Overview

Dataset statistics

Number of variables6
Number of observations184
Missing cells20
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory49.7 B

Variable types

Numeric1
Categorical1
Text3
DateTime1

Dataset

Description인천광역시 남동구 관내 세탁업 영업신고 현황에 대한 자료로 업종, 상호, 영업소소재지,전화번호, 데이터기준일자를 공개합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15038945&srcSe=7661IVAWM27C61E190

Alerts

업종명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
소재지전화 has 20 (10.9%) missing valuesMissing
연번 has unique valuesUnique
업소소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:40:14.280594
Analysis finished2024-01-28 16:40:14.905859
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.5
Minimum1
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:40:14.985514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.15
Q146.75
median92.5
Q3138.25
95-th percentile174.85
Maximum184
Range183
Interquartile range (IQR)91.5

Descriptive statistics

Standard deviation53.260367
Coefficient of variation (CV)0.57578775
Kurtosis-1.2
Mean92.5
Median Absolute Deviation (MAD)46
Skewness0
Sum17020
Variance2836.6667
MonotonicityStrictly increasing
2024-01-29T01:40:15.142305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
128 1
 
0.5%
119 1
 
0.5%
120 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
Other values (174) 174
94.6%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
184 1
0.5%
183 1
0.5%
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%
177 1
0.5%
176 1
0.5%
175 1
0.5%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
세탁업
184 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 184
100.0%

Length

2024-01-29T01:40:15.293926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:40:15.402798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 184
100.0%

업소명
Text

Distinct171
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-01-29T01:40:15.631114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length5.3913043
Min length2

Characters and Unicode

Total characters992
Distinct characters190
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique163 ?
Unique (%)88.6%

Sample

1st row영신사세탁소
2nd row현대세탁소
3rd row형제세탁소
4th row수지세탁소
5th row은행사세탁소
ValueCountFrequency (%)
현대세탁소 4
 
2.0%
주공세탁소 4
 
2.0%
백양세탁소 3
 
1.5%
럭키세탁 2
 
1.0%
세탁전문점 2
 
1.0%
세탁 2
 
1.0%
세탁소 2
 
1.0%
크린 2
 
1.0%
현대힐세탁소 2
 
1.0%
충남세탁소 2
 
1.0%
Other values (169) 171
87.2%
2024-01-29T01:40:16.093296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 146
 
14.7%
탁 145
 
14.6%
소 85
 
8.6%
사 29
 
2.9%
크 17
 
1.7%
동 16
 
1.6%
신 15
 
1.5%
대 14
 
1.4%
14
 
1.4%
화 13
 
1.3%
Other values (180) 498
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 957
96.5%
Space Separator 14
 
1.4%
Decimal Number 11
 
1.1%
Open Punctuation 4
 
0.4%
Close Punctuation 4
 
0.4%
Lowercase Letter 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 146
 
15.3%
탁 145
 
15.2%
소 85
 
8.9%
사 29
 
3.0%
크 17
 
1.8%
동 16
 
1.7%
신 15
 
1.6%
대 14
 
1.5%
화 13
 
1.4%
린 13
 
1.4%
Other values (171) 464
48.5%
Decimal Number
ValueCountFrequency (%)
1 7
63.6%
9 2
 
18.2%
2 1
 
9.1%
4 1
 
9.1%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 957
96.5%
Common 34
 
3.4%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 146
 
15.3%
탁 145
 
15.2%
소 85
 
8.9%
사 29
 
3.0%
크 17
 
1.8%
동 16
 
1.7%
신 15
 
1.6%
대 14
 
1.5%
화 13
 
1.4%
린 13
 
1.4%
Other values (171) 464
48.5%
Common
ValueCountFrequency (%)
14
41.2%
1 7
20.6%
( 4
 
11.8%
) 4
 
11.8%
9 2
 
5.9%
2 1
 
2.9%
4 1
 
2.9%
- 1
 
2.9%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 957
96.5%
ASCII 35
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 146
 
15.3%
탁 145
 
15.2%
소 85
 
8.9%
사 29
 
3.0%
크 17
 
1.8%
동 16
 
1.7%
신 15
 
1.6%
대 14
 
1.5%
화 13
 
1.4%
린 13
 
1.4%
Other values (171) 464
48.5%
ASCII
ValueCountFrequency (%)
14
40.0%
1 7
20.0%
( 4
 
11.4%
) 4
 
11.4%
9 2
 
5.7%
2 1
 
2.9%
4 1
 
2.9%
e 1
 
2.9%
- 1
 
2.9%

업소소재지(도로명)
Text

UNIQUE 

Distinct184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-01-29T01:40:16.413303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length49
Mean length36.798913
Min length21

