Overview

Dataset statistics

Number of variables4
Number of observations93
Missing cells7
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory34.4 B

Variable types

Numeric1
Text3

Dataset

Description부평구 안경업소 현황(안경업소명칭 사업장소재지(도로명) 사업장전화번호)예) 1,2층에 안경,인천광역시 부평구 체육관로 14, 삼산지구 복합시설 205호 (삼산동),032-724-8686
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3045124&srcSe=7661IVAWM27C61E190

Alerts

사업장전화번호 has 7 (7.5%) missing valuesMissing
순번 has unique valuesUnique
사업장소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2024-03-18 02:04:20.421597
Analysis finished2024-03-18 02:04:20.900910
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2024-03-18T11:04:20.978143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.6
Q124
median47
Q370
95-th percentile88.4
Maximum93
Range92
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.990739
Coefficient of variation (CV)0.57427105
Kurtosis-1.2
Mean47
Median Absolute Deviation (MAD)23
Skewness0
Sum4371
Variance728.5
MonotonicityStrictly increasing
2024-03-18T11:04:21.103142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
60 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
Other values (83) 83
89.2%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%

안경업소명칭
Text

Distinct91
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size876.0 B
2024-03-18T11:04:21.302466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length7.5698925
Min length4

Characters and Unicode

Total characters704
Distinct characters175
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

Unique89 ?
Unique (%)95.7%

Sample

1st row안테나안경원
2nd row이목안경원
3rd row으뜸플러스인천삼산점
4th row으뜸50안경인천부평역점
5th row바른안경
ValueCountFrequency (%)
안경 5
 
4.3%
안경박사 3
 
2.6%
부평점 3
 
2.6%
행복한안경 2
 
1.7%
렌즈미 2
 
1.7%
다비치안경 2
 
1.7%
안경콘택트 2
 
1.7%
오렌즈 2
 
1.7%
글라스박스 1
 
0.9%
안경창고싸군부평아이즈빌점 1
 
0.9%
Other values (92) 92
80.0%
2024-03-18T11:04:21.711260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 78
 
11.1%
경 77
 
10.9%
스 25
 
3.6%
22
 
3.1%
이 21
 
3.0%
점 21
 
3.0%
아 19
 
2.7%
원 19
 
2.7%
트 17
 
2.4%
평 15
 
2.1%
Other values (165) 390
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 613
87.1%
Space Separator 22
 
3.1%
Uppercase Letter 17
 
2.4%
Decimal Number 15
 
2.1%
Lowercase Letter 14
 
2.0%
Open Punctuation 11
 
1.6%
Close Punctuation 11
 
1.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 78
 
12.7%
경 77
 
12.6%
스 25
 
4.1%
이 21
 
3.4%
점 21
 
3.4%
아 19
 
3.1%
원 19
 
3.1%
트 17
 
2.8%
평 15
 
2.4%
부 15
 
2.4%
Other values (132) 306
49.9%
Uppercase Letter
ValueCountFrequency (%)
E 3
17.6%
S 2
11.8%
G 2
11.8%
M 1
 
5.9%
K 1
 
5.9%
C 1
 
5.9%
A 1
 
5.9%
P 1
 
5.9%
T 1
 
5.9%
N 1
 
5.9%
Other values (3) 3
17.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
14.3%
i 2
14.3%
s 2
14.3%
c 1
7.1%
k 1
7.1%
a 1
7.1%
m 1
7.1%
p 1
7.1%
t 1
7.1%
q 1
7.1%
Decimal Number
ValueCountFrequency (%)
1 6
40.0%
0 5
33.3%
2 2
 
13.3%
5 1
 
6.7%
7 1
 
6.7%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 613
87.1%
Common 60
 
8.5%
Latin 31
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 78
 
12.7%
경 77
 
12.6%
스 25
 
4.1%
이 21
 
3.4%
점 21
 
3.4%
아 19
 
3.1%
원 19
 
3.1%
트 17
 
2.8%
평 15
 
2.4%
부 15
 
2.4%
Other values (132) 306
49.9%
Latin
ValueCountFrequency (%)
E 3
 
9.7%
S 2
 
6.5%
G 2
 
6.5%
e 2
 
6.5%
i 2
 
6.5%
s 2
 
6.5%
M 1
 
3.2%
K 1
 
3.2%
C 1
 
3.2%
A 1
 
3.2%
Other values (14) 14
45.2%
Common
ValueCountFrequency (%)
22
36.7%
( 11
18.3%
) 11
18.3%
1 6
 
