Overview

Dataset statistics

Number of variables51
Number of observations10000
Missing cells100120
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory449.0 B

Variable types

Numeric16
Categorical20
Text7
Unsupported5
DateTime1
Boolean2

Dataset

Description2021-04-01
Author지방행정인허가공개데이터
URLhttps://bigdata.busan.go.kr/data/bigDataDetailView.do?menuCode=M00000000007&hdfs_file_sn=20230901050101123088

Alerts

개방서비스명 has constant value ""Constant
개방서비스id has constant value ""Constant
발한실여부 has constant value ""Constant
업태구분명 is highly imbalanced (50.9%)Imbalance
사용시작지하층 is highly imbalanced (52.5%)Imbalance
사용끝지하층 is highly imbalanced (57.1%)Imbalance
조건부허가시작일자 is highly imbalanced (99.8%)Imbalance
조건부허가종료일자 is highly imbalanced (99.7%)Imbalance
남성종사자수 is highly imbalanced (60.0%)Imbalance
다중이용업소여부 is highly imbalanced (99.6%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 4918 (49.2%) missing valuesMissing
휴업시작일자 has 10000 (100.0%) missing valuesMissing
휴업종료일자 has 10000 (100.0%) missing valuesMissing
재개업일자 has 10000 (100.0%) missing valuesMissing
소재지전화 has 2963 (29.6%) missing valuesMissing
도로명전체주소 has 3047 (30.5%) missing valuesMissing
도로명우편번호 has 3123 (31.2%) missing valuesMissing
좌표정보(x) has 278 (2.8%) missing valuesMissing
좌표정보(y) has 278 (2.8%) missing valuesMissing
건물지상층수 has 2099 (21.0%) missing valuesMissing
건물지하층수 has 3024 (30.2%) missing valuesMissing
사용시작지상층 has 2753 (27.5%) missing valuesMissing
사용끝지상층 has 4153 (41.5%) missing valuesMissing
발한실여부 has 161 (1.6%) missing valuesMissing
의자수 has 730 (7.3%) missing valuesMissing
조건부허가신고사유 has 9998 (> 99.9%) missing valuesMissing
여성종사자수 has 7661 (76.6%) missing valuesMissing
침대수 has 4785 (47.9%) missing valuesMissing
Unnamed: 50 has 10000 (100.0%) missing valuesMissing
사용끝지상층 is highly skewed (γ1 = 55.61355833)Skewed
번호 has unique valuesUnique
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 50 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 2924 (29.2%) zerosZeros
건물지하층수 has 5095 (50.9%) zerosZeros
사용시작지상층 has 1548 (15.5%) zerosZeros
사용끝지상층 has 1050 (10.5%) zerosZeros
의자수 has 1396 (14.0%) zerosZeros
여성종사자수 has 2189 (21.9%) zerosZeros
침대수 has 3547 (35.5%) zerosZeros

Reproduction

Analysis started2024-04-17 03:26:56.634271
Analysis finished2024-04-17 03:26:58.851505
Duration2.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11595.722
Minimum1
Maximum23129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:58.903848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1155.95
Q15795.5
median11631
Q317375.75
95-th percentile22007.5
Maximum23129
Range23128
Interquartile range (IQR)11580.25

Descriptive statistics

Standard deviation6687.3853
Coefficient of variation (CV)0.57671141
Kurtosis-1.1984856
Mean11595.722
Median Absolute Deviation (MAD)5794.5
Skewness-0.0073077183
Sum1.1595722 × 108
Variance44721122
MonotonicityNot monotonic
2024-04-17T12:26:59.003608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21355 1
 
< 0.1%
5636 1
 
< 0.1%
1280 1
 
< 0.1%
21235 1
 
< 0.1%
7743 1
 
< 0.1%
10484 1
 
< 0.1%
14472 1
 
< 0.1%
1900 1
 
< 0.1%
21656 1
 
< 0.1%
15718 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 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%
11 1
< 0.1%
12 1
< 0.1%
ValueCountFrequency (%)
23129 1
< 0.1%
23126 1
< 0.1%
23124 1
< 0.1%
23117 1
< 0.1%
23115 1
< 0.1%
23111 1
< 0.1%
23110 1
< 0.1%
23108 1
< 0.1%
23105 1
< 0.1%
23104 1
< 0.1%

개방서비스명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미용업
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 10000
100.0%

Length

2024-04-17T12:26:59.125561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:59.209949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 10000
100.0%

개방서비스id
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
05_18_01_P
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row05_18_01_P
2nd row05_18_01_P
3rd row05_18_01_P
4th row05_18_01_P
5th row05_18_01_P

Common Values

ValueCountFrequency (%)
05_18_01_P 10000
100.0%

Length

2024-04-17T12:26:59.299022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:59.375487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
05_18_01_p 10000
100.0%

개방자치단체코드
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3325041
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:59.450803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13300000
median3330000
Q33350000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation37624.724
Coefficient of variation (CV)0.011315567
Kurtosis-0.7487947
Mean3325041
Median Absolute Deviation (MAD)30000
Skewness0.033822445
Sum3.325041 × 1010
Variance1.4156199 × 109
MonotonicityNot monotonic
2024-04-17T12:26:59.545529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3330000 1296
13.0%
3290000 1199
12.0%
3340000 939
9.4%
3300000 931
9.3%
3310000 774
7.7%
3350000 747
7.5%
3370000 736
7.4%
3380000 725
7.2%
3320000 707
7.1%
3390000 394
 
3.9%
Other values (6) 1552
15.5%
ValueCountFrequency (%)
3250000 340
 
3.4%
3260000 299
 
3.0%
3270000 299
 
3.0%
3280000 303
 
3.0%
3290000 1199
12.0%
3300000 931
9.3%
3310000 774
7.7%
3320000 707
7.1%
3330000 1296
13.0%
3340000 939
9.4%
ValueCountFrequency (%)
3400000 177
 
1.8%
3390000 394
 
3.9%
3380000 725
7.2%
3370000 736
7.4%
3360000 134
 
1.3%
3350000 747
7.5%
3340000 939
9.4%
3330000 1296
13.0%
3320000 707
7.1%
3310000 774
7.7%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:26:59.707465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters220000
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row3290000-204-2006-00027
2nd row3340000-204-1986-00713
3rd row3320000-212-2011-00015
4th row3340000-211-1986-00001
5th row3300000-211-2014-00025
ValueCountFrequency (%)
3290000-204-2006-00027 1
 
< 0.1%
3370000-204-2004-00046 1
 
< 0.1%
3350000-217-2016-00001 1
 
< 0.1%
3290000-204-1999-02447 1
 
< 0.1%
3290000-204-1997-02447 1
 
< 0.1%
3330000-211-2014-00007 1
 
< 0.1%
3350000-212-2009-00003 1
 
< 0.1%
3360000-211-2015-00018 1
 
< 0.1%
3330000-204-2004-00078 1
 
< 0.1%
3320000-211-2005-00023 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-04-17T12:26:59.993865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 87805
39.9%
- 30000
 
13.6%
2 26026
 
11.8%
3 22131
 
10.1%
1 22070
 
10.0%
9 8789
 
4.0%
4 7912
 
3.6%
8 4420
 
2.0%
5 4210
 
1.9%
7 3659
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190000
86.4%
Dash Punctuation 30000
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87805
46.2%
2 26026
 
13.7%
3 22131
 
11.6%
1 22070
 
11.6%
9 8789
 
4.6%
4 7912
 
4.2%
8 4420
 
2.3%
5 4210
 
2.2%
7 3659
 
1.9%
6 2978
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87805
39.9%
- 30000
 
13.6%
2 26026
 
11.8%
3 22131
 
10.1%
1 22070
 
10.0%
9 8789
 
4.0%
4 7912
 
3.6%
8 4420
 
2.0%
5 4210
 
1.9%
7 3659
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87805
39.9%
- 30000
 
13.6%
2 26026
 
11.8%
3 22131
 
10.1%
1 22070
 
10.0%
9 8789
 
4.0%
4 7912
 
3.6%
8 4420
 
2.0%
5 4210
 
1.9%
7 3659
 
1.7%

인허가일자
Real number (ℝ)

