Overview

Dataset statistics

Number of variables44
Number of observations240
Missing cells2215
Missing cells (%)21.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory88.0 KiB
Average record size in memory375.6 B

Variable types

Categorical20
Text7
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
Author강북구
URLhttps://data.seoul.go.kr/dataList/OA-18260/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
영업상태코드 is highly imbalanced (50.5%)Imbalance
영업상태명 is highly imbalanced (50.5%)Imbalance
상세영업상태코드 is highly imbalanced (50.5%)Imbalance
상세영업상태명 is highly imbalanced (50.5%)Imbalance
업태구분명 is highly imbalanced (56.0%)Imbalance
여성종사자수 is highly imbalanced (52.5%)Imbalance
총인원 is highly imbalanced (78.9%)Imbalance
보증액 is highly imbalanced (66.8%)Imbalance
월세액 is highly imbalanced (66.8%)Imbalance
인허가취소일자 has 240 (100.0%) missing valuesMissing
폐업일자 has 26 (10.8%) missing valuesMissing
휴업시작일자 has 240 (100.0%) missing valuesMissing
휴업종료일자 has 240 (100.0%) missing valuesMissing
재개업일자 has 240 (100.0%) missing valuesMissing
전화번호 has 62 (25.8%) missing valuesMissing
소재지면적 has 9 (3.8%) missing valuesMissing
도로명주소 has 124 (51.7%) missing valuesMissing
도로명우편번호 has 126 (52.5%) missing valuesMissing
좌표정보(X) has 24 (10.0%) missing valuesMissing
좌표정보(Y) has 24 (10.0%) missing valuesMissing
공장생산직종업원수 has 95 (39.6%) missing valuesMissing
다중이용업소여부 has 26 (10.8%) missing valuesMissing
시설총규모 has 26 (10.8%) missing valuesMissing
전통업소지정번호 has 240 (100.0%) missing valuesMissing
전통업소주된음식 has 240 (100.0%) missing valuesMissing
홈페이지 has 233 (97.1%) 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
전통업소지정번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소주된음식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공장생산직종업원수 has 132 (55.0%) zerosZeros
시설총규모 has 198 (82.5%) zerosZeros

Reproduction

Analysis started2024-05-11 06:19:23.485106
Analysis finished2024-05-11 06:19:24.997391
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
3080000
240 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3080000
2nd row3080000
3rd row3080000
4th row3080000
5th row3080000

Common Values

ValueCountFrequency (%)
3080000 240
100.0%

Length

2024-05-11T06:19:25.199213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:25.556461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3080000 240
100.0%

관리번호
Text

UNIQUE 

Distinct240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-05-11T06:19:25.983467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters5280
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

Unique240 ?
Unique (%)100.0%

Sample

1st row3080000-106-1971-00120
2nd row3080000-106-1971-00125
3rd row3080000-106-1974-00126
4th row3080000-106-1975-00112
5th row3080000-106-1982-00137
ValueCountFrequency (%)
3080000-106-1971-00120 1
 
0.4%
3080000-106-1971-00125 1
 
0.4%
3080000-106-2011-00005 1
 
0.4%
3080000-106-2010-00003 1
 
0.4%
3080000-106-2010-00004 1
 
0.4%
3080000-106-2010-00005 1
 
0.4%
3080000-106-2010-00006 1
 
0.4%
3080000-106-2010-00007 1
 
0.4%
3080000-106-2010-00008 1
 
0.4%
3080000-106-2010-00009 1
 
0.4%
Other values (230) 230
95.8%
2024-05-11T06:19:26.975243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2578
48.8%
- 720
 
13.6%
1 491
 
9.3%
3 332
 
6.3%
6 285
 
5.4%
8 282
 
5.3%
2 272
 
5.2%
9 147
 
2.8%
4 78
 
1.5%
7 48
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4560
86.4%
Dash Punctuation 720
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2578
56.5%
1 491
 
10.8%
3 332
 
7.3%
6 285
 
6.2%
8 282
 
6.2%
2 272
 
6.0%
9 147
 
3.2%
4 78
 
1.7%
7 48
 
1.1%
5 47
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5280
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2578
48.8%
- 720
 
13.6%
1 491
 
9.3%
3 332
 
6.3%
6 285
 
5.4%
8 282
 
5.3%
2 272
 
5.2%
9 147
 
2.8%
4 78
 
1.5%
7 48
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2578
48.8%
- 720
 
13.6%
1 491
 
9.3%
3 332
 
6.3%
6 285
 
5.4%
8 282
 
5.3%
2 272
 
5.2%
9 147
 
2.8%
4 78
 
1.5%
7 48
 
0.9%
Distinct232
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum1971-08-10 00:00:00
Maximum2023-10-04 00:00:00
2024-05-11T06:19:27.502072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:19:28.047149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing240
Missing (%)100.0%
Memory size2.2 KiB

영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
3
214 
1
26 

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 (%)
3 214
89.2%
1 26
 
10.8%

Length

2024-05-11T06:19:28.514741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:28.814341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 214
89.2%
1 26
 
10.8%

영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
폐업
214 
영업/정상
26 

Length

Max length5
Median length2
Mean length2.325
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 214
89.2%
영업/정상 26
 
10.8%

Length

2024-05-11T06:19:29.163828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:29.496633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 214
89.2%
영업/정상 26
 
10.8%

상세영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2
214 
1
26 

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 (%)
2 214
89.2%
1 26
 
10.8%

Length

2024-05-11T06:19:29.904085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:30.182886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 214
89.2%
1 26
 
10.8%

상세영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
폐업
214 
영업
26 

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 214
89.2%
영업 26
 
10.8%

Length

2024-05-11T06:19:30.484070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:30.817739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 214
89.2%
영업 26
 
