Overview

Dataset statistics

Number of variables26
Number of observations101
Missing cells277
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.2 KiB
Average record size in memory225.3 B

Variable types

Numeric9
Categorical9
Text7
Boolean1

Dataset

Description시군구코드,업종코드,년도,업소일련번호,업종명,신고일자,업소명,영업장면적(㎡),소재지전화번호,영업자시작일,법인명,소재지시작일,행정동명,폐업일자,업태명,위생교육수료일자,객실수,한실수,양실수,의자수,욕실수,발한실,세탁기수,허가(신고)번호,소재지도로명,소재지지번
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20880/S/1/datasetView.do

Alerts

업종코드 has constant value ""Constant
업종명 has constant value ""Constant
법인명 has constant value ""Constant
업태명 has constant value ""Constant
객실수 has constant value ""Constant
발한실 has constant value ""Constant
영업장면적(㎡) has 2 (2.0%) missing valuesMissing
소재지전화번호 has 3 (3.0%) missing valuesMissing
법인명 has 100 (99.0%) missing valuesMissing
폐업일자 has 26 (25.7%) missing valuesMissing
위생교육수료일자 has 79 (78.2%) missing valuesMissing
소재지도로명 has 66 (65.3%) missing valuesMissing
허가(신고)번호 has unique valuesUnique
영업장면적(㎡) has 31 (30.7%) zerosZeros

Reproduction

Analysis started2024-05-11 08:21:58.448286
Analysis finished2024-05-11 08:21:58.843028
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

Distinct24
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3145346.5
Minimum3000000
Maximum3240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:21:58.892593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3030000
Q13090000
median3160000
Q33220000
95-th percentile3230000
Maximum3240000
Range240000
Interquartile range (IQR)130000

Descriptive statistics

Standard deviation74263.913
Coefficient of variation (CV)0.023610725
Kurtosis-1.175936
Mean3145346.5
Median Absolute Deviation (MAD)70000
Skewness-0.35105169
Sum3.1768 × 108
Variance5.5151287 × 109
MonotonicityNot monotonic
2024-05-11T17:21:58.992137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3230000 20
19.8%
3160000 9
 
8.9%
3040000 9
 
8.9%
3030000 6
 
5.9%
3240000 5
 
5.0%
3130000 5
 
5.0%
3150000 4
 
4.0%
3110000 4
 
4.0%
3120000 4
 
4.0%
3140000 4
 
4.0%
Other values (14) 31
30.7%
ValueCountFrequency (%)
3000000 2
 
2.0%
3020000 2
 
2.0%
3030000 6
5.9%
3040000 9
8.9%
3050000 1
 
1.0%
3060000 2
 
2.0%
3070000 1
 
1.0%
3080000 2
 
2.0%
3090000 2
 
2.0%
3100000 2
 
2.0%
ValueCountFrequency (%)
3240000 5
 
5.0%
3230000 20
19.8%
3220000 4
 
4.0%
3210000 3
 
3.0%
3200000 2
 
2.0%
3190000 3
 
3.0%
3180000 2
 
2.0%
3170000 3
 
3.0%
3160000 9
8.9%
3150000 4
 
4.0%

업종코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
207
101 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row207
2nd row207
3rd row207
4th row207
5th row207

Common Values

ValueCountFrequency (%)
207 101
100.0%

Length

2024-05-11T17:21:59.091443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:21:59.166985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
207 101
100.0%

년도
Real number (ℝ)

Distinct28
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.1485
Minimum1984
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:21:59.237092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1987
Q11990
median1995
Q32001
95-th percentile2010
Maximum2017
Range33
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.1977582
Coefficient of variation (CV)0.003605823
Kurtosis0.12538856
Mean1996.1485
Median Absolute Deviation (MAD)5
Skewness0.73092435
Sum201611
Variance51.807723
MonotonicityDecreasing
2024-05-11T17:21:59.341310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1995 10
 
9.9%
1997 8
 
7.9%
1987 7
 
6.9%
1990 6
 
5.9%
1992 6
 
5.9%
1994 6
 
5.9%
1988 6
 
5.9%
1989 5
 
5.0%
1996 5
 
5.0%
2002 5
 
5.0%
Other values (18) 37
36.6%
ValueCountFrequency (%)
1984 1
 
1.0%
1986 2
 
2.0%
1987 7
6.9%
1988 6
5.9%
1989 5
5.0%
1990 6
5.9%
1991 1
 
1.0%
1992 6
5.9%
1993 4
4.0%
1994 6
5.9%
ValueCountFrequency (%)
2017 1
 
1.0%
2015 1
 
1.0%
2014 1
 
1.0%
2010 3
3.0%
2009 2
2.0%
2008 2
2.0%
2007 1
 
1.0%
2005 2
2.0%
2004 2
2.0%
2003 3
3.0%

업소일련번호
Real number (ℝ)

Distinct64
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1472.7525
Minimum1
Maximum3246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:21:59.464717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1903
Q32167
95-th percentile3242
Maximum3246
Range3245
Interquartile range (IQR)2166

Descriptive statistics

Standard deviation1208.6558
Coefficient of variation (CV)0.82067814
Kurtosis-1.4271493
Mean1472.7525
Median Absolute Deviation (MAD)1330
Skewness-0.079799078
Sum148748
Variance1460848.7
MonotonicityNot monotonic
2024-05-11T17:21:59.767020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 34
33.7%
2 2
 