Distinct5774
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20053129
Minimum19630110
Maximum20210226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:00.108871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19630110
5-th percentile19840330
Q119980711
median20071118
Q320150812
95-th percentile20191204
Maximum20210226
Range580116
Interquartile range (IQR)170101.25

Descriptive statistics

Standard deviation115662.7
Coefficient of variation (CV)0.005767813
Kurtosis0.12914176
Mean20053129
Median Absolute Deviation (MAD)80997.5
Skewness-0.79959653
Sum2.0053129 × 1011
Variance1.3377859 × 1010
MonotonicityNot monotonic
2024-04-17T12:27:00.219115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000712 26
 
0.3%
20000415 19
 
0.2%
20030224 15
 
0.1%
20030225 12
 
0.1%
20001114 12
 
0.1%
20020508 11
 
0.1%
20170403 10
 
0.1%
20020122 9
 
0.1%
19980930 9
 
0.1%
20180416 8
 
0.1%
Other values (5764) 9869
98.7%
ValueCountFrequency (%)
19630110 4
< 0.1%
19630617 1
 
< 0.1%
19640630 1
 
< 0.1%
19660107 1
 
< 0.1%
19660301 2
 
< 0.1%
19660331 5
0.1%
19660516 1
 
< 0.1%
19660520 1
 
< 0.1%
19660604 1
 
< 0.1%
19660628 1
 
< 0.1%
ValueCountFrequency (%)
20210226 2
 
< 0.1%
20210225 2
 
< 0.1%
20210224 1
 
< 0.1%
20210223 3
< 0.1%
20210222 3
< 0.1%
20210219 1
 
< 0.1%
20210218 5
0.1%
20210217 2
 
< 0.1%
20210216 1
 
< 0.1%
20210215 3
< 0.1%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
5082 
1
4918 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 5082
50.8%
1 4918
49.2%

Length

2024-04-17T12:27:00.319776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:00.390740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5082
50.8%
1 4918
49.2%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5082 
영업/정상
4918 

Length

Max length5
Median length2
Mean length3.4754
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row폐업
3rd row폐업
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
폐업 5082
50.8%
영업/정상 4918
49.2%

Length

2024-04-17T12:27:00.468516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:00.542627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5082
50.8%
영업/정상 4918
49.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5082 
1
4918 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 5082
50.8%
1 4918
49.2%

Length

2024-04-17T12:27:00.627357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:00.716228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5082
50.8%
1 4918
49.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5082 
영업
4918 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row폐업
3rd row폐업
4th row영업
5th row영업

Common Values

ValueCountFrequency (%)
폐업 5082
50.8%
영업 4918
49.2%

Length

2024-04-17T12:27:00.792830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:00.859363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5082
50.8%
영업 4918
49.2%

폐업일자
Real number (ℝ)

MISSING 

Distinct2965
Distinct (%)58.3%
Missing4918
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean20095013
Minimum19821109
Maximum20210226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:00.946656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19821109
5-th percentile19990733
Q120040311
median20091102
Q320151185
95-th percentile20200302
Maximum20210226
Range389117
Interquartile range (IQR)110873.75

Descriptive statistics

Standard deviation66751.333
Coefficient of variation (CV)0.003321786
Kurtosis-1.0887544
Mean20095013
Median Absolute Deviation (MAD)59818
Skewness-0.031815173
Sum1.0212285 × 1011
Variance4.4557404 × 109
MonotonicityNot monotonic
2024-04-17T12:27:01.057804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030227 150
 
1.5%
20050117 54
 
0.5%
20030226 38
 
0.4%
20050510 38
 
0.4%
20000712 33
 
0.3%
20010321 31
 
0.3%
20030101 23
 
0.2%
20030606 20
 
0.2%
20051222 19
 
0.2%
20000531 18
 
0.2%
Other values (2955) 4658
46.6%
(Missing) 4918
49.2%
ValueCountFrequency (%)
19821109 1
< 0.1%
19851118 1
< 0.1%
19891116 1
< 0.1%
19900615 1
< 0.1%
19910205 1
< 0.1%
19921012 1
< 0.1%
19930308 1
< 0.1%
19931207 1
< 0.1%
19940125 1
< 0.1%
19950612 1
< 0.1%
ValueCountFrequency (%)
20210226 2
< 0.1%
20210224 1
< 0.1%
20210223 1
< 0.1%
20210222 1
< 0.1%
20210218 2
< 0.1%
20210217 2
< 0.1%
20210216 1
< 0.1%
20210209 2
< 0.1%
20210208 1
< 0.1%
20210205 1
< 0.1%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

소재지전화
Text

MISSING 

Distinct6275
Distinct (%)89.2%
Missing2963
Missing (%)29.6%
Memory size156.2 KiB
2024-04-17T12:27:01.374830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.634219
Min length3

Characters and Unicode

Total characters74833
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6107 ?
Unique (%)86.8%

Sample

1st row051 8918824
2nd row051
3rd row051 333 5805
4th row051 2012417
5th row051 5187739
ValueCountFrequency (%)
051 6580
41.9%
070 127
 
0.8%
747 30
 
0.2%
868 30
 
0.2%
746 27
 
0.2%
852 27
 
0.2%
516 25
 
0.2%
203 25
 
0.2%
611 24
 
0.2%
727 23
 
0.1%
Other values (6219) 8768
55.9%
2024-04-17T12:27:01.766107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12501
16.7%
0 11203
15.0%
1 11107
14.8%
8698
11.6%
2 5714
7.6%
7 4850
 
6.5%
3 4647
 
6.2%
8 4428
 
5.9%
6 4405
 
5.9%
4 4328
 
5.8%
Other values (3) 2952
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66131
88.4%
Space Separator 8698
 
11.6%
Dash Punctuation 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12501
18.9%
0 11203
16.9%
1 11107
16.8%
2 5714
8.6%
7 4850
 
7.3%
3 4647
 
7.0%
8 4428
 
6.7%
6 4405
 
6.7%
4 4328
 
6.5%
9 2948
 
4.5%
Space Separator
ValueCountFrequency (%)
8698
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74833
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12501
16.7%
0 11203
15.0%
1 11107
14.8%
8698
11.6%
2 5714
7.6%
7 4850
 
6.5%
3 4647
 
6.2%
8 4428
 
5.9%
6 4405
 
5.9%
4 4328
 
5.8%
Other values (3) 2952
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12501
16.7%
0 11203
15.0%
1 11107
14.8%
8698
11.6%
2 5714
7.6%
7 4850
 
6.5%
3 4647
 
6.2%
8 4428
 
5.9%
6 4405
 
5.9%
4 4328
 
5.8%
Other values (3) 2952
 
3.9%
Distinct4110
Distinct (%)41.3%
Missing37
Missing (%)0.4%
Memory size156.2 KiB
2024-04-17T12:27:02.050007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.9166918
Min length3

Characters and Unicode

Total characters48985
Distinct characters12
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

Unique2551 ?
Unique (%)25.6%

Sample

1st row43.21
2nd row.00
3rd row64.58
4th row26.98
5th row28.53
ValueCountFrequency (%)
00 751
 
7.5%
33.00 127
 
1.3%
30.00 63
 
0.6%
18.00 56
 
0.6%
24.00 52
 
0.5%
20.00 47
 
0.5%
15.00 43
 
0.4%
27.00 42
 
0.4%
16.50 42
 
0.4%
23.00 41
 
0.4%
Other values (4100) 8699
87.3%
2024-04-17T12:27:02.422364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9963
20.3%
0 8640
17.6%
2 4975
10.2%
1 4871
9.9%
3 3813
 
7.8%
4 3336
 
6.8%
5 3127
 
6.4%
6 3112
 
6.4%
8 2567
 
5.2%
7 2346
 
4.8%
Other values (2) 2235
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39019
79.7%
Other Punctuation 9966
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8640
22.1%
2 4975
12.8%
1 4871
12.5%
3 3813
9.8%
4 3336
 