Characters and Unicode

Total characters6771
Distinct characters219
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184 ?
Unique (%)100.0%

Sample

1st row인천광역시 남동구 예술로360번길 11, 1층 일부호 (간석동)
2nd row인천광역시 남동구 구월로 65, 상가동 117호 (간석동, 현대홈타운아파트)
3rd row인천광역시 남동구 석산로 194 (간석동,1층일부)
4th row인천광역시 남동구 동암남로20번길 28 (간석동)
5th row인천광역시 남동구 인주대로763번길 33, 1층 일부호 (구월동)
ValueCountFrequency (%)
인천광역시 184
 
14.7%
남동구 184
 
14.7%
1층 63
 
5.0%
간석동 41
 
3.3%
구월동 38
 
3.0%
만수동 30
 
2.4%
상가동 30
 
2.4%
논현동 22
 
1.8%
2층 16
 
1.3%
남촌동 10
 
0.8%
Other values (419) 636
50.7%
2024-01-29T01:40:16.942432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1070
 
15.8%
동 438
 
6.5%
1 321
 
4.7%
구 264
 
3.9%
남 221
 
3.3%
인 210
 
3.1%
) 200
 
3.0%
( 200
 
3.0%
로 197
 
2.9%
시 191
 
2.8%
Other values (209) 3459
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3956
58.4%
Decimal Number 1126
 
16.6%
Space Separator 1070
 
15.8%
Close Punctuation 200
 
3.0%
Open Punctuation 200
 
3.0%
Other Punctuation 184
 
2.7%
Dash Punctuation 25
 
0.4%
Uppercase Letter 9
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 438
 
11.1%
구 264
 
6.7%
남 221
 
5.6%
인 210
 
5.3%
로 197
 
5.0%
시 191
 
4.8%
천 190
 
4.8%
역 187
 
4.7%
광 185
 
4.7%
길 123
 
3.1%
Other values (185) 1750
44.2%
Decimal Number
ValueCountFrequency (%)
1 321
28.5%
2 166
14.7%
0 116
 
10.3%
4 93
 
8.3%
3 88
 
7.8%
6 87
 
7.7%
5 84
 
7.5%
7 84
 
7.5%
8 47
 
4.2%
9 40
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 3
33.3%
B 2
22.2%
H 1
 
11.1%
L 1
 
11.1%
T 1
 
11.1%
P 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 180
97.8%
@ 3
 
1.6%
/ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
1070
100.0%
Close Punctuation
ValueCountFrequency (%)
) 200
100.0%
Open Punctuation
ValueCountFrequency (%)
( 200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3955
58.4%
Common 2806
41.4%
Latin 9
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 438
 
11.1%
구 264
 
6.7%
남 221
 
5.6%
인 210
 
5.3%
로 197
 
5.0%
시 191
 
4.8%
천 190
 
4.8%
역 187
 
4.7%
광 185
 
4.7%
길 123
 
3.1%
Other values (184) 1749
44.2%
Common
ValueCountFrequency (%)
1070
38.1%
1 321
 
11.4%
) 200
 
7.1%
( 200
 
7.1%
, 180
 
6.4%
2 166
 
5.9%
0 116
 
4.1%
4 93
 
3.3%
3 88
 
3.1%
6 87
 
3.1%
Other values (8) 285
 
10.2%
Latin
ValueCountFrequency (%)
A 3
33.3%
B 2
22.2%
H 1
 
11.1%
L 1
 
11.1%
T 1
 
11.1%
P 1
 
11.1%
Han
ValueCountFrequency (%)
內 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3955
58.4%
ASCII 2815
41.6%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1070
38.0%
1 321
 
11.4%
) 200
 
7.1%
( 200
 
7.1%
, 180
 
6.4%
2 166
 
5.9%
0 116
 
4.1%
4 93
 
3.3%
3 88
 
3.1%
6 87
 
3.1%
Other values (14) 294
 
10.4%
Hangul
ValueCountFrequency (%)
동 438
 
11.1%
구 264
 
6.7%
남 221
 
5.6%
인 210
 
5.3%
로 197
 
5.0%
시 191
 
4.8%
천 190
 
4.8%
역 187
 
4.7%
광 185
 
4.7%
길 123
 
3.1%
Other values (184) 1749
44.2%
CJK
ValueCountFrequency (%)
內 1
100.0%

소재지전화
Text

MISSING 

Distinct164
Distinct (%)100.0%
Missing20
Missing (%)10.9%
Memory size1.6 KiB
2024-01-29T01:40:17.261495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1968
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

Unique164 ?
Unique (%)100.0%

Sample

1st row032-426-7319
2nd row032-435-4209
3rd row032-468-0607
4th row032-431-2762
5th row032-469-6752
ValueCountFrequency (%)
032-461-6022 1
 
