Overview

Dataset statistics

Number of variables44
Number of observations4972
Missing cells51518
Missing cells (%)23.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory375.0 B

Variable types

Categorical19
Text8
DateTime4
Unsupported6
Numeric6
Boolean1

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,남성종사자수,여성종사자수,영업장주변구분명,등급구분명,급수시설구분명,총인원,본사종업원수,공장사무직종업원수,공장판매직종업원수,공장생산직종업원수,건물소유구분명,보증액,월세액,다중이용업소여부,시설총규모,전통업소지정번호,전통업소주된음식,홈페이지
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-18689/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
영업장주변구분명 is highly imbalanced (55.0%)Imbalance
등급구분명 is highly imbalanced (52.7%)Imbalance
총인원 is highly imbalanced (72.5%)Imbalance
본사종업원수 is highly imbalanced (72.2%)Imbalance
공장사무직종업원수 is highly imbalanced (72.2%)Imbalance
공장판매직종업원수 is highly imbalanced (72.2%)Imbalance
공장생산직종업원수 is highly imbalanced (72.2%)Imbalance
보증액 is highly imbalanced (72.2%)Imbalance
월세액 is highly imbalanced (72.2%)Imbalance
다중이용업소여부 is highly imbalanced (88.3%)Imbalance
전통업소지정번호 is highly imbalanced (99.7%)Imbalance
인허가취소일자 has 4972 (100.0%) missing valuesMissing
폐업일자 has 1169 (23.5%) missing valuesMissing
휴업시작일자 has 4972 (100.0%) missing valuesMissing
휴업종료일자 has 4972 (100.0%) missing valuesMissing
재개업일자 has 4972 (100.0%) missing valuesMissing
전화번호 has 2535 (51.0%) missing valuesMissing
소재지면적 has 124 (2.5%) missing valuesMissing
도로명주소 has 1864 (37.5%) missing valuesMissing
도로명우편번호 has 1894 (38.1%) missing valuesMissing
좌표정보(X) has 274 (5.5%) missing valuesMissing
좌표정보(Y) has 274 (5.5%) missing valuesMissing
남성종사자수 has 3120 (62.8%) missing valuesMissing
여성종사자수 has 3114 (62.6%) missing valuesMissing
건물소유구분명 has 4972 (100.0%) missing valuesMissing
다중이용업소여부 has 1153 (23.2%) missing valuesMissing
시설총규모 has 1153 (23.2%) missing valuesMissing
전통업소주된음식 has 4970 (> 99.9%) missing valuesMissing
홈페이지 has 4972 (100.0%) missing valuesMissing
여성종사자수 is highly skewed (γ1 = 31.77334613)Skewed
시설총규모 is highly skewed (γ1 = 61.61354339)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
건물소유구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
홈페이지 is an unsupported type, check if it needs cleaning or further analysisUnsupported
남성종사자수 has 1492 (30.0%) zerosZeros
여성종사자수 has 1162 (23.4%) zerosZeros
시설총규모 has 54 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-11 07:05:13.511964
Analysis finished2024-05-11 07:05:17.938363
Duration4.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
3120000
4972 

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 4972
100.0%

Length

2024-05-11T07:05:18.182390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:18.453707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3120000 4972
100.0%

관리번호
Text

UNIQUE 

Distinct4972
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
2024-05-11T07:05:18.992411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters109384
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

Unique4972 ?
Unique (%)100.0%

Sample

1st row3120000-104-1931-04254
2nd row3120000-104-1967-02921
3rd row3120000-104-1967-03011
4th row3120000-104-1967-03100
5th row3120000-104-1967-04253
ValueCountFrequency (%)
3120000-104-1931-04254 1
 
< 0.1%
3120000-104-2017-00040 1
 
< 0.1%
3120000-104-2017-00047 1
 
< 0.1%
3120000-104-2017-00046 1
 
< 0.1%
3120000-104-2017-00045 1
 
< 0.1%
3120000-104-2017-00044 1
 
< 0.1%
3120000-104-2017-00043 1
 
< 0.1%
3120000-104-2017-00033 1
 
< 0.1%
3120000-104-2017-00041 1
 
< 0.1%
3120000-104-2017-00039 1
 
< 0.1%
Other values (4962) 4962
99.8%
2024-05-11T07:05:20.193161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41859
38.3%
1 16215
 
14.8%
- 14916
 
13.6%
2 11928
 
10.9%
3 7262
 
6.6%
4 6714
 
6.1%
9 3600
 
3.3%
8 2115
 
1.9%
5 1640
 
1.5%
7 1592
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94468
86.4%
Dash Punctuation 14916
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41859
44.3%
1 16215
 
17.2%
2 11928
 
12.6%
3 7262
 
7.7%
4 6714
 
7.1%
9 3600
 
3.8%
8 2115
 
2.2%
5 1640
 
1.7%
7 1592
 
1.7%
6 1543
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 14916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41859
38.3%
1 16215
 
14.8%
- 14916
 
13.6%
2 11928
 
10.9%
3 7262
 
6.6%
4 6714
 
6.1%
9 3600
 
3.3%
8 2115
 
1.9%
5 1640
 
1.5%
7 1592
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41859
38.3%
1 16215
 
14.8%
- 14916
 
13.6%
2 11928
 
10.9%
3 7262
 
6.6%
4 6714
 
6.1%
9 3600
 
3.3%
8 2115
 
1.9%
5 1640
 
1.5%
7 1592
 
1.5%
Distinct3599
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
Minimum1931-11-06 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T07:05:20.660328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:05:21.313150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4972
Missing (%)100.0%
Memory size43.8 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
3
3803 
1
1169 

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 3803
76.5%
1 1169
 
23.5%

Length

2024-05-11T07:05:21.774830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:22.153849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3803
76.5%
1 1169
 
23.5%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
폐업
3803 
영업/정상
1169 

Length

Max length5
Median length2
Mean length2.70535
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3803
76.5%
영업/정상 1169
 
23.5%

Length

2024-05-11T07:05:22.502817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:23.077392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3803
76.5%
영업/정상 1169
 
23.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
2
3803 
1
1169 

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 3803
76.5%
1 1169
 
23.5%

Length

2024-05-11T07:05:23.470864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:23.835798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3803
76.5%
1 1169
 
23.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
폐업
3803 
영업
1169 

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 3803
76.5%
영업 1169
 
23.5%

Length

2024-05-11T07:05:24.398328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:24.671071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3803
76.5%
영업 1169
 
23.5%

폐업일자
Date

MISSING 

Distinct2713
Distinct (%)71.3%
Missing1169
Missing (%)23.5%
Memory size39.0 KiB
Minimum1980-08-05 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T07:05:25.011783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:05:25.512208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4972
Missing (%)100.0%
Memory size43.8 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4972
Missing (%)100.0%
Memory size43.8 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4972
Missing (%)100.0%
Memory size43.8 KiB

