Overview

Dataset statistics

Number of variables47
Number of observations2835
Missing cells33882
Missing cells (%)25.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory404.0 B

Variable types

Categorical17
Text7
DateTime4
Unsupported7
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
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-17914/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 is highly imbalanced (57.1%)Imbalance
사용시작지하층 is highly imbalanced (61.4%)Imbalance
사용끝지하층 is highly imbalanced (76.5%)Imbalance
발한실여부 is highly imbalanced (97.8%)Imbalance
남성종사자수 is highly imbalanced (66.0%)Imbalance
다중이용업소여부 is highly imbalanced (99.5%)Imbalance
인허가취소일자 has 2835 (100.0%) missing valuesMissing
폐업일자 has 848 (29.9%) missing valuesMissing
휴업시작일자 has 2835 (100.0%) missing valuesMissing
휴업종료일자 has 2835 (100.0%) missing valuesMissing
재개업일자 has 2835 (100.0%) missing valuesMissing
전화번호 has 776 (27.4%) missing valuesMissing
도로명주소 has 1176 (41.5%) missing valuesMissing
도로명우편번호 has 1192 (42.0%) missing valuesMissing
좌표정보(X) has 201 (7.1%) missing valuesMissing
좌표정보(Y) has 201 (7.1%) missing valuesMissing
건물지상층수 has 623 (22.0%) missing valuesMissing
건물지하층수 has 1014 (35.8%) missing valuesMissing
사용시작지상층 has 852 (30.1%) missing valuesMissing
사용끝지상층 has 1350 (47.6%) missing valuesMissing
발한실여부 has 509 (18.0%) missing valuesMissing
좌석수 has 517 (18.2%) missing valuesMissing
조건부허가신고사유 has 2835 (100.0%) missing valuesMissing
조건부허가시작일자 has 2835 (100.0%) missing valuesMissing
조건부허가종료일자 has 2835 (100.0%) missing valuesMissing
여성종사자수 has 2345 (82.7%) missing valuesMissing
침대수 has 1964 (69.3%) missing valuesMissing
다중이용업소여부 has 469 (16.5%) missing valuesMissing
사용시작지상층 is highly skewed (γ1 = 40.62488785)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
조건부허가종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 1184 (41.8%) zerosZeros
건물지하층수 has 1261 (44.5%) zerosZeros
사용시작지상층 has 503 (17.7%) zerosZeros
사용끝지상층 has 52 (1.8%) zerosZeros
좌석수 has 197 (6.9%) zerosZeros
여성종사자수 has 470 (16.6%) zerosZeros
침대수 has 630 (22.2%) zerosZeros

Reproduction

Analysis started2024-04-06 11:41:26.625824
Analysis finished2024-04-06 11:41:29.762684
Duration3.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
3120000
2835 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3120000
2nd row3120000
3rd row3120000
4th row3120000
5th row3120000

Common Values

ValueCountFrequency (%)
3120000 2835
100.0%

Length

2024-04-06T20:41:29.964358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:30.249117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3120000 2835
100.0%

관리번호
Text

UNIQUE 

Distinct2835
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
2024-04-06T20:41:30.723375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters62370
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

Unique2835 ?
Unique (%)100.0%

Sample

1st row3120000-204-1906-00294
2nd row3120000-204-1967-01562
3rd row3120000-204-1968-00404
4th row3120000-204-1968-01558
5th row3120000-204-1969-00431
ValueCountFrequency (%)
3120000-204-1906-00294 1
 
< 0.1%
3120000-211-2017-00041 1
 
< 0.1%
3120000-211-2018-00017 1
 
< 0.1%
3120000-211-2018-00009 1
 
< 0.1%
3120000-211-2018-00010 1
 
< 0.1%
3120000-211-2018-00011 1
 
< 0.1%
3120000-211-2018-00012 1
 
< 0.1%
3120000-211-2018-00013 1
 
< 0.1%
3120000-211-2018-00014 1
 
< 0.1%
3120000-211-2018-00015 1
 
< 0.1%
Other values (2825) 2825
99.6%
2024-04-06T20:41:32.560709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23972
38.4%
2 9437
 
15.1%
1 8858
 
14.2%
- 8505
 
13.6%
3 4001
 
6.4%
9 2186
 
3.5%
4 1958
 
3.1%
8 947
 
1.5%
5 939
 
1.5%
7 787
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53865
86.4%
Dash Punctuation 8505
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23972
44.5%
2 9437
 
17.5%
1 8858
 
16.4%
3 4001
 
7.4%
9 2186
 
4.1%
4 1958
 
3.6%
8 947
 
1.8%
5 939
 
1.7%
7 787
 
1.5%
6 780
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 8505
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62370
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23972
38.4%
2 9437
 
15.1%
1 8858
 
14.2%
- 8505
 
13.6%
3 4001
 
6.4%
9 2186
 
3.5%
4 1958
 
3.1%
8 947
 
1.5%
5 939
 
1.5%
7 787
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23972
38.4%
2 9437
 
15.1%
1 8858
 
14.2%
- 8505
 
13.6%
3 4001
 
6.4%
9 2186
 
3.5%
4 1958
 
3.1%
8 947
 
1.5%
5 939
 
1.5%
7 787
 
1.3%
Distinct2141
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
Minimum1906-02-16 00:00:00
Maximum2024-04-03 00:00:00
2024-04-06T20:41:33.093694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:41:33.445005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2835
Missing (%)100.0%
Memory size25.0 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
3
1987 
1
848 

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 1987
70.1%
1 848
29.9%

Length

2024-04-06T20:41:33.792325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:34.014290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1987
70.1%
1 848
29.9%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
폐업
1987 
영업/정상
848 

Length

Max length5
Median length2
Mean length2.8973545
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1987
70.1%
영업/정상 848
29.9%

Length

2024-04-06T20:41:34.465147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:34.825777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1987
70.1%
영업/정상 848
29.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
2
1987 
1
848 

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 1987
70.1%
1 848
29.9%

Length

2024-04-06T20:41:35.019803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:35.191807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1987
70.1%
1 848
29.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
폐업
1987 
영업
848 

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 1987
70.1%
영업 848
29.9%

Length

2024-04-06T20:41:35.448589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:35.663033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1987
70.1%
영업 848
29.9%

폐업일자
Date

MISSING 

Distinct1515
Distinct (%)76.2%
Missing848
Missing (%)29.9%
Memory size22.3 KiB
Minimum1989-12-19 00:00:00
Maximum2024-04-02 00:00:00
2024-04-06T20:41:35.930859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:41:36.209971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2835
Missing (%)100.0%
Memory size25.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2835
Missing (%)100.0%
Memory size25.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2835
Missing (%)100.0%
Memory size25.0 KiB

