Overview

Dataset statistics

Number of variables6
Number of observations1420
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.1 KiB
Average record size in memory49.1 B

Variable types

Numeric1
Categorical2
Text2
DateTime1

Dataset

Description광주광역시 서구 관내의 미용업체에 대한 업종명, 업소명, 도로명주소, 신고일자, 데이터기준일자 등에 관한 정보 현황입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15038903/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:18:59.773252
Analysis finished2023-12-12 22:19:00.689381
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1420
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.5
Minimum1
Maximum1420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-13T07:19:00.759085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile71.95
Q1355.75
median710.5
Q31065.25
95-th percentile1349.05
Maximum1420
Range1419
Interquartile range (IQR)709.5

Descriptive statistics

Standard deviation410.063
Coefficient of variation (CV)0.57714708
Kurtosis-1.2
Mean710.5
Median Absolute Deviation (MAD)355
Skewness0
Sum1008910
Variance168151.67
MonotonicityStrictly increasing
2023-12-13T07:19:00.910892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
978 1
 
0.1%
954 1
 
0.1%
953 1
 
0.1%
952 1
 
0.1%
951 1
 
0.1%
950 1
 
0.1%
949 1
 
0.1%
948 1
 
0.1%
947 1
 
0.1%
Other values (1410) 1410
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1420 1
0.1%
1419 1
0.1%
1418 1
0.1%
1417 1
0.1%
1416 1
0.1%
1415 1
0.1%
1414 1
0.1%
1413 1
0.1%
1412 1
0.1%
1411 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
일반미용업
750 
피부미용업
251 
네일미용업
122 
이용업
85 
화장ㆍ분장 미용업
 
45
Other values (11)
167 

Length

Max length23
Median length5
Mean length6.0661972
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 750
52.8%
피부미용업 251
 
17.7%
네일미용업 122
 
8.6%
이용업 85
 
6.0%
화장ㆍ분장 미용업 45
 
3.2%
종합미용업 29
 
2.0%
네일미용업, 화장ㆍ분장 미용업 27
 
1.9%
피부미용업, 네일미용업 25
 
1.8%
피부미용업, 화장ㆍ분장 미용업 23
 
1.6%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 20
 
1.4%
Other values (6) 43
 
3.0%

Length

2023-12-13T07:19:01.083647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 792
46.1%
피부미용업 333
19.4%
네일미용업 215
 
12.5%
미용업 132
 
7.7%
화장ㆍ분장 131
 
7.6%
이용업 85
 
5.0%
종합미용업 29
 
1.7%

업소명
Text

Distinct1388
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2023-12-13T07:19:01.373002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length6.3598592
Min length1

Characters and Unicode

Total characters9031
Distinct characters603
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1361 ?
Unique (%)95.8%

Sample

1st row성원
2nd row친선이용원
3rd row차엔차이용원
4th row우영이용원
5th row우주이용
ValueCountFrequency (%)
헤어 34
 
1.8%
미용실 31
 
1.7%
hair 18
 
1.0%
nail 17
 
0.9%
헤어샵 10
 
0.5%
에스테틱 8
 
0.4%
beauty 8
 
0.4%
상무점 7
 
0.4%
네일 7
 
0.4%
태후사랑 7
 
0.4%
Other values (1549) 1727
92.2%
2023-12-13T07:19:02.115712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
454
 
5.0%
어 452
 
5.0%
헤 426
 
4.7%
스 219
 
2.4%
이 204
 
2.3%
( 201
 
2.2%
) 201
 
2.2%
일 198
 
2.2%
네 176
 
1.9%
리 158
 
1.7%
Other values (593) 6342
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6854
75.9%
Lowercase Letter 642
 
7.1%
Uppercase Letter 494
 
5.5%
Space Separator 454
 
5.0%
Open Punctuation 202
 
2.2%
Close Punctuation 202
 
2.2%
Decimal Number 101
 
1.1%
Other Punctuation 70
 
0.8%
Dash Punctuation 8
 
0.1%
Connector Punctuation 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 452
 
6.6%
헤 426
 
6.2%
스 219
 
3.2%
이 204
 
3.0%
일 198
 
2.9%
네 176
 
2.6%
리 158
 
2.3%
미 144
 
2.1%
라 109
 
1.6%
티 105
 
1.5%
Other values (516) 4663
68.0%
Lowercase Letter
ValueCountFrequency (%)
a 80
12.5%
e 73
11.4%
i 68
10.6%
l 51
 
7.9%
o 45
 
7.0%
n 45
 
7.0%
r 42
 
6.5%
t 31
 
4.8%
s 30
 
4.7%
u 30
 
4.7%
Other values (16) 147
22.9%
Uppercase Letter
ValueCountFrequency (%)
A 61
12.3%
N 39
 
