Overview

Dataset statistics

Number of variables47
Number of observations2798
Missing cells27356
Missing cells (%)20.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory405.0 B

Variable types

Categorical19
Text6
DateTime4
Unsupported6
Numeric10
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author도봉구
URLhttps://data.seoul.go.kr/dataList/OA-17911/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
데이터갱신구분 is highly imbalanced (52.0%)Imbalance
업태구분명 is highly imbalanced (59.7%)Imbalance
사용시작지하층 is highly imbalanced (51.0%)Imbalance
사용끝지하층 is highly imbalanced (68.0%)Imbalance
조건부허가종료일자 is highly imbalanced (98.8%)Imbalance
건물소유구분명 is highly imbalanced (55.5%)Imbalance
여성종사자수 is highly imbalanced (66.3%)Imbalance
남성종사자수 is highly imbalanced (52.5%)Imbalance
인허가취소일자 has 2798 (100.0%) missing valuesMissing
폐업일자 has 906 (32.4%) missing valuesMissing
휴업시작일자 has 2798 (100.0%) missing valuesMissing
휴업종료일자 has 2798 (100.0%) missing valuesMissing
재개업일자 has 2798 (100.0%) missing valuesMissing
전화번호 has 957 (34.2%) missing valuesMissing
도로명주소 has 1096 (39.2%) missing valuesMissing
도로명우편번호 has 1104 (39.5%) missing valuesMissing
좌표정보(X) has 60 (2.1%) missing valuesMissing
좌표정보(Y) has 60 (2.1%) missing valuesMissing
건물지상층수 has 698 (24.9%) missing valuesMissing
건물지하층수 has 867 (31.0%) missing valuesMissing
사용시작지상층 has 626 (22.4%) missing valuesMissing
사용끝지상층 has 1314 (47.0%) missing valuesMissing
발한실여부 has 421 (15.0%) missing valuesMissing
좌석수 has 414 (14.8%) missing valuesMissing
조건부허가신고사유 has 2798 (100.0%) missing valuesMissing
조건부허가시작일자 has 2798 (100.0%) missing valuesMissing
침대수 has 1682 (60.1%) missing valuesMissing
다중이용업소여부 has 363 (13.0%) missing valuesMissing
도로명우편번호 is highly skewed (γ1 = 38.45323055)Skewed
좌석수 is highly skewed (γ1 = 45.59626781)Skewed
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
조건부허가신고사유 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적 has 71 (2.5%) zerosZeros
건물지상층수 has 1631 (58.3%) zerosZeros
건물지하층수 has 1714 (61.3%) zerosZeros
사용시작지상층 has 504 (18.0%) zerosZeros
사용끝지상층 has 75 (2.7%) zerosZeros
좌석수 has 592 (21.2%) zerosZeros
침대수 has 943 (33.7%) zerosZeros

Reproduction

Analysis started2024-05-18 00:56:58.877713
Analysis finished2024-05-18 00:57:01.624815
Duration2.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
3090000
2798 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3090000
2nd row3090000
3rd row3090000
4th row3090000
5th row3090000

Common Values

ValueCountFrequency (%)
3090000 2798
100.0%

Length

2024-05-18T09:57:01.952860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:02.266870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3090000 2798
100.0%

관리번호
Text

UNIQUE 

Distinct2798
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2024-05-18T09:57:02.686744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters61556
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

Unique2798 ?
Unique (%)100.0%

Sample

1st row3090000-204-1965-00391
2nd row3090000-204-1974-00311
3rd row3090000-204-1976-00316
4th row3090000-204-1976-00722
5th row3090000-204-1977-00553
ValueCountFrequency (%)
3090000-204-1965-00391 1
 
< 0.1%
3090000-211-2017-00018 1
 
< 0.1%
3090000-211-2017-00051 1
 
< 0.1%
3090000-211-2017-00021 1
 
< 0.1%
3090000-211-2017-00012 1
 
< 0.1%
3090000-211-2017-00013 1
 
< 0.1%
3090000-211-2017-00014 1
 
< 0.1%
3090000-211-2017-00015 1
 
< 0.1%
3090000-211-2017-00016 1
 
< 0.1%
3090000-211-2017-00017 1
 
< 0.1%
Other values (2788) 2788
99.6%
2024-05-18T09:57:03.923850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26692
43.4%
- 8394
 
13.6%
2 6488
 
10.5%
1 5998
 
9.7%
9 4901
 
8.0%
3 3821
 
6.2%
4 1903
 
3.1%
5 920
 
1.5%
8 901
 
1.5%
6 790
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53162
86.4%
Dash Punctuation 8394
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26692
50.2%
2 6488
 
12.2%
1 5998
 
11.3%
9 4901
 
9.2%
3 3821
 
7.2%
4 1903
 
3.6%
5 920
 
1.7%
8 901
 
1.7%
6 790
 
1.5%
7 748
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 8394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61556
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26692
43.4%
- 8394
 
13.6%
2 6488
 
10.5%
1 5998
 
9.7%
9 4901
 
8.0%
3 3821
 
6.2%
4 1903
 
3.1%
5 920
 
1.5%
8 901
 
1.5%
6 790
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26692
43.4%
- 8394
 
13.6%
2 6488
 
10.5%
1 5998
 
9.7%
9 4901
 
8.0%
3 3821
 
6.2%
4 1903
 
3.1%
5 920
 
1.5%
8 901
 
1.5%
6 790
 
1.3%
Distinct2250
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
Minimum1965-01-18 00:00:00
Maximum2024-05-16 00:00:00
2024-05-18T09:57:04.466991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:57:05.068774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2798
Missing (%)100.0%
Memory size24.7 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
3
1892 
1
906 

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 1892
67.6%
1 906
32.4%

Length

2024-05-18T09:57:05.526638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:05.905963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1892
67.6%
1 906
32.4%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
폐업
1892 
영업/정상
906 

Length

Max length5
Median length2
Mean length2.9714081
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1892
67.6%
영업/정상 906
32.4%

Length

2024-05-18T09:57:06.399225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:06.843852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1892
67.6%
영업/정상 906
32.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2
1892 
1
906 

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 1892
67.6%
1 906
32.4%

Length

2024-05-18T09:57:07.335505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:07.750719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1892
67.6%
1 906
32.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
폐업
1892 
영업
906 

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 1892
67.6%
영업 906
32.4%

Length

2024-05-18T09:57:08.216806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:08.585875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1892
67.6%
영업 906
32.4%