전화번호
Text

MISSING 

Distinct1886
Distinct (%)91.6%
Missing776
Missing (%)27.4%
Memory size22.3 KiB
2024-04-06T20:41:36.608127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.4351627
Min length2

Characters and Unicode

Total characters19427
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

Unique1760 ?
Unique (%)85.5%

Sample

1st row02 3920413
2nd row0203521677
3rd row3794163
4th row0203862248
5th row3964222
ValueCountFrequency (%)
02 1168
35.4%
0 24
 
0.7%
0200000000 8
 
0.2%
0203341859 7
 
0.2%
070 6
 
0.2%
00000 5
 
0.2%
3735025 4
 
0.1%
362 4
 
0.1%
3932717 4
 
0.1%
394 4
 
0.1%
Other values (1831) 2066
62.6%
2024-04-06T20:41:37.351815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 3363
17.3%
0 3248
16.7%
2 3185
16.4%
1439
7.4%
6 1384
7.1%
7 1335
 
6.9%
1 1181
 
6.1%
9 1171
 
6.0%
5 1116
 
5.7%
4 1108
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17988
92.6%
Space Separator 1439
 
7.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3363
18.7%
0 3248
18.1%
2 3185
17.7%
6 1384
7.7%
7 1335
 
7.4%
1 1181
 
6.6%
9 1171
 
6.5%
5 1116
 
6.2%
4 1108
 
6.2%
8 897
 
5.0%
Space Separator
ValueCountFrequency (%)
1439
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3363
17.3%
0 3248
16.7%
2 3185
16.4%
1439
7.4%
6 1384
7.1%
7 1335
 
6.9%
1 1181
 
6.1%
9 1171
 
6.0%
5 1116
 
5.7%
4 1108
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3363
17.3%
0 3248
16.7%
2 3185
16.4%
1439
7.4%
6 1384
7.1%
7 1335
 
6.9%
1 1181
 
6.1%
9 1171
 
6.0%
5 1116
 
5.7%
4 1108
 
5.7%
Distinct1452
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
2024-04-06T20:41:37.986169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.0303351
Min length3

Characters and Unicode

Total characters14261
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1071 ?
Unique (%)37.8%

Sample

1st row72.71
2nd row10.73
3rd row12.22
4th row11.13
5th row59.44
ValueCountFrequency (%)
33.00 116
 
4.1%
00 85
 
3.0%
30.00 38
 
1.3%
16.50 32
 
1.1%
26.40 31
 
1.1%
20.00 26
 
0.9%
26.42 23
 
0.8%
49.50 23
 
0.8%
26.00 23
 
0.8%
66.00 22
 
0.8%
Other values (1442) 2416
85.2%
2024-04-06T20:41:38.888199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2835
19.9%
0 2471
17.3%
1 1472
10.3%
2 1391
9.8%
3 1179
8.3%
4 975
 
6.8%
6 973
 
6.8%
5 945
 
6.6%
8 717
 
5.0%
9 691
 
4.8%
Other values (2) 612
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11425
80.1%
Other Punctuation 2836
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2471
21.6%
1 1472
12.9%
2 1391
12.2%
3 1179
10.3%
4 975
 
8.5%
6 973
 
8.5%
5 945
 
8.3%
8 717
 
6.3%
9 691
 
6.0%
7 611
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 2835
> 99.9%
, 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14261
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2835
19.9%
0 2471
17.3%
1 1472
10.3%
2 1391
9.8%
3 1179
8.3%
4 975
 
6.8%
6 973
 
6.8%
5 945
 
6.6%
8 717
 
5.0%
9 691
 
4.8%
Other values (2) 612
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2835
19.9%
0 2471
17.3%
1 1472
10.3%
2 1391
9.8%
3 1179
8.3%
4 975
 
6.8%
6 973
 
6.8%
5 945
 
6.6%
8 717
 
5.0%
9 691
 
4.8%
Other values (2) 612
 
4.3%
Distinct154
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
2024-04-06T20:41:39.395744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0857143
Min length6

Characters and Unicode

Total characters17253
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

Unique28 ?
Unique (%)1.0%

Sample

1st row120833
2nd row120841
3rd row120842
4th row120100
5th row120861
ValueCountFrequency (%)
120808 319
 
11.3%
120809 121
 
4.3%
120834 111
 
3.9%
120100 93
 
3.3%
120833 91
 
3.2%
120857 91
 
3.2%
120807 82
 
2.9%
120805 79
 
2.8%
120825 69
 
2.4%
120812 63
 
2.2%
Other values (144) 1716
60.5%
2024-04-06T20:41:40.086765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4510
26.1%
1 3701
21.5%
2 3301
19.1%
8 2832
16.4%
3 663
 
3.8%
5 564
 
3.3%
4 460
 
2.7%
7 372
 
2.2%
9 353
 
2.0%
6 254
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17010
98.6%
Dash Punctuation 243
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4510
26.5%
1 3701
21.8%
2 3301
19.4%
8 2832
16.6%
3 663
 
3.9%
5 564
 
3.3%
4 460
 
2.7%
7 372
 
2.2%
9 353
 
2.1%
6 254
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4510
26.1%
1 3701
21.5%
2 3301
19.1%
8 2832
16.4%
3 663
 
3.8%
5 564
 
3.3%
4 460
 
2.7%
7 372
 
2.2%
9 353
 
2.0%
6 254
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4510
26.1%
1 3701
21.5%
2 3301
19.1%
8 2832
16.4%
3 663
 
3.8%
5 564
 
3.3%
4 460
 
2.7%
7 372
 
2.2%
9 353
 
2.0%
6 254
 
1.5%
Distinct2407
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
2024-04-06T20:41:40.643722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length50
Mean length26.659259
Min length17

Characters and Unicode

Total characters75579
Distinct characters317
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2084 ?
Unique (%)73.5%

Sample

1st row서울특별시 서대문구 창천동 20-27번지
2nd row서울특별시 서대문구 홍은동 9-38번지
3rd row서울특별시 서대문구 홍은동 11-388번지
4th row서울특별시 서대문구 홍은동 48-201번지
5th row서울특별시 서대문구 홍제동 334-87번지
ValueCountFrequency (%)
서울특별시 2835
21.1%
서대문구 2835
21.1%
대현동 519
 
3.9%
1층 387
 
2.9%
남가좌동 380
 
2.8%
홍제동 376
 
2.8%
북가좌동 334
 
2.5%
창천동 319
 
2.4%
홍은동 279
 
2.1%
연희동 226
 
1.7%
Other values (2533) 4949
36.8%
2024-04-06T20:41:41.429636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12895
 
17.1%
5679
 
7.5%
3415
 
4.5%
1 3013
 
4.0%
2920
 
3.9%
2860
 
3.8%
2849
 
3.8%
2844
 
3.8%
2842
 
3.8%
2839
 
3.8%
Other values (307) 33423
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44597
59.0%
Decimal Number 14214
 
