Overview

Dataset statistics

Number of variables51
Number of observations10000
Missing cells110834
Missing cells (%)21.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory449.0 B

Variable types

Numeric17
Categorical19
Text7
Unsupported5
DateTime1
Boolean2

Dataset

Description2021-02-01
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 (51.3%)Imbalance
사용시작지하층 is highly imbalanced (57.7%)Imbalance
사용끝지하층 is highly imbalanced (57.8%)Imbalance
조건부허가시작일자 is highly imbalanced (99.7%)Imbalance
남성종사자수 is highly imbalanced (60.2%)Imbalance
다중이용업소여부 is highly imbalanced (99.7%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 4890 (48.9%) 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 2989 (29.9%) missing valuesMissing
소재지우편번호 has 109 (1.1%) missing valuesMissing
도로명전체주소 has 3098 (31.0%) missing valuesMissing
도로명우편번호 has 3184 (31.8%) missing valuesMissing
좌표정보(x) has 310 (3.1%) missing valuesMissing
좌표정보(y) has 310 (3.1%) missing valuesMissing
건물지상층수 has 2217 (22.2%) missing valuesMissing
건물지하층수 has 3126 (31.3%) missing valuesMissing
사용시작지상층 has 2800 (28.0%) missing valuesMissing
사용끝지상층 has 4224 (42.2%) missing valuesMissing
발한실여부 has 157 (1.6%) missing valuesMissing
의자수 has 823 (8.2%) missing valuesMissing
조건부허가신고사유 has 9996 (> 99.9%) missing valuesMissing
조건부허가종료일자 has 9994 (99.9%) missing valuesMissing
여성종사자수 has 7683 (76.8%) missing valuesMissing
침대수 has 4872 (48.7%) missing valuesMissing
Unnamed: 50 has 10000 (100.0%) missing valuesMissing
인허가일자 is highly skewed (γ1 = -28.57227672)Skewed
폐업일자 is highly skewed (γ1 = -64.7408994)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 2823 (28.2%) zerosZeros
건물지하층수 has 5047 (50.5%) zerosZeros
사용시작지상층 has 1436 (14.4%) zerosZeros
사용끝지상층 has 985 (9.8%) zerosZeros
의자수 has 1375 (13.8%) zerosZeros
여성종사자수 has 2177 (21.8%) zerosZeros
침대수 has 3473 (34.7%) zerosZeros

Reproduction

Analysis started2024-04-17 03:27:37.822397
Analysis finished2024-04-17 03:27:39.812735
Duration1.99 second
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%
Mean11584.832
Minimum1
Maximum23137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:39.864095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1142.75
Q15760.25
median11616.5
Q317383.25
95-th percentile21979.05
Maximum23137
Range23136
Interquartile range (IQR)11623

Descriptive statistics

Standard deviation6688.3052
Coefficient of variation (CV)0.57733295
Kurtosis-1.2089139
Mean11584.832
Median Absolute Deviation (MAD)5804
Skewness-0.0040641201
Sum1.1584832 × 108
Variance44733426
MonotonicityNot monotonic
2024-04-17T12:27:39.962349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12669 1
 
< 0.1%
98 1
 
< 0.1%
741 1
 
< 0.1%
5267 1
 
< 0.1%
19543 1
 
< 0.1%
2757 1
 
< 0.1%
3759 1
 
< 0.1%
12223 1
 
< 0.1%
20617 1
 
< 0.1%
21871 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
23137 1
< 0.1%
23136 1
< 0.1%
23135 1
< 0.1%
23134 1
< 0.1%
23133 1
< 0.1%
23132 1
< 0.1%
23131 1
< 0.1%
23129 1
< 0.1%
23128 1
< 0.1%
23127 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

2024-04-17T12:27:40.055696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:40.308203image/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

2024-04-17T12:27:40.377854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:40.443609image/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%
Mean3325135
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:40.509639image/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 deviation37490.296
Coefficient of variation (CV)0.011274819
Kurtosis-0.7493814
Mean3325135
Median Absolute Deviation (MAD)30000
Skewness0.050628556
Sum3.325135 × 1010
Variance1.4055223 × 109
MonotonicityNot monotonic
2024-04-17T12:27:40.609484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3330000 1232
12.3%
3290000 1200
12.0%
3340000 980
9.8%
3300000 908
9.1%
3310000 802
8.0%
3350000 785
7.8%
3380000 725
7.2%
3320000 723
7.2%
3370000 708
7.1%
3390000 407
 
4.1%
Other values (6) 1530
15.3%
ValueCountFrequency (%)
3250000 315
 
3.1%
3260000 285
 
2.9%
3270000 302
 
3.0%
3280000 343
 
3.4%
3290000 1200
12.0%
3300000 908
9.1%
3310000 802
8.0%
3320000 723
7.2%
3330000 1232
12.3%
3340000 980
9.8%
ValueCountFrequency (%)
3400000 181
 
1.8%
3390000 407
 
4.1%
3380000 725
7.2%
3370000 708
7.1%
3360000 104
 
1.0%
3350000 785
7.8%
3340000 980
9.8%
3330000 1232
12.3%
3320000 723
7.2%
3310000 802
8.0%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:27:40.790311image/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 row3290000-204-2003-00039
2nd row3350000-212-2016-00013
3rd row3340000-204-2005-00007
4th row3270000-212-2017-00003
5th row3390000-204-2006-00026
ValueCountFrequency (%)
3290000-204-2003-00039 1
 
< 0.1%
3290000-204-2004-00073 1
 
< 0.1%
3370000-204-2006-00027 1
 
< 0.1%
3310000-211-2006-00016 1
 
< 0.1%
3350000-211-2019-00010 1
 
< 0.1%
3380000-211-2000-00009 1
 
< 0.1%
3310000-204-1997-00080 1
 
< 0.1%
3340000-212-2015-00002 1
 
< 0.1%
3350000-204-2005-00017 1
 
< 0.1%
3290000-211-2013-00021 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-04-17T12:27:41.046601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 87583
39.8%
- 30000
 
13.6%
2 25921
 
11.8%
1 22182
 
10.1%
3 22138
 
10.1%
9 8785
 
4.0%
4 7992
 
3.6%
8 4454
 
2.0%
5 4267
 
1.9%
7 3649
 
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 87583
46.1%
2 25921
 
13.6%
1 22182
 
11.7%
3 22138
 
11.7%
9 8785
 
4.6%
4 7992
 
4.2%
8 4454
 
2.3%
5 4267
 
2.2%
7 3649
 
1.9%
6 3029
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87583
39.8%
- 30000
 
13.6%
2 25921
 
11.8%
1 22182
 
10.1%
3 22138
 
10.1%
9 8785
 
4.0%
4 7992
 
3.6%
8 4454
 
2.0%
5 4267
 
1.9%
7 3649
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87583
39.8%
- 30000
 
13.6%
2 25921
 
11.8%
1 22182
 
10.1%
3 22138
 
10.1%
9 8785
 
4.0%
4 7992
 
3.6%
8 4454
 
2.0%
5 4267
 
1.9%
7 3649
 
1.7%

인허가일자
Real number (ℝ)

SKEWED 

Distinct5749
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20051466
Minimum9990827
Maximum20201230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:41.168914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9990827
5-th percentile19840330
Q119980618
median20070914
Q320150718
95-th percentile20191021
Maximum20201230
Range10210403
Interquartile range (IQR)170100

Descriptive statistics

Standard deviation153372.26
Coefficient of variation (CV)0.0076489303
Kurtosis1850.8018
Mean20051466
Median Absolute Deviation (MAD)80712
Skewness-28.572277
Sum2.0051466 × 1011
Variance2.3523052 × 1010
MonotonicityNot monotonic
2024-04-17T12:27:41.303491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000712 22
 
0.2%
20030225 16
 
0.2%
20000415 16
 
0.2%
20030224 14
 
0.1%
20180102 10
 
0.1%
20020508 10
 
0.1%
20180115 9
 
0.1%
20180305 8
 
0.1%
20130103 8
 
0.1%
20100222 7
 
0.1%
Other values (5739) 9880
98.8%
ValueCountFrequency (%)
9990827 1
< 0.1%
19330331 1
< 0.1%
19581017 1
< 0.1%
19630110 2
< 0.1%
19630130 1
< 0.1%
19630206 1
< 0.1%
19630617 1
< 0.1%
19640630 1
< 0.1%
19650113 1
< 0.1%
19650426 1
< 0.1%
ValueCountFrequency (%)
20201230 1
 
