Overview

Dataset statistics

Number of variables6
Number of observations649
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.6 KiB
Average record size in memory48.2 B

Variable types

Categorical4
Text2

Dataset

Description서울특별시 금천구 공중위생업 서비스 평가결과로서 업종명, 업소명, 업소소재지 주소, 평가등급, 평가연월 등의 항목을 제공하고 있습니다.
Author서울특별시 금천구
URLhttps://www.data.go.kr/data/3081162/fileData.do

Alerts

평가연월 has constant value ""Constant
데이터기준일자 has constant value ""Constant

Reproduction

Analysis started2023-12-12 05:59:45.947685
Analysis finished2023-12-12 05:59:46.488174
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
미용업
233 
미용업(일반)
159 
이용업
86 
미용업(종합)
65 
미용업(피부)
57 
Other values (4)
49 

Length

Max length11
Median length10
Mean length5.2619414
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row미용업(일반)
2nd row미용업(일반)
3rd row미용업(일반)
4th row미용업(일반)
5th row미용업(일반)

Common Values

ValueCountFrequency (%)
미용업 233
35.9%
미용업(일반) 159
24.5%
이용업 86
 
13.3%
미용업(종합) 65
 
10.0%
미용업(피부) 57
 
8.8%
미용업(손톱ㆍ발톱) 35
 
5.4%
미용업(화장ㆍ분장) 10
 
1.5%
미용업(화장ㆍ분장) 3
 
0.5%
미용업(피부) 1
 
0.2%

Length

2023-12-12T14:59:46.575548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:59:46.729801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 233
35.9%
미용업(일반 159
24.5%
이용업 86
 
13.3%
미용업(종합 65
 
10.0%
미용업(피부 58
 
8.9%
미용업(손톱ㆍ발톱 35
 
5.4%
미용업(화장ㆍ분장 13
 
2.0%

업소명
Text

Distinct589
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-12-12T14:59:47.084725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length5.2696456
Min length1

Characters and Unicode

Total characters3420
Distinct characters429
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique539 ?
Unique (%)83.1%

Sample

1st row박승철 헤어스튜디오 가산역1호점
2nd row스위트벙커(sweet bunker)
3rd row토리헤어
4th row이훈All30000
5th row일랑헤어
ValueCountFrequency (%)
헤어 11
 
1.4%
미용실 11
 
1.4%
리안헤어 5
 
0.6%
hair 5
 
0.6%
가위소리 4
 
0.5%
수 4
 
0.5%
가산점 4
 
0.5%
헤어샵 4
 
0.5%
머리못하는집 4
 
0.5%
nail 4
 
0.5%
Other values (639) 716
92.7%
2023-12-12T14:59:47.625874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 265
 
7.7%
헤 252
 
7.4%
123
 
3.6%
미 100
 
2.9%
리 91
 
2.7%
용 76
 
2.2%
이 66
 
1.9%
실 64
 
1.9%
스 55
 
1.6%
머 40
 
1.2%
Other values (419) 2288
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2953
86.3%
Lowercase Letter 150
 
4.4%
Space Separator 123
 
3.6%
Uppercase Letter 81
 
2.4%
Close Punctuation 35
 
1.0%
Open Punctuation 35
 
1.0%
Decimal Number 27
 
0.8%
Other Punctuation 15
 
0.4%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 265
 
9.0%
헤 252
 
8.5%
미 100
 
3.4%
리 91
 
3.1%
용 76
 
2.6%
이 66
 
2.2%
실 64
 
2.2%
스 55
 
1.9%
머 40
 
1.4%
일 39
 
1.3%
Other values (360) 1905
64.5%
Uppercase Letter
ValueCountFrequency (%)
A 9
11.1%
I 8
 
9.9%
L 7
 
8.6%
J 7
 
8.6%
T 6
 
7.4%
N 6
 
7.4%
M 5
 
6.2%
G 4
 
4.9%
S 4
 
4.9%
R 3
 
3.7%
Other values (12) 22
27.2%
Lowercase Letter
ValueCountFrequency (%)
i 20
13.3%
e 17
11.3%
n 16
10.7%
a 14
9.3%
r 13
8.7%
s 12
8.0%
h 11
7.3%
l 10
6.7%
m 7
 
4.7%
o 7
 
4.7%
Other values (10) 23
15.3%
Decimal Number
ValueCountFrequency (%)
0 8
29.6%
1 6
22.2%
2 5
18.5%
8 2
 
7.4%
9 2
 
7.4%
7 1
 
3.7%
4 1
 
3.7%
6 1
 
3.7%
3 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
# 6
40.0%
. 5
33.3%
& 3
20.0%
＆ 1
 