18.8%
Space Separator 12895
 
17.1%
Dash Punctuation 2600
 
3.4%
Close Punctuation 412
 
0.5%
Open Punctuation 410
 
0.5%
Uppercase Letter 220
 
0.3%
Other Punctuation 201
 
0.3%
Lowercase Letter 25
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5679
12.7%
3415
 
7.7%
2920
 
6.5%
2860
 
6.4%
2849
 
6.4%
2844
 
6.4%
2842
 
6.4%
2839
 
6.4%
2835
 
6.4%
2308
 
5.2%
Other values (262) 13206
29.6%
Uppercase Letter
ValueCountFrequency (%)
D 38
17.3%
C 37
16.8%
M 36
16.4%
A 34
15.5%
B 32
14.5%
T 13
 
5.9%
P 11
 
5.0%
S 5
 
2.3%
E 3
 
1.4%
O 3
 
1.4%
Other values (7) 8
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 3013
21.2%
2 2170
15.3%
3 1911
13.4%
4 1417
10.0%
0 1283
9.0%
5 1124
 
7.9%
7 926
 
6.5%
6 869
 
6.1%
9 773
 
5.4%
8 728
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 187
93.0%
. 9
 
4.5%
& 2
 
1.0%
? 1
 
0.5%
# 1
 
0.5%
/ 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
e 20
80.0%
s 1
 
4.0%
k 1
 
4.0%
r 1
 
4.0%
w 1
 
4.0%
o 1
 
4.0%
Space Separator
ValueCountFrequency (%)
12895
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2600
100.0%
Close Punctuation
ValueCountFrequency (%)
) 412
100.0%
Open Punctuation
ValueCountFrequency (%)
( 410
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44596
59.0%
Common 30736
40.7%
Latin 246
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5679
12.7%
3415
 
7.7%
2920
 
6.5%
2860
 
6.4%
2849
 
6.4%
2844
 
6.4%
2842
 
6.4%
2839
 
6.4%
2835
 
6.4%
2308
 
5.2%
Other values (261) 13205
29.6%
Latin
ValueCountFrequency (%)
D 38
15.4%
C 37
15.0%
M 36
14.6%
A 34
13.8%
B 32
13.0%
e 20
8.1%
T 13
 
5.3%
P 11
 
4.5%
S 5
 
2.0%
E 3
 
1.2%
Other values (14) 17
6.9%
Common
ValueCountFrequency (%)
12895
42.0%
1 3013
 
9.8%
- 2600
 
8.5%
2 2170
 
7.1%
3 1911
 
6.2%
4 1417
 
4.6%
0 1283
 
4.2%
5 1124
 
3.7%
7 926
 
3.0%
6 869
 
2.8%
Other values (11) 2528
 
8.2%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44595
59.0%
ASCII 30981
41.0%
Compat Jamo 1
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12895
41.6%
1 3013
 
9.7%
- 2600
 
8.4%
2 2170
 
7.0%
3 1911
 
6.2%
4 1417
 
4.6%
0 1283
 
4.1%
5 1124
 
3.6%
7 926
 
3.0%
6 869
 
2.8%
Other values (34) 2773
 
9.0%
Hangul
ValueCountFrequency (%)
5679
12.7%
3415
 
7.7%
2920
 
6.5%
2860
 
6.4%
2849
 
6.4%
2844
 
6.4%
2842
 
6.4%
2839
 
6.4%
2835
 
6.4%
2308
 
5.2%
Other values (260) 13204
29.6%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct1570
Distinct (%)94.6%
Missing1176
Missing (%)41.5%
Memory size22.3 KiB
2024-04-06T20:41:42.002076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length59
Mean length33.531646
Min length22

Characters and Unicode

Total characters55629
Distinct characters313
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1491 ?
Unique (%)89.9%

Sample

1st row서울특별시 서대문구 거북골로 210 (북가좌동)
2nd row서울특별시 서대문구 북아현로 26-14 (북아현동, 1층)
3rd row서울특별시 서대문구 북아현로6길 27 (북아현동)
4th row서울특별시 서대문구 연대동문길 37 (대신동)
5th row서울특별시 서대문구 연희로41길 4 (홍은동, 2층)
ValueCountFrequency (%)
서울특별시 1659
 
15.5%
서대문구 1659
 
15.5%
1층 547
 
5.1%
2층 263
 
2.5%
대현동 252
 
2.4%
홍제동 216
 
2.0%
창천동 202
 
1.9%
북가좌동 181
 
1.7%
남가좌동 154
 
1.4%
홍은동 153
 
1.4%
Other values (1365) 5427
50.7%
2024-04-06T20:41:42.933535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9058
 
16.3%
3375
 
6.1%
2306
 
4.1%
1 2304
 
4.1%
) 1819
 
3.3%
( 1818
 
3.3%
1799
 
3.2%
1723
 
3.1%
1716
 
3.1%
1686
 
3.0%
Other values (303) 28025
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32897
59.1%
Space Separator 9058
 
16.3%
Decimal Number 7759
 
13.9%
Close Punctuation 1819
 
3.3%
Open Punctuation 1818
 
3.3%
Other Punctuation 1685
 
3.0%
Dash Punctuation 340
 
0.6%
Uppercase Letter 219
 
0.4%
Lowercase Letter 27
 
< 0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3375
 
10.3%
2306
 
7.0%
1799
 
5.5%
1723
 
5.2%
1716
 
5.2%
1686
 
5.1%
1668
 
5.1%
1663
 
5.1%
1659
 
5.0%
1300
 
4.0%
Other values (259) 14002
42.6%
Uppercase Letter
ValueCountFrequency (%)
B 51
23.3%
C 38
17.4%
D 38
17.4%
M 37
16.9%
A 20
 
9.1%
T 6
 
2.7%
S 6
 
2.7%
E 5
 
2.3%
P 4
 
1.8%
O 4
 
1.8%
Other values (7) 10
 
4.6%
Decimal Number
ValueCountFrequency (%)
1 2304
29.7%
2 1377
17.7%
3 874
 
11.3%
0 766
 
9.9%
4 549
 
7.1%
5 529
 
6.8%
7 348
 
4.5%
6 343
 
4.4%
8 338
 
4.4%
9 331
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
e 22
81.5%
o 1
 
3.7%
w 1
 
3.7%
r 1
 
3.7%
s 1
 
3.7%
k 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 1678
99.6%
. 2
 
0.1%
# 2
 
0.1%
& 2
 
0.1%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
9058
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1819
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1818
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 340
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32897
59.1%
Common 22485
40.4%
Latin 247
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3375
 
