.3%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 321
90.2%
Common 23
 
6.5%
Latin 12
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 36
 
11.2%
경 35
 
10.9%
원 15
 
4.7%
점 13
 
4.0%
이 12
 
3.7%
트 12
 
3.7%
스 12
 
3.7%
콘 10
 
3.1%
아 10
 
3.1%
리 8
 
2.5%
Other values (97) 158
49.2%
Latin
ValueCountFrequency (%)
e 3
25.0%
n 2
16.7%
l 1
 
8.3%
a 1
 
8.3%
h 1
 
8.3%
c 1
 
8.3%
S 1
 
8.3%
K 1
 
8.3%
O 1
 
8.3%
Common
ValueCountFrequency (%)
8
34.8%
( 7
30.4%
) 7
30.4%
. 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 321
90.2%
ASCII 35
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 36
 
11.2%
경 35
 
10.9%
원 15
 
4.7%
점 13
 
4.0%
이 12
 
3.7%
트 12
 
3.7%
스 12
 
3.7%
콘 10
 
3.1%
아 10
 
3.1%
리 8
 
2.5%
Other values (97) 158
49.2%
ASCII
ValueCountFrequency (%)
8
22.9%
( 7
20.0%
) 7
20.0%
e 3
 
8.6%
n 2
 
5.7%
l 1
 
2.9%
a 1
 
2.9%
h 1
 
2.9%
c 1
 
2.9%
S 1
 
2.9%
Other values (3) 3
 
8.6%

사업장소재지
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size568.0 B
2024-03-15T11:13:57.264285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length28
Mean length23.163636
Min length19

Characters and Unicode

Total characters1274
Distinct characters57
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row대구광역시 서구 평리동 1524-4
2nd row대구광역시 서구 원대동3가 1367-2
3rd row대구광역시 서구 평리동 1680 평리 푸르지오
4th row대구광역시 서구 원대동3가 1461번지 1호
5th row대구광역시 서구 평리동 1676번지 평리롯데캐슬
ValueCountFrequency (%)
대구광역시 55
19.7%
서구 55
19.7%
1호 7
 
2.5%
원대동3가 6
 
2.2%
평리4동 6
 
2.2%
평리3동 5
 
1.8%
평리동 5
 
1.8%
5호 4
 
1.4%
중리동 4
 
1.4%
내당1동 4
 
1.4%
Other values (95) 128
45.9%
2024-03-15T11:13:58.948443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
225
17.7%
구 111
 
8.7%
1 69
 
5.4%
대 62
 
4.9%
광 55
 
4.3%
역 55
 
4.3%
시 55
 
4.3%
서 55
 
4.3%
동 55
 
4.3%
지 53
 
4.2%
Other values (47) 479
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 739
58.0%
Decimal Number 301
23.6%
Space Separator 225
 
17.7%
Other Punctuation 3
 
0.2%
Dash Punctuation 3
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 111
15.0%
대 62
8.4%
광 55
 
7.4%
역 55
 
7.4%
시 55
 
7.4%
서 55
 
7.4%
동 55
 
7.4%
지 53
 
7.2%
번 51
 
6.9%
호 44
 
6.0%
Other values (31) 143
19.4%
Decimal Number
ValueCountFrequency (%)
1 69
22.9%
3 47
15.6%
6 39
13.0%
2 36
12.0%
4 32
10.6%
0 22
 
7.3%
5 17
 
5.6%
7 14
 
4.7%
9 13
 
4.3%
8 12
 
4.0%
Space Separator
ValueCountFrequency (%)
225
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 739
58.0%
Common 534
41.9%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 111
15.0%
대 62
8.4%
광 55
 
7.4%
역 55
 
7.4%
시 55
 
7.4%
서 55
 
7.4%
동 55
 
7.4%
지 53
 
7.2%
번 51
 
6.9%
호 44
 
6.0%
Other values (31) 143
19.4%
Common
ValueCountFrequency (%)
225
42.1%
1 69
 
12.9%
3 47
 
8.8%
6 39
 
7.3%
2 36
 
6.7%
4 32
 
6.0%
0 22
 
4.1%
5 17
 
3.2%
7 14
 
2.6%
9 13
 
2.4%
Other values (5) 20
 
3.7%
Latin
ValueCountFrequency (%)
E 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 739
58.0%
ASCII 535
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225
42.1%
1 69
 
12.9%
3 47
 
8.8%
6 39
 
7.3%
2 36
 
6.7%
4 32
 
6.0%
0 22
 
4.1%
5 17
 
3.2%
7 14
 
2.6%
9 13
 
2.4%
Other values (6) 21
 
3.9%
Hangul
ValueCountFrequency (%)
구 111
15.0%
대 62
8.4%
광 55
 
7.4%
역 55
 
7.4%
시 55
 
7.4%
서 55
 
7.4%
동 55
 
7.4%
지 53
 
7.2%
번 51
 
6.9%
호 44
 
6.0%
Other values (31) 143
19.4%

사업장전화번호
Text

MISSING 

Distinct54
Distinct (%)100.0%
Missing1
Missing (%)1.8%
Memory size568.0 B
2024-03-15T11:13:59.785829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.037037
Min length12