폐업일자
Date

MISSING 

Distinct1469
Distinct (%)77.6%
Missing906
Missing (%)32.4%
Memory size22.0 KiB
Minimum1991-10-10 00:00:00
Maximum2024-04-30 00:00:00
2024-05-18T09:57:09.146226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:57:09.779354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2798
Missing (%)100.0%
Memory size24.7 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2798
Missing (%)100.0%
Memory size24.7 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2798
Missing (%)100.0%
Memory size24.7 KiB

전화번호
Text

MISSING 

Distinct1643
Distinct (%)89.2%
Missing957
Missing (%)34.2%
Memory size22.0 KiB
2024-05-18T09:57:10.753284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.128734
Min length2

Characters and Unicode

Total characters18647
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1508 ?
Unique (%)81.9%

Sample

1st row02 9939118
2nd row0209933593
3rd row0209936611
4th row02 9977877
5th row0209034086
ValueCountFrequency (%)
02 1165
35.3%
955 25
 
0.8%
070 21
 
0.6%
954 20
 
0.6%
0200000000 18
 
0.5%
990 17
 
0.5%
999 16
 
0.5%
900 16
 
0.5%
956 14
 
0.4%
995 13
 
0.4%
Other values (1677) 1977
59.9%
2024-05-18T09:57:12.562956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3652
19.6%
9 2978
16.0%
2 2605
14.0%
1764
9.5%
5 1355
 
7.3%
3 1297
 
7.0%
4 1200
 
6.4%
6 977
 
5.2%
8 944
 
5.1%
1 937
 
5.0%
Other values (2) 938
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16882
90.5%
Space Separator 1764
 
9.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3652
21.6%
9 2978
17.6%
2 2605
15.4%
5 1355
 
8.0%
3 1297
 
7.7%
4 1200
 
7.1%
6 977
 
5.8%
8 944
 
5.6%
1 937
 
5.6%
7 937
 
5.6%
Space Separator
ValueCountFrequency (%)
1764
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18647
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3652
19.6%
9 2978
16.0%
2 2605
14.0%
1764
9.5%
5 1355
 
7.3%
3 1297
 
7.0%
4 1200
 
6.4%
6 977
 
5.2%
8 944
 
5.1%
1 937
 
5.0%
Other values (2) 938
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3652
19.6%
9 2978
16.0%
2 2605
14.0%
1764
9.5%
5 1355
 
7.3%
3 1297
 
7.0%
4 1200
 
6.4%
6 977
 
5.2%
8 944
 
5.1%
1 937
 
5.0%
Other values (2) 938
 
5.0%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1169
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.393245
Minimum0
Maximum288.51
Zeros71
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:13.176502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.897
Q119.44
median26.51
Q339
95-th percentile82.5
Maximum288.51
Range288.51
Interquartile range (IQR)19.56

Descriptive statistics

Standard deviation24.482719
Coefficient of variation (CV)0.73316381
Kurtosis12.160132
Mean33.393245
Median Absolute Deviation (MAD)8.41
Skewness2.6949235
Sum93434.3
Variance599.40352
MonotonicityNot monotonic
2024-05-18T09:57:13.827911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 92
 
3.3%
30.0 77
 
2.8%
0.0 71
 
2.5%
26.4 52
 
1.9%
23.0 50
 
1.8%
20.0 50
 
1.8%
24.0 49
 
1.8%
26.0 39
 
1.4%
23.1 35
 
1.3%
27.0 33
 
1.2%
Other values (1159) 2250
80.4%
ValueCountFrequency (%)
0.0 71
2.5%
1.98 1
 
< 0.1%
2.0 1
 
< 0.1%
3.0 1
 
< 0.1%
3.2 1
 
< 0.1%
4.95 1
 
< 0.1%
6.0 1
 
< 0.1%
6.5 2
 
0.1%
6.6 5
 
0.2%
6.9 2
 
0.1%
ValueCountFrequency (%)
288.51 1
< 0.1%
234.71 1
< 0.1%
203.09 1
< 0.1%
198.0 1
< 0.1%
186.0 1
< 0.1%
170.0 1
< 0.1%
166.01 2
0.1%
161.0 1
< 0.1%
150.0 1
< 0.1%
149.95 1
< 0.1%
Distinct211
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2024-05-18T09:57:15.058507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0779128
Min length6

Characters and Unicode

Total characters17006
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

Unique36 ?
Unique (%)1.3%

Sample

1st row132918
2nd row132890
3rd row132863
4th row132919
5th row132815
ValueCountFrequency (%)
132917 98
 
3.5%
132924 88
 
3.1%
132854 88
 
3.1%
132864 79
 
2.8%
132850 73
 
2.6%
132040 71
 
2.5%
132916 64
 
2.3%
132919 63
 
2.3%
132866 61
 
2.2%
132925 60
 
2.1%
Other values (201) 2053
73.4%
2024-05-18T09:57:16.651023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3536
20.8%
1 3494
20.5%
3 3145
18.5%
8 2128
12.5%
9 1306
 
7.7%
0 875
 
5.1%
4 788
 
4.6%
5 565
 
3.3%
6 553
 
3.3%
7 398
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16788
98.7%
Dash Punctuation 218
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3536
21.1%
1 3494
20.8%
3 3145
18.7%
8 2128
12.7%
9 1306
 
7.8%
0 875
 
5.2%
4 788
 
4.7%
5 565
 
3.4%
6 553
 
3.3%
7 398
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17006
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3536
20.8%
1 3494
20.5%
3 3145
18.5%
8 2128
12.5%
9 1306
 
7.7%
0 875
 
5.1%
4 788
 
4.6%
5 565
 
3.3%
6 553
 
3.3%
7 398
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3536
20.8%
1 3494
20.5%
3 3145
18.5%
8 2128
12.5%
9 1306
 
7.7%
0 875
 
5.1%
4 788
 
4.6%
5 565
 
3.3%
6 553
 
3.3%
7 398
 
2.3%
Distinct2289
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2024-05-18T09:57:17.987005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length45
Mean length26.520014
Min length16

Characters and Unicode

Total characters74203
Distinct characters284
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1942 ?
Unique (%)69.4%

Sample

1st row서울특별시 도봉구 창동 585-37번지
2nd row서울특별시 도봉구 쌍문동 495-13번지
3rd row서울특별시 도봉구 쌍문동 85-4번지
4th row서울특별시 도봉구 창동 593-12번지
5th row서울특별시 도봉구 도봉동 574-21번지
ValueCountFrequency (%)
서울특별시 2797
19.8%
도봉구 2797
19.8%
창동 1020
 