10.3%
2306
 
7.0%
1799
 
5.5%
1723
 
5.2%
1716
 
5.2%
1686
 
5.1%
1668
 
5.1%
1663
 
5.1%
1659
 
5.0%
1300
 
4.0%
Other values (259) 14002
42.6%
Latin
ValueCountFrequency (%)
B 51
20.6%
C 38
15.4%
D 38
15.4%
M 37
15.0%
e 22
8.9%
A 20
 
8.1%
T 6
 
2.4%
S 6
 
2.4%
E 5
 
2.0%
P 4
 
1.6%
Other values (14) 20
 
8.1%
Common
ValueCountFrequency (%)
9058
40.3%
1 2304
 
10.2%
) 1819
 
8.1%
( 1818
 
8.1%
, 1678
 
7.5%
2 1377
 
6.1%
3 874
 
3.9%
0 766
 
3.4%
4 549
 
2.4%
5 529
 
2.4%
Other values (10) 1713
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32897
59.1%
ASCII 22731
40.9%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9058
39.8%
1 2304
 
10.1%
) 1819
 
8.0%
( 1818
 
8.0%
, 1678
 
7.4%
2 1377
 
6.1%
3 874
 
3.8%
0 766
 
3.4%
4 549
 
2.4%
5 529
 
2.3%
Other values (33) 1959
 
8.6%
Hangul
ValueCountFrequency (%)
3375
 
10.3%
2306
 
7.0%
1799
 
5.5%
1723
 
5.2%
1716
 
5.2%
1686
 
5.1%
1668
 
5.1%
1663
 
5.1%
1659
 
5.0%
1300
 
4.0%
Other values (259) 14002
42.6%
Number Forms
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct166
Distinct (%)10.1%
Missing1192
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean3712.0791
Minimum3600
Maximum3791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:43.251582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3600
5-th percentile3616
Q13667
median3713
Q33766
95-th percentile3787
Maximum3791
Range191
Interquartile range (IQR)99

Descriptive statistics

Standard deviation57.140923
Coefficient of variation (CV)0.01539324
Kurtosis-1.1906087
Mean3712.0791
Median Absolute Deviation (MAD)53
Skewness-0.33003627
Sum6098946
Variance3265.0851
MonotonicityNot monotonic
2024-04-06T20:41:43.593176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3766 194
 
6.8%
3779 51
 
1.8%
3789 45
 
1.6%
3767 41
 
1.4%
3665 38
 
1.3%
3765 34
 
1.2%
3709 34
 
1.2%
3780 29
 
1.0%
3712 26
 
0.9%
3675 25
 
0.9%
Other values (156) 1126
39.7%
(Missing) 1192
42.0%
ValueCountFrequency (%)
3600 7
 
0.2%
3601 5
 
0.2%
3602 4
 
0.1%
3604 7
 
0.2%
3605 21
0.7%
3606 3
 
0.1%
3607 1
 
< 0.1%
3610 1
 
< 0.1%
3611 3
 
0.1%
3612 7
 
0.2%
ValueCountFrequency (%)
3791 5
 
0.2%
3789 45
1.6%
3788 20
0.7%
3787 14
 
0.5%
3786 13
 
0.5%
3785 13
 
0.5%
3784 12
 
0.4%
3783 3
 
0.1%
3782 1
 
< 0.1%
3781 4
 
0.1%
Distinct2430
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
2024-04-06T20:41:44.170778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length5.7425044
Min length1

Characters and Unicode

Total characters16280
Distinct characters695
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2159 ?
Unique (%)76.2%

Sample

1st row가위앤가위
2nd row희망
3rd row
4th row우리
5th row예스(Yes)헤어스케치
ValueCountFrequency (%)
헤어 94
 
2.6%
미용실 31
 
0.9%
네일 30
 
0.8%
hair 29
 
0.8%
신촌점 20
 
0.6%
에스테틱 18
 
0.5%
헤어샵 14
 
0.4%
헤어살롱 13
 
0.4%
13
 
0.4%
스킨케어 11
 
0.3%
Other values (2589) 3297
92.4%
2024-04-06T20:41:45.081787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1109
 
6.8%
1076
 
6.6%
736
 
4.5%
506
 
3.1%
378
 
2.3%
366
 
2.2%
363
 
2.2%
330
 
2.0%
329
 
2.0%
210
 
1.3%
Other values (685) 10877
66.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13687
84.1%
Space Separator 736
 
4.5%
Lowercase Letter 636
 
3.9%
Uppercase Letter 585
 
3.6%
Open Punctuation 180
 
1.1%
Close Punctuation 180
 
1.1%
Decimal Number 147
 
0.9%
Other Punctuation 121
 
0.7%
Dash Punctuation 7
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1109
 
8.1%
1076
 
7.9%
506
 
3.7%
378
 
2.8%
366
 
2.7%
363
 
2.7%
330
 
2.4%
329
 
2.4%
210
 
1.5%
209
 
1.5%
Other values (611) 8811
64.4%
Uppercase Letter
ValueCountFrequency (%)
A 62
 
10.6%
H 57
 
9.7%
I 48
 
8.2%
N 46
 
7.9%
S 37
 
6.3%
L 37
 
6.3%
O 36
 
6.2%
B 30
 
5.1%
R 27
 
4.6%
E 25
 
4.3%
Other values (16) 180
30.8%
Lowercase Letter
ValueCountFrequency (%)
a 101
15.9%
i 82
12.9%
n 56
8.8%
e 54
8.5%
l 50
7.9%
o 50
7.9%
r 49
7.7%
t 28
 
4.4%
h 26
 
4.1%
y 25
 
3.9%
Other values (13) 115
18.1%
Decimal Number
ValueCountFrequency (%)
0 44
29.9%
2 31
21.1%
1 24
16.3%
3 14
 
9.5%
5 8
 
5.4%
9 7
 
4.8%
8 7
 
4.8%
4 7
 
4.8%
7 4
 
2.7%
6 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
? 52
43.0%
& 29
24.0%
, 12
 
9.9%
. 11
 
9.1%
' 7
 
5.8%
# 6
 
5.0%
! 3
 
2.5%
/ 1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 178
98.9%
[ 2
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 178
98.9%
] 2
 
1.1%
Space Separator
ValueCountFrequency (%)
736
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13683
84.0%
Common 1372
 
