Overview

Dataset statistics

Number of variables47
Number of observations750
Missing cells8067
Missing cells (%)22.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory295.3 KiB
Average record size in memory403.2 B

Variable types

Categorical20
Text8
DateTime4
Unsupported6
Numeric7
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
Author성북구
URLhttps://data.seoul.go.kr/dataList/OA-17934/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
발한실여부 has constant value ""Constant
조건부허가신고사유 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
사용시작지하층 is highly imbalanced (50.1%)Imbalance
사용끝지하층 is highly imbalanced (79.2%)Imbalance
건물소유구분명 is highly imbalanced (85.1%)Imbalance
여성종사자수 is highly imbalanced (70.5%)Imbalance
남성종사자수 is highly imbalanced (80.4%)Imbalance
회수건조수 is highly imbalanced (51.0%)Imbalance
침대수 is highly imbalanced (51.4%)Imbalance
인허가취소일자 has 750 (100.0%) missing valuesMissing
폐업일자 has 107 (14.3%) missing valuesMissing
휴업시작일자 has 750 (100.0%) missing valuesMissing
휴업종료일자 has 750 (100.0%) missing valuesMissing
재개업일자 has 750 (100.0%) missing valuesMissing
전화번호 has 141 (18.8%) missing valuesMissing
도로명주소 has 514 (68.5%) missing valuesMissing
도로명우편번호 has 521 (69.5%) missing valuesMissing
좌표정보(X) has 82 (10.9%) missing valuesMissing
좌표정보(Y) has 82 (10.9%) missing valuesMissing
건물지상층수 has 228 (30.4%) missing valuesMissing
사용시작지상층 has 289 (38.5%) missing valuesMissing
사용끝지상층 has 535 (71.3%) missing valuesMissing
발한실여부 has 95 (12.7%) missing valuesMissing
좌석수 has 135 (18.0%) missing valuesMissing
조건부허가신고사유 has 749 (99.9%) missing valuesMissing
조건부허가시작일자 has 750 (100.0%) missing valuesMissing
조건부허가종료일자 has 750 (100.0%) missing valuesMissing
다중이용업소여부 has 87 (11.6%) missing valuesMissing
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
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 302 (40.3%) zerosZeros
사용시작지상층 has 243 (32.4%) zerosZeros
좌석수 has 19 (2.5%) zerosZeros

Reproduction

Analysis started2024-05-11 07:54:47.969760
Analysis finished2024-05-11 07:54:50.345087
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
3070000
750 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3070000
2nd row3070000
3rd row3070000
4th row3070000
5th row3070000

Common Values

ValueCountFrequency (%)
3070000 750
100.0%

Length

2024-05-11T07:54:50.544963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:54:50.873967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3070000 750
100.0%

관리번호
Text

UNIQUE 

Distinct750
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-05-11T07:54:51.337067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters16500
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

Unique750 ?
Unique (%)100.0%

Sample

1st row3070000-203-1963-00505
2nd row3070000-203-1964-00781
3rd row3070000-203-1966-00459
4th row3070000-203-1967-00445
5th row3070000-203-1967-00499
ValueCountFrequency (%)
3070000-203-1963-00505 1
 
0.1%
3070000-203-2002-00002 1
 
0.1%
3070000-203-2001-02297 1
 
0.1%
3070000-203-2002-00014 1
 
0.1%
3070000-203-2001-02298 1
 
0.1%
3070000-203-2001-02299 1
 
0.1%
3070000-203-2001-02300 1
 
0.1%
3070000-203-2001-02302 1
 
0.1%
3070000-203-2001-02304 1
 
0.1%
3070000-203-2001-02305 1
 
0.1%
Other values (740) 740
98.7%
2024-05-11T07:54:52.247049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7051
42.7%
- 2250
 
13.6%
3 1741
 
10.6%
2 1407
 
8.5%
7 1123
 
6.8%
9 830
 
5.0%
1 793
 
4.8%
8 421
 
2.6%
6 311
 
1.9%
5 295
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14250
86.4%
Dash Punctuation 2250
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7051
49.5%
3 1741
 
12.2%
2 1407
 
9.9%
7 1123
 
7.9%
9 830
 
5.8%
1 793
 
5.6%
8 421
 
3.0%
6 311
 
2.2%
5 295
 
2.1%
4 278
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 2250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7051
42.7%
- 2250
 
13.6%
3 1741
 
10.6%
2 1407
 
8.5%
7 1123
 
6.8%
9 830
 
5.0%
1 793
 
4.8%
8 421
 
2.6%
6 311
 
1.9%
5 295
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7051
42.7%
- 2250
 
13.6%
3 1741
 
10.6%
2 1407
 
8.5%
7 1123
 
6.8%
9 830
 
5.0%
1 793
 
4.8%
8 421
 
2.6%
6 311
 
1.9%
5 295
 
1.8%
Distinct672
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum1963-06-08 00:00:00
Maximum2023-11-27 00:00:00
2024-05-11T07:54:52.676342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:54:53.040529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing750
Missing (%)100.0%
Memory size6.7 KiB
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
3
643 
1
107 

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 (%)
3 643
85.7%
1 107
 
14.3%

Length

2024-05-11T07:54:53.465826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:54:53.754557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 643
85.7%
1 107
 
14.3%

영업상태명
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
폐업
643 
영업/정상
107 

Length

Max length5
Median length2
Mean length2.428
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 643
85.7%
영업/정상 107
 
14.3%

Length

2024-05-11T07:54:54.058067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:54:54.333149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 643
85.7%
영업/정상 107
 
14.3%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2
643 
1
107 

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 (%)
2 643
85.7%
1 107
 
14.3%

Length

2024-05-11T07:54:54.663469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:54:55.004840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 643
85.7%
1 107
 
14.3%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
폐업
643 
영업
107 

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 643
85.7%
영업 107
 
14.3%

Length

2024-05-11T07:54:55.392183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:54:55.778268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 643
85.7%
영업 107
 