< 0.1%
20201228 4
< 0.1%
20201223 1
 
< 0.1%
20201222 2
< 0.1%
20201221 3
< 0.1%
20201218 2
< 0.1%
20201217 1
 
< 0.1%
20201216 1
 
< 0.1%
20201211 3
< 0.1%
20201210 1
 
< 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
5110 
1
4890 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 5110
51.1%
1 4890
48.9%

Length

2024-04-17T12:27:41.426909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:41.495474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5110
51.1%
1 4890
48.9%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5110 
영업/정상
4890 

Length

Max length5
Median length2
Mean length3.467
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5110
51.1%
영업/정상 4890
48.9%

Length

2024-04-17T12:27:41.571713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:41.642637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5110
51.1%
영업/정상 4890
48.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5110 
1
4890 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5110
51.1%
1 4890
48.9%

Length

2024-04-17T12:27:41.728956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:41.799719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5110
51.1%
1 4890
48.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5110 
영업
4890 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5110
51.1%
영업 4890
48.9%

Length

2024-04-17T12:27:41.877614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:41.955082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5110
51.1%
영업 4890
48.9%

폐업일자
Real number (ℝ)

MISSING  SKEWED 

Distinct2924
Distinct (%)57.2%
Missing4890
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean20090365
Minimum2013102
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:42.044455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013102
5-th percentile19991019
Q120040220
median20090620
Q320151020
95-th percentile20200115
Maximum20201231
Range18188129
Interquartile range (IQR)110800.5

Descriptive statistics

Standard deviation261423.74
Coefficient of variation (CV)0.013012394
Kurtosis4477.4882
Mean20090365
Median Absolute Deviation (MAD)59717.5
Skewness-64.740899
Sum1.0266177 × 1011
Variance6.8342372 × 1010
MonotonicityNot monotonic
2024-04-17T12:27:42.158461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030227 180
 
1.8%
20050117 65
 
0.7%
20030226 52
 
0.5%
20050510 41
 
0.4%
20000712 33
 
0.3%
20010321 28
 
0.3%
20030606 24
 
0.2%
20030101 19
 
0.2%
20031107 17
 
0.2%
20000531 17
 
0.2%
Other values (2914) 4634
46.3%
(Missing) 4890
48.9%
ValueCountFrequency (%)
2013102 1
< 0.1%
19851118 1
< 0.1%
19891116 1
< 0.1%
19921012 1
< 0.1%
19921210 1
< 0.1%
19930202 1
< 0.1%
19930209 1
< 0.1%
19930308 1
< 0.1%
19940125 1
< 0.1%
19940506 1
< 0.1%
ValueCountFrequency (%)
20201231 1
 
< 0.1%
20201230 3
< 0.1%
20201229 2
 
< 0.1%
20201228 1
 
< 0.1%
20201224 5
0.1%
20201223 1
 
< 0.1%
20201222 7
0.1%
20201221 1
 
< 0.1%
20201218 3
< 0.1%
20201216 3
< 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 

Distinct6235
Distinct (%)88.9%
Missing2989
Missing (%)29.9%
Memory size156.2 KiB
2024-04-17T12:27:42.433659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.636856
Min length3

Characters and Unicode

Total characters74575
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6046 ?
Unique (%)86.2%

Sample

1st row051 8086999
2nd row051 516 7707
3rd row051 2070710
4th row051 3228546
5th row051 521 0444
ValueCountFrequency (%)
051 6574
42.0%
070 127
 
0.8%
868 35
 
0.2%
747 33
 
0.2%
852 31
 
0.2%
808 31
 
0.2%
515 30
 
0.2%
727 27
 
0.2%
728 25
 
0.2%
701 23
 
0.1%
Other values (6161) 8722
55.7%
2024-04-17T12:27:42.802626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12349
16.6%
0 11145
14.9%
1 11073
14.8%
8694
11.7%
2 5787
7.8%
7 4873
 
6.5%
3 4726
 
6.3%
6 4454
 
6.0%
8 4396
 
5.9%
4 4151
 
5.6%
Other values (2) 2927
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65879
88.3%
Space Separator 8694
 
11.7%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12349
18.7%
0 11145
16.9%
1 11073
16.8%
2 5787
8.8%
7 4873
 
7.4%
3 4726
 
7.2%
6 4454
 
6.8%
8 4396
 
6.7%
4 4151
 
6.3%
9 2925
 
4.4%
Space Separator
ValueCountFrequency (%)
8694
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12349
16.6%
0 11145
14.9%
1 11073
14.8%
8694
11.7%
2 5787
7.8%
7 4873
 
6.5%
3 4726
 
6.3%
6 4454
 
6.0%
8 4396
 
5.9%
4 4151
 
5.6%
Other values (2) 2927
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12349
16.6%
0 11145
14.9%
1 11073
14.8%
8694
11.7%
2 5787
7.8%
7 4873
 
6.5%
3 4726
 
6.3%
6 4454
 
6.0%
8 4396
 
5.9%
4 4151
 
5.6%
Other values (2) 2927
 
3.9%
Distinct4064
Distinct (%)40.8%
Missing36
Missing (%)0.4%
Memory size156.2 KiB
2024-04-17T12:27:43.106683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.9037535
Min length3

Characters and Unicode

Total characters48861
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

Unique2485 ?
Unique (%)24.9%

Sample

1st row93.58
2nd row49.23
3rd row14.80
4th row62.40
5th row38.00
ValueCountFrequency (%)
00 794
 
8.0%
33.00 134
 
1.3%
30.00 64
 
0.6%
24.00 57
 
0.6%
26.40 55
 
0.6%
20.00 53
 
0.5%
23.00 47
 
0.5%
18.00 44
 
0.4%
16.50 43
 
0.4%
15.00 40
 
0.4%
Other values (4054) 8633
86.6%
2024-04-17T12:27:43.496803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9964
20.4%
0 8747
17.9%
2 4958
10.1%
1 4879
10.0%
3 3749
 
7.7%
4 3362
 
6.9%
5 3066
 
6.3%
6 2962
 
6.1%
8 2615
 
5.4%
7 2287
 
4.7%
Other values (2) 2272
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38895
79.6%
Other Punctuation 9966
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8747
22.5%
2 4958
12.7%
1 4879
12.5%
3 3749
9.6%
4 3362
 
8.6%
5 3066
 
7.9%
6 2962
 
7.6%
8 2615
 
6.7%
7 2287
 
5.9%
9 2270
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 9964
> 99.9%
, 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 48861
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9964
20.4%
0 8747
17.9%
2 4958
10.1%
1 4879
10.0%
3 3749
 
7.7%
4 3362
 
6.9%
5 3066
 
6.3%
6 2962
 
6.1%
8 2615
 
5.4%
7 2287
 
4.7%
Other values (2) 2272
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9964
20.4%
0 8747
17.9%
2 4958
10.1%
1 4879
10.0%
3 3749
 
7.7%
4 3362
 
6.9%
5 3066
 
6.3%
6 2962
 
6.1%
8 2615
 
5.4%
7 2287
 
4.7%
Other values (2) 2272
 
4.6%

소재지우편번호
Real number (ℝ)

MISSING 

Distinct863
Distinct (%)8.7%
Missing109
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean610581.21
Minimum361856
Maximum619953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:43.616414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum361856
5-th percentile601820
Q1607810
median611812
Q3614800
95-th percentile617808
Maximum619953
Range258097
Interquartile range (IQR)6990

Descriptive statistics

Standard deviation5360.8653
Coefficient of variation (CV)0.0087799382
Kurtosis467.22748
Mean610581.21
Median Absolute Deviation (MAD)3037
Skewness-10.328428
Sum6.0392588 × 109
Variance28738877
MonotonicityNot monotonic
2024-04-17T12:27:43.720352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
609839 99
 