2.0%
1753 2
 
2.0%
3242 2
 
2.0%
2143 2
 
2.0%
1644 1
 
1.0%
2140 1
 
1.0%
2108 1
 
1.0%
3238 1
 
1.0%
1941 1
 
1.0%
Other values (54) 54
53.5%
ValueCountFrequency (%)
1 34
33.7%
2 2
 
2.0%
911 1
 
1.0%
929 1
 
1.0%
1477 1
 
1.0%
1478 1
 
1.0%
1588 1
 
1.0%
1589 1
 
1.0%
1643 1
 
1.0%
1644 1
 
1.0%
ValueCountFrequency (%)
3246 1
1.0%
3245 1
1.0%
3244 1
1.0%
3243 1
1.0%
3242 2
2.0%
3241 1
1.0%
3240 1
1.0%
3239 1
1.0%
3238 1
1.0%
3237 1
1.0%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
위생처리업
101 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위생처리업 101
100.0%

Length

2024-05-11T17:21:59.901345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:21:59.996110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위생처리업 101
100.0%

신고일자
Real number (ℝ)

Distinct99
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19962185
Minimum19841205
Maximum20171102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:22:00.098330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19841205
5-th percentile19870530
Q119900929
median19950812
Q320011025
95-th percentile20100408
Maximum20171102
Range329897
Interquartile range (IQR)110096

Descriptive statistics

Standard deviation71998.716
Coefficient of variation (CV)0.0036067553
Kurtosis0.12860978
Mean19962185
Median Absolute Deviation (MAD)50506
Skewness0.73232694
Sum2.0161807 × 109
Variance5.1838151 × 109
MonotonicityDecreasing
2024-05-11T17:22:00.232904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19970801 2
 
2.0%
19940412 2
 
2.0%
20171102 1
 
1.0%
19900929 1
 
1.0%
19901229 1
 
1.0%
19911107 1
 
1.0%
19920321 1
 
1.0%
19920417 1
 
1.0%
19920520 1
 
1.0%
19920808 1
 
1.0%
Other values (89) 89
88.1%
ValueCountFrequency (%)
19841205 1
1.0%
19860123 1
1.0%
19860926 1
1.0%
19870401 1
1.0%
19870526 1
1.0%
19870530 1
1.0%
19870630 1
1.0%
19870812 1
1.0%
19870922 1
1.0%
19871005 1
1.0%
ValueCountFrequency (%)
20171102 1
1.0%
20151111 1
1.0%
20140416 1
1.0%
20101014 1
1.0%
20100607 1
1.0%
20100408 1
1.0%
20091026 1
1.0%
20091008 1
1.0%
20080731 1
1.0%
20080229 1
1.0%

업소명
Text

Distinct89
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-05-11T17:22:00.463321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.5247525
Min length2

Characters and Unicode

Total characters457
Distinct characters95
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)77.2%

Sample

1st row강남사(세탁)
2nd row증산산업
3rd row잠실기업
4th row거기상사
5th row고양산업
ValueCountFrequency (%)
해동기업 3
 
3.0%
장수산업 2
 
2.0%
고양산업 2
 
2.0%
명성실업 2
 
2.0%
영동산업 2
 
2.0%
서부산업 2
 
2.0%
제일상사 2
 
2.0%
백두산위생물수건 2
 
2.0%
성동산업 2
 
2.0%
성은수건 2
 
2.0%
Other values (79) 80
79.2%
2024-05-11T17:22:00.825499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 59
 
12.9%
산 48
 
10.5%
성 25
 
5.5%
수 25
 
5.5%
물 23
 
5.0%
건 19
 
4.2%
위 16
 
3.5%
생 16
 
3.5%
기 13
 
2.8%
동 12
 
2.6%
Other values (85) 201
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 449
98.2%
Close Punctuation 4
 
0.9%
Open Punctuation 4
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 59
 
13.1%
산 48
 
10.7%
성 25
 
5.6%
수 25
 
5.6%
물 23
 
5.1%
건 19
 
4.2%
위 16
 
3.6%
생 16
 
3.6%
기 13
 
2.9%
동 12
 
2.7%
Other values (83) 193
43.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 449
98.2%
Common 8
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 59
 
13.1%
산 48
 
10.7%
성 25
 
5.6%
수 25
 
5.6%
물 23
 
5.1%
건 19
 
4.2%
위 16
 
3.6%
생 16
 
3.6%
기 13
 
2.9%
동 12
 
2.7%
Other values (83) 193
43.0%
Common
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 449
98.2%
ASCII 8
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 59
 
13.1%
산 48
 
10.7%
성 25
 
5.6%
수 25
 
5.6%
물 23
 
5.1%
건 19
 
4.2%
위 16
 
3.6%
생 16
 
3.6%
기 13
 
2.9%
동 12
 
2.7%
Other values (83) 193
43.0%
ASCII
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

영업장면적(㎡)
Real number (ℝ)

MISSING  ZEROS 

Distinct69
Distinct (%)69.7%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean97.587778
Minimum0
Maximum912
Zeros31
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:22:00.957807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median86.81
Q3110.2
95-th percentile300.725
Maximum912
Range912
Interquartile range (IQR)110.2