6.7%
Space Separator
ValueCountFrequency (%)
123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2946
86.1%
Common 236
 
6.9%
Latin 231
 
6.8%
Han 7
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 265
 
9.0%
헤 252
 
8.6%
미 100
 
3.4%
리 91
 
3.1%
용 76
 
2.6%
이 66
 
2.2%
실 64
 
2.2%
스 55
 
1.9%
머 40
 
1.4%
일 39
 
1.3%
Other values (356) 1898
64.4%
Latin
ValueCountFrequency (%)
i 20
 
8.7%
e 17
 
7.4%
n 16
 
6.9%
a 14
 
6.1%
r 13
 
5.6%
s 12
 
5.2%
h 11
 
4.8%
l 10
 
4.3%
A 9
 
3.9%
I 8
 
3.5%
Other values (32) 101
43.7%
Common
ValueCountFrequency (%)
123
52.1%
) 35
 
14.8%
( 35
 
14.8%
0 8
 
3.4%
1 6
 
2.5%
# 6
 
2.5%
. 5
 
2.1%
2 5
 
2.1%
& 3
 
1.3%
8 2
 
0.8%
Other values (7) 8
 
3.4%
Han
ValueCountFrequency (%)
美 3
42.9%
愛 2
28.6%
尙 1
 
14.3%
引 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2946
86.1%
ASCII 466
 
13.6%
CJK 7
 
0.2%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 265
 
9.0%
헤 252
 
8.6%
미 100
 
3.4%
리 91
 
3.1%
용 76
 
2.6%
이 66
 
2.2%
실 64
 
2.2%
스 55
 
1.9%
머 40
 
1.4%
일 39
 
1.3%
Other values (356) 1898
64.4%
ASCII
ValueCountFrequency (%)
123
26.4%
) 35
 
7.5%
( 35
 
7.5%
i 20
 
4.3%
e 17
 
3.6%
n 16
 
3.4%
a 14
 
3.0%
r 13
 
2.8%
s 12
 
2.6%
h 11
 
2.4%
Other values (48) 170
36.5%
CJK
ValueCountFrequency (%)
美 3
42.9%
愛 2
28.6%
尙 1
 
14.3%
引 1
 
14.3%
None
ValueCountFrequency (%)
＆ 1
100.0%

업소소재지 주소
Text

Distinct609
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-12-12T14:59:47.932751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length47
Mean length29.331279
Min length21

Characters and Unicode

Total characters19036
Distinct characters175
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

Unique574 ?
Unique (%)88.4%

Sample

1st row서울특별시 금천구 가산디지털1로 168, A동 B323호 (가산동, 우림라이온스밸리)
2nd row서울특별시 금천구 벚꽃로 298, B123호 (가산동, 대륭포스트타워6차)
3rd row서울특별시 금천구 두산로 83, 2층 (독산동)
4th row서울특별시 금천구 시흥대로 235, 2층 (시흥동)
5th row서울특별시 금천구 시흥대로 273, 2층 (시흥동)
ValueCountFrequency (%)
서울특별시 649
17.1%
금천구 649
17.1%
독산동 291
 
7.7%
시흥동 288
 
7.6%
1층 138
 
3.6%
가산동 69
 
1.8%
독산로 58
 
1.5%
시흥대로 46
 
1.2%
금하로 45
 
1.2%
2층 45
 
1.2%
Other values (575) 1508
39.8%
2023-12-12T14:59:48.433650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3140
 
16.5%
시 1144
 
6.0%
1 808
 
4.2%
금 743
 
3.9%
동 698
 
3.7%
천 655
 
3.4%
울 653
 
3.4%
서 653
 
3.4%
산 651
 
3.4%
구 649
 
3.4%
Other values (165) 9242
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10992
57.7%
Space Separator 3140
 
16.5%
Decimal Number 3062
 
16.1%
Open Punctuation 649
 
3.4%
Close Punctuation 649
 
3.4%
Other Punctuation 427
 
2.2%
Dash Punctuation 65
 
0.3%
Uppercase Letter 49
 
0.3%
Math Symbol 2
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1144
 
10.4%
금 743
 
6.8%
동 698
 
6.4%
천 655
 
6.0%
울 653
 
5.9%
서 653
 
5.9%
산 651
 
5.9%
구 649
 
5.9%
별 649
 
5.9%
특 649
 
5.9%
Other values (139) 3848
35.0%
Decimal Number
ValueCountFrequency (%)
1 808
26.4%
2 445
14.5%
3 320
 
