Overview

Dataset statistics

Number of variables51
Number of observations10000
Missing cells100444
Missing cells (%)19.7%
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

Description2021-01-04
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 (57.4%)Imbalance
사용끝지하층 is highly imbalanced (61.6%)Imbalance
조건부허가시작일자 is highly imbalanced (99.8%)Imbalance
조건부허가종료일자 is highly imbalanced (99.6%)Imbalance
남성종사자수 is highly imbalanced (59.8%)Imbalance
다중이용업소여부 is highly imbalanced (99.5%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 4928 (49.3%) 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 2978 (29.8%) missing valuesMissing
소재지우편번호 has 103 (1.0%) missing valuesMissing
도로명전체주소 has 3046 (30.5%) missing valuesMissing
도로명우편번호 has 3130 (31.3%) missing valuesMissing
좌표정보(x) has 287 (2.9%) missing valuesMissing
좌표정보(y) has 287 (2.9%) missing valuesMissing
건물지상층수 has 2182 (21.8%) missing valuesMissing
건물지하층수 has 3080 (30.8%) missing valuesMissing
사용시작지상층 has 2796 (28.0%) missing valuesMissing
사용끝지상층 has 4194 (41.9%) missing valuesMissing
발한실여부 has 161 (1.6%) missing valuesMissing
의자수 has 802 (8.0%) missing valuesMissing
조건부허가신고사유 has 9997 (> 99.9%) missing valuesMissing
여성종사자수 has 7636 (76.4%) missing valuesMissing
침대수 has 4774 (47.7%) missing valuesMissing
Unnamed: 50 has 10000 (100.0%) missing valuesMissing
폐업일자 is highly skewed (γ1 = -64.54498083)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 2841 (28.4%) zerosZeros
건물지하층수 has 4993 (49.9%) zerosZeros
사용시작지상층 has 1426 (14.3%) zerosZeros
사용끝지상층 has 1007 (10.1%) zerosZeros
의자수 has 1367 (13.7%) zerosZeros
여성종사자수 has 2225 (22.2%) zerosZeros
침대수 has 3561 (35.6%) zerosZeros

Reproduction

Analysis started2024-04-17 03:27:58.202338
Analysis finished2024-04-17 03:28:00.294268
Duration2.09 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%
Mean11524.488
Minimum5
Maximum23135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:00.349973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1126.75
Q15822.5
median11480.5
Q317272.25
95-th percentile21926.2
Maximum23135
Range23130
Interquartile range (IQR)11449.75

Descriptive statistics

Standard deviation6650.8064
Coefficient of variation (CV)0.57710213
Kurtosis-1.1903322
Mean11524.488
Median Absolute Deviation (MAD)5734
Skewness0.0036159814
Sum1.1524488 × 108
Variance44233226
MonotonicityNot monotonic
2024-04-17T12:28:00.451084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17250 1
 
< 0.1%
1364 1
 
< 0.1%
19165 1
 
< 0.1%
5398 1
 
< 0.1%
3187 1
 
< 0.1%
3916 1
 
< 0.1%
19413 1
 
< 0.1%
11941 1
 
< 0.1%
18140 1
 
< 0.1%
3158 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
21 1
< 0.1%
24 1
< 0.1%
26 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
30 1
< 0.1%
31 1
< 0.1%
ValueCountFrequency (%)
23135 1
< 0.1%
23132 1
< 0.1%
23128 1
< 0.1%
23124 1
< 0.1%
23121 1
< 0.1%
23120 1
< 0.1%
23117 1
< 0.1%
23116 1
< 0.1%
23113 1
< 0.1%
23105 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

2024-04-17T12:28:00.544009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:00.615745image/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

2024-04-17T12:28:00.685781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:00.751530image/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%
Mean3325008
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:00.825549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13290000
median3330000
Q33350000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation37804.799
Coefficient of variation (CV)0.011369837
Kurtosis-0.76722855
Mean3325008
Median Absolute Deviation (MAD)30000
Skewness0.050927095
Sum3.325008 × 1010
Variance1.4292029 × 109
MonotonicityNot monotonic
2024-04-17T12:28:00.939646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3330000 1280
12.8%
3290000 1264
12.6%
3340000 970
9.7%
3300000 861
8.6%
3310000 787
7.9%
3350000 730
7.3%
3380000 720
7.2%
3370000 717
7.2%
3320000 686
6.9%
3390000 424
 
4.2%
Other values (6) 1561
15.6%
ValueCountFrequency (%)
3250000 329
 
3.3%
3260000 290
 
2.9%
3270000 327
 
3.3%
3280000 312
 
3.1%
3290000 1264
12.6%
3300000 861
8.6%
3310000 787
7.9%
3320000 686
6.9%
3330000 1280
12.8%
3340000 970
9.7%
ValueCountFrequency (%)
3400000 185
 
1.8%
3390000 424
 
4.2%
3380000 720
7.2%
3370000 717
7.2%
3360000 118
 
1.2%
3350000 730
7.3%
3340000 970
9.7%
3330000 1280
12.8%
3320000 686
6.9%
3310000 787
7.9%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:28:01.115858image/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 row3270000-204-2005-00008
2nd row3330000-204-1666-00001
3rd row3330000-204-1999-00905
4th row3320000-204-1979-00414
5th row3250000-204-2000-00036
ValueCountFrequency (%)
3270000-204-2005-00008 1
 
< 0.1%
3370000-212-2011-00005 1
 
< 0.1%
3320000-215-2018-00017 1
 
< 0.1%
3350000-211-2019-00012 1
 
< 0.1%
3310000-204-1981-01146 1
 
< 0.1%
3250000-211-2014-00006 1
 
< 0.1%
3400000-212-2019-00005 1
 
< 0.1%
3260000-204-2003-00002 1
 
< 0.1%
3280000-204-2000-00012 1
 
< 0.1%
3330000-204-1993-00685 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-04-17T12:28:01.365016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 87766
39.9%
- 30000
 
13.6%
2 26144
 
11.9%
3 22069
 
10.0%
1 22023
 
10.0%
9 8744
 
4.0%
4 7900
 
3.6%
8 4444
 
2.0%
5 4212
 
1.9%
7 3721
 
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 87766
46.2%
2 26144
 
13.8%
3 22069
 
11.6%
1 22023
 
11.6%
9 8744
 
4.6%
4 7900
 
4.2%
8 4444
 
2.3%
5 4212
 
2.2%
7 3721
 
2.0%
6 2977
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87766
39.9%
- 30000
 
13.6%
2 26144
 
11.9%
3 22069
 
10.0%
1 22023
 
10.0%
9 8744
 
4.0%
4 7900
 
3.6%
8 4444
 
2.0%
5 4212
 
1.9%
7 3721
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87766
39.9%
- 30000
 
13.6%
2 26144
 
11.9%
3 22069
 
10.0%
1 22023
 
10.0%
9 8744
 
4.0%
4 7900
 
3.6%
8 4444
 
2.0%
5 4212
 
1.9%
7 3721
 
1.7%

인허가일자
Real number (ℝ)

Distinct5745
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20054447
Minimum19630110
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:01.660747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19630110
5-th percentile19840721
Q119980917
median20080208
Q320150821
95-th percentile20191016
Maximum20201231
Range571121
Interquartile range (IQR)169904

Descriptive statistics

Standard deviation114283.95
Coefficient of variation (CV)0.0056986839
Kurtosis0.1178339
Mean20054447
Median Absolute Deviation (MAD)80211
Skewness-0.80705113
Sum2.0054447 × 1011
Variance1.3060822 × 1010
MonotonicityNot monotonic
2024-04-17T12:28:01.761788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000415 25
 
0.2%
20000712 22
 
0.2%
20030224 20
 
0.2%
20030225 16
 
0.2%
20001114 11
 
0.1%
20180102 10
 
0.1%
19980930 9
 
0.1%
20110906 8
 
0.1%
20150908 8
 
0.1%
20000831 8
 
0.1%
Other values (5735) 9863
98.6%
ValueCountFrequency (%)
19630110 3
< 0.1%
19630130 1
 