10.8%

폐업일자
Date

MISSING 

Distinct209
Distinct (%)97.7%
Missing26
Missing (%)10.8%
Memory size2.0 KiB
Minimum1996-01-11 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T06:19:31.186914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:19:31.652056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing240
Missing (%)100.0%
Memory size2.2 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing240
Missing (%)100.0%
Memory size2.2 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing240
Missing (%)100.0%
Memory size2.2 KiB

전화번호
Text

MISSING 

Distinct164
Distinct (%)92.1%
Missing62
Missing (%)25.8%
Memory size2.0 KiB
2024-05-11T06:19:32.272255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.275281
Min length2

Characters and Unicode

Total characters1829
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

Unique156 ?
Unique (%)87.6%

Sample

1st row02 9890425
2nd row02 9898904
3rd row02 9834040
4th row02 9028477
5th row02 9827667
ValueCountFrequency (%)
02 161
42.5%
070 12
 
3.2%
945 3
 
0.8%
984 2
 
0.5%
9898201 2
 
0.5%
988 2
 
0.5%
989 2
 
0.5%
742 2
 
0.5%
997 2
 
0.5%
900 2
 
0.5%
Other values (178) 189
49.9%
2024-05-11T06:19:33.309080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 321
17.6%
9 263
14.4%
261
14.3%
2 246
13.4%
8 178
9.7%
4 105
 
5.7%
7 98
 
5.4%
5 95
 
5.2%
6 93
 
5.1%
1 89
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1568
85.7%
Space Separator 261
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 321
20.5%
9 263
16.8%
2 246
15.7%
8 178
11.4%
4 105
 
6.7%
7 98
 
6.2%
5 95
 
6.1%
6 93
 
5.9%
1 89
 
5.7%
3 80
 
5.1%
Space Separator
ValueCountFrequency (%)
261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 321
17.6%
9 263
14.4%
261
14.3%
2 246
13.4%
8 178
9.7%
4 105
 
5.7%
7 98
 
5.4%
5 95
 
5.2%
6 93
 
5.1%
1 89
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 321
17.6%
9 263
14.4%
261
14.3%
2 246
13.4%
8 178
9.7%
4 105
 
5.7%
7 98
 
5.4%
5 95
 
5.2%
6 93
 
5.1%
1 89
 
4.9%

소재지면적
Real number (ℝ)

MISSING 

Distinct204
Distinct (%)88.3%
Missing9
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean65.607446
Minimum0
Maximum465.24
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-05-11T06:19:33.754867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.35
Q126.095
median46.08
Q392.795
95-th percentile160.64
Maximum465.24
Range465.24
Interquartile range (IQR)66.7

Descriptive statistics

Standard deviation57.870716
Coefficient of variation (CV)0.88207543
Kurtosis11.777687
Mean65.607446
Median Absolute Deviation (MAD)26.13
Skewness2.5883245
Sum15155.32
Variance3349.0198
MonotonicityNot monotonic
2024-05-11T06:19:34.200341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.0 5
 
2.1%
33.0 4
 
1.7%
36.0 3
 
1.2%
50.0 3
 
1.2%
30.0 3
 
1.2%
18.0 2
 
0.8%
37.38 2
 
0.8%
17.17 2
 
0.8%
80.0 2
 
0.8%
0.0 2
 
0.8%
Other values (194) 203
84.6%
(Missing) 9
 
3.8%
ValueCountFrequency (%)
0.0 2
0.8%
5.5 1
0.4%
6.6 1
0.4%
9.19 1
0.4%
9.8 1
0.4%
9.88 1
0.4%
10.03 1
0.4%
10.75 1
0.4%
11.22 1
0.4%
12.0 1
0.4%
ValueCountFrequency (%)
465.24 1
0.4%
352.53 1
0.4%
249.92 1
0.4%
238.5 1
0.4%
218.0 1
0.4%
208.52 1
0.4%
180.9 1
0.4%
179.7 1
0.4%
177.06 1
0.4%
174.9 1
0.4%
Distinct71
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-05-11T06:19:34.745898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0833333
Min length6

Characters and Unicode

Total characters1460
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

Unique22 ?
Unique (%)9.2%

Sample

1st row142809
2nd row142100
3rd row142100
4th row142872
5th row142805
ValueCountFrequency (%)
142874 19
 
7.9%
142877 16
 
6.7%
142070 12
 
5.0%
142100 9
 
3.8%
142884 9
 
3.8%
142864 8
 
3.3%
142886 7
 
2.9%
142879 7
 
2.9%
142805 7
 
2.9%
142875 6
 
2.5%
Other values (61) 140
58.3%
2024-05-11T06:19:35.768013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 293
20.1%
1 286
19.6%
2 272
18.6%
8 255
17.5%
7 121
8.3%
0 98
 
6.7%
6 45
 
3.1%
5 26
 
1.8%
3 24
 
1.6%
9 20
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1440
98.6%
Dash Punctuation 20
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 293
20.3%
1 286
19.9%
2 272
18.9%
8 255
17.7%
7 121
8.4%
0 98
 
6.8%
6 45
 
3.1%
5 26
 
1.8%
3 24
 
1.7%
9 20
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 293
20.1%
1 286
19.6%
2 272
18.6%
8 255
17.5%
7 121
8.3%
0 98
 
6.7%
6 45
 
3.1%
5 26
 
1.8%
3 24
 
1.6%
9 20
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 293
20.1%
1 286
19.6%
2 272
18.6%
8 255
17.5%
7 121
8.3%
0 98
 
6.7%
6 45
 
3.1%
5 26
 
1.8%
3 24
 
1.6%
9 20
 
1.4%
Distinct234
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-05-11T06:19:36.516078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length38
Mean length25.129167
Min length18

Characters and Unicode

Total characters6031
Distinct characters114
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