8.4%
Latin 1221
 
7.5%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1109
 
8.1%
1076
 
7.9%
506
 
3.7%
378
 
2.8%
366
 
2.7%
363
 
2.7%
330
 
2.4%
329
 
2.4%
210
 
1.5%
209
 
1.5%
Other values (609) 8807
64.4%
Latin
ValueCountFrequency (%)
a 101
 
8.3%
i 82
 
6.7%
A 62
 
5.1%
H 57
 
4.7%
n 56
 
4.6%
e 54
 
4.4%
l 50
 
4.1%
o 50
 
4.1%
r 49
 
4.0%
I 48
 
3.9%
Other values (39) 612
50.1%
Common
ValueCountFrequency (%)
736
53.6%
( 178
 
13.0%
) 178
 
13.0%
? 52
 
3.8%
0 44
 
3.2%
2 31
 
2.3%
& 29
 
2.1%
1 24
 
1.7%
3 14
 
1.0%
, 12
 
0.9%
Other values (15) 74
 
5.4%
Han
ValueCountFrequency (%)
3
75.0%
1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13682
84.0%
ASCII 2593
 
15.9%
CJK 4
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1109
 
8.1%
1076
 
7.9%
506
 
3.7%
378
 
2.8%
366
 
2.7%
363
 
2.7%
330
 
2.4%
329
 
2.4%
210
 
1.5%
209
 
1.5%
Other values (608) 8806
64.4%
ASCII
ValueCountFrequency (%)
736
28.4%
( 178
 
6.9%
) 178
 
6.9%
a 101
 
3.9%
i 82
 
3.2%
A 62
 
2.4%
H 57
 
2.2%
n 56
 
2.2%
e 54
 
2.1%
? 52
 
2.0%
Other values (64) 1037
40.0%
CJK
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Distinct2146
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
Minimum1999-02-01 00:00:00
Maximum2024-04-03 10:40:32
2024-04-06T20:41:45.454872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:41:45.788770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
I
2122 
U
707 
D
 
6

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 2122
74.9%
U 707
 
24.9%
D 6
 
0.2%

Length

2024-04-06T20:41:46.133399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:46.372613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2122
74.9%
u 707
 
24.9%
d 6
 
0.2%
Distinct621
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:06:00
2024-04-06T20:41:46.576788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:41:46.846309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
일반미용업
2268 
피부미용업
306 
네일아트업
 
211
메이크업업
 
43
기타
 
7

Length

Max length5
Median length5
Mean length4.9925926
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2268
80.0%
피부미용업 306
 
10.8%
네일아트업 211
 
7.4%
메이크업업 43
 
1.5%
기타 7
 
0.2%

Length

2024-04-06T20:41:47.081833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:47.422861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2268
80.0%
피부미용업 306
 
10.8%
네일아트업 211
 
7.4%
메이크업업 43
 
1.5%
기타 7
 
0.2%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1490
Distinct (%)56.6%
Missing201
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean194427.9
Minimum191497.87
Maximum197144.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:47.677582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191497.87
5-th percentile192183.49
Q1193458.89
median194851.66
Q3195186.1
95-th percentile196382.47
Maximum197144.02
Range5646.1484
Interquartile range (IQR)1727.2083

Descriptive statistics

Standard deviation1248.4625
Coefficient of variation (CV)0.0064212103
Kurtosis-0.6383401
Mean194427.9
Median Absolute Deviation (MAD)676.32678
Skewness-0.33674268
Sum5.121231 × 108
Variance1558658.5
MonotonicityNot monotonic
2024-04-06T20:41:47.927645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194474.81938437 34
 
1.2%
194960.929138694 16
 
0.6%
194907.857352331 13
 
0.5%
195057.033841623 12
 
0.4%
192874.514454351 11
 
0.4%
195477.913016234 11
 
0.4%
192672.067512964 11
 
0.4%
195030.072804422 10
 
0.4%
192209.323632506 10
 
0.4%
194265.067639805 8
 
0.3%
Other values (1480) 2498
88.1%
(Missing) 201
 
7.1%
ValueCountFrequency (%)
191497.867040904 2
0.1%
191526.125663524 1
< 0.1%
191540.260492627 1
< 0.1%
191552.612967645 1
< 0.1%
191580.964968934 2
0.1%
191581.803953246 1
< 0.1%
191615.037104598 1
< 0.1%
191669.676111651 1
< 0.1%
191673.051071881 1
< 0.1%
191699.043549683 1
< 0.1%
ValueCountFrequency (%)
197144.015440398 1
 
< 0.1%
197044.746327258 1
 
< 0.1%
197028.785113745 1
 
< 0.1%
196945.454641052 3
0.1%
196899.242661444 1
 
< 0.1%
196884.166631688 2
0.1%
196872.448012674 2
0.1%
196866.910861104 2
0.1%
196861.226817567 1
 
< 0.1%
196853.605651298 3
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1490
Distinct (%)56.6%
Missing201
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean452175.1
Minimum450379.22
Maximum455710.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:48.160613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum450379.22
5-th percentile450501.07
Q1450708.89
median451976.36
Q3453307.55
95-th percentile454725.82
Maximum455710.43
Range5331.2135
Interquartile range (IQR)2598.665

Descriptive statistics

Standard deviation1477.2516
Coefficient of variation (CV)0.0032669901
Kurtosis-1.094302
Mean452175.1
Median Absolute Deviation (MAD)1287.4455
Skewness0.42113606
Sum1.1910292 × 109
Variance2182272.2
MonotonicityNot monotonic
2024-04-06T20:41:48.476976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450422.374151297 34
 
1.2%
450652.212057734 16
 
0.6%
450641.278768893 13
 
0.5%
454786.019433577 12
 
0.4%
452525.219515622 11
 
0.4%
453929.325030582 11
 
0.4%
452221.960661496 11
 
0.4%
450537.21301229 10
 
0.4%
452067.039429636 10
 
0.4%
450433.691021245 8
 
0.3%
Other values (1480) 2498
88.1%
(Missing) 201
 
7.1%
ValueCountFrequency (%)
450379.216435725 1
 
< 0.1%
450387.139950052 2
 
0.1%
450388.329359668 5
0.2%
450391.53849739 1
 
< 0.1%
450392.774100774 3
0.1%
450400.49762862 2
 
0.1%
450404.173010287 3
0.1%
450404.707893709 1
 
< 0.1%
450406.804177438 3
0.1%
450415.700852123 2
 
0.1%
ValueCountFrequency (%)
455710.429910779 8
0.3%
455689.567479919 2
 
0.1%
455588.317096857 3
 
0.1%
455510.91644523 2
 
0.1%
455484.478875325 1
 
< 0.1%
455430.406456381 1
 
< 0.1%
455426.290378741 1
 
< 0.1%
455422.498445639 1
 
< 0.1%
455411.720077741 1
 
< 0.1%
455362.439652942 1
 
< 0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
미용업
1121 
일반미용업
688 
<NA>
469 
피부미용업
221 
종합미용업
170 
Other values (11)
166 

Length

Max length23
Median length16
Mean length4.3552028
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1121
39.5%
일반미용업 688
24.3%
<NA> 469
16.5%
피부미용업 221
 