전화번호
Text

MISSING 

Distinct2194
Distinct (%)90.0%
Missing2535
Missing (%)51.0%
Memory size39.0 KiB
2024-05-11T07:05:26.416024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.278211
Min length2

Characters and Unicode

Total characters25048
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

Unique2103 ?
Unique (%)86.3%

Sample

1st row02 3633429
2nd row0203630855
3rd row0203921843
4th row02 7359618
5th row02 3627286
ValueCountFrequency (%)
02 1634
35.6%
070 41
 
0.9%
312 36
 
0.8%
0 28
 
0.6%
393 27
 
0.6%
313 27
 
0.6%
0200000000 21
 
0.5%
363 20
 
0.4%
031 18
 
0.4%
304 16
 
0.3%
Other values (2277) 2725
59.3%
2024-05-11T07:05:27.908107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4684
18.7%
2 4070
16.2%
3 3672
14.7%
2812
11.2%
7 1606
 
6.4%
1 1504
 
6.0%
6 1498
 
6.0%
9 1340
 
5.3%
4 1338
 
5.3%
5 1323
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22236
88.8%
Space Separator 2812
 
11.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4684
21.1%
2 4070
18.3%
3 3672
16.5%
7 1606
 
7.2%
1 1504
 
6.8%
6 1498
 
6.7%
9 1340
 
6.0%
4 1338
 
6.0%
5 1323
 
5.9%
8 1201
 
5.4%
Space Separator
ValueCountFrequency (%)
2812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4684
18.7%
2 4070
16.2%
3 3672
14.7%
2812
11.2%
7 1606
 
6.4%
1 1504
 
6.0%
6 1498
 
6.0%
9 1340
 
5.3%
4 1338
 
5.3%
5 1323
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4684
18.7%
2 4070
16.2%
3 3672
14.7%
2812
11.2%
7 1606
 
6.4%
1 1504
 
6.0%
6 1498
 
6.0%
9 1340
 
5.3%
4 1338
 
5.3%
5 1323
 
5.3%

소재지면적
Text

MISSING 

Distinct2316
Distinct (%)47.8%
Missing124
Missing (%)2.5%
Memory size39.0 KiB
2024-05-11T07:05:29.137749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.9212046
Min length3

Characters and Unicode

Total characters23858
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

Unique1766 ?
Unique (%)36.4%

Sample

1st row46.40
2nd row23.66
3rd row82.09
4th row104.22
5th row.00
ValueCountFrequency (%)
3.30 225
 
4.6%
6.60 183
 
3.8%
10.00 116
 
2.4%
33.00 87
 
1.8%
15.00 58
 
1.2%
16.50 52
 
1.1%
26.40 51
 
1.1%
9.90 49
 
1.0%
00 46
 
0.9%
20.00 45
 
0.9%
Other values (2306) 3936
81.2%
2024-05-11T07:05:31.038658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4848
20.3%
0 4723
19.8%
1 2145
9.0%
3 2110
8.8%
2 1910
 
8.0%
6 1777
 
7.4%
5 1557
 
6.5%
4 1363
 
5.7%
9 1243
 
5.2%
8 1145
 
4.8%
Other values (2) 1037
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19008
79.7%
Other Punctuation 4850
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4723
24.8%
1 2145
11.3%
3 2110
11.1%
2 1910
10.0%
6 1777
 
9.3%
5 1557
 
8.2%
4 1363
 
7.2%
9 1243
 
6.5%
8 1145
 
6.0%
7 1035
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 4848
> 99.9%
, 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4848
20.3%
0 4723
19.8%
1 2145
9.0%
3 2110
8.8%
2 1910
 
8.0%
6 1777
 
7.4%
5 1557
 
6.5%
4 1363
 
5.7%
9 1243
 
5.2%
8 1145
 
4.8%
Other values (2) 1037
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4848
20.3%
0 4723
19.8%
1 2145
9.0%
3 2110
8.8%
2 1910
 
8.0%
6 1777
 
7.4%
5 1557
 
6.5%
4 1363
 
5.7%
9 1243
 
5.2%
8 1145
 
4.8%
Other values (2) 1037
 
4.3%
Distinct195
Distinct (%)3.9%
Missing21
Missing (%)0.4%
Memory size39.0 KiB
2024-05-11T07:05:32.883259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1642093
Min length6

Characters and Unicode

Total characters30519
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

Unique29 ?
Unique (%)0.6%

Sample

1st row120837
2nd row120808
3rd row120833
4th row120834
5th row120809
ValueCountFrequency (%)
120834 464
 
9.4%
120808 405
 
8.2%
120706 185
 
3.7%
120833 179
 
3.6%
120825 175
 
3.5%
120809 146
 
2.9%
120848 142
 
2.9%
120857 113
 
2.3%
120-706 109
 
2.2%
120805 99
 
2.0%
Other values (185) 2934
59.3%
2024-05-11T07:05:34.986905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7756
25.4%
1 6147
20.1%
2 5748
18.8%
8 4456
14.6%
3 1463
 
4.8%
4 1192
 
3.9%
5 883
 
2.9%
7 859
 
2.8%
- 813
 
2.7%
6 749
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29706
97.3%
Dash Punctuation 813
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7756
26.1%
1 6147
20.7%
2 5748
19.3%
8 4456
15.0%
3 1463
 
4.9%
4 1192
 
4.0%
5 883
 
3.0%
7 859
 
2.9%
6 749
 
2.5%
9 453
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7756
25.4%
1 6147
20.1%
2 5748
18.8%
8 4456
14.6%
3 1463
 
4.8%
4 1192
 
3.9%
5 883
 
2.9%
7 859
 
2.8%
- 813
 
2.7%
6 749
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7756
25.4%
1 6147
20.1%
2 5748
18.8%
8 4456
14.6%
3 1463
 
4.8%
4 1192
 
3.9%
5 883
 
2.9%
7 859
 
2.8%
- 813
 
2.7%
6 749
 
2.5%
Distinct3787
Distinct (%)76.5%
Missing21
Missing (%)0.4%
Memory size39.0 KiB
2024-05-11T07:05:35.818629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length58
Mean length26.65098
Min length15

Characters and Unicode

Total characters131949
Distinct characters381
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

Unique3278 ?
Unique (%)66.2%

Sample

1st row서울특별시 서대문구 충정로3가 32-10번지
2nd row서울특별시 서대문구 대현동 54-24번지
3rd row서울특별시 서대문구 창천동 18-52번지
4th row서울특별시 서대문구 창천동 30-6번지
5th row서울특별시 서대문구 대현동 121-8번지
ValueCountFrequency (%)
서울특별시 4951
20.9%
서대문구 4951
20.9%
창천동 1117
 
