Overview

Dataset statistics

Number of variables50
Number of observations10000
Missing cells91994
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory440.0 B

Variable types

Numeric19
Categorical18
Text6
Unsupported4
DateTime1
Boolean2

Dataset

Description6270000_대구광역시_05_18_01_P_미용업_1월
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=DMI_0000092350&dataSetDetailId=DDI_0000092347&provdMethod=FILE

Alerts

개방서비스명 has constant value ""Constant
개방서비스아이디 has constant value ""Constant
업태구분명 is highly imbalanced (54.0%)Imbalance
발한실여부 is highly imbalanced (99.1%)Imbalance
조건부허가시작일자 is highly imbalanced (99.8%)Imbalance
조건부허가종료일자 is highly imbalanced (99.8%)Imbalance
남성종사자수 is highly imbalanced (61.1%)Imbalance
다중이용업소여부 is highly imbalanced (98.7%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 4513 (45.1%) 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 3610 (36.1%) missing valuesMissing
도로명전체주소 has 2844 (28.4%) missing valuesMissing
도로명우편번호 has 2871 (28.7%) missing valuesMissing
좌표정보(X) has 252 (2.5%) missing valuesMissing
좌표정보(Y) has 252 (2.5%) missing valuesMissing
건물지상층수 has 1331 (13.3%) missing valuesMissing
건물지하층수 has 2555 (25.6%) missing valuesMissing
사용시작지상층 has 1871 (18.7%) missing valuesMissing
사용끝지상층 has 2318 (23.2%) missing valuesMissing
사용시작지하층 has 3996 (40.0%) missing valuesMissing
사용끝지하층 has 4535 (45.4%) missing valuesMissing
발한실여부 has 123 (1.2%) missing valuesMissing
의자수 has 818 (8.2%) missing valuesMissing
조건부허가신고사유 has 9998 (> 99.9%) missing valuesMissing
여성종사자수 has 6123 (61.2%) missing valuesMissing
침대수 has 3856 (38.6%) missing valuesMissing
폐업일자 is highly skewed (γ1 = -23.17865531)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
소재지면적 has 933 (9.3%) zerosZeros
건물지상층수 has 1935 (19.4%) zerosZeros
건물지하층수 has 5154 (51.5%) zerosZeros
사용시작지상층 has 1056 (10.6%) zerosZeros
사용끝지상층 has 912 (9.1%) zerosZeros
사용시작지하층 has 5845 (58.5%) zerosZeros
사용끝지하층 has 5322 (53.2%) zerosZeros
의자수 has 1262 (12.6%) zerosZeros
여성종사자수 has 1515 (15.2%) zerosZeros
침대수 has 4529 (45.3%) zerosZeros

Reproduction

Analysis started2023-12-10 20:43:51.435958
Analysis finished2023-12-10 20:43:54.830590
Duration3.39 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%
Mean9865.6909
Minimum1
Maximum19846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:43:54.948849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile911.95
Q14880.75
median9911
Q314828.5
95-th percentile18803.05
Maximum19846
Range19845
Interquartile range (IQR)9947.75

Descriptive statistics

Standard deviation5741.4048
Coefficient of variation (CV)0.58195669
Kurtosis-1.1982811
Mean9865.6909
Median Absolute Deviation (MAD)4974.5
Skewness-0.0030393381
Sum98656909
Variance32963729
MonotonicityNot monotonic
2023-12-11T05:43:55.198796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5666 1
 
< 0.1%
3437 1
 
< 0.1%
15306 1
 
< 0.1%
8828 1
 
< 0.1%
1066 1
 
< 0.1%
6038 1
 
< 0.1%
11157 1
 
< 0.1%
712 1
 
< 0.1%
5839 1
 
< 0.1%
8057 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
19846 1
< 0.1%
19844 1
< 0.1%
19843 1
< 0.1%
19842 1
< 0.1%
19840 1
< 0.1%
19838 1
< 0.1%
19837 1
< 0.1%
19834 1
< 0.1%
19833 1
< 0.1%
19832 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

2023-12-11T05:43:55.432760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:43:55.815392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 10000
100.0%

개방서비스아이디
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

2023-12-11T05:43:56.003901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:43:56.291537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
05_18_01_p 10000
100.0%

개방자치단체코드
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3447945
Minimum3410000
Maximum3480000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:43:56.507061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3410000
5-th percentile3410000
Q13430000
median3450000
Q33470000
95-th percentile3480000
Maximum3480000
Range70000
Interquartile range (IQR)40000

Descriptive statistics

Standard deviation21213.014
Coefficient of variation (CV)0.0061523645
Kurtosis-1.1223807
Mean3447945
Median Absolute Deviation (MAD)20000
Skewness-0.37472127
Sum3.447945 × 1010
Variance4.4999197 × 108
MonotonicityNot monotonic
2023-12-11T05:43:56.781132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3470000 2229
22.3%
3460000 1789
17.9%
3450000 1577
15.8%
3420000 1315
13.2%
3430000 890
 
8.9%
3410000 851
 
8.5%
3440000 805
 
8.1%
3480000 544
 
5.4%
ValueCountFrequency (%)
3410000 851
 
8.5%
3420000 1315
13.2%
3430000 890
 
8.9%
3440000 805
 
8.1%
3450000 1577
15.8%
3460000 1789
17.9%
3470000 2229
22.3%
3480000 544
 
5.4%
ValueCountFrequency (%)
3480000 544
 
5.4%
3470000 2229
22.3%
3460000 1789
17.9%
3450000 1577
15.8%
3440000 805
 
8.1%
3430000 890
 
8.9%
3420000 1315
13.2%
3410000 851
 
8.5%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T05:43:57.138798image/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 row3430000-211-2003-00108
2nd row3470000-211-2002-00003
3rd row3440000-204-2001-00017
4th row3470000-211-2009-00004
5th row3460000-212-2010-00044
ValueCountFrequency (%)
3430000-211-2003-00108 1
 
< 0.1%
3410000-204-2006-00001 1
 
< 0.1%
3410000-219-2020-00001 1
 
< 0.1%
3430000-211-2020-00015 1
 
< 0.1%
3470000-212-2015-00012 1
 
< 0.1%
3450000-211-1996-00024 1
 
< 0.1%
3410000-221-2018-00002 1
 
< 0.1%
3440000-204-1984-00006 1
 
< 0.1%
3460000-204-1999-00013 1
 
< 0.1%
3420000-211-2008-00007 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-11T05:43:57.636525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 88630
40.3%
- 30000
 
13.6%
2 25289
 
11.5%
1 23358
 
10.6%
4 16179
 
7.4%
3 14302
 
6.5%
9 5615
 
2.6%
7 4716
 
2.1%
5 4444
 
2.0%
6 4405
 
2.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88630
46.6%
2 25289
 
13.3%
1 23358
 
12.3%
4 16179
 
8.5%
3 14302
 
7.5%
9 5615
 
3.0%
7 4716
 
2.5%
5 4444
 
2.3%
6 4405
 
2.3%
8 3062
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 88630
40.3%
- 30000
 
13.6%
2 25289
 
11.5%
1 23358
 
10.6%
4 16179
 
7.4%
3 14302
 
6.5%
9 5615
 
2.6%
7 4716
 
2.1%
5 4444
 
2.0%
6 4405
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88630
40.3%
- 30000
 
13.6%
2 25289
 
11.5%
1 23358
 
10.6%
4 16179
 
7.4%
3 14302
 
6.5%
9 5615
 
2.6%
7 4716
 
2.1%
5 4444
 
2.0%
6 4405
 
2.0%

인허가일자
Real number (ℝ)

Distinct5001
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20083650
Minimum19630817
Maximum20220128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:43:57.865767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19630817
5-th percentile19940912
Q120020328
median20100322
Q320160930
95-th percentile20201228
Maximum20220128
Range589311
Interquartile range (IQR)140602.5

Descriptive statistics

Standard deviation92925.507
Coefficient of variation (CV)0.0046269232
Kurtosis0.53852173
Mean20083650
Median Absolute Deviation (MAD)70094.5
Skewness-0.74164847
Sum2.008365 × 1011
Variance8.6351498 × 109
MonotonicityNot monotonic
2023-12-11T05:43:58.091543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030823 116
 