Descriptive statistics

Standard deviation132.94436
Coefficient of variation (CV)1.3623055
Kurtosis17.137456
Mean97.587778
Median Absolute Deviation (MAD)36.81
Skewness3.6071552
Sum9661.19
Variance17674.204
MonotonicityNot monotonic
2024-05-11T17:22:01.094694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
30.7%
152.54 1
 
1.0%
272.34 1
 
1.0%
99.18 1
 
1.0%
101.83 1
 
1.0%
118.65 1
 
1.0%
85.54 1
 
1.0%
81.9 1
 
1.0%
99.57 1
 
1.0%
162.3 1
 
1.0%
Other values (59) 59
58.4%
(Missing) 2
 
2.0%
ValueCountFrequency (%)
0.0 31
30.7%
19.0 1
 
1.0%
50.0 1
 
1.0%
58.68 1
 
1.0%
59.9 1
 
1.0%
65.6 1
 
1.0%
70.0 1
 
1.0%
73.1 1
 
1.0%
80.52 1
 
1.0%
81.0 1
 
1.0%
ValueCountFrequency (%)
912.0 1
1.0%
657.86 1
1.0%
496.0 1
1.0%
389.12 1
1.0%
370.25 1
1.0%
293.0 1
1.0%
272.34 1
1.0%
266.12 1
1.0%
179.82 1
1.0%
162.3 1
1.0%

소재지전화번호
Text

MISSING 

Distinct87
Distinct (%)88.8%
Missing3
Missing (%)3.0%
Memory size940.0 B
2024-05-11T17:22:01.288645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.7653061
Min length2

Characters and Unicode

Total characters957
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

Unique80 ?
Unique (%)81.6%

Sample

1st row02 3765426
2nd row02 420 1750
3rd row02 431 1677
4th row00117912672
5th row02 375 2943
ValueCountFrequency (%)
02 72
41.1%
0 5
 
2.9%
4667232 3
 
1.7%
3356410 2
 
1.1%
8549544 2
 
1.1%
4447311 2
 
1.1%
4482508 2
 
1.1%
2021996 1
 
0.6%
0204480400 1
 
0.6%
0203062473 1
 
0.6%
Other values (84) 84
48.0%
2024-05-11T17:22:01.614717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 177
18.5%
2 168
17.6%
4 98
10.2%
95
9.9%
6 67
 
7.0%
7 67
 
7.0%
3 66
 
6.9%
8 57
 
6.0%
1 56
 
5.9%
5 54
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 862
90.1%
Space Separator 95
 
9.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 177
20.5%
2 168
19.5%
4 98
11.4%
6 67
 
7.8%
7 67
 
7.8%
3 66
 
7.7%
8 57
 
6.6%
1 56
 
6.5%
5 54
 
6.3%
9 52
 
6.0%
Space Separator
ValueCountFrequency (%)
95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 957
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 177
18.5%
2 168
17.6%
4 98
10.2%
95
9.9%
6 67
 
7.0%
7 67
 
7.0%
3 66
 
6.9%
8 57
 
6.0%
1 56
 
5.9%
5 54
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 177
18.5%
2 168
17.6%
4 98
10.2%
95
9.9%
6 67
 
7.0%
7 67
 
7.0%
3 66
 
6.9%
8 57
 
6.0%
1 56
 
5.9%
5 54
 
5.6%

영업자시작일
Real number (ℝ)

Distinct91
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20014452
Minimum19841205
Maximum20171102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:22:01.742681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19841205
5-th percentile19931028
Q119970825
median19990208
Q320070323
95-th percentile20150514
Maximum20171102
Range329897
Interquartile range (IQR)99498

Descriptive statistics

Standard deviation69226.593
Coefficient of variation (CV)0.0034588304
Kurtosis-0.015720066
Mean20014452
Median Absolute Deviation (MAD)30090
Skewness0.56854379
Sum2.0214596 × 109
Variance4.7923212 × 109
MonotonicityNot monotonic
2024-05-11T17:22:01.864749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19951115 4
 
4.0%
19980724 3
 
3.0%
19980706 2
 
2.0%
19980514 2
 
2.0%
19980903 2
 
2.0%
20150514 2
 
2.0%
19970728 2
 
2.0%
20040114 1
 
1.0%
19980804 1
 
1.0%
19971104 1
 
1.0%
Other values (81) 81
80.2%
ValueCountFrequency (%)
19841205 1
 
1.0%
19870630 1
 
1.0%
19900113 1
 
1.0%
19900929 1
 
1.0%
19921217 1
 
1.0%
19931028 1
 
1.0%
19950209 1
 
1.0%
19950331 1
 
1.0%
19951028 1
 
1.0%
19951115 4
4.0%
ValueCountFrequency (%)
20171102 1
1.0%
20170707 1
1.0%
20170302 1
1.0%
20161229 1
1.0%
20160728 1
1.0%
20150514 2
2.0%
20141230 1
1.0%
20140416 1
1.0%
20120531 1
1.0%
20110714 1
1.0%