4.7%
대현동 687
 
2.9%
1층 560
 
2.4%
연희동 556
 
2.4%
남가좌동 496
 
2.1%
홍제동 490
 
2.1%
홍은동 434
 
1.8%
지상1층 346
 
1.5%
Other values (3692) 9069
38.3%
2024-05-11T07:05:37.284394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22419
 
17.0%
9936
 
7.5%
6188
 
4.7%
1 5415
 
4.1%
4986
 
3.8%
4974
 
3.8%
4961
 
3.8%
4961
 
3.8%
4952
 
3.8%
4951
 
3.8%
Other values (371) 58206
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79500
60.3%
Decimal Number 23708
 
18.0%
Space Separator 22419
 
17.0%
Dash Punctuation 4517
 
3.4%
Close Punctuation 560
 
0.4%
Open Punctuation 560
 
0.4%
Uppercase Letter 348
 
0.3%
Other Punctuation 271
 
0.2%
Math Symbol 42
 
< 0.1%
Lowercase Letter 23
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9936
12.5%
6188
 
7.8%
4986
 
6.3%
4974
 
6.3%
4961
 
6.2%
4961
 
6.2%
4952
 
6.2%
4951
 
6.2%
4911
 
6.2%
4115
 
5.2%
Other values (321) 24565
30.9%
Uppercase Letter
ValueCountFrequency (%)
C 67
19.3%
D 65
18.7%
M 63
18.1%
B 51
14.7%
A 33
9.5%
S 12
 
3.4%
K 9
 
2.6%
G 7
 
2.0%
N 5
 
1.4%
E 5
 
1.4%
Other values (12) 31
8.9%
Decimal Number
ValueCountFrequency (%)
1 5415
22.8%
3 4496
19.0%
2 2988
12.6%
0 2357
9.9%
4 2069
 
8.7%
5 1531
 
6.5%
7 1263
 
5.3%
6 1255
 
5.3%
9 1187
 
5.0%
8 1147
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 258
95.2%
/ 5
 
1.8%
. 4
 
1.5%
: 2
 
0.7%
& 1
 
0.4%
@ 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 17
73.9%
b 3
 
13.0%
t 1
 
4.3%
r 1
 
4.3%
a 1
 
4.3%
Math Symbol
ValueCountFrequency (%)
~ 41
97.6%
+ 1
 
2.4%
Space Separator
ValueCountFrequency (%)
22419
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4517
100.0%
Close Punctuation
ValueCountFrequency (%)
) 560
100.0%
Open Punctuation
ValueCountFrequency (%)
( 560
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79500
60.3%
Common 52077
39.5%
Latin 372
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9936
12.5%
6188
 
7.8%
4986
 
6.3%
4974
 
6.3%
4961
 
6.2%
4961
 
6.2%
4952
 
6.2%
4951
 
6.2%
4911
 
6.2%
4115
 
5.2%
Other values (321) 24565
30.9%
Latin
ValueCountFrequency (%)
C 67
18.0%
D 65
17.5%
M 63
16.9%
B 51
13.7%
A 33
8.9%
e 17
 
4.6%
S 12
 
3.2%
K 9
 
2.4%
G 7
 
1.9%
N 5
 
1.3%
Other values (18) 43
11.6%
Common
ValueCountFrequency (%)
22419
43.0%
1 5415
 
10.4%
- 4517
 
8.7%
3 4496
 
8.6%
2 2988
 
5.7%
0 2357
 
4.5%
4 2069
 
4.0%
5 1531
 
2.9%
7 1263
 
2.4%
6 1255
 
2.4%
Other values (12) 3767
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79500
60.3%
ASCII 52448
39.7%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22419
42.7%
1 5415
 
10.3%
- 4517
 
8.6%
3 4496
 
8.6%
2 2988
 
5.7%
0 2357
 
4.5%
4 2069
 
3.9%
5 1531
 
2.9%
7 1263
 
2.4%
6 1255
 
2.4%
Other values (39) 4138
 
7.9%
Hangul
ValueCountFrequency (%)
9936
12.5%
6188
 
7.8%
4986
 
6.3%
4974
 
6.3%
4961
 
6.2%
4961
 
6.2%
4952
 
6.2%
4951
 
6.2%
4911
 
6.2%
4115
 
5.2%
Other values (321) 24565
30.9%
Number Forms
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct2518
Distinct (%)81.0%
Missing1864
Missing (%)37.5%
Memory size39.0 KiB
2024-05-11T07:05:38.199645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length58
Mean length33.492921
Min length22

Characters and Unicode

Total characters104096
Distinct characters391
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

Unique2289 ?
Unique (%)73.6%

Sample

1st row서울특별시 서대문구 북아현로 2 (북아현동,(지하1층))
2nd row서울특별시 서대문구 신촌로 77 (창천동)
3rd row서울특별시 서대문구 명지대3길 1 (남가좌동)
4th row서울특별시 서대문구 통일로 107-15 (미근동,(지하1층))
5th row서울특별시 서대문구 수색로 28-3 (남가좌동)
ValueCountFrequency (%)
서대문구 3109
 
15.6%
서울특별시 3108
 
15.6%
1층 1136
 
5.7%
창천동 664
 
3.3%
신촌로 541
 
2.7%
지하1층 473
 
2.4%
83 406
 
2.0%
연희동 383
 
1.9%
대현동 355
 
1.8%
홍제동 286
 
1.4%
Other values (1804) 9507
47.6%
2024-05-11T07:05:40.000581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16867
 
16.2%
6316
 
6.1%
1 4567
 
4.4%
4487
 
4.3%
) 3288
 
3.2%
( 3288
 
3.2%
3229
 
3.1%
3208
 
3.1%
3176
 
3.1%
, 3160
 
3.0%
Other values (381) 52510
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63220
60.7%
Space Separator 16867
 
16.2%
Decimal Number 13256
 
12.7%
Close Punctuation 3288
 
3.2%
Open Punctuation 3288
 
3.2%
Other Punctuation 3166
 
3.0%
Dash Punctuation 486
 
0.5%
Uppercase Letter 435
 
0.4%
Math Symbol 61
 
0.1%
Lowercase Letter 28
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6316
 
10.0%
4487
 
7.1%
3229
 
5.1%
3208
 
5.1%
3176
 
5.0%
3138
 
5.0%
3118
 
4.9%
3114
 
4.9%
3108
 
4.9%
2659
 
4.2%
Other values (330) 27667
43.8%
Uppercase Letter
ValueCountFrequency (%)
B 94
21.6%
C 82
18.9%
M 70
16.1%
D 68
15.6%
A 35
 