8.5%
5 3127
 
8.0%
6 3112
 
8.0%
8 2567
 
6.6%
7 2346
 
6.0%
9 2232
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 9963
> 99.9%
, 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 48985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9963
20.3%
0 8640
17.6%
2 4975
10.2%
1 4871
9.9%
3 3813
 
7.8%
4 3336
 
6.8%
5 3127
 
6.4%
6 3112
 
6.4%
8 2567
 
5.2%
7 2346
 
4.8%
Other values (2) 2235
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9963
20.3%
0 8640
17.6%
2 4975
10.2%
1 4871
9.9%
3 3813
 
7.8%
4 3336
 
6.8%
5 3127
 
6.4%
6 3112
 
6.4%
8 2567
 
5.2%
7 2346
 
4.8%
Other values (2) 2235
 
4.6%

소재지우편번호
Real number (ℝ)

Distinct866
Distinct (%)8.7%
Missing97
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean610637.96
Minimum600012
Maximum619952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:02.540963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600012
5-th percentile601817
Q1607814
median611816
Q3614800.5
95-th percentile617813
Maximum619952
Range19940
Interquartile range (IQR)6986.5

Descriptive statistics

Standard deviation4769.7543
Coefficient of variation (CV)0.0078111003
Kurtosis-0.65783737
Mean610637.96
Median Absolute Deviation (MAD)3031
Skewness-0.37193784
Sum6.0471477 × 109
Variance22750556
MonotonicityNot monotonic
2024-04-17T12:27:02.654099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
609839 104
 
1.0%
614847 86
 
0.9%
608805 81
 
0.8%
604851 80
 
0.8%
614845 75
 
0.8%
612842 72
 
0.7%
612824 69
 
0.7%
616852 65
 
0.7%
607826 56
 
0.6%
608832 55
 
0.5%
Other values (856) 9160
91.6%
(Missing) 97
 
1.0%
ValueCountFrequency (%)
600012 2
 
< 0.1%
600013 4
< 0.1%
600015 2
 
< 0.1%
600016 6
0.1%
600017 4
< 0.1%
600021 2
 
< 0.1%
600022 2
 
< 0.1%
600023 1
 
< 0.1%
600024 3
< 0.1%
600025 7
0.1%
ValueCountFrequency (%)
619952 2
 
< 0.1%
619951 1
 
< 0.1%
619913 3
 
< 0.1%
619912 6
 
0.1%
619911 1
 
< 0.1%
619906 4
 
< 0.1%
619905 26
0.3%
619904 1
 
< 0.1%
619903 30
0.3%
619901 19
0.2%
Distinct9330
Distinct (%)93.4%
Missing15
Missing (%)0.1%
Memory size156.2 KiB
2024-04-17T12:27:03.103593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length50
Mean length24.95333
Min length14

Characters and Unicode

Total characters249159
Distinct characters501
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8774 ?
Unique (%)87.9%

Sample

1st row부산광역시 부산진구 개금동 197-5번지 외 5필지 성원상떼뷰 201호
2nd row부산광역시 사하구 하단동 812-4번지
3rd row부산광역시 북구 덕천동 388-1번지 대방상가 2동 307호
4th row부산광역시 사하구 괴정동 1074-8 지하 1층 지하2호
5th row부산광역시 동래구 수안동 4-8번지 1층
ValueCountFrequency (%)
부산광역시 9985
 
21.2%
해운대구 1296
 
2.8%
부산진구 1191
 
2.5%
사하구 937
 
2.0%
동래구 931
 
2.0%
t통b반 905
 
1.9%
남구 774
 
1.6%
금정구 747
 
1.6%
연제구 731
 
1.6%
수영구 725
 
1.5%
Other values (10147) 28797
61.2%
2024-04-17T12:27:03.474982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37048
 
14.9%
12172
 
4.9%
12066
 
4.8%
12047
 
4.8%
1 11940
 
4.8%
10386
 
4.2%
10199
 
4.1%
10137
 
4.1%
9998
 
4.0%
- 8988
 
3.6%
Other values (491) 114178
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 147127
59.0%
Decimal Number 52629
 
21.1%
Space Separator 37048
 
14.9%
Dash Punctuation 8988
 
3.6%
Uppercase Letter 2248
 
0.9%
Open Punctuation 394
 
0.2%
Close Punctuation 390
 
0.2%
Other Punctuation 277
 
0.1%
Lowercase Letter 46
 
< 0.1%
Math Symbol 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12172
 
8.3%
12066
 
8.2%
12047
 
8.2%
10386
 
7.1%
10199
 
6.9%
10137
 
6.9%
9998
 
6.8%
8983
 
6.1%
8564
 
5.8%
2388
 
1.6%
Other values (436) 50187
34.1%
Uppercase Letter
ValueCountFrequency (%)
B 977
43.5%
T 915
40.7%
A 64
 
2.8%
S 64
 
2.8%
K 53
 
2.4%
H 22
 
1.0%
G 21
 
0.9%
I 20
 
0.9%
E 18
 
0.8%
L 14
 
0.6%
Other values (14) 80
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 11940
22.7%
2 7451
14.2%
3 5886
11.2%
4 4778
9.1%
0 4492
 
8.5%
5 4385
 
8.3%
6 3667
 
7.0%
7 3615
 
6.9%
8 3288
 
6.2%
9 3127
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
e 16
34.8%
l 10
21.7%
i 6
 
13.0%
s 5
 
10.9%
c 3
 
6.5%
o 2
 
4.3%
k 2
 
4.3%
n 1
 
2.2%
a 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 216
78.0%
@ 35
 
12.6%
. 12
 
4.3%
/ 9
 
3.2%
· 4
 
1.4%
& 1
 
0.4%
Space Separator
ValueCountFrequency (%)
37048
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8988
100.0%
Open Punctuation
ValueCountFrequency (%)
( 394
100.0%
Close Punctuation
ValueCountFrequency (%)
) 390
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 147127
59.0%
Common 99736
40.0%
Latin 2296
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12172
 
8.3%
12066
 
8.2%
12047
 
8.2%
10386
 
7.1%
10199
 
6.9%
10137
 
6.9%
9998
 
6.8%
8983
 
6.1%
8564
 
5.8%
2388
 
1.6%
Other values (436) 50187
34.1%
Latin
ValueCountFrequency (%)
B 977
42.6%
T 915
39.9%
A 64
 
2.8%
S 64
 
2.8%
K 53
 
2.3%
H 22
 
1.0%
G 21
 
0.9%
I 20
 
0.9%
E 18
 
0.8%
e 16
 
0.7%
Other values (24) 126
 
5.5%
Common
ValueCountFrequency (%)
37048
37.1%
1 11940
 
12.0%
- 8988
 
9.0%
2 7451
 
7.5%
3 5886
 
5.9%
4 4778
 
4.8%
0 4492
 
4.5%
5 4385
 
4.4%
6 3667
 
3.7%
7 3615
 
3.6%
Other values (11) 7486
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 147126
59.0%
ASCII 102026
40.9%
None 4
 
< 0.1%
Number Forms 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37048
36.3%
1 11940
 
11.7%
- 8988
 
8.8%
2 7451
 
7.3%
3 5886
 
5.8%
4 4778
 
4.7%
0 4492
 
4.4%
5 4385
 
4.3%
6 3667
 
3.6%
7 3615
 
3.5%
Other values (43) 9776
 
9.6%
Hangul
ValueCountFrequency (%)
12172
 
8.3%
12066
 
8.2%
12047
 
8.2%
10386
 
7.1%
10199
 
6.9%
10137
 
6.9%
9998
 
6.8%
8983
 
6.1%
8564
 
5.8%
2388
 
1.6%
Other values (435) 50186
34.1%
None
ValueCountFrequency (%)
· 4
100.0%
Number Forms
ValueCountFrequency (%)
2
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명전체주소
Text

MISSING 

Distinct6754
Distinct (%)97.1%
Missing3047
Missing (%)30.5%
Memory size156.2 KiB
2024-04-17T12:27:03.778704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length55
Mean length32.05005
Min length18

Characters and Unicode

Total characters222844
Distinct characters530
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