Unique228 ?
Unique (%)95.0%

Sample

1st row서울특별시 강북구 미아동 138-19번지
2nd row서울특별시 강북구 미아동 산 318-5번지
3rd row서울특별시 강북구 미아동 산 833-0번지
4th row서울특별시 강북구 수유동 32-2번지
5th row서울특별시 강북구 미아동 473-62번지 (송천길 49-8)
ValueCountFrequency (%)
서울특별시 240
21.3%
강북구 240
21.3%
수유동 126
 
11.2%
미아동 77
 
6.8%
번동 29
 
2.6%
1층 16
 
1.4%
16
 
1.4%
지하1층 11
 
1.0%
우이동 8
 
0.7%
지상1층 6
 
0.5%
Other values (313) 356
31.6%
2024-05-11T06:19:37.926926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1093
 
18.1%
248
 
4.1%
242
 
4.0%
241
 
4.0%
241
 
4.0%
240
 
4.0%
240
 
4.0%
240
 
4.0%
240
 
4.0%
240
 
4.0%
Other values (104) 2766
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3339
55.4%
Decimal Number 1275
 
21.1%
Space Separator 1093
 
18.1%
Dash Punctuation 239
 
4.0%
Close Punctuation 37
 
0.6%
Open Punctuation 37
 
0.6%
Other Punctuation 5
 
0.1%
Lowercase Letter 3
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
248
 
7.4%
242
 
7.2%
241
 
7.2%
241
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
228
 
6.8%
Other values (84) 939
28.1%
Decimal Number
ValueCountFrequency (%)
1 231
18.1%
4 168
13.2%
2 158
12.4%
3 152
11.9%
5 133
10.4%
7 110
8.6%
0 88
 
6.9%
8 87
 
6.8%
6 78
 
6.1%
9 70
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 4
80.0%
, 1
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
b 2
66.7%
a 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%
Space Separator
ValueCountFrequency (%)
1093
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 239
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3339
55.4%
Common 2686
44.5%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
248
 
7.4%
242
 
7.2%
241
 
7.2%
241
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
228
 
6.8%
Other values (84) 939
28.1%
Common
ValueCountFrequency (%)
1093
40.7%
- 239
 
8.9%
1 231
 
8.6%
4 168
 
6.3%
2 158
 
5.9%
3 152
 
5.7%
5 133
 
5.0%
7 110
 
4.1%
0 88
 
3.3%
8 87
 
3.2%
Other values (6) 227
 
8.5%
Latin
ValueCountFrequency (%)
b 2
33.3%
A 2
33.3%
a 1
16.7%
B 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3339
55.4%
ASCII 2692
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1093
40.6%
- 239
 
8.9%
1 231
 
8.6%
4 168
 
6.2%
2 158
 
5.9%
3 152
 
5.6%
5 133
 
4.9%
7 110
 
4.1%
0 88
 
3.3%
8 87
 
3.2%
Other values (10) 233
 
8.7%
Hangul
ValueCountFrequency (%)
248
 
7.4%
242
 
7.2%
241
 
7.2%
241
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
240
 
7.2%
228
 
6.8%
Other values (84) 939
28.1%

도로명주소
Text

MISSING 

Distinct113
Distinct (%)97.4%
Missing124
Missing (%)51.7%
Memory size2.0 KiB
2024-05-11T06:19:38.817524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length37
Mean length28.482759
Min length22

Characters and Unicode

Total characters3304
Distinct characters91
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

Unique110 ?
Unique (%)94.8%

Sample

1st row서울특별시 강북구 도봉로 104 (미아동)
2nd row서울특별시 강북구 도봉로69길 10 (수유동)
3rd row서울특별시 강북구 삼각산로 10-3 (수유동)
4th row서울특별시 강북구 인수봉로 306 (수유동,(인수봉길 306))
5th row서울특별시 강북구 삼양로139길 5 (수유동,(대동천길 3))
ValueCountFrequency (%)
서울특별시 116
17.5%
강북구 116
17.5%
수유동 60
 
9.0%
미아동 27
 
4.1%
1층 23
 
3.5%
번동 14
 
2.1%
지하1층 11
 
1.7%
삼양로 10
 
1.5%
2층 9
 
1.4%
인수봉로 8
 
1.2%
Other values (187) 270
40.7%
2024-05-11T06:19:40.178305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
548
 
16.6%
1 151
 
4.6%
) 121
 
3.7%
121
 
3.7%
( 121
 
3.7%
116
 
3.5%
116
 
3.5%
116
 
3.5%
116
 
3.5%
116
 
3.5%
Other values (81) 1662
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1899
57.5%
Space Separator 548
 
16.6%
Decimal Number 520
 
15.7%
Close Punctuation 121
 
3.7%
Open Punctuation 121
 
3.7%
Other Punctuation 78
 
2.4%
Dash Punctuation 13
 
0.4%
Uppercase Letter 2
 
0.1%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
121
 
6.4%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
Other values (62) 734
38.7%
Decimal Number
ValueCountFrequency (%)
1 151
29.0%
2 68
13.1%
3 53
 
10.2%
7 50
 
9.6%
4 44
 
8.5%
6 36
 
6.9%
8 35
 
6.7%
0 31
 
6.0%
9 31
 
6.0%
5 21
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 75
96.2%
. 3
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
548
100.0%
Close Punctuation
ValueCountFrequency (%)
) 121
100.0%
Open Punctuation
ValueCountFrequency (%)
( 121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1899
57.5%
Common 1401
42.4%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
121
 
6.4%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
Other values (62) 734
38.7%
Common
ValueCountFrequency (%)
548
39.1%
1 151
 
10.8%
) 121
 
8.6%
( 121
 
8.6%
, 75
 
5.4%
2 68
 
4.9%
3 53
 
3.8%
7 50
 
3.6%
4 44
 
3.1%
6 36
 
2.6%
Other values (6) 134
 
9.6%
Latin
ValueCountFrequency (%)
B 2
50.0%
s 1
25.0%
k 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1899
57.5%
ASCII 1405
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
548
39.0%
1 151
 