1.0%
614847 89
 
0.9%
604851 83
 
0.8%
608805 79
 
0.8%
612842 78
 
0.8%
612824 78
 
0.8%
616852 74
 
0.7%
614845 74
 
0.7%
607826 58
 
0.6%
608832 57
 
0.6%
Other values (853) 9122
91.2%
(Missing) 109
 
1.1%
ValueCountFrequency (%)
361856 1
 
< 0.1%
600012 3
< 0.1%
600013 5
0.1%
600016 5
0.1%
600017 2
 
< 0.1%
600021 2
 
< 0.1%
600022 2
 
< 0.1%
600023 1
 
< 0.1%
600024 2
 
< 0.1%
600025 3
< 0.1%
ValueCountFrequency (%)
619953 2
 
< 0.1%
619952 3
 
< 0.1%
619951 4
 
< 0.1%
619913 2
 
< 0.1%
619912 11
0.1%
619911 1
 
< 0.1%
619906 2
 
< 0.1%
619905 21
0.2%
619903 24
0.2%
619902 4
 
< 0.1%
Distinct9314
Distinct (%)93.3%
Missing16
Missing (%)0.2%
Memory size156.2 KiB
2024-04-17T12:27:43.981840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length51
Mean length24.986178
Min length16

Characters and Unicode

Total characters249462
Distinct characters499
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8720 ?
Unique (%)87.3%

Sample

1st row부산광역시 부산진구 부전동 264-22번지
2nd row부산광역시 금정구 장전동 652-49
3rd row부산광역시 사하구 감천동 30-18번지
4th row부산광역시 동구 범일동 640-15번지
5th row부산광역시 사상구 학장동 541번지
ValueCountFrequency (%)
부산광역시 9983
 
21.2%
해운대구 1232
 
2.6%
부산진구 1192
 
2.5%
사하구 978
 
2.1%
동래구 907
 
1.9%
t통b반 876
 
1.9%
남구 802
 
1.7%
금정구 784
 
1.7%
북구 731
 
1.6%
수영구 725
 
1.5%
Other values (10159) 28781
61.2%
2024-04-17T12:27:44.367469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37016
 
14.8%
12136
 
4.9%
12053
 
4.8%
12051
 
4.8%
1 11980
 
4.8%
10382
 
4.2%
10201
 
4.1%
10124
 
4.1%
9996
 
4.0%
9234
 
3.7%
Other values (489) 114289
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 147573
59.2%
Decimal Number 52606
 
21.1%
Space Separator 37016
 
14.8%
Dash Punctuation 9021
 
3.6%
Uppercase Letter 2170
 
0.9%
Open Punctuation 364
 
0.1%
Close Punctuation 361
 
0.1%
Other Punctuation 282
 
0.1%
Lowercase Letter 60
 
< 0.1%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12136
 
8.2%
12053
 
8.2%
12051
 
8.2%
10382
 
7.0%
10201
 
6.9%
10124
 
6.9%
9996
 
6.8%
9234
 
6.3%
8793
 
6.0%
2336
 
1.6%
Other values (430) 50267
34.1%
Uppercase Letter
ValueCountFrequency (%)
B 954
44.0%
T 883
40.7%
A 69
 
3.2%
S 55
 
2.5%
K 47
 
2.2%
G 22
 
1.0%
H 20
 
0.9%
E 15
 
0.7%
I 15
 
0.7%
C 15
 
0.7%
Other values (14) 75
 
3.5%
Lowercase Letter
ValueCountFrequency (%)
e 15
25.0%
l 12
20.0%
s 8
13.3%
i 7
11.7%
k 4
 
6.7%
b 2
 
3.3%
c 2
 
3.3%
a 2
 
3.3%
o 2
 
3.3%
t 1
 
1.7%
Other values (5) 5
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 11980
22.8%
2 7581
14.4%
3 5834
11.1%
4 4839
9.2%
0 4457
 
8.5%
5 4287
 
8.1%
6 3680
 
7.0%
7 3550
 
6.7%
8 3320
 
6.3%
9 3078
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 217
77.0%
@ 36
 
12.8%
/ 14
 
5.0%
. 13
 
4.6%
· 2
 
0.7%
Space Separator
ValueCountFrequency (%)
37016
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9021
100.0%
Open Punctuation
ValueCountFrequency (%)
( 364
100.0%
Close Punctuation
ValueCountFrequency (%)
) 361
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 147573
59.2%
Common 99659
39.9%
Latin 2230
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12136
 
8.2%
12053
 
8.2%
12051
 
8.2%
10382
 
7.0%
10201
 
6.9%
10124
 
6.9%
9996
 
6.8%
9234
 
6.3%
8793
 
6.0%
2336
 
1.6%
Other values (430) 50267
34.1%
Latin
ValueCountFrequency (%)
B 954
42.8%
T 883
39.6%
A 69
 
3.1%
S 55
 
2.5%
K 47
 
2.1%
G 22
 
1.0%
H 20
 
0.9%
e 15
 
0.7%
E 15
 
0.7%
I 15
 
0.7%
Other values (29) 135
 
6.1%
Common
ValueCountFrequency (%)
37016
37.1%
1 11980
 
12.0%
- 9021
 
9.1%
2 7581
 
7.6%
3 5834
 
5.9%
4 4839
 
4.9%
0 4457
 
4.5%
5 4287
 
4.3%
6 3680
 
3.7%
7 3550
 
3.6%
Other values (10) 7414
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 147572
59.2%
ASCII 101887
40.8%
None 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37016
36.3%
1 11980
 
11.8%
- 9021
 
8.9%
2 7581
 
7.4%
3 5834
 
5.7%
4 4839
 
4.7%
0 4457
 
4.4%
5 4287
 
4.2%
6 3680
 
3.6%
7 3550
 
3.5%
Other values (48) 9642
 
9.5%
Hangul
ValueCountFrequency (%)
12136
 
8.2%
12053
 
8.2%
12051
 
8.2%
10382
 
7.0%
10201
 
6.9%
10124
 
6.9%
9996
 
6.8%
9234
 
6.3%
8793
 
6.0%
2336
 
1.6%
Other values (429) 50266
34.1%
None
ValueCountFrequency (%)
· 2
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명전체주소
Text

MISSING 

Distinct6706
Distinct (%)97.2%
Missing3098
Missing (%)31.0%
Memory size156.2 KiB
2024-04-17T12:27:44.669495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length57
Mean length32.059548
Min length17

Characters and Unicode

Total characters221275
Distinct characters516
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6520 ?
Unique (%)94.5%

Sample

1st row부산광역시 금정구 온천장로 129, 2층 (장전동)
2nd row부산광역시 동구 진시장로 21-4 (범일동, 3층)
3rd row부산광역시 해운대구 해운대로 399-1 (우동, 제1층)
4th row부산광역시 수영구 연수로310번길 74, 상가동동 4층 (망미동, 삼성@)
5th row부산광역시 수영구 수영로725번길 48, 3층 (수영동)
ValueCountFrequency (%)
부산광역시 6901
 
16.1%
1층 1717
 
4.0%
부산진구 944
 
2.2%
2층 860
 
2.0%
해운대구 818
 
1.9%
동래구 642
 
1.5%
사하구 606
 
1.4%
남구 552
 
1.3%
수영구 533
 
1.2%
금정구 529
 
1.2%
Other values (5666) 28777
67.1%
2024-04-17T12:27:45.119033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35983
 
16.3%
9524
 
4.3%
1 9394
 
4.2%
8697
 
3.9%
8571
 
3.9%
7469
 
3.4%
7332
 
3.3%
7088
 
3.2%
6918
 
3.1%
( 6903
 
3.1%
Other values (506) 113396
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127750
57.7%
Decimal Number 36061
 
16.3%
Space Separator 35983
 
16.3%
Open Punctuation 6903
 
3.1%
Close Punctuation 6902
 
3.1%
Other Punctuation 5999
 
2.7%
Dash Punctuation 1154
 
0.5%
Uppercase Letter 433
 
0.2%
Lowercase Letter 64
 
< 0.1%
Math Symbol 26
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9524
 
7.5%
8697
 
6.8%
8571
 
6.7%
7469
 
5.8%
7332
 
5.7%
7088
 
5.5%
6918
 
5.4%
6783
 
5.3%
3448
 
2.7%
3388
 
2.7%
Other values (449) 58532
45.8%
Uppercase Letter
ValueCountFrequency (%)
B 94
21.7%
A 73
16.9%
S 63
14.5%
K 46
10.6%
H 22
 