14.3%

폐업일자
Date

MISSING 

Distinct519
Distinct (%)80.7%
Missing107
Missing (%)14.3%
Memory size6.0 KiB
Minimum1992-07-14 00:00:00
Maximum2024-05-03 00:00:00
2024-05-11T07:54:56.206578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:54:56.611574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing750
Missing (%)100.0%
Memory size6.7 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing750
Missing (%)100.0%
Memory size6.7 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing750
Missing (%)100.0%
Memory size6.7 KiB

전화번호
Text

MISSING 

Distinct514
Distinct (%)84.4%
Missing141
Missing (%)18.8%
Memory size6.0 KiB
2024-05-11T07:54:57.251869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.8308703
Min length2

Characters and Unicode

Total characters5987
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

Unique474 ?
Unique (%)77.8%

Sample

1st row02 7647020
2nd row0209824394
3rd row0209120593
4th row02 9137842
5th row02 9280312
ValueCountFrequency (%)
02 386
38.1%
0 28
 
2.8%
0200000000 14
 
1.4%
00000 8
 
0.8%
9210359 4
 
0.4%
9576923 4
 
0.4%
9166508 3
 
0.3%
917 3
 
0.3%
9809070 3
 
0.3%
9262578 3
 
0.3%
Other values (521) 558
55.0%
2024-05-11T07:54:58.299232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1290
21.5%
2 1021
17.1%
9 786
13.1%
494
 
8.3%
1 486
 
8.1%
4 348
 
5.8%
7 333
 
5.6%
6 316
 
5.3%
3 315
 
5.3%
8 313
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5493
91.7%
Space Separator 494
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1290
23.5%
2 1021
18.6%
9 786
14.3%
1 486
 
8.8%
4 348
 
6.3%
7 333
 
6.1%
6 316
 
5.8%
3 315
 
5.7%
8 313
 
5.7%
5 285
 
5.2%
Space Separator
ValueCountFrequency (%)
494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5987
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1290
21.5%
2 1021
17.1%
9 786
13.1%
494
 
8.3%
1 486
 
8.1%
4 348
 
5.8%
7 333
 
5.6%
6 316
 
5.3%
3 315
 
5.3%
8 313
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5987
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1290
21.5%
2 1021
17.1%
9 786
13.1%
494
 
8.3%
1 486
 
8.1%
4 348
 
5.8%
7 333
 
5.6%
6 316
 
5.3%
3 315
 
5.3%
8 313
 
5.2%
Distinct444
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-05-11T07:54:59.199257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.6986667
Min length3

Characters and Unicode

Total characters3524
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

Unique356 ?
Unique (%)47.5%

Sample

1st row14.12
2nd row21.00
3rd row15.84
4th row10.37
5th row16.32
ValueCountFrequency (%)
00 62
 
8.3%
6.60 21
 
2.8%
16.50 20
 
2.7%
9.90 15
 
2.0%
13.20 15
 
2.0%
12.00 12
 
1.6%
33.00 12
 
1.6%
10.00 12
 
1.6%
19.80 11
 
1.5%
23.10 8
 
1.1%
Other values (434) 562
74.9%
2024-05-11T07:55:00.541996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 750
21.3%
0 721
20.5%
1 431
12.2%
2 325
9.2%
6 247
 
7.0%
3 220
 
6.2%
4 189
 
5.4%
5 186
 
5.3%
9 167
 
4.7%
8 145
 
4.1%
Other values (2) 143
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2773
78.7%
Other Punctuation 751
 
21.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 721
26.0%
1 431
15.5%
2 325
11.7%
6 247
 
8.9%
3 220
 
7.9%
4 189
 
6.8%
5 186
 
6.7%
9 167
 
6.0%
8 145
 
5.2%
7 142
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 750
99.9%
, 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 750
21.3%
0 721
20.5%
1 431
12.2%
2 325
9.2%
6 247
 
7.0%
3 220
 
6.2%
4 189
 
5.4%
5 186
 
5.3%
9 167
 
4.7%
8 145
 
4.1%
Other values (2) 143
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 750
21.3%
0 721
20.5%
1 431
12.2%
2 325
9.2%
6 247
 
7.0%
3 220
 
6.2%
4 189
 
5.4%
5 186
 
5.3%
9 167
 
4.7%
8 145
 
4.1%
Other values (2) 143
 
4.1%
Distinct152
Distinct (%)20.3%
Missing1
Missing (%)0.1%
Memory size6.0 KiB
2024-05-11T07:55:01.468710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0961282
Min length6

Characters and Unicode

Total characters4566
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

Unique46 ?
Unique (%)6.1%

Sample

1st row136082
2nd row136800
3rd row136800
4th row136812
5th row136892
ValueCountFrequency (%)
136865 29
 
3.9%
136800 24
 
3.2%
136864 23
 
3.1%
136802 22
 
2.9%
136818 21
 
2.8%
136833 21
 
2.8%
136858 20
 
2.7%
136871 16
 
2.1%
136826 16
 
2.1%
136863 15
 
2.0%
Other values (142) 542
72.4%
2024-05-11T07:55:02.785683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 941
20.6%
3 917
20.1%
6 903
19.8%
8 644
14.1%
0 365
 
8.0%
4 188
 
4.1%
5 176
 
3.9%
2 162
 
3.5%
7 142
 
3.1%
- 72
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4494
98.4%
Dash Punctuation 72
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 941
20.9%
3 917
20.4%
6 903
20.1%
8 644
14.3%
0 365
 
8.1%
4 188
 
4.2%
5 176
 
3.9%
2 162
 
3.6%
7 142
 
3.2%
9 56
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4566
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 941
20.6%
3 917
20.1%
6 903
19.8%
8 644
14.1%
0 365
 
8.0%
4 188
 
4.1%
5 176
 
3.9%
2 162
 
3.5%
7 142
 
3.1%
- 72
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 941
20.6%
3 917
20.1%
6 903
19.8%
8 644
14.1%
0 365
 