8.0%
S 15
 
3.4%
K 14
 
3.2%
E 11
 
2.5%
G 6
 
1.4%
N 5
 
1.1%
Other values (11) 35
 
8.0%
Decimal Number
ValueCountFrequency (%)
1 4567
34.5%
2 1759
 
13.3%
3 1614
 
12.2%
0 1137
 
8.6%
8 898
 
6.8%
4 852
 
6.4%
5 820
 
6.2%
7 613
 
4.6%
6 541
 
4.1%
9 455
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
e 15
53.6%
s 3
 
10.7%
b 3
 
10.7%
k 2
 
7.1%
r 1
 
3.6%
t 1
 
3.6%
u 1
 
3.6%
o 1
 
3.6%
a 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 3160
99.8%
/ 3
 
0.1%
? 2
 
0.1%
& 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 60
98.4%
+ 1
 
1.6%
Space Separator
ValueCountFrequency (%)
16867
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3288
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3288
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 486
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63220
60.7%
Common 40412
38.8%
Latin 464
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6316
 
10.0%
4487
 
7.1%
3229
 
5.1%
3208
 
5.1%
3176
 
5.0%
3138
 
5.0%
3118
 
4.9%
3114
 
4.9%
3108
 
4.9%
2659
 
4.2%
Other values (330) 27667
43.8%
Latin
ValueCountFrequency (%)
B 94
20.3%
C 82
17.7%
M 70
15.1%
D 68
14.7%
A 35
 
7.5%
e 15
 
3.2%
S 15
 
3.2%
K 14
 
3.0%
E 11
 
2.4%
G 6
 
1.3%
Other values (21) 54
11.6%
Common
ValueCountFrequency (%)
16867
41.7%
1 4567
 
11.3%
) 3288
 
8.1%
( 3288
 
8.1%
, 3160
 
7.8%
2 1759
 
4.4%
3 1614
 
4.0%
0 1137
 
2.8%
8 898
 
2.2%
4 852
 
2.1%
Other values (10) 2982
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63220
60.7%
ASCII 40875
39.3%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16867
41.3%
1 4567
 
11.2%
) 3288
 
8.0%
( 3288
 
8.0%
, 3160
 
7.7%
2 1759
 
4.3%
3 1614
 
3.9%
0 1137
 
2.8%
8 898
 
2.2%
4 852
 
2.1%
Other values (40) 3445
 
8.4%
Hangul
ValueCountFrequency (%)
6316
 
10.0%
4487
 
7.1%
3229
 
5.1%
3208
 
5.1%
3176
 
5.0%
3138
 
5.0%
3118
 
4.9%
3114
 
4.9%
3108
 
4.9%
2659
 
4.2%
Other values (330) 27667
43.8%
Number Forms
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct179
Distinct (%)5.8%
Missing1894
Missing (%)38.1%
Infinite0
Infinite (%)0.0%
Mean3724.0543
Minimum3600
Maximum3791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2024-05-11T07:05:40.616775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3600
5-th percentile3624
Q13680
median3727
Q33776
95-th percentile3789
Maximum3791
Range191
Interquartile range (IQR)96

Descriptive statistics

Standard deviation54.104558
Coefficient of variation (CV)0.014528402
Kurtosis-0.9120984
Mean3724.0543
Median Absolute Deviation (MAD)48
Skewness-0.4685907
Sum11462639
Variance2927.3032
MonotonicityNot monotonic
2024-05-11T07:05:41.264594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3789 447
 
9.0%
3766 196
 
3.9%
3722 109
 
2.2%
3707 81
 
1.6%
3665 65
 
1.3%
3675 56
 
1.1%
3777 55
 
1.1%
3767 55
 
1.1%
3776 51
 
1.0%
3780 49
 
1.0%
Other values (169) 1914
38.5%
(Missing) 1894
38.1%
ValueCountFrequency (%)
3600 1
 
< 0.1%
3601 4
 
0.1%
3602 5
 
0.1%
3603 1
 
< 0.1%
3604 4
 
0.1%
3605 24
0.5%
3606 5
 
0.1%
3609 1
 
< 0.1%
3610 5
 
0.1%
3611 9
 
0.2%
ValueCountFrequency (%)
3791 11
 
0.2%
3790 1
 
< 0.1%
3789 447
9.0%
3788 29
 
0.6%
3787 18
 
0.4%
3786 6
 
0.1%
3785 37
 
0.7%
3784 20
 
0.4%
3783 3
 
0.1%
3782 3
 
0.1%
Distinct4494
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
2024-05-11T07:05:42.183511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38
Mean length7.3185841
Min length1

Characters and Unicode

Total characters36388
Distinct characters923
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4195 ?
Unique (%)84.4%

Sample

1st row터틀
2nd row호원당
3rd row노타리
4th row코드리옹
5th row역전
ValueCountFrequency (%)
세븐일레븐 73
 
1.1%
신촌점 69
 
1.0%
카페 69
 
1.0%
씨유 65
 
1.0%
gs25 59
 
0.9%
이대점 48
 
0.7%
지에스25 33
 
0.5%
coffee 30
 
0.4%
커피 30
 
0.4%
명지대점 28
 
0.4%
Other values (4885) 6323
92.6%
2024-05-11T07:05:43.506058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1859
 
5.1%
1376
 
3.8%
1073
 
2.9%
1013
 
2.8%
) 692
 
1.9%
( 690
 
1.9%
671
 
1.8%
623
 
1.7%
577
 
1.6%
529
 
1.5%
Other values (913) 27285
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28934
79.5%
Space Separator 1859
 
5.1%
Lowercase Letter 1769
 
4.9%
Uppercase Letter 1542
 
4.2%
Decimal Number 758
 
2.1%
Close Punctuation 692
 
1.9%
Open Punctuation 690
 
1.9%
Other Punctuation 107
 
0.3%
Dash Punctuation 35
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1376
 
4.8%
1073
 
3.7%
1013
 
3.5%
671
 
2.3%
623
 
2.2%
577
 
2.0%
529
 
1.8%
370
 
1.3%
368
 
1.3%
353
 
1.2%
Other values (834) 21981
76.0%
Uppercase Letter
ValueCountFrequency (%)
C 210
13.6%
S 140
 
9.1%
E 129
 
8.4%
G 122
 
7.9%
A 85
 
5.5%
O 82
 
5.3%
U 81
 
5.3%
P 80
 
5.2%
L 64
 
4.2%
R 63
 
4.1%
Other values (16) 486
31.5%
Lowercase Letter
ValueCountFrequency (%)
e 294
16.6%
a 176
 