1.2%
19961228 93
 
0.9%
20030821 76
 
0.8%
19961210 62
 
0.6%
20030708 33
 
0.3%
19970210 29
 
0.3%
19961224 22
 
0.2%
19970305 17
 
0.2%
19970221 16
 
0.2%
19961230 16
 
0.2%
Other values (4991) 9520
95.2%
ValueCountFrequency (%)
19630817 1
< 0.1%
19640721 1
< 0.1%
19650201 1
< 0.1%
19650520 1
< 0.1%
19651201 1
< 0.1%
19690319 1
< 0.1%
19690712 1
< 0.1%
19690718 1
< 0.1%
19690808 1
< 0.1%
19700420 1
< 0.1%
ValueCountFrequency (%)
20220128 4
< 0.1%
20220126 2
< 0.1%
20220125 1
 
< 0.1%
20220124 2
< 0.1%
20220119 1
 
< 0.1%
20220118 4
< 0.1%
20220117 2
< 0.1%
20220114 2
< 0.1%
20220113 4
< 0.1%
20220111 3
< 0.1%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
5487 
1
4513 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 5487
54.9%
1 4513
45.1%

Length

2023-12-11T05:43:58.292323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:43:58.451380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5487
54.9%
1 4513
45.1%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5487 
영업/정상
4513 

Length

Max length5
Median length2
Mean length3.3539
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5487
54.9%
영업/정상 4513
45.1%

Length

2023-12-11T05:43:58.629637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:43:58.813849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5487
54.9%
영업/정상 4513
45.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5487 
1
4513 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5487
54.9%
1 4513
45.1%

Length

2023-12-11T05:43:58.965134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:43:59.099607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5487
54.9%
1 4513
45.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5487 
영업
4513 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5487
54.9%
영업 4513
45.1%

Length

2023-12-11T05:43:59.238268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:43:59.381507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5487
54.9%
영업 4513
45.1%

폐업일자
Real number (ℝ)

MISSING  SKEWED 

Distinct3034
Distinct (%)55.3%
Missing4513
Missing (%)45.1%
Infinite0
Infinite (%)0.0%
Mean20078460
Minimum200001
Maximum20220128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:43:59.542084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200001
5-th percentile20030821
Q120051025
median20110922
Q320170918
95-th percentile20210428
Maximum20220128
Range20020127
Interquartile range (IQR)119893

Descriptive statistics

Standard deviation851750.37
Coefficient of variation (CV)0.0424211
Kurtosis538.35861
Mean20078460
Median Absolute Deviation (MAD)59912
Skewness-23.178655
Sum1.1017051 × 1011
Variance7.254787 × 1011
MonotonicityNot monotonic
2023-12-11T05:43:59.804899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20031231 117
 
1.2%
20040731 66
 
0.7%
20031128 64
 
0.6%
20031229 29
 
0.3%
20031230 28
 
0.3%
20040417 24
 
0.2%
20050930 22
 
0.2%
20031226 21
 
0.2%
20031222 21
 
0.2%
20031204 19
 
0.2%
Other values (3024) 5076
50.8%
(Missing) 4513
45.1%
ValueCountFrequency (%)
200001 1
< 0.1%
200010 1
< 0.1%
200104 1
< 0.1%
200110 1
< 0.1%
200203 1
< 0.1%
200204 1
< 0.1%
200209 1
< 0.1%
200212 1
< 0.1%
200302 1
< 0.1%
200310 1
< 0.1%
ValueCountFrequency (%)
20220128 1
< 0.1%
20220127 2
< 0.1%
20220124 2
< 0.1%
20220121 2
< 0.1%
20220119 1
< 0.1%
20220117 1
< 0.1%
20220114 2
< 0.1%
20220113 2
< 0.1%
20220112 1
< 0.1%
20220111 2
< 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 

Distinct6099
Distinct (%)95.4%
Missing3610
Missing (%)36.1%
Memory size156.2 KiB
2023-12-11T05:44:00.350284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.10626
Min length3

Characters and Unicode

Total characters70969
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

Unique5827 ?
Unique (%)91.2%

Sample

1st row053 5581362
2nd row053 5231004
3rd row053 6269549
4th row053 7669336
5th row053 791 0578
ValueCountFrequency (%)
053 5658
39.6%
070 119
 
0.8%
765 35
 
0.2%
782 32
 
0.2%
781 31
 
0.2%
741 31
 
0.2%
767 29
 
0.2%
322 28
 
0.2%
791 28
 
0.2%
766 27
 
0.2%
Other values (6077) 8266
57.9%
2023-12-11T05:44:01.082767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12152
17.1%
3 10597
14.9%
0 9239
13.0%
7975
11.2%
2 5370
7.6%
6 5207
7.3%
7 4824
 
6.8%
4 4159
 
5.9%
1 3959
 
5.6%
9 3774
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62994
88.8%
Space Separator 7975
 
11.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12152
19.3%
3 10597
16.8%
0 9239
14.7%
2 5370
8.5%
6 5207
8.3%
7 4824
 
7.7%
4 4159
 
6.6%
1 3959
 
6.3%
9 3774
 
6.0%
8 3713
 
5.9%
Space Separator
ValueCountFrequency (%)
7975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70969
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12152
17.1%
3 10597
14.9%
0 9239
13.0%
7975
11.2%
2 5370
7.6%
6 5207
7.3%
7 4824
 
6.8%
4 4159
 
5.9%
1 3959
 
5.6%
9 3774
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12152
17.1%
3 10597
14.9%
0 9239
13.0%
7975
11.2%
2 5370
7.6%
6 5207
7.3%
7 4824
 
6.8%
4 4159
 
5.9%
1 3959
 
5.6%
9 3774
 
5.3%

소재지면적
Real number (ℝ)

ZEROS 

Distinct3460
Distinct (%)34.7%
Missing41
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean37.988078
Minimum0
Maximum923.4
Zeros933
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:01.315402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.8
median29.7
Q343.46
95-th percentile105.407
Maximum923.4
Range923.4
Interquartile range (IQR)23.66

Descriptive statistics

Standard deviation39.067516
Coefficient of variation (CV)1.0284152
Kurtosis50.406432
Mean37.988078
Median Absolute Deviation (MAD)11.43
Skewness4.8646098
Sum378323.27
Variance1526.2708
MonotonicityNot monotonic
2023-12-11T05:44:01.505305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 933
 
9.3%
33.0 189
 
1.9%
30.0 136
 
1.4%
26.4 92
 
0.9%
18.0 88
 
0.9%
24.0 81
 
0.8%
20.0 81
 
0.8%
15.0 79
 
0.8%
21.0 78
 
0.8%
19.8 72
 
0.7%
Other values (3450) 8130
81.3%
ValueCountFrequency (%)
0.0 933
9.3%
1.0 1
 
< 0.1%
2.25 1
 
< 0.1%
3.0 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
4.0 1
 
< 0.1%
4.09 1
 
< 0.1%
4.29 1
 
< 0.1%
4.44 1
 
< 0.1%
ValueCountFrequency (%)
923.4 1
< 0.1%
514.19 1
< 0.1%
507.7 1
< 0.1%
493.7 1
< 0.1%
460.97 1
< 0.1%
452.77 1
< 0.1%
401.63 1
< 0.1%
400.0 1
< 0.1%
397.0 1
< 0.1%
396.56 1
< 0.1%

소재지우편번호
Real number (ℝ)

Distinct644
Distinct (%)6.5%
Missing87
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean704346.99
Minimum621901
Maximum711893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:01.693520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum621901
5-th percentile700806
Q1702804
median704400
Q3705826
95-th percentile706853
Maximum711893
Range89992
Interquartile range (IQR)3022

Descriptive statistics

Standard deviation2665.2694
Coefficient of variation (CV)0.0037840289
Kurtosis92.959817
Mean704346.99
Median Absolute Deviation (MAD)1571
Skewness-2.2376641
Sum6.9821917 × 109
Variance7103660.9
MonotonicityNot monotonic
2023-12-11T05:44:01.887036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
704080 126
 