8.0%
4 188
 
4.1%
5 176
 
3.9%
2 162
 
3.5%
7 142
 
3.1%
- 72
 
1.6%
Distinct622
Distinct (%)83.0%
Missing1
Missing (%)0.1%
Memory size6.0 KiB
2024-05-11T07:55:03.597308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length43
Mean length23.865154
Min length18

Characters and Unicode

Total characters17875
Distinct characters156
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique530 ?
Unique (%)70.8%

Sample

1st row서울특별시 성북구 보문동2가 7-0번지
2nd row서울특별시 성북구 길음동 481-0번지
3rd row서울특별시 성북구 길음동 46-0번지
4th row서울특별시 성북구 길음동 1271-91번지
5th row서울특별시 성북구 돈암동 578-1번지
ValueCountFrequency (%)
서울특별시 749
23.3%
성북구 749
23.3%
장위동 117
 
3.6%
정릉동 94
 
2.9%
하월곡동 91
 
2.8%
석관동 80
 
2.5%
종암동 79
 
2.5%
길음동 76
 
2.4%
돈암동 19
 
0.6%
상월곡동 18
 
0.6%
Other values (709) 1143
35.6%
2024-05-11T07:55:05.106162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3118
 
17.4%
828
 
4.6%
770
 
4.3%
764
 
4.3%
754
 
4.2%
753
 
4.2%
750
 
4.2%
749
 
4.2%
749
 
4.2%
749
 
4.2%
Other values (146) 7891
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10498
58.7%
Decimal Number 3467
 
19.4%
Space Separator 3118
 
17.4%
Dash Punctuation 700
 
3.9%
Open Punctuation 31
 
0.2%
Close Punctuation 31
 
0.2%
Uppercase Letter 22
 
0.1%
Other Punctuation 7
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
828
 
7.9%
770
 
7.3%
764
 
7.3%
754
 
7.2%
753
 
7.2%
750
 
7.1%
749
 
7.1%
749
 
7.1%
749
 
7.1%
672
 
6.4%
Other values (122) 2960
28.2%
Decimal Number
ValueCountFrequency (%)
1 743
21.4%
2 508
14.7%
3 406
11.7%
5 312
9.0%
0 295
 
8.5%
4 281
 
8.1%
7 259
 
7.5%
6 252
 
7.3%
8 220
 
6.3%
9 191
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
S 6
27.3%
K 6
27.3%
B 3
13.6%
P 2
 
9.1%
T 2
 
9.1%
A 2
 
9.1%
M 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
@ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
3118
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 700
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10498
58.7%
Common 7355
41.1%
Latin 22
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
828
 
7.9%
770
 
7.3%
764
 
7.3%
754
 
7.2%
753
 
7.2%
750
 
7.1%
749
 
7.1%
749
 
7.1%
749
 
7.1%
672
 
6.4%
Other values (122) 2960
28.2%
Common
ValueCountFrequency (%)
3118
42.4%
1 743
 
10.1%
- 700
 
9.5%
2 508
 
6.9%
3 406
 
5.5%
5 312
 
4.2%
0 295
 
4.0%
4 281
 
3.8%
7 259
 
3.5%
6 252
 
3.4%
Other values (7) 481
 
6.5%
Latin
ValueCountFrequency (%)
S 6
27.3%
K 6
27.3%
B 3
13.6%
P 2
 
9.1%
T 2
 
9.1%
A 2
 
9.1%
M 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10498
58.7%
ASCII 7377
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3118
42.3%
1 743
 
10.1%
- 700
 
9.5%
2 508
 
6.9%
3 406
 
5.5%
5 312
 
4.2%
0 295
 
4.0%
4 281
 
3.8%
7 259
 
3.5%
6 252
 
3.4%
Other values (14) 503
 
6.8%
Hangul
ValueCountFrequency (%)
828
 
7.9%
770
 
7.3%
764
 
7.3%
754
 
7.2%
753
 
7.2%
750
 
7.1%
749
 
7.1%
749
 
7.1%
749
 
7.1%
672
 
6.4%
Other values (122) 2960
28.2%

도로명주소
Text

MISSING 

Distinct225
Distinct (%)95.3%
Missing514
Missing (%)68.5%
Memory size6.0 KiB
2024-05-11T07:55:06.284772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length46
Mean length29.466102
Min length22

Characters and Unicode

Total characters6954
Distinct characters161
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique216 ?
Unique (%)91.5%

Sample

1st row서울특별시 성북구 아리랑로5길 45 (돈암동)
2nd row서울특별시 성북구 성북로2길 46 (동소문동1가)
3rd row서울특별시 성북구 솔샘로6길 24-17 (정릉동)
4th row서울특별시 성북구 삼양로8길 8 (길음동)
5th row서울특별시 성북구 종암로21길 11 (종암동)
ValueCountFrequency (%)
서울특별시 236
 
17.5%
성북구 236
 
17.5%
정릉동 36
 
2.7%
1층 35
 
2.6%
장위동 32
 
2.4%
하월곡동 28
 
2.1%
석관동 22
 
1.6%
종암동 20
 
1.5%
2층 15
 
1.1%
길음동 15
 
1.1%
Other values (378) 671
49.9%
2024-05-11T07:55:07.921282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1110
 
16.0%
279
 
4.0%
260
 
3.7%
258
 
3.7%
) 250
 
3.6%
( 250
 
3.6%
1 243
 
3.5%
241
 
3.5%
239
 
3.4%
237
 
3.4%
Other values (151) 3587
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4159
59.8%
Space Separator 1110
 
16.0%
Decimal Number 1003
 
14.4%
Close Punctuation 250
 
3.6%
Open Punctuation 250
 
3.6%
Other Punctuation 127
 
1.8%
Dash Punctuation 44
 
0.6%
Uppercase Letter 10
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
279
 
6.7%
260
 
6.3%
258
 
6.2%
241
 
5.8%
239
 
5.7%
237
 
5.7%
236
 
5.7%
236
 
5.7%
236
 
5.7%
236
 
5.7%
Other values (128) 1701
40.9%
Decimal Number
ValueCountFrequency (%)
1 243
24.2%
2 193
19.2%
3 97
 