Unique6563 ?
Unique (%)94.4%

Sample

1st row부산광역시 북구 만덕대로90번길 13 (덕천동, 대방상가2동 307호)
2nd row부산광역시 사하구 마하로 54, 지하1층 지하2호 (괴정동)
3rd row부산광역시 동래구 충렬대로218번길 43, 1층 (수안동)
4th row부산광역시 동래구 명장로57번길 3 (명장동)
5th row부산광역시 기장군 기장읍 차성남로 54
ValueCountFrequency (%)
부산광역시 6953
 
16.1%
1층 1681
 
3.9%
부산진구 965
 
2.2%
2층 886
 
2.1%
해운대구 848
 
2.0%
동래구 660
 
1.5%
사하구 585
 
1.4%
남구 531
 
1.2%
수영구 531
 
1.2%
금정구 515
 
1.2%
Other values (5706) 29003
67.2%
2024-04-17T12:27:04.204088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36214
 
16.3%
9634
 
4.3%
1 9378
 
4.2%
8800
 
3.9%
8638
 
3.9%
7511
 
3.4%
7386
 
3.3%
7168
 
3.2%
6966
 
3.1%
( 6963
 
3.1%
Other values (520) 114186
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128731
57.8%
Decimal Number 36279
 
16.3%
Space Separator 36214
 
16.3%
Open Punctuation 6963
 
3.1%
Close Punctuation 6961
 
3.1%
Other Punctuation 5981
 
2.7%
Dash Punctuation 1158
 
0.5%
Uppercase Letter 474
 
0.2%
Lowercase Letter 49
 
< 0.1%
Math Symbol 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9634
 
7.5%
8800
 
6.8%
8638
 
6.7%
7511
 
5.8%
7386
 
5.7%
7168
 
5.6%
6966
 
5.4%
6855
 
5.3%
3438
 
2.7%
3436
 
2.7%
Other values (464) 58899
45.8%
Uppercase Letter
ValueCountFrequency (%)
B 101
21.3%
S 72
15.2%
A 72
15.2%
K 55
11.6%
H 25
 
5.3%
E 22
 
4.6%
I 16
 
3.4%
C 13
 
2.7%
W 12
 
2.5%
Y 11
 
2.3%
Other values (14) 75
15.8%
Decimal Number
ValueCountFrequency (%)
1 9378
25.8%
2 6281
17.3%
3 3931
10.8%
0 3510
 
9.7%
4 2885
 
8.0%
5 2506
 
6.9%
6 2237
 
6.2%
8 1941
 
5.4%
7 1929
 
5.3%
9 1681
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
e 17
34.7%
l 10
20.4%
i 6
 
12.2%
s 6
 
12.2%
k 5
 
10.2%
o 2
 
4.1%
n 1
 
2.0%
a 1
 
2.0%
c 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 5942
99.3%
@ 25
 
0.4%
/ 4
 
0.1%
· 4
 
0.1%
. 3
 
0.1%
& 2
 
< 0.1%
# 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
36214
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6963
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6961
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1158
100.0%
Math Symbol
ValueCountFrequency (%)
~ 32
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128731
57.8%
Common 93588
42.0%
Latin 525
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9634
 
7.5%
8800
 
6.8%
8638
 
6.7%
7511
 
5.8%
7386
 
5.7%
7168
 
5.6%
6966
 
5.4%
6855
 
5.3%
3438
 
2.7%
3436
 
2.7%
Other values (464) 58899
45.8%
Latin
ValueCountFrequency (%)
B 101
19.2%
S 72
13.7%
A 72
13.7%
K 55
10.5%
H 25
 
4.8%
E 22
 
4.2%
e 17
 
3.2%
I 16
 
3.0%
C 13
 
2.5%
W 12
 
2.3%
Other values (24) 120
22.9%
Common
ValueCountFrequency (%)
36214
38.7%
1 9378
 
10.0%
( 6963
 
7.4%
) 6961
 
7.4%
2 6281
 
6.7%
, 5942
 
6.3%
3 3931
 
4.2%
0 3510
 
3.8%
4 2885
 
3.1%
5 2506
 
2.7%
Other values (12) 9017
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128731
57.8%
ASCII 94107
42.2%
None 4
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36214
38.5%
1 9378
 
10.0%
( 6963
 
7.4%
) 6961
 
7.4%
2 6281
 
6.7%
, 5942
 
6.3%
3 3931
 
4.2%
0 3510
 
3.7%
4 2885
 
3.1%
5 2506
 
2.7%
Other values (44) 9536
 
10.1%
Hangul
ValueCountFrequency (%)
9634
 
7.5%
8800
 
6.8%
8638
 
6.7%
7511
 
5.8%
7386
 
5.7%
7168
 
5.6%
6966
 
5.4%
6855
 
5.3%
3438
 
2.7%
3436
 
2.7%
Other values (464) 58899
45.8%
None
ValueCountFrequency (%)
· 4
100.0%
Number Forms
ValueCountFrequency (%)
2
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct1568
Distinct (%)22.8%
Missing3123
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean47811.483
Minimum46007
Maximum49524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:04.317461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46007
5-th percentile46246
Q147152
median47851
Q348484
95-th percentile49374.2
Maximum49524
Range3517
Interquartile range (IQR)1332

Descriptive statistics

Standard deviation948.76735
Coefficient of variation (CV)0.019843922
Kurtosis-0.87081856
Mean47811.483
Median Absolute Deviation (MAD)652
Skewness-0.03069952
Sum3.2879957 × 108
Variance900159.49
MonotonicityNot monotonic
2024-04-17T12:27:04.426555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46726 49
 
0.5%
48059 46
 
0.5%
48111 46
 
0.5%
48060 31
 
0.3%
47292 31
 
0.3%
48498 30
 
0.3%
46291 30
 
0.3%
47286 28
 
0.3%
48947 27
 
0.3%
48110 26
 
0.3%
Other values (1558) 6533
65.3%
(Missing) 3123
31.2%
ValueCountFrequency (%)
46007 7
0.1%
46008 11
0.1%
46009 1
 
< 0.1%
46010 3
 
< 0.1%
46011 1
 
< 0.1%
46012 5
 
0.1%
46013 2
 
< 0.1%
46014 1
 
< 0.1%
46015 14
0.1%
46016 2
 
< 0.1%
ValueCountFrequency (%)
49524 2
 
< 0.1%
49523 1
 
< 0.1%
49522 1
 
< 0.1%
49521 3
 
< 0.1%
49520 12
0.1%
49519 9
0.1%
49518 12
0.1%
49516 2
 
< 0.1%
49515 6
0.1%
49514 1
 
< 0.1%
Distinct8198
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:27:04.672742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length34
Mean length5.5819
Min length1

Characters and Unicode

Total characters55819
Distinct characters896
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7299 ?
Unique (%)73.0%

Sample

1st row김현정헤어샵
2nd row미당
3rd row힐에스테틱
4th row핑크헤어타운
5th row엘샤론염색방
ValueCountFrequency (%)
미용실 297
 
2.4%
헤어 276
 
2.2%
에스테틱 101
 
0.8%
네일 95
 
0.8%
헤어샵 92
 
0.7%
hair 64
 
0.5%
nail 53
 
0.4%
뷰티 52
 
0.4%
36
 
0.3%
by 30
 
0.2%
Other values (8193) 11528
91.3%
2024-04-17T12:27:05.024579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3573
 
6.4%
3480
 
6.2%
2629
 
4.7%
1883
 
3.4%
1353
 
2.4%
1154
 
2.1%
1146
 
2.1%
1145
 
2.1%
1032
 
1.8%
813
 
1.5%
Other values (886) 37611
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46941
84.1%
Space Separator 2629
 
4.7%
Lowercase Letter 2361
 
4.2%
Uppercase Letter 2048
 
3.7%
Close Punctuation 557
 
1.0%
Open Punctuation 556
 
1.0%
Other Punctuation 361
 
0.6%
Decimal Number 327
 
0.6%
Dash Punctuation 29
 
0.1%
Connector Punctuation 4
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3573
 