< 0.1%
19631111 1
 
< 0.1%
19650113 1
 
< 0.1%
19650426 1
 
< 0.1%
19660301 1
 
< 0.1%
19660331 3
< 0.1%
19660416 1
 
< 0.1%
19660516 1
 
< 0.1%
19660604 1
 
< 0.1%
ValueCountFrequency (%)
20201231 1
 
< 0.1%
20201230 3
< 0.1%
20201229 3
< 0.1%
20201228 2
< 0.1%
20201224 1
 
< 0.1%
20201223 3
< 0.1%
20201222 1
 
< 0.1%
20201221 2
< 0.1%
20201218 2
< 0.1%
20201216 1
 
< 0.1%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

영업상태구분코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
5072 
1
4928 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 5072
50.7%
1 4928
49.3%

Length

2024-04-17T12:28:01.854584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:01.923121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5072
50.7%
1 4928
49.3%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5072 
영업/정상
4928 

Length

Max length5
Median length2
Mean length3.4784
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5072
50.7%
영업/정상 4928
49.3%

Length

2024-04-17T12:28:01.998985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:02.072071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5072
50.7%
영업/정상 4928
49.3%

상세영업상태코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5072 
1
4928 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5072
50.7%
1 4928
49.3%

Length

2024-04-17T12:28:02.162056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:02.229003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5072
50.7%
1 4928
49.3%

상세영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5072 
영업
4928 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5072
50.7%
영업 4928
49.3%

Length

2024-04-17T12:28:02.301737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:02.370055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5072
50.7%
영업 4928
49.3%

폐업일자
Real number (ℝ)

MISSING  SKEWED 

Distinct2901
Distinct (%)57.2%
Missing4928
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean20091254
Minimum2013102
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:02.459630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013102
5-th percentile19991030
Q120040226
median20091020
Q320151202
95-th percentile20200115
Maximum20201231
Range18188129
Interquartile range (IQR)110976.5

Descriptive statistics

Standard deviation262353.66
Coefficient of variation (CV)0.013058103
Kurtosis4448.3158
Mean20091254
Median Absolute Deviation (MAD)59816.5
Skewness-64.544981
Sum1.0190284 × 1011
Variance6.8829445 × 1010
MonotonicityNot monotonic
2024-04-17T12:28:02.599120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030227 174
 
1.7%
20050117 54
 
0.5%
20030226 44
 
0.4%
20050510 33
 
0.3%
20000712 30
 
0.3%
20010321 29
 
0.3%
20030101 25
 
0.2%
20030606 23
 
0.2%
20051222 23
 
0.2%
20000531 18
 
0.2%
Other values (2891) 4619
46.2%
(Missing) 4928
49.3%
ValueCountFrequency (%)
2013102 1
< 0.1%
19891116 1
< 0.1%
19900615 1
< 0.1%
19910430 1
< 0.1%
19911029 1
< 0.1%
19921012 1
< 0.1%
19930924 1
< 0.1%
19940623 1
< 0.1%
19940725 1
< 0.1%
19950208 1
< 0.1%
ValueCountFrequency (%)
20201231 1
 
< 0.1%
20201230 4
< 0.1%
20201229 2
 
< 0.1%
20201228 3
< 0.1%
20201224 5
0.1%
20201222 4
< 0.1%
20201221 1
 
< 0.1%
20201218 2
 
< 0.1%
20201217 2
 
< 0.1%
20201216 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 

Distinct6230
Distinct (%)88.7%
Missing2978
Missing (%)29.8%
Memory size156.2 KiB
2024-04-17T12:28:02.895769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.639134
Min length3

Characters and Unicode

Total characters74708
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

Unique6025 ?
Unique (%)85.8%

Sample

1st row4667144
2nd row051 7466830
3rd row051 5455827
4th row051
5th row051 2536880
ValueCountFrequency (%)
051 6554
41.8%
070 119
 
0.8%
747 34
 
0.2%
868 31
 
0.2%
852 27
 
0.2%
757 25
 
0.2%
701 25
 
0.2%
515 24
 
0.2%
746 24
 
0.2%
727 22
 
0.1%
Other values (6175) 8787
56.1%
2024-04-17T12:28:03.296048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12301
16.5%
0 11203
15.0%
1 11153
14.9%
8695
11.6%
2 5708
7.6%
7 4773
 
6.4%
3 4627
 
6.2%
6 4559
 
6.1%
4 4373
 
5.9%
8 4345
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66013
88.4%
Space Separator 8695
 
11.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12301
18.6%
0 11203
17.0%
1 11153
16.9%
2 5708
8.6%
7 4773
 
7.2%
3 4627
 
7.0%
6 4559
 
6.9%
4 4373
 
6.6%
8 4345
 
6.6%
9 2971
 
4.5%
Space Separator
ValueCountFrequency (%)
8695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74708
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12301
16.5%
0 11203
15.0%
1 11153
14.9%
8695
11.6%
2 5708
7.6%
7 4773
 
6.4%
3 4627
 
6.2%
6 4559
 
6.1%
4 4373
 
5.9%
8 4345
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12301
16.5%
0 11203
15.0%
1 11153
14.9%
8695
11.6%
2 5708
7.6%
7 4773
 
6.4%
3 4627
 
6.2%
6 4559
 
6.1%
4 4373
 
5.9%
8 4345
 
5.8%

소재지면적
Text

Distinct4015
Distinct (%)40.3%
Missing46
Missing (%)0.5%
Memory size156.2 KiB
2024-04-17T12:28:03.601758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.8995379
Min length3

Characters and Unicode

Total characters48770
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

Unique2493 ?
Unique (%)25.0%

Sample

1st row19.69
2nd row12.90
3rd row15.04
4th row22.63
5th row127.70
ValueCountFrequency (%)
00 816
 
8.2%
33.00 121
 
1.2%
30.00 63
 
0.6%
24.00 57
 
0.6%
26.40 49
 
0.5%
16.50 45
 
0.5%
15.00 44
 
0.4%
18.00 44
 
0.4%
16.00 42
 
0.4%
20.00 41
 
0.4%
Other values (4005) 8632
86.7%
2024-04-17T12:28:04.001772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9954
20.4%
0 8814
18.1%
2 4964
10.2%
1 4749
9.7%
3 3711
 
7.6%
4 3264
 
6.7%
5 3109
 
6.4%
6 3066
 
6.3%
8 2637
 
5.4%
9 2267
 
4.6%
Other values (2) 2235
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38815
79.6%
Other Punctuation 9955
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8814
22.7%
2 4964
12.8%
1 4749
12.2%
3 3711
9.6%
4 3264
 
8.4%
5 3109
 
8.0%
6 3066
 
7.9%
8 2637
 
6.8%
9 2267
 
5.8%
7 2234
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 9954
> 99.9%
, 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 48770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9954
20.4%
0 8814
18.1%
2 4964
10.2%
1 4749
9.7%
3 3711
 
7.6%
4 3264
 
6.7%
5 3109
 
6.4%
6 3066
 
6.3%
8 2637
 
5.4%
9 2267
 
4.6%
Other values (2) 2235
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9954
20.4%
0 8814
18.1%
2 4964
10.2%
1 4749
9.7%
3 3711
 
7.6%
4 3264
 
6.7%
5 3109
 
6.4%
6 3066
 
6.3%
8 2637
 
5.4%
9 2267
 
4.6%
Other values (2) 2235
 
4.6%

소재지우편번호
Real number (ℝ)

MISSING 

Distinct868
Distinct (%)8.8%
Missing103
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean610626.33
Minimum361856
Maximum619953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:04.115697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum361856
5-th percentile601817
Q1607813
median611817
Q3614803
95-th percentile617813
Maximum619953
Range258097
Interquartile range (IQR)6990

Descriptive statistics

Standard deviation5404.8509
Coefficient of variation (CV)0.0088513231
Kurtosis452.22099
Mean610626.33
Median Absolute Deviation (MAD)3032
Skewness-10.104176
Sum6.0433688 × 109
Variance29212414
MonotonicityNot monotonic
2024-04-17T12:28:04.220393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
604851 97
 