9.7%
4 96
 
9.6%
5 84
 
8.4%
6 75
 
7.5%
0 59
 
5.9%
7 58
 
5.8%
9 49
 
4.9%
8 49
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
K 2
20.0%
S 2
20.0%
B 2
20.0%
T 1
10.0%
A 1
10.0%
P 1
10.0%
M 1
10.0%
Space Separator
ValueCountFrequency (%)
1110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 250
100.0%
Open Punctuation
ValueCountFrequency (%)
( 250
100.0%
Other Punctuation
ValueCountFrequency (%)
, 127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4159
59.8%
Common 2785
40.0%
Latin 10
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
279
 
6.7%
260
 
6.3%
258
 
6.2%
241
 
5.8%
239
 
5.7%
237
 
5.7%
236
 
5.7%
236
 
5.7%
236
 
5.7%
236
 
5.7%
Other values (128) 1701
40.9%
Common
ValueCountFrequency (%)
1110
39.9%
) 250
 
9.0%
( 250
 
9.0%
1 243
 
8.7%
2 193
 
6.9%
, 127
 
4.6%
3 97
 
3.5%
4 96
 
3.4%
5 84
 
3.0%
6 75
 
2.7%
Other values (6) 260
 
9.3%
Latin
ValueCountFrequency (%)
K 2
20.0%
S 2
20.0%
B 2
20.0%
T 1
10.0%
A 1
10.0%
P 1
10.0%
M 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4159
59.8%
ASCII 2795
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1110
39.7%
) 250
 
8.9%
( 250
 
8.9%
1 243
 
8.7%
2 193
 
6.9%
, 127
 
4.5%
3 97
 
3.5%
4 96
 
3.4%
5 84
 
3.0%
6 75
 
2.7%
Other values (13) 270
 
9.7%
Hangul
ValueCountFrequency (%)
279
 
6.7%
260
 
6.3%
258
 
6.2%
241
 
5.8%
239
 
5.7%
237
 
5.7%
236
 
5.7%
236
 
5.7%
236
 
5.7%
236
 
5.7%
Other values (128) 1701
40.9%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct96
Distinct (%)41.9%
Missing521
Missing (%)69.5%
Infinite0
Infinite (%)0.0%
Mean2782.8035
Minimum2700
Maximum2880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-05-11T07:55:08.571452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2700
5-th percentile2709
Q12739
median2781
Q32820
95-th percentile2864
Maximum2880
Range180
Interquartile range (IQR)81

Descriptive statistics

Standard deviation49.560355
Coefficient of variation (CV)0.017809506
Kurtosis-1.0407919
Mean2782.8035
Median Absolute Deviation (MAD)42
Skewness0.18673304
Sum637262
Variance2456.2288
MonotonicityNot monotonic
2024-05-11T07:55:09.101623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2737 12
 
1.6%
2850 9
 
1.2%
2751 7
 
0.9%
2797 7
 
0.9%
2755 5
 
0.7%
2798 5
 
0.7%
2704 5
 
0.7%
2784 5
 
0.7%
2709 5
 
0.7%
2785 5
 
0.7%
Other values (86) 164
 
21.9%
(Missing) 521
69.5%
ValueCountFrequency (%)
2700 1
 
0.1%
2702 3
0.4%
2704 5
0.7%
2709 5
0.7%
2710 3
0.4%
2711 1
 
0.1%
2713 1
 
0.1%
2715 1
 
0.1%
2717 1
 
0.1%
2718 2
 
0.3%
ValueCountFrequency (%)
2880 2
0.3%
2873 4
0.5%
2872 3
0.4%
2865 1
 
0.1%
2864 3
0.4%
2862 2
0.3%
2861 4
0.5%
2860 1
 
0.1%
2857 1
 
0.1%
2856 3
0.4%
Distinct575
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-05-11T07:55:10.030332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length2
Mean length3.8733333
Min length1

Characters and Unicode

Total characters2905
Distinct characters310
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique461 ?
Unique (%)61.5%

Sample

1st row동명
2nd row반도
3rd row옥천
4th row새서울
5th row신흥
ValueCountFrequency (%)
이용원 13
 
1.6%
13
 
1.6%
제일 8
 
1.0%
새마을 8
 
1.0%
현대 8
 
1.0%
삼성 6
 
0.7%
이발소 6
 
0.7%
대성 5
 
0.6%
성심 5
 
0.6%
수정 4
 
0.5%
Other values (584) 745
90.7%
2024-05-11T07:55:11.451079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
241
 
8.3%
165
 
5.7%
156
 
5.4%
78
 
2.7%
71
 
2.4%
71
 
2.4%
69
 
2.4%
54
 
1.9%
53
 
1.8%
50
 
1.7%
Other values (300) 1897
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2649
91.2%
Uppercase Letter 75
 
2.6%
Space Separator 71
 
2.4%
Lowercase Letter 38
 
1.3%
Close Punctuation 22
 
0.8%
Open Punctuation 22
 
0.8%
Other Punctuation 14
 
0.5%
Decimal Number 14
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
241
 
9.1%
165
 
6.2%
156
 
5.9%
78
 
2.9%
71
 
2.7%
69
 
2.6%
54
 
2.0%
53
 
2.0%
50
 
1.9%
49
 
1.8%
Other values (255) 1663
62.8%
Uppercase Letter
ValueCountFrequency (%)
S 15
20.0%
K 9
12.0%
B 9
12.0%
O 7
9.3%
R 5
 
6.7%
H 5
 
6.7%
A 4
 
5.3%
E 4
 
5.3%
I 3
 
4.0%
D 3
 
4.0%
Other values (6) 11
14.7%
Lowercase Letter
ValueCountFrequency (%)
a 5
13.2%
e 5
13.2%
r 4
10.5%
t 3
 