7.6%
3480
 
7.4%
1883
 
4.0%
1353
 
2.9%
1154
 
2.5%
1146
 
2.4%
1145
 
2.4%
1032
 
2.2%
813
 
1.7%
753
 
1.6%
Other values (800) 30609
65.2%
Lowercase Letter
ValueCountFrequency (%)
a 326
13.8%
i 253
10.7%
e 236
10.0%
o 202
8.6%
n 192
 
8.1%
l 167
 
7.1%
r 154
 
6.5%
y 128
 
5.4%
h 117
 
5.0%
s 100
 
4.2%
Other values (16) 486
20.6%
Uppercase Letter
ValueCountFrequency (%)
A 191
 
9.3%
N 161
 
7.9%
S 158
 
7.7%
I 141
 
6.9%
H 131
 
6.4%
J 130
 
6.3%
B 123
 
6.0%
M 115
 
5.6%
E 105
 
5.1%
L 102
 
5.0%
Other values (16) 691
33.7%
Other Punctuation
ValueCountFrequency (%)
& 121
33.5%
. 92
25.5%
# 47
 
13.0%
, 40
 
11.1%
' 32
 
8.9%
: 7
 
1.9%
· 7
 
1.9%
; 6
 
1.7%
3
 
0.8%
" 2
 
0.6%
Other values (3) 4
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 89
27.2%
2 67
20.5%
0 49
15.0%
3 31
 
9.5%
9 28
 
8.6%
5 18
 
5.5%
4 15
 
4.6%
7 15
 
4.6%
8 8
 
2.4%
6 7
 
2.1%
Close Punctuation
ValueCountFrequency (%)
) 556
99.8%
] 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 555
99.8%
[ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
2629
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46892
84.0%
Common 4468
 
8.0%
Latin 4410
 
7.9%
Han 49
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3573
 
7.6%
3480
 
7.4%
1883
 
4.0%
1353
 
2.9%
1154
 
2.5%
1146
 
2.4%
1145
 
2.4%
1032
 
2.2%
813
 
1.7%
753
 
1.6%
Other values (775) 30560
65.2%
Latin
ValueCountFrequency (%)
a 326
 
7.4%
i 253
 
5.7%
e 236
 
5.4%
o 202
 
4.6%
n 192
 
4.4%
A 191
 
4.3%
l 167
 
3.8%
N 161
 
3.7%
S 158
 
3.6%
r 154
 
3.5%
Other values (43) 2370
53.7%
Common
ValueCountFrequency (%)
2629
58.8%
) 556
 
12.4%
( 555
 
12.4%
& 121
 
2.7%
. 92
 
2.1%
1 89
 
2.0%
2 67
 
1.5%
0 49
 
1.1%
# 47
 
1.1%
, 40
 
0.9%
Other values (23) 223
 
5.0%
Han
ValueCountFrequency (%)
22
44.9%
2
 
4.1%
2
 
4.1%
2
 
4.1%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (15) 15
30.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46891
84.0%
ASCII 8867
 
15.9%
CJK 48
 
0.1%
None 10
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3573
 
7.6%
3480
 
7.4%
1883
 
4.0%
1353
 
2.9%
1154
 
2.5%
1146
 
2.4%
1145
 
2.4%
1032
 
2.2%
813
 
1.7%
753
 
1.6%
Other values (774) 30559
65.2%
ASCII
ValueCountFrequency (%)
2629
29.6%
) 556
 
6.3%
( 555
 
6.3%
a 326
 
3.7%
i 253
 
2.9%
e 236
 
2.7%
o 202
 
2.3%
n 192
 
2.2%
A 191
 
2.2%
l 167
 
1.9%
Other values (73) 3560
40.1%
CJK
ValueCountFrequency (%)
22
45.8%
2
 
4.2%
2
 
4.2%
2
 
4.2%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Other values (14) 14
29.2%
None
ValueCountFrequency (%)
· 7
70.0%
3
30.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

최종수정시점
Real number (ℝ)

Distinct8131
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0129567 × 1013
Minimum1.9990125 × 1013
Maximum2.0210226 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:05.135848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990125 × 1013
5-th percentile2.0010807 × 1013
Q12.0060816 × 1013
median2.015052 × 1013
Q32.0190922 × 1013
95-th percentile2.0201208 × 1013
Maximum2.0210226 × 1013
Range2.2010118 × 1011
Interquartile range (IQR)1.3010563 × 1011

Descriptive statistics

Standard deviation6.8575438 × 1010
Coefficient of variation (CV)0.0034067021
Kurtosis-1.1272402
Mean2.0129567 × 1013
Median Absolute Deviation (MAD)4.9887534 × 1010
Skewness-0.56452112
Sum2.0129567 × 1017
Variance4.7025907 × 1021
MonotonicityNot monotonic
2024-04-17T12:27:05.242775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030402000000 98
 
1.0%
19990429000000 35
 
0.4%
20020823000000 33
 
0.3%
20030627000000 32
 
0.3%
20001209000000 31
 
0.3%
20030503000000 30
 
0.3%
20061113000000 29
 
0.3%
20040827000000 29
 
0.3%
20040719000000 27
 
0.3%
20040102000000 24
 
0.2%
Other values (8121) 9632
96.3%
ValueCountFrequency (%)
19990125000000 2
 
< 0.1%
19990126000000 8
0.1%
19990222000000 2
 
< 0.1%
19990223000000 3
 
< 0.1%
19990224000000 5
0.1%
19990225000000 7
0.1%
19990303000000 1
 
< 0.1%
19990304000000 10
0.1%
19990305000000 11
0.1%
19990308000000 12
0.1%
ValueCountFrequency (%)
20210226175314 1
< 0.1%
20210226174411 1
< 0.1%
20210226142033 1
< 0.1%
20210226141033 1
< 0.1%
20210226115540 1
< 0.1%
20210226105502 1
< 0.1%
20210226091336 1
< 0.1%
20210225162306 1
< 0.1%
20210225162108 1
< 0.1%
20210225142225 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
7163 
U
2837 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowI
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
I 7163
71.6%
U 2837
 
28.4%

Length

2024-04-17T12:27:05.333165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:05.399749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7163
71.6%
u 2837
 
28.4%
Distinct892
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-02-28 02:40:00
2024-04-17T12:27:05.476586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T12:27:05.579062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반미용업
7060 
피부미용업
1822 
네일아트업
859 
메이크업업
 
162
기타
 
94

Length

Max length6
Median length5
Mean length4.9721
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반미용업
2nd row일반미용업
3rd row피부미용업
4th row일반미용업
5th row일반미용업

Common Values

ValueCountFrequency (%)
일반미용업 7060
70.6%
피부미용업 1822
 
18.2%
네일아트업 859
 
8.6%
메이크업업 162
 
1.6%
기타 94
 
0.9%
미용업 기타 3
 
< 0.1%

Length

2024-04-17T12:27:05.687507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:05.778318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 7060
70.6%
피부미용업 1822
 
18.2%
네일아트업 859
 
8.6%
메이크업업 162
 
1.6%
기타 97
 
1.0%
미용업 3
 
< 0.1%

좌표정보(x)
Real number (ℝ)

MISSING 

Distinct7500
Distinct (%)77.1%
Missing278
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean388304.49
Minimum366931.44
Maximum407824.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:05.874606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum366931.44
5-th percentile379723.3
Q1384502.37
median388736.69
Q3391815.75
95-th percentile397650.02
Maximum407824.89
Range40893.451
Interquartile range (IQR)7313.3841

Descriptive statistics

Standard deviation5305.76
Coefficient of variation (CV)0.013663916
Kurtosis0.15508786
Mean388304.49
Median Absolute Deviation (MAD)3536.358
Skewness-0.14063193
Sum3.7750962 × 109
Variance28151090
MonotonicityNot monotonic
2024-04-17T12:27:05.979112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
398237.363461482 19
 