법인명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing100
Missing (%)99.0%
Memory size940.0 B
2024-05-11T17:22:01.983278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row한국공항 주식회사
ValueCountFrequency (%)
한국공항 1
50.0%
주식회사 1
50.0%
2024-05-11T17:22:02.212390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
한 1
11.1%
국 1
11.1%
공 1
11.1%
항 1
11.1%
1
11.1%
주 1
11.1%
식 1
11.1%
회 1
11.1%
사 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8
88.9%
Space Separator 1
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 1
12.5%
국 1
12.5%
공 1
12.5%
항 1
12.5%
주 1
12.5%
식 1
12.5%
회 1
12.5%
사 1
12.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8
88.9%
Common 1
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 1
12.5%
국 1
12.5%
공 1
12.5%
항 1
12.5%
주 1
12.5%
식 1
12.5%
회 1
12.5%
사 1
12.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8
88.9%
ASCII 1
 
11.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
한 1
12.5%
국 1
12.5%
공 1
12.5%
항 1
12.5%
주 1
12.5%
식 1
12.5%
회 1
12.5%
사 1
12.5%
ASCII
ValueCountFrequency (%)
1
100.0%

소재지시작일
Real number (ℝ)

Distinct91
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20002374
Minimum19870630
Maximum20171109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:22:02.327560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870630
5-th percentile19900929
Q119961031
median19980724
Q320030806
95-th percentile20140416
Maximum20171109
Range300479
Interquartile range (IQR)69775

Descriptive statistics

Standard deviation67298.576
Coefficient of variation (CV)0.0033645294
Kurtosis0.15409597
Mean20002374
Median Absolute Deviation (MAD)30281
Skewness0.74011853
Sum2.0202398 × 109
Variance4.5290983 × 109
MonotonicityNot monotonic
2024-05-11T17:22:02.444510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19980724 4
 
4.0%
19951115 3
 
3.0%
20091026 2
 
2.0%
19980903 2
 
2.0%
19980514 2
 
2.0%
19970728 2
 
2.0%
19970825 2
 
2.0%
19980804 1
 
1.0%
19960302 1
 
1.0%
19980122 1
 
1.0%
Other values (81) 81
80.2%
ValueCountFrequency (%)
19870630 1
1.0%
19880303 1
1.0%
19890501 1
1.0%
19900106 1
1.0%
19900113 1
1.0%
19900929 1
1.0%
19911107 1
1.0%
19920808 1
1.0%
19921217 1
1.0%
19930310 1
1.0%
ValueCountFrequency (%)
20171109 1
1.0%
20171102 1
1.0%
20161006 1
1.0%
20151111 1
1.0%
20151005 1
1.0%
20140416 1
1.0%
20130520 1
1.0%
20110623 1
1.0%
20110610 1
1.0%
20110315 1
1.0%

행정동명
Text

Distinct74
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-05-11T17:22:02.666660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.4950495
Min length3

Characters and Unicode

Total characters454
Distinct characters96
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)58.4%

Sample

1st row성수2가제1동
2nd row응암제3동
3rd row잠실본동
4th row개봉제3동
5th row문정1동
ValueCountFrequency (%)
장지동 8
 
7.9%
군자동 4
 
4.0%
문정1동 4
 
4.0%
성수2가제1동 3
 
3.0%
개봉제3동 3
 
3.0%
서초제3동 2
 
2.0%
망원제1동 2
 
2.0%
천호제2동 2
 
2.0%
자양제1동 2
 
2.0%
방화제1동 2
 
2.0%
Other values (64) 69
68.3%
2024-05-11T17:22:03.030505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 101
22.2%
제 55
 
12.1%
1 35
 
7.7%
2 25
 
5.5%
가 11
 
2.4%
장 9
 
2.0%
3 9
 
2.0%
지 8
 
1.8%
수 8
 
1.8%
성 7
 
1.5%
Other values (86) 186
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 372
81.9%
Decimal Number 80
 
17.6%
Other Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
27.2%
제 55
 
14.8%
가 11
 
3.0%
장 9
 
2.4%
지 8
 
2.2%
수 8
 
2.2%
성 7
 
1.9%
자 7
 
1.9%
신 7
 
1.9%
화 6
 
1.6%
Other values (77) 153
41.1%
Decimal Number
ValueCountFrequency (%)
1 35
43.8%
2 25
31.2%
3 9
 
11.2%
4 4
 
5.0%
5 3
 
3.8%
6 2
 
2.5%
8 1
 
1.2%
7 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 372
81.9%
Common 82
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 101
27.2%
제 55
 
14.8%
가 11
 
3.0%
장 9
 
2.4%
지 8
 
2.2%
수 8
 
2.2%
성 7
 
1.9%
자 7
 
1.9%
신 7
 
1.9%
화 6
 
1.6%
Other values (77) 153
41.1%
Common
ValueCountFrequency (%)
1 35
42.7%
2 25
30.5%
3 9
 
11.0%
4 4
 
4.9%
5 3
 
3.7%
6 2
 
2.4%
. 2
 
2.4%
8 1
 
1.2%
7 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 372
81.9%
ASCII 82
 
18.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 101
27.2%
제 55
 
14.8%
가 11
 
3.0%
장 9
 
2.4%
지 8
 
2.2%
수 8
 
2.2%
성 7
 
1.9%
자 7
 
1.9%
신 7
 
1.9%
화 6
 
1.6%
Other values (77) 153
41.1%
ASCII
ValueCountFrequency (%)
1 35
42.7%
2 25
30.5%
3 9
 