7.9%
n 2
 
5.3%
p 2
 
5.3%
l 2
 
5.3%
s 2
 
5.3%
b 2
 
5.3%
u 2
 
5.3%
Other values (6) 9
23.7%
Decimal Number
ValueCountFrequency (%)
1 3
21.4%
2 3
21.4%
8 2
14.3%
9 2
14.3%
3 2
14.3%
7 2
14.3%
Other Punctuation
ValueCountFrequency (%)
. 8
57.1%
? 4
28.6%
& 1
 
7.1%
' 1
 
7.1%
Space Separator
ValueCountFrequency (%)
71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2649
91.2%
Common 143
 
4.9%
Latin 113
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
241
 
9.1%
165
 
6.2%
156
 
5.9%
78
 
2.9%
71
 
2.7%
69
 
2.6%
54
 
2.0%
53
 
2.0%
50
 
1.9%
49
 
1.8%
Other values (255) 1663
62.8%
Latin
ValueCountFrequency (%)
S 15
 
13.3%
K 9
 
8.0%
B 9
 
8.0%
O 7
 
6.2%
R 5
 
4.4%
a 5
 
4.4%
e 5
 
4.4%
H 5
 
4.4%
A 4
 
3.5%
E 4
 
3.5%
Other values (22) 45
39.8%
Common
ValueCountFrequency (%)
71
49.7%
) 22
 
15.4%
( 22
 
15.4%
. 8
 
5.6%
? 4
 
2.8%
1 3
 
2.1%
2 3
 
2.1%
8 2
 
1.4%
9 2
 
1.4%
3 2
 
1.4%
Other values (3) 4
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2649
91.2%
ASCII 256
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
241
 
9.1%
165
 
6.2%
156
 
5.9%
78
 
2.9%
71
 
2.7%
69
 
2.6%
54
 
2.0%
53
 
2.0%
50
 
1.9%
49
 
1.8%
Other values (255) 1663
62.8%
ASCII
ValueCountFrequency (%)
71
27.7%
) 22
 
8.6%
( 22
 
8.6%
S 15
 
5.9%
K 9
 
3.5%
B 9
 
3.5%
. 8
 
3.1%
O 7
 
2.7%
R 5
 
2.0%
a 5
 
2.0%
Other values (35) 83
32.4%
Distinct434
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum1999-01-09 00:00:00
Maximum2024-05-03 14:44:12
2024-05-11T07:55:12.130627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:55:12.779385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
I
632 
U
118 

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 632
84.3%
U 118
 
15.7%

Length

2024-05-11T07:55:13.524554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:13.984122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 632
84.3%
u 118
 
15.7%
Distinct72
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:05:00
2024-05-11T07:55:14.408777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:55:14.966842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
일반이용업
750 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반이용업 750
100.0%

Length

2024-05-11T07:55:15.397203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:15.697915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반이용업 750
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct447
Distinct (%)66.9%
Missing82
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean202795.12
Minimum199628.17
Maximum205996.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-05-11T07:55:16.131186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199628.17
5-th percentile200524.88
Q1201493.15
median202800.83
Q3204260.86
95-th percentile205381.17
Maximum205996.72
Range6368.5451
Interquartile range (IQR)2767.7148

Descriptive statistics

Standard deviation1577.0637
Coefficient of variation (CV)0.0077766352
Kurtosis-1.0992134
Mean202795.12
Median Absolute Deviation (MAD)1366.2973
Skewness0.15348534
Sum1.3546714 × 108
Variance2487129.8
MonotonicityNot monotonic
2024-05-11T07:55:17.006125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201778.047712477 11
 
1.5%
203196.800963466 11
 
1.5%
203109.497601817 8
 
1.1%
205254.262597746 6
 
0.8%
200841.726990037 6
 
0.8%
201245.960798798 6
 
0.8%
201968.831466182 5
 
0.7%
200087.547476586 5
 
0.7%
204342.019457429 5
 
0.7%
201653.494954375 4
 
0.5%
Other values (437) 601
80.1%
(Missing) 82
 
10.9%
ValueCountFrequency (%)
199628.172805473 1
 
0.1%
199665.525800799 1
 
0.1%
199831.236585382 1
 
0.1%
199866.373947177 1
 
0.1%
199870.752714322 1
 
0.1%
199888.694291511 1
 
0.1%
199955.212381036 1
 
0.1%
199972.889954824 1
 
0.1%
200032.272260327 1
 
0.1%
200087.547476586 5
0.7%
ValueCountFrequency (%)
205996.717928956 1
0.1%
205728.738159295 1
0.1%
205672.181109817 1
0.1%
205669.62299598 2
0.3%
205656.786301307 1
0.1%
205646.680457527 1
0.1%
205645.834743576 1
0.1%
205643.223108087 2
0.3%
205619.709546648 1
0.1%
205604.6712436 1
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct447
Distinct (%)66.9%
Missing82
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean455630.87
Minimum452872.83
Maximum457738.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-05-11T07:55:17.619507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum452872.83
5-th percentile453522.77
Q1454907.87
median455856.79
Q3456517.92
95-th percentile457211.88
Maximum457738.39
Range4865.5673
Interquartile range (IQR)1610.0547

Descriptive statistics

Standard deviation1137.7702
Coefficient of variation (CV)0.0024971315
Kurtosis-0.63494258
Mean455630.87
Median Absolute Deviation (MAD)724.71341
Skewness-0.51980796
Sum3.0436142 × 108
Variance1294521
MonotonicityNot monotonic
2024-05-11T07:55:18.141478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453855.56648031 11
 
1.5%
455429.846270094 11
 
1.5%
456180.732746123 8
 
1.1%
456319.556671704 6
 
0.8%
454721.505180141 6
 
0.8%
454944.923369993 6
 
0.8%
455669.921091142 5
 
0.7%
457201.065544346 5
 
0.7%
456218.615137622 5
 
0.7%
454684.553804488 4
 
0.5%
Other values (437) 601
80.1%
(Missing) 82
 
10.9%
ValueCountFrequency (%)
452872.827007071 2
0.3%
453026.691758481 2
0.3%
453034.369076605 1
0.1%
453044.258471435 1
0.1%
453070.548276 1
0.1%
453071.709292695 2
0.3%
453092.122987948 1
0.1%
453112.857906238 1
0.1%
453134.984441454 1
0.1%
453143.824331525 1
0.1%
ValueCountFrequency (%)
457738.394259698 1
 