7.2%
방학동 733
 
5.2%
쌍문동 676
 
4.8%
도봉동 372
 
2.6%
지상1층 329
 
2.3%
1층 318
 
2.3%
상가동 145
 
1.0%
2층 98
 
0.7%
Other values (2277) 4808
34.1%
2024-05-18T09:57:19.883801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13453
 
18.1%
1 3313
 
4.5%
3249
 
4.4%
3229
 
4.4%
3223
 
4.3%
2830
 
3.8%
2817
 
3.8%
2811
 
3.8%
2801
 
3.8%
2797
 
3.8%
Other values (274) 33680
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42085
56.7%
Decimal Number 15865
 
21.4%
Space Separator 13453
 
18.1%
Dash Punctuation 2401
 
3.2%
Uppercase Letter 189
 
0.3%
Other Punctuation 91
 
0.1%
Open Punctuation 56
 
0.1%
Close Punctuation 56
 
0.1%
Lowercase Letter 4
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3249
 
7.7%
3229
 
7.7%
3223
 
7.7%
2830
 
6.7%
2817
 
6.7%
2811
 
6.7%
2801
 
6.7%
2797
 
6.6%
2797
 
6.6%
2769
 
6.6%
Other values (231) 12762
30.3%
Uppercase Letter
ValueCountFrequency (%)
A 58
30.7%
S 34
18.0%
E 34
18.0%
B 18
 
9.5%
T 9
 
4.8%
P 8
 
4.2%
C 4
 
2.1%
R 3
 
1.6%
G 3
 
1.6%
Q 3
 
1.6%
Other values (10) 15
 
7.9%
Decimal Number
ValueCountFrequency (%)
1 3313
20.9%
2 2136
13.5%
6 1902
12.0%
0 1628
10.3%
3 1596
10.1%
5 1418
8.9%
4 1157
 
7.3%
7 1020
 
6.4%
8 1003
 
6.3%
9 692
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 50
54.9%
@ 33
36.3%
. 4
 
4.4%
/ 3
 
3.3%
& 1
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
a 2
50.0%
t 1
25.0%
p 1
25.0%
Space Separator
ValueCountFrequency (%)
13453
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2401
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42085
56.7%
Common 31925
43.0%
Latin 193
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3249
 
7.7%
3229
 
7.7%
3223
 
7.7%
2830
 
6.7%
2817
 
6.7%
2811
 
6.7%
2801
 
6.7%
2797
 
6.6%
2797
 
6.6%
2769
 
6.6%
Other values (231) 12762
30.3%
Latin
ValueCountFrequency (%)
A 58
30.1%
S 34
17.6%
E 34
17.6%
B 18
 
9.3%
T 9
 
4.7%
P 8
 
4.1%
C 4
 
2.1%
R 3
 
1.6%
G 3
 
1.6%
Q 3
 
1.6%
Other values (13) 19
 
9.8%
Common
ValueCountFrequency (%)
13453
42.1%
1 3313
 
10.4%
- 2401
 
7.5%
2 2136
 
6.7%
6 1902
 
6.0%
0 1628
 
5.1%
3 1596
 
5.0%
5 1418
 
4.4%
4 1157
 
3.6%
7 1020
 
3.2%
Other values (10) 1901
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42085
56.7%
ASCII 32118
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13453
41.9%
1 3313
 
10.3%
- 2401
 
7.5%
2 2136
 
6.7%
6 1902
 
5.9%
0 1628
 
5.1%
3 1596
 
5.0%
5 1418
 
4.4%
4 1157
 
3.6%
7 1020
 
3.2%
Other values (33) 2094
 
6.5%
Hangul
ValueCountFrequency (%)
3249
 
7.7%
3229
 
7.7%
3223
 
7.7%
2830
 
6.7%
2817
 
6.7%
2811
 
6.7%
2801
 
6.7%
2797
 
6.6%
2797
 
6.6%
2769
 
6.6%
Other values (231) 12762
30.3%

도로명주소
Text

MISSING 

Distinct1536
Distinct (%)90.2%
Missing1096
Missing (%)39.2%
Memory size22.0 KiB
2024-05-18T09:57:20.853886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length52
Mean length33.609871
Min length22

Characters and Unicode

Total characters57204
Distinct characters267
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1398 ?
Unique (%)82.1%

Sample

1st row서울특별시 도봉구 시루봉로 278 (도봉동)
2nd row서울특별시 도봉구 덕릉로59바길 25 (창동)
3rd row서울특별시 도봉구 시루봉로17길 6 (방학동)
4th row서울특별시 도봉구 덕릉로57길 60 (창동)
5th row서울특별시 도봉구 도봉로180가길 96, 104호 (도봉동)
ValueCountFrequency (%)
서울특별시 1701
 
15.3%
도봉구 1701
 
15.3%
창동 590
 
5.3%
1층 439
 
4.0%
방학동 397
 
3.6%
쌍문동 387
 
3.5%
지상1층 349
 
3.1%
도봉동 199
 
1.8%
상가동 146
 
1.3%
해등로 126
 
1.1%
Other values (1149) 5075
45.7%
2024-05-18T09:57:22.449505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9410
 
16.4%
1 3287
 
5.7%
2763
 
4.8%
2635
 
4.6%
2105
 
3.7%
, 1805
 
3.2%
1784
 
3.1%
) 1729
 
3.0%
( 1729
 
3.0%
1711
 
3.0%
Other values (257) 28246
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31928
55.8%
Decimal Number 10268
 
17.9%
Space Separator 9410
 
16.4%
Other Punctuation 1820
 
3.2%
Close Punctuation 1729
 
3.0%
Open Punctuation 1729
 
3.0%
Dash Punctuation 191
 
0.3%
Uppercase Letter 125
 
0.2%
Math Symbol 3
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2763
 
8.7%
2635
 
8.3%
2105
 
6.6%
1784
 
5.6%
1711
 
5.4%
1706
 
5.3%
1703
 
5.3%
1702
 
5.3%
1701
 
5.3%
1692
 
5.3%
Other values (217) 12426
38.9%
Uppercase Letter
ValueCountFrequency (%)
A 37
29.6%
S 25
20.0%
E 25
20.0%
B 15
12.0%
Q 3
 
2.4%
R 3
 
2.4%
N 2
 
1.6%
K 2
 
1.6%
T 2
 
1.6%
C 2
 
1.6%
Other values (9) 9
 
7.2%
Decimal Number
ValueCountFrequency (%)
1 3287
32.0%
2 1322
12.9%
0 1051
 
10.2%
3 873
 
8.5%
6 806
 
7.8%
5 772
 
7.5%
4 762
 
7.4%
7 489
 
4.8%
9 461
 
4.5%
8 445
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 1805
99.2%
@ 9
 
0.5%
. 4
 
0.2%
/ 1
 
0.1%
& 1
 
0.1%
Space Separator
ValueCountFrequency (%)
9410
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1729
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1729
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 191
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31928
55.8%
Common 25151
44.0%
Latin 125
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2763
 