1.3%
706170 123
 
1.2%
702886 86
 
0.9%
706800 83
 
0.8%
702040 81
 
0.8%
704060 79
 
0.8%
701847 77
 
0.8%
704834 76
 
0.8%
704837 75
 
0.8%
700411 75
 
0.8%
Other values (634) 9032
90.3%
(Missing) 87
 
0.9%
ValueCountFrequency (%)
621901 1
 
< 0.1%
700010 5
 
0.1%
700020 4
 
< 0.1%
700030 3
 
< 0.1%
700040 9
 
0.1%
700050 1
 
< 0.1%
700060 11
 
0.1%
700070 37
0.4%
700082 7
 
0.1%
700091 4
 
< 0.1%
ValueCountFrequency (%)
711893 1
 
< 0.1%
711891 24
0.2%
711874 14
 
0.1%
711873 15
 
0.1%
711872 6
 
0.1%
711871 1
 
< 0.1%
711864 5
 
0.1%
711863 2
 
< 0.1%
711852 58
0.6%
711851 4
 
< 0.1%
Distinct9067
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T05:44:02.639590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length53
Mean length25.2293
Min length16

Characters and Unicode

Total characters252293
Distinct characters439
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

Unique8264 ?
Unique (%)82.6%

Sample

1st row대구광역시 서구 내당동 417-9번지
2nd row대구광역시 달서구 용산동 931-5번지 (지상2층)
3rd row대구광역시 남구 대명동 1890-9번지
4th row대구광역시 달서구 감삼동 146-5
5th row대구광역시 수성구 상동 62-4번지
ValueCountFrequency (%)
대구광역시 9998
 
21.4%
달서구 2226
 
4.8%
수성구 1789
 
3.8%
북구 1577
 
3.4%
동구 1315
 
2.8%
서구 889
 
1.9%
중구 852
 
1.8%
남구 806
 
1.7%
1층 640
 
1.4%
대명동 555
 
1.2%
Other values (9608) 26177
55.9%
2023-12-11T05:44:03.742182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45792
18.2%
19720
 
7.8%
1 13274
 
5.3%
12069
 
4.8%
11099
 
4.4%
10217
 
4.0%
10062
 
4.0%
10017
 
4.0%
9534
 
3.8%
- 8310
 
3.3%
Other values (429) 102199
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 140475
55.7%
Decimal Number 55634
 
22.1%
Space Separator 45792
 
18.2%
Dash Punctuation 8310
 
3.3%
Close Punctuation 773
 
0.3%
Open Punctuation 773
 
0.3%
Uppercase Letter 316
 
0.1%
Other Punctuation 180
 
0.1%
Lowercase Letter 32
 
< 0.1%
Math Symbol 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
19720
14.0%
12069
 
8.6%
11099
 
7.9%
10217
 
7.3%
10062
 
7.2%
10017
 
7.1%
9534
 
6.8%
7980
 
5.7%
3473
 
2.5%
3448
 
2.5%
Other values (371) 42856
30.5%
Uppercase Letter
ValueCountFrequency (%)
A 74
23.4%
B 44
13.9%
S 29
 
9.2%
T 20
 
6.3%
K 18
 
5.7%
P 16
 
5.1%
E 15
 
4.7%
L 15
 
4.7%
C 14
 
4.4%
H 14
 
4.4%
Other values (12) 57
18.0%
Lowercase Letter
ValueCountFrequency (%)
e 15
46.9%
a 5
 
15.6%
n 2
 
6.2%
h 1
 
3.1%
b 1
 
3.1%
o 1
 
3.1%
l 1
 
3.1%
m 1
 
3.1%
c 1
 
3.1%
i 1
 
3.1%
Other values (3) 3
 
9.4%
Decimal Number
ValueCountFrequency (%)
1 13274
23.9%
0 7882
14.2%
2 7049
12.7%
3 5201
 
9.3%
4 4462
 
8.0%
5 4234
 
7.6%
6 3611
 
6.5%
8 3370
 
6.1%
7 3320
 
6.0%
9 3231
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 141
78.3%
/ 14
 
7.8%
@ 11
 
6.1%
. 11
 
6.1%
· 1
 
0.6%
! 1
 
0.6%
1
 
0.6%
Math Symbol
ValueCountFrequency (%)
~ 7
87.5%
+ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
45792
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8310
100.0%
Close Punctuation
ValueCountFrequency (%)
) 773
100.0%
Open Punctuation
ValueCountFrequency (%)
( 773
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 140475
55.7%
Common 111470
44.2%
Latin 348
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
19720
14.0%
12069
 
8.6%
11099
 
7.9%
10217
 
7.3%
10062
 
7.2%
10017
 
7.1%
9534
 
6.8%
7980
 
5.7%
3473
 
2.5%
3448
 
2.5%
Other values (371) 42856
30.5%
Latin
ValueCountFrequency (%)
A 74
21.3%
B 44
12.6%
S 29
 
8.3%
T 20
 
5.7%
K 18
 
5.2%
P 16
 
4.6%
E 15
 
4.3%
e 15
 
4.3%
L 15
 
4.3%
C 14
 
4.0%
Other values (25) 88
25.3%
Common
ValueCountFrequency (%)
45792
41.1%
1 13274
 
11.9%
- 8310
 
7.5%
0 7882
 
7.1%
2 7049
 
6.3%
3 5201
 
4.7%
4 4462
 
4.0%
5 4234
 
3.8%
6 3611
 
3.2%
8 3370
 
3.0%
Other values (13) 8285
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 140475
55.7%
ASCII 111816
44.3%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45792
41.0%
1 13274
 
11.9%
- 8310
 
7.4%
0 7882
 
7.0%
2 7049
 
6.3%
3 5201
 
4.7%
4 4462
 
4.0%
5 4234
 
3.8%
6 3611
 
3.2%
8 3370
 
3.0%
Other values (46) 8631
 
7.7%
Hangul
ValueCountFrequency (%)
19720
14.0%
12069
 
8.6%
11099
 
7.9%
10217
 
7.3%
10062
 
7.2%
10017
 
7.1%
9534
 
6.8%
7980
 
5.7%
3473
 
2.5%
3448
 
2.5%
Other values (371) 42856
30.5%
None
ValueCountFrequency (%)
· 1
50.0%
1
50.0%

도로명전체주소
Text

MISSING 

Distinct6808
Distinct (%)95.1%
Missing2844
Missing (%)28.4%
Memory size156.2 KiB
2023-12-11T05:44:04.459343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length54
Mean length31.002655
Min length18

Characters and Unicode

Total characters221855
Distinct characters463
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

Unique6498 ?
Unique (%)90.8%

Sample

1st row대구광역시 서구 당산로51길 8 (내당동)
2nd row대구광역시 달서구 용산서로 10 (용산동,(지상2층))
3rd row대구광역시 달서구 감삼2길 10, 1층 (감삼동)
4th row대구광역시 수성구 수성로 120 (상동)
5th row대구광역시 수성구 달구벌대로 3197 (매호동)
ValueCountFrequency (%)
대구광역시 7154
 
16.1%
1층 2487
 
5.6%
달서구 1654
 
3.7%
수성구 1336
 
3.0%
북구 1095
 
2.5%
동구 855
 
1.9%
중구 668
 
1.5%
서구 553
 
1.2%
남구 544
 
1.2%
2층 508
 
1.1%
Other values (5001) 27590
62.1%
2023-12-11T05:44:05.349214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37293
 
16.8%
14835
 
6.7%
1 10868
 
4.9%
10098
 
4.6%
9327
 
4.2%
7390
 
3.3%
7273
 
3.3%
7175
 
3.2%
) 7131
 
3.2%
( 7131
 
3.2%
Other values (453) 103334
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 125376
56.5%
Space Separator 37293
 
16.8%
Decimal Number 36881
 
16.6%
Close Punctuation 7131
 
3.2%
Open Punctuation 7131
 
3.2%
Other Punctuation 6292
 
2.8%
Dash Punctuation 1352
 
0.6%
Uppercase Letter 361
 
0.2%
Lowercase Letter 29
 
< 0.1%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14835
 
11.8%
10098
 
8.1%
9327
 
7.4%
7390
 
5.9%
7273
 
5.8%
7175
 
5.7%
6984
 
5.6%
4139
 
3.3%
4110
 
3.3%
3208
 
2.6%
Other values (399) 50837
40.5%
Uppercase Letter
ValueCountFrequency (%)
A 92
25.5%
B 53
14.7%
S 33
 