9.9%
o 173
 
9.8%
f 119
 
6.7%
n 95
 
5.4%
i 93
 
5.3%
c 90
 
5.1%
r 83
 
4.7%
s 81
 
4.6%
t 81
 
4.6%
Other values (15) 484
27.4%
Other Punctuation
ValueCountFrequency (%)
. 28
26.2%
& 26
24.3%
' 21
19.6%
? 12
11.2%
, 11
 
10.3%
! 2
 
1.9%
% 2
 
1.9%
# 1
 
0.9%
1
 
0.9%
/ 1
 
0.9%
Other values (2) 2
 
1.9%
Decimal Number
ValueCountFrequency (%)
2 224
29.6%
5 167
22.0%
1 78
 
10.3%
3 62
 
8.2%
9 59
 
7.8%
4 52
 
6.9%
0 45
 
5.9%
8 37
 
4.9%
6 18
 
2.4%
7 16
 
2.1%
Space Separator
ValueCountFrequency (%)
1859
100.0%
Close Punctuation
ValueCountFrequency (%)
) 692
100.0%
Open Punctuation
ValueCountFrequency (%)
( 690
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28921
79.5%
Common 4143
 
11.4%
Latin 3311
 
9.1%
Han 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1376
 
4.8%
1073
 
3.7%
1013
 
3.5%
671
 
2.3%
623
 
2.2%
577
 
2.0%
529
 
1.8%
370
 
1.3%
368
 
1.3%
353
 
1.2%
Other values (824) 21968
76.0%
Latin
ValueCountFrequency (%)
e 294
 
8.9%
C 210
 
6.3%
a 176
 
5.3%
o 173
 
5.2%
S 140
 
4.2%
E 129
 
3.9%
G 122
 
3.7%
f 119
 
3.6%
n 95
 
2.9%
i 93
 
2.8%
Other values (41) 1760
53.2%
Common
ValueCountFrequency (%)
1859
44.9%
) 692
 
16.7%
( 690
 
16.7%
2 224
 
5.4%
5 167
 
4.0%
1 78
 
1.9%
3 62
 
1.5%
9 59
 
1.4%
4 52
 
1.3%
0 45
 
1.1%
Other values (18) 215
 
5.2%
Han
ValueCountFrequency (%)
4
30.8%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28919
79.5%
ASCII 7453
 
20.5%
CJK 11
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%
Compat Jamo 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1859
24.9%
) 692
 
9.3%
( 690
 
9.3%
e 294
 
3.9%
2 224
 
3.0%
C 210
 
2.8%
a 176
 
2.4%
o 173
 
2.3%
5 167
 
2.2%
S 140
 
1.9%
Other values (68) 2828
37.9%
Hangul
ValueCountFrequency (%)
1376
 
4.8%
1073
 
3.7%
1013
 
3.5%
671
 
2.3%
623
 
2.2%
577
 
2.0%
529
 
1.8%
370
 
1.3%
368
 
1.3%
353
 
1.2%
Other values (822) 21966
76.0%
CJK
ValueCountFrequency (%)
4
36.4%
1
 
9.1%
1
 
9.1%
1
 
9.1%
1
 
9.1%
1
 
9.1%
1
 
9.1%
1
 
9.1%
CJK Compat Ideographs
ValueCountFrequency (%)
1
50.0%
1
50.0%
Compat Jamo
ValueCountFrequency (%)
1
50.0%
1
50.0%
None
ValueCountFrequency (%)
1
100.0%
Distinct3900
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
Minimum1999-02-02 00:00:00
Maximum2024-05-09 14:23:15
2024-05-11T07:05:43.945243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:05:44.647596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
I
3163 
U
1809 

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 3163
63.6%
U 1809
36.4%

Length

2024-05-11T07:05:45.080729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:45.520999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 3163
63.6%
u 1809
36.4%
Distinct1100
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T07:05:45.928851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:05:46.475394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
커피숍
1295 
기타 휴게음식점
1082 
일반조리판매
651 
다방
621 
편의점
445 
Other values (11)
878 

Length

Max length8
Median length6
Mean length4.5080451
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row다방
2nd row과자점
3rd row다방
4th row다방
5th row다방

Common Values

ValueCountFrequency (%)
커피숍 1295
26.0%
기타 휴게음식점 1082
21.8%
일반조리판매 651
13.1%
다방 621
12.5%
편의점 445
 
9.0%
과자점 353
 
7.1%
패스트푸드 331
 
6.7%
백화점 117
 
2.4%
아이스크림 25
 
0.5%
푸드트럭 24
 
0.5%
Other values (6) 28
 
0.6%

Length

2024-05-11T07:05:46.887702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
커피숍 1295
21.4%
기타 1082
17.9%
휴게음식점 1082
17.9%
일반조리판매 651
10.8%
다방 621
10.3%
편의점 445
 
7.4%
과자점 353
 
5.8%
패스트푸드 331
 
5.5%
백화점 117
 
1.9%
아이스크림 25
 
0.4%
Other values (7) 52
 
0.9%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct2063
Distinct (%)43.9%
Missing274
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean194450.08
Minimum191484.64
Maximum197170.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2024-05-11T07:05:47.254869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191484.64
5-th percentile192349.33
Q1193730.38
median194375.5
Q3195132.47
95-th percentile196711.58
Maximum197170.64
Range5686.0034
Interquartile range (IQR)1402.0887

Descriptive statistics

Standard deviation1158.9444
Coefficient of variation (CV)0.0059601127
Kurtosis-0.085697053
Mean194450.08
Median Absolute Deviation (MAD)732.07213
Skewness-0.014713886
Sum9.1352649 × 108
Variance1343152.1
MonotonicityNot monotonic
2024-05-11T07:05:47.725235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194265.067639805 517
 
10.4%
194584.959249312 122
 
2.5%
195287.766418517 47
 
0.9%
194326.41268289 34
 
0.7%
192672.067512964 23
 
0.5%
194955.406353595 19
 
0.4%
193759.805626607 13
 
0.3%
192209.323632506 13
 
0.3%
195155.26476378 12
 
0.2%
195119.290041442 12
 
0.2%
Other values (2053) 3886
78.2%
(Missing) 274
 
5.5%
ValueCountFrequency (%)
191484.638855239 1
 
< 0.1%
191497.867040904 1
 
< 0.1%
191519.132033814 1
 
< 0.1%
191555.173434967 2
 
< 0.1%
191559.918118738 6
0.1%
191560.52084165 1
 
< 0.1%
191570.192406712 1
 
< 0.1%
191572.384552653 4
0.1%
191580.964968934 1
 
< 0.1%
191590.248604043 1
 
< 0.1%
ValueCountFrequency (%)
197170.642282034 2
 
< 0.1%
197147.394631349 5
0.1%
197144.015440398 11
0.2%
197121.041523674 1
 
< 0.1%
197080.294787436 1
 
< 0.1%
197073.021981074 1
 
< 0.1%
197064.31975275 1
 
< 0.1%
197062.989911966 2
 
< 0.1%
197061.149730671 1
 
< 0.1%
197053.685372291 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct2062
Distinct (%)43.9%
Missing274
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean451857.01
Minimum450367.37
Maximum455710.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2024-05-11T07:05:48.168268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum450367.37
5-th percentile450433.69
Q1450645.32
median451381.59
Q3453002.27
95-th percentile454477.71
Maximum455710.43
Range5343.0582
Interquartile range (IQR)2356.9451