10.0%
0 5
 
8.3%
2 2
 
3.3%
. 1
 
1.7%
5 1
 
1.7%
7 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 613
87.1%
ASCII 91
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 78
 
12.7%
경 77
 
12.6%
스 25
 
4.1%
이 21
 
3.4%
점 21
 
3.4%
아 19
 
3.1%
원 19
 
3.1%
트 17
 
2.8%
평 15
 
2.4%
부 15
 
2.4%
Other values (132) 306
49.9%
ASCII
ValueCountFrequency (%)
22
24.2%
( 11
12.1%
) 11
12.1%
1 6
 
6.6%
0 5
 
5.5%
E 3
 
3.3%
S 2
 
2.2%
G 2
 
2.2%
e 2
 
2.2%
i 2
 
2.2%
Other values (23) 25
27.5%

사업장소재지(도로명)
Text

UNIQUE 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2024-03-18T11:04:21.940862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length40
Mean length30.731183
Min length22

Characters and Unicode

Total characters2858
Distinct characters143
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 경인로 947, 태성메디칼 110호, 111호 (부평동)
2nd row인천광역시 부평구 주부토로 236, A동 134호, 135호 (갈산동)
3rd row인천광역시 부평구 충선로203번길 24, 삼산프라자 3층 304호 (삼산동)
4th row인천광역시 부평구 부평대로 24, 가나빌딩 2층 202호 (부평동)
5th row인천광역시 부평구 부흥로 416, 1층 102일부호, 103호 (부개동, 골드마운틴)
ValueCountFrequency (%)
인천광역시 93
 
16.0%
부평구 93
 
16.0%
부평동 35
 
6.0%
1층 15
 
2.6%
삼산동 14
 
2.4%
부개동 13
 
2.2%
산곡동 12
 
2.1%
부평대로 9
 
1.5%
경원대로 8
 
1.4%
청천동 7
 
1.2%
Other values (202) 282
48.5%
2024-03-18T11:04:22.330470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
489
 
17.1%
부 174
 
6.1%
평 146
 
5.1%
1 112
 
3.9%
동 107
 
3.7%
천 102
 
3.6%
인 99
 
3.5%
시 99
 
3.5%
광 98
 
3.4%
역 95
 
3.3%
Other values (133) 1337
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1660
58.1%
Space Separator 489
 
17.1%
Decimal Number 429
 
15.0%
Close Punctuation 94
 
3.3%
Open Punctuation 94
 
3.3%
Other Punctuation 80
 
2.8%
Dash Punctuation 7
 
0.2%
Uppercase Letter 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 174
 
10.5%
평 146
 
8.8%
동 107
 
6.4%
천 102
 
6.1%
인 99
 
6.0%
시 99
 
6.0%
광 98
 
5.9%
역 95
 
5.7%
구 94
 
5.7%
로 93
 
5.6%
Other values (114) 553
33.3%
Decimal Number
ValueCountFrequency (%)
1 112
26.1%
2 65
15.2%
3 50
11.7%
0 46
10.7%
4 45
10.5%
5 34
 
7.9%
6 24
 
5.6%
8 22
 
5.1%
7 18
 
4.2%
9 13
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
C 1
25.0%
B 1
25.0%
Space Separator
ValueCountFrequency (%)
489
100.0%
Close Punctuation
ValueCountFrequency (%)
) 94
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Other Punctuation
ValueCountFrequency (%)
, 80
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1660
58.1%
Common 1194
41.8%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 174
 
10.5%
평 146
 
8.8%
동 107
 
6.4%
천 102
 
6.1%
인 99
 
6.0%
시 99
 
6.0%
광 98
 
5.9%
역 95
 
5.7%
구 94
 
5.7%
로 93
 
5.6%
Other values (114) 553
33.3%
Common
ValueCountFrequency (%)
489
41.0%
1 112
 
9.4%
) 94
 
7.9%
( 94
 
7.9%
, 80
 
6.7%
2 65
 
5.4%
3 50
 
4.2%
0 46
 
3.9%
4 45
 
3.8%
5 34
 
2.8%
Other values (6) 85
 
7.1%
Latin
ValueCountFrequency (%)
A 2
50.0%
C 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1660
58.1%
ASCII 1198
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
489
40.8%
1 112
 
