Overview

Dataset statistics

Number of variables4
Number of observations328
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory33.4 B

Variable types

Numeric1
Categorical1
Text2

Dataset

Description영천시 공중위생업소(미용업, 이용업 등) 현황
Author경상북도 영천시
URLhttps://www.data.go.kr/data/15077340/fileData.do

Alerts

연 번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연 번High correlation
연 번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:36:30.945448
Analysis finished2023-12-12 07:36:31.409838
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연 번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct328
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.5
Minimum1
Maximum328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T16:36:31.467039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.35
Q182.75
median164.5
Q3246.25
95-th percentile311.65
Maximum328
Range327
Interquartile range (IQR)163.5

Descriptive statistics

Standard deviation94.829672
Coefficient of variation (CV)0.57647217
Kurtosis-1.2
Mean164.5
Median Absolute Deviation (MAD)82
Skewness0
Sum53956
Variance8992.6667
MonotonicityStrictly increasing
2023-12-12T16:36:31.600335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
227 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
Other values (318) 318
97.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
328 1
0.3%
327 1
0.3%
326 1
0.3%
325 1
0.3%
324 1
0.3%
323 1
0.3%
322 1
0.3%
321 1
0.3%
320 1
0.3%
319 1
0.3%

업종명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
미용업
134 
일반미용업
71 
이용업
62 
피부미용업
23 
종합미용업
17 
Other values (6)
21 

Length

Max length23
Median length3
Mean length4.0457317
Min length3

Unique

Unique3 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 134
40.9%
일반미용업 71
21.6%
이용업 62
18.9%
피부미용업 23
 
7.0%
종합미용업 17
 
5.2%
네일미용업 14
 
4.3%
피부미용업, 네일미용업 2
 
0.6%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 2
 
0.6%
일반미용업, 네일미용업 1
 
0.3%
일반미용업, 화장ㆍ분장 미용업 1
 
0.3%

Length

2023-12-12T16:36:31.716582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 138
40.5%
일반미용업 75
22.0%
이용업 62
18.2%
피부미용업 25
 
7.3%
네일미용업 20
 
5.9%
종합미용업 17
 
5.0%
화장ㆍ분장 4
 
1.2%

업소명
Text

Distinct320
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T16:36:31.960577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length5.5884146
Min length2

Characters and Unicode

Total characters1833
Distinct characters328
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique313 ?
Unique (%)95.4%

Sample

1st row영흥이용소
2nd row청통이발관
3rd row영천이용소
4th row신흥이용소
5th row보남이용소
ValueCountFrequency (%)
선미용실 3
 
0.8%
hair 3
 
0.8%
덕성이용소 2
 
0.6%
영천점 2
 
0.6%
피부샵 2
 
0.6%
한일자미용실 2
 
0.6%
헤어샵 2
 
0.6%
송화미용실 2
 
0.6%
그린미용실 2
 
0.6%
덕신이용소 2
 
0.6%
Other values (339) 340
93.9%
2023-12-12T16:36:32.691762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 144
 
7.9%
미 112
 
6.1%
어 92
 
5.0%
실 88
 
4.8%
헤 86
 
4.7%
이 85
 
4.6%
소 58
 
3.2%
37
 
2.0%
리 34
 
1.9%
스 31
 
1.7%
Other values (318) 1066
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1690
92.2%
Lowercase Letter 46
 
2.5%
Space Separator 37
 
2.0%
Uppercase Letter 23
 
1.3%
Close Punctuation 14
 
0.8%
Open Punctuation 14
 
0.8%
Other Punctuation 8
 
0.4%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 144
 
8.5%
미 112
 
6.6%
어 92
 
5.4%
실 88
 
5.2%
헤 86
 
5.1%
이 85
 
5.0%
소 58
 
3.4%
리 34
 
2.0%
스 31
 
1.8%
일 22
 
1.3%
Other values (282) 938
55.5%
Lowercase Letter
ValueCountFrequency (%)
a 6
13.0%
e 5
10.9%
h 5
10.9%
s 5
10.9%
i 4
8.7%
o 4
8.7%
u 4
8.7%
r 3
6.5%
y 2
 
4.3%
b 2
 
4.3%
Other values (4) 6
13.0%
Uppercase Letter
ValueCountFrequency (%)
J 4
17.4%
N 3
13.0%
B 2
8.7%
L 2
8.7%
A 2
8.7%
I 2
8.7%
H 2
8.7%
D 1
 