0.1%
457681.833795388 1
 
0.1%
457616.529941971 1
 
0.1%
457533.773053035 1
 
0.1%
457511.040836432 1
 
0.1%
457503.002571573 2
0.3%
457496.512410178 4
0.5%
457483.977193855 1
 
0.1%
457428.955302233 1
 
0.1%
457423.617145425 1
 
0.1%

위생업태명
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
일반이용업
663 
<NA>
87 

Length

Max length5
Median length5
Mean length4.884
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반이용업 663
88.4%
<NA> 87
 
11.6%

Length

2024-05-11T07:55:18.805402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:19.257529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반이용업 663
88.4%
na 87
 
11.6%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)1.7%
Missing228
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean1.2298851
Minimum0
Maximum10
Zeros302
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-05-11T07:55:19.700440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7206066
Coefficient of variation (CV)1.3989979
Kurtosis1.4090498
Mean1.2298851
Median Absolute Deviation (MAD)0
Skewness1.322351
Sum642
Variance2.9604871
MonotonicityNot monotonic
2024-05-11T07:55:20.302927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 302
40.3%
3 77
 
10.3%
2 44
 
5.9%
1 39
 
5.2%
4 36
 
4.8%
5 12
 
1.6%
6 7
 
0.9%
7 4
 
0.5%
10 1
 
0.1%
(Missing) 228
30.4%
ValueCountFrequency (%)
0 302
40.3%
1 39
 
5.2%
2 44
 
5.9%
3 77
 
10.3%
4 36
 
4.8%
5 12
 
1.6%
6 7
 
0.9%
7 4
 
0.5%
10 1
 
0.1%
ValueCountFrequency (%)
10 1
 
0.1%
7 4
 
0.5%
6 7
 
0.9%
5 12
 
1.6%
4 36
 
4.8%
3 77
 
10.3%
2 44
 
5.9%
1 39
 
5.2%
0 302
40.3%
Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
327 
0
323 
1
91 
2
 
8
4
 
1

Length

Max length4
Median length1
Mean length2.308
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 327
43.6%
0 323
43.1%
1 91
 
12.1%
2 8
 
1.1%
4 1
 
0.1%

Length

2024-05-11T07:55:21.013685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:21.523117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 327
43.6%
0 323
43.1%
1 91
 
12.1%
2 8
 
1.1%
4 1
 
0.1%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)1.5%
Missing289
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean0.75704989
Minimum0
Maximum6
Zeros243
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-05-11T07:55:21.862182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0862676
Coefficient of variation (CV)1.4348693
Kurtosis6.3860774
Mean0.75704989
Median Absolute Deviation (MAD)0
Skewness2.1909689
Sum349
Variance1.1799774
MonotonicityNot monotonic
2024-05-11T07:55:22.338607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 243
32.4%
1 141
18.8%
2 48
 
6.4%
3 18
 
2.4%
6 5
 
0.7%
5 4
 
0.5%
4 2
 
0.3%
(Missing) 289
38.5%
ValueCountFrequency (%)
0 243
32.4%
1 141
18.8%
2 48
 
6.4%
3 18
 
2.4%
4 2
 
0.3%
5 4
 
0.5%
6 5
 
0.7%
ValueCountFrequency (%)
6 5
 
0.7%
5 4
 
0.5%
4 2
 
0.3%
3 18
 
2.4%
2 48
 
6.4%
1 141
18.8%
0 243
32.4%

사용끝지상층
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)3.7%
Missing535
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean1.5534884
Minimum0
Maximum10
Zeros6
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-05-11T07:55:22.955152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1783381
Coefficient of variation (CV)0.75851108
Kurtosis15.018774
Mean1.5534884
Median Absolute Deviation (MAD)0
Skewness3.1918802
Sum334
Variance1.3884808
MonotonicityNot monotonic
2024-05-11T07:55:23.455899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 138
 
18.4%
2 43
 
5.7%
3 19
 
2.5%
0 6
 
0.8%
6 4
 
0.5%
5 3
 
0.4%
10 1
 
0.1%
4 1
 
0.1%
(Missing) 535
71.3%
ValueCountFrequency (%)
0 6
 
0.8%
1 138
18.4%
2 43
 
5.7%
3 19
 
2.5%
4 1
 
0.1%
5 3
 
0.4%
6 4
 
0.5%
10 1
 
0.1%
ValueCountFrequency (%)
10 1
 
0.1%
6 4
 
0.5%
5 3
 
0.4%
4 1
 
0.1%
3 19
 
2.5%
2 43
 
5.7%
1 138
18.4%
0 6
 
0.8%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
445 
0
256 
1
 
41
2
 
5
3
 
2

Length

Max length4
Median length4
Mean length2.78
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 445
59.3%
0 256
34.1%
1 41
 
5.5%
2 5
 
0.7%
3 2
 
0.3%
5 1
 
0.1%

Length

2024-05-11T07:55:23.979491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:24.448507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 445
59.3%
0 256
34.1%
1 41
 
5.5%
2 5
 
0.7%
3 2
 
0.3%
5 1
 
0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
687 
1
 
39
0
 
17
2
 
4
3
 
2

Length

Max length4
Median length4
Mean length3.748
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 687
91.6%
1 39
 
5.2%
0 17
 
2.3%
2 4
 
0.5%
3 2
 
0.3%
5 1
 
0.1%

Length

2024-05-11T07:55:25.022724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:25.551616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 687
91.6%
1 39
 
5.2%
0 17
 
2.3%
2 4
 
0.5%
3 2
 
0.3%
5 1
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
418 
0
332 

Length

Max length4
Median length4
Mean length2.672
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 418
55.7%
0 332
44.3%

Length

2024-05-11T07:55:26.162416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:26.697275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 418
55.7%
0 332
44.3%

양실수
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
418 
0
332 

Length

Max length4
Median length4
Mean length2.672
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 418
55.7%
0 332
44.3%