9.3%
) 94
 
7.8%
( 94
 
7.8%
, 80
 
6.7%
2 65
 
5.4%
3 50
 
4.2%
0 46
 
3.8%
4 45
 
3.8%
5 34
 
2.8%
Other values (9) 89
 
7.4%
Hangul
ValueCountFrequency (%)
부 174
 
10.5%
평 146
 
8.8%
동 107
 
6.4%
천 102
 
6.1%
인 99
 
6.0%
시 99
 
6.0%
광 98
 
5.9%
역 95
 
5.7%
구 94
 
5.7%
로 93
 
5.6%
Other values (114) 553
33.3%

사업장전화번호
Text

MISSING 

Distinct86
Distinct (%)100.0%
Missing7
Missing (%)7.5%
Memory size876.0 B
2024-03-18T11:04:22.554511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1032
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

Unique86 ?
Unique (%)100.0%

Sample

1st row032-330-9001
2nd row032-565-0110
3rd row032-502-5552
4th row032-507-1001
5th row032-511-8977
ValueCountFrequency (%)
032-503-0853 1
 
1.2%
032-525-0353 1
 
1.2%
032-522-2863 1
 
1.2%
032-513-0101 1
 
1.2%
032-270-2950 1
 
1.2%
032-527-5339 1
 
1.2%
032-515-0801 1
 
1.2%
032-525-2491 1
 
1.2%
032-521-1100 1
 
1.2%
032-517-9080 1
 
1.2%
Other values (76) 76
88.4%
2024-03-18T11:04:22.876463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 194
18.8%
- 172
16.7%
2 158
15.3%
3 140
13.6%
5 109
10.6%
1 80
7.8%
7 49
 
4.7%
8 36
 
3.5%
6 34
 
3.3%
9 30
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 860
83.3%
Dash Punctuation 172
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 194
22.6%
2 158
18.4%
3 140
16.3%
5 109
12.7%
1 80
9.3%
7 49
 
5.7%
8 36
 
4.2%
6 34
 
4.0%
9 30
 
3.5%
4 30
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 194
18.8%
- 172
16.7%
2 158
15.3%
3 140
13.6%
5 109
10.6%
1 80
7.8%
7 49
 
4.7%
8 36
 
3.5%
6 34
 
3.3%
9 30
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 194
18.8%
- 172
16.7%
2 158
15.3%
3 140
13.6%
5 109
10.6%
1 80
7.8%
7 49
 
4.7%
8 36
 
3.5%
6 34
 
3.3%
9 30
 
2.9%

Interactions

2024-03-18T11:04:20.700087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:04:22.983588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번안경업소명칭사업장소재지(도로명)사업장전화번호
순번1.0000.8671.0001.000
안경업소명칭0.8671.0001.0001.000
사업장소재지(도로명)1.0001.0001.0001.000
사업장전화번호1.0001.0001.0001.000

Missing values

2024-03-18T11:04:20.787994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:04:20.863381image/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

순번안경업소명칭사업장소재지(도로명)사업장전화번호
01안테나안경원인천광역시 부평구 경인로 947, 태성메디칼 110호, 111호 (부평동)<NA>
12이목안경원인천광역시 부평구 주부토로 236, A동 134호, 135호 (갈산동)<NA>
23으뜸플러스인천삼산점인천광역시 부평구 충선로203번길 24, 삼산프라자 3층 304호 (삼산동)032-330-9001
34으뜸50안경인천부평역점인천광역시 부평구 부평대로 24, 가나빌딩 2층 202호 (부평동)032-565-0110
45바른안경인천광역시 부평구 부흥로 416, 1층 102일부호, 103호 (부개동, 골드마운틴)032-502-5552
56글라스월드안경산곡점인천광역시 부평구 마장로272번길 76, 상가동 103호 (산곡동, 경남아파트)032-507-1001
67제임스옵티크(James Optique)인천광역시 부평구 시장로 13-1, 2동 1,2층 (부평동)032-511-8977
782층에 안경인천광역시 부평구 체육관로 14, 삼산지구 복합시설 205호 (삼산동)032-724-8686
89꼬모안경인천광역시 부평구 부흥로 393, 1층 (부개동)032-527-7717
910안경매니져인천광역시 부평구 부영로 161, 주안빌딩 1층 105호 (산곡동)032-874-8408
순번안경업소명칭사업장소재지(도로명)사업장전화번호
8384한일안경인천광역시 부평구 부평대로 12 (부평동)032-515-7220
8485한국안경인천광역시 부평구 경인로1024번길 42 (부개동)032-519-7329
8586아이아트안경인천광역시 부평구 대정로 45 (부평동)032-503-3181
8687탑클래스 안경체인(동암역점)인천광역시 부평구 열우물로 29 (십정동)032-432-0100
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