10.7%
) 121
 
8.6%
( 121
 
8.6%
, 75
 
5.3%
2 68
 
4.8%
3 53
 
3.8%
7 50
 
3.6%
4 44
 
3.1%
6 36
 
2.6%
Other values (9) 138
 
9.8%
Hangul
ValueCountFrequency (%)
121
 
6.4%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
116
 
6.1%
Other values (62) 734
38.7%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct82
Distinct (%)71.9%
Missing126
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean1098.2281
Minimum1004
Maximum1236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-05-11T06:19:40.640233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1004
5-th percentile1013.3
Q11044.5
median1083
Q31129
95-th percentile1223.35
Maximum1236
Range232
Interquartile range (IQR)84.5

Descriptive statistics

Standard deviation65.076814
Coefficient of variation (CV)0.059256193
Kurtosis-0.60574063
Mean1098.2281
Median Absolute Deviation (MAD)40
Skewness0.6628374
Sum125198
Variance4234.9918
MonotonicityNot monotonic
2024-05-11T06:19:41.365222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1041 5
 
2.1%
1129 4
 
1.7%
1233 4
 
1.7%
1043 3
 
1.2%
1117 3
 
1.2%
1061 3
 
1.2%
1093 2
 
0.8%
1070 2
 
0.8%
1019 2
 
0.8%
1048 2
 
0.8%
Other values (72) 84
35.0%
(Missing) 126
52.5%
ValueCountFrequency (%)
1004 1
0.4%
1005 1
0.4%
1006 1
0.4%
1011 1
0.4%
1012 2
0.8%
1014 1
0.4%
1019 2
0.8%
1021 2
0.8%
1024 1
0.4%
1027 1
0.4%
ValueCountFrequency (%)
1236 1
 
0.4%
1233 4
1.7%
1224 1
 
0.4%
1223 1
 
0.4%
1219 1
 
0.4%
1216 1
 
0.4%
1215 1
 
0.4%
1211 1
 
0.4%
1209 1
 
0.4%
1204 1
 
0.4%
Distinct227
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-05-11T06:19:41.972338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length18
Mean length5.8875
Min length2

Characters and Unicode

Total characters1413
Distinct characters349
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

Unique215 ?
Unique (%)89.6%

Sample

1st row금호식품
2nd row신신식품
3rd row대천식품(공)
4th row국융식품
5th row간송전통한과
ValueCountFrequency (%)
주식회사 7
 
2.6%
연화당 3
 
1.1%
식품 3
 
1.1%
핸드메이드통 2
 
0.7%
커피 2
 
0.7%
88쭈꾸미 2
 
0.7%
해담솔 2
 
0.7%
두부사랑 2
 
0.7%
금조홍어 2
 
0.7%
coffee 2
 
0.7%
Other values (236) 242
90.0%
2024-05-11T06:19:42.995379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
 
4.7%
55
 
3.9%
31
 
2.2%
) 30
 
2.1%
( 30
 
2.1%
29
 
2.1%
27
 
1.9%
25
 
1.8%
24
 
1.7%
24
 
1.7%
Other values (339) 1072
75.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1216
86.1%
Uppercase Letter 43
 
3.0%
Lowercase Letter 41
 
2.9%
Close Punctuation 30
 
2.1%
Open Punctuation 30
 
2.1%
Space Separator 29
 
2.1%
Decimal Number 16
 
1.1%
Other Punctuation 6
 
0.4%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
66
 
5.4%
55
 
4.5%
31
 
2.5%
27
 
2.2%
25
 
2.1%
24
 
2.0%
24
 
2.0%
18
 
1.5%
17
 
1.4%
17
 
1.4%
Other values (298) 912
75.0%
Uppercase Letter
ValueCountFrequency (%)
F 6
14.0%
S 6
14.0%
E 5
11.6%
O 5
11.6%
T 4
9.3%
A 4
9.3%
R 4
9.3%
C 3
7.0%
I 1
 
2.3%
M 1
 
2.3%
Other values (4) 4
9.3%
Lowercase Letter
ValueCountFrequency (%)
o 10
24.4%
e 6
14.6%
r 3
 
7.3%
n 3
 
7.3%
s 3
 
7.3%
d 3
 
7.3%
m 2
 
4.9%
c 2
 
4.9%
f 2
 
4.9%
i 2
 
4.9%
Other values (3) 5
12.2%
Decimal Number
ValueCountFrequency (%)
8 4
25.0%
2 3
18.8%
1 3
18.8%
3 2
12.5%
9 1
 
6.2%
6 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
& 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1216
86.1%
Common 113
 
8.0%
Latin 84
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
66
 
5.4%
55
 
4.5%
31
 
2.5%
27
 
2.2%
25
 
2.1%
24
 
2.0%
24
 
2.0%
18
 
1.5%
17
 
1.4%
17
 
1.4%
Other values (298) 912
75.0%
Latin
ValueCountFrequency (%)
o 10
 
11.9%
e 6
 
7.1%
F 6
 
7.1%
S 6
 
7.1%
E 5
 
6.0%
O 5
 
6.0%
T 4
 
4.8%
A 4
 
4.8%
R 4
 
4.8%
r 3
 
3.6%
Other values (17) 31
36.9%
Common
ValueCountFrequency (%)
) 30
26.5%
( 30
26.5%
29
25.7%
. 5
 
4.4%
8 4
 
3.5%
2 3
 
2.7%
1 3
 
2.7%
3 2
 
1.8%
- 2
 
1.8%
9 1
 
0.9%
Other values (4) 4
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1216
86.1%
ASCII 197
 