Length

2024-05-11T07:55:27.205389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:27.732898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 418
55.7%
0 332
44.3%

욕실수
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
418 
0
332 

Length

Max length4
Median length4
Mean length2.672
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 418
55.7%
0 332
44.3%

Length

2024-05-11T07:55:28.257466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:28.718980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 418
55.7%
0 332
44.3%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing95
Missing (%)12.7%
Memory size1.6 KiB
False
655 
(Missing)
95 
ValueCountFrequency (%)
False 655
87.3%
(Missing) 95
 
12.7%
2024-05-11T07:55:29.119626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)2.4%
Missing135
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean3.7853659
Minimum0
Maximum15
Zeros19
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-05-11T07:55:29.468045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34.5
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.7268923
Coefficient of variation (CV)0.72037749
Kurtosis1.8519345
Mean3.7853659
Median Absolute Deviation (MAD)1
Skewness1.4790105
Sum2328
Variance7.4359418
MonotonicityNot monotonic
2024-05-11T07:55:29.864564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 194
25.9%
3 144
19.2%
4 68
 
9.1%
1 36
 
4.8%
7 30
 
4.0%
5 29
 
3.9%
6 23
 
3.1%
8 20
 
2.7%
9 20
 
2.7%
0 19
 
2.5%
Other values (5) 32
 
4.3%
(Missing) 135
18.0%
ValueCountFrequency (%)
0 19
 
2.5%
1 36
 
4.8%
2 194
25.9%
3 144
19.2%
4 68
 
9.1%
5 29
 
3.9%
6 23
 
3.1%
7 30
 
4.0%
8 20
 
2.7%
9 20
 
2.7%
ValueCountFrequency (%)
15 1
 
0.1%
13 6
 
0.8%
12 8
 
1.1%
11 8
 
1.1%
10 9
 
1.2%
9 20
2.7%
8 20
2.7%
7 30
4.0%
6 23
3.1%
5 29
3.9%

조건부허가신고사유
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing749
Missing (%)99.9%
Memory size6.0 KiB
2024-05-11T07:55:30.268866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters22
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row시설기준위반 유선통보(2008.5.29)
ValueCountFrequency (%)
시설기준위반 1
50.0%
유선통보(2008.5.29 1
50.0%
2024-05-11T07:55:31.003178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2
 
9.1%
0 2
 
9.1%
2 2
 
9.1%
1
 
4.5%
1
 
4.5%
9 1
 
4.5%
5 1
 
4.5%
8 1
 
4.5%
( 1
 
4.5%
1
 
4.5%
Other values (9) 9
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
45.5%
Decimal Number 7
31.8%
Other Punctuation 2
 
9.1%
Open Punctuation 1
 
4.5%
Space Separator 1
 
4.5%
Close Punctuation 1
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Decimal Number
ValueCountFrequency (%)
0 2
28.6%
2 2
28.6%
9 1
14.3%
5 1
14.3%
8 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
54.5%
Hangul 10
45.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Common
ValueCountFrequency (%)
. 2
16.7%
0 2
16.7%
2 2
16.7%
9 1
8.3%
5 1
8.3%
8 1
8.3%
( 1
8.3%
1
8.3%
) 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
54.5%
Hangul 10
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
16.7%
0 2
16.7%
2 2
16.7%
9 1
8.3%
5 1
8.3%
8 1
8.3%
( 1
8.3%
1
8.3%
) 1
8.3%
Hangul
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing750
Missing (%)100.0%
Memory size6.7 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing750
Missing (%)100.0%
Memory size6.7 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
734 
임대
 
16

Length

Max length4
Median length4
Mean length3.9573333
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> 734
97.9%
임대 16
 
2.1%

Length

2024-05-11T07:55:31.572608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:31.938993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 734
97.9%
임대 16
 
2.1%

세탁기수
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
657 
0
93 

Length

Max length4
Median length4
Mean length3.628
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> 657
87.6%
0 93
 
12.4%

Length

2024-05-11T07:55:32.295913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:32.678502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 657
87.6%
0 93
 
12.4%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
711 
0
 
39

Length

Max length4
Median length4
Mean length3.844
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> 711
94.8%
0 39
 
5.2%

Length

2024-05-11T07:55:33.038305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:33.407200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 711
94.8%
0 39
 
5.2%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
711 
0
 
37
1
 
2

Length

Max length4
Median length4
Mean length3.844
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> 711
94.8%
0 37
 
4.9%
1 2
 
0.3%

Length

2024-05-11T07:55:33.880795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:34.338256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 711
94.8%
0 37
 
4.9%
1 2
 
0.3%

회수건조수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
670 
0
80 

Length

Max length4
Median length4
Mean length3.68
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> 670
89.3%
0 80
 
10.7%

Length

2024-05-11T07:55:34.855890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:35.219806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 670
89.3%
0 80
 
10.7%

침대수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
671 
0
79 

Length

Max length4
Median length4
Mean length3.684
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> 671
89.5%
0 79
 
10.5%

Length

2024-05-11T07:55:35.639706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:55:36.180655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 671
89.5%
0 79
 
10.5%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing87
Missing (%)11.6%
Memory size1.6 KiB
False
663 
(Missing)
87 
ValueCountFrequency (%)
False 663
88.4%
(Missing) 87
 
