Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells524
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory312.5 KiB
Average record size in memory32.0 B

Variable types

Text2
DateTime1

Dataset

Description전국에 위치한 세탁소의 현황을 나타내는 데이터입니다. 전국 세탁소의 상호명과 도로명주소 및 개업일을 항목으로 제공합니다.
Author소상공인시장진흥공단
URLhttps://www.data.go.kr/data/15069604/fileData.do

Alerts

개업일 has 524 (5.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:14:29.482699
Analysis finished2023-12-12 15:14:30.367391
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상호명
Text

Distinct5397
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:14:30.593062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length5.2142
Min length1

Characters and Unicode

Total characters52142
Distinct characters722
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4265 ?
Unique (%)42.6%

Sample

1st row더삽명품크리닝
2nd row현대세탁
3rd row대호
4th row크린위드
5th row크린토피아등촌역점
ValueCountFrequency (%)
크린토피아 654
 
6.5%
현대세탁소 94
 
0.9%
월드크리닝 58
 
0.6%
크린에이드 57
 
0.6%
제일세탁소 50
 
0.5%
크린위드 50
 
0.5%
백양세탁소 42
 
0.4%
백조세탁소 42
 
0.4%
그린세탁소 41
 
0.4%
크린하우스 39
 
0.4%
Other values (5386) 8873
88.7%
2023-12-13T00:14:30.998535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 5946
 
11.4%
탁 5851
 
11.2%
소 3677
 
7.1%
크 2077
 
4.0%
린 1520
 
2.9%
아 1094
 
2.1%
사 1042
 
2.0%
리 1029
 
2.0%
피 854
 
1.6%
토 791
 
1.5%
Other values (712) 28261
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51521
98.8%
Decimal Number 334
 
0.6%
Uppercase Letter 181
 
0.3%
Other Punctuation 55
 
0.1%
Lowercase Letter 28
 
0.1%
Dash Punctuation 12
 
< 0.1%
Space Separator 6
 
< 0.1%
Math Symbol 2
 
< 0.1%
Letter Number 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 5946
 
11.5%
탁 5851
 
11.4%
소 3677
 
7.1%
크 2077
 
4.0%
린 1520
 
3.0%
아 1094
 
2.1%
사 1042
 
2.0%
리 1029
 
2.0%
피 854
 
1.7%
토 791
 
1.5%
Other values (653) 27640
53.6%
Uppercase Letter
ValueCountFrequency (%)
K 26
14.4%
O 16
 
8.8%
L 16
 
8.8%
G 15
 
8.3%
S 15
 
8.3%
C 11
 
6.1%
P 11
 
6.1%
E 11
 
6.1%
I 9
 
5.0%
M 8
 
4.4%
Other values (13) 43
23.8%
Lowercase Letter
ValueCountFrequency (%)
e 8
28.6%
r 3
 
10.7%
l 2
 
7.1%
y 2
 
7.1%
c 2
 
7.1%
k 2
 
7.1%
a 2
 
7.1%
d 1
 
3.6%
s 1
 
3.6%
f 1
 
3.6%
Other values (4) 4
14.3%
Decimal Number
ValueCountFrequency (%)
2 103
30.8%
4 85
25.4%
1 79
23.7%
9 19
 
5.7%
3 18
 
5.4%
8 8
 
2.4%
5 8
 
2.4%
0 6
 
1.8%
6 5
 
1.5%
7 3
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 24
43.6%
& 18
32.7%
, 9
 
16.4%
/ 2
 
3.6%
! 1
 
1.8%
# 1
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51521
98.8%
Common 411
 
0.8%
Latin 210
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 5946
 
11.5%
탁 5851
 
11.4%
소 3677
 
7.1%
크 2077
 
4.0%
린 1520
 
3.0%
아 1094
 
2.1%
사 1042
 
2.0%
리 1029
 
2.0%
피 854
 
1.7%
토 791
 
1.5%
Other values (653) 27640
53.6%
Latin
ValueCountFrequency (%)
K 26
 
12.4%
O 16
 
7.6%
L 16
 
7.6%
G 15
 
7.1%
S 15
 
7.1%
C 11
 
5.2%
P 11
 
5.2%
E 11
 
5.2%
I 9
 
4.3%
M 8
 
3.8%
Other values (28) 72
34.3%
Common
ValueCountFrequency (%)
2 103
25.1%
4 85
20.7%
1 79
19.2%
. 24
 
5.8%
9 19
 
4.6%
3 18
 
4.4%
& 18
 
4.4%
- 12
 
2.9%
, 9
 
2.2%
8 8
 
1.9%
Other values (11) 36
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51519
98.8%
ASCII 620
 
1.2%
Compat Jamo 2
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 5946
 
11.5%
탁 5851
 
11.4%
소 3677
 
7.1%
크 2077
 
4.0%
린 1520
 
3.0%
아 1094
 
2.1%
사 1042
 
2.0%
리 1029
 
2.0%
피 854
 
1.7%
토 791
 
1.5%
Other values (651) 27638
53.6%
ASCII
ValueCountFrequency (%)
2 103
16.6%
4 85
 
13.7%
1 79
 
12.7%
K 26
 
4.2%
. 24
 
3.9%
9 19
 
3.1%
3 18
 
2.9%
& 18
 
2.9%
O 16
 
2.6%
L 16
 
2.6%
Other values (48) 216
34.8%
Compat Jamo
ValueCountFrequency (%)
ㅇ 1
50.0%
ㅅ 1
50.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

도로명
Text

Distinct9615
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:14:31.375491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length19.2289
Min length1