13.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
66
 
5.4%
55
 
4.5%
31
 
2.5%
27
 
2.2%
25
 
2.1%
24
 
2.0%
24
 
2.0%
18
 
1.5%
17
 
1.4%
17
 
1.4%
Other values (298) 912
75.0%
ASCII
ValueCountFrequency (%)
) 30
15.2%
( 30
15.2%
29
14.7%
o 10
 
5.1%
e 6
 
3.0%
F 6
 
3.0%
S 6
 
3.0%
E 5
 
2.5%
O 5
 
2.5%
. 5
 
2.5%
Other values (31) 65
33.0%
Distinct198
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2000-10-13 00:00:00
Maximum2024-05-09 15:06:44
2024-05-11T06:19:43.457491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:19:43.943175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
I
192 
U
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 192
80.0%
U 48
 
20.0%

Length

2024-05-11T06:19:44.378155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:44.686082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 192
80.0%
u 48
 
20.0%
Distinct55
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 23:01:00
2024-05-11T06:19:45.113293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:19:45.805027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
식품제조가공업
198 
기타 식품제조가공업
41 
도시락제조업
 
1

Length

Max length10
Median length7
Mean length7.5083333
Min length6

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 198
82.5%
기타 식품제조가공업 41
 
17.1%
도시락제조업 1
 
0.4%

Length

2024-05-11T06:19:46.463007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:46.969840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 239
85.1%
기타 41
 
14.6%
도시락제조업 1
 
0.4%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct191
Distinct (%)88.4%
Missing24
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean201838.24
Minimum200449.66
Maximum203932.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-05-11T06:19:47.572434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200449.66
5-th percentile200902.81
Q1201373.63
median201811.15
Q3202217.18
95-th percentile203009.79
Maximum203932.99
Range3483.327
Interquartile range (IQR)843.54181

Descriptive statistics

Standard deviation674.35848
Coefficient of variation (CV)0.0033410838
Kurtosis-0.0034384812
Mean201838.24
Median Absolute Deviation (MAD)429.14318
Skewness0.43979188
Sum43597061
Variance454759.36
MonotonicityNot monotonic
2024-05-11T06:19:48.443252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200902.806414022 3
 
1.2%
201606.445832012 3
 
1.2%
201811.151102087 3
 
1.2%
202066.153326647 2
 
0.8%
201473.551545568 2
 
0.8%
201974.958688991 2
 
0.8%
201279.139507879 2
 
0.8%
200491.916160361 2
 
0.8%
202604.496481713 2
 
0.8%
201391.74874284 2
 
0.8%
Other values (181) 193
80.4%
(Missing) 24
 
10.0%
ValueCountFrequency (%)
200449.664521046 1
 
0.4%
200491.306966193 1
 
0.4%
200491.916160361 2
0.8%
200500.130999999 1
 
0.4%
200542.427835442 1
 
0.4%
200623.807302059 1
 
0.4%
200827.522471672 1
 
0.4%
200887.492333424 1
 
0.4%
200897.095518246 1
 
0.4%
200902.806414022 3
1.2%
ValueCountFrequency (%)
203932.991517795 1
0.4%
203631.978357584 2
0.8%
203465.746576461 1
0.4%
203390.507370819 1
0.4%
203299.090419064 1
0.4%
203242.908057171 1
0.4%
203083.008678785 1
0.4%
203051.697617861 1
0.4%
203025.291433973 1
0.4%
203014.528088231 1
0.4%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct191
Distinct (%)88.4%
Missing24
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean459026.01
Minimum456402.68
Maximum461975.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-05-11T06:19:49.343211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum456402.68
5-th percentile456939.6
Q1458326.96
median459113.24
Q3459919.1
95-th percentile460595.33
Maximum461975.21
Range5572.5314
Interquartile range (IQR)1592.1381

Descriptive statistics

Standard deviation1175.3582
Coefficient of variation (CV)0.0025605481
Kurtosis-0.48070954
Mean459026.01
Median Absolute Deviation (MAD)806.53581
Skewness-0.2166857
Sum99149618
Variance1381466.8
MonotonicityNot monotonic
2024-05-11T06:19:49.970964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
459803.243745625 3
 
1.2%
460001.632075829 3
 
1.2%
458962.835543942 3
 
1.2%
457826.028657726 2
 
0.8%
458556.077986908 2
 
0.8%
459917.064973621 2
 
0.8%
460131.940233388 2
 
0.8%
459472.82637676 2
 
0.8%
459375.1611479 2
 
0.8%
458596.483422406 2
 
0.8%
Other values (181) 193
80.4%
(Missing) 24
 
10.0%
ValueCountFrequency (%)
456402.681578677 1
0.4%
456690.622279341 1
0.4%
456702.757817774 1
0.4%
456785.529790353 1
0.4%
456808.972105684 1
0.4%
456871.461840582 1
0.4%
456875.973976242 1
0.4%
456901.846675483 1
0.4%
456903.77424907 1
0.4%
456909.363722543 1
0.4%
ValueCountFrequency (%)
461975.213006348 1
0.4%
461924.948245352 1
0.4%
461741.554943103 1
0.4%
461717.450652619 1
0.4%
461449.073521991 1
0.4%
460853.681103507 1
0.4%
460809.785861848 1
0.4%
460746.864832433 1
0.4%
460623.667465739 1
0.4%
460610.639647348 1
0.4%

위생업태명
Categorical

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
식품제조가공업
187 
기타 식품제조가공업
26 
<NA>
26 
도시락제조업
 
1

Length

Max length10
Median length7
Mean length6.9958333
Min length4

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 187
77.9%
기타 식품제조가공업 26
 
10.8%
<NA> 26
 
10.8%
도시락제조업 1
 
0.4%

Length

2024-05-11T06:19:50.593278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:50.951704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 213
80.1%
기타 26
 