9.1%
K 19
 
5.3%
D 17
 
4.7%
H 17
 
4.7%
C 17
 
4.7%
E 16
 
4.4%
T 16
 
4.4%
L 13
 
3.6%
Other values (11) 68
18.8%
Lowercase Letter
ValueCountFrequency (%)
e 16
55.2%
a 4
 
13.8%
h 1
 
3.4%
n 1
 
3.4%
l 1
 
3.4%
c 1
 
3.4%
i 1
 
3.4%
d 1
 
3.4%
m 1
 
3.4%
u 1
 
3.4%
Decimal Number
ValueCountFrequency (%)
1 10868
29.5%
2 5500
14.9%
3 3824
 
10.4%
0 3694
 
10.0%
4 2811
 
7.6%
5 2782
 
7.5%
6 2230
 
6.0%
7 1987
 
5.4%
8 1670
 
4.5%
9 1515
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 6264
99.6%
@ 15
 
0.2%
. 7
 
0.1%
/ 3
 
< 0.1%
· 2
 
< 0.1%
! 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 8
88.9%
+ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
37293
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7131
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1352
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 125376
56.5%
Common 96089
43.3%
Latin 390
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14835
 
11.8%
10098
 
8.1%
9327
 
7.4%
7390
 
5.9%
7273
 
5.8%
7175
 
5.7%
6984
 
5.6%
4139
 
3.3%
4110
 
3.3%
3208
 
2.6%
Other values (399) 50837
40.5%
Latin
ValueCountFrequency (%)
A 92
23.6%
B 53
13.6%
S 33
 
8.5%
K 19
 
4.9%
D 17
 
4.4%
H 17
 
4.4%
C 17
 
4.4%
E 16
 
4.1%
e 16
 
4.1%
T 16
 
4.1%
Other values (22) 94
24.1%
Common
ValueCountFrequency (%)
37293
38.8%
1 10868
 
11.3%
) 7131
 
7.4%
( 7131
 
7.4%
, 6264
 
6.5%
2 5500
 
5.7%
3 3824
 
4.0%
0 3694
 
3.8%
4 2811
 
2.9%
5 2782
 
2.9%
Other values (12) 8791
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 125376
56.5%
ASCII 96477
43.5%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37293
38.7%
1 10868
 
11.3%
) 7131
 
7.4%
( 7131
 
7.4%
, 6264
 
6.5%
2 5500
 
5.7%
3 3824
 
4.0%
0 3694
 
3.8%
4 2811
 
2.9%
5 2782
 
2.9%
Other values (43) 9179
 
9.5%
Hangul
ValueCountFrequency (%)
14835
 
11.8%
10098
 
8.1%
9327
 
7.4%
7390
 
5.9%
7273
 
5.8%
7175
 
5.7%
6984
 
5.6%
4139
 
3.3%
4110
 
3.3%
3208
 
2.6%
Other values (399) 50837
40.5%
None
ValueCountFrequency (%)
· 2
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct1162
Distinct (%)16.3%
Missing2871
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean42084.95
Minimum14676
Maximum50905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:05.564647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14676
5-th percentile41120
Q141567
median42113
Q342634
95-th percentile42920
Maximum50905
Range36229
Interquartile range (IQR)1067

Descriptive statistics

Standard deviation663.63188
Coefficient of variation (CV)0.015768865
Kurtosis410.95987
Mean42084.95
Median Absolute Deviation (MAD)531
Skewness-9.6583377
Sum3.0002361 × 108
Variance440407.28
MonotonicityNot monotonic
2023-12-11T05:44:05.807096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41942 73
 
0.7%
41941 41
 
0.4%
42760 40
 
0.4%
41957 34
 
0.3%
41438 33
 
0.3%
42614 32
 
0.3%
41938 32
 
0.3%
41544 31
 
0.3%
42918 31
 
0.3%
42688 31
 
0.3%
Other values (1152) 6751
67.5%
(Missing) 2871
28.7%
ValueCountFrequency (%)
14676 1
 
< 0.1%
41001 1
 
< 0.1%
41002 6
 
0.1%
41003 5
 
0.1%
41005 12
0.1%
41009 4
 
< 0.1%
41022 2
 
< 0.1%
41023 1
 
< 0.1%
41024 1
 
< 0.1%
41026 17
0.2%
ValueCountFrequency (%)
50905 1
 
< 0.1%
43019 5
 
0.1%
43018 16
0.2%
43017 8
 
0.1%
43016 3
 
< 0.1%
43015 2
 
< 0.1%
43014 20
0.2%
43010 4
 
< 0.1%
43009 5
 
0.1%
43008 15
0.1%
Distinct7993
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T05:44:06.355724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length5.8024
Min length1

Characters and Unicode

Total characters58024
Distinct characters910
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6918 ?
Unique (%)69.2%

Sample

1st row국제미용실
2nd row캐슬헤어모드
3rd row바네사꾸아퓌르
4th row낭만미장원
5th row보금피부미용실
ValueCountFrequency (%)
헤어 93
 
0.8%
미용실 90
 
0.8%
hair 70
 
0.6%
헤어샵 41
 
0.4%
nail 38
 
0.3%
네일 26
 
0.2%
머리하는날 22
 
0.2%
에스테틱 21
 
0.2%
블루클럽 19
 
0.2%
헤어스토리 18
 
0.2%
Other values (8178) 10659
96.1%
2023-12-11T05:44:07.068213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3575
 
6.2%
3464
 
6.0%
3110
 
5.4%
2379
 
4.1%
2034
 
3.5%
1141
 
2.0%
1115
 
1.9%
1098
 
1.9%
1093
 
1.9%
( 813
 
1.4%
Other values (900) 38202
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49896
86.0%
Lowercase Letter 2752
 
4.7%
Uppercase Letter 2061
 
3.6%
Space Separator 1098
 
1.9%
Open Punctuation 813
 
1.4%
Close Punctuation 813
 
1.4%
Other Punctuation 295
 
0.5%
Decimal Number 261
 
0.4%
Dash Punctuation 18
 
< 0.1%
Modifier Symbol 4
 
< 0.1%
Other values (5) 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3575
 
7.2%
3464
 
6.9%
3110
 
6.2%
2379
 
4.8%
2034
 
4.1%
1141
 
2.3%
1115
 
2.2%
1093
 
2.2%
801
 
1.6%
726
 
1.5%
Other values (814) 30458
61.0%
Lowercase Letter
ValueCountFrequency (%)
a 374
13.6%
i 330
12.0%
e 261
9.5%
o 201
 
7.3%
n 200
 
7.3%
l 197
 
7.2%
r 170
 
6.2%
y 154
 
5.6%
h 132
 
4.8%
u 129
 
4.7%
Other values (16) 604
21.9%
Uppercase Letter
ValueCountFrequency (%)
A 195
 
9.5%
N 173
 
8.4%
S 148
 
7.2%
H 146
 
7.1%
I 144
 
7.0%
B 132
 
6.4%
O 114
 
5.5%
M 109
 
5.3%
J 107
 
5.2%
L 107
 
5.2%
Other values (16) 686
33.3%
Other Punctuation
ValueCountFrequency (%)
& 104
35.3%
. 66
22.4%
# 47
15.9%
, 30
 
10.2%
' 24
 
8.1%
: 15
 
5.1%
! 2
 
0.7%
2
 
0.7%
@ 2
 
0.7%
% 2
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 67
25.7%
2 40
15.3%
0 39
14.9%
3 27
10.3%
8 23
 
8.8%
5 20
 
7.7%
9 20
 
7.7%
7 15
 
5.7%
6 6
 
2.3%
4 4
 
1.5%
Modifier Symbol
ValueCountFrequency (%)
` 3
75.0%
˚ 1
 
25.0%
Math Symbol
ValueCountFrequency (%)
+ 3
75.0%
~ 1
 
25.0%
Other Symbol
ValueCountFrequency (%)
° 3
75.0%
1
 
25.0%
Space Separator
ValueCountFrequency (%)
1098
100.0%
Open Punctuation
ValueCountFrequency (%)
( 813
100.0%
Close Punctuation
ValueCountFrequency (%)
) 813
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49869
85.9%
Latin 4815
 