7.8%
종합미용업 170
 
6.0%
네일미용업 81
 
2.9%
피부미용업, 네일미용업 23
 
0.8%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 15
 
0.5%
일반미용업, 화장ㆍ분장 미용업 10
 
0.4%
화장ㆍ분장 미용업 9
 
0.3%
Other values (6) 28
 
1.0%

Length

2024-04-06T20:41:48.688667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1172
39.3%
일반미용업 725
24.3%
na 469
15.7%
피부미용업 258
 
8.6%
종합미용업 170
 
5.7%
네일미용업 138
 
4.6%
화장ㆍ분장 51
 
1.7%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)0.9%
Missing623
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean1.6428571
Minimum0
Maximum21
Zeros1184
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:49.342012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile5
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4160118
Coefficient of variation (CV)1.4706159
Kurtosis13.319737
Mean1.6428571
Median Absolute Deviation (MAD)0
Skewness2.7932409
Sum3634
Variance5.8371131
MonotonicityNot monotonic
2024-04-06T20:41:49.512544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1184
41.8%
3 332
 
11.7%
2 242
 
8.5%
4 192
 
6.8%
5 99
 
3.5%
1 78
 
2.8%
7 23
 
0.8%
6 23
 
0.8%
15 10
 
0.4%
8 6
 
0.2%
Other values (10) 23
 
0.8%
(Missing) 623
22.0%
ValueCountFrequency (%)
0 1184
41.8%
1 78
 
2.8%
2 242
 
8.5%
3 332
 
11.7%
4 192
 
6.8%
5 99
 
3.5%
6 23
 
0.8%
7 23
 
0.8%
8 6
 
0.2%
9 4
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 3
 
0.1%
18 3
 
0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 10
0.4%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
0.1%
10 4
 
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.4%
Missing1014
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean0.36628226
Minimum0
Maximum7
Zeros1261
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:49.682263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.68688248
Coefficient of variation (CV)1.8752819
Kurtosis23.112742
Mean0.36628226
Median Absolute Deviation (MAD)0
Skewness3.6660741
Sum667
Variance0.47180754
MonotonicityNot monotonic
2024-04-06T20:41:49.856339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1261
44.5%
1 509
18.0%
2 24
 
0.8%
3 15
 
0.5%
5 5
 
0.2%
6 3
 
0.1%
7 2
 
0.1%
4 2
 
0.1%
(Missing) 1014
35.8%
ValueCountFrequency (%)
0 1261
44.5%
1 509
18.0%
2 24
 
0.8%
3 15
 
0.5%
4 2
 
0.1%
5 5
 
0.2%
6 3
 
0.1%
7 2
 
0.1%
ValueCountFrequency (%)
7 2
 
0.1%
6 3
 
0.1%
5 5
 
0.2%
4 2
 
0.1%
3 15
 
0.5%
2 24
 
0.8%
1 509
18.0%
0 1261
44.5%

사용시작지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct12
Distinct (%)0.6%
Missing852
Missing (%)30.1%
Infinite0
Infinite (%)0.0%
Mean1.2945033
Minimum0
Maximum206
Zeros503
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:50.042871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum206
Range206
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.7435766
Coefficient of variation (CV)3.664399
Kurtosis1752.1769
Mean1.2945033
Median Absolute Deviation (MAD)1
Skewness40.624888
Sum2567
Variance22.501519
MonotonicityNot monotonic
2024-04-06T20:41:50.225795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 915
32.3%
0 503
17.7%
2 389
13.7%
3 113
 
4.0%
4 33
 
1.2%
5 11
 
0.4%
7 8
 
0.3%
6 4
 
0.1%
10 3
 
0.1%
9 2
 
0.1%
Other values (2) 2
 
0.1%
(Missing) 852
30.1%
ValueCountFrequency (%)
0 503
17.7%
1 915
32.3%
2 389
13.7%
3 113
 
4.0%
4 33
 
1.2%
5 11
 
0.4%
6 4
 
0.1%
7 8
 
0.3%
9 2
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
206 1
 
< 0.1%
14 1
 
< 0.1%
10 3
 
0.1%
9 2
 
0.1%
7 8
 
0.3%
6 4
 
0.1%
5 11
 
0.4%
4 33
 
1.2%
3 113
 
4.0%
2 389
13.7%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.7%
Missing1350
Missing (%)47.6%
Infinite0
Infinite (%)0.0%
Mean1.5488215
Minimum0
Maximum14
Zeros52
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:50.414456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1049114
Coefficient of variation (CV)0.71338843
Kurtosis22.782129
Mean1.5488215
Median Absolute Deviation (MAD)0
Skewness3.516912
Sum2300
Variance1.2208291
MonotonicityNot monotonic
2024-04-06T20:41:50.733893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 885
31.2%
2 369
 
13.0%
3 115
 
4.1%
0 52
 
1.8%
4 38
 
1.3%
5 9
 
0.3%
7 7
 
0.2%
6 4
 
0.1%
10 3
 
0.1%
9 2
 
0.1%
(Missing) 1350
47.6%
ValueCountFrequency (%)
0 52
 
1.8%
1 885
31.2%
2 369
13.0%
3 115
 
4.1%
4 38
 
1.3%
5 9
 
0.3%
6 4
 
0.1%
7 7
 
0.2%
9 2
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 3
 
0.1%
9 2
 
0.1%
7 7
 
0.2%
6 4
 
0.1%
5 9
 
0.3%
4 38
 
1.3%
3 115
 
4.1%
2 369
13.0%
1 885
31.2%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
<NA>
2120 
0
601 
1
 
103
2
 
7
3
 
3

Length

Max length4
Median length4
Mean length3.2433862
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2120
74.8%
0 601
 
21.2%
1 103
 
3.6%
2 7
 
0.2%
3 3
 
0.1%
4 1
 
< 0.1%

Length

2024-04-06T20:41:50.994017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:51.192742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2120
74.8%
0 601
 
21.2%
1 103
 
3.6%
2 7
 
0.2%
3 3
 
0.1%
4 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
<NA>
2579 
0
 
147
1
 
99
2
 
7
3
 
3

Length

Max length4
Median length4
Mean length3.7291005
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> 2579
91.0%
0 147
 
5.2%
1 99
 
3.5%
2 7
 
0.2%
3 3
 
0.1%

Length

2024-04-06T20:41:51.386073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:51.585672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2579
91.0%
0 147
 
5.2%
1 99
 
3.5%
2 7
 
0.2%
3 3
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
0
1426 
<NA>
1409 

Length

Max length4
Median length1
Mean length2.4910053
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1426
50.3%
<NA> 1409
49.7%

Length

2024-04-06T20:41:51.795557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:51.999361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1426
50.3%
na 1409
49.7%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
0
1426 
<NA>
1409 

Length

Max length4
Median length1
Mean length2.4910053
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1426
50.3%
<NA> 1409
49.7%