1.0%
609839 91
 
0.9%
614847 85
 
0.9%
608805 78
 
0.8%
616852 72
 
0.7%
612824 71
 
0.7%
614845 70
 
0.7%
608832 62
 
0.6%
612842 61
 
0.6%
614846 56
 
0.6%
Other values (858) 9154
91.5%
(Missing) 103
 
1.0%
ValueCountFrequency (%)
361856 1
 
< 0.1%
600012 3
 
< 0.1%
600013 4
 
< 0.1%
600016 4
 
< 0.1%
600017 4
 
< 0.1%
600021 1
 
< 0.1%
600022 3
 
< 0.1%
600023 3
 
< 0.1%
600025 6
 
0.1%
600031 17
0.2%
ValueCountFrequency (%)
619953 1
 
< 0.1%
619952 3
 
< 0.1%
619951 3
 
< 0.1%
619913 3
 
< 0.1%
619912 8
 
0.1%
619911 2
 
< 0.1%
619906 2
 
< 0.1%
619905 21
0.2%
619904 1
 
< 0.1%
619903 32
0.3%

소재지전체주소
Text

Distinct9292
Distinct (%)93.1%
Missing17
Missing (%)0.2%
Memory size156.2 KiB
2024-04-17T12:28:04.520231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length51
Mean length24.985776
Min length16

Characters and Unicode

Total characters249433
Distinct characters498
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

Unique8702 ?
Unique (%)87.2%

Sample

1st row부산광역시 동구 수정동 116-8번지
2nd row부산광역시 해운대구 중동 1392-69번지
3rd row부산광역시 해운대구 반송동 354-0번지 T통B반
4th row부산광역시 북구 구포동 915-31번지 T통B반
5th row부산광역시 중구 대청동2가 25-2번지 (3층)
ValueCountFrequency (%)
부산광역시 9982
 
21.2%
해운대구 1280
 
2.7%
부산진구 1256
 
2.7%
사하구 968
 
2.1%
t통b반 922
 
2.0%
동래구 861
 
1.8%
남구 787
 
1.7%
금정구 729
 
1.6%
수영구 720
 
1.5%
연제구 711
 
1.5%
Other values (10036) 28786
61.2%
2024-04-17T12:28:04.943511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37035
 
14.8%
산 12136
 
4.9%
부 12101
 
4.9%
동 12093
 
4.8%
1 12001
 
4.8%
광 10372
 
4.2%
구 10203
 
4.1%
시 10130
 
4.1%
역 9999
 
4.0%
지 9206
 
3.7%
Other values (488) 114157
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 147364
59.1%
Decimal Number 52557
 
21.1%
Space Separator 37035
 
14.8%
Dash Punctuation 9011
 
3.6%
Uppercase Letter 2348
 
0.9%
Close Punctuation 384
 
0.2%
Open Punctuation 384
 
0.2%
Other Punctuation 280
 
0.1%
Lowercase Letter 55
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 12136
 
8.2%
부 12101
 
8.2%
동 12093
 
8.2%
광 10372
 
7.0%
구 10203
 
6.9%
시 10130
 
6.9%
역 9999
 
6.8%
지 9206
 
6.2%
번 8779
 
6.0%
대 2388
 
1.6%
Other values (434) 49957
33.9%
Uppercase Letter
ValueCountFrequency (%)
B 999
42.5%
T 932
39.7%
A 73
 
3.1%
S 69
 
2.9%
K 57
 
2.4%
G 30
 
1.3%
H 22
 
0.9%
C 21
 
0.9%
I 21
 
0.9%
L 20
 
0.9%
Other values (12) 104
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
l 14
25.5%
e 13
23.6%
s 10
18.2%
i 8
14.5%
k 3
 
5.5%
a 1
 
1.8%
c 1
 
1.8%
h 1
 
1.8%
w 1
 
1.8%
v 1
 
1.8%
Other values (2) 2
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 12001
22.8%
2 7566
14.4%
3 5856
11.1%
4 4733
 
9.0%
0 4461
 
8.5%
5 4362
 
8.3%
6 3716
 
7.1%
7 3500
 
6.7%
8 3294
 
6.3%
9 3068
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 211
75.4%
@ 47
 
16.8%
. 12
 
4.3%
/ 8
 
2.9%
· 2
 
0.7%
Space Separator
ValueCountFrequency (%)
37035
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9011
100.0%
Close Punctuation
ValueCountFrequency (%)
) 384
100.0%
Open Punctuation
ValueCountFrequency (%)
( 384
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 147364
59.1%
Common 99666
40.0%
Latin 2403
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 12136
 
8.2%
부 12101
 
8.2%
동 12093
 
8.2%
광 10372
 
7.0%
구 10203
 
6.9%
시 10130
 
6.9%
역 9999
 
6.8%
지 9206
 
6.2%
번 8779
 
6.0%
대 2388
 
1.6%
Other values (434) 49957
33.9%
Latin
ValueCountFrequency (%)
B 999
41.6%
T 932
38.8%
A 73
 
3.0%
S 69
 
2.9%
K 57
 
2.4%
G 30
 
1.2%
H 22
 
0.9%
C 21
 
0.9%
I 21
 
0.9%
L 20
 
0.8%
Other values (24) 159
 
6.6%
Common
ValueCountFrequency (%)
37035
37.2%
1 12001
 
12.0%
- 9011
 
9.0%
2 7566
 
7.6%
3 5856
 
5.9%
4 4733
 
4.7%
0 4461
 
4.5%
5 4362
 
4.4%
6 3716
 
3.7%
7 3500
 
3.5%
Other values (10) 7425
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 147363
59.1%
ASCII 102067
40.9%
None 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37035
36.3%
1 12001
 
11.8%
- 9011
 
8.8%
2 7566
 
7.4%
3 5856
 
5.7%
4 4733
 
4.6%
0 4461
 
4.4%
5 4362
 
4.3%
6 3716
 
3.6%
7 3500
 
3.4%
Other values (43) 9826
 
9.6%
Hangul
ValueCountFrequency (%)
산 12136
 
8.2%
부 12101
 
8.2%
동 12093
 
8.2%
광 10372
 
7.0%
구 10203
 
6.9%
시 10130
 
6.9%
역 9999
 
6.8%
지 9206
 
6.2%
번 8779
 
6.0%
대 2388
 
1.6%
Other values (433) 49956
33.9%
None
ValueCountFrequency (%)
· 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅀ 1
100.0%

도로명전체주소
Text

MISSING 

Distinct6732
Distinct (%)96.8%
Missing3046
Missing (%)30.5%
Memory size156.2 KiB
2024-04-17T12:28:05.235769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length55
Mean length32.012079
Min length17

Characters and Unicode

Total characters222612
Distinct characters522
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

Unique6522 ?
Unique (%)93.8%

Sample

1st row부산광역시 중구 광복중앙로 34, 3층 (대청동2가)
2nd row부산광역시 영도구 번영길 57 (신선동1가)
3rd row부산광역시 사하구 제석로17번길 1, 2층 (하단동)
4th row부산광역시 수영구 광서로9번길 4, 1층 (광안동)
5th row부산광역시 수영구 수영로705번길 5, 5층 (수영동)
ValueCountFrequency (%)
부산광역시 6953
 
16.1%
1층 1668
 
3.9%
부산진구 1002
 
2.3%
2층 889
 
2.1%
해운대구 851
 
2.0%
동래구 629
 
1.5%
사하구 592
 
1.4%
남구 552
 
1.3%
수영구 523
 
1.2%
금정구 507
 
1.2%
Other values (5759) 28964
67.2%
2024-04-17T12:28:05.637253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36182
 
16.3%
동 9610
 
4.3%
1 9414
 
4.2%
부 8818
 
4.0%
산 8678
 
3.9%
광 7473
 
3.4%
시 7377
 
3.3%
구 7166
 
3.2%
역 6969
 
3.1%
) 6950
 
3.1%
Other values (512) 113975
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128587
57.8%
Space Separator 36182
 