5.1%
E 18
 
4.2%
C 16
 
3.7%
G 15
 
3.5%
I 13
 
3.0%
W 10
 
2.3%
Other values (14) 63
14.5%
Lowercase Letter
ValueCountFrequency (%)
e 20
31.2%
l 12
18.8%
s 9
14.1%
i 7
 
10.9%
k 5
 
7.8%
c 3
 
4.7%
w 2
 
3.1%
o 2
 
3.1%
n 1
 
1.6%
a 1
 
1.6%
Other values (2) 2
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 9394
26.1%
2 6069
16.8%
3 3887
10.8%
0 3480
 
9.7%
4 2944
 
8.2%
5 2486
 
6.9%
6 2238
 
6.2%
7 2018
 
5.6%
8 1837
 
5.1%
9 1708
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 5965
99.4%
@ 21
 
0.4%
/ 6
 
0.1%
. 5
 
0.1%
· 2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 25
96.2%
1
 
3.8%
Space Separator
ValueCountFrequency (%)
35983
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6903
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6902
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127750
57.7%
Common 93028
42.0%
Latin 497
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9524
 
7.5%
8697
 
6.8%
8571
 
6.7%
7469
 
5.8%
7332
 
5.7%
7088
 
5.5%
6918
 
5.4%
6783
 
5.3%
3448
 
2.7%
3388
 
2.7%
Other values (449) 58532
45.8%
Latin
ValueCountFrequency (%)
B 94
18.9%
A 73
14.7%
S 63
12.7%
K 46
 
9.3%
H 22
 
4.4%
e 20
 
4.0%
E 18
 
3.6%
C 16
 
3.2%
G 15
 
3.0%
I 13
 
2.6%
Other values (26) 117
23.5%
Common
ValueCountFrequency (%)
35983
38.7%
1 9394
 
10.1%
( 6903
 
7.4%
) 6902
 
7.4%
2 6069
 
6.5%
, 5965
 
6.4%
3 3887
 
4.2%
0 3480
 
3.7%
4 2944
 
3.2%
5 2486
 
2.7%
Other values (11) 9015
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127750
57.7%
ASCII 93522
42.3%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35983
38.5%
1 9394
 
10.0%
( 6903
 
7.4%
) 6902
 
7.4%
2 6069
 
6.5%
, 5965
 
6.4%
3 3887
 
4.2%
0 3480
 
3.7%
4 2944
 
3.1%
5 2486
 
2.7%
Other values (45) 9509
 
10.2%
Hangul
ValueCountFrequency (%)
9524
 
7.5%
8697
 
6.8%
8571
 
6.7%
7469
 
5.8%
7332
 
5.7%
7088
 
5.5%
6918
 
5.4%
6783
 
5.3%
3448
 
2.7%
3388
 
2.7%
Other values (449) 58532
45.8%
None
ValueCountFrequency (%)
· 2
66.7%
1
33.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct1565
Distinct (%)23.0%
Missing3184
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean47817.184
Minimum28465
Maximum49525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:45.246371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28465
5-th percentile46245
Q147164
median47857
Q348497
95-th percentile49384
Maximum49525
Range21060
Interquartile range (IQR)1333

Descriptive statistics

Standard deviation984.21138
Coefficient of variation (CV)0.020582797
Kurtosis20.836819
Mean47817.184
Median Absolute Deviation (MAD)651
Skewness-1.1487666
Sum3.2592192 × 108
Variance968672.03
MonotonicityNot monotonic
2024-04-17T12:27:45.345841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48111 49
 
0.5%
46726 43
 
0.4%
48059 40
 
0.4%
48110 33
 
0.3%
47287 32
 
0.3%
47292 30
 
0.3%
48119 30
 
0.3%
46526 30
 
0.3%
48060 28
 
0.3%
46291 28
 
0.3%
Other values (1555) 6473
64.7%
(Missing) 3184
31.8%
ValueCountFrequency (%)
28465 1
 
< 0.1%
46007 6
0.1%
46008 14
0.1%
46009 1
 
< 0.1%
46010 4
 
< 0.1%
46011 1
 
< 0.1%
46012 4
 
< 0.1%
46013 2
 
< 0.1%
46014 1
 
< 0.1%
46015 14
0.1%
ValueCountFrequency (%)
49525 4
 
< 0.1%
49524 3
 
< 0.1%
49523 1
 
< 0.1%
49522 1
 
< 0.1%
49521 3
 
< 0.1%
49520 15
0.1%
49519 10
0.1%
49518 14
0.1%
49515 10
0.1%
49514 2
 
< 0.1%
Distinct8166
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:27:45.609845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30.5
Mean length5.5225
Min length1

Characters and Unicode

Total characters55225
Distinct characters898
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7267 ?
Unique (%)72.7%

Sample

1st row헤어카페준
2nd row블루밍영(young)
3rd row영미용실
4th row힐링카페
5th row서준헤어샵
ValueCountFrequency (%)
미용실 295
 
2.4%
헤어 266
 
2.1%
에스테틱 115
 
0.9%
네일 93
 
0.7%
헤어샵 76
 
0.6%
hair 67
 
0.5%
뷰티 49
 
0.4%
39
 
0.3%
nail 39
 
0.3%
35
 
0.3%
Other values (8164) 11468
91.4%
2024-04-17T12:27:45.998415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3538
 
6.4%
3475
 
6.3%
2547
 
4.6%
1819
 
3.3%
1335
 
2.4%
1182
 
2.1%
1116
 
2.0%
1091
 
2.0%
967
 
1.8%
840
 
1.5%
Other values (888) 37315
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46684
84.5%
Space Separator 2547
 
4.6%
Lowercase Letter 2257
 
4.1%
Uppercase Letter 1870
 
3.4%
Open Punctuation 549
 
1.0%
Close Punctuation 549
 
1.0%
Other Punctuation 386
 
0.7%
Decimal Number 331
 
0.6%
Dash Punctuation 37
 
0.1%
Math Symbol 8
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3538
 
7.6%
3475
 
7.4%
1819
 
3.9%
1335
 
2.9%
1182
 
2.5%
1116
 
2.4%
1091
 
2.3%
967
 
2.1%
840
 
1.8%
768
 
1.6%
Other values (800) 30553
65.4%
Lowercase Letter
ValueCountFrequency (%)
a 299
13.2%
e 252
11.2%
i 251
11.1%
n 188
8.3%
o 171
 
7.6%
l 162
 
7.2%
r 145
 
6.4%
y 118
 
5.2%
h 114
 
5.1%
s 104
 
4.6%
Other values (16) 453
20.1%
Uppercase Letter
ValueCountFrequency (%)
A 176
 
9.4%
N 153
 
8.2%
S 137
 
7.3%
H 130
 
7.0%
I 118
 
6.3%
J 108
 
5.8%
B 102
 
5.5%
O 102
 
5.5%
M 99
 
5.3%
L 96
 
5.1%
Other values (16) 649
34.7%
Other Punctuation
ValueCountFrequency (%)
& 137
35.5%
. 90
23.3%
# 49
 
12.7%
, 46
 
11.9%
' 29
 
7.5%
· 10
 
2.6%
: 7
 
1.8%
5
 
1.3%
; 4
 
1.0%
/ 3
 
0.8%
Other values (5) 6
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 76
23.0%
2 71
21.5%
0 57
17.2%
9 26
 
7.9%
3 26
 
7.9%
7 20
 
6.0%
8 17
 
5.1%
5 16
 
4.8%
4 15
 
4.5%
6 7
 
2.1%
Open Punctuation
ValueCountFrequency (%)
( 546
99.5%
[ 3
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 546
99.5%
] 3
 
0.5%
Space Separator
ValueCountFrequency (%)
2547
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Math Symbol
ValueCountFrequency (%)
+ 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Number
ValueCountFrequency (%)
1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46632
84.4%
Common 4413
 