Length

2024-04-06T20:41:52.187424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:52.348572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1426
50.3%
na 1409
49.7%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
0
1426 
<NA>
1409 

Length

Max length4
Median length1
Mean length2.4910053
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1426
50.3%
<NA> 1409
49.7%

Length

2024-04-06T20:41:52.509184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:52.700353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1426
50.3%
na 1409
49.7%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing509
Missing (%)18.0%
Memory size5.7 KiB
False
2321 
True
 
5
(Missing)
509 
ValueCountFrequency (%)
False 2321
81.9%
True 5
 
0.2%
(Missing) 509
 
18.0%
2024-04-06T20:41:52.853537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct33
Distinct (%)1.4%
Missing517
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean4.0500431
Minimum0
Maximum131
Zeros197
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:53.045385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median3
Q35
95-th percentile10
Maximum131
Range131
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.2666918
Coefficient of variation (CV)1.0534929
Kurtosis345.60712
Mean4.0500431
Median Absolute Deviation (MAD)1
Skewness13.054007
Sum9388
Variance18.204659
MonotonicityNot monotonic
2024-04-06T20:41:53.292748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3 789
27.8%
4 356
12.6%
2 333
11.7%
0 197
 
6.9%
5 174
 
6.1%
6 125
 
4.4%
8 76
 
2.7%
7 59
 
2.1%
10 46
 
1.6%
1 45
 
1.6%
Other values (23) 118
 
4.2%
(Missing) 517
18.2%
ValueCountFrequency (%)
0 197
 
6.9%
1 45
 
1.6%
2 333
11.7%
3 789
27.8%
4 356
12.6%
5 174
 
6.1%
6 125
 
4.4%
7 59
 
2.1%
8 76
 
2.7%
9 28
 
1.0%
ValueCountFrequency (%)
131 1
< 0.1%
40 1
< 0.1%
36 1
< 0.1%
34 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
28 1
< 0.1%
27 1
< 0.1%
25 2
0.1%
24 1
< 0.1%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2835
Missing (%)100.0%
Memory size25.0 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2835
Missing (%)100.0%
Memory size25.0 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2835
Missing (%)100.0%
Memory size25.0 KiB
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
<NA>
1945 
임대
865 
자가
 
25

Length

Max length4
Median length4
Mean length3.372134
Min length2

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> 1945
68.6%
임대 865
30.5%
자가 25
 
0.9%

Length

2024-04-06T20:41:53.560614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:53.765453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1945
68.6%
임대 865
30.5%
자가 25
 
0.9%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
<NA>
1861 
0
974 

Length

Max length4
Median length4
Mean length2.9693122
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> 1861
65.6%
0 974
34.4%

Length

2024-04-06T20:41:53.987026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:54.189385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1861
65.6%
0 974
34.4%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)1.2%
Missing2345
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean0.081632653
Minimum0
Maximum13
Zeros470
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:54.378363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.66881254
Coefficient of variation (CV)8.1929536
Kurtosis289.33177
Mean0.081632653
Median Absolute Deviation (MAD)0
Skewness15.67768
Sum40
Variance0.44731021
MonotonicityNot monotonic
2024-04-06T20:41:54.585916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 470
 
16.6%
1 15
 
0.5%
3 2
 
0.1%
13 1
 
< 0.1%
4 1
 
< 0.1%
2 1
 
< 0.1%
(Missing) 2345
82.7%
ValueCountFrequency (%)
0 470
16.6%
1 15
 
0.5%
2 1
 
< 0.1%
3 2
 
0.1%
4 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
4 1
 
< 0.1%
3 2
 
0.1%
2 1
 
< 0.1%
1 15
 
0.5%
0 470
16.6%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
<NA>
2345 
0
485 
1
 
3
2
 
2

Length

Max length4
Median length4
Mean length3.4814815
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> 2345
82.7%
0 485
 
17.1%
1 3
 
0.1%
2 2
 
0.1%

Length

2024-04-06T20:41:54.813807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:55.005926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2345
82.7%
0 485
 
17.1%
1 3
 
0.1%
2 2
 
0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
<NA>
1940 
0
895 

Length

Max length4
Median length4
Mean length3.0529101
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> 1940
68.4%
0 895
31.6%

Length

2024-04-06T20:41:55.223219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:41:55.389055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1940
68.4%
0 895
31.6%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)1.4%
Missing1964
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean0.91504018
Minimum0
Maximum14
Zeros630
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2024-04-06T20:41:55.633657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9463237
Coefficient of variation (CV)2.1270363
Kurtosis9.3771738
Mean0.91504018
Median Absolute Deviation (MAD)0
Skewness2.8254879
Sum797
Variance3.7881758
MonotonicityNot monotonic
2024-04-06T20:41:55.944294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 630
 
22.2%
2 67
 
2.4%
1 51
 
1.8%
3 48
 
1.7%
4 17
 
0.6%
5 17
 
0.6%
6 15
 
0.5%
7 8
 
0.3%
8 7
 
0.2%
9 5
 
0.2%
Other values (2) 6
 
0.2%
(Missing) 1964
69.3%
ValueCountFrequency (%)
0 630
22.2%
1 51
 
1.8%
2 67
 
2.4%
3 48
 
1.7%
4 17
 
0.6%
5 17
 
0.6%
6 15
 
0.5%
7 8
 
0.3%
8 7
 
0.2%
9 5
 
0.2%
ValueCountFrequency (%)
14 2
 
0.1%
10 4
 
0.1%
9 5
 
0.2%
8 7
 
0.2%
7 8
 
0.3%
6 15
 
0.5%
5 17
 
0.6%
4 17
 
0.6%
3 48
1.7%
2 67
2.4%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing469
Missing (%)16.5%
Memory size5.7 KiB
False
2365 
True
 