16.3%
Decimal Number 36102
 
16.2%
Close Punctuation 6950
 
3.1%
Open Punctuation 6950
 
3.1%
Other Punctuation 6035
 
2.7%
Dash Punctuation 1168
 
0.5%
Uppercase Letter 542
 
0.2%
Lowercase Letter 59
 
< 0.1%
Math Symbol 37
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9610
 
7.5%
부 8818
 
6.9%
산 8678
 
6.7%
광 7473
 
5.8%
시 7377
 
5.7%
구 7166
 
5.6%
역 6969
 
5.4%
로 6849
 
5.3%
대 3475
 
2.7%
층 3430
 
2.7%
Other values (461) 58742
45.7%
Uppercase Letter
ValueCountFrequency (%)
B 104
19.2%
A 86
15.9%
S 83
15.3%
K 63
11.6%
H 28
 
5.2%
E 21
 
3.9%
I 21
 
3.9%
C 20
 
3.7%
G 18
 
3.3%
Y 15
 
2.8%
Other values (11) 83
15.3%
Decimal Number
ValueCountFrequency (%)
1 9414
26.1%
2 6187
17.1%
3 4085
11.3%
0 3453
 
9.6%
4 2843
 
7.9%
5 2472
 
6.8%
6 2181
 
6.0%
7 1956
 
5.4%
8 1815
 
5.0%
9 1696
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
e 15
25.4%
l 14
23.7%
s 11
18.6%
i 8
13.6%
k 5
 
8.5%
c 2
 
3.4%
a 1
 
1.7%
h 1
 
1.7%
w 1
 
1.7%
v 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 5984
99.2%
@ 37
 
0.6%
. 7
 
0.1%
/ 5
 
0.1%
· 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
36182
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6950
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6950
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1168
100.0%
Math Symbol
ValueCountFrequency (%)
~ 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128587
57.8%
Common 93424
42.0%
Latin 601
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9610
 
7.5%
부 8818
 
6.9%
산 8678
 
6.7%
광 7473
 
5.8%
시 7377
 
5.7%
구 7166
 
5.6%
역 6969
 
5.4%
로 6849
 
5.3%
대 3475
 
2.7%
층 3430
 
2.7%
Other values (461) 58742
45.7%
Latin
ValueCountFrequency (%)
B 104
17.3%
A 86
14.3%
S 83
13.8%
K 63
10.5%
H 28
 
4.7%
E 21
 
3.5%
I 21
 
3.5%
C 20
 
3.3%
G 18
 
3.0%
Y 15
 
2.5%
Other values (21) 142
23.6%
Common
ValueCountFrequency (%)
36182
38.7%
1 9414
 
10.1%
) 6950
 
7.4%
( 6950
 
7.4%
2 6187
 
6.6%
, 5984
 
6.4%
3 4085
 
4.4%
0 3453
 
3.7%
4 2843
 
3.0%
5 2472
 
2.6%
Other values (10) 8904
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128587
57.8%
ASCII 94023
42.2%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36182
38.5%
1 9414
 
10.0%
) 6950
 
7.4%
( 6950
 
7.4%
2 6187
 
6.6%
, 5984
 
6.4%
3 4085
 
4.3%
0 3453
 
3.7%
4 2843
 
3.0%
5 2472
 
2.6%
Other values (40) 9503
 
10.1%
Hangul
ValueCountFrequency (%)
동 9610
 
7.5%
부 8818
 
6.9%
산 8678
 
6.7%
광 7473
 
5.8%
시 7377
 
5.7%
구 7166
 
5.6%
역 6969
 
5.4%
로 6849
 
5.3%
대 3475
 
2.7%
층 3430
 
2.7%
Other values (461) 58742
45.7%
None
ValueCountFrequency (%)
· 2
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct1568
Distinct (%)22.8%
Missing3130
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean47813.301
Minimum28465
Maximum49525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:05.752620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28465
5-th percentile46242
Q147164
median47858
Q348491.75
95-th percentile49371
Maximum49525
Range21060
Interquartile range (IQR)1327.75

Descriptive statistics

Standard deviation976.84616
Coefficient of variation (CV)0.020430427
Kurtosis21.320158
Mean47813.301
Median Absolute Deviation (MAD)650
Skewness-1.1615976
Sum3.2847738 × 108
Variance954228.41
MonotonicityNot monotonic
2024-04-17T12:28:05.852530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46726 44
 
0.4%
48059 43
 
0.4%
48111 39
 
0.4%
48060 36
 
0.4%
48110 29
 
0.3%
46291 29
 
0.3%
47295 28
 
0.3%
48947 27
 
0.3%
48515 26
 
0.3%
46525 25
 
0.2%
Other values (1558) 6544
65.4%
(Missing) 3130
31.3%
ValueCountFrequency (%)
28465 1
 
< 0.1%
46002 1
 
< 0.1%
46007 5
 
0.1%
46008 20
0.2%
46010 2
 
< 0.1%
46011 1
 
< 0.1%
46012 3
 
< 0.1%
46013 7
 
0.1%
46014 1
 
< 0.1%
46015 11
0.1%
ValueCountFrequency (%)
49525 2
 
< 0.1%
49524 1
 
< 0.1%
49523 1
 
< 0.1%
49521 1
 
< 0.1%
49520 17
0.2%
49519 10
0.1%
49518 18
0.2%
49515 6
 
0.1%
49511 9
0.1%
49510 3
 
< 0.1%

사업장명
Text

Distinct8146
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:28:06.069172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length5.5903
Min length1

Characters and Unicode

Total characters55903
Distinct characters899
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7225 ?
Unique (%)72.2%

Sample

1st row강보경헤어나라
2nd row백화
3rd row모아
4th row제일
5th row예지테라피
ValueCountFrequency (%)
미용실 303
 
2.4%
헤어 237
 
1.9%
에스테틱 103
 
0.8%
헤어샵 96
 
0.8%
네일 94
 
0.7%
hair 68
 
0.5%
뷰티 52
 
0.4%
nail 48
 
0.4%
더 37
 
0.3%
by 32
 
0.3%
Other values (8184) 11484
91.5%
2024-04-17T12:28:06.414666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 3548
 
6.3%
헤 3456
 
6.2%
2557
 
4.6%
미 1895
 
3.4%
스 1366
 
2.4%
실 1163
 
2.1%
리 1154
 
2.1%
용 1135
 
2.0%
이 992
 
1.8%
일 834
 
1.5%
Other values (889) 37803
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46914
83.9%
Space Separator 2557
 
4.6%
Lowercase Letter 2437
 
4.4%
Uppercase Letter 2087
 
3.7%
Close Punctuation 599
 
1.1%
Open Punctuation 597
 
1.1%
Other Punctuation 372
 
0.7%
Decimal Number 286
 
0.5%
Dash Punctuation 40
 
0.1%
Connector Punctuation 7
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 3548
 
7.6%
헤 3456
 
7.4%
미 1895
 
4.0%
스 1366
 
2.9%
실 1163
 
2.5%
리 1154
 
2.5%
용 1135
 
2.4%
이 992
 
2.1%
일 834
 
1.8%
네 781
 
1.7%
Other values (802) 30590
65.2%
Lowercase Letter
ValueCountFrequency (%)
a 339
13.9%
i 286
11.7%
e 254
10.4%
n 190
 
7.8%
o 186
 
7.6%
l 165
 
6.8%
r 155
 
6.4%
h 137
 
5.6%
s 117
 
4.8%
y 116
 
4.8%
Other values (16) 492
20.2%
Uppercase Letter
ValueCountFrequency (%)
A 189
 
9.1%
N 183
 
8.8%
S 154
 
7.4%
I 141
 
6.8%
H 124
 
5.9%
O 122
 
5.8%
J 115
 
5.5%
M 114
 
5.5%
B 112
 
5.4%
L 107
 
5.1%
Other values (16) 726
34.8%
Other Punctuation
ValueCountFrequency (%)
& 145
39.0%
. 73
19.6%
, 45
 
12.1%
# 42
 
11.3%
' 34
 
9.1%
: 10
 
2.7%
· 7
 
1.9%
＆ 4
 
1.1%
? 3
 
0.8%
; 3
 
0.8%
Other values (5) 6
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 69
24.1%
2 63
22.0%
0 42
14.7%
3 24
 