7.9%
H 36
 
7.3%
O 36
 
7.3%
S 33
 
6.7%
E 32
 
6.5%
I 30
 
6.1%
L 29
 
5.9%
R 28
 
5.7%
M 25
 
5.1%
Other values (14) 145
29.4%
Decimal Number
ValueCountFrequency (%)
0 18
17.8%
1 18
17.8%
3 14
13.9%
2 13
12.9%
8 10
9.9%
7 9
8.9%
5 6
 
5.9%
9 6
 
5.9%
6 4
 
4.0%
4 3
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 18
25.7%
& 16
22.9%
# 16
22.9%
, 10
14.3%
: 4
 
5.7%
' 4
 
5.7%
· 1
 
1.4%
% 1
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 201
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 201
99.5%
] 1
 
0.5%
Space Separator
ValueCountFrequency (%)
454
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Other Symbol
ValueCountFrequency (%)
♡ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6850
75.8%
Latin 1136
 
12.6%
Common 1041
 
11.5%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 452
 
6.6%
헤 426
 
6.2%
스 219
 
3.2%
이 204
 
3.0%
일 198
 
2.9%
네 176
 
2.6%
리 158
 
2.3%
미 144
 
2.1%
라 109
 
1.6%
티 105
 
1.5%
Other values (514) 4659
68.0%
Latin
ValueCountFrequency (%)
a 80
 
7.0%
e 73
 
6.4%
i 68
 
6.0%
A 61
 
5.4%
l 51
 
4.5%
o 45
 
4.0%
n 45
 
4.0%
r 42
 
3.7%
N 39
 
3.4%
H 36
 
3.2%
Other values (40) 596
52.5%
Common
ValueCountFrequency (%)
454
43.6%
( 201
19.3%
) 201
19.3%
. 18
 
1.7%
0 18
 
1.7%
1 18
 
1.7%
& 16
 
1.5%
# 16
 
1.5%
3 14
 
1.3%
2 13
 
1.2%
Other values (17) 72
 
6.9%
Han
ValueCountFrequency (%)
美 3
75.0%
林 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6850
75.8%
ASCII 2174
 
24.1%
CJK 4
 
< 0.1%
None 2
 
< 0.1%
Misc Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
454
20.9%
( 201
 
9.2%
) 201
 
9.2%
a 80
 
3.7%
e 73
 
3.4%
i 68
 
3.1%
A 61
 
2.8%
l 51
 
2.3%
o 45
 
2.1%
n 45
 
2.1%
Other values (64) 895
41.2%
Hangul
ValueCountFrequency (%)
어 452
 
6.6%
헤 426
 
6.2%
스 219
 
3.2%
이 204
 
3.0%
일 198
 
2.9%
네 176
 
2.6%
리 158
 
2.3%
미 144
 
2.1%
라 109
 
1.6%
티 105
 
1.5%
Other values (514) 4659
68.0%
CJK
ValueCountFrequency (%)
美 3
75.0%
林 1
 
25.0%
None
ValueCountFrequency (%)
· 1
50.0%
´ 1
50.0%
Misc Symbols
ValueCountFrequency (%)
♡ 1
100.0%

도로명주소
Text

Distinct1372
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2023-12-13T07:19:02.436632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length53
Mean length32.446479
Min length20

Characters and Unicode

Total characters46074
Distinct characters278
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1326 ?
Unique (%)93.4%

Sample

1st row광주광역시 서구 마륵복개로 108 (마륵동)
2nd row광주광역시 서구 구성로 97 (양동)
3rd row광주광역시 서구 경열로66번길 2, 1층 (농성동)
4th row광주광역시 서구 천변좌로 216-3 (양동)
5th row광주광역시 서구 경열로146번길 1-1, 2층 (양동)
ValueCountFrequency (%)
광주광역시 1420
 
15.5%
서구 1420
 
15.5%
1층 619
 
6.8%
쌍촌동 286
 
3.1%
화정동 244
 
2.7%
2층 172
 
1.9%
치평동 167
 
1.8%
금호동 142
 
1.6%
풍암동 136
 
1.5%
상가동 135
 
1.5%
Other values (1234) 4396
48.1%
2023-12-13T07:19:02.882181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7717
 
16.7%
광 2946
 
6.4%
1 2443
 
5.3%
동 1743
 
3.8%
) 1622
 
3.5%
( 1622
 
3.5%
, 1574
 
3.4%
시 1485
 
3.2%
주 1477
 
3.2%
서 1429
 
3.1%
Other values (268) 22016
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25491
55.3%
Space Separator 7717
 
16.7%
Decimal Number 7629
 
16.6%
Close Punctuation 1622
 
3.5%
Open Punctuation 1622
 
3.5%
Other Punctuation 1590
 
3.5%
Dash Punctuation 311
 
0.7%
Uppercase Letter 69
 
0.1%
Lowercase Letter 21
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 2946
 