1
(Missing)
469 
ValueCountFrequency (%)
False 2365
83.4%
True 1
 
< 0.1%
(Missing) 469
 
16.5%
2024-04-06T20:41:56.152783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031200003120000-204-1906-0029419060216<NA>3폐업2폐업19971010<NA><NA><NA>02 392041372.71120833서울특별시 서대문구 창천동 20-27번지<NA><NA>가위앤가위2002-04-26 00:00:00I2018-08-31 23:59:59.0일반미용업194636.40433450457.88555미용업000<NA>0<NA>000N8<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131200003120000-204-1967-0156219670125<NA>3폐업2폐업19990625<NA><NA><NA>020352167710.73120841서울특별시 서대문구 홍은동 9-38번지<NA><NA>희망1999-06-30 00:00:00I2018-08-31 23:59:59.0일반미용업195454.033625455426.290379미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231200003120000-204-1968-0040419680817<NA>3폐업2폐업20090313<NA><NA><NA>379416312.22120842서울특별시 서대문구 홍은동 11-388번지<NA><NA>2003-06-30 00:00:00I2018-08-31 23:59:59.0일반미용업195173.040326455110.696225미용업4<NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331200003120000-204-1968-0155819680530<NA>3폐업2폐업19990318<NA><NA><NA>020386224811.13120100서울특별시 서대문구 홍은동 48-201번지<NA><NA>우리2002-04-09 00:00:00I2018-08-31 23:59:59.0일반미용업194874.258801454437.09684미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431200003120000-204-1969-0043119690701<NA>3폐업2폐업20070205<NA><NA><NA>396422259.44120861서울특별시 서대문구 홍제동 334-87번지<NA><NA>예스(Yes)헤어스케치2004-02-26 00:00:00I2018-08-31 23:59:59.0일반미용업194323.20508453944.564301미용업3111<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531200003120000-204-1969-0138119690319<NA>3폐업2폐업20070220<NA><NA><NA>313070916.17120160서울특별시 서대문구 대신동 124-8번지<NA><NA>롯데2003-07-07 00:00:00I2018-08-31 23:59:59.0일반미용업195183.609736451674.477574미용업2<NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631200003120000-204-1969-0139819690128<NA>3폐업2폐업20090528<NA><NA><NA>392495415.60120160서울특별시 서대문구 대신동 124-16번지<NA><NA>깔롱헤어라인2007-05-31 00:00:00I2018-08-31 23:59:59.0일반미용업195196.312336451647.254957미용업5<NA>11<NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731200003120000-204-1969-0155919691030<NA>3폐업2폐업19980409<NA><NA><NA>02 356326227.13120841서울특별시 서대문구 홍은동 9-92번지<NA><NA>조이머리방2001-10-04 00:00:00I2018-08-31 23:59:59.0일반미용업195369.036375455205.60923미용업000<NA>0<NA>000N5<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831200003120000-204-1970-0028219700922<NA>3폐업2폐업20001029<NA><NA><NA>020324368123.76120834서울특별시 서대문구 창천동 33-45번지<NA><NA>계명2002-12-09 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N5<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931200003120000-204-1970-0034819701123<NA>3폐업2폐업20020903<NA><NA><NA>020312372913.78120818서울특별시 서대문구 북아현동 197-8번지<NA><NA>2002-04-08 00:00:00I2018-08-31 23:59:59.0일반미용업195658.621292451092.809067미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
282531200003120000-226-2014-0000120140617<NA>3폐업2폐업20190531<NA><NA><NA>02 312293426.40120808서울특별시 서대문구 대현동 60-9번지서울특별시 서대문구 이화여대길 42 (대현동)3764봉숭아 손톱2019-05-31 12:52:22U2019-06-02 02:40:00.0네일아트업195147.402737450735.341627피부미용업, 네일미용업, 화장ㆍ분장 미용업2122<NA><NA>000N6<NA><NA><NA>임대0<NA><NA>01N
282631200003120000-226-2016-0000120160322<NA>3폐업2폐업20171027<NA><NA><NA>02 394 234518.97120857서울특별시 서대문구 홍제동 238-2번지 1층서울특별시 서대문구 세무서길 40, 1층 (홍제동)3627핑키네일2017-10-27 13:41:09I2018-08-31 23:59:59.0네일아트업195153.605303454157.737568피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N4<NA><NA><NA>임대00001N
282731200003120000-226-2018-0000120180725<NA>1영업/정상1영업<NA><NA><NA><NA>02 332424187.91120834서울특별시 서대문구 창천동 72-12번지 거화빌딩 케이스퀘어 신촌 2층 우측일부서울특별시 서대문구 신촌로 73, 케이스퀘어 신촌 2층 우측일부호 (창천동)3789포쉬네일 신촌점2019-03-12 09:51:34U2019-03-14 02:40:00.0네일아트업194170.408173450453.360532피부미용업, 네일미용업, 화장ㆍ분장 미용업0022<NA><NA>000N12<NA><NA><NA>임대00002N
282831200003120000-226-2019-0000120190611<NA>1영업/정상1영업<NA><NA><NA><NA>02 308318845.00120812서울특별시 서대문구 북가좌동 294-21서울특별시 서대문구 증가로25길 11, 1층 일부호 (북가좌동)3682보라다(BORADA)2022-11-10 15:17:36U2021-10-31 23:02:00.0네일아트업192301.772545453221.262281<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
282931200003120000-226-2021-0000120210126<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.40120807서울특별시 서대문구 남가좌동 346-24서울특별시 서대문구 가재울로6길 53-55, 1층 (남가좌동)3692동네살롱2021-01-26 11:53:48I2021-01-28 00:23:13.0네일아트업193162.168636452713.30798피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N7<NA><NA><NA><NA>00001N
283031200003120000-226-2021-000022021-02-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>127.18120-833서울특별시 서대문구 창천동 18-5서울특별시 서대문구 명물길 6, 9층 (창천동)3777더스무스 블랑드 신촌점2023-05-10 11:33:30U2022-12-04 23:02:00.0네일아트업194398.900504450561.690367<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
283131200003120000-226-2022-0000120220228<NA>1영업/정상1영업<NA><NA><NA><NA><NA>43.19120857서울특별시 서대문구 홍제동 270-1 홍제역해링턴플레이스서울특별시 서대문구 세무서8길 30, 41호 (홍제동, 홍제역해링턴플레이스)3626디디뷰티2022-02-28 11:40:03I2022-03-02 00:22:36.0네일아트업195387.622258454191.447093피부미용업, 네일미용업, 화장ㆍ분장 미용업000000000N9<NA><NA><NA><NA>00001N
283231200003120000-226-2022-0000220220901<NA>1영업/정상1영업<NA><NA><NA><NA><NA>34.60120012서울특별시 서대문구 충정로2가 113-1서울특별시 서대문구 충정로9길 4, 2층 (충정로2가)3736네일온2022-09-01 13:45:01I2021-12-09 00:03:00.0네일아트업196853.605651451352.1472<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
283331200003120000-226-2022-000032022-09-13<NA>3폐업2폐업2023-05-31<NA><NA><NA><NA>28.53120-808서울특별시 서대문구 대현동 37-32 영타운 지웰 에스테이트서울특별시 서대문구 이화여대5길 35, 영타운 지웰 에스테이트 지하1층 114호 (대현동)3766예넬2023-05-31 13:15:24U2022-12-06 00:02:00.0네일아트업194960.929139450652.212058<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
283431200003120000-226-2023-000012023-01-09<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.00120-808서울특별시 서대문구 대현동 37-49서울특별시 서대문구 이화여대5길 24, 2층 201호 (대현동)3766로렐아이래쉬2023-07-31 15:06:27I2022-12-08 00:02:00.0메이크업업195010.725621450686.278583<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>