Descriptive statistics

Standard deviation1379.1321
Coefficient of variation (CV)0.0030521428
Kurtosis-0.65708543
Mean451857.01
Median Absolute Deviation (MAD)880.41663
Skewness0.7322006
Sum2.1228242 × 109
Variance1902005.4
MonotonicityNot monotonic
2024-05-11T07:05:48.620364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450433.691021245 517
 
10.4%
451381.585492051 123
 
2.5%
451143.789306014 47
 
0.9%
450501.1688616 34
 
0.7%
452221.960661496 23
 
0.5%
450620.463748751 19
 
0.4%
451374.136885633 13
 
0.3%
452067.039429636 13
 
0.3%
450709.840433705 12
 
0.2%
450712.203292397 12
 
0.2%
Other values (2052) 3885
78.1%
(Missing) 274
 
5.5%
ValueCountFrequency (%)
450367.371729263 1
 
< 0.1%
450371.790065047 1
 
< 0.1%
450374.297075689 1
 
< 0.1%
450375.488731434 2
< 0.1%
450378.207939919 2
< 0.1%
450379.216435725 2
< 0.1%
450384.575342365 2
< 0.1%
450387.139950052 4
0.1%
450390.671043197 3
0.1%
450391.53849739 3
0.1%
ValueCountFrequency (%)
455710.429910779 1
 
< 0.1%
455588.317096857 4
0.1%
455532.552666932 1
 
< 0.1%
455484.478875325 1
 
< 0.1%
455478.495118012 1
 
< 0.1%
455436.091152491 1
 
< 0.1%
455414.392407621 1
 
< 0.1%
455404.807687089 1
 
< 0.1%
455396.252167005 1
 
< 0.1%
455381.480302685 1
 
< 0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
1153 
커피숍
834 
기타 휴게음식점
788 
다방
606 
일반조리판매
508 
Other values (12)
1083 

Length

Max length8
Median length6
Mean length4.3445294
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row다방
2nd row과자점
3rd row다방
4th row다방
5th row다방

Common Values

ValueCountFrequency (%)
<NA> 1153
23.2%
커피숍 834
16.8%
기타 휴게음식점 788
15.8%
다방 606
12.2%
일반조리판매 508
10.2%
과자점 350
 
7.0%
패스트푸드 306
 
6.2%
편의점 266
 
5.3%
백화점 107
 
2.2%
푸드트럭 15
 
0.3%
Other values (7) 39
 
0.8%

Length

2024-05-11T07:05:49.098022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1153
20.0%
커피숍 834
14.5%
기타 788
13.7%
휴게음식점 788
13.7%
다방 606
10.5%
일반조리판매 508
8.8%
과자점 350
 
6.1%
패스트푸드 306
 
5.3%
편의점 266
 
4.6%
백화점 107
 
1.9%
Other values (8) 54
 
0.9%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.4%
Missing3120
Missing (%)62.8%
Infinite0
Infinite (%)0.0%
Mean0.22948164
Minimum0
Maximum8
Zeros1492
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2024-05-11T07:05:49.455886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54384742
Coefficient of variation (CV)2.3698951
Kurtosis34.483976
Mean0.22948164
Median Absolute Deviation (MAD)0
Skewness4.1615246
Sum425
Variance0.29577002
MonotonicityNot monotonic
2024-05-11T07:05:50.075290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1492
30.0%
1 317
 
6.4%
2 31
 
0.6%
3 8
 
0.2%
4 2
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
(Missing) 3120
62.8%
ValueCountFrequency (%)
0 1492
30.0%
1 317
 
6.4%
2 31
 
0.6%
3 8
 
0.2%
4 2
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 1
 
< 0.1%
4 2
 
< 0.1%
3 8
 
0.2%
2 31
 
0.6%
1 317
 
6.4%
0 1492
30.0%

여성종사자수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct9
Distinct (%)0.5%
Missing3114
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean0.74219591
Minimum0
Maximum94
Zeros1162
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2024-05-11T07:05:50.443769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum94
Range94
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.3990116
Coefficient of variation (CV)3.2323159
Kurtosis1230.8582
Mean0.74219591
Median Absolute Deviation (MAD)0
Skewness31.773346
Sum1379
Variance5.7552568
MonotonicityNot monotonic
2024-05-11T07:05:50.792312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1162
 
23.4%
1 273
 
5.5%
2 272
 
5.5%
3 140
 
2.8%
4 7
 
0.1%
6 1
 
< 0.1%
94 1
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 3114
62.6%
ValueCountFrequency (%)
0 1162
23.4%
1 273
 
5.5%
2 272
 
5.5%
3 140
 
2.8%
4 7
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
94 1
 
< 0.1%
ValueCountFrequency (%)
94 1
 
< 0.1%
9 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 7
 
0.1%
3 140
 
2.8%
2 272
 
5.5%
1 273
 
5.5%
0 1162
23.4%

영업장주변구분명
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
3611 
기타
564 
주택가주변
512 
유흥업소밀집지역
 
191
아파트지역
 
38
Other values (3)
 
56

Length

Max length8
Median length4
Mean length4.0818584
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주택가주변
2nd row학교정화(상대)
3rd row유흥업소밀집지역
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
<NA> 3611
72.6%
기타 564
 
11.3%
주택가주변 512
 
10.3%
유흥업소밀집지역 191
 
3.8%
아파트지역 38
 
0.8%
학교정화(상대) 35
 
0.7%
학교정화(절대) 18
 
0.4%
결혼예식장주변 3
 
0.1%

Length

2024-05-11T07:05:51.234720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:51.601936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3611
72.6%
기타 564
 
11.3%
주택가주변 512
 
10.3%
유흥업소밀집지역 191
 
3.8%
아파트지역 38
 
0.8%
학교정화(상대 35
 
0.7%
학교정화(절대 18
 
0.4%
결혼예식장주변 3
 
0.1%

등급구분명
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
3641 
기타
484 
지도
 
304
 
230
자율
 
221
Other values (3)
 
92

Length

Max length4
Median length4
Mean length3.4004425
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row지도
3rd row지도
4th row
5th row지도