8.3%
Common 3313
 
5.7%
Han 27
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3575
 
7.2%
3464
 
6.9%
3110
 
6.2%
2379
 
4.8%
2034
 
4.1%
1141
 
2.3%
1115
 
2.2%
1093
 
2.2%
801
 
1.6%
726
 
1.5%
Other values (804) 30431
61.0%
Latin
ValueCountFrequency (%)
a 374
 
7.8%
i 330
 
6.9%
e 261
 
5.4%
o 201
 
4.2%
n 200
 
4.2%
l 197
 
4.1%
A 195
 
4.0%
N 173
 
3.6%
r 170
 
3.5%
y 154
 
3.2%
Other values (43) 2560
53.2%
Common
ValueCountFrequency (%)
1098
33.1%
( 813
24.5%
) 813
24.5%
& 104
 
3.1%
1 67
 
2.0%
. 66
 
2.0%
# 47
 
1.4%
2 40
 
1.2%
0 39
 
1.2%
, 30
 
0.9%
Other values (23) 196
 
5.9%
Han
ValueCountFrequency (%)
18
66.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49865
85.9%
ASCII 8118
 
14.0%
CJK 27
 
< 0.1%
None 6
 
< 0.1%
Compat Jamo 4
 
< 0.1%
Number Forms 2
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3575
 
7.2%
3464
 
6.9%
3110
 
6.2%
2379
 
4.8%
2034
 
4.1%
1141
 
2.3%
1115
 
2.2%
1093
 
2.2%
801
 
1.6%
726
 
1.5%
Other values (800) 30427
61.0%
ASCII
ValueCountFrequency (%)
1098
 
13.5%
( 813
 
10.0%
) 813
 
10.0%
a 374
 
4.6%
i 330
 
4.1%
e 261
 
3.2%
o 201
 
2.5%
n 200
 
2.5%
l 197
 
2.4%
A 195
 
2.4%
Other values (70) 3636
44.8%
CJK
ValueCountFrequency (%)
18
66.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
None
ValueCountFrequency (%)
° 3
50.0%
2
33.3%
½ 1
 
16.7%
Number Forms
ValueCountFrequency (%)
2
100.0%
Compat Jamo
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%

최종수정시점
Real number (ℝ)

Distinct8444
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0136163 × 1013
Minimum2.0011006 × 1013
Maximum2.0220128 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:07.285590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0011006 × 1013
5-th percentile2.0030513 × 1013
Q12.0080923 × 1013
median2.0150925 × 1013
Q32.0191219 × 1013
95-th percentile2.0211001 × 1013
Maximum2.0220128 × 1013
Range2.0912218 × 1011
Interquartile range (IQR)1.1029604 × 1011

Descriptive statistics

Standard deviation6.5574876 × 1010
Coefficient of variation (CV)0.0032565726
Kurtosis-1.226593
Mean2.0136163 × 1013
Median Absolute Deviation (MAD)4.9896522 × 1010
Skewness-0.471437
Sum2.0136163 × 1017
Variance4.3000644 × 1021
MonotonicityNot monotonic
2023-12-11T05:44:07.511507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20031008000000 57
 
0.6%
20031007000000 52
 
0.5%
20031006000000 52
 
0.5%
20031203000000 41
 
0.4%
20031202000000 40
 
0.4%
20060111000000 34
 
0.3%
20040206000000 32
 
0.3%
20040207000000 31
 
0.3%
20031013000000 29
 
0.3%
20031115000000 26
 
0.3%
Other values (8434) 9606
96.1%
ValueCountFrequency (%)
20011006000000 2
 
< 0.1%
20020119000000 1
 
< 0.1%
20020205000000 1
 
< 0.1%
20020304000000 1
 
< 0.1%
20020314000000 1
 
< 0.1%
20020321000000 5
0.1%
20020401000000 1
 
< 0.1%
20020411000000 2
 
< 0.1%
20020416000000 1
 
< 0.1%
20020418000000 12
0.1%
ValueCountFrequency (%)
20220128175148 1
< 0.1%
20220128154506 1
< 0.1%
20220128131925 1
< 0.1%
20220128131648 1
< 0.1%
20220128114641 1
< 0.1%
20220128111348 1
< 0.1%
20220128105033 1
< 0.1%
20220128095551 1
< 0.1%
20220127171511 1
< 0.1%
20220127171232 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
6849 
U
3151 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 6849
68.5%
U 3151
31.5%

Length

2023-12-11T05:44:07.701584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:08.262349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 6849
68.5%
u 3151
31.5%
Distinct1178
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2022-01-30 02:40:00
2023-12-11T05:44:08.435923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:44:08.675692image/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
7543 
피부미용업
1237 
네일아트업
875 
기타
 
183
메이크업업
 
161

Length

Max length6
Median length5
Mean length4.9452
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 7543
75.4%
피부미용업 1237
 
12.4%
네일아트업 875
 
8.8%
기타 183
 
1.8%
메이크업업 161
 
1.6%
미용업 기타 1
 
< 0.1%

Length

2023-12-11T05:44:08.874807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:09.072037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 7543
75.4%
피부미용업 1237
 
12.4%
네일아트업 875
 
8.7%
기타 184
 
1.8%
메이크업업 161
 
1.6%
미용업 1
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct7225
Distinct (%)74.1%
Missing252
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean343024.48
Minimum183293.84
Maximum370287.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:09.241740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183293.84
5-th percentile335089
Q1339671.69
median342856.72
Q3346290.97
95-th percentile353451.77
Maximum370287.67
Range186993.83
Interquartile range (IQR)6619.2853

Descriptive statistics

Standard deviation5369.3851
Coefficient of variation (CV)0.015653067
Kurtosis80.16822
Mean343024.48
Median Absolute Deviation (MAD)3298.6043
Skewness-2.5400721
Sum3.3438027 × 109
Variance28830296
MonotonicityNot monotonic
2023-12-11T05:44:09.461174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
343820.40768 25
 
0.2%
345663.599839 17
 
0.2%
345383.649328 16
 
0.2%
344083.488663 15
 
0.1%
346571.168775 14
 
0.1%
338687.880127 14
 
0.1%
338969.480757 13
 
0.1%
346586.097961 13
 
0.1%
337473.886467 10
 
0.1%
345053.536451 10
 
0.1%
Other values (7215) 9601
96.0%
(Missing) 252
 
2.5%
ValueCountFrequency (%)
183293.83598401 1
< 0.1%
327220.136878 1
< 0.1%
327251.925896 1
< 0.1%
327697.977321 1
< 0.1%
327853.173809 1
< 0.1%
327864.71443 1
< 0.1%
327984.386741 1
< 0.1%
328011.919639 1
< 0.1%
328037.595154 1
< 0.1%
328045.45569 1
< 0.1%
ValueCountFrequency (%)
370287.666377797 1
< 0.1%
358228.205269 1
< 0.1%
357925.220488 1
< 0.1%
357924.560528 1
< 0.1%
357918.706351 1
< 0.1%
357870.136201 1
< 0.1%
357867.135305 1
< 0.1%
356544.223723 1
< 0.1%
356490.183327 2
< 0.1%
356468.087524 1
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct7223
Distinct (%)74.1%
Missing252
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean263300.36
Minimum196022.32
Maximum442516.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:09.680545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum196022.32
5-th percentile257626.5
Q1261180.81
median263211.28
Q3265366.05
95-th percentile271372.96
Maximum442516.96
Range246494.65
Interquartile range (IQR)4185.2394

Descriptive statistics

Standard deviation4752.7338
Coefficient of variation (CV)0.018050617
Kurtosis214.0006
Mean263300.36
Median Absolute Deviation (MAD)2064.3763
Skewness4.5181211
Sum2.5666519 × 109
Variance22588479
MonotonicityNot monotonic
2023-12-11T05:44:09.919300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
264018.87607 25
 