Characters and Unicode

Total characters192289
Distinct characters495
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9255 ?
Unique (%)92.5%

Sample

1st row경상남도 창원시 진해구 조천북로 65
2nd row경기도 부천시 조마루로 84
3rd row전라남도 목포시 연동로4번길 8
4th row경기도 성남시 중원구 자혜로57번길 15
5th row서울특별시 강서구 등촌로55길 8
ValueCountFrequency (%)
경기도 2321
 
5.4%
서울특별시 1584
 
3.7%
부산광역시 680
 
1.6%
경상북도 574
 
1.3%
인천광역시 556
 
1.3%
경상남도 550
 
1.3%
대구광역시 526
 
1.2%
광주광역시 441
 
1.0%
전라북도 438
 
1.0%
남구 425
 
1.0%
Other values (9634) 35157
81.3%
2023-12-13T00:14:31.994564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33254
 
17.3%
시 9724
 
5.1%
로 8697
 
4.5%
1 7657
 
4.0%
구 7162
 
3.7%
도 5958
 
3.1%
길 5674
 
3.0%
2 5087
 
2.6%
3 4050
 
2.1%
경 3766
 
2.0%
Other values (485) 101260
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122875
63.9%
Decimal Number 34406
 
17.9%
Space Separator 33254
 
17.3%
Dash Punctuation 1754
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 9724
 
7.9%
로 8697
 
7.1%
구 7162
 
5.8%
도 5958
 
4.8%
길 5674
 
4.6%
경 3766
 
3.1%
광 3710
 
3.0%
서 3039
 
2.5%
남 2894
 
2.4%
역 2831
 
2.3%
Other values (473) 69420
56.5%
Decimal Number
ValueCountFrequency (%)
1 7657
22.3%
2 5087
14.8%
3 4050
11.8%
4 3143
9.1%
5 2920
 
8.5%
6 2674
 
7.8%
7 2452
 
7.1%
8 2229
 
6.5%
0 2154
 
6.3%
9 2040
 
5.9%
Space Separator
ValueCountFrequency (%)
33254
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122875
63.9%
Common 69414
36.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 9724
 
7.9%
로 8697
 
7.1%
구 7162
 
5.8%
도 5958
 
4.8%
길 5674
 
4.6%
경 3766
 
3.1%
광 3710
 
3.0%
서 3039
 
2.5%
남 2894
 
2.4%
역 2831
 
2.3%
Other values (473) 69420
56.5%
Common
ValueCountFrequency (%)
33254
47.9%
1 7657
 
11.0%
2 5087
 
7.3%
3 4050
 
5.8%
4 3143
 
4.5%
5 2920
 
4.2%
6 2674
 
3.9%
7 2452
 
3.5%
8 2229
 
3.2%
0 2154
 
3.1%
Other values (2) 3794
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122875
63.9%
ASCII 69414
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33254
47.9%
1 7657
 
11.0%
2 5087
 
7.3%
3 4050
 
5.8%
4 3143
 
4.5%
5 2920
 
4.2%
6 2674
 
3.9%
7 2452
 
3.5%
8 2229
 
3.2%
0 2154
 
3.1%
Other values (2) 3794
 
5.5%
Hangul
ValueCountFrequency (%)
시 9724
 
7.9%
로 8697
 
7.1%
구 7162
 
5.8%
도 5958
 
4.8%
길 5674
 
4.6%
경 3766
 
3.1%
광 3710
 
3.0%
서 3039
 
2.5%
남 2894
 
2.4%
역 2831
 
2.3%
Other values (473) 69420
56.5%

개업일
Date

MISSING 

Distinct3937
Distinct (%)41.5%
Missing524
Missing (%)5.2%
Memory size156.2 KiB
Minimum1971-04-16 00:00:00
Maximum2017-06-29 00:00:00
2023-12-13T00:14:32.233024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:32.473960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2023-12-13T00:14:30.239220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:14:30.329149image/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

상호명도로명개업일
6118더삽명품크리닝경상남도 창원시 진해구 조천북로 652014-08-30
37981현대세탁경기도 부천시 조마루로 842014-10-31
6020대호전라남도 목포시 연동로4번길 81996-05-08
30384크린위드경기도 성남시 중원구 자혜로57번길 152014-08-30
33226크린토피아등촌역점서울특별시 강서구 등촌로55길 82015-01-02
704강북소파천갈이대구광역시 북구 태암로 462012-08-01
19410신월코아루세탁전라남도 여수시 신월2길 9-11990-12-22
2155그랜드세탁소강원도 춘천시 우석로101번길 201998-10-30
31627크린토피아충청북도 청주시 청원구 새터로 1712013-08-05
35497하늘채세탁소대구광역시 수성구 들안로 3002009-03-10
상호명도로명개업일
479VIP세탁소울산광역시 남구 남산로338번길 232002-01-10
25261이안유림세탁경기도 부천시 상동로117번길 642002-11-19
16941세탁소충청남도 천안시 서북구 쌍용11길 582016-04-15
22537우만세탁소경기도 수원시 팔달구 중부대로223번길 1021992-11-30
16762세탁마을서울특별시 동대문구 장한로30길 312015-11-03
32119크린토피아서울특별시 영등포구 영신로 1662015-01-01
7310두손크리닝전라북도 남원시 광한서로 782002-10-14
31765크린토피아대구광역시 수성구 지산로 532015-01-02
30470크린위드세탁멀티샵경기도 파주시 가람로 702014-08-30
21278영광컴퓨터세탁소경상북도 포항시 남구 연일읍 연일로159번길 58-161997-01-27