Common Values

ValueCountFrequency (%)
<NA> 3641
73.2%
기타 484
 
9.7%
지도 304
 
6.1%
230
 
4.6%
자율 221
 
4.4%
89
 
1.8%
우수 2
 
< 0.1%
관리 1
 
< 0.1%

Length

2024-05-11T07:05:52.019747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:52.390051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3641
73.2%
기타 484
 
9.7%
지도 304
 
6.1%
230
 
4.6%
자율 221
 
4.4%
89
 
1.8%
우수 2
 
< 0.1%
관리 1
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
3237 
상수도전용
1710 
상수도(음용)지하수(주방용)겸용
 
25

Length

Max length17
Median length4
Mean length4.409292
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3237
65.1%
상수도전용 1710
34.4%
상수도(음용)지하수(주방용)겸용 25
 
0.5%

Length

2024-05-11T07:05:52.802238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:53.197605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3237
65.1%
상수도전용 1710
34.4%
상수도(음용)지하수(주방용)겸용 25
 
0.5%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4737 
0
 
235

Length

Max length4
Median length4
Mean length3.858206
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> 4737
95.3%
0 235
 
4.7%

Length

2024-05-11T07:05:53.562793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:53.881273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4737
95.3%
0 235
 
4.7%

본사종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4733 
0
 
239

Length

Max length4
Median length4
Mean length3.8557924
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> 4733
95.2%
0 239
 
4.8%

Length

2024-05-11T07:05:54.248143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:54.544458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4733
95.2%
0 239
 
4.8%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4733 
0
 
239

Length

Max length4
Median length4
Mean length3.8557924
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> 4733
95.2%
0 239
 
4.8%

Length

2024-05-11T07:05:55.008690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:55.333400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4733
95.2%
0 239
 
4.8%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4733 
0
 
239

Length

Max length4
Median length4
Mean length3.8557924
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> 4733
95.2%
0 239
 
4.8%

Length

2024-05-11T07:05:55.611249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:55.872340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4733
95.2%
0 239
 
4.8%

공장생산직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4733 
0
 
239

Length

Max length4
Median length4
Mean length3.8557924
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> 4733
95.2%
0 239
 
4.8%

Length

2024-05-11T07:05:56.200048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:56.578008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4733
95.2%
0 239
 
4.8%

건물소유구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4972
Missing (%)100.0%
Memory size43.8 KiB

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4733 
0
 
239

Length

Max length4
Median length4
Mean length3.8557924
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> 4733
95.2%
0 239
 
4.8%

Length

2024-05-11T07:05:56.928972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:57.267878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4733
95.2%
0 239
 
4.8%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4733 
0
 
239

Length

Max length4
Median length4
Mean length3.8557924
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> 4733
95.2%
0 239
 
4.8%

Length

2024-05-11T07:05:57.658673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:05:58.013070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4733
95.2%
0 239
 
4.8%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing1153
Missing (%)23.2%
Memory size9.8 KiB
False
3759 
True
 
60
(Missing)
1153 
ValueCountFrequency (%)
False 3759
75.6%
True 60
 
1.2%
(Missing) 1153
 
23.2%
2024-05-11T07:05:58.388769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1956
Distinct (%)51.2%
Missing1153
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean69.207368
Minimum0
Maximum80069.2
Zeros54
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2024-05-11T07:05:58.765929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.354
Q114.17
median29.7
Q363
95-th percentile150
Maximum80069.2
Range80069.2
Interquartile range (IQR)48.83

Descriptive statistics

Standard deviation1296.1712
Coefficient of variation (CV)18.728803
Kurtosis3803.7702
Mean69.207368
Median Absolute Deviation (MAD)19.8
Skewness61.613543
Sum264302.94
Variance1680059.7
MonotonicityNot monotonic
2024-05-11T07:05:59.308345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 138
 
2.8%
3.3 115
 
2.3%
10.0 85
 
1.7%
33.0 73
 
1.5%
0.0 54
 
1.1%
26.4 45
 
0.9%
15.0 44
 
0.9%
16.5 43
 
0.9%
9.9 39
 
0.8%
30.0 37
 
0.7%
Other values (1946) 3146
63.3%
(Missing) 1153
 
23.2%
ValueCountFrequency (%)
0.0 54
1.1%
0.99 1
 
< 0.1%
1.44 1
 
< 0.1%
1.5 1
 
< 0.1%
1.56 1
 
< 0.1%
1.65 1
 
< 0.1%
1.7 1
 
< 0.1%
1.8 1
 
< 0.1%
2.0 2
 
< 0.1%
2.1 1
 
< 0.1%
ValueCountFrequency (%)
80069.2 1
< 0.1%
1080.0 1
< 0.1%
528.0 1
< 0.1%
472.02 1
< 0.1%
465.6 1
< 0.1%
455.4 1
< 0.1%
404.8 1
< 0.1%
396.13 1
< 0.1%
396.0 1
< 0.1%
390.91 1
< 0.1%

전통업소지정번호
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
<NA>
4970 
32
 
1
1218
 
1

Length

Max length4
Median length4
Mean length3.9995977
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4970
> 99.9%
32 1
 
< 0.1%
1218 1
 
< 0.1%

Length

2024-05-11T07:05:59.926196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:06:00.293309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4970
> 99.9%
32 1
 
< 0.1%
1218 1
 
< 0.1%
Distinct2
Distinct (%)100.0%
Missing4970
Missing (%)> 99.9%
Memory size39.0 KiB
2024-05-11T07:06:00.559445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Characters and Unicode