9.8%
na 26
 
9.8%
도시락제조업 1
 
0.4%
Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
173 
0
43 
2
 
12
1
 
11
3
 
1

Length

Max length4
Median length4
Mean length3.1625
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row<NA>
2nd row3
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
<NA> 173
72.1%
0 43
 
17.9%
2 12
 
5.0%
1 11
 
4.6%
3 1
 
0.4%

Length

2024-05-11T06:19:51.480906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:51.877418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 173
72.1%
0 43
 
17.9%
2 12
 
5.0%
1 11
 
4.6%
3 1
 
0.4%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
176 
0
43 
1
 
10
2
 
7
3
 
2

Length

Max length4
Median length4
Mean length3.2
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row<NA>
4th row2
5th row3

Common Values

ValueCountFrequency (%)
<NA> 176
73.3%
0 43
 
17.9%
1 10
 
4.2%
2 7
 
2.9%
3 2
 
0.8%
4 2
 
0.8%

Length

2024-05-11T06:19:52.468121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:52.851833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 176
73.3%
0 43
 
17.9%
1 10
 
4.2%
2 7
 
2.9%
3 2
 
0.8%
4 2
 
0.8%
Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
178 
주택가주변
37 
기타
23 
아파트지역
 
2

Length

Max length5
Median length4
Mean length3.9708333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row기타
3rd row기타
4th row기타
5th row주택가주변

Common Values

ValueCountFrequency (%)
<NA> 178
74.2%
주택가주변 37
 
15.4%
기타 23
 
9.6%
아파트지역 2
 
0.8%

Length

2024-05-11T06:19:53.357749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:53.760070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 178
74.2%
주택가주변 37
 
15.4%
기타 23
 
9.6%
아파트지역 2
 
0.8%

등급구분명
Categorical

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
178 
기타
46 
자율
 
16

Length

Max length4
Median length4
Mean length3.4833333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 178
74.2%
기타 46
 
19.2%
자율 16
 
6.7%

Length

2024-05-11T06:19:54.346015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:54.783377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 178
74.2%
기타 46
 
19.2%
자율 16
 
6.7%
Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
상수도전용
120 
<NA>
116 
상수도(음용)지하수(주방용)겸용
 
2
간이상수도
 
2

Length

Max length17
Median length5
Mean length4.6166667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 120
50.0%
<NA> 116
48.3%
상수도(음용)지하수(주방용)겸용 2
 
0.8%
간이상수도 2
 
0.8%

Length

2024-05-11T06:19:55.358315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:55.804305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 120
50.0%
na 116
48.3%
상수도(음용)지하수(주방용)겸용 2
 
0.8%
간이상수도 2
 
0.8%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
232 
0
 
8

Length

Max length4
Median length4
Mean length3.9
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> 232
96.7%
0 8
 
3.3%

Length

2024-05-11T06:19:56.390256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:56.854433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 232
96.7%
0 8
 
3.3%
Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
141 
<NA>
98 
5
 
1

Length

Max length4
Median length1
Mean length2.225
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 141
58.8%
<NA> 98
40.8%
5 1
 
0.4%

Length

2024-05-11T06:19:57.270917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:57.723724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 141
58.8%
na 98
40.8%
5 1
 
0.4%
Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
140 
<NA>
97 
2
 
2
1
 
1

Length

Max length4
Median length1
Mean length2.2125
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 140
58.3%
<NA> 97
40.4%
2 2
 
0.8%
1 1
 
0.4%

Length

2024-05-11T06:19:58.086671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:58.437894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 140
58.3%
na 97
40.4%
2 2
 
0.8%
1 1
 
0.4%
Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
140 
<NA>
98 
1
 
2

Length

Max length4
Median length1
Mean length2.225
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 140
58.3%
<NA> 98
40.8%
1 2
 
0.8%

Length

2024-05-11T06:19:58.947711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:19:59.353314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 140
58.3%
na 98
40.8%
1 2
 
0.8%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)4.1%
Missing95
Missing (%)39.6%
Infinite0
Infinite (%)0.0%
Mean0.20689655
Minimum0
Maximum5
Zeros132
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-05-11T06:19:59.661601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.8
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76282144
Coefficient of variation (CV)3.6869703
Kurtosis18.342681
Mean0.20689655
Median Absolute Deviation (MAD)0
Skewness4.1991857
Sum30
Variance0.58189655
MonotonicityNot monotonic
2024-05-11T06:20:00.198609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 132
55.0%
1 5
 
2.1%
3 4
 
1.7%
2 2
 
0.8%
5 1
 
0.4%
4 1
 
0.4%
(Missing) 95
39.6%
ValueCountFrequency (%)
0 132
55.0%
1 5
 
2.1%
2 2
 
0.8%
3 4
 
1.7%
4 1
 
0.4%
5 1
 
0.4%
ValueCountFrequency (%)
5 1
 
0.4%
4 1
 
0.4%
3 4
 
1.7%
2 2
 
0.8%
1 5
 
2.1%
0 132
55.0%
Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
130 
자가
56 
임대
54 

Length

Max length4
Median length4
Mean length3.0833333
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> 130
54.2%
자가 56
23.3%
임대 54
22.5%

Length

2024-05-11T06:20:00.653468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:20:00.976258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 130
54.2%
자가 56
23.3%
임대 54
22.5%

보증액
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
204 
0
34 
40000000
 
1
30000000
 
1

Length

Max length8
Median length4
Mean length3.6083333
Min length1

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 204
85.0%
0 34
 
14.2%
40000000 1
 
0.4%
30000000 1
 
0.4%

Length

2024-05-11T06:20:01.372613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:20:01.717841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 204
85.0%
0 34
 