8.4%
9 24
 
8.4%
5 21
 
7.3%
7 15
 
5.2%
4 12
 
4.2%
6 8
 
2.8%
8 8
 
2.8%
Close Punctuation
ValueCountFrequency (%)
) 597
99.7%
] 2
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 595
99.7%
[ 2
 
0.3%
Space Separator
ValueCountFrequency (%)
2557
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46874
83.8%
Latin 4524
 
8.1%
Common 4465
 
8.0%
Han 40
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 3548
 
7.6%
헤 3456
 
7.4%
미 1895
 
4.0%
스 1366
 
2.9%
실 1163
 
2.5%
리 1154
 
2.5%
용 1135
 
2.4%
이 992
 
2.1%
일 834
 
1.8%
네 781
 
1.7%
Other values (783) 30550
65.2%
Latin
ValueCountFrequency (%)
a 339
 
7.5%
i 286
 
6.3%
e 254
 
5.6%
n 190
 
4.2%
A 189
 
4.2%
o 186
 
4.1%
N 183
 
4.0%
l 165
 
3.6%
r 155
 
3.4%
S 154
 
3.4%
Other values (42) 2423
53.6%
Common
ValueCountFrequency (%)
2557
57.3%
) 597
 
13.4%
( 595
 
13.3%
& 145
 
3.2%
. 73
 
1.6%
1 69
 
1.5%
2 63
 
1.4%
, 45
 
1.0%
0 42
 
0.9%
# 42
 
0.9%
Other values (25) 237
 
5.3%
Han
ValueCountFrequency (%)
美 18
45.0%
間 2
 
5.0%
休 2
 
5.0%
空 2
 
5.0%
愛 2
 
5.0%
裕 1
 
2.5%
場 1
 
2.5%
水 1
 
2.5%
手 1
 
2.5%
高 1
 
2.5%
Other values (9) 9
22.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46873
83.8%
ASCII 8976
 
16.1%
CJK 39
 
0.1%
None 13
 
< 0.1%
Compat Jamo 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 3548
 
7.6%
헤 3456
 
7.4%
미 1895
 
4.0%
스 1366
 
2.9%
실 1163
 
2.5%
리 1154
 
2.5%
용 1135
 
2.4%
이 992
 
2.1%
일 834
 
1.8%
네 781
 
1.7%
Other values (782) 30549
65.2%
ASCII
ValueCountFrequency (%)
2557
28.5%
) 597
 
6.7%
( 595
 
6.6%
a 339
 
3.8%
i 286
 
3.2%
e 254
 
2.8%
n 190
 
2.1%
A 189
 
2.1%
o 186
 
2.1%
N 183
 
2.0%
Other values (74) 3600
40.1%
CJK
ValueCountFrequency (%)
美 18
46.2%
間 2
 
5.1%
休 2
 
5.1%
空 2
 
5.1%
愛 2
 
5.1%
裕 1
 
2.6%
場 1
 
2.6%
水 1
 
2.6%
手 1
 
2.6%
高 1
 
2.6%
Other values (8) 8
20.5%
None
ValueCountFrequency (%)
· 7
53.8%
＆ 4
30.8%
＃ 2
 
15.4%
Compat Jamo
ValueCountFrequency (%)
ㅍ 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
李 1
100.0%

최종수정시점
Real number (ℝ)

Distinct8084
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0128598 × 1013
Minimum1.9990125 × 1013
Maximum2.0201231 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:06.523177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990125 × 1013
5-th percentile2.0011205 × 1013
Q12.0060718 × 1013
median2.0150407 × 1013
Q32.0190624 × 1013
95-th percentile2.0201126 × 1013
Maximum2.0201231 × 1013
Range2.1110617 × 1011
Interquartile range (IQR)1.2990616 × 1011

Descriptive statistics

Standard deviation6.7734546 × 1010
Coefficient of variation (CV)0.0033650902
Kurtosis-1.1183326
Mean2.0128598 × 1013
Median Absolute Deviation (MAD)4.9712985 × 1010
Skewness-0.57569345
Sum2.0128598 × 1017
Variance4.5879687 × 1021
MonotonicityNot monotonic
2024-04-17T12:28:06.625573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030402000000 116
 
1.2%
20020823000000 45
 
0.4%
20030403000000 39
 
0.4%
20001209000000 36
 
0.4%
20040719000000 34
 
0.3%
19990429000000 34
 
0.3%
20030627000000 31
 
0.3%
20040827000000 28
 
0.3%
20061113000000 27
 
0.3%
20040102000000 24
 
0.2%
Other values (8074) 9586
95.9%
ValueCountFrequency (%)
19990125000000 4
 
< 0.1%
19990126000000 6
 
0.1%
19990211000000 1
 
< 0.1%
19990222000000 2
 
< 0.1%
19990223000000 4
 
< 0.1%
19990224000000 6
 
0.1%
19990225000000 6
 
0.1%
19990303000000 6
 
0.1%
19990304000000 14
0.1%
19990305000000 15
0.1%
ValueCountFrequency (%)
20201231173319 1
< 0.1%
20201231164153 1
< 0.1%
20201231155348 1
< 0.1%
20201231145557 1
< 0.1%
20201231105024 1
< 0.1%
20201231093602 1
< 0.1%
20201231093459 1
< 0.1%
20201231093247 1
< 0.1%
20201231092849 1
< 0.1%
20201230172050 1
< 0.1%

데이터갱신구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
7314 
U
2686 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 7314
73.1%
U 2686
 
26.9%

Length

2024-04-17T12:28:06.718733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:06.787809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7314
73.1%
u 2686
 
26.9%

데이터갱신일자
Date

Distinct877
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-01-02 02:40:00
2024-04-17T12:28:06.862665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T12:28:06.965382image/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
7057 
피부미용업
1805 
네일아트업
885 
메이크업업
 
170
기타
 
81

Length

Max length6
Median length5
Mean length4.9759
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 7057
70.6%
피부미용업 1805
 
18.1%
네일아트업 885
 
8.8%
메이크업업 170
 
1.7%
기타 81
 
0.8%
미용업 기타 2
 
< 0.1%

Length

2024-04-17T12:28:07.070215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:07.395318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 7057
70.6%
피부미용업 1805
 
18.0%
네일아트업 885
 
8.8%
메이크업업 170
 
1.7%
기타 83
 
0.8%
미용업 2
 
< 0.1%

좌표정보(x)
Real number (ℝ)

MISSING 

Distinct7505
Distinct (%)77.3%
Missing287
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean388276.99
Minimum241128.92
Maximum407739.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:07.487516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum241128.92
5-th percentile379697.63
Q1384456.21
median388670.52
Q3391842.69
95-th percentile397608.72
Maximum407739.05
Range166610.12
Interquartile range (IQR)7386.471

Descriptive statistics

Standard deviation5501.1177
Coefficient of variation (CV)0.014168024
Kurtosis52.415609
Mean388276.99
Median Absolute Deviation (MAD)3564.6821
Skewness-2.0473823
Sum3.7713344 × 109
Variance30262296
MonotonicityNot monotonic
2024-04-17T12:28:07.591436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
380398.062015138 22
 
0.2%
392474.578116018 19
 
0.2%
398237.363461482 18
 
0.2%
389816.233000769 17
 
0.2%
393239.237933586 16
 
0.2%
398491.352089522 14
 
0.1%
392446.756833326 14
 
0.1%
394201.81589403 13
 
0.1%
383934.836497921 12
 
0.1%
391411.212179843 12
 
0.1%
Other values (7495) 9556
95.6%
(Missing) 287
 
2.9%
ValueCountFrequency (%)
241128.922467 1
 
< 0.1%
367051.926419356 2
< 0.1%
367062.132343737 1
 
< 0.1%
367088.901392071 1
 
< 0.1%
367108.112280274 1
 
< 0.1%
367177.362435871 1
 
< 0.1%
367193.583454597 1
 
< 0.1%
367195.063042763 3
< 0.1%
367226.483222999 1
 
< 0.1%
370718.68095386 1
 
< 0.1%
ValueCountFrequency (%)
407739.046710947 1
< 0.1%
407652.048377874 1
< 0.1%
407446.00678665 1
< 0.1%
407161.891842701 1
< 0.1%
407121.882187494 1
< 0.1%
406982.053033795 1
< 0.1%
405396.620775536 1
< 0.1%
405172.859381319 1
< 0.1%
403996.140697028 1
< 0.1%
403472.718473818 1
< 0.1%