11.0%
4 4
 
4.9%
5 3
 
3.7%
6 2
 
2.4%
. 2
 
2.4%
8 1
 
1.2%
7 1
 
1.2%

폐업일자
Real number (ℝ)

MISSING 

Distinct72
Distinct (%)96.0%
Missing26
Missing (%)25.7%
Infinite0
Infinite (%)0.0%
Mean20011739
Minimum18991230
Maximum20171114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:22:03.175945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18991230
5-th percentile19947197
Q119980566
median20000524
Q320085964
95-th percentile20153666
Maximum20171114
Range1179884
Interquartile range (IQR)105398

Descriptive statistics

Standard deviation135894.21
Coefficient of variation (CV)0.0067907246
Kurtosis43.672805
Mean20011739
Median Absolute Deviation (MAD)29799
Skewness-5.7258219
Sum1.5008805 × 109
Variance1.8467237 × 1010
MonotonicityNot monotonic
2024-05-11T17:22:03.296407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20161006 2
 
2.0%
20100607 2
 
2.0%
19970710 2
 
2.0%
19990304 1
 
1.0%
19920305 1
 
1.0%
19970806 1
 
1.0%
20010629 1
 
1.0%
19971009 1
 
1.0%
20170926 1
 
1.0%
19970725 1
 
1.0%
Other values (62) 62
61.4%
(Missing) 26
25.7%
ValueCountFrequency (%)
18991230 1
1.0%
19900430 1
1.0%
19920305 1
1.0%
19940415 1
1.0%
19950104 1
1.0%
19960528 1
1.0%
19961022 1
1.0%
19961104 1
1.0%
19970315 1
1.0%
19970624 1
1.0%
ValueCountFrequency (%)
20171114 1
1.0%
20170926 1
1.0%
20161006 2
2.0%
20150520 1
1.0%
20140610 1
1.0%
20130826 1
1.0%
20120611 1
1.0%
20120524 1
1.0%
20120503 1
1.0%
20110802 1
1.0%

업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
물수건위생처리업
101 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
물수건위생처리업 101
100.0%

Length

2024-05-11T17:22:03.405270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:22:03.479365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물수건위생처리업 101
100.0%

위생교육수료일자
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)59.1%
Missing79
Missing (%)78.2%
Infinite0
Infinite (%)0.0%
Mean20063506
Minimum20020423
Maximum20160720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-11T17:22:03.547934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020423
5-th percentile20031125
Q120040921
median20050426
Q320080878
95-th percentile20100996
Maximum20160720
Range140297
Interquartile range (IQR)39957.5

Descriptive statistics

Standard deviation33081.85
Coefficient of variation (CV)0.0016488569
Kurtosis1.9810427
Mean20063506
Median Absolute Deviation (MAD)19301
Skewness1.2778706
Sum4.4139713 × 108
Variance1.0944088 × 109
MonotonicityNot monotonic
2024-05-11T17:22:03.641373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20040921 6
 
5.9%
20050426 3
 
3.0%
20080424 2
 
2.0%
20031125 2
 
2.0%
20101026 1
 
1.0%
20091222 1
 
1.0%
20100429 1
 
1.0%
20020423 1
 
1.0%
20160720 1
 
1.0%
20071227 1
 
1.0%
Other values (3) 3
 
3.0%
(Missing) 79
78.2%
ValueCountFrequency (%)
20020423 1
 
1.0%
20031125 2
 
2.0%
20040921 6
5.9%
20050426 3
3.0%
20050721 1
 
1.0%
20071227 1
 
1.0%
20080424 2
 
2.0%
20081030 1
 
1.0%
20091222 1
 
1.0%
20100428 1
 
1.0%
ValueCountFrequency (%)
20160720 1
 
1.0%
20101026 1
 
1.0%
20100429 1
 
1.0%
20100428 1
 
1.0%
20091222 1
 
1.0%
20081030 1
 
1.0%
20080424 2
2.0%
20071227 1
 
1.0%
20050721 1
 
1.0%
20050426 3
3.0%

객실수
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
0
101 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101
100.0%

Length

2024-05-11T17:22:03.749176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:22:03.829901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 101
100.0%

한실수
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
0
70 
<NA>
31 

Length

Max length4
Median length1
Mean length1.9207921
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70
69.3%
<NA> 31
30.7%

Length

2024-05-11T17:22:03.917074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:22:04.008597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 70
69.3%
na 31
30.7%

양실수
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
0
70 
<NA>
31 

Length

Max length4
Median length1
Mean length1.9207921
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70
69.3%
<NA> 31
30.7%

Length

2024-05-11T17:22:04.099455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:22:04.187151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 70
69.3%
na 31
30.7%

의자수
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
0
70 
<NA>
31 

Length

Max length4
Median length1
Mean length1.9207921
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70
69.3%
<NA> 31
30.7%

Length

2024-05-11T17:22:04.287614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:22:04.377285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 70
69.3%
na 31
30.7%

욕실수
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
0
70 
<NA>
31 

Length

Max length4
Median length1
Mean length1.9207921
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70
69.3%
<NA> 31
30.7%

Length

2024-05-11T17:22:04.474094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:22:04.570401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 70
69.3%
na 31
30.7%

발한실
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing1
Missing (%)1.0%
Memory size334.0 B
False
100 
(Missing)
 
1
ValueCountFrequency (%)
False 100
99.0%
(Missing) 1
 
1.0%
2024-05-11T17:22:04.645455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