0.2%
393239.237933586 17
 
0.2%
392474.578116018 16
 
0.2%
394112.524871033 16
 
0.2%
395388.715069604 16
 
0.2%
380398.062015138 15
 
0.1%
398330.516530402 15
 
0.1%
398491.352089522 14
 
0.1%
389816.233000769 14
 
0.1%
392235.198208233 14
 
0.1%
Other values (7490) 9566
95.7%
(Missing) 278
 
2.8%
ValueCountFrequency (%)
366931.435995074 1
< 0.1%
367051.926419356 1
< 0.1%
367058.667450096 1
< 0.1%
367062.132343737 1
< 0.1%
367108.112280274 1
< 0.1%
367145.447260979 1
< 0.1%
367177.362435871 1
< 0.1%
367193.583454597 1
< 0.1%
367205.763155348 1
< 0.1%
367226.95953767 1
< 0.1%
ValueCountFrequency (%)
407824.887002439 1
< 0.1%
407739.046710947 1
< 0.1%
405172.859381319 1
< 0.1%
404952.299376958 1
< 0.1%
404194.966298269 1
< 0.1%
403422.902434994 1
< 0.1%
403420.122313532 1
< 0.1%
403382.323810557 1
< 0.1%
403220.11445311 1
< 0.1%
403117.408357422 1
< 0.1%

좌표정보(y)
Real number (ℝ)

MISSING 

Distinct7501
Distinct (%)77.2%
Missing278
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean187006.57
Minimum173895.66
Maximum210001.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:06.089694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173895.66
5-th percentile178256.59
Q1183253.49
median187294.63
Q3190879.96
95-th percentile195540.75
Maximum210001.95
Range36106.292
Interquartile range (IQR)7626.4746

Descriptive statistics

Standard deviation5487.8725
Coefficient of variation (CV)0.029345881
Kurtosis0.034574181
Mean187006.57
Median Absolute Deviation (MAD)3688.1257
Skewness0.13057231
Sum1.8180779 × 109
Variance30116745
MonotonicityNot monotonic
2024-04-17T12:27:06.209041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187720.511056894 19
 
0.2%
188619.149645081 17
 
0.2%
183052.21115244 16
 
0.2%
187887.931705979 16
 
0.2%
186268.853282623 16
 
0.2%
175314.286676535 15
 
0.1%
187771.511373596 15
 
0.1%
187644.220019205 14
 
0.1%
193329.605871168 14
 
0.1%
190531.622508818 14
 
0.1%
Other values (7491) 9566
95.7%
(Missing) 278
 
2.8%
ValueCountFrequency (%)
173895.655729558 1
 
< 0.1%
173942.787360397 1
 
< 0.1%
173961.914773076 2
< 0.1%
173969.719902491 2
< 0.1%
173994.386578688 1
 
< 0.1%
174016.551235181 1
 
< 0.1%
174031.935803657 3
< 0.1%
174035.700224564 1
 
< 0.1%
174092.497564851 1
 
< 0.1%
174096.498143437 1
 
< 0.1%
ValueCountFrequency (%)
210001.947526977 1
 
< 0.1%
206512.517255249 1
 
< 0.1%
206353.855586145 2
 
< 0.1%
206248.235800687 1
 
< 0.1%
206184.609573703 6
0.1%
206174.795860476 1
 
< 0.1%
206145.8644854 1
 
< 0.1%
206120.302153948 1
 
< 0.1%
206102.964822493 1
 
< 0.1%
206089.035101112 1
 
< 0.1%

위생업태명
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미용업
3960 
미용업(일반)
2887 
미용업(피부)
933 
일반미용업
526 
미용업(손톱ㆍ발톱)
 
364
Other values (26)
1330 

Length

Max length31
Median length28
Mean length6.0522
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 3960
39.6%
미용업(일반) 2887
28.9%
미용업(피부) 933
 
9.3%
일반미용업 526
 
5.3%
미용업(손톱ㆍ발톱) 364
 
3.6%
미용업(종합) 310
 
3.1%
피부미용업 199
 
2.0%
네일미용업 128
 
1.3%
종합미용업 74
 
0.7%
미용업(피부), 미용업(손톱ㆍ발톱) 73
 
0.7%
Other values (21) 546
 
5.5%

Length

2024-04-17T12:27:06.323595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 4092
38.0%
미용업(일반 3048
28.3%
미용업(피부 1122
 
10.4%
미용업(손톱ㆍ발톱 620
 
5.8%
일반미용업 575
 
5.3%
미용업(종합 310
 
2.9%
미용업(화장ㆍ분장 293
 
2.7%
피부미용업 276
 
2.6%
네일미용업 217
 
2.0%
화장ㆍ분장 132
 
1.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct41
Distinct (%)0.5%
Missing2099
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean2.6039742
Minimum0
Maximum51
Zeros2924
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:06.416035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile7
Maximum51
Range51
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.024794
Coefficient of variation (CV)1.5456351
Kurtosis37.980778
Mean2.6039742
Median Absolute Deviation (MAD)2
Skewness4.9758816
Sum20574
Variance16.198966
MonotonicityNot monotonic
2024-04-17T12:27:06.515575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 2924
29.2%
2 1309
13.1%
3 1094
 
10.9%
4 986
 
9.9%
5 500
 
5.0%
1 428
 
4.3%
6 194
 
1.9%
7 82
 
0.8%
8 60
 
0.6%
9 59
 
0.6%
Other values (31) 265
 
2.6%
(Missing) 2099
21.0%
ValueCountFrequency (%)
0 2924
29.2%
1 428
 
4.3%
2 1309
13.1%
3 1094
 
10.9%
4 986
 
9.9%
5 500
 
5.0%
6 194
 
1.9%
7 82
 
0.8%
8 60
 
0.6%
9 59
 
0.6%
ValueCountFrequency (%)
51 2
 
< 0.1%
49 2
 
< 0.1%
47 3
< 0.1%
43 2
 
< 0.1%
42 5
0.1%
41 1
 
< 0.1%
39 1
 
< 0.1%
38 4
< 0.1%
37 2
 
< 0.1%
36 1
 
< 0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)0.2%
Missing3024
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean0.39506881
Minimum0
Maximum24
Zeros5095
Zeros (%)50.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:06.601714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95016998
Coefficient of variation (CV)2.4050747
Kurtosis97.507785
Mean0.39506881
Median Absolute Deviation (MAD)0
Skewness6.7924622
Sum2756
Variance0.90282299
MonotonicityNot monotonic
2024-04-17T12:27:06.684693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 5095
50.9%
1 1506
 
15.1%
2 173
 
1.7%
3 84
 
0.8%
5 52
 
0.5%
4 36
 
0.4%
6 15
 
0.1%
7 5
 
0.1%
8 4
 
< 0.1%
24 1
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 3024
30.2%
ValueCountFrequency (%)
0 5095
50.9%
1 1506
 
15.1%
2 173
 
1.7%
3 84
 
0.8%
4 36
 
0.4%
5 52
 
0.5%
6 15
 
0.1%
7 5
 
0.1%
8 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
18 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
8 4
 
< 0.1%
7 5
 
0.1%
6 15
 
0.1%
5 52
0.5%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)0.2%
Missing2753
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean1.3084035
Minimum0
Maximum37
Zeros1548
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:06.781777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3513223
Coefficient of variation (CV)1.0328025
Kurtosis82.699724
Mean1.3084035
Median Absolute Deviation (MAD)1
Skewness5.1837941
Sum9482
Variance1.8260721
MonotonicityNot monotonic
2024-04-17T12:27:06.894164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 3537
35.4%
0 1548
15.5%
2 1373
 
13.7%
3 458
 
4.6%
4 150
 
1.5%
5 73
 
0.7%
6 40
 
0.4%
7 29
 
0.3%
8 13
 
0.1%
9 9
 
0.1%
Other values (8) 17
 
0.2%
(Missing) 2753
27.5%
ValueCountFrequency (%)
0 1548
15.5%
1 3537
35.4%
2 1373
 
13.7%
3 458
 
4.6%
4 150
 
1.5%
5 73
 
0.7%
6 40
 
0.4%
7 29
 
0.3%
8 13
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
37 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 4
 