0.2%
263506.50013 17
 
0.2%
267901.294823 16
 
0.2%
263931.205868 15
 
0.1%
261988.432193 14
 
0.1%
260826.314023 14
 
0.1%
261066.095208 13
 
0.1%
257548.491949 13
 
0.1%
262713.424359 10
 
0.1%
258645.860959 10
 
0.1%
Other values (7213) 9601
96.0%
(Missing) 252
 
2.5%
ValueCountFrequency (%)
196022.315868021 1
< 0.1%
240225.954338 1
< 0.1%
240358.722944 1
< 0.1%
240365.71605 1
< 0.1%
240381.508517 1
< 0.1%
240452.846684 1
< 0.1%
240482.499847 1
< 0.1%
240527.237745 1
< 0.1%
240528.740086 1
< 0.1%
240529.270585 2
< 0.1%
ValueCountFrequency (%)
442516.963197419 1
 
< 0.1%
277736.133758 1
 
< 0.1%
274196.40201 1
 
< 0.1%
274168.895966 2
< 0.1%
274148.35881 2
< 0.1%
273719.251935 1
 
< 0.1%
273712.082489 4
< 0.1%
273568.291421 1
 
< 0.1%
273565.445411 1
 
< 0.1%
273550.298095 1
 
< 0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반미용업
4625 
미용업
2786 
피부미용업
1112 
네일미용업
501 
종합미용업
 
333
Other values (11)
643 

Length

Max length23
Median length5
Mean length5.0876
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 4625
46.2%
미용업 2786
27.9%
피부미용업 1112
 
11.1%
네일미용업 501
 
5.0%
종합미용업 333
 
3.3%
피부미용업, 네일미용업 110
 
1.1%
네일미용업, 화장ㆍ분장 미용업 89
 
0.9%
화장ㆍ분장 미용업 86
 
0.9%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 86
 
0.9%
일반미용업, 네일미용업 71
 
0.7%
Other values (6) 201
 
2.0%

Length

2023-12-11T05:44:10.159372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 4836
43.6%
미용업 3201
28.8%
피부미용업 1429
 
12.9%
네일미용업 885
 
8.0%
화장ㆍ분장 415
 
3.7%
종합미용업 333
 
3.0%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct35
Distinct (%)0.4%
Missing1331
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean2.5680009
Minimum0
Maximum42
Zeros1935
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:10.360303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum42
Range42
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9460063
Coefficient of variation (CV)1.1471983
Kurtosis55.845536
Mean2.5680009
Median Absolute Deviation (MAD)1
Skewness5.7325773
Sum22262
Variance8.6789529
MonotonicityNot monotonic
2023-12-11T05:44:10.543828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3 2023
20.2%
0 1935
19.4%
2 1889
18.9%
4 1216
12.2%
1 762
 
7.6%
5 381
 
3.8%
6 148
 
1.5%
7 75
 
0.8%
8 64
 
0.6%
10 34
 
0.3%
Other values (25) 142
 
1.4%
(Missing) 1331
13.3%
ValueCountFrequency (%)
0 1935
19.4%
1 762
 
7.6%
2 1889
18.9%
3 2023
20.2%
4 1216
12.2%
5 381
 
3.8%
6 148
 
1.5%
7 75
 
0.8%
8 64
 
0.6%
9 31
 
0.3%
ValueCountFrequency (%)
42 6
0.1%
40 2
 
< 0.1%
36 4
< 0.1%
35 4
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
29 3
< 0.1%
28 3
< 0.1%
26 2
 
< 0.1%
25 5
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing2555
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean0.40523842
Minimum0
Maximum11
Zeros5154
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:10.726877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80186474
Coefficient of variation (CV)1.9787481
Kurtosis23.675377
Mean0.40523842
Median Absolute Deviation (MAD)0
Skewness3.8411826
Sum3017
Variance0.64298706
MonotonicityNot monotonic
2023-12-11T05:44:10.899797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 5154
51.5%
1 1948
 
19.5%
2 151
 
1.5%
3 103
 
1.0%
4 44
 
0.4%
6 21
 
0.2%
5 10
 
0.1%
7 10
 
0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
(Missing) 2555
25.6%
ValueCountFrequency (%)
0 5154
51.5%
1 1948
 
19.5%
2 151
 
1.5%
3 103
 
1.0%
4 44
 
0.4%
5 10
 
0.1%
6 21
 
0.2%
7 10
 
0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 10
 
0.1%
6 21
 
0.2%
5 10
 
0.1%
4 44
 
0.4%
3 103
 
1.0%
2 151
 
1.5%
1 1948
19.5%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.2%
Missing1871
Missing (%)18.7%
Infinite0
Infinite (%)0.0%
Mean1.1490958
Minimum0
Maximum20
Zeros1056
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:11.082319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95295953
Coefficient of variation (CV)0.8293125
Kurtosis55.494303
Mean1.1490958
Median Absolute Deviation (MAD)0
Skewness4.8881978
Sum9341
Variance0.90813187
MonotonicityNot monotonic
2023-12-11T05:44:11.286341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 5685
56.9%
0 1056
 
10.6%
2 954
 
9.5%
3 254
 
2.5%
4 76
 
0.8%
5 47
 
0.5%
6 22
 
0.2%
7 11
 
0.1%
9 9
 
0.1%
8 8
 
0.1%
Other values (4) 7
 
0.1%
(Missing) 1871
 
18.7%
ValueCountFrequency (%)
0 1056
 
10.6%
1 5685
56.9%
2 954
 
9.5%
3 254
 
2.5%
4 76
 
0.8%
5 47
 
0.5%
6 22
 
0.2%
7 11
 
0.1%
8 8
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
20 2
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 9
 
0.1%
8 8
 
0.1%
7 11
 
0.1%
6 22
 
0.2%
5 47
0.5%
4 76
0.8%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)0.2%
Missing2318
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean1.1735225
Minimum0
Maximum50
Zeros912
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:11.480284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1589974
Coefficient of variation (CV)0.9876226
Kurtosis485.97129
Mean1.1735225
Median Absolute Deviation (MAD)0
Skewness14.636957
Sum9015
Variance1.3432749
MonotonicityNot monotonic
2023-12-11T05:44:11.659350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 5461
54.6%
0 912
 
9.1%
2 885
 
8.8%
3 249
 
2.5%
4 75
 
0.8%
5 42
 
0.4%
6 20
 
0.2%
7 11
 
0.1%
9 8
 
0.1%
8 8
 
0.1%
Other values (6) 11
 
0.1%
(Missing) 2318
23.2%
ValueCountFrequency (%)
0 912
 
9.1%
1 5461
54.6%
2 885
 
8.8%
3 249
 
2.5%
4 75
 
0.8%
5 42
 
0.4%
6 20
 
0.2%
7 11
 
0.1%
8 8
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
30 1
 
< 0.1%
20 2
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
10 4
 
< 0.1%
9 8
 
0.1%
8 8
 
0.1%
7 11
0.1%
6 20
0.2%

사용시작지하층
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.1%
Missing3996
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean0.03431046
Minimum0
Maximum6
Zeros5845
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:11.835322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24857818
Coefficient of variation (CV)7.244968
Kurtosis210.11935
Mean0.03431046
Median Absolute Deviation (MAD)0
Skewness11.972641
Sum206
Variance0.061791112
MonotonicityNot monotonic
2023-12-11T05:44:12.033391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5845
58.5%
1 132
 
1.3%
2 17
 
0.2%
3 6
 
0.1%
6 3
 
< 0.1%
4 1
 
< 0.1%
(Missing) 3996
40.0%
ValueCountFrequency (%)
0 5845
58.5%
1 132
 
1.3%
2 17
 
0.2%
3 6
 
0.1%
4 1
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
4 1
 
< 0.1%
3 6
 
0.1%
2 17
 
0.2%
1 132
 
1.3%
0 5845
58.5%

사용끝지하층
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.1%
Missing4535
Missing (%)45.4%
Infinite0
Infinite (%)0.0%
Mean0.035132662
Minimum0
Maximum6
Zeros5322
Zeros (%)53.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:12.196387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26415094
Coefficient of variation (CV)7.5186713
Kurtosis226.00231
Mean0.035132662
Median Absolute Deviation (MAD)0
Skewness12.710396
Sum192
Variance0.069775719
MonotonicityNot monotonic
2023-12-11T05:44:12.349239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5322
53.2%
1 118
 