10.5%
0 284
 
9.3%
4 264
 
8.6%
6 220
 
7.2%
5 195
 
6.4%
9 185
 
6.0%
8 175
 
5.7%
7 166
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 29
59.2%
A 10
 
20.4%
C 3
 
6.1%
Y 2
 
4.1%
F 2
 
4.1%
D 1
 
2.0%
X 1
 
2.0%
E 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 426
99.8%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
3140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 649
100.0%
Close Punctuation
ValueCountFrequency (%)
) 649
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
w 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10992
57.7%
Common 7994
42.0%
Latin 50
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1144
 
10.4%
금 743
 
6.8%
동 698
 
6.4%
천 655
 
6.0%
울 653
 
5.9%
서 653
 
5.9%
산 651
 
5.9%
구 649
 
5.9%
별 649
 
5.9%
특 649
 
5.9%
Other values (139) 3848
35.0%
Common
ValueCountFrequency (%)
3140
39.3%
1 808
 
10.1%
( 649
 
8.1%
) 649
 
8.1%
2 445
 
5.6%
, 426
 
5.3%
3 320
 
4.0%
0 284
 
3.6%
4 264
 
3.3%
6 220
 
2.8%
Other values (7) 789
 
9.9%
Latin
ValueCountFrequency (%)
B 29
58.0%
A 10
 
20.0%
C 3
 
6.0%
Y 2
 
4.0%
F 2
 
4.0%
D 1
 
2.0%
X 1
 
2.0%
E 1
 
2.0%
w 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10992
57.7%
ASCII 8044
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3140
39.0%
1 808
 
10.0%
( 649
 
8.1%
) 649
 
8.1%
2 445
 
5.5%
, 426
 
5.3%
3 320
 
4.0%
0 284
 
3.5%
4 264
 
3.3%
6 220
 
2.7%
Other values (16) 839
 
10.4%
Hangul
ValueCountFrequency (%)
시 1144
 
10.4%
금 743
 
6.8%
동 698
 
6.4%
천 655
 
6.0%
울 653
 
5.9%
서 653
 
5.9%
산 651
 
5.9%
구 649
 
5.9%
별 649
 
5.9%
특 649
 
5.9%
Other values (139) 3848
35.0%

평가등급
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
황색등급
228 
녹색등급
221 
백색등급
200 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
황색등급 228
35.1%
녹색등급 221
34.1%
백색등급 200
30.8%

Length

2023-12-12T14:59:48.566071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:59:48.668511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
황색등급 228
35.1%
녹색등급 221
34.1%
백색등급 200
30.8%

평가연월
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2022-12
649 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12
2nd row2022-12
3rd row2022-12
4th row2022-12
5th row2022-12

Common Values

ValueCountFrequency (%)
2022-12 649
100.0%

Length

2023-12-12T14:59:48.782622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:59:48.887640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12 649
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-09-19
649 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-19
2nd row2023-09-19
3rd row2023-09-19
4th row2023-09-19
5th row2023-09-19

Common Values

ValueCountFrequency (%)
2023-09-19 649
100.0%

Length

2023-12-12T14:59:48.985515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:59:49.078516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-19 649
100.0%

Correlations

2023-12-12T14:59:49.139937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명평가등급
업종명1.0000.490
평가등급0.4901.000
2023-12-12T14:59:49.229347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평가등급업종명
평가등급1.0000.247
업종명0.2471.000
2023-12-12T14:59:49.307636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명평가등급
업종명1.0000.247
평가등급0.2471.000

Missing values

2023-12-12T14:59:46.340582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:59:46.445447image/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

업종명업소명업소소재지 주소평가등급평가연월데이터기준일자
0미용업(일반)박승철 헤어스튜디오 가산역1호점서울특별시 금천구 가산디지털1로 168, A동 B323호 (가산동, 우림라이온스밸리)녹색등급2022-122023-09-19
1미용업(일반)스위트벙커(sweet bunker)서울특별시 금천구 벚꽃로 298, B123호 (가산동, 대륭포스트타워6차)녹색등급2022-122023-09-19
2미용업(일반)토리헤어서울특별시 금천구 두산로 83, 2층 (독산동)녹색등급2022-122023-09-19
3미용업(일반)이훈All30000서울특별시 금천구 시흥대로 235, 2층 (시흥동)녹색등급2022-122023-09-19
4미용업(일반)일랑헤어서울특별시 금천구 시흥대로 273, 2층 (시흥동)녹색등급2022-122023-09-19
5미용업(일반)리본헤어서울특별시 금천구 시흥대로 223 (시흥동, 유진빌딩 2층)녹색등급2022-122023-09-19
6미용업(일반)헤어박스서울특별시 금천구 독산로 92 (시흥동)녹색등급2022-122023-09-19
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