11.6%
동 1743
 
6.8%
시 1485
 
5.8%
주 1477
 
5.8%
서 1429
 
5.6%
구 1428
 
5.6%
역 1420
 
5.6%
로 1391
 
5.5%
층 1045
 
4.1%
길 686
 
2.7%
Other values (228) 10441
41.0%
Uppercase Letter
ValueCountFrequency (%)
B 14
20.3%
C 8
11.6%
A 8
11.6%
D 6
8.7%
E 5
 
7.2%
S 5
 
7.2%
Y 4
 
5.8%
W 3
 
4.3%
I 3
 
4.3%
V 3
 
4.3%
Other values (5) 10
14.5%
Decimal Number
ValueCountFrequency (%)
1 2443
32.0%
2 1138
14.9%
0 770
 
10.1%
4 615
 
8.1%
3 606
 
7.9%
5 468
 
6.1%
9 420
 
5.5%
7 411
 
5.4%
8 388
 
5.1%
6 370
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
e 6
28.6%
n 3
14.3%
t 3
14.3%
r 3
14.3%
a 3
14.3%
l 3
14.3%
Other Punctuation
ValueCountFrequency (%)
, 1574
99.0%
@ 11
 
0.7%
. 3
 
0.2%
& 2
 
0.1%
Space Separator
ValueCountFrequency (%)
7717
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1622
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1622
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 311
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25491
55.3%
Common 20493
44.5%
Latin 90
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 2946
 
11.6%
동 1743
 
6.8%
시 1485
 
5.8%
주 1477
 
5.8%
서 1429
 
5.6%
구 1428
 
5.6%
역 1420
 
5.6%
로 1391
 
5.5%
층 1045
 
4.1%
길 686
 
2.7%
Other values (228) 10441
41.0%
Latin
ValueCountFrequency (%)
B 14
15.6%
C 8
 
8.9%
A 8
 
8.9%
e 6
 
6.7%
D 6
 
6.7%
E 5
 
5.6%
S 5
 
5.6%
Y 4
 
4.4%
W 3
 
3.3%
n 3
 
3.3%
Other values (11) 28
31.1%
Common
ValueCountFrequency (%)
7717
37.7%
1 2443
 
11.9%
) 1622
 
7.9%
( 1622
 
7.9%
, 1574
 
7.7%
2 1138
 
5.6%
0 770
 
3.8%
4 615
 
3.0%
3 606
 
3.0%
5 468
 
2.3%
Other values (9) 1918
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25491
55.3%
ASCII 20583
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7717
37.5%
1 2443
 
11.9%
) 1622
 
7.9%
( 1622
 
7.9%
, 1574
 
7.6%
2 1138
 
5.5%
0 770
 
3.7%
4 615
 
3.0%
3 606
 
2.9%
5 468
 
2.3%
Other values (30) 2008
 
9.8%
Hangul
ValueCountFrequency (%)
광 2946
 
11.6%
동 1743
 
6.8%
시 1485
 
5.8%
주 1477
 
5.8%
서 1429
 
5.6%
구 1428
 
5.6%
역 1420
 
5.6%
로 1391
 
5.5%
층 1045
 
4.1%
길 686
 
2.7%
Other values (228) 10441
41.0%

신고일자
Date

Distinct1030
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Minimum1985-04-17 00:00:00
Maximum2021-09-24 00:00:00
2023-12-13T07:19:03.018932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:03.169061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2021-09-24
1420 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-09-24
2nd row2021-09-24
3rd row2021-09-24
4th row2021-09-24
5th row2021-09-24

Common Values

ValueCountFrequency (%)
2021-09-24 1420
100.0%

Length

2023-12-13T07:19:03.282196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:19:03.367859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-09-24 1420
100.0%

Interactions

2023-12-13T07:19:00.362164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:19:03.422958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.882
업종명0.8821.000
2023-12-13T07:19:03.513745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.606
업종명0.6061.000

Missing values

2023-12-13T07:19:00.527852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:19:00.639009image/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

연번업종명업소명도로명주소신고일자데이터기준일자
01이용업성원광주광역시 서구 마륵복개로 108 (마륵동)2003-02-272021-09-24
12이용업친선이용원광주광역시 서구 구성로 97 (양동)2003-02-272021-09-24
23이용업차엔차이용원광주광역시 서구 경열로66번길 2, 1층 (농성동)2003-02-272021-09-24
34이용업우영이용원광주광역시 서구 천변좌로 216-3 (양동)2003-02-272021-09-24
45이용업우주이용광주광역시 서구 경열로146번길 1-1, 2층 (양동)2003-02-272021-09-24
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