1.2%
2 14
 
0.1%
3 6
 
0.1%
6 4
 
< 0.1%
4 1
 
< 0.1%
(Missing) 4535
45.4%
ValueCountFrequency (%)
0 5322
53.2%
1 118
 
1.2%
2 14
 
0.1%
3 6
 
0.1%
4 1
 
< 0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
6 4
 
< 0.1%
4 1
 
< 0.1%
3 6
 
0.1%
2 14
 
0.1%
1 118
 
1.2%
0 5322
53.2%

한실수
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
7037 
<NA>
2962 
6
 
1

Length

Max length4
Median length1
Mean length1.8886
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 7037
70.4%
<NA> 2962
29.6%
6 1
 
< 0.1%

Length

2023-12-11T05:44:12.540129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:12.699290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7037
70.4%
na 2962
29.6%
6 1
 
< 0.1%

양실수
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
7037 
<NA>
2962 
39
 
1

Length

Max length4
Median length1
Mean length1.8887
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 7037
70.4%
<NA> 2962
29.6%
39 1
 
< 0.1%

Length

2023-12-11T05:44:12.864756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:13.028045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7037
70.4%
na 2962
29.6%
39 1
 
< 0.1%

욕실수
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
7037 
<NA>
2960 
2
 
3

Length

Max length4
Median length1
Mean length1.888
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 7037
70.4%
<NA> 2960
29.6%
2 3
 
< 0.1%

Length

2023-12-11T05:44:13.200299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:13.366753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7037
70.4%
na 2960
29.6%
2 3
 
< 0.1%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing123
Missing (%)1.2%
Memory size97.7 KiB
False
9869 
True
 
8
(Missing)
 
123
ValueCountFrequency (%)
False 9869
98.7%
True 8
 
0.1%
(Missing) 123
 
1.2%
2023-12-11T05:44:13.493896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

의자수
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)0.3%
Missing818
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean3.0722065
Minimum0
Maximum30
Zeros1262
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:13.640700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2872169
Coefficient of variation (CV)0.74448671
Kurtosis17.99886
Mean3.0722065
Median Absolute Deviation (MAD)1
Skewness2.7970136
Sum28209
Variance5.2313612
MonotonicityNot monotonic
2023-12-11T05:44:13.813011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3 3669
36.7%
4 1409
 
14.1%
2 1407
 
14.1%
0 1262
 
12.6%
5 487
 
4.9%
6 268
 
2.7%
1 237
 
2.4%
8 117
 
1.2%
7 98
 
1.0%
10 50
 
0.5%
Other values (16) 178
 
1.8%
(Missing) 818
 
8.2%
ValueCountFrequency (%)
0 1262
 
12.6%
1 237
 
2.4%
2 1407
 
14.1%
3 3669
36.7%
4 1409
 
14.1%
5 487
 
4.9%
6 268
 
2.7%
7 98
 
1.0%
8 117
 
1.2%
9 47
 
0.5%
ValueCountFrequency (%)
30 2
 
< 0.1%
27 2
 
< 0.1%
26 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
20 9
0.1%
19 4
 
< 0.1%
18 5
0.1%
17 5
0.1%
16 10
0.1%
Distinct2
Distinct (%)100.0%
Missing9998
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-11T05:44:14.096298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length25.5
Mean length25.5
Min length19

Characters and Unicode

Total characters51
Distinct characters34
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowF-4 체류기간동안 한시적 영업가능
2nd row가설건축물 존치기간( 2015.9.22~2018.3.21)
ValueCountFrequency (%)
f-4 1
14.3%
체류기간동안 1
14.3%
한시적 1
14.3%
영업가능 1
14.3%
가설건축물 1
14.3%
존치기간 1
14.3%
2015.9.22~2018.3.21 1
14.3%
2023-12-11T05:44:14.531800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
9.8%
2 5
 
9.8%
. 4
 
7.8%
1 3
 
5.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
0 2
 
3.9%
1
 
2.0%
1
 
2.0%
Other values (24) 24
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22
43.1%
Decimal Number 15
29.4%
Space Separator 5
 
9.8%
Other Punctuation 4
 
7.8%
Open Punctuation 1
 
2.0%
Uppercase Letter 1
 
2.0%
Math Symbol 1
 
2.0%
Dash Punctuation 1
 
2.0%
Close Punctuation 1
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (9) 9
40.9%
Decimal Number
ValueCountFrequency (%)
2 5
33.3%
1 3
20.0%
0 2
 
13.3%
5 1
 
6.7%
9 1
 
6.7%
8 1
 
6.7%
3 1
 
6.7%
4 1
 
6.7%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28
54.9%
Hangul 22
43.1%
Latin 1
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (9) 9
40.9%
Common
ValueCountFrequency (%)
5
17.9%
2 5
17.9%
. 4
14.3%
1 3
10.7%
0 2
 
7.1%
( 1
 
3.6%
5 1
 
3.6%
9 1
 
3.6%
~ 1
 
3.6%
8 1
 
3.6%
Other values (4) 4
14.3%
Latin
ValueCountFrequency (%)
F 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29
56.9%
Hangul 22
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5
17.2%
2 5
17.2%
. 4
13.8%
1 3
10.3%
0 2
 
6.9%
( 1
 
3.4%
F 1
 
3.4%
5 1
 
3.4%
9 1
 
3.4%
~ 1
 
3.4%
Other values (5) 5
17.2%
Hangul
ValueCountFrequency (%)
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (9) 9
40.9%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9998 
20211215
 
1
20150922
 
1

Length

Max length8
Median length4
Mean length4.0008
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9998
> 99.9%
20211215 1
 
< 0.1%
20150922 1
 
< 0.1%

Length

2023-12-11T05:44:14.756247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:14.931522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9998
> 99.9%
20211215 1
 
< 0.1%
20150922 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9998 
20231018
 
1
20180321
 
1

Length

Max length8
Median length4
Mean length4.0008
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9998
> 99.9%
20231018 1
 
< 0.1%
20180321 1
 
< 0.1%

Length

2023-12-11T05:44:15.113697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:15.277850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9998
> 99.9%
20231018 1
 
< 0.1%
20180321 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
임대
5673 
<NA>
3954 
자가
 
373

Length

Max length4
Median length2
Mean length2.7908
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임대 5673
56.7%
<NA> 3954
39.5%
자가 373
 
3.7%

Length

2023-12-11T05:44:15.470162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:15.650346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대 5673
56.7%
na 3954
39.5%
자가 373
 
3.7%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6498 
<NA>
3502 

Length

Max length4
Median length1
Mean length2.0506
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6498
65.0%
<NA> 3502
35.0%

Length

2023-12-11T05:44:15.842150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:15.990961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6498
65.0%
na 3502
35.0%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)0.4%
Missing6123
Missing (%)61.2%
Infinite0
Infinite (%)0.0%
Mean0.79468661
Minimum0
Maximum24
Zeros1515
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:16.142706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.99955751
Coefficient of variation (CV)1.2578009
Kurtosis95.029619
Mean0.79468661
Median Absolute Deviation (MAD)0
Skewness6.0552269
Sum3081
Variance0.99911521
MonotonicityNot monotonic
2023-12-11T05:44:16.340551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 1948
 
19.5%
0 1515
 
15.2%
2 268
 
2.7%
3 88
 
0.9%
4 27
 
0.3%
5 15
 
0.1%
8 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
9 1
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 6123
61.2%
ValueCountFrequency (%)
0 1515
15.2%
1 1948
19.5%
2 268
 
2.7%
3 88
 
0.9%
4 27
 
0.3%
5 15
 
0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
6 3
 
< 0.1%
5 15
0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7064 
0
2704 
1
 
204
2
 
19
3
 
6

Length

Max length4
Median length4
Mean length3.1192
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7064
70.6%
0 2704
 
27.0%
1 204
 
2.0%
2 19
 
0.2%
3 6
 
0.1%
4 3
 
< 0.1%

Length

2023-12-11T05:44:16.519589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:16.697020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7064
70.6%
0 2704
 
27.0%
1 204
 
2.0%
2 19
 
0.2%
3 6
 
0.1%
4 3
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6176 
<NA>
3824 

Length

Max length4
Median length1
Mean length2.1472
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6176
61.8%
<NA> 3824
38.2%