좌표정보(y)
Real number (ℝ)

MISSING 

Distinct7506
Distinct (%)77.3%
Missing287
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean186970.09
Minimum173961.75
Maximum349970.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:07.695602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173961.75
5-th percentile178254.24
Q1183188.19
median187140.47
Q3190770.08
95-th percentile195745.62
Maximum349970.06
Range176008.3
Interquartile range (IQR)7581.8862

Descriptive statistics

Standard deviation5758.1253
Coefficient of variation (CV)0.03079704
Kurtosis65.815745
Mean186970.09
Median Absolute Deviation (MAD)3699.498
Skewness2.5077655
Sum1.8160405 × 109
Variance33156007
MonotonicityNot monotonic
2024-04-17T12:28:07.813956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175314.286676535 22
 
0.2%
183052.21115244 19
 
0.2%
187720.511056894 18
 
0.2%
193329.605871168 17
 
0.2%
188619.149645081 16
 
0.2%
187644.220019205 14
 
0.1%
184648.922305219 14
 
0.1%
187760.860165772 13
 
0.1%
192378.532914385 12
 
0.1%
184052.409245407 12
 
0.1%
Other values (7496) 9556
95.6%
(Missing) 287
 
2.9%
ValueCountFrequency (%)
173961.753544726 1
 
< 0.1%
173961.914773076 3
< 0.1%
173969.719902491 2
< 0.1%
173982.009447475 1
 
< 0.1%
174016.551235181 1
 
< 0.1%
174031.935803657 2
< 0.1%
174035.700224564 3
< 0.1%
174046.5147373 1
 
< 0.1%
174092.497564851 1
 
< 0.1%
174101.406639044 3
< 0.1%
ValueCountFrequency (%)
349970.057043 1
 
< 0.1%
206512.517255249 2
 
< 0.1%
206353.855586145 4
< 0.1%
206298.919203021 2
 
< 0.1%
206248.235800687 1
 
< 0.1%
206246.675982691 1
 
< 0.1%
206242.308287228 1
 
< 0.1%
206184.609573703 5
0.1%
206175.365141713 1
 
< 0.1%
206174.795860476 1
 
< 0.1%

위생업태명
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미용업
3949 
미용업(일반)
2981 
미용업(피부)
974 
일반미용업
420 
미용업(손톱ㆍ발톱)
 
386
Other values (26)
1290 

Length

Max length31
Median length28
Mean length6.1019
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 3949
39.5%
미용업(일반) 2981
29.8%
미용업(피부) 974
 
9.7%
일반미용업 420
 
4.2%
미용업(손톱ㆍ발톱) 386
 
3.9%
미용업(종합) 310
 
3.1%
피부미용업 185
 
1.8%
네일미용업 115
 
1.1%
미용업(일반), 미용업(손톱ㆍ발톱) 77
 
0.8%
미용업(피부), 미용업(손톱ㆍ발톱) 75
 
0.8%
Other values (21) 528
 
5.3%

Length

2024-04-17T12:28:07.950521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 4064
37.9%
미용업(일반 3170
29.5%
미용업(피부 1172
 
10.9%
미용업(손톱ㆍ발톱 662
 
6.2%
일반미용업 456
 
4.2%
미용업(종합 310
 
2.9%
미용업(화장ㆍ분장 302
 
2.8%
피부미용업 239
 
2.2%
네일미용업 188
 
1.8%
화장ㆍ분장 115
 
1.1%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct44
Distinct (%)0.6%
Missing2182
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean2.7097723
Minimum0
Maximum61
Zeros2841
Zeros (%)28.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:08.059922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile8
Maximum61
Range61
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3518125
Coefficient of variation (CV)1.6059698
Kurtosis38.602838
Mean2.7097723
Median Absolute Deviation (MAD)2
Skewness5.1411636
Sum21185
Variance18.938272
MonotonicityNot monotonic
2024-04-17T12:28:08.159768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 2841
28.4%
2 1308
13.1%
3 1067
 
10.7%
4 953
 
9.5%
5 513
 
5.1%
1 437
 
4.4%
6 202
 
2.0%
7 85
 
0.9%
8 70
 
0.7%
9 65
 
0.7%
Other values (34) 277
 
2.8%
(Missing) 2182
21.8%
ValueCountFrequency (%)
0 2841
28.4%
1 437
 
4.4%
2 1308
13.1%
3 1067
 
10.7%
4 953
 
9.5%
5 513
 
5.1%
6 202
 
2.0%
7 85
 
0.9%
8 70
 
0.7%
9 65
 
0.7%
ValueCountFrequency (%)
61 1
 
< 0.1%
51 2
 
< 0.1%
49 2
 
< 0.1%
47 4
< 0.1%
43 3
< 0.1%
42 6
0.1%
41 1
 
< 0.1%
39 2
 
< 0.1%
38 5
0.1%
37 7
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.2%
Missing3080
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean0.40722543
Minimum0
Maximum24
Zeros4993
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:08.250915image/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 deviation0.96314802
Coefficient of variation (CV)2.365147
Kurtosis94.76741
Mean0.40722543
Median Absolute Deviation (MAD)0
Skewness6.7221074
Sum2818
Variance0.9276541
MonotonicityNot monotonic
2024-04-17T12:28:08.335072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 4993
49.9%
1 1544
 
15.4%
2 184
 
1.8%
3 80
 
0.8%
5 57
 
0.6%
4 31
 
0.3%
6 14
 
0.1%
7 8
 
0.1%
10 3
 
< 0.1%
8 2
 
< 0.1%
Other values (4) 4
 
< 0.1%
(Missing) 3080
30.8%
ValueCountFrequency (%)
0 4993
49.9%
1 1544
 
15.4%
2 184
 
1.8%
3 80
 
0.8%
4 31
 
0.3%
5 57
 
0.6%
6 14
 
0.1%
7 8
 
0.1%
8 2
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
18 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
10 3
 
< 0.1%
8 2
 
< 0.1%
7 8
 
0.1%
6 14
 
0.1%
5 57
0.6%
4 31
0.3%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)0.2%
Missing2796
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean1.3349528
Minimum0
Maximum37
Zeros1426
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:08.415971image/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 deviation1.4080528
Coefficient of variation (CV)1.0547585
Kurtosis123.55472
Mean1.3349528
Median Absolute Deviation (MAD)0
Skewness6.6318254
Sum9617
Variance1.9826126
MonotonicityNot monotonic
2024-04-17T12:28:08.501282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3609
36.1%
0 1426
 
14.3%
2 1382
 
13.8%
3 464
 
4.6%
4 133
 
1.3%
5 64
 
0.6%
6 45
 
0.4%
7 40
 
0.4%
8 14
 
0.1%
9 9
 
0.1%
Other values (6) 18
 
0.2%
(Missing) 2796
28.0%
ValueCountFrequency (%)
0 1426
 
14.3%
1 3609
36.1%
2 1382
 
13.8%
3 464
 
4.6%
4 133
 
1.3%
5 64
 
0.6%
6 45
 
0.4%
7 40
 
0.4%
8 14
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
37 2
 
< 0.1%
16 1
 
< 0.1%
13 3
 
< 0.1%
12 2
 
< 0.1%
11 4
 
< 0.1%
10 6
 
0.1%
9 9
 
0.1%
8 14
 
0.1%
7 40
0.4%
6 45
0.4%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)0.3%
Missing4194
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean1.3448157
Minimum0
Maximum20
Zeros1007
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:08.589322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2864951
Coefficient of variation (CV)0.956633
Kurtosis27.102121
Mean1.3448157
Median Absolute Deviation (MAD)0
Skewness3.6407758
Sum7808
Variance1.6550696
MonotonicityNot monotonic
2024-04-17T12:28:08.681810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3058
30.6%
2 1120
 