세탁기수
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
<NA>
84 
0
17 

Length

Max length4
Median length4
Mean length3.4950495
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 84
83.2%
0 17
 
16.8%

Length

2024-05-11T17:22:04.758351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:22:04.841141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 84
83.2%
0 17
 
16.8%

허가(신고)번호
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-05-11T17:22:04.999607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2222
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

Unique101 ?
Unique (%)100.0%

Sample

1st row3030000-207-2017-00001
2nd row3110000-207-2015-00001
3rd row3230000-207-2014-00001
4th row3160000-207-2010-00001
5th row3230000-207-2010-00001
ValueCountFrequency (%)
3030000-207-2017-00001 1
 
1.0%
3230000-207-1995-03244 1
 
1.0%
3040000-207-1990-01941 1
 
1.0%
3200000-207-1991-00001 1
 
1.0%
3000000-207-1992-01753 1
 
1.0%
3160000-207-1992-02141 1
 
1.0%
3080000-207-1992-02109 1
 
1.0%
3230000-207-1992-03239 1
 
1.0%
3230000-207-1992-03240 1
 
1.0%
3110000-207-1992-02148 1
 
1.0%
Other values (91) 91
90.1%
2024-05-11T17:22:05.298835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 822
37.0%
- 303
 
13.6%
2 235
 
10.6%
1 206
 
9.3%
3 173
 
7.8%
9 161
 
7.2%
7 134
 
6.0%
4 73
 
3.3%
8 49
 
2.2%
6 35
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1919
86.4%
Dash Punctuation 303
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 822
42.8%
2 235
 
12.2%
1 206
 
10.7%
3 173
 
9.0%
9 161
 
8.4%
7 134
 
7.0%
4 73
 
3.8%
8 49
 
2.6%
6 35
 
1.8%
5 31
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 303
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 822
37.0%
- 303
 
13.6%
2 235
 
10.6%
1 206
 
9.3%
3 173
 
7.8%
9 161
 
7.2%
7 134
 
6.0%
4 73
 
3.3%
8 49
 
2.2%
6 35
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 822
37.0%
- 303
 
13.6%
2 235
 
10.6%
1 206
 
9.3%
3 173
 
7.8%
9 161
 
7.2%
7 134
 
6.0%
4 73
 
3.3%
8 49
 
2.2%
6 35
 
1.6%

소재지도로명
Text

MISSING 

Distinct34
Distinct (%)97.1%
Missing66
Missing (%)65.3%
Memory size940.0 B
2024-05-11T17:22:05.570426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length29.114286
Min length23

Characters and Unicode

Total characters1019
Distinct characters115
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)94.3%

Sample

1st row서울특별시 성동구 뚝섬로13길 32-24, 1층 (성수동2가)
2nd row서울특별시 은평구 응암로20길 18-1, 지하1층 (응암동)
3rd row서울특별시 송파구 백제고분로12길 10, 지하1층 (잠실동)
4th row서울특별시 구로구 개봉로1나길 46, (개봉동)
5th row서울특별시 마포구 임정로 59, (신공덕동,지하1층)
ValueCountFrequency (%)
서울특별시 35
 
18.9%
지하1층 6
 
3.2%
성동구 4
 
2.2%
구로구 3
 
1.6%
강서구 3
 
1.6%
강동구 3
 
1.6%
광진구 3
 
1.6%
은평구 3
 
1.6%
방화동 3
 
1.6%
송파구 3
 
1.6%
Other values (107) 119
64.3%
2024-05-11T17:22:05.974991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
150
 
14.7%
, 47
 
4.6%
1 45
 
4.4%
동 45
 
4.4%
서 41
 
4.0%
구 38
 
3.7%
( 38
 
3.7%
) 38
 
3.7%
로 37
 
3.6%
시 37
 
3.6%
Other values (105) 503
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 594
58.3%
Space Separator 150
 
14.7%
Decimal Number 148
 
14.5%
Other Punctuation 47
 
4.6%
Open Punctuation 38
 
3.7%
Close Punctuation 38
 
3.7%
Dash Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 45
 
7.6%
서 41
 
6.9%
구 38
 
6.4%
로 37
 
6.2%
시 37
 
6.2%
울 35
 
5.9%
별 35
 
5.9%
특 35
 
5.9%
길 29
 
4.9%
지 18
 
3.0%
Other values (90) 244
41.1%
Decimal Number
ValueCountFrequency (%)
1 45
30.4%
2 23
15.5%
4 17
 
11.5%
3 15
 
10.1%
0 12
 
8.1%
5 9
 
6.1%
6 8
 
5.4%
8 7
 
4.7%
9 7
 
4.7%
7 5
 
3.4%
Space Separator
ValueCountFrequency (%)
150
100.0%
Other Punctuation
ValueCountFrequency (%)
, 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 594
58.3%
Common 425
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 45
 
7.6%
서 41
 
6.9%
구 38
 
6.4%
로 37
 
6.2%
시 37
 
6.2%
울 35
 
5.9%
별 35
 
5.9%
특 35
 
5.9%
길 29
 
4.9%
지 18
 
3.0%
Other values (90) 244
41.1%
Common
ValueCountFrequency (%)
150
35.3%
, 47
 
11.1%
1 45
 
10.6%
( 38
 
8.9%
) 38
 
8.9%
2 23
 
5.4%
4 17
 
4.0%
3 15
 
3.5%
0 12
 
2.8%
5 9
 
2.1%
Other values (5) 31
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 594
58.3%
ASCII 425
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
150
35.3%
, 47
 