< 0.1%
10 4
 
< 0.1%
9 9
0.1%
8 13
0.1%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct19
Distinct (%)0.3%
Missing4153
Missing (%)41.5%
Infinite0
Infinite (%)0.0%
Mean1.3827604
Minimum0
Maximum206
Zeros1050
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:06.995877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum206
Range206
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.9820819
Coefficient of variation (CV)2.1566151
Kurtosis3793.9289
Mean1.3827604
Median Absolute Deviation (MAD)0
Skewness55.613558
Sum8085
Variance8.8928126
MonotonicityNot monotonic
2024-04-17T12:27:07.077799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 3011
30.1%
2 1153
 
11.5%
0 1050
 
10.5%
3 380
 
3.8%
4 109
 
1.1%
5 58
 
0.6%
6 31
 
0.3%
7 19
 
0.2%
8 10
 
0.1%
10 8
 
0.1%
Other values (9) 18
 
0.2%
(Missing) 4153
41.5%
ValueCountFrequency (%)
0 1050
 
10.5%
1 3011
30.1%
2 1153
 
11.5%
3 380
 
3.8%
4 109
 
1.1%
5 58
 
0.6%
6 31
 
0.3%
7 19
 
0.2%
8 10
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
206 1
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
13 2
 
< 0.1%
12 4
< 0.1%
10 8
0.1%
9 6
0.1%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5740 
0
4081 
1
 
154
2
 
21
3
 
4

Length

Max length4
Median length4
Mean length2.722
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5740
57.4%
0 4081
40.8%
1 154
 
1.5%
2 21
 
0.2%
3 4
 
< 0.1%

Length

2024-04-17T12:27:07.167684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:07.253911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5740
57.4%
0 4081
40.8%
1 154
 
1.5%
2 21
 
0.2%
3 4
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6840 
0
3011 
1
 
128
2
 
19
3
 
2

Length

Max length4
Median length4
Mean length3.052
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6840
68.4%
0 3011
30.1%
1 128
 
1.3%
2 19
 
0.2%
3 2
 
< 0.1%

Length

2024-04-17T12:27:07.341928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:07.422203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6840
68.4%
0 3011
30.1%
1 128
 
1.3%
2 19
 
0.2%
3 2
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6598 
<NA>
3402 

Length

Max length4
Median length1
Mean length2.0206
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 6598
66.0%
<NA> 3402
34.0%

Length

2024-04-17T12:27:07.509960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:07.600902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6598
66.0%
na 3402
34.0%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6598 
<NA>
3402 

Length

Max length4
Median length1
Mean length2.0206
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 6598
66.0%
<NA> 3402
34.0%

Length

2024-04-17T12:27:07.934030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:08.007909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6598
66.0%
na 3402
34.0%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6598 
<NA>
3402 

Length

Max length4
Median length1
Mean length2.0206
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 6598
66.0%
<NA> 3402
34.0%

Length

2024-04-17T12:27:08.088558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:08.169844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6598
66.0%
na 3402
34.0%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing161
Missing (%)1.6%
Memory size97.7 KiB
False
9839 
(Missing)
 
161
ValueCountFrequency (%)
False 9839
98.4%
(Missing) 161
 
1.6%
2024-04-17T12:27:08.229516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

의자수
Real number (ℝ)

MISSING  ZEROS 

Distinct31
Distinct (%)0.3%
Missing730
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean3.3266451
Minimum0
Maximum37
Zeros1396
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:08.306813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum37
Range37
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8377364
Coefficient of variation (CV)0.8530325
Kurtosis16.292893
Mean3.3266451
Median Absolute Deviation (MAD)1
Skewness2.8964846
Sum30838
Variance8.0527477
MonotonicityNot monotonic
2024-04-17T12:27:08.398563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 3145
31.4%
2 1436
14.4%
4 1417
14.2%
0 1396
14.0%
5 575
 
5.8%
6 365
 
3.6%
1 221
 
2.2%
8 161
 
1.6%
7 139
 
1.4%
10 102
 
1.0%
Other values (21) 313
 
3.1%
(Missing) 730
 
7.3%
ValueCountFrequency (%)
0 1396
14.0%
1 221
 
2.2%
2 1436
14.4%
3 3145
31.4%
4 1417
14.2%
5 575
 
5.8%
6 365
 
3.6%
7 139
 
1.4%
8 161
 
1.6%
9 62
 
0.6%
ValueCountFrequency (%)
37 1
 
< 0.1%
36 1
 
< 0.1%
31 1
 
< 0.1%
29 1
 
< 0.1%
28 2
 
< 0.1%
27 2
 
< 0.1%
24 8
0.1%
23 2
 
< 0.1%
22 3
 
< 0.1%
21 3
 
< 0.1%
Distinct2
Distinct (%)100.0%
Missing9998
Missing (%)> 99.9%
Memory size156.2 KiB
2024-04-17T12:27:08.538786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length46.5
Mean length46.5
Min length10

Characters and Unicode

Total characters93
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row가설건축물 존치기간
2nd row기한부영업신고기간:2006.10.19~2008.10.18 3. 이후 영업신고기간이 만료되어 연장신청을 하지않을경우 영업신고가 자동으로 말소됨
ValueCountFrequency (%)
가설건축물 1
8.3%
존치기간 1
8.3%
기한부영업신고기간:2006.10.19~2008.10.18 1
8.3%
3 1
8.3%
이후 1
8.3%
영업신고기간이 1
8.3%
만료되어 1
8.3%
연장신청을 1
8.3%
하지않을경우 1
8.3%
영업신고가 1
8.3%
Other values (2) 2
16.7%
2024-04-17T12:27:08.813808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
16.1%
0 6
 
6.5%
. 5
 
5.4%
4
 
4.3%
1 4
 
4.3%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
Other values (38) 43
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
58.1%
Decimal Number 17
 
18.3%
Space Separator 15
 
16.1%
Other Punctuation 6
 
6.5%
Math Symbol 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
 
7.4%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
1
 
1.9%
Other values (27) 27
50.0%
Decimal Number
ValueCountFrequency (%)
0 6
35.3%
1 4
23.5%
2 2
 
11.8%
8 2
 
11.8%
3 1
 
5.9%
9 1
 
5.9%
6 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
: 1
 
16.7%
Space Separator
ValueCountFrequency (%)
15
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
58.1%
Common 39
41.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4
 
7.4%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
1
 
1.9%
Other values (27) 27
50.0%
Common
ValueCountFrequency (%)
15
38.5%
0 6
 
15.4%
. 5
 
12.8%
1 4
 
10.3%
2 2
 
5.1%
8 2
 
5.1%
: 1
 
2.6%
3 1
 
2.6%
~ 1
 
2.6%
9 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
58.1%
ASCII 39
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
38.5%
0 6
 
15.4%
. 5
 
12.8%
1 4
 
10.3%
2 2
 
5.1%
8 2
 
5.1%
: 1
 
2.6%
3 1
 
2.6%
~ 1
 
2.6%
9 1
 
2.6%
Hangul
ValueCountFrequency (%)
4
 
7.4%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
1
 
1.9%
Other values (27) 27
50.0%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9998 
20141231
 
1
20051019
 
1

Length

Max length8
Median length4
Mean length4.0008
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9998
> 99.9%
20141231 1
 
< 0.1%
20051019 1
 
< 0.1%

Length

2024-04-17T12:27:08.948931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:09.026746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9998
> 99.9%
20141231 1
 
< 0.1%
20051019 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9995 
2
 
2
20161230
 
1
20080929
 
1
20061018
 
1

Length

Max length8
Median length4
Mean length4.0006
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9995
> 99.9%
2 2
 
< 0.1%
20161230 1
 
< 0.1%
20080929 1
 
< 0.1%
20061018 1
 
< 0.1%

Length

2024-04-17T12:27:09.109282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:09.188159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9995
> 99.9%
2 2
 
< 0.1%
20161230 1
 
< 0.1%
20080929 1
 
< 0.1%
20061018 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7690 
임대
2213 
자가
 
97

Length

Max length4
Median length4
Mean length3.538
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row임대
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 7690
76.9%
임대 2213
 
22.1%
자가 97
 
1.0%

Length

2024-04-17T12:27:09.278575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:09.361962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7690
76.9%
임대 2213
 