4.3%
E 1
 
4.3%
S 1
 
4.3%
Other values (3) 3
13.0%
Other Punctuation
ValueCountFrequency (%)
' 2
25.0%
, 2
25.0%
& 2
25.0%
; 1
12.5%
# 1
12.5%
Space Separator
ValueCountFrequency (%)
37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Decimal Number
ValueCountFrequency (%)
9 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1689
92.1%
Common 74
 
4.0%
Latin 69
 
3.8%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 144
 
8.5%
미 112
 
6.6%
어 92
 
5.4%
실 88
 
5.2%
헤 86
 
5.1%
이 85
 
5.0%
소 58
 
3.4%
리 34
 
2.0%
스 31
 
1.8%
일 22
 
1.3%
Other values (281) 937
55.5%
Latin
ValueCountFrequency (%)
a 6
 
8.7%
e 5
 
7.2%
h 5
 
7.2%
s 5
 
7.2%
i 4
 
5.8%
o 4
 
5.8%
u 4
 
5.8%
J 4
 
5.8%
r 3
 
4.3%
N 3
 
4.3%
Other values (17) 26
37.7%
Common
ValueCountFrequency (%)
37
50.0%
) 14
 
18.9%
( 14
 
18.9%
' 2
 
2.7%
, 2
 
2.7%
& 2
 
2.7%
9 1
 
1.4%
; 1
 
1.4%
# 1
 
1.4%
Han
ValueCountFrequency (%)
美 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1689
92.1%
ASCII 143
 
7.8%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 144
 
8.5%
미 112
 
6.6%
어 92
 
5.4%
실 88
 
5.2%
헤 86
 
5.1%
이 85
 
5.0%
소 58
 
3.4%
리 34
 
2.0%
스 31
 
1.8%
일 22
 
1.3%
Other values (281) 937
55.5%
ASCII
ValueCountFrequency (%)
37
25.9%
) 14
 
9.8%
( 14
 
9.8%
a 6
 
4.2%
e 5
 
3.5%
h 5
 
3.5%
s 5
 
3.5%
i 4
 
2.8%
o 4
 
2.8%
u 4
 
2.8%
Other values (26) 45
31.5%
CJK
ValueCountFrequency (%)
美 1
100.0%

영업소 주소(도로명)
Text

Distinct317
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T16:36:33.021253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length43.5
Mean length25.067073
Min length18

Characters and Unicode

Total characters8222
Distinct characters178
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

Unique306 ?
Unique (%)93.3%

Sample

1st row경상북도 영천시 화남면 천문로 1294-4
2nd row경상북도 영천시 청통면 원촌리 522-3
3rd row경상북도 영천시 고경면 호국로 391
4th row경상북도 영천시 북안면 명주리 335
5th row경상북도 영천시 화남면 천문로 1617
ValueCountFrequency (%)
경상북도 328
 
18.2%
영천시 328
 
18.2%
야사동 76
 
4.2%
완산동 60
 
3.3%
문외동 36
 
2.0%
망정동 30
 
1.7%
호국로 30
 
1.7%
금호읍 24
 
1.3%
충효로 23
 
1.3%
장수로 17
 
0.9%
Other values (421) 853
47.3%
2023-12-12T16:36:33.508595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1484
18.0%
시 371
 
4.5%
상 361
 
4.4%
북 344
 
4.2%
영 339
 
4.1%
천 339
 
4.1%
경 333
 
4.1%
도 328
 
4.0%
동 324
 
3.9%
1 312
 
3.8%
Other values (168) 3687
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4781
58.1%
Space Separator 1484
 
18.0%
Decimal Number 1182
 
14.4%
Close Punctuation 270
 
3.3%
Open Punctuation 270
 
3.3%
Other Punctuation 122
 
1.5%
Dash Punctuation 101
 
1.2%
Uppercase Letter 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 371
 
7.8%
상 361
 
7.6%
북 344
 
7.2%
영 339
 
7.1%
천 339
 
7.1%
경 333
 
7.0%
도 328
 
6.9%
동 324
 
6.8%
길 183
 
3.8%
로 140
 
2.9%
Other values (148) 1719
36.0%
Decimal Number
ValueCountFrequency (%)
1 312
26.4%
2 170
14.4%
3 132
11.2%
4 109
 