Characters and Unicode

Total characters650
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)100.0%

Sample

1st row053-526-0077
2nd row053-351-3171
3rd row053-553-9779
4th row070-8822-2080
5th row053-567-0171
ValueCountFrequency (%)
053-526-0077 1
 
1.9%
053)572-2737 1
 
1.9%
053)357-1797 1
 
1.9%
053)357-6522 1
 
1.9%
053)555-1266 1
 
1.9%
053)565-3365 1
 
1.9%
053)573-5678 1
 
1.9%
053)557-3635 1
 
1.9%
053)557-8712 1
 
1.9%
053)556-0700 1
 
1.9%
Other values (44) 44
81.5%
2024-03-15T11:14:01.125960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 157
24.2%
3 106
16.3%
0 98
15.1%
- 73
11.2%
7 40
 
6.2%
6 35
 
5.4%
) 35
 
5.4%
2 30
 
4.6%
8 26
 
4.0%
1 23
 
3.5%
Other values (2) 27
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 542
83.4%
Dash Punctuation 73
 
11.2%
Close Punctuation 35
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 157
29.0%
3 106
19.6%
0 98
18.1%
7 40
 
7.4%
6 35
 
6.5%
2 30
 
5.5%
8 26
 
4.8%
1 23
 
4.2%
9 14
 
2.6%
4 13
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 157
24.2%
3 106
16.3%
0 98
15.1%
- 73
11.2%
7 40
 
6.2%
6 35
 
5.4%
) 35
 
5.4%
2 30
 
4.6%
8 26
 
4.0%
1 23
 
3.5%
Other values (2) 27
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 157
24.2%
3 106
16.3%
0 98
15.1%
- 73
11.2%
7 40
 
6.2%
6 35
 
5.4%
) 35
 
5.4%
2 30
 
4.6%
8 26
 
4.0%
1 23
 
3.5%
Other values (2) 27
 
4.2%

기타유의사항
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing54
Missing (%)98.2%
Memory size568.0 B
2024-03-15T11:14:01.589707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
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

Unique1 ?
Unique (%)100.0%

Sample

1st row데이터 미집계
ValueCountFrequency (%)
데이터 1
50.0%
미집계 1
50.0%
2024-03-15T11:14:02.503783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
데 1
14.3%
이 1
14.3%
터 1
14.3%
1
14.3%
미 1
14.3%
집 1
14.3%
계 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
85.7%
Space Separator 1
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
데 1
16.7%
이 1
16.7%
터 1
16.7%
미 1
16.7%
집 1
16.7%
계 1
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
85.7%
Common 1
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
데 1
16.7%
이 1
16.7%
터 1
16.7%
미 1
16.7%
집 1
16.7%
계 1
16.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
85.7%
ASCII 1
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
데 1
16.7%
이 1
16.7%
터 1
16.7%
미 1
16.7%
집 1
16.7%
계 1
16.7%
ASCII
ValueCountFrequency (%)
1
100.0%

Interactions

2024-03-15T11:13:52.128963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:14:02.878671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번안경업소명칭사업장소재지사업장전화번호
순번1.0001.0001.0001.000
안경업소명칭1.0001.0001.0001.000
사업장소재지1.0001.0001.0001.000
사업장전화번호1.0001.0001.0001.000

Missing values

2024-03-15T11:13:52.475424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:13:52.825832image/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.
2024-03-15T11:13:53.051270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순번안경업소명칭사업장소재지사업장전화번호기타유의사항
01아이데코 안경대구광역시 서구 평리동 1524-4053-526-0077<NA>
12렌즈미(팔달시장점)대구광역시 서구 원대동3가 1367-2053-351-3171<NA>
23눈사랑안경원(평리점)대구광역시 서구 평리동 1680 평리 푸르지오053-553-9779<NA>
34안경하우스안경점대구광역시 서구 원대동3가 1461번지 1호070-8822-2080<NA>
45글라스스토리대구평리점대구광역시 서구 평리동 1676번지 평리롯데캐슬053-567-0171<NA>
56안경매니져안경원대구광역시 서구 중리동 113번지 1호053-525-3979<NA>
67쓰리팩토리안경원 대구본점대구광역시 서구 원대동3가 1374번지 16호053-358-1150<NA>
78광장안경콘택트대구광역시 서구 내당4동 468번지 2호 1층053-522-2025<NA>
89씨채널안경광장점대구광역시 서구 내당동 468번지 10호053-563-1001<NA>
910앙드레안경콘택트대구광역시 서구 내당4동 465번지 6호053-624-0302<NA>
순번안경업소명칭사업장소재지사업장전화번호기타유의사항
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5051정안경원대구광역시 서구 비산4동 159번지 24호053)553-7180<NA>
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