11.6%
2024-05-11T07:55:36.574137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030700003070000-203-1963-0050519630608<NA>3폐업2폐업20021107<NA><NA><NA>02 764702014.12136082서울특별시 성북구 보문동2가 7-0번지<NA><NA>동명2003-06-03 00:00:00I2018-08-31 23:59:59.0일반이용업201468.363097453902.751787일반이용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130700003070000-203-1964-0078119640115<NA>3폐업2폐업19940201<NA><NA><NA>020982439421.00136800서울특별시 성북구 길음동 481-0번지<NA><NA>반도2001-09-27 00:00:00I2018-08-31 23:59:59.0일반이용업202399.999797456681.35978일반이용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230700003070000-203-1966-0045919661207<NA>3폐업2폐업20030226<NA><NA><NA>020912059315.84136800서울특별시 성북구 길음동 46-0번지<NA><NA>옥천2003-03-04 00:00:00I2018-08-31 23:59:59.0일반이용업<NA><NA>일반이용업<NA><NA><NA>1<NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330700003070000-203-1967-0044519671103<NA>3폐업2폐업20021107<NA><NA><NA>02 913784210.37136812서울특별시 성북구 길음동 1271-91번지<NA><NA>새서울2003-06-03 00:00:00I2018-08-31 23:59:59.0일반이용업<NA><NA>일반이용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430700003070000-203-1967-0049919670728<NA>3폐업2폐업20131114<NA><NA><NA>02 928031216.32136892서울특별시 성북구 돈암동 578-1번지서울특별시 성북구 아리랑로5길 45 (돈암동)2830신흥2004-01-10 00:00:00I2018-08-31 23:59:59.0일반이용업201050.561558454960.759258일반이용업6<NA>11<NA><NA><NA><NA><NA>N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530700003070000-203-1967-0052819670822<NA>3폐업2폐업19990128<NA><NA><NA>020765178622.60136824서울특별시 성북구 성북동 136-16번지<NA><NA>성원1999-01-29 00:00:00I2018-08-31 23:59:59.0일반이용업<NA><NA>일반이용업000<NA>0<NA>000N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630700003070000-203-1967-0072819671109<NA>3폐업2폐업19970721<NA><NA><NA>02 012.00136827서울특별시 성북구 장위동 76-15번지<NA><NA>영풍2001-09-27 00:00:00I2018-08-31 23:59:59.0일반이용업204641.417757457070.333127일반이용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730700003070000-203-1968-005251968-06-15<NA>1영업/정상1영업<NA><NA><NA><NA>02 929934714.21136-031서울특별시 성북구 동소문동1가 105서울특별시 성북구 성북로2길 46 (동소문동1가)2833미광2023-06-02 14:50:29U2022-12-06 00:04:00.0일반이용업200655.700011454161.452128<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
830700003070000-203-1968-0062719680603<NA>3폐업2폐업19931104<NA><NA><NA>020919273424.84136864서울특별시 성북구 종암동 98-9번지<NA><NA>장단2001-09-27 00:00:00I2018-08-31 23:59:59.0일반이용업203095.929354455318.468873일반이용업000<NA>0<NA>000N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
930700003070000-203-1969-004981969-04-15<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.15136-850서울특별시 성북구 정릉동 685-83서울특별시 성북구 솔샘로6길 24-17 (정릉동)2709대성2023-06-02 15:59:53U2022-12-06 00:04:00.0일반이용업200501.70389456268.450795<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
74030700003070000-203-2022-0000120220502<NA>1영업/정상1영업<NA><NA><NA><NA><NA>.00136045서울특별시 성북구 삼선동5가 93-2서울특별시 성북구 보문로31길 41, 1층 (삼선동5가)2862오도이바버샵(ODOI BARBER SHOP)2022-05-02 13:10:36I2021-12-05 00:04:00.0일반이용업201233.233845453946.968469<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74130700003070000-203-2022-0000220220518<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.16136110서울특별시 성북구 길음동 1285 길음7구역 두산위브아파트서울특별시 성북구 길음로13길 22, 상가동 지2층 124호 (길음동, 길음7구역 두산위브아파트)2715더살롱염색&두피클리닉2022-06-27 15:37:08U2021-12-05 22:09:00.0일반이용업201592.991324456382.966062<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74230700003070000-203-2022-0000320221013<NA>1영업/정상1영업<NA><NA><NA><NA><NA>13.22136023서울특별시 성북구 성북동1가 35-17서울특별시 성북구 창경궁로 317, 지하1층 (성북동1가)2880위카 바버샵2022-10-13 11:44:14I2021-10-30 23:05:00.0일반이용업200358.43597453997.38123<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74330700003070000-203-2022-0000420221026<NA>1영업/정상1영업<NA><NA><NA><NA><NA>6.60136878서울특별시 성북구 정릉동 786-9서울특별시 성북구 보국문로 175 (정릉동)2704청수탕 내 이용원2022-10-26 14:55:33I2021-10-30 22:08:00.0일반이용업200087.547477457201.065544<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74430700003070000-203-2022-0000520221031<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.00136863서울특별시 성북구 종암동 82-56서울특별시 성북구 종암로21길 63, 1층 (종암동)2803서래이발실2022-10-31 11:22:49I2021-11-01 00:02:00.0일반이용업202800.83317455422.622729<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74530700003070000-203-2022-000062022-11-30<NA>3폐업2폐업2023-06-26<NA><NA><NA><NA>8.00136-869서울특별시 성북구 하월곡동 76-5서울특별시 성북구 오패산로 65-1, 2층 (하월곡동)2737호수목욕탕2023-06-26 13:58:01U2022-12-05 22:08:00.0일반이용업203109.497602456180.732746<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74630700003070000-203-2023-000012023-05-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA>8.22136-825서울특별시 성북구 성북동 173-26서울특별시 성북구 성북로 52, 1층 좌측호 (성북동)2835성북동 염색2023-05-02 14:03:58I2022-12-05 00:04:00.0일반이용업200183.72073454453.587737<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74730700003070000-203-2023-000022023-09-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>10.00136-869서울특별시 성북구 하월곡동 76-5서울특별시 성북구 오패산로 65-1, 2층 (하월곡동)2737호수탕2023-09-12 15:01:10I2022-12-08 23:04:00.0일반이용업203109.497602456180.732746<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
74830700003070000-203-2023-000032023-10-25<NA>1영업/정상1영업<NA><NA><NA><NA><NA>42.99136-865서울특별시 성북구 하월곡동 67-18서울특별시 성북구 오패산로 6-13, 2층 (하월곡동)2751에프엘바버샵(FL BarBershop)2023-10-25 15:19:19I2022-10-30 22:07:00.0일반이용업203267.86885455693.019826<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
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