8.7%
2635
 
8.3%
2105
 
6.6%
1784
 
5.6%
1711
 
5.4%
1706
 
5.3%
1703
 
5.3%
1702
 
5.3%
1701
 
5.3%
1692
 
5.3%
Other values (217) 12426
38.9%
Common
ValueCountFrequency (%)
9410
37.4%
1 3287
 
13.1%
, 1805
 
7.2%
) 1729
 
6.9%
( 1729
 
6.9%
2 1322
 
5.3%
0 1051
 
4.2%
3 873
 
3.5%
6 806
 
3.2%
5 772
 
3.1%
Other values (11) 2367
 
9.4%
Latin
ValueCountFrequency (%)
A 37
29.6%
S 25
20.0%
E 25
20.0%
B 15
12.0%
Q 3
 
2.4%
R 3
 
2.4%
N 2
 
1.6%
K 2
 
1.6%
T 2
 
1.6%
C 2
 
1.6%
Other values (9) 9
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31928
55.8%
ASCII 25276
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9410
37.2%
1 3287
 
13.0%
, 1805
 
7.1%
) 1729
 
6.8%
( 1729
 
6.8%
2 1322
 
5.2%
0 1051
 
4.2%
3 873
 
3.5%
6 806
 
3.2%
5 772
 
3.1%
Other values (30) 2492
 
9.9%
Hangul
ValueCountFrequency (%)
2763
 
8.7%
2635
 
8.3%
2105
 
6.6%
1784
 
5.6%
1711
 
5.4%
1706
 
5.3%
1703
 
5.3%
1702
 
5.3%
1701
 
5.3%
1692
 
5.3%
Other values (217) 12426
38.9%

도로명우편번호
Real number (ℝ)

MISSING  SKEWED 

Distinct172
Distinct (%)10.2%
Missing1104
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean1402.5567
Minimum1301
Maximum11632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:22.921339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1301
5-th percentile1314
Q11349
median1400
Q31447.75
95-th percentile1474
Maximum11632
Range10331
Interquartile range (IQR)98.75

Descriptive statistics

Standard deviation254.37747
Coefficient of variation (CV)0.18136699
Kurtosis1547.1549
Mean1402.5567
Median Absolute Deviation (MAD)48
Skewness38.453231
Sum2375931
Variance64707.9
MonotonicityNot monotonic
2024-05-18T09:57:23.408828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1455 64
 
2.3%
1340 50
 
1.8%
1394 43
 
1.5%
1332 38
 
1.4%
1454 38
 
1.4%
1402 36
 
1.3%
1357 34
 
1.2%
1443 32
 
1.1%
1403 31
 
1.1%
1318 30
 
1.1%
Other values (162) 1298
46.4%
(Missing) 1104
39.5%
ValueCountFrequency (%)
1301 1
 
< 0.1%
1302 7
0.3%
1303 6
0.2%
1304 14
0.5%
1305 6
0.2%
1306 11
0.4%
1307 14
0.5%
1308 4
 
0.1%
1309 4
 
0.1%
1310 6
0.2%
ValueCountFrequency (%)
11632 1
 
< 0.1%
1489 10
0.4%
1488 6
0.2%
1487 3
 
0.1%
1484 1
 
< 0.1%
1482 4
 
0.1%
1481 9
0.3%
1480 7
0.3%
1479 8
0.3%
1478 9
0.3%
Distinct2356
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2024-05-18T09:57:24.132887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length5.4857041
Min length1

Characters and Unicode

Total characters15349
Distinct characters660
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2068 ?
Unique (%)73.9%

Sample

1st row동보
2nd row은하
3rd row유경
4th row이송 헤어크럽
5th row가림
ValueCountFrequency (%)
헤어 77
 
2.3%
미용실 42
 
1.3%
네일 24
 
0.7%
hair 23
 
0.7%
헤어클럽 15
 
0.5%
헤어샵 14
 
0.4%
nail 12
 
0.4%
블루클럽 12
 
0.4%
까꼬뽀꼬 10
 
0.3%
헤어살롱 9
 
0.3%
Other values (2469) 3085
92.8%
2024-05-18T09:57:25.344848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1157
 
7.5%
1106
 
7.2%
583
 
3.8%
528
 
3.4%
418
 
2.7%
416
 
2.7%
405
 
2.6%
314
 
2.0%
295
 
1.9%
217
 
1.4%
Other values (650) 9910
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13346
87.0%
Lowercase Letter 630
 
4.1%
Space Separator 528
 
3.4%
Uppercase Letter 386
 
2.5%
Other Punctuation 122
 
0.8%
Open Punctuation 121
 
0.8%
Close Punctuation 120
 
0.8%
Decimal Number 80
 
0.5%
Dash Punctuation 11
 
0.1%
Connector Punctuation 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1157
 
8.7%
1106
 
8.3%
583
 
4.4%
418
 
3.1%
416
 
3.1%
405
 
3.0%
314
 
2.4%
295
 
2.2%
217
 
1.6%
197
 
1.5%
Other values (577) 8238
61.7%
Lowercase Letter
ValueCountFrequency (%)
a 84
13.3%
i 72
11.4%
e 52
 
8.3%
r 50
 
7.9%
n 49
 
7.8%
h 43
 
6.8%
o 42
 
6.7%
s 37
 
5.9%
l 37
 
5.9%
y 32
 
5.1%
Other values (13) 132
21.0%
Uppercase Letter
ValueCountFrequency (%)
N 31
 
8.0%
J 30
 
7.8%
S 30
 
7.8%
A 30
 
7.8%
H 28
 
7.3%
I 23
 
6.0%
B 23
 
6.0%
O 22
 
5.7%
T 18
 
4.7%
M 18
 
4.7%
Other values (13) 133
34.5%
Decimal Number
ValueCountFrequency (%)
0 19
23.8%
2 16
20.0%
1 15
18.8%
3 9
11.2%
5 7
 
8.8%
7 5
 
6.2%
9 4
 
5.0%
4 2
 
2.5%
6 2
 
2.5%
8 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
? 42
34.4%
& 26
21.3%
. 16
 