14.2%
40000000 1
 
0.4%
30000000 1
 
0.4%

월세액
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
204 
0
34 
750000
 
1
600000
 
1

Length

Max length6
Median length4
Mean length3.5916667
Min length1

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 204
85.0%
0 34
 
14.2%
750000 1
 
0.4%
600000 1
 
0.4%

Length

2024-05-11T06:20:02.075742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:20:02.419825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 204
85.0%
0 34
 
14.2%
750000 1
 
0.4%
600000 1
 
0.4%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing26
Missing (%)10.8%
Memory size612.0 B
False
214 
(Missing)
26 
ValueCountFrequency (%)
False 214
89.2%
(Missing) 26
 
10.8%
2024-05-11T06:20:02.747398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)7.9%
Missing26
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean1.5307009
Minimum0
Maximum99
Zeros198
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-05-11T06:20:03.044773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.2215
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.3406073
Coefficient of variation (CV)5.448881
Kurtosis92.600063
Mean1.5307009
Median Absolute Deviation (MAD)0
Skewness8.7911358
Sum327.57
Variance69.56573
MonotonicityNot monotonic
2024-05-11T06:20:03.546176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 198
82.5%
24.17 1
 
0.4%
99.0 1
 
0.4%
25.5 1
 
0.4%
13.1 1
 
0.4%
26.43 1
 
0.4%
2.81 1
 
0.4%
14.72 1
 
0.4%
47.4 1
 
0.4%
4.5 1
 
0.4%
Other values (7) 7
 
2.9%
(Missing) 26
 
10.8%
ValueCountFrequency (%)
0.0 198
82.5%
2.08 1
 
0.4%
2.81 1
 
0.4%
3.67 1
 
0.4%
4.5 1
 
0.4%
6.51 1
 
0.4%
11.4 1
 
0.4%
12.0 1
 
0.4%
13.1 1
 
0.4%
14.72 1
 
0.4%
ValueCountFrequency (%)
99.0 1
0.4%
47.4 1
0.4%
26.43 1
0.4%
25.5 1
0.4%
24.17 1
0.4%
17.28 1
0.4%
17.0 1
0.4%
14.72 1
0.4%
13.1 1
0.4%
12.0 1
0.4%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing240
Missing (%)100.0%
Memory size2.2 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing240
Missing (%)100.0%
Memory size2.2 KiB

홈페이지
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing233
Missing (%)97.1%
Memory size2.0 KiB
2024-05-11T06:20:03.974339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.571429
Min length18

Characters and Unicode

Total characters137
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowmarineleeho@naver.com
2nd rowperry6611@nate.com
3rd rowesperecoffee@naver.com
4th rowkijoy0717@daum.net
5th rowlokum@yenicheri.co.kr
ValueCountFrequency (%)
marineleeho@naver.com 1
14.3%
perry6611@nate.com 1
14.3%
esperecoffee@naver.com 1
14.3%
kijoy0717@daum.net 1
14.3%
lokum@yenicheri.co.kr 1
14.3%
yuns1313@naver.com 1
14.3%
jo8409031@naver.com 1
14.3%
2024-05-11T06:20:05.045237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 17
 
12.4%
o 11
 
8.0%
r 10
 
7.3%
n 9
 
6.6%
m 8
 
5.8%
c 8
 
5.8%
. 8
 
5.8%
a 7
 
5.1%
@ 7
 
5.1%
1 6
 
4.4%
Other values (20) 46
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103
75.2%
Decimal Number 19
 
13.9%
Other Punctuation 15
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17
16.5%
o 11
10.7%
r 10
9.7%
n 9
8.7%
m 8
 
7.8%
c 8
 
7.8%
a 7
 
6.8%
v 4
 
3.9%
y 4
 
3.9%
i 4
 
3.9%
Other values (10) 21
20.4%
Decimal Number
ValueCountFrequency (%)
1 6
31.6%
3 3
15.8%
0 3
15.8%
7 2
 
10.5%
6 2
 
10.5%
8 1
 
5.3%
4 1
 
5.3%
9 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 8
53.3%
@ 7
46.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 103
75.2%
Common 34
 
24.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17
16.5%
o 11
10.7%
r 10
9.7%
n 9
8.7%
m 8
 
7.8%
c 8
 
7.8%
a 7
 
6.8%
v 4
 
3.9%
y 4
 
3.9%
i 4
 
3.9%
Other values (10) 21
20.4%
Common
ValueCountFrequency (%)
. 8
23.5%
@ 7
20.6%
1 6
17.6%
3 3
 