Length

2023-12-11T05:44:16.872530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:44:17.036047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6176
61.8%
na 3824
38.2%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)0.3%
Missing3856
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean0.73681641
Minimum0
Maximum15
Zeros4529
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T05:44:17.166291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6138458
Coefficient of variation (CV)2.1902956
Kurtosis12.923839
Mean0.73681641
Median Absolute Deviation (MAD)0
Skewness3.1544515
Sum4527
Variance2.6044981
MonotonicityNot monotonic
2023-12-11T05:44:17.867088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4529
45.3%
2 546
 
5.5%
1 405
 
4.0%
3 279
 
2.8%
4 154
 
1.5%
5 77
 
0.8%
6 54
 
0.5%
7 40
 
0.4%
8 17
 
0.2%
10 16
 
0.2%
Other values (6) 27
 
0.3%
(Missing) 3856
38.6%
ValueCountFrequency (%)
0 4529
45.3%
1 405
 
4.0%
2 546
 
5.5%
3 279
 
2.8%
4 154
 
1.5%
5 77
 
0.8%
6 54
 
0.5%
7 40
 
0.4%
8 17
 
0.2%
9 12
 
0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 3
 
< 0.1%
11 6
 
0.1%
10 16
 
0.2%
9 12
 
0.1%
8 17
 
0.2%
7 40
0.4%
6 54
0.5%

다중이용업소여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9988 
True
 
12
ValueCountFrequency (%)
False 9988
99.9%
True 12
 
0.1%
2023-12-11T05:44:18.177734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

번호개방서비스명개방서비스아이디개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
56655666미용업05_18_01_P34300003430000-211-2003-0010820030823<NA>1영업/정상1영업<NA><NA><NA><NA>053 558136215.00703805대구광역시 서구 내당동 417-9번지대구광역시 서구 당산로51길 8 (내당동)41847국제미용실20090707112437I2018-08-31 23:59:59.0일반미용업339605.993095263174.791093일반미용업201100000N3<NA><NA><NA>임대0<NA><NA>00N
1535915360미용업05_18_01_P34700003470000-211-2002-0000320020420<NA>3폐업2폐업20120709<NA><NA><NA>053 523100490.00704936대구광역시 달서구 용산동 931-5번지 (지상2층)대구광역시 달서구 용산서로 10 (용산동,(지상2층))42628캐슬헤어모드20110120100849I2018-08-31 23:59:59.0일반미용업338084.419035263033.563019일반미용업4122<NA><NA><NA><NA><NA>N3<NA><NA><NA>임대<NA><NA><NA><NA><NA>N
65226523미용업05_18_01_P34400003440000-204-2001-0001720010524<NA>3폐업2폐업20051025<NA><NA><NA>053 626954964.96705819대구광역시 남구 대명동 1890-9번지<NA><NA>바네사꾸아퓌르20050929000000I2018-08-31 23:59:59.0일반미용업342931.790278262925.695252미용업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1544715448미용업05_18_01_P34700003470000-211-2009-0000420090202<NA>3폐업2폐업20200714<NA><NA><NA><NA>20.67704954대구광역시 달서구 감삼동 146-5대구광역시 달서구 감삼2길 10, 1층 (감삼동)42643낭만미장원20200714092327U2020-07-16 02:40:00.0일반미용업339040.650602262151.77187일반미용업301100000N2<NA><NA><NA>임대01<NA>00N
1284212843미용업05_18_01_P34600003460000-212-2010-0004420101110<NA>1영업/정상1영업<NA><NA><NA><NA>053 766933623.00706827대구광역시 수성구 상동 62-4번지대구광역시 수성구 수성로 120 (상동)42160보금피부미용실20180320174144I2018-08-31 23:59:59.0피부미용업345598.242263260763.642994피부미용업101100000N0<NA><NA><NA>임대01<NA>02N
1422614227미용업05_18_01_P34600003460000-211-2016-0006620160905<NA>1영업/정상1영업<NA><NA><NA><NA>053 791 057856.00706140대구광역시 수성구 매호동 841번지 (가동)대구광역시 수성구 달구벌대로 3197 (매호동)42271헤어두20170411092855I2018-08-31 23:59:59.0일반미용업354118.307787261487.106903일반미용업201100000N7<NA><NA><NA>임대03<NA>00N
84978498미용업05_18_01_P34500003450000-211-2005-0001120051114<NA>1영업/정상1영업<NA><NA><NA><NA>053 321221429.40702885대구광역시 북구 동천동 872번지대구광역시 북구 팔거천동로 104, 105동 202호 (동천동, 칠곡2차 보성서한타운)41431부티 헤어20111118145314I2018-08-31 23:59:59.0일반미용업340439.491773271441.238804일반미용업2<NA>22<NA><NA><NA><NA><NA>N3<NA><NA><NA>임대<NA><NA><NA><NA><NA>N
44984499미용업05_18_01_P34300003430000-204-1997-0022919970128<NA>3폐업2폐업20031128<NA><NA><NA><NA>.00703842대구광역시 서구 평리동 710-3번지<NA><NA>새한미용실20031203000000I2018-08-31 23:59:59.0일반미용업341167.465715265197.681694미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
10091010미용업05_18_01_P34100003410000-221-2016-0000420160909<NA>1영업/정상1영업<NA><NA><NA><NA><NA>65.00700192대구광역시 중구 종로2가 0045-0001 지상3층대구광역시 중구 종로 25 (종로2가, 지상3층)41934쁘띠럭키20201216110831U2020-12-18 02:40:00.0피부미용업343681.586341264267.941905피부미용업, 화장ㆍ분장 미용업000000000N1<NA><NA><NA><NA>00000N
193194미용업05_18_01_P34100003410000-213-2018-0000520180514<NA>3폐업2폐업20200312<NA><NA><NA><NA>.00700093대구광역시 중구 동성로3가 0027-0004번지 근린생활시설대구광역시 중구 동성로 3, 3층 (동성로3가)41937더왁싱바속눈썹미인20200312140733U2020-03-14 02:40:00.0일반미용업343889.809365264202.807753종합미용업413300000N3<NA><NA><NA><NA>02003N
번호개방서비스명개방서비스아이디개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
64896490미용업05_18_01_P34400003440000-204-2008-0000720080312<NA>3폐업2폐업20081222<NA><NA><NA><NA>26.52705801대구광역시 남구 대명동 55-11번지 (명덕시장길 22)<NA><NA>비즈헤어샵20080312104435I2018-08-31 23:59:59.0일반미용업343497.200702261747.994117미용업2011<NA><NA>000<NA>3<NA><NA><NA>임대0<NA><NA><NA><NA>N
98589859미용업05_18_01_P34500003450000-204-2000-0006120000609<NA>3폐업2폐업20031231<NA><NA><NA>9550292.00702846대구광역시 북구 산격동 794-28번지<NA><NA>이숙희20040127000000I2018-08-31 23:59:59.0일반미용업343869.15697267325.194336미용업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
93539354미용업05_18_01_P34500003450000-204-1996-0015219961213<NA>3폐업2폐업20031231<NA><NA><NA>314872923.36702865대구광역시 북구 태전동 532-1번지 전원맨션상가107<NA><NA>한마음20020718000000I2018-08-31 23:59:59.0일반미용업339746.983521269847.453329미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1375413755미용업05_18_01_P34600003460000-211-2013-0006920130912<NA>1영업/정상1영업<NA><NA><NA><NA>053 764 115237.83706853대구광역시 수성구 황금동 809-27번지대구광역시 수성구 들안로32길 43 (황금동)42145O2미장20130916192449I2018-08-31 23:59:59.0일반미용업346232.44892261699.630569일반미용업201100000N4<NA><NA><NA>자가02000N
51125113미용업05_18_01_P34300003430000-215-2016-0000720160712<NA>3폐업2폐업20190107<NA><NA><NA>053 358 012230.72703851대구광역시 서구 원대동3가 1408-1번지대구광역시 서구 달서로 265 (원대동3가)41716슬긔네일20190107155647U2019-01-09 02:40:00.0네일아트업342042.016866266198.241071네일미용업101100000N2<NA><NA><NA>임대0<NA><NA>01N
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