13.1%
, 15
 
12.3%
# 10
 
8.2%
' 6
 
4.9%
: 5
 
4.1%
! 1
 
0.8%
1
 
0.8%
Math Symbol
ValueCountFrequency (%)
= 1
50.0%
+ 1
50.0%
Space Separator
ValueCountFrequency (%)
528
100.0%
Open Punctuation
ValueCountFrequency (%)
( 121
100.0%
Close Punctuation
ValueCountFrequency (%)
) 120
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13338
86.9%
Latin 1016
 
6.6%
Common 987
 
6.4%
Han 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1157
 
8.7%
1106
 
8.3%
583
 
4.4%
418
 
3.1%
416
 
3.1%
405
 
3.0%
314
 
2.4%
295
 
2.2%
217
 
1.6%
197
 
1.5%
Other values (572) 8230
61.7%
Latin
ValueCountFrequency (%)
a 84
 
8.3%
i 72
 
7.1%
e 52
 
5.1%
r 50
 
4.9%
n 49
 
4.8%
h 43
 
4.2%
o 42
 
4.1%
s 37
 
3.6%
l 37
 
3.6%
y 32
 
3.1%
Other values (36) 518
51.0%
Common
ValueCountFrequency (%)
528
53.5%
( 121
 
12.3%
) 120
 
12.2%
? 42
 
4.3%
& 26
 
2.6%
0 19
 
1.9%
2 16
 
1.6%
. 16
 
1.6%
, 15
 
1.5%
1 15
 
1.5%
Other values (17) 69
 
7.0%
Han
ValueCountFrequency (%)
3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13337
86.9%
ASCII 2001
 
13.0%
CJK 8
 
0.1%
None 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1157
 
8.7%
1106
 
8.3%
583
 
4.4%
418
 
3.1%
416
 
3.1%
405
 
3.0%
314
 
2.4%
295
 
2.2%
217
 
1.6%
197
 
1.5%
Other values (571) 8229
61.7%
ASCII
ValueCountFrequency (%)
528
26.4%
( 121
 
6.0%
) 120
 
6.0%
a 84
 
4.2%
i 72
 
3.6%
e 52
 
2.6%
r 50
 
2.5%
n 49
 
2.4%
h 43
 
2.1%
? 42
 
2.1%
Other values (61) 840
42.0%
CJK
ValueCountFrequency (%)
3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
None
ValueCountFrequency (%)
´ 1
50.0%
1
50.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Distinct2206
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
Minimum1999-02-22 00:00:00
Maximum2024-05-16 13:49:30
2024-05-18T09:57:26.219654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:57:26.915292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
I
2231 
U
553 
D
 
14

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 2231
79.7%
U 553
 
19.8%
D 14
 
0.5%

Length

2024-05-18T09:57:27.526995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:27.952471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2231
79.7%
u 553
 
19.8%
d 14
 
0.5%
Distinct632
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-18T09:57:28.481028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:57:29.049929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
일반미용업
2214 
피부미용업
315 
네일아트업
 
212
메이크업업
 
34
기타
 
21

Length

Max length6
Median length5
Mean length4.9781987
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2214
79.1%
피부미용업 315
 
11.3%
네일아트업 212
 
7.6%
메이크업업 34
 
1.2%
기타 21
 
0.8%
미용업 기타 2
 
0.1%

Length

2024-05-18T09:57:29.675293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:30.222196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2214
79.1%
피부미용업 315
 
11.2%
네일아트업 212
 
7.6%
메이크업업 34
 
1.2%
기타 23
 
0.8%
미용업 2
 
0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1261
Distinct (%)46.1%
Missing60
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean203307.72
Minimum201081.92
Maximum204719.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:30.804874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201081.92
5-th percentile202237.41
Q1202974.62
median203308.01
Q3203695.54
95-th percentile204192.92
Maximum204719.28
Range3637.3621
Interquartile range (IQR)720.91419

Descriptive statistics

Standard deviation592.92317
Coefficient of variation (CV)0.0029163829
Kurtosis1.2592971
Mean203307.72
Median Absolute Deviation (MAD)360.41052
Skewness-0.66699015
Sum5.5665655 × 108
Variance351557.89
MonotonicityNot monotonic
2024-05-18T09:57:31.346020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204169.847670316 31
 
1.1%
202422.313606872 25
 
0.9%
203039.327074879 22
 
0.8%
204192.915094501 21
 
0.8%
204083.769271308 21
 
0.8%
203089.442773514 19
 
0.7%
203242.968498731 19
 
0.7%
204136.58447783 17
 
0.6%
202807.230907617 15
 
0.5%
203946.218925234 14
 
0.5%
Other values (1251) 2534
90.6%
(Missing) 60
 
2.1%
ValueCountFrequency (%)
201081.915285607 1
 
< 0.1%
201094.369420008 1
 
< 0.1%
201096.680640152 3
0.1%
201109.927189862 1
 
< 0.1%
201111.814808903 2
0.1%
201112.939161727 1
 
< 0.1%
201116.976996056 1
 
< 0.1%
201117.225298634 1
 
< 0.1%
201122.155870704 1
 
< 0.1%
201128.59404573 3
0.1%
ValueCountFrequency (%)
204719.277363253 1
 
< 0.1%
204623.018873403 11
0.4%
204569.769384803 2
 
0.1%
204506.921108086 1
 
< 0.1%
204502.118604099 1
 
< 0.1%
204468.657003222 7
0.3%
204428.262955437 9
0.3%
204419.422902458 1
 
< 0.1%
204414.113861369 6
0.2%
204413.616574592 3
 
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1261
Distinct (%)46.1%
Missing60
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean461658.04
Minimum458927.89
Maximum469593.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:31.992239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum458927.89
5-th percentile459539.96
Q1460591.04
median461652.61
Q3462627.76
95-th percentile464092.19
Maximum469593.62
Range10665.73
Interquartile range (IQR)2036.725

Descriptive statistics

Standard deviation1358.529
Coefficient of variation (CV)0.002942717
Kurtosis-0.10468347
Mean461658.04
Median Absolute Deviation (MAD)1030.8371
Skewness0.3098024
Sum1.2640197 × 109
Variance1845601
MonotonicityNot monotonic
2024-05-18T09:57:32.714232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464814.717432497 31
 