8.0%
Latin 4128
 
7.5%
Han 52
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3538
 
7.6%
3475
 
7.5%
1819
 
3.9%
1335
 
2.9%
1182
 
2.5%
1116
 
2.4%
1091
 
2.3%
967
 
2.1%
840
 
1.8%
768
 
1.6%
Other values (777) 30501
65.4%
Latin
ValueCountFrequency (%)
a 299
 
7.2%
e 252
 
6.1%
i 251
 
6.1%
n 188
 
4.6%
A 176
 
4.3%
o 171
 
4.1%
l 162
 
3.9%
N 153
 
3.7%
r 145
 
3.5%
S 137
 
3.3%
Other values (43) 2194
53.1%
Common
ValueCountFrequency (%)
2547
57.7%
( 546
 
12.4%
) 546
 
12.4%
& 137
 
3.1%
. 90
 
2.0%
1 76
 
1.7%
2 71
 
1.6%
0 57
 
1.3%
# 49
 
1.1%
, 46
 
1.0%
Other values (25) 248
 
5.6%
Han
ValueCountFrequency (%)
26
50.0%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%
Other values (13) 13
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46632
84.4%
ASCII 8523
 
15.4%
CJK 52
 
0.1%
None 17
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3538
 
7.6%
3475
 
7.5%
1819
 
3.9%
1335
 
2.9%
1182
 
2.5%
1116
 
2.4%
1091
 
2.3%
967
 
2.1%
840
 
1.8%
768
 
1.6%
Other values (777) 30501
65.4%
ASCII
ValueCountFrequency (%)
2547
29.9%
( 546
 
6.4%
) 546
 
6.4%
a 299
 
3.5%
e 252
 
3.0%
i 251
 
2.9%
n 188
 
2.2%
A 176
 
2.1%
o 171
 
2.0%
l 162
 
1.9%
Other values (73) 3385
39.7%
CJK
ValueCountFrequency (%)
26
50.0%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%
Other values (13) 13
25.0%
None
ValueCountFrequency (%)
· 10
58.8%
5
29.4%
1
 
5.9%
1
 
5.9%
Number Forms
ValueCountFrequency (%)
1
100.0%

최종수정시점
Real number (ℝ)

Distinct8027
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0127984 × 1013
Minimum1.9990125 × 1013
Maximum2.0201231 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:46.110448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990125 × 1013
5-th percentile2.0011212 × 1013
Q12.0060221 × 1013
median2.0150226 × 1013
Q32.0190624 × 1013
95-th percentile2.0201126 × 1013
Maximum2.0201231 × 1013
Range2.1110616 × 1011
Interquartile range (IQR)1.3040365 × 1011

Descriptive statistics

Standard deviation6.8096214 × 1010
Coefficient of variation (CV)0.0033831611
Kurtosis-1.1517252
Mean2.0127984 × 1013
Median Absolute Deviation (MAD)4.9887003 × 1010
Skewness-0.55727462
Sum2.0127984 × 1017
Variance4.6370944 × 1021
MonotonicityNot monotonic
2024-04-17T12:27:46.222322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030402000000 121
 
1.2%
20020823000000 43
 
0.4%
20040827000000 39
 
0.4%
20040719000000 35
 
0.4%
20030403000000 33
 
0.3%
19990429000000 31
 
0.3%
20001209000000 30
 
0.3%
20030627000000 30
 
0.3%
20031023000000 25
 
0.2%
20030707000000 25
 
0.2%
Other values (8017) 9588
95.9%
ValueCountFrequency (%)
19990125000000 4
 
< 0.1%
19990126000000 3
 
< 0.1%
19990211000000 1
 
< 0.1%
19990222000000 1
 
< 0.1%
19990223000000 4
 
< 0.1%
19990224000000 5
 
0.1%
19990225000000 5
 
0.1%
19990226000000 1
 
< 0.1%
19990303000000 7
0.1%
19990304000000 16
0.2%
ValueCountFrequency (%)
20201231164153 1
< 0.1%
20201231162945 1
< 0.1%
20201231145340 1
< 0.1%
20201231105024 1
< 0.1%
20201231093459 1
< 0.1%
20201230164003 1
< 0.1%
20201230160116 1
< 0.1%
20201230151252 1
< 0.1%
20201230143917 1
< 0.1%
20201230133855 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
7306 
U
2694 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 7306
73.1%
U 2694
 
26.9%

Length

2024-04-17T12:27:46.349661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:46.436708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7306
73.1%
u 2694
 
26.9%
Distinct881
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-01-02 02:40:00
2024-04-17T12:27:46.537369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T12:27:46.654222image/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
7092 
피부미용업
1793 
네일아트업
874 
메이크업업
 
152
기타
 
86

Length

Max length6
Median length5
Mean length4.9745
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 7092
70.9%
피부미용업 1793
 
17.9%
네일아트업 874
 
8.7%
메이크업업 152
 
1.5%
기타 86
 
0.9%
미용업 기타 3
 
< 0.1%

Length

2024-04-17T12:27:46.993353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:47.078849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 7092
70.9%
피부미용업 1793
 
17.9%
네일아트업 874
 
8.7%
메이크업업 152
 
1.5%
기타 89
 
0.9%
미용업 3
 
< 0.1%

좌표정보(x)
Real number (ℝ)

MISSING 

Distinct7542
Distinct (%)77.8%
Missing310
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean388287.5
Minimum241128.92
Maximum407824.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:47.172537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum241128.92
5-th percentile379797.47
Q1384499.98
median388707.95
Q3391745.05
95-th percentile397581.77
Maximum407824.89
Range166695.96
Interquartile range (IQR)7245.0774

Descriptive statistics

Standard deviation5447.6105
Coefficient of variation (CV)0.014029837
Kurtosis54.601452
Mean388287.5
Median Absolute Deviation (MAD)3526.8368
Skewness-2.0870671
Sum3.7625059 × 109
Variance29676460
MonotonicityNot monotonic
2024-04-17T12:27:47.280283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
398330.516530402 23
 
0.2%
395388.715069604 19
 
0.2%
380398.062015138 18
 
0.2%
398491.352089522 17
 
0.2%
398237.363461482 15
 
0.1%
392235.198208233 13
 
0.1%
394112.524871033 13
 
0.1%
393239.237933586 12
 
0.1%
391411.212179843 12
 
0.1%
392474.578116018 12
 
0.1%
Other values (7532) 9536
95.4%
(Missing) 310
 
3.1%
ValueCountFrequency (%)
241128.922467 1
< 0.1%
366931.435995074 1
< 0.1%
367058.667450096 1
< 0.1%
367062.132343737 1
< 0.1%
367108.112280274 1
< 0.1%
367195.063042763 1
< 0.1%
367226.483222999 1
< 0.1%
367226.95953767 1
< 0.1%
367451.087635496 1
< 0.1%
370718.68095386 1
< 0.1%
ValueCountFrequency (%)
407824.887002439 1
< 0.1%
407663.462947301 1
< 0.1%
407472.686463578 1
< 0.1%
407161.891842701 1
< 0.1%
407121.882187494 1
< 0.1%
407037.787059465 1
< 0.1%
406982.053033795 1
< 0.1%
405370.623777615 1
< 0.1%
403981.485891989 1
< 0.1%
403804.630703364 1
< 0.1%

좌표정보(y)
Real number (ℝ)

MISSING 

Distinct7543
Distinct (%)77.8%
Missing310
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean187012.71
Minimum173942.79
Maximum349970.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:47.400018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173942.79
5-th percentile178253.72
Q1183202.38
median187252.53
Q3190887.54
95-th percentile195691.51
Maximum349970.06
Range176027.27
Interquartile range (IQR)7685.167

Descriptive statistics

Standard deviation5802.4031
Coefficient of variation (CV)0.031026784
Kurtosis63.86851
Mean187012.71
Median Absolute Deviation (MAD)3713.3835
Skewness2.441295
Sum1.8121532 × 109
Variance33667882
MonotonicityNot monotonic
2024-04-17T12:27:47.502352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187771.511373596 23
 