11.1%
1 45
 
10.6%
( 38
 
8.9%
) 38
 
8.9%
2 23
 
5.4%
4 17
 
4.0%
3 15
 
3.5%
0 12
 
2.8%
5 9
 
2.1%
Other values (5) 31
 
7.3%
Hangul
ValueCountFrequency (%)
동 45
 
7.6%
서 41
 
6.9%
구 38
 
6.4%
로 37
 
6.2%
시 37
 
6.2%
울 35
 
5.9%
별 35
 
5.9%
특 35
 
5.9%
길 29
 
4.9%
지 18
 
3.0%
Other values (90) 244
41.1%

소재지지번
Text

Distinct98
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-05-11T17:22:06.272021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length35
Mean length26.613861
Min length24

Characters and Unicode

Total characters2688
Distinct characters116
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)95.0%

Sample

1st row서울특별시 성동구 성수동2가 269번지 20호 1층
2nd row서울특별시 은평구 응암동 397번지 281호 지하1층
3rd row서울특별시 송파구 잠실동 297번지 3호 지하1층
4th row서울특별시 구로구 개봉동 368번지 20호
5th row서울특별시 송파구 문정동 116번지 6호
ValueCountFrequency (%)
서울특별시 101
 
19.0%
송파구 20
 
3.8%
지하1층 11
 
2.1%
광진구 9
 
1.7%
구로구 9
 
1.7%
산 8
 
1.5%
장지동 7
 
1.3%
2호 7
 
1.3%
3호 7
 
1.3%
1호 6
 
1.1%
Other values (218) 347
65.2%
2024-05-11T17:22:06.687076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
716
26.6%
지 127
 
4.7%
서 115
 
4.3%
동 115
 
4.3%
구 113
 
4.2%
시 103
 
3.8%
호 103
 
3.8%
번 101
 
3.8%
특 101
 
3.8%
별 101
 
3.8%
Other values (106) 993
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1487
55.3%
Space Separator 716
26.6%
Decimal Number 470
 
17.5%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 127
 
8.5%
서 115
 
7.7%
동 115
 
7.7%
구 113
 
7.6%
시 103
 
6.9%
호 103
 
6.9%
번 101
 
6.8%
특 101
 
6.8%
별 101
 
6.8%
울 101
 
6.8%
Other values (92) 407
27.4%
Decimal Number
ValueCountFrequency (%)
1 97
20.6%
2 68
14.5%
3 63
13.4%
5 41
8.7%
7 38
 
8.1%
4 37
 
7.9%
0 34
 
7.2%
9 33
 
7.0%
6 33
 
7.0%
8 26
 
5.5%
Space Separator
ValueCountFrequency (%)
716
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1487
55.3%
Common 1201
44.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 127
 
8.5%
서 115
 
7.7%
동 115
 
7.7%
구 113
 
7.6%
시 103
 
6.9%
호 103
 
6.9%
번 101
 
6.8%
특 101
 
6.8%
별 101
 
6.8%
울 101
 
6.8%
Other values (92) 407
27.4%
Common
ValueCountFrequency (%)
716
59.6%
1 97
 
8.1%
2 68
 
5.7%
3 63
 
5.2%
5 41
 
3.4%
7 38
 
3.2%
4 37
 
3.1%
0 34
 
2.8%
9 33
 
2.7%
6 33
 
2.7%
Other values (4) 41
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1487
55.3%
ASCII 1201
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
716
59.6%
1 97
 