0.6%
032-812-6704 1
 
0.6%
032-427-8095 1
 
0.6%
032-446-3001 1
 
0.6%
032-441-4004 1
 
0.6%
032-426-1044 1
 
0.6%
032-462-4434 1
 
0.6%
032-868-0739 1
 
0.6%
032-446-4146 1
 
0.6%
032-463-2115 1
 
0.6%
Other values (154) 154
93.9%
2024-01-29T01:40:17.708544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 328
16.7%
2 284
14.4%
3 269
13.7%
0 263
13.4%
4 251
12.8%
6 169
8.6%
7 95
 
4.8%
8 83
 
4.2%
5 80
 
4.1%
1 74
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1640
83.3%
Dash Punctuation 328
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 284
17.3%
3 269
16.4%
0 263
16.0%
4 251
15.3%
6 169
10.3%
7 95
 
5.8%
8 83
 
5.1%
5 80
 
4.9%
1 74
 
4.5%
9 72
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 328
16.7%
2 284
14.4%
3 269
13.7%
0 263
13.4%
4 251
12.8%
6 169
8.6%
7 95
 
4.8%
8 83
 
4.2%
5 80
 
4.1%
1 74
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 328
16.7%
2 284
14.4%
3 269
13.7%
0 263
13.4%
4 251
12.8%
6 169
8.6%
7 95
 
4.8%
8 83
 
4.2%
5 80
 
4.1%
1 74
 
3.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2023-09-07 00:00:00
Maximum2023-09-07 00:00:00
2024-01-29T01:40:17.851442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:40:17.959485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-29T01:40:14.579764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-01-29T01:40:14.723781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:40:14.853203image/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

연번업종명업소명업소소재지(도로명)소재지전화데이터기준일자
01세탁업영신사세탁소인천광역시 남동구 예술로360번길 11, 1층 일부호 (간석동)032-426-73192023-09-07
12세탁업현대세탁소인천광역시 남동구 구월로 65, 상가동 117호 (간석동, 현대홈타운아파트)032-435-42092023-09-07
23세탁업형제세탁소인천광역시 남동구 석산로 194 (간석동,1층일부)032-468-06072023-09-07
34세탁업수지세탁소인천광역시 남동구 동암남로20번길 28 (간석동)032-431-27622023-09-07
45세탁업은행사세탁소인천광역시 남동구 인주대로763번길 33, 1층 일부호 (구월동)032-469-67522023-09-07
56세탁업백조세탁소인천광역시 남동구 경인로667번길 17 (간석동)032-439-64522023-09-07
67세탁업주공세탁소인천광역시 남동구 백범로124번길 7 (만수동,만수주공상가 나동 211~212호)032-461-88572023-09-07
78세탁업안국백양세탁인천광역시 남동구 문화서로23번길 49, 101호 (구월동)032-423-05282023-09-07
89세탁업천국세탁소인천광역시 남동구 경인로524번길 16 (간석동)032-426-32132023-09-07
910세탁업현대세탁소인천광역시 남동구 석촌로36번길 20-15 (간석동)032-424-98152023-09-07
연번업종명업소명업소소재지(도로명)소재지전화데이터기준일자
174175세탁업크린덕인천광역시 남동구 남촌로 74, 1층 2호 (남촌동)<NA>2023-09-07
175176세탁업하마운동화손세탁인천광역시 남동구 구월남로 116, 하늘지움2차 1층 105호 (구월동)<NA>2023-09-07
176177세탁업명품세탁소인천광역시 남동구 소래역동로 8, 1층 103호 (논현동)<NA>2023-09-07
177178세탁업세탁은여기인천광역시 남동구 인하로 608, 구월메디컬프라자 1층 106호 (구월동)<NA>2023-09-07
178179세탁업현세탁인천광역시 남동구 구월로137번길 33, 102호 (간석동)032-429-87852023-09-07
179180세탁업미소세탁소인천광역시 남동구 백범로206번길 15, 1층 (만수동)<NA>2023-09-07
180181세탁업명품수호인천광역시 남동구 만수서로37번길 82, 1층 일부호 (만수동)<NA>2023-09-07
181182세탁업푸르네세탁소인천광역시 남동구 포구로 96, 휴먼시아푸르내마을13단지 상가 102호일부호 (논현동)032-442-65652023-09-07
182183세탁업스웨덴세탁인천광역시 남동구 석산로 175, 정문프라자 110호 (간석동)032-424-24692023-09-07
183184세탁업시대세탁소인천광역시 남동구 남동대로799번길 34, B동 1층 117호 (구월동, 신영구월지웰시티푸르지오)<NA>2023-09-07