0.2%
186268.853282623 19
 
0.2%
175314.286676535 18
 
0.2%
187644.220019205 17
 
0.2%
187720.511056894 15
 
0.1%
187887.931705979 13
 
0.1%
190531.622508818 13
 
0.1%
188619.149645081 12
 
0.1%
183052.21115244 12
 
0.1%
187784.843934451 12
 
0.1%
Other values (7533) 9536
95.4%
(Missing) 310
 
3.1%
ValueCountFrequency (%)
173942.787360397 1
 
< 0.1%
173961.753544726 1
 
< 0.1%
173961.914773076 1
 
< 0.1%
173969.719902491 1
 
< 0.1%
174031.935803657 1
 
< 0.1%
174035.700224564 2
< 0.1%
174101.406639044 3
< 0.1%
174107.728805438 1
 
< 0.1%
174149.475814107 1
 
< 0.1%
174156.617297535 1
 
< 0.1%
ValueCountFrequency (%)
349970.057043 1
 
< 0.1%
206512.517255249 2
 
< 0.1%
206353.855586145 2
 
< 0.1%
206285.479401732 1
 
< 0.1%
206184.609573703 5
0.1%
206175.365141713 1
 
< 0.1%
206166.902182052 1
 
< 0.1%
206145.8644854 1
 
< 0.1%
206138.724866676 1
 
< 0.1%
206120.302153948 1
 
< 0.1%

위생업태명
Categorical

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미용업
3996 
미용업(일반)
2980 
미용업(피부)
945 
일반미용업
437 
미용업(손톱ㆍ발톱)
 
385
Other values (25)
1257 

Length

Max length31
Median length28
Mean length6.0845
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 3996
40.0%
미용업(일반) 2980
29.8%
미용업(피부) 945
 
9.4%
일반미용업 437
 
4.4%
미용업(손톱ㆍ발톱) 385
 
3.9%
미용업(종합) 322
 
3.2%
피부미용업 172
 
1.7%
네일미용업 102
 
1.0%
미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장) 76
 
0.8%
미용업(피부), 미용업(손톱ㆍ발톱) 72
 
0.7%
Other values (20) 513
 
5.1%

Length

2024-04-17T12:27:47.608133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 4094
38.2%
미용업(일반 3144
29.3%
미용업(피부 1160
 
10.8%
미용업(손톱ㆍ발톱 666
 
6.2%
일반미용업 475
 
4.4%
미용업(종합 322
 
3.0%
미용업(화장ㆍ분장 306
 
2.9%
피부미용업 215
 
2.0%
네일미용업 174
 
1.6%
화장ㆍ분장 98
 
0.9%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct42
Distinct (%)0.5%
Missing2217
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean2.6282924
Minimum0
Maximum61
Zeros2823
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:47.698199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile7
Maximum61
Range61
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9548605
Coefficient of variation (CV)1.5047262
Kurtosis36.314714
Mean2.6282924
Median Absolute Deviation (MAD)2
Skewness4.8221801
Sum20456
Variance15.640921
MonotonicityNot monotonic
2024-04-17T12:27:47.808756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 2823
28.2%
2 1309
13.1%
3 1077
 
10.8%
4 973
 
9.7%
5 523
 
5.2%
1 416
 
4.2%
6 185
 
1.8%
7 94
 
0.9%
9 60
 
0.6%
8 59
 
0.6%
Other values (32) 264
 
2.6%
(Missing) 2217
22.2%
ValueCountFrequency (%)
0 2823
28.2%
1 416
 
4.2%
2 1309
13.1%
3 1077
 
10.8%
4 973
 
9.7%
5 523
 
5.2%
6 185
 
1.8%
7 94
 
0.9%
8 59
 
0.6%
9 60
 
0.6%
ValueCountFrequency (%)
61 1
 
< 0.1%
47 1
 
< 0.1%
43 4
< 0.1%
42 3
< 0.1%
41 1
 
< 0.1%
39 2
< 0.1%
38 4
< 0.1%
37 3
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)0.2%
Missing3126
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean0.3868199
Minimum0
Maximum15
Zeros5047
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:47.897420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89800253
Coefficient of variation (CV)2.3215003
Kurtosis43.312975
Mean0.3868199
Median Absolute Deviation (MAD)0
Skewness4.995767
Sum2659
Variance0.80640854
MonotonicityNot monotonic
2024-04-17T12:27:47.983694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5047
50.5%
1 1458
 
14.6%
2 178
 
1.8%
3 76
 
0.8%
5 52
 
0.5%
4 34
 
0.3%
6 14
 
0.1%
7 6
 
0.1%
8 4
 
< 0.1%
10 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
(Missing) 3126
31.3%
ValueCountFrequency (%)
0 5047
50.5%
1 1458
 
14.6%
2 178
 
1.8%
3 76
 
0.8%
4 34
 
0.3%
5 52
 
0.5%
6 14
 
0.1%
7 6
 
0.1%
8 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
13 1
 
< 0.1%
10 2
 
< 0.1%
8 4
 
< 0.1%
7 6
 
0.1%
6 14
 
0.1%
5 52
 
0.5%
4 34
 
0.3%
3 76
0.8%
2 178
1.8%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)0.2%
Missing2800
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean1.3356944
Minimum0
Maximum37
Zeros1436
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:48.069050image/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 deviation1.4112454
Coefficient of variation (CV)1.0565631
Kurtosis121.53466
Mean1.3356944
Median Absolute Deviation (MAD)0
Skewness6.5231143
Sum9617
Variance1.9916136
MonotonicityNot monotonic
2024-04-17T12:27:48.150511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 3626
36.3%
0 1436
 
14.4%
2 1327
 
13.3%
3 456
 
4.6%
4 160
 
1.6%
5 78
 
0.8%
6 45
 
0.4%
7 30
 
0.3%
8 14
 
0.1%
10 8
 
0.1%
Other values (5) 20
 
0.2%
(Missing) 2800
28.0%
ValueCountFrequency (%)
0 1436
 
14.4%
1 3626
36.3%
2 1327
 
13.3%
3 456
 
4.6%
4 160
 
1.6%
5 78
 
0.8%
6 45
 
0.4%
7 30
 
0.3%
8 14
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
37 2
 
< 0.1%
13 3
 
< 0.1%
12 3
 
< 0.1%
11 5
 
0.1%
10 8
 
0.1%
9 7
 
0.1%
8 14
 
0.1%
7 30
 
0.3%
6 45
0.4%
5 78
0.8%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.2%
Missing4224
Missing (%)42.2%
Infinite0
Infinite (%)0.0%
Mean1.3573407
Minimum0
Maximum18
Zeros985
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:48.235650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2593167
Coefficient of variation (CV)0.92778231
Kurtosis18.111539
Mean1.3573407
Median Absolute Deviation (MAD)0
Skewness3.0411297
Sum7840
Variance1.5858786
MonotonicityNot monotonic
2024-04-17T12:27:48.317865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 3038
30.4%
2 1102
 
11.0%
0 985
 
9.8%
3 385
 
3.9%
4 117
 
1.2%
5 62
 
0.6%
6 33
 
0.3%
7 21
 
0.2%
8 13
 
0.1%
10 10
 
0.1%
Other values (4) 10
 
0.1%
(Missing) 4224
42.2%
ValueCountFrequency (%)
0 985
 
9.8%
1 3038
30.4%
2 1102
 
11.0%
3 385
 
3.9%
4 117
 
1.2%
5 62
 
0.6%
6 33
 
0.3%
7 21
 
0.2%
8 13
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
13 2
 
< 0.1%
12 3
 
< 0.1%
10 10
 
0.1%
9 4
 
< 0.1%
8 13
 
0.1%
7 21
 
0.2%
6 33
 
0.3%
5 62
0.6%
4 117
1.2%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5808 
0
4021 
1
 
151
2
 
17
3
 
2

Length

Max length4
Median length4
Mean length2.7424
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row0
3rd row1
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5808
58.1%
0 4021
40.2%
1 151
 
1.5%
2 17
 
0.2%
3 2
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-17T12:27:48.416133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:48.511429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5808
58.1%
0 4021
40.2%
1 151
 
1.5%
2 17
 
0.2%
3 2
 
< 0.1%
4 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6901 
0
2964 
1
 
120
2
 
14
4
 
1

Length

Max length4
Median length4
Mean length3.0703
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row0
3rd row1
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6901
69.0%
0 2964
29.6%
1 120
 
1.2%
2 14
 
0.1%
4 1
 
< 0.1%

Length

2024-04-17T12:27:48.603853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:48.684036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6901
69.0%
0 2964
29.6%
1 120
 