1.1%
461665.385672458 25
 
0.9%
460524.018199419 22
 
0.8%
461653.03000502 21
 
0.8%
462849.020752258 21
 
0.8%
461770.488758803 19
 
0.7%
460001.081136921 19
 
0.7%
463573.102150294 17
 
0.6%
461791.719629655 15
 
0.5%
462919.219463289 14
 
0.5%
Other values (1251) 2534
90.6%
(Missing) 60
 
2.1%
ValueCountFrequency (%)
458927.890005777 1
 
< 0.1%
458954.54912126 1
 
< 0.1%
458967.340530703 4
0.1%
458990.391547966 1
 
< 0.1%
459003.103267266 1
 
< 0.1%
459006.081127987 1
 
< 0.1%
459012.255071829 1
 
< 0.1%
459026.486447676 1
 
< 0.1%
459027.916487009 1
 
< 0.1%
459037.113074794 1
 
< 0.1%
ValueCountFrequency (%)
469593.619656499 1
 
< 0.1%
465122.678825118 1
 
< 0.1%
465042.493324016 7
 
0.3%
464941.802561026 1
 
< 0.1%
464926.884466418 1
 
< 0.1%
464905.033086911 4
 
0.1%
464855.988967255 1
 
< 0.1%
464840.350966167 3
 
0.1%
464840.183818755 3
 
0.1%
464814.717432497 31
1.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
미용업
1061 
일반미용업
918 
<NA>
363 
피부미용업
223 
네일미용업
 
78
Other values (12)
155 

Length

Max length23
Median length19
Mean length4.3624017
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1061
37.9%
일반미용업 918
32.8%
<NA> 363
 
13.0%
피부미용업 223
 
8.0%
네일미용업 78
 
2.8%
종합미용업 74
 
2.6%
피부미용업, 네일미용업 22
 
0.8%
일반미용업, 네일미용업 16
 
0.6%
네일미용업, 화장ㆍ분장 미용업 13
 
0.5%
화장ㆍ분장 미용업 10
 
0.4%
Other values (7) 20
 
0.7%

Length

2024-05-18T09:57:33.334131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1098
37.7%
일반미용업 946
32.5%
na 363
 
12.5%
피부미용업 260
 
8.9%
네일미용업 136
 
4.7%
종합미용업 74
 
2.5%
화장ㆍ분장 37
 
1.3%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)0.8%
Missing698
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean0.71238095
Minimum0
Maximum15
Zeros1631
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:33.890092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6354631
Coefficient of variation (CV)2.2957703
Kurtosis19.041336
Mean0.71238095
Median Absolute Deviation (MAD)0
Skewness3.521594
Sum1496
Variance2.6747394
MonotonicityNot monotonic
2024-05-18T09:57:34.418498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1631
58.3%
2 128
 
4.6%
3 128
 
4.6%
4 97
 
3.5%
1 56
 
2.0%
5 34
 
1.2%
6 9
 
0.3%
15 3
 
0.1%
7 3
 
0.1%
14 3
 
0.1%
Other values (6) 8
 
0.3%
(Missing) 698
24.9%
ValueCountFrequency (%)
0 1631
58.3%
1 56
 
2.0%
2 128
 
4.6%
3 128
 
4.6%
4 97
 
3.5%
5 34
 
1.2%
6 9
 
0.3%
7 3
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
15 3
 
0.1%
14 3
 
0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
0.1%
8 2
 
0.1%
7 3
 
0.1%
6 9
0.3%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.3%
Missing867
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean0.12221647
Minimum0
Maximum5
Zeros1714
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:34.882717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37482771
Coefficient of variation (CV)3.0669165
Kurtosis29.634137
Mean0.12221647
Median Absolute Deviation (MAD)0
Skewness4.2652168
Sum236
Variance0.14049581
MonotonicityNot monotonic
2024-05-18T09:57:35.227485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1714
61.3%
1 207
 
7.4%
2 4
 
0.1%
3 4
 
0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 867
31.0%
ValueCountFrequency (%)
0 1714
61.3%
1 207
 
7.4%
2 4
 
0.1%
3 4
 
0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 1
 
< 0.1%
3 4
 
0.1%
2 4
 
0.1%
1 207
 
7.4%
0 1714
61.3%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.4%
Missing626
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean1.0069061
Minimum0
Maximum12
Zeros504
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:35.650123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82535154
Coefficient of variation (CV)0.81969069
Kurtosis18.325908
Mean1.0069061
Median Absolute Deviation (MAD)0
Skewness2.2947048
Sum2187
Variance0.68120516
MonotonicityNot monotonic
2024-05-18T09:57:36.163292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1278
45.7%
0 504
 
18.0%
2 308
 
11.0%
3 54
 
1.9%
4 20
 
0.7%
5 4
 
0.1%
6 2
 
0.1%
12 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 626
22.4%
ValueCountFrequency (%)
0 504
 
18.0%
1 1278
45.7%
2 308
 
11.0%
3 54
 
1.9%
4 20
 
0.7%
5 4
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
0.1%
5 4
 
0.1%
4 20
 
0.7%
3 54
 
1.9%
2 308
 
11.0%
1 1278
45.7%
0 504
 
18.0%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.5%
Missing1314
Missing (%)47.0%
Infinite0
Infinite (%)0.0%
Mean1.2412399
Minimum0
Maximum7
Zeros75
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:36.663916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q11
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69853721
Coefficient of variation (CV)0.56277373
Kurtosis9.5809637
Mean1.2412399
Median Absolute Deviation (MAD)0
Skewness2.2453319
Sum1842
Variance0.48795423
MonotonicityNot monotonic
2024-05-18T09:57:37.153119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1079
38.6%
2 260
 
9.3%
0 75
 
2.7%
3 47
 
1.7%
4 17
 
0.6%
5 3
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
(Missing) 1314
47.0%
ValueCountFrequency (%)
0 75
 
2.7%
1 1079
38.6%
2 260
 
9.3%
3 47
 
1.7%
4 17
 
0.6%
5 3
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
0.1%
5 3
 
0.1%
4 17
 
0.6%
3 47
 
1.7%
2 260
 
9.3%
1 1079
38.6%
0 75
 
2.7%

사용시작지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
1959 
0
792 
1
 
44
2
 
3

Length

Max length4
Median length4
Mean length3.1004289
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 (%)
<NA> 1959
70.0%
0 792
28.3%
1 44
 
1.6%
2 3
 
0.1%

Length

2024-05-18T09:57:37.652529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:38.065994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1959
70.0%
0 792
28.3%
1 44
 
1.6%
2 3
 
0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
2424 
0
332 
1
 
39
2
 
3

Length

Max length4
Median length4
Mean length3.5989993
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> 2424
86.6%
0 332
 
11.9%
1 39
 
1.4%
2 3
 
0.1%

Length

2024-05-18T09:57:38.534571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:38.989811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2424
86.6%
0 332
 
11.9%
1 39
 
1.4%
2 3
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
0
1764 
<NA>
1034 

Length

Max length4
Median length1
Mean length2.108649
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 1764
63.0%
<NA> 1034
37.0%