9.2%
0 108
 
9.1%
5 88
 
7.4%
6 80
 
6.8%
7 76
 
6.4%
9 56
 
4.7%
8 51
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
A 4
33.3%
B 4
33.3%
C 2
16.7%
H 1
 
8.3%
D 1
 
8.3%
Space Separator
ValueCountFrequency (%)
1484
100.0%
Close Punctuation
ValueCountFrequency (%)
) 270
100.0%
Open Punctuation
ValueCountFrequency (%)
( 270
100.0%
Other Punctuation
ValueCountFrequency (%)
, 122
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4781
58.1%
Common 3429
41.7%
Latin 12
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 371
 
7.8%
상 361
 
7.6%
북 344
 
7.2%
영 339
 
7.1%
천 339
 
7.1%
경 333
 
7.0%
도 328
 
6.9%
동 324
 
6.8%
길 183
 
3.8%
로 140
 
2.9%
Other values (148) 1719
36.0%
Common
ValueCountFrequency (%)
1484
43.3%
1 312
 
9.1%
) 270
 
7.9%
( 270
 
7.9%
2 170
 
5.0%
3 132
 
3.8%
, 122
 
3.6%
4 109
 
3.2%
0 108
 
3.1%
- 101
 
2.9%
Other values (5) 351
 
10.2%
Latin
ValueCountFrequency (%)
A 4
33.3%
B 4
33.3%
C 2
16.7%
H 1
 
8.3%
D 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4781
58.1%
ASCII 3441
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1484
43.1%
1 312
 
9.1%
) 270
 
7.8%
( 270
 
7.8%
2 170
 
4.9%
3 132
 
3.8%
, 122
 
3.5%
4 109
 
3.2%
0 108
 
3.1%
- 101
 
2.9%
Other values (10) 363
 
10.5%
Hangul
ValueCountFrequency (%)
시 371
 
7.8%
상 361
 
7.6%
북 344
 
7.2%
영 339
 
7.1%
천 339
 
7.1%
경 333
 
7.0%
도 328
 
6.9%
동 324
 
6.8%
길 183
 
3.8%
로 140
 
2.9%
Other values (148) 1719
36.0%

Interactions

2023-12-12T16:36:31.221770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:36:33.613630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 번업종명
연 번1.0000.865
업종명0.8651.000
2023-12-12T16:36:33.741875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 번업종명
연 번1.0000.604
업종명0.6041.000

Missing values

2023-12-12T16:36:31.311222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:36:31.380681image/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

연 번업종명업소명영업소 주소(도로명)
01이용업영흥이용소경상북도 영천시 화남면 천문로 1294-4
12이용업청통이발관경상북도 영천시 청통면 원촌리 522-3
23이용업영천이용소경상북도 영천시 고경면 호국로 391
34이용업신흥이용소경상북도 영천시 북안면 명주리 335
45이용업보남이용소경상북도 영천시 화남면 천문로 1617
56이용업오거리이용소경상북도 영천시 완산동 1965
67이용업유성이용소경상북도 영천시 서문길 28 (성내동)
78이용업향토이용소경상북도 영천시 장수로 24 (화룡동)
89이용업신우이용소경상북도 영천시 화산면 장수로 573
910이용업시대이용소경상북도 영천시 고경면 호국로 387
연 번업종명업소명영업소 주소(도로명)
318319네일미용업네일난다름경상북도 영천시 충효로 124 (야사동)
319320네일미용업미르네일경상북도 영천시 호국로 76-1 (야사동)
320321네일미용업미애가하는 뷰티샵경상북도 영천시 야사시장길 43, 103호 (야사동)
321322일반미용업, 네일미용업손톱공주경상북도 영천시 호국로 38 (야사동)
322323피부미용업, 네일미용업네일더예쁨경상북도 영천시 창신길 140-11 (망정동)
323324피부미용업, 네일미용업동그라미네일경상북도 영천시 호국로 54-1 (야사동)
324325일반미용업, 화장ㆍ분장 미용업리프(leaf)경상북도 영천시 충효로 112-1 (야사동)
325326네일미용업, 화장ㆍ분장 미용업너,참 예쁘다경상북도 영천시 역전로 33-2 (완산동)
326327일반미용업, 네일미용업, 화장ㆍ분장 미용업리안헤어경상북도 영천시 금호읍 교대길 30, 2층
327328일반미용업, 네일미용업, 화장ㆍ분장 미용업지민헤어경상북도 영천시 호국로 23, 영진궁전빌라 (문외동)