Total characters5
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row갈비탕
2nd row피자
ValueCountFrequency (%)
갈비탕 1
50.0%
피자 1
50.0%
2024-05-11T07:06:01.499732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4972
Missing (%)100.0%
Memory size43.8 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031200003120000-104-1931-0425419311106<NA>3폐업2폐업19970311<NA><NA><NA>02 363342946.40120837서울특별시 서대문구 충정로3가 32-10번지<NA><NA>터틀2001-09-30 00:00:00I2018-08-31 23:59:59.0다방196544.199575451004.003187다방11주택가주변상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N46.4<NA><NA><NA>
131200003120000-104-1967-0292119670523<NA>3폐업2폐업20050314<NA><NA><NA>020363085523.66120808서울특별시 서대문구 대현동 54-24번지<NA><NA>호원당2001-09-30 00:00:00I2018-08-31 23:59:59.0과자점195159.503584450575.481509과자점02학교정화(상대)지도상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N23.66<NA><NA><NA>
231200003120000-104-1967-0301119671010<NA>3폐업2폐업19931008<NA><NA><NA>020392184382.09120833서울특별시 서대문구 창천동 18-52번지<NA><NA>노타리2001-09-30 00:00:00I2018-08-31 23:59:59.0다방194375.526997450378.20794다방13유흥업소밀집지역지도상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N82.09<NA><NA><NA>
331200003120000-104-1967-0310019670623<NA>3폐업2폐업19960604<NA><NA><NA>02 7359618104.22120834서울특별시 서대문구 창천동 30-6번지<NA><NA>코드리옹2001-09-30 00:00:00I2018-08-31 23:59:59.0다방194333.411173450462.360244다방02기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N104.22<NA><NA><NA>
431200003120000-104-1967-0425319671010<NA>3폐업2폐업19971014<NA><NA><NA>02 3627286.00120809서울특별시 서대문구 대현동 121-8번지<NA><NA>역전2001-09-30 00:00:00I2018-08-31 23:59:59.0다방<NA><NA>다방02기타지도상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
531200003120000-104-1968-0286719680522<NA>3폐업2폐업19890323<NA><NA><NA>0203624270113.20120012서울특별시 서대문구 충정로2가 139-2번지<NA><NA>뉴-이태리2001-09-30 00:00:00I2018-08-31 23:59:59.0과자점<NA><NA>과자점03주택가주변지도상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N113.2<NA><NA><NA>
631200003120000-104-1968-0846919680406<NA>3폐업2폐업19960415<NA><NA><NA>02 738872958.87120859서울특별시 서대문구 홍제동 173-29번지<NA><NA>팡세2001-09-30 00:00:00I2018-08-31 23:59:59.0다방195036.294849453980.142683다방02유흥업소밀집지역지도상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N58.87<NA><NA><NA>
731200003120000-104-1968-0851319680617<NA>3폐업2폐업20020529<NA><NA><NA>02 392830854.70120012서울특별시 서대문구 충정로2가 141-3번지<NA><NA>화양2001-09-30 00:00:00I2018-08-31 23:59:59.0다방<NA><NA>다방02주택가주변상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N54.7<NA><NA><NA>
831200003120000-104-1969-0304419690812<NA>3폐업2폐업20200929<NA><NA><NA>0203621991111.03120819서울특별시 서대문구 북아현동 135-9 (지하1층)서울특별시 서대문구 북아현로 2 (북아현동,(지하1층))3758이삭2020-09-29 17:49:45U2020-10-01 02:40:00.0다방196079.049171450605.895857다방12주택가주변지도상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N111.03<NA><NA><NA>
931200003120000-104-1969-0305219690912<NA>3폐업2폐업20001121<NA><NA><NA>020363915788.33120070서울특별시 서대문구 영천동 287-0번지<NA><NA>로타리커피숍2001-09-30 00:00:00I2018-08-31 23:59:59.0다방<NA><NA>다방22기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N88.33<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
496231200003120000-104-2024-000802024-04-26<NA>1영업/정상1영업<NA><NA><NA><NA><NA>40.26120-120서울특별시 서대문구 남가좌동 381 DMC센트레빌서울특별시 서대문구 거북골로 120, 상가1동 지2층 1-8b호 (남가좌동, DMC센트레빌)3690카페 마주2024-04-26 14:47:38I2023-12-03 22:08:00.0커피숍192480.029441452634.557674<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
496331200003120000-104-2024-000812024-04-29<NA>1영업/정상1영업<NA><NA><NA><NA><NA>19.00120-814서울특별시 서대문구 북가좌동 307-9 1층 우측서울특별시 서대문구 거북골로 192, 1층 우측 (북가좌동)3681몬스터커피 북가좌동점2024-04-29 13:32:05I2023-12-05 00:01:00.0커피숍191944.952028452871.31002<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
496431200003120000-104-2024-000822024-04-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>31.55120-810서울특별시 서대문구 북가좌동 3-29 1층 우측서울특별시 서대문구 증가로12나길 63-6, 1층 우측 (북가좌동)3671성북동키친2024-04-30 15:52:06I2023-12-05 00:02:00.0기타 휴게음식점192930.675878453276.809507<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
496531200003120000-104-2024-000832024-05-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.25120-800서울특별시 서대문구 남가좌동 5-205 베어205 1층 101호서울특별시 서대문구 명지대1라길 13, 베어205 1층 101호 (남가좌동)3673윈오트르메종2024-05-01 09:42:25I2023-12-05 00:03:00.0커피숍193127.221322453280.412637<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
496631200003120000-104-2024-000842024-05-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA><NA>120-706서울특별시 서대문구 창천동 30-33 현대백화점신촌점 지하1층 행사장서울특별시 서대문구 신촌로 83, 현대백화점신촌점 지하1층 행사장 (창천동)3789(주)갑성2024-05-02 13:23:40I2023-12-05 00:04:00.0백화점194265.06764450433.691021<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
496731200003120000-104-2024-000852024-05-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA><NA>120-706서울특별시 서대문구 창천동 30-33 현대백화점신촌점 지하1층 행사장서울특별시 서대문구 신촌로 83, 현대백화점신촌점 지하1층 행사장 (창천동)3789(주)갑성2024-05-02 13:31:17I2023-12-05 00:04:00.0백화점194265.06764450433.691021<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
496831200003120000-104-2024-000862024-05-03<NA>3폐업2폐업2024-05-05<NA><NA><NA><NA><NA>120-848서울특별시 서대문구 홍은동 429서울특별시 서대문구 연희로 262-24, 홍제천 산책로 (홍은동)3653울리에(Ulie)2024-05-06 04:15:09U2023-12-05 00:08:00.0커피숍194366.912686453218.662022<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
496931200003120000-104-2024-000872024-05-07<NA>1영업/정상1영업<NA><NA><NA><NA><NA><NA>120-706서울특별시 서대문구 창천동 30-33 현대백화점신촌점서울특별시 서대문구 신촌로 83, 현대백화점신촌점 지하1층 (창천동)3789(주)참살이유통2024-05-07 14:24:22I2023-12-05 00:09:00.0일반조리판매194265.06764450433.691021<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
497031200003120000-104-2024-000882024-05-07<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3.30120-835서울특별시 서대문구 창천동 89-27 1층서울특별시 서대문구 성산로22길 22, 1층 (창천동)3727세븐일레븐 신촌원룸점2024-05-07 16:54:21I2023-12-05 00:09:00.0편의점194102.837564450848.309912<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
497131200003120000-104-2024-000892024-05-08<NA>1영업/정상1영업<NA><NA><NA><NA><NA><NA>120-706서울특별시 서대문구 창천동 30-33 현대백화점신촌점서울특별시 서대문구 신촌로 83, 현대백화점신촌점 지하1층 (창천동)3789서울키친(어랑사랑)2024-05-08 13:06:27I2023-12-04 23:00:00.0일반조리판매194265.06764450433.691021<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>