Length

2024-05-18T09:57:39.562340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:39.996655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1764
63.0%
na 1034
37.0%

양실수
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
0
1763 
<NA>
1034 
20
 
1

Length

Max length4
Median length1
Mean length2.1090064
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 1763
63.0%
<NA> 1034
37.0%
20 1
 
< 0.1%

Length

2024-05-18T09:57:40.408770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:40.722441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1763
63.0%
na 1034
37.0%
20 1
 
< 0.1%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
0
1764 
<NA>
1034 

Length

Max length4
Median length1
Mean length2.108649
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 1764
63.0%
<NA> 1034
37.0%

Length

2024-05-18T09:57:41.226825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:41.529202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1764
63.0%
na 1034
37.0%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing421
Missing (%)15.0%
Memory size5.6 KiB
False
2377 
(Missing)
421 
ValueCountFrequency (%)
False 2377
85.0%
(Missing) 421
 
15.0%
2024-05-18T09:57:41.749321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct18
Distinct (%)0.8%
Missing414
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean2.9916107
Minimum0
Maximum482
Zeros592
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:42.039143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum482
Range482
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.041538
Coefficient of variation (CV)3.3565658
Kurtosis2175.4253
Mean2.9916107
Median Absolute Deviation (MAD)1
Skewness45.596268
Sum7132
Variance100.83249
MonotonicityNot monotonic
2024-05-18T09:57:42.393440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 872
31.2%
0 592
21.2%
4 417
14.9%
2 184
 
6.6%
5 141
 
5.0%
6 59
 
2.1%
8 50
 
1.8%
7 31
 
1.1%
1 9
 
0.3%
9 8
 
0.3%
Other values (8) 21
 
0.8%
(Missing) 414
14.8%
ValueCountFrequency (%)
0 592
21.2%
1 9
 
0.3%
2 184
 
6.6%
3 872
31.2%
4 417
14.9%
5 141
 
5.0%
6 59
 
2.1%
7 31
 
1.1%
8 50
 
1.8%
9 8
 
0.3%
ValueCountFrequency (%)
482 1
 
< 0.1%
18 1
 
< 0.1%
16 2
 
0.1%
14 2
 
0.1%
13 1
 
< 0.1%
12 4
 
0.1%
11 2
 
0.1%
10 8
 
0.3%
9 8
 
0.3%
8 50
1.8%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2798
Missing (%)100.0%
Memory size24.7 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2798
Missing (%)100.0%
Memory size24.7 KiB

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
2795 
2
 
3

Length

Max length4
Median length4
Mean length3.9967834
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> 2795
99.9%
2 3
 
0.1%

Length

2024-05-18T09:57:42.849132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:43.127180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2795
99.9%
2 3
 
0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
2300 
임대
486 
자가
 
12

Length

Max length4
Median length4
Mean length3.6440315
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2300
82.2%
임대 486
 
17.4%
자가 12
 
0.4%

Length

2024-05-18T09:57:43.438150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:43.757587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2300
82.2%
임대 486
 
17.4%
자가 12
 
0.4%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
1496 
0
1302 

Length

Max length4
Median length4
Mean length2.6040029
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> 1496
53.5%
0 1302
46.5%

Length

2024-05-18T09:57:44.234189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:44.520885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1496
53.5%
0 1302
46.5%

여성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
2201 
0
585 
1
 
10
4
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.3598999
Min length1

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> 2201
78.7%
0 585
 
20.9%
1 10
 
0.4%
4 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-05-18T09:57:44.794409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:45.158200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2201
78.7%
0 585
 
20.9%
1 10
 
0.4%
4 1
 
< 0.1%
2 1
 
< 0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
2204 
0
592 
1
 
2

Length

Max length4
Median length4
Mean length3.3631165
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> 2204
78.8%
0 592
 
21.2%
1 2
 
0.1%

Length

2024-05-18T09:57:45.509289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:45.806884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2204
78.8%
0 592
 
21.2%
1 2
 
0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
<NA>
1672 
0
1126 

Length

Max length4
Median length4
Mean length2.7927091
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> 1672
59.8%
0 1126
40.2%

Length

2024-05-18T09:57:46.274184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:57:46.636432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1672
59.8%
0 1126
40.2%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)1.1%
Missing1682
Missing (%)60.1%
Infinite0
Infinite (%)0.0%
Mean0.48835125
Minimum0
Maximum13
Zeros943
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2024-05-18T09:57:46.965016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3908247
Coefficient of variation (CV)2.8480007
Kurtosis17.136344
Mean0.48835125
Median Absolute Deviation (MAD)0
Skewness3.7299768
Sum545
Variance1.9343933
MonotonicityNot monotonic
2024-05-18T09:57:47.366389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 943
33.7%
2 56
 
2.0%
1 29
 
1.0%
3 29
 
1.0%
4 24
 
0.9%
5 19
 
0.7%
6 6
 
0.2%
9 4
 
0.1%
8 3
 
0.1%
10 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 1682
60.1%
ValueCountFrequency (%)
0 943
33.7%
1 29
 
1.0%
2 56
 
2.0%
3 29
 
1.0%
4 24
 
0.9%
5 19
 
0.7%
6 6
 
0.2%
7 1
 
< 0.1%
8 3
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
10 1
 
< 0.1%
9 4
 
0.1%
8 3
 
0.1%
7 1
 
< 0.1%
6 6
 
0.2%
5 19
 
0.7%
4 24
0.9%
3 29
1.0%
2 56
2.0%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing363
Missing (%)13.0%
Memory size5.6 KiB
False
2435 
(Missing)
363 
ValueCountFrequency (%)
False 2435
87.0%
(Missing) 363
 