8.8%
0 3
 
8.8%
7 2
 
5.9%
6 2
 
5.9%
8 1
 
2.9%
4 1
 
2.9%
9 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17
 
12.4%
o 11
 
8.0%
r 10
 
7.3%
n 9
 
6.6%
m 8
 
5.8%
c 8
 
5.8%
. 8
 
5.8%
a 7
 
5.1%
@ 7
 
5.1%
1 6
 
4.4%
Other values (20) 46
33.6%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
030800003080000-106-1971-0012019710908<NA>3폐업2폐업20181008<NA><NA><NA>02 9890425208.52142809서울특별시 강북구 미아동 138-19번지서울특별시 강북구 도봉로 104 (미아동)1165금호식품2018-10-08 16:50:41U2018-10-10 02:36:44.0식품제조가공업202549.34072457334.840983식품제조가공업<NA>1기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
130800003080000-106-1971-0012519710810<NA>3폐업2폐업19980714<NA><NA><NA>02 9898904101.89142100서울특별시 강북구 미아동 산 318-5번지<NA><NA>신신식품2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업31기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
230800003080000-106-1974-0012619740214<NA>3폐업2폐업19970122<NA><NA><NA>02 9834040101.58142100서울특별시 강북구 미아동 산 833-0번지<NA><NA>대천식품(공)2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업2<NA>기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
330800003080000-106-1975-0011219750508<NA>3폐업2폐업20040621<NA><NA><NA>02 9028477238.5142872서울특별시 강북구 수유동 32-2번지<NA><NA>국융식품2002-07-30 00:00:00I2018-08-31 23:59:59.0식품제조가공업201812.904685459775.99283식품제조가공업22기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
430800003080000-106-1982-0013719821011<NA>3폐업2폐업20120207<NA><NA><NA>02 982766778.86142805서울특별시 강북구 미아동 473-62번지 (송천길 49-8)<NA><NA>간송전통한과2011-02-16 19:07:49I2018-08-31 23:59:59.0식품제조가공업202233.006581456970.634971식품제조가공업13주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
530800003080000-106-1984-0011819841115<NA>3폐업2폐업19990417<NA><NA><NA>0247.64142100서울특별시 강북구 미아동 산 701-3번지<NA><NA>주-성근식품2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업00기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
630800003080000-106-1987-0014319870122<NA>3폐업2폐업19990608<NA><NA><NA>02 906410099.51142871서울특별시 강북구 우이동 산 72-133번지<NA><NA>씨웨이산업(주)2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업24기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
730800003080000-106-1991-0013819910910<NA>3폐업2폐업19981216<NA><NA><NA>02 904016379.21142070서울특별시 강북구 수유동 산 330-4번지<NA><NA>에덴제과2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업22주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
830800003080000-106-1991-0013919911115<NA>3폐업2폐업20020305<NA><NA><NA>02 9989495103.31142070서울특별시 강북구 수유동 산 408-41번지<NA><NA>연화당2000-10-13 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업12주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
930800003080000-106-1992-0012319921207<NA>3폐업2폐업19980326<NA><NA><NA>02 991870081.95142090서울특별시 강북구 우이동 산 37-1번지<NA><NA>장인대가2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업200958.173572461924.948245식품제조가공업24주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
23030800003080000-106-2020-000042020-07-08<NA>1영업/정상1영업<NA><NA><NA><NA><NA>50.0142-884서울특별시 강북구 수유동 410-34 경원빌딩 1층서울특별시 강북구 인수봉로 222-1, 경원빌딩 1층 (수유동)1084카페델마티노2023-10-13 14:03:59U2022-10-30 23:05:00.0기타 식품제조가공업201103.922689459664.993381<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23130800003080000-106-2020-0000520200922<NA>1영업/정상1영업<NA><NA><NA><NA>02 990 988210.03142878서울특별시 강북구 수유동 221-18 1층서울특별시 강북구 노해로8가길 45, 1층 (수유동)1072수유리조트2020-09-24 09:01:40I2020-09-26 00:23:11.0기타 식품제조가공업201991.050727459747.450064기타 식품제조가공업<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA>임대<NA><NA>N0.0<NA><NA><NA>
23230800003080000-106-2020-000062020-11-16<NA>3폐업2폐업2024-03-26<NA><NA><NA><NA>30.0142-814서울특별시 강북구 미아동 775-42서울특별시 강북구 솔매로 60, 지상1층 (미아동)1168영자네곱창2024-03-26 11:55:23U2023-12-02 22:08:00.0기타 식품제조가공업201592.785667458045.187963<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23330800003080000-106-2021-0000120210723<NA>3폐업2폐업20220520<NA><NA><NA><NA>28.86142864서울특별시 강북구 번동 412-97 지상1층서울특별시 강북구 덕릉로41길 38, 지상1층 (번동)1066서울앤로스터스2022-05-20 13:19:14U2021-12-04 22:02:00.0기타 식품제조가공업202604.496482459375.161148<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23430800003080000-106-2021-000022021-10-26<NA>3폐업2폐업2023-11-20<NA><NA><NA><NA>41.58142-864서울특별시 강북구 번동 416-19서울특별시 강북구 한천로131길 17, 지상6층 (번동)1064리브커피컴퍼니2023-11-20 09:46:02U2022-10-31 22:02:00.0기타 식품제조가공업202350.743493459564.650497<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23530800003080000-106-2021-000032021-11-30<NA>3폐업2폐업2023-07-31<NA><NA><NA>02 984 618354.43142-883서울특별시 강북구 수유동 524-5서울특별시 강북구 인수봉로 245, 1층 (수유동)1021핸드메이드통2023-07-31 09:22:39U2022-12-08 00:02:00.0기타 식품제조가공업200902.806414459803.243746<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23630800003080000-106-2022-000012022-09-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>25.0142-865서울특별시 강북구 번동 454-6서울특별시 강북구 덕릉로51길 7, 1층 102호 (번동)1061로쿠바 커피 로스터즈(ROCUBA COFFEE ROASTERS)2024-01-02 16:20:36U2023-12-01 00:04:00.0기타 식품제조가공업203083.008679459370.753161<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23730800003080000-106-2022-0000220221025<NA>1영업/정상1영업<NA><NA><NA><NA><NA>40.66142874서울특별시 강북구 수유동 90-18서울특별시 강북구 도봉로77길 17, 1층 (수유동)1114금조홍어2022-10-25 13:08:35I2021-10-30 22:07:00.0기타 식품제조가공업201896.627984459017.991685<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23830800003080000-106-2023-000012023-09-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.48142-803서울특별시 강북구 미아동 162-7서울특별시 강북구 도봉로78길 18, 1층 (미아동)1129아크로스팅팩토리2023-09-21 16:04:43I2022-12-08 22:03:00.0기타 식품제조가공업202101.958725459026.9337<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23930800003080000-106-2023-000022023-10-04<NA>1영업/정상1영업<NA><NA><NA><NA><NA>80.0142-875서울특별시 강북구 수유동 480-7서울특별시 강북구 삼양로77길 30, 2층 (수유동)1102파티세리 묘2023-10-04 17:47:06I2022-10-31 00:06:00.0기타 식품제조가공업201391.748743458596.483422<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>