1.2%
2 14
 
0.1%
4 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6497 
<NA>
3503 

Length

Max length4
Median length1
Mean length2.0509
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6497
65.0%
<NA> 3503
35.0%

Length

2024-04-17T12:27:48.769158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:48.847587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6497
65.0%
na 3503
35.0%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6497 
<NA>
3503 

Length

Max length4
Median length1
Mean length2.0509
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6497
65.0%
<NA> 3503
35.0%

Length

2024-04-17T12:27:48.926071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:49.001239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6497
65.0%
na 3503
35.0%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6497 
<NA>
3503 

Length

Max length4
Median length1
Mean length2.0509
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6497
65.0%
<NA> 3503
35.0%

Length

2024-04-17T12:27:49.080315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:49.154794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6497
65.0%
na 3503
35.0%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing157
Missing (%)1.6%
Memory size97.7 KiB
False
9843 
(Missing)
 
157
ValueCountFrequency (%)
False 9843
98.4%
(Missing) 157
 
1.6%
2024-04-17T12:27:49.212910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

의자수
Real number (ℝ)

MISSING  ZEROS 

Distinct33
Distinct (%)0.4%
Missing823
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean3.2956304
Minimum0
Maximum38
Zeros1375
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:49.290096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum38
Range38
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8029781
Coefficient of variation (CV)0.8505135
Kurtosis19.171329
Mean3.2956304
Median Absolute Deviation (MAD)1
Skewness3.0964529
Sum30244
Variance7.8566864
MonotonicityNot monotonic
2024-04-17T12:27:49.386539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3 3165
31.6%
2 1434
14.3%
4 1409
14.1%
0 1375
13.8%
5 539
 
5.4%
6 377
 
3.8%
1 209
 
2.1%
8 165
 
1.7%
7 135
 
1.4%
10 83
 
0.8%
Other values (23) 286
 
2.9%
(Missing) 823
 
8.2%
ValueCountFrequency (%)
0 1375
13.8%
1 209
 
2.1%
2 1434
14.3%
3 3165
31.6%
4 1409
14.1%
5 539
 
5.4%
6 377
 
3.8%
7 135
 
1.4%
8 165
 
1.7%
9 58
 
0.6%
ValueCountFrequency (%)
38 1
 
< 0.1%
36 2
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 2
< 0.1%
27 2
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 4
< 0.1%
23 3
< 0.1%
Distinct4
Distinct (%)100.0%
Missing9996
Missing (%)> 99.9%
Memory size156.2 KiB
2024-04-17T12:27:49.553896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length43
Mean length50.75
Min length34

Characters and Unicode

Total characters203
Distinct characters74
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row기한부영업신고기간:2006.10.19~2008.10.18 3. 이후 영업신고기간이 만료되어 연장신청을 하지않을경우 영업신고가 자동으로 말소됨
2nd row가설건축물 존치기간 연장, 건축과-9379(2014.12.22)호 관련
3rd row특정건축물 사용승인하였으나, 2007.7.13자 조건부영업신고 해제함(담당자:고제현)
4th row가설건축물존치기한까지연장(영업신고기간:2017.03.01까지)
ValueCountFrequency (%)
기한부영업신고기간:2006.10.19~2008.10.18 1
 
4.8%
존치기간 1
 
4.8%
해제함(담당자:고제현 1
 
4.8%
조건부영업신고 1
 
4.8%
2007.7.13자 1
 
4.8%
사용승인하였으나 1
 
4.8%
특정건축물 1
 
4.8%
관련 1
 
4.8%
건축과-9379(2014.12.22)호 1
 
4.8%
연장 1
 
4.8%
Other values (11) 11
52.4%
2024-04-17T12:27:49.804430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
10.8%
0 12
 
5.9%
. 11
 
5.4%
1 9
 
4.4%
2 8
 
3.9%
6
 
3.0%
6
 
3.0%
6
 
3.0%
5
 
2.5%
5
 
2.5%
Other values (64) 113
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
55.7%
Decimal Number 44
 
21.7%
Space Separator 22
 
10.8%
Other Punctuation 16
 
7.9%
Open Punctuation 3
 
1.5%
Close Punctuation 3
 
1.5%
Dash Punctuation 1
 
0.5%
Math Symbol 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
5.3%
6
 
5.3%
6
 
5.3%
5
 
4.4%
5
 
4.4%
5
 
4.4%
4
 
3.5%
4
 
3.5%
3
 
2.7%
3
 
2.7%
Other values (47) 66
58.4%
Decimal Number
ValueCountFrequency (%)
0 12
27.3%
1 9
20.5%
2 8
18.2%
7 4
 
9.1%
3 4
 
9.1%
9 3
 
6.8%
8 2
 
4.5%
6 1
 
2.3%
4 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 11
68.8%
: 3
 
18.8%
, 2
 
12.5%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
55.7%
Common 90
44.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
5.3%
6
 
5.3%
6
 
5.3%
5
 
4.4%
5
 
4.4%
5
 
4.4%
4
 
3.5%
4
 
3.5%
3
 
2.7%
3
 
2.7%
Other values (47) 66
58.4%
Common
ValueCountFrequency (%)
22
24.4%
0 12
13.3%
. 11
12.2%
1 9
10.0%
2 8
 
8.9%
7 4
 
4.4%
3 4
 
4.4%
( 3
 
3.3%
9 3
 
3.3%
) 3
 
3.3%
Other values (7) 11
12.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
55.7%
ASCII 90
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
24.4%
0 12
13.3%
. 11
12.2%
1 9
10.0%
2 8
 
8.9%
7 4
 
4.4%
3 4
 
4.4%
( 3
 
3.3%
9 3
 
3.3%
) 3
 
3.3%
Other values (7) 11
12.2%
Hangul
ValueCountFrequency (%)
6
 
5.3%
6
 
5.3%
6
 
5.3%
5
 
4.4%
5
 
4.4%
5
 
4.4%
4
 
3.5%
4
 
3.5%
3
 
2.7%
3
 
2.7%
Other values (47) 66
58.4%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
20051019
 
1
20141231
 
1
20060728
 
1
20150331
 
1

Length

Max length8
Median length4
Mean length4.0016
Min length4

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9996
> 99.9%
20051019 1
 
< 0.1%
20141231 1
 
< 0.1%
20060728 1
 
< 0.1%
20150331 1
 
< 0.1%

Length

2024-04-17T12:27:49.916412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:50.008380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
20051019 1
 
< 0.1%
20141231 1
 
< 0.1%
20060728 1
 
< 0.1%
20150331 1
 
< 0.1%

조건부허가종료일자
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)100.0%
Missing9994
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean16757364
Minimum2
Maximum20170301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:50.104039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5015256
Q120063440
median20075818
Q320141155
95-th percentile20168033
Maximum20170301
Range20170299
Interquartile range (IQR)77714.75

Descriptive statistics

Standard deviation8209531.9
Coefficient of variation (CV)0.48990592
Kurtosis5.9993123
Mean16757364
Median Absolute Deviation (MAD)50106
Skewness-2.449309
Sum1.0054419 × 108
Variance6.7396415 × 1013
MonotonicityNot monotonic
2024-04-17T12:27:50.200142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1
 
< 0.1%
20061018 1
 
< 0.1%
20161230 1
 
< 0.1%
20070706 1
 
< 0.1%
20170301 1
 
< 0.1%
20080929 1
 
< 0.1%
(Missing) 9994
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
20061018 1
< 0.1%
20070706 1
< 0.1%
20080929 1
< 0.1%
20161230 1
< 0.1%
20170301 1
< 0.1%
ValueCountFrequency (%)
20170301 1
< 0.1%
20161230 1
< 0.1%
20080929 1
< 0.1%
20070706 1
< 0.1%
20061018 1
< 0.1%
2 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7716 
임대
2199 
자가
 
85

Length

Max length4
Median length4
Mean length3.5432
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7716
77.2%
임대 2199
 
22.0%
자가 85
 
0.9%

Length

2024-04-17T12:27:50.303717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:50.385942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7716
77.2%
임대 2199
 