13.0%
2024-05-18T09:57:47.727988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030900003090000-204-1965-0039119650118<NA>3폐업2폐업20110113<NA><NA><NA>02 99391189.0132918서울특별시 도봉구 창동 585-37번지<NA><NA>동보2007-07-02 11:38:15I2018-08-31 23:59:59.0일반미용업203130.327141459695.203016미용업1<NA>11<NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130900003090000-204-1974-0031119740918<NA>3폐업2폐업20041230<NA><NA><NA>020993359321.0132890서울특별시 도봉구 쌍문동 495-13번지<NA><NA>은하2003-12-30 00:00:00I2018-08-31 23:59:59.0일반미용업201229.620479461451.675165미용업<NA><NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230900003090000-204-1976-0031619760920<NA>3폐업2폐업20030630<NA><NA><NA>020993661112.04132863서울특별시 도봉구 쌍문동 85-4번지<NA><NA>유경2003-07-01 00:00:00I2018-08-31 23:59:59.0일반미용업203054.699736461010.980312미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330900003090000-204-1976-0072219761122<NA>3폐업2폐업20081218<NA><NA><NA>02 997787768.7132919서울특별시 도봉구 창동 593-12번지<NA><NA>이송 헤어크럽2007-12-07 17:47:18I2018-08-31 23:59:59.0일반미용업203289.769006460101.676599미용업41<NA><NA><NA><NA><NA><NA><NA>N7<NA><NA><NA>임대<NA><NA><NA><NA><NA>N
430900003090000-204-1977-0055319770602<NA>3폐업2폐업20021031<NA><NA><NA>020903408623.6132815서울특별시 도봉구 도봉동 574-21번지<NA><NA>가림2002-11-01 00:00:00I2018-08-31 23:59:59.0일반미용업203689.208731464742.292766미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530900003090000-204-1978-0029819780602<NA>3폐업2폐업20110601<NA><NA><NA>02 905645514.85132896서울특별시 도봉구 쌍문동 711-0번지<NA><NA>쎄미2009-05-21 10:53:13I2018-08-31 23:59:59.0일반미용업203296.164642462113.860477미용업202200000N1<NA><NA><NA><NA>0<NA><NA>00N
630900003090000-204-1978-0039719780107<NA>3폐업2폐업20000621<NA><NA><NA>020902852512.76132924서울특별시 도봉구 창동 657-137번지<NA><NA>단골2000-06-26 00:00:00I2018-08-31 23:59:59.0일반미용업203107.978095460561.096925미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730900003090000-204-1980-0039319801226<NA>3폐업2폐업19980323<NA><NA><NA>020900206412.61132924서울특별시 도봉구 창동 657-30번지<NA><NA>안희진헤어아트2002-01-18 00:00:00I2018-08-31 23:59:59.0일반미용업203146.637858460512.429609미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
830900003090000-204-1980-0049419800204<NA>3폐업2폐업19971204<NA><NA><NA>020992092311.68132850서울특별시 도봉구 방학동 685-21번지<NA><NA>희정2002-01-18 00:00:00I2018-08-31 23:59:59.0일반미용업203445.862859462535.354761미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
930900003090000-204-1982-0028119821125<NA>3폐업2폐업20021031<NA><NA><NA>020906716214.4132884서울특별시 도봉구 쌍문동 382-110번지<NA><NA>현미2002-11-01 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
278830900003090000-225-2022-0000120220330<NA>1영업/정상1영업<NA><NA><NA><NA><NA>22.25132850서울특별시 도봉구 방학동 682-33서울특별시 도봉구 도봉로145길 14, 1층 (방학동)1340은선헤어2022-03-30 16:13:18I2021-12-04 00:01:00.0일반미용업203616.697316462479.758657<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
278930900003090000-225-2023-000012023-05-24<NA>1영업/정상1영업<NA><NA><NA><NA>026448406635.74132-844서울특별시 도봉구 방학동 642-29 1층서울특별시 도봉구 도당로13가길 81, 1층 (방학동)1348J헤어갤러리2023-05-24 10:47:50I2022-12-04 22:06:00.0일반미용업203372.55394462828.392003<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
279030900003090000-225-2023-000022023-06-28<NA>1영업/정상1영업<NA><NA><NA><NA><NA>31.32132-918서울특별시 도봉구 창동 582-49 1층서울특별시 도봉구 덕릉로59바길 25, 1층 (창동)1468가인헤어샵2024-02-19 13:21:42U2023-12-01 22:01:00.0일반미용업203155.982183459776.336002<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
279130900003090000-226-2016-0000120161013<NA>3폐업2폐업20201116<NA><NA><NA><NA>45.09132040서울특별시 도봉구 창동 808 상가동 지층04호서울특별시 도봉구 노해로69길 97, 상가동 지하1층 지층04호 (창동)1411조현정토탈뷰티2020-11-16 11:13:43U2020-11-18 02:40:00.0네일아트업204192.915095461653.030005피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA>11000N0<NA><NA><NA><NA>00000N
279230900003090000-226-2017-0000120170608<NA>1영업/정상1영업<NA><NA><NA><NA><NA>24.0132924서울특별시 도봉구 창동 657-139번지 지상1층서울특별시 도봉구 도봉로114길 28, 지상1층 (창동)1455네일 더(the)예쁨2017-06-08 11:05:28I2018-08-31 23:59:59.0네일아트업203091.641406460559.430362피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N0<NA><NA><NA><NA>00000N
279330900003090000-226-2018-0000120180807<NA>3폐업2폐업20230118<NA><NA><NA><NA>16.59132924서울특별시 도봉구 창동 659-29 삼성쉐르빌퍼스티 407호 지상4층서울특별시 도봉구 도봉로 476, 삼성쉐르빌퍼스티 지상4층 407호 (창동)1454유나뷰티2023-01-18 14:26:58U2022-11-30 22:00:00.0피부미용업202999.267517460616.701328<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
279430900003090000-226-2018-000022018-01-05<NA>1영업/정상1영업<NA><NA><NA><NA><NA>18.2132-863서울특별시 도봉구 쌍문동 80-50 지상1층서울특별시 도봉구 도봉로121길 5, 지상1층 (쌍문동)1440핑크모모(PINK MOMO)2023-10-23 10:59:10I2022-10-30 22:05:00.0네일아트업203112.303197461051.32785<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
279530900003090000-226-2020-000012020-07-03<NA>1영업/정상1영업<NA><NA><NA><NA>023493100626.4132-845서울특별시 도봉구 방학동 648-41 1층서울특별시 도봉구 도당로27길 15, 1층 (방학동)1352설레임2023-03-02 15:10:32U2022-12-03 00:04:00.0네일아트업203531.738029462847.426744<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
279630900003090000-226-2021-0000120210121<NA>1영업/정상1영업<NA><NA><NA><NA><NA>24.0132916서울특별시 도봉구 창동 478-3 1층서울특별시 도봉구 우이천로4다길 32, 1층 (창동)1479네일가(家)2021-01-21 17:06:12I2021-01-23 00:23:11.0네일아트업203514.222316459219.034904피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N2<NA><NA><NA><NA>00001N
279730900003090000-226-2024-000012024-03-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>16.83132-906서울특별시 도봉구 창동 262-18 1층서울특별시 도봉구 노해로63가길 29, 1층 (창동)1403퍼플뷰티 창동본점2024-03-18 17:00:48I2023-12-02 22:00:00.0메이크업업203901.872574461299.292702<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>