8.1%
2 68
 
5.7%
3 63
 
5.2%
5 41
 
3.4%
7 38
 
3.2%
4 37
 
3.1%
0 34
 
2.8%
9 33
 
2.7%
6 33
 
2.7%
Other values (4) 41
 
3.4%
Hangul
ValueCountFrequency (%)
지 127
 
8.5%
서 115
 
7.7%
동 115
 
7.7%
구 113
 
7.6%
시 103
 
6.9%
호 103
 
6.9%
번 101
 
6.8%
특 101
 
6.8%
별 101
 
6.8%
울 101
 
6.8%
Other values (92) 407
27.4%

Sample

시군구코드업종코드년도업소일련번호업종명신고일자업소명영업장면적(㎡)소재지전화번호영업자시작일법인명소재지시작일행정동명폐업일자업태명위생교육수료일자객실수한실수양실수의자수욕실수발한실세탁기수허가(신고)번호소재지도로명소재지지번
0303000020720171위생처리업20171102강남사(세탁)99.17<NA>20171102<NA>20171102성수2가제1동20171114물수건위생처리업<NA>00000N03030000-207-2017-00001서울특별시 성동구 뚝섬로13길 32-24, 1층 (성수동2가)서울특별시 성동구 성수동2가 269번지 20호 1층
1311000020720151위생처리업20151111증산산업97.6302 376542620170302<NA>20151111응암제3동<NA>물수건위생처리업<NA>00000N03110000-207-2015-00001서울특별시 은평구 응암로20길 18-1, 지하1층 (응암동)서울특별시 은평구 응암동 397번지 281호 지하1층
2323000020720141위생처리업20140416잠실기업135.7802 420 175020140416<NA>20140416잠실본동<NA>물수건위생처리업<NA>00000N03230000-207-2014-00001서울특별시 송파구 백제고분로12길 10, 지하1층 (잠실동)서울특별시 송파구 잠실동 297번지 3호 지하1층
3316000020720101위생처리업20101014거기상사103.32<NA>20101014<NA>20101014개봉제3동<NA>물수건위생처리업2010102600000N03160000-207-2010-00001서울특별시 구로구 개봉로1나길 46, (개봉동)서울특별시 구로구 개봉동 368번지 20호
4323000020720101위생처리업20100607고양산업496.002 431 167720100607<NA>20100607문정1동20120611물수건위생처리업<NA>00000N03230000-207-2010-00001<NA>서울특별시 송파구 문정동 116번지 6호
5313000020720101위생처리업20100408서부산업97.130011791267220100408<NA>20100408공덕동<NA>물수건위생처리업<NA>00000N03130000-207-2010-00001서울특별시 마포구 임정로 59, (신공덕동,지하1층)서울특별시 마포구 신공덕동 2번지 94호 지하1층
6313000020720091위생처리업20091026대성산업93.9502 375 294320120531<NA>20091026성산제1동<NA>물수건위생처리업<NA>00000N03130000-207-2009-00001서울특별시 마포구 월드컵북로26길 17, (성산동)서울특별시 마포구 성산동 137번지 4호
7311000020720091위생처리업20091008선우산업154.1802 356 365820091008<NA>20110228신사제1동<NA>물수건위생처리업<NA>00000N03110000-207-2009-00001서울특별시 은평구 갈현로3길 50, (신사동, 지하1층,지상1층)서울특별시 은평구 신사동 283번지 30호 (지하1층, 지상1층)
8318000020720081위생처리업20080731대성실업95.16<NA>20091102<NA>20080731대림제2동<NA>물수건위생처리업2009122200000N03180000-207-2008-00001서울특별시 영등포구 대림로23나길 6, (대림동)서울특별시 영등포구 대림동 705번지 2호
9323000020720081위생처리업20080229해창물산142.902 449 471220090910<NA>20080229가락본동<NA>물수건위생처리업<NA>00000<NA>03230000-207-2008-00001서울특별시 송파구 송이로15길 14, (가락동,지하1층)서울특별시 송파구 가락동 44번지 3호 지하1층
시군구코드업종코드년도업소일련번호업종명신고일자업소명영업장면적(㎡)소재지전화번호영업자시작일법인명소재지시작일행정동명폐업일자업태명위생교육수료일자객실수한실수양실수의자수욕실수발한실세탁기수허가(신고)번호소재지도로명소재지지번
91306000020719872449위생처리업19871005경성사107.502 492233920110114<NA>20091026중화제2동<NA>물수건위생처리업2010042800000N03060000-207-1987-02449서울특별시 중랑구 중랑천로 104, (중화동)서울특별시 중랑구 중화동 323번지 63호
92304000020719871940위생처리업19870922삼일위생107.802 468278019971223<NA>19971223군자동<NA>물수건위생처리업<NA>0<NA><NA><NA><NA>N<NA>3040000-207-1987-01940서울특별시 광진구 동일로40길 26, (군자동)서울특별시 광진구 군자동 343번지 11호
93306000020719872448위생처리업19870812동아산업0.002 495968619961031<NA>19961031면목제3.8동20030103물수건위생처리업<NA>00000N<NA>3060000-207-1987-02448<NA>서울특별시 중랑구 면목동 527번지 75호
94322000020719871위생처리업19870630아람통상0.0020549770819870630<NA>19870630논현1동20020702물수건위생처리업<NA>00000N<NA>3220000-207-1987-00001<NA>서울특별시 강남구 논현동 139번지 34호
95316000020719872139위생처리업19870530금성물산73.102 854954419960118<NA>19960118구로제5동19950104물수건위생처리업<NA>00000N<NA>3160000-207-1987-02139<NA>서울특별시 구로구 구로동 31번지 3호
96323000020719873234위생처리업19870526영동산업0.002 413712119951115<NA>19951115장지동19900430물수건위생처리업<NA>00000N<NA>3230000-207-1987-03234<NA>서울특별시 송파구 장지동 산 250번지 11호
97321000020719872168위생처리업19870401부흥물산134.0802 586227619990208<NA>19970825서초제3동19990208물수건위생처리업<NA>00000N<NA>3210000-207-1987-02168<NA>서울특별시 서초구 서초동 1475번지 1호
98323000020719863233위생처리업19860926제일상사59.902 413928819970701<NA>19970701장지동19970624물수건위생처리업<NA>00000N<NA>3230000-207-1986-03233<NA>서울특별시 송파구 장지동 산 237번지 5호
99321000020719862167위생처리업19860123강남기업(물수건)152.5402 585593319970825<NA>19970825서초제1동19970315물수건위생처리업<NA>00000N<NA>3210000-207-1986-02167<NA>서울특별시 서초구 서초동 1613번지 16호
100313000020719842046위생처리업19841205일원산업0.002 335549719841205<NA>19930310성산제2동20020927물수건위생처리업<NA>00000N<NA>3130000-207-1984-02046<NA>서울특별시 마포구 성산동 572번지 197호