22.0%
자가 85
 
0.9%

세탁기수
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5420 
<NA>
4579 
5
 
1

Length

Max length4
Median length1
Mean length2.3737
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 5420
54.2%
<NA> 4579
45.8%
5 1
 
< 0.1%

Length

2024-04-17T12:27:50.465146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:50.538683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5420
54.2%
na 4579
45.8%
5 1
 
< 0.1%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.3%
Missing7683
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean0.079413034
Minimum0
Maximum8
Zeros2177
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:50.607241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39062193
Coefficient of variation (CV)4.9188642
Kurtosis117.97989
Mean0.079413034
Median Absolute Deviation (MAD)0
Skewness8.8502866
Sum184
Variance0.15258549
MonotonicityNot monotonic
2024-04-17T12:27:50.687727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2177
 
21.8%
1 119
 
1.2%
2 11
 
0.1%
4 4
 
< 0.1%
3 3
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
(Missing) 7683
76.8%
ValueCountFrequency (%)
0 2177
21.8%
1 119
 
1.2%
2 11
 
0.1%
3 3
 
< 0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
5 2
 
< 0.1%
4 4
 
< 0.1%
3 3
 
< 0.1%
2 11
 
0.1%
1 119
 
1.2%
0 2177
21.8%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7698 
0
2281 
1
 
20
2
 
1

Length

Max length4
Median length4
Mean length3.3094
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7698
77.0%
0 2281
 
22.8%
1 20
 
0.2%
2 1
 
< 0.1%

Length

2024-04-17T12:27:50.787183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:50.890002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7698
77.0%
0 2281
 
22.8%
1 20
 
0.2%
2 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5170 
<NA>
4830 

Length

Max length4
Median length1
Mean length2.449
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 5170
51.7%
<NA> 4830
48.3%

Length

2024-04-17T12:27:50.976910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:27:51.054501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5170
51.7%
na 4830
48.3%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)0.4%
Missing4872
Missing (%)48.7%
Infinite0
Infinite (%)0.0%
Mean0.94052262
Minimum0
Maximum20
Zeros3473
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:27:51.124040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.812385
Coefficient of variation (CV)1.9269978
Kurtosis12.338406
Mean0.94052262
Median Absolute Deviation (MAD)0
Skewness2.8576338
Sum4823
Variance3.2847395
MonotonicityNot monotonic
2024-04-17T12:27:51.215572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3473
34.7%
2 533
 
5.3%
1 405
 
4.0%
3 266
 
2.7%
4 155
 
1.6%
5 104
 
1.0%
6 89
 
0.9%
7 47
 
0.5%
8 26
 
0.3%
10 12
 
0.1%
Other values (9) 18
 
0.2%
(Missing) 4872
48.7%
ValueCountFrequency (%)
0 3473
34.7%
1 405
 
4.0%
2 533
 
5.3%
3 266
 
2.7%
4 155
 
1.6%
5 104
 
1.0%
6 89
 
0.9%
7 47
 
0.5%
8 26
 
0.3%
9 7
 
0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 3
 
< 0.1%
11 1
 
< 0.1%
10 12
0.1%
9 7
0.1%

다중이용업소여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9998 
True
 
2
ValueCountFrequency (%)
False 9998
> 99.9%
True 2
 
< 0.1%
2024-04-17T12:27:51.294904image/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
1266812669미용업05_18_01_P32900003290000-204-2003-0003920030828<NA>3폐업2폐업20050718<NA><NA><NA>051 808699993.58614844부산광역시 부산진구 부전동 264-22번지<NA><NA>헤어카페준20030828000000I2018-08-31 23:59:59.0일반미용업387502.655662186462.259017미용업4<NA>2<NA><NA><NA><NA><NA><NA>N5<NA><NA><NA>임대<NA><NA><NA><NA><NA>N<NA>
65086509미용업05_18_01_P33500003350000-212-2016-0001320160926<NA>1영업/정상1영업<NA><NA><NA><NA>051 516 770749.23609842부산광역시 금정구 장전동 652-49부산광역시 금정구 온천장로 129, 2층 (장전동)46300블루밍영(young)20200818132750U2020-08-20 02:40:00.0기타389754.925451193431.957648미용업(피부)000000000N0<NA><NA><NA><NA>00001N<NA>
1646016461미용업05_18_01_P33400003340000-204-2005-0000720050105<NA>3폐업2폐업20051227<NA><NA><NA>051 207071014.80604802부산광역시 사하구 감천동 30-18번지<NA><NA>영미용실20050926000000I2018-08-31 23:59:59.0일반미용업382913.276313178497.055783미용업<NA><NA><NA><NA>11<NA><NA><NA>N3<NA><NA><NA>임대<NA><NA><NA><NA><NA>N<NA>
85748575미용업05_18_01_P32700003270000-212-2017-0000320170601<NA>1영업/정상1영업<NA><NA><NA><NA><NA>62.40601803부산광역시 동구 범일동 640-15번지부산광역시 동구 진시장로 21-4 (범일동, 3층)48735힐링카페20170615094902I2018-08-31 23:59:59.0피부미용업387595.818539183961.615876미용업(피부)403300000N4<NA><NA><NA>임대00002N<NA>
2197821979미용업05_18_01_P33900003390000-204-2006-0002620060705<NA>3폐업2폐업20100524<NA><NA><NA>051 322854638.00617841부산광역시 사상구 학장동 541번지<NA><NA>서준헤어샵20080711134028I2018-08-31 23:59:59.0일반미용업381079.343586184407.88759미용업4111<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
2010420105미용업05_18_01_P33300003330000-212-2013-0002720130903<NA>3폐업2폐업20171024<NA><NA><NA>051 521 044425.09612826부산광역시 해운대구 우동 1088-8번지 제1층부산광역시 해운대구 해운대로 399-1 (우동, 제1층)48063뷰티러쉬20171024164802I2018-08-31 23:59:59.0피부미용업394908.867518187557.937694미용업(피부)101<NA><NA><NA>000N6<NA><NA><NA><NA>0<NA><NA>02N<NA>
51905191미용업05_18_01_P33800003380000-211-1992-0000219921026<NA>1영업/정상1영업<NA><NA><NA><NA>051 758145024.39613825부산광역시 수영구 망미동 777번지 삼성@ 상가동 4층부산광역시 수영구 연수로310번길 74, 상가동동 4층 (망미동, 삼성@)48238태양20130610110557I2018-08-31 23:59:59.0일반미용업391420.713034187567.309029미용업(일반)404400000N3<NA><NA><NA><NA>0<NA><NA>00N<NA>
1994919950미용업05_18_01_P33800003380000-213-2008-0000120081217<NA>3폐업2폐업20130409<NA><NA><NA>051 753939933.03613830부산광역시 수영구 수영동 447-22번지부산광역시 수영구 수영로725번길 48, 3층 (수영동)48228민스킨 & 바디20130218133456I2018-08-31 23:59:59.0피부미용업392720.341025187539.405652미용업(종합)403300000N0<NA><NA><NA>임대0<NA><NA>03N<NA>
1115211153미용업05_18_01_P33400003340000-211-2018-0004820181024<NA>1영업/정상1영업<NA><NA><NA><NA><NA>22.30604833부산광역시 사하구 신평동 33-127번지부산광역시 사하구 다대로120번길 1, 1층 (신평동)49440해피염색방20200106112724U2020-01-08 02:40:00.0일반미용업379978.650291178887.571799미용업(일반)<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
1308713088미용업05_18_01_P33700003370000-204-1984-0088219841211<NA>3폐업2폐업20050913<NA><NA><NA>05170.09611831부산광역시 연제구 연산동 1124-3번지 T통B반<NA><NA>목화20050913000000I2018-08-31 23:59:59.0일반미용업389427.914338189624.47218미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부Unnamed: 50
99819982미용업05_18_01_P33700003370000-212-2016-0001720160429<NA>1영업/정상1영업<NA><NA><NA><NA><NA>24.50611816부산광역시 연제구 연산동 365-19번지부산광역시 연제구 안연로23번길 51, 1층 (연산동)47562LUCE(루케뷰티)20200429102945U2020-05-01 02:40:00.0피부미용업390980.369443189872.403845미용업(피부)0011<NA><NA>000N1<NA><NA><NA><NA>00001N<NA>
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