11.2%
0 1007
 
10.1%
3 382
 
3.8%
4 99
 
1.0%
5 46
 
0.5%
6 32
 
0.3%
7 28
 
0.3%
8 10
 
0.1%
10 9
 
0.1%
Other values (7) 15
 
0.1%
(Missing) 4194
41.9%
ValueCountFrequency (%)
0 1007
 
10.1%
1 3058
30.6%
2 1120
 
11.2%
3 382
 
3.8%
4 99
 
1.0%
5 46
 
0.5%
6 32
 
0.3%
7 28
 
0.3%
8 10
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%
10 9
 
0.1%
9 6
 
0.1%
8 10
 
0.1%
7 28
0.3%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5798 
0
4018 
1
 
161
2
 
21
3
 
1

Length

Max length4
Median length4
Mean length2.7394
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5798
58.0%
0 4018
40.2%
1 161
 
1.6%
2 21
 
0.2%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-17T12:28:08.785684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:08.868087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5798
58.0%
0 4018
40.2%
1 161
 
1.6%
2 21
 
0.2%
3 1
 
< 0.1%
4 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6861 
0
2992 
1
 
128
2
 
17
109
 
1

Length

Max length4
Median length4
Mean length3.0585
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6861
68.6%
0 2992
29.9%
1 128
 
1.3%
2 17
 
0.2%
109 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-17T12:28:08.963422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:09.048012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6861
68.6%
0 2992
29.9%
1 128
 
1.3%
2 17
 
0.2%
109 1
 
< 0.1%
4 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6534 
<NA>
3466 

Length

Max length4
Median length1
Mean length2.0398
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6534
65.3%
<NA> 3466
34.7%

Length

2024-04-17T12:28:09.137658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:09.210895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6534
65.3%
na 3466
34.7%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6534 
<NA>
3466 

Length

Max length4
Median length1
Mean length2.0398
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6534
65.3%
<NA> 3466
34.7%

Length

2024-04-17T12:28:09.290786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:09.367420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6534
65.3%
na 3466
34.7%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6534 
<NA>
3466 

Length

Max length4
Median length1
Mean length2.0398
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6534
65.3%
<NA> 3466
34.7%

Length

2024-04-17T12:28:09.452108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:09.531470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6534
65.3%
na 3466
34.7%

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%
2024-04-17T12:28:09.586818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

의자수
Real number (ℝ)

MISSING  ZEROS 

Distinct32
Distinct (%)0.3%
Missing802
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean3.3303979
Minimum0
Maximum36
Zeros1367
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:09.663801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum36
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.817043
Coefficient of variation (CV)0.84585779
Kurtosis16.28924
Mean3.3303979
Median Absolute Deviation (MAD)1
Skewness2.9085713
Sum30633
Variance7.9357313
MonotonicityNot monotonic
2024-04-17T12:28:09.786871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3 3145
31.4%
2 1429
14.3%
4 1418
14.2%
0 1367
13.7%
5 570
 
5.7%
6 359
 
3.6%
1 200
 
2.0%
8 165
 
1.7%
7 140
 
1.4%
10 105
 
1.1%
Other values (22) 300
 
3.0%
(Missing) 802
 
8.0%
ValueCountFrequency (%)
0 1367
13.7%
1 200
 
2.0%
2 1429
14.3%
3 3145
31.4%
4 1418
14.2%
5 570
 
5.7%
6 359
 
3.6%
7 140
 
1.4%
8 165
 
1.7%
9 68
 
0.7%
ValueCountFrequency (%)
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
24 7
0.1%
23 3
< 0.1%
22 2
 
< 0.1%

조건부허가신고사유
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing9997
Missing (%)> 99.9%
Memory size156.2 KiB
2024-04-17T12:28:09.929983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length44
Mean length45.666667
Min length10

Characters and Unicode

Total characters137
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row가설건축물 존치기간(2004.01.08부터 2005.01.07)까지 영업신고수리
2nd row가설건축물 존치기간
3rd row기한부영업신고기간:2006.10.19~2008.10.18 3. 이후 영업신고기간이 만료되어 연장신청을 하지않을경우 영업신고가 자동으로 말소됨
ValueCountFrequency (%)
가설건축물 2
 
12.5%
존치기간(2004.01.08부터 1
 
6.2%
2005.01.07)까지 1
 
6.2%
영업신고수리 1
 
6.2%
존치기간 1
 
6.2%
기한부영업신고기간:2006.10.19~2008.10.18 1
 
6.2%
3 1
 
6.2%
이후 1
 
6.2%
영업신고기간이 1
 
6.2%
만료되어 1
 
6.2%
Other values (5) 5
31.2%
2024-04-17T12:28:10.163757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
13.1%
0 14
 
10.2%
. 9
 
6.6%
1 6
 
4.4%
신 5
 
3.6%
기 5
 
3.6%
고 4
 
2.9%
업 4
 
2.9%
영 4
 
2.9%
간 4
 
2.9%
Other values (47) 64
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73
53.3%
Decimal Number 33
24.1%
Space Separator 18
 
13.1%
Other Punctuation 10
 
7.3%
Open Punctuation 1
 
0.7%
Close Punctuation 1
 
0.7%
Math Symbol 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 5
 
6.8%
기 5
 
6.8%
고 4
 
5.5%
업 4
 
5.5%
영 4
 
5.5%
간 4
 
5.5%
가 3
 
4.1%
이 2
 
2.7%
치 2
 
2.7%
부 2
 
2.7%
Other values (31) 38
52.1%
Decimal Number
ValueCountFrequency (%)
0 14
42.4%
1 6
18.2%
2 4
 
12.1%
8 3
 
9.1%
4 1
 
3.0%
5 1
 
3.0%
7 1
 
3.0%
6 1
 
3.0%
9 1
 
3.0%
3 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 9
90.0%
: 1
 
10.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73
53.3%
Common 64
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 5
 
6.8%
기 5
 
6.8%
고 4
 
5.5%
업 4
 
5.5%
영 4
 
5.5%
간 4
 
5.5%
가 3
 
4.1%
이 2
 
2.7%
치 2
 
2.7%
부 2
 
2.7%
Other values (31) 38
52.1%
Common
ValueCountFrequency (%)
18
28.1%
0 14
21.9%
. 9
14.1%
1 6
 
9.4%
2 4
 
6.2%
8 3
 
4.7%
( 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
7 1
 
1.6%
Other values (6) 6
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73
53.3%
ASCII 64
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
28.1%
0 14
21.9%
. 9
14.1%
1 6
 
9.4%
2 4
 
6.2%
8 3
 
4.7%
( 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
7 1
 
1.6%
Other values (6) 6
 
9.4%
Hangul
ValueCountFrequency (%)
신 5
 
6.8%
기 5
 
6.8%
고 4
 
5.5%
업 4
 
5.5%
영 4
 
5.5%
간 4
 
5.5%
가 3
 
4.1%
이 2
 
2.7%
치 2
 
2.7%
부 2
 
2.7%
Other values (31) 38
52.1%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9997 
20040119
 
1
20141231
 
1
20051019
 
1

Length

Max length8
Median length4
Mean length4.0012
Min length4

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> 9997
> 99.9%
20040119 1
 
< 0.1%
20141231 1
 
< 0.1%
20051019 1
 
< 0.1%

Length

2024-04-17T12:28:10.276869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:10.389494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9997
> 99.9%
20040119 1
 
< 0.1%
20141231 1
 
< 0.1%
20051019 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9993 
2
 
4
20050107
 
1
20161230
 
1
20061018
 
1

Length

Max length8
Median length4
Mean length4
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> 9993
99.9%
2 4
 
< 0.1%
20050107 1
 
< 0.1%
20161230 1
 
< 0.1%
20061018 1
 
< 0.1%

Length

2024-04-17T12:28:10.478826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:10.557902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9993
99.9%
2 4
 
< 0.1%
20050107 1
 
< 0.1%
20161230 1
 
< 0.1%
20061018 1
 
< 0.1%

건물소유구분명
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7628 
임대
2293 
자가
 
79

Length

Max length4
Median length4
Mean length3.5256
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7628
76.3%
임대 2293
 