22.1%
자가 97
 
1.0%

세탁기수
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5514 
<NA>
4485 
5
 
1

Length

Max length4
Median length1
Mean length2.3455
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 5514
55.1%
<NA> 4485
44.9%
5 1
 
< 0.1%

Length

2024-04-17T12:27:09.451540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:09.536015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5514
55.1%
na 4485
44.9%
5 1
 
< 0.1%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.3%
Missing7661
Missing (%)76.6%
Infinite0
Infinite (%)0.0%
Mean0.081231295
Minimum0
Maximum7
Zeros2189
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:09.604982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37487726
Coefficient of variation (CV)4.6149363
Kurtosis95.504305
Mean0.081231295
Median Absolute Deviation (MAD)0
Skewness7.9524142
Sum190
Variance0.14053296
MonotonicityNot monotonic
2024-04-17T12:27:09.683229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2189
 
21.9%
1 128
 
1.3%
2 15
 
0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 7661
76.6%
ValueCountFrequency (%)
0 2189
21.9%
1 128
 
1.3%
2 15
 
0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 2
 
< 0.1%
4 3
 
< 0.1%
3 1
 
< 0.1%
2 15
 
0.1%
1 128
 
1.3%
0 2189
21.9%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7675 
0
2306 
1
 
17
2
 
2

Length

Max length4
Median length4
Mean length3.3025
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 7675
76.8%
0 2306
 
23.1%
1 17
 
0.2%
2 2
 
< 0.1%

Length

2024-04-17T12:27:09.774355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:09.851045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7675
76.8%
0 2306
 
23.1%
1 17
 
0.2%
2 2
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5244 
<NA>
4756 

Length

Max length4
Median length1
Mean length2.4268
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 5244
52.4%
<NA> 4756
47.6%

Length

2024-04-17T12:27:09.935333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:10.008790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5244
52.4%
na 4756
47.6%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)0.3%
Missing4785
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean0.95800575
Minimum0
Maximum19
Zeros3547
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:10.077704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8419422
Coefficient of variation (CV)1.9226838
Kurtosis10.482262
Mean0.95800575
Median Absolute Deviation (MAD)0
Skewness2.7488172
Sum4996
Variance3.3927509
MonotonicityNot monotonic
2024-04-17T12:27:10.161800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3547
35.5%
2 519
 
5.2%
1 373
 
3.7%
3 307
 
3.1%
4 174
 
1.7%
5 107
 
1.1%
6 75
 
0.8%
7 41
 
0.4%
8 29
 
0.3%
9 15
 
0.1%
Other values (7) 28
 
0.3%
(Missing) 4785
47.9%
ValueCountFrequency (%)
0 3547
35.5%
1 373
 
3.7%
2 519
 
5.2%
3 307
 
3.1%
4 174
 
1.7%
5 107
 
1.1%
6 75
 
0.8%
7 41
 
0.4%
8 29
 
0.3%
9 15
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
16 3
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 5
 
0.1%
11 2
 
< 0.1%
10 14
 
0.1%
9 15
 
0.1%
8 29
0.3%
7 41
0.4%

다중이용업소여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9997 
True
 
3
ValueCountFrequency (%)
False 9997
> 99.9%
True 3
 
< 0.1%
2024-04-17T12:27:10.250836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Unnamed: 50
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Sample

번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부Unnamed: 50
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1886718868미용업05_18_01_P33400003340000-204-1986-0071319861028<NA>3폐업2폐업20030227<NA><NA><NA>051.00604849부산광역시 사하구 하단동 812-4번지<NA><NA>미당20030402000000I2018-08-31 23:59:59.0일반미용업379062.276388181095.506265미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
1530015301미용업05_18_01_P33200003320000-212-2011-0001520111110<NA>3폐업2폐업20140210<NA><NA><NA>051 333 580564.58616819부산광역시 북구 덕천동 388-1번지 대방상가 2동 307호부산광역시 북구 만덕대로90번길 13 (덕천동, 대방상가2동 307호)46568힐에스테틱20121113100212I2018-08-31 23:59:59.0피부미용업383443.264424192181.296405미용업(피부)303300000N0<NA><NA><NA><NA>0<NA><NA>05N<NA>
26952696미용업05_18_01_P33400003340000-211-1986-0000119860615<NA>1영업/정상1영업<NA><NA><NA><NA>051 201241726.98604813부산광역시 사하구 괴정동 1074-8 지하 1층 지하2호부산광역시 사하구 마하로 54, 지하1층 지하2호 (괴정동)49335핑크헤어타운20201130180356U2020-12-02 02:40:00.0일반미용업380810.624375180152.185461미용업(일반)<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
69997000미용업05_18_01_P33000003300000-211-2014-0002520140507<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.53607822부산광역시 동래구 수안동 4-8번지 1층부산광역시 동래구 충렬대로218번길 43, 1층 (수안동)47818엘샤론염색방20181123103135U2018-11-25 02:37:03.0일반미용업389670.237427190950.945377미용업(일반)1011<NA><NA>000N2<NA><NA><NA><NA>0<NA><NA>00N<NA>
2294922950미용업05_18_01_P33500003350000-204-1996-0040719960110<NA>3폐업2폐업20000712<NA><NA><NA>051 518773918.27609816부산광역시 금정구 남산동 995-8번지<NA><NA>핑크20010418000000I2018-08-31 23:59:59.0피부미용업389894.581722198202.472852미용업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
2193821939미용업05_18_01_P33000003300000-211-1998-0001319981127<NA>3폐업2폐업20150225<NA><NA><NA>051 525564926.40607809부산광역시 동래구 명장동 146-30번지부산광역시 동래구 명장로57번길 3 (명장동)<NA>야시머리방20140328114535I2018-08-31 23:59:59.0일반미용업391811.895485191537.774722미용업(일반)<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
95829583미용업05_18_01_P34000003400000-211-2003-0002020030408<NA>1영업/정상1영업<NA><NA><NA><NA>051 722538633.00619903부산광역시 기장군 기장읍 대라리 128-3번지부산광역시 기장군 기장읍 차성남로 5446070뷰림헤어케어살롱20190404171456U2019-04-06 02:40:00.0일반미용업401464.380542195778.549187미용업(일반)101100000N5<NA><NA><NA>임대0<NA><NA>00N<NA>
15081509미용업05_18_01_P33700003370000-204-1991-0099019911212<NA>1영업/정상1영업<NA><NA><NA><NA>051 863 648518.62611836부산광역시 연제구 연산동 2121-6번지 T통B반부산광역시 연제구 배산로 32 (연산동)47594주은헤어20200427141600U2020-04-29 02:40:00.0일반미용업390843.383112188097.99605미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
2220122202미용업05_18_01_P33000003300000-212-2015-0000220150204<NA>3폐업2폐업20160331<NA><NA><NA><NA>88.81607842부산광역시 동래구 온천동 1445-77번지부산광역시 동래구 미남로132번길 39, 지하 1층 (온천동)47822스코리아 에스테틱20150204171209I2018-08-31 23:59:59.0피부미용업388592.140079191618.163814미용업(피부)41<NA><NA>11000N0<NA><NA><NA><NA>00002N<NA>
번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부Unnamed: 50
79137914미용업05_18_01_P33500003350000-204-1989-0035519890817<NA>1영업/정상1영업<NA><NA><NA><NA>051 516407826.55609802부산광역시 금정구 구서동 1016-11번지부산광역시 금정구 두실로45번길 45 (구서동)46228서진20130312142414I2018-08-31 23:59:59.0일반미용업389778.587142197583.71409미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
1411414115미용업05_18_01_P33400003340000-211-2000-0001220000701<NA>3폐업2폐업20141202<NA><NA><NA>051 207525261.56604807부산광역시 사하구 괴정동 310-4번지부산광역시 사하구 낙동대로 154 (괴정동)49319모리스미용실20100415145004I2018-08-31 23:59:59.0일반미용업382207.849674180047.142973미용업(일반)000000000N8<NA><NA><NA><NA>0<NA><NA>00N<NA>
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