22.9%
자가 79
 
0.8%

Length

2024-04-17T12:28:10.644188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:10.726201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7628
76.3%
임대 2293
 
22.9%
자가 79
 
0.8%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5482 
<NA>
4518 

Length

Max length4
Median length1
Mean length2.3554
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 5482
54.8%
<NA> 4518
45.2%

Length

2024-04-17T12:28:10.810539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:10.883597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5482
54.8%
na 4518
45.2%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.3%
Missing7636
Missing (%)76.4%
Infinite0
Infinite (%)0.0%
Mean0.079949239
Minimum0
Maximum8
Zeros2225
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:10.960323image/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 deviation0.42253973
Coefficient of variation (CV)5.2851001
Kurtosis154.11615
Mean0.079949239
Median Absolute Deviation (MAD)0
Skewness10.389973
Sum189
Variance0.17853982
MonotonicityNot monotonic
2024-04-17T12:28:11.079195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 2225
 
22.2%
1 118
 
1.2%
2 10
 
0.1%
3 5
 
0.1%
4 2
 
< 0.1%
8 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 7636
76.4%
ValueCountFrequency (%)
0 2225
22.2%
1 118
 
1.2%
2 10
 
0.1%
3 5
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 5
 
0.1%
2 10
 
0.1%
1 118
 
1.2%
0 2225
22.2%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7648 
0
2333 
1
 
18
2
 
1

Length

Max length4
Median length4
Mean length3.2944
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7648
76.5%
0 2333
 
23.3%
1 18
 
0.2%
2 1
 
< 0.1%

Length

2024-04-17T12:28:11.195325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:11.285770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7648
76.5%
0 2333
 
23.3%
1 18
 
0.2%
2 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5241 
<NA>
4759 

Length

Max length4
Median length1
Mean length2.4277
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 5241
52.4%
<NA> 4759
47.6%

Length

2024-04-17T12:28:11.380261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:28:11.457865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5241
52.4%
na 4759
47.6%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)0.4%
Missing4774
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean0.96134711
Minimum0
Maximum60
Zeros3561
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:28:11.536991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum60
Range60
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0027824
Coefficient of variation (CV)2.0833083
Kurtosis150.51523
Mean0.96134711
Median Absolute Deviation (MAD)0
Skewness6.9284569
Sum5024
Variance4.0111372
MonotonicityNot monotonic
2024-04-17T12:28:11.636948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3561
35.6%
2 527
 
5.3%
1 384
 
3.8%
3 291
 
2.9%
4 147
 
1.5%
5 108
 
1.1%
6 86
 
0.9%
7 59
 
0.6%
8 24
 
0.2%
9 14
 
0.1%
Other values (9) 25
 
0.2%
(Missing) 4774
47.7%
ValueCountFrequency (%)
0 3561
35.6%
1 384
 
3.8%
2 527
 
5.3%
3 291
 
2.9%
4 147
 
1.5%
5 108
 
1.1%
6 86
 
0.9%
7 59
 
0.6%
8 24
 
0.2%
9 14
 
0.1%
ValueCountFrequency (%)
60 1
 
< 0.1%
20 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 3
 
< 0.1%
11 1
 
< 0.1%
10 14
0.1%
9 14
0.1%

다중이용업소여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9996 
True
 
4
ValueCountFrequency (%)
False 9996
> 99.9%
True 4
 
< 0.1%
2024-04-17T12:28:11.731773image/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
1724917250미용업05_18_01_P32700003270000-204-2005-0000820050830<NA>3폐업2폐업20100810<NA><NA><NA>466714419.69601812부산광역시 동구 수정동 116-8번지<NA><NA>강보경헤어나라20050830000000I2018-08-31 23:59:59.0일반미용업386355.254335182793.73435미용업4111<NA><NA><NA><NA><NA>N3<NA><NA><NA>임대<NA><NA><NA><NA><NA>N<NA>
1340913410미용업05_18_01_P33300003330000-204-1666-0000119660628<NA>3폐업2폐업20091113<NA><NA><NA>051 746683012.90612847부산광역시 해운대구 중동 1392-69번지<NA><NA>백화20091106111357I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
2102621027미용업05_18_01_P33300003330000-204-1999-0090519990520<NA>3폐업2폐업20040304<NA><NA><NA>051 545582715.04612807부산광역시 해운대구 반송동 354-0번지 T통B반<NA><NA>모아20040305000000I2018-08-31 23:59:59.0일반미용업395907.835567194516.468744미용업31<NA>1<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
1469614697미용업05_18_01_P33200003320000-204-1979-0041419790418<NA>3폐업2폐업20050117<NA><NA><NA>05122.63616806부산광역시 북구 구포동 915-31번지 T통B반<NA><NA>제일19990420000000I2018-08-31 23:59:59.0피부미용업381739.315588190552.030888미용업000<NA>0<NA>000N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
2040920410미용업05_18_01_P32500003250000-204-2000-0003620000219<NA>3폐업2폐업20131216<NA><NA><NA>051 2536880127.70600092부산광역시 중구 대청동2가 25-2번지 (3층)부산광역시 중구 광복중앙로 34, 3층 (대청동2가)48949예지테라피20130212144044I2018-08-31 23:59:59.0일반미용업385127.995081180033.573038미용업303300000N4<NA><NA><NA>임대0<NA><NA>00N<NA>
52105211미용업05_18_01_P32800003280000-211-1998-0000119980112<NA>1영업/정상1영업<NA><NA><NA><NA>051 414858617.88606051부산광역시 영도구 신선동1가 4-4번지부산광역시 영도구 번영길 57 (신선동1가)49059리라헤어라인20200406184746U2020-04-08 02:40:00.0일반미용업386537.239836178957.700974미용업(일반)<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
33533354미용업05_18_01_P33400003340000-212-2019-0000820190612<NA>1영업/정상1영업<NA><NA><NA><NA><NA>46.00604848부산광역시 사하구 하단동 620-8번지부산광역시 사하구 제석로17번길 1, 2층 (하단동)49408가인에스테틱20190612135540U2019-06-14 02:40:00.0피부미용업382048.919446179748.886147미용업(피부)002<NA><NA><NA>000N2<NA><NA><NA><NA>00002N<NA>
10741075미용업05_18_01_P33800003380000-222-2017-0000520170925<NA>1영업/정상1영업<NA><NA><NA><NA><NA>27.96613807부산광역시 수영구 광안동 691-7번지부산광역시 수영구 광서로9번길 4, 1층 (광안동)48247도로시네일20170925122003I2018-08-31 23:59:59.0네일아트업392485.237526187028.322641미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)0011<NA><NA>000N4<NA><NA><NA><NA>00001N<NA>
1036610367미용업05_18_01_P33800003380000-211-2003-0002420030812<NA>1영업/정상1영업<NA><NA><NA><NA>051 756567764.14613830부산광역시 수영구 수영동 445-26부산광역시 수영구 수영로705번길 5, 5층 (수영동)48228가발스토리20201006140218I2020-10-08 00:23:10.0일반미용업392746.856046187489.875425일반미용업200100000N2<NA><NA><NA>임대0<NA><NA>00N<NA>
90009001미용업05_18_01_P33600003360000-211-2014-0001120141226<NA>1영업/정상1영업<NA><NA><NA><NA>051 202 202325.80618814부산광역시 강서구 명지동 3237-1번지 1층부산광역시 강서구 명지오션시티3로 31, 1층 (명지동)46765리얼미헤어20170925142144I2018-08-31 23:59:59.0일반미용업373486.741258177476.378092미용업(일반)4011<NA><NA>000N2<NA><NA><NA><NA>00000N<NA>
번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부Unnamed: 50
2303923040미용업05_18_01_P33000003300000-204-1999-0131819991227<NA>3폐업2폐업20030605<NA><NA><NA>051 506577621.39607840부산광역시 동래구 온천동 1600-0번지 화신동영A상가206호<NA><NA>머리하기좋은날20030605000000I2018-08-31 23:59:59.0일반미용업387522.460942191450.032892미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
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