Overview

Dataset statistics

Number of variables44
Number of observations338
Missing cells2989
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.9 KiB
Average record size in memory375.4 B

Variable types

Categorical22
Text7
DateTime4
Unsupported7
Numeric3
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
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-18195/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
남성종사자수 is highly imbalanced (58.6%)Imbalance
여성종사자수 is highly imbalanced (58.6%)Imbalance
영업장주변구분명 is highly imbalanced (70.1%)Imbalance
등급구분명 is highly imbalanced (72.6%)Imbalance
급수시설구분명 is highly imbalanced (80.8%)Imbalance
본사종업원수 is highly imbalanced (60.1%)Imbalance
공장판매직종업원수 is highly imbalanced (60.1%)Imbalance
시설총규모 is highly imbalanced (70.3%)Imbalance
인허가취소일자 has 338 (100.0%) missing valuesMissing
폐업일자 has 83 (24.6%) missing valuesMissing
휴업시작일자 has 338 (100.0%) missing valuesMissing
휴업종료일자 has 338 (100.0%) missing valuesMissing
재개업일자 has 338 (100.0%) missing valuesMissing
전화번호 has 124 (36.7%) missing valuesMissing
소재지면적 has 51 (15.1%) missing valuesMissing
소재지우편번호 has 9 (2.7%) missing valuesMissing
지번주소 has 9 (2.7%) missing valuesMissing
도로명주소 has 131 (38.8%) missing valuesMissing
도로명우편번호 has 138 (40.8%) missing valuesMissing
좌표정보(X) has 14 (4.1%) missing valuesMissing
좌표정보(Y) has 14 (4.1%) missing valuesMissing
다중이용업소여부 has 50 (14.8%) missing valuesMissing
전통업소지정번호 has 338 (100.0%) missing valuesMissing
전통업소주된음식 has 338 (100.0%) missing valuesMissing
홈페이지 has 338 (100.0%) 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
홈페이지 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 06:51:50.327134
Analysis finished2024-05-11 06:51:51.562826
Duration1.24 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
3180000
338 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 338
100.0%

Length

2024-05-11T15:51:51.669587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:51.813297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 338
100.0%

관리번호
Text

UNIQUE 

Distinct338
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-05-11T15:51:52.071539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters7436
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

Unique338 ?
Unique (%)100.0%

Sample

1st row3180000-109-1981-00094
2nd row3180000-109-1989-00095
3rd row3180000-109-1989-00096
4th row3180000-109-1990-00096
5th row3180000-109-1990-00097
ValueCountFrequency (%)
3180000-109-1981-00094 1
 
0.3%
3180000-109-2015-00001 1
 
0.3%
3180000-109-2013-00005 1
 
0.3%
3180000-109-2013-00004 1
 
0.3%
3180000-109-2013-00003 1
 
0.3%
3180000-109-2013-00002 1
 
0.3%
3180000-109-2013-00001 1
 
0.3%
3180000-109-2012-00013 1
 
0.3%
3180000-109-2012-00012 1
 
0.3%
3180000-109-2012-00010 1
 
0.3%
Other values (328) 328
97.0%
2024-05-11T15:51:52.792687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3388
45.6%
- 1014
 
13.6%
1 992
 
13.3%
9 480
 
6.5%
2 445
 
6.0%
3 412
 
5.5%
8 406
 
5.5%
4 88
 
1.2%
5 80
 
1.1%
6 66
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6422
86.4%
Dash Punctuation 1014
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3388
52.8%
1 992
 
15.4%
9 480
 
7.5%
2 445
 
6.9%
3 412
 
6.4%
8 406
 
6.3%
4 88
 
1.4%
5 80
 
1.2%
6 66
 
1.0%
7 65
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 1014
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3388
45.6%
- 1014
 
13.6%
1 992
 
13.3%
9 480
 
6.5%
2 445
 
6.0%
3 412
 
5.5%
8 406
 
5.5%
4 88
 
1.2%
5 80
 
1.1%
6 66
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3388
45.6%
- 1014
 
13.6%
1 992
 
13.3%
9 480
 
6.5%
2 445
 
6.0%
3 412
 
5.5%
8 406
 
5.5%
4 88
 
1.2%
5 80
 
1.1%
6 66
 
0.9%
Distinct319
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum1981-10-08 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T15:51:53.033772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:53.273955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing338
Missing (%)100.0%
Memory size3.1 KiB
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
3
255 
1
83 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 255
75.4%
1 83
 
24.6%

Length

2024-05-11T15:51:53.529474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:53.703233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 255
75.4%
1 83
 
24.6%

영업상태명
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
폐업
255 
영업/정상
83 

Length

Max length5
Median length2
Mean length2.7366864
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업/정상
2nd row폐업
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 255
75.4%
영업/정상 83
 
24.6%

Length

2024-05-11T15:51:53.880496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:54.054990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 255
75.4%
영업/정상 83
 
24.6%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2
255 
1
83 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 255
75.4%
1 83
 
24.6%

Length

2024-05-11T15:51:54.205518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:54.374609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 255
75.4%
1 83
 
24.6%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
폐업
255 
영업
83 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 255
75.4%
영업 83
 
24.6%

Length

2024-05-11T15:51:54.561931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:54.734853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 255
75.4%
영업 83
 
24.6%

폐업일자
Date

MISSING 

Distinct215
Distinct (%)84.3%
Missing83
Missing (%)24.6%
Memory size2.8 KiB
Minimum1998-02-16 00:00:00
Maximum2023-10-27 00:00:00
2024-05-11T15:51:54.913701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:55.145883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing338
Missing (%)100.0%
Memory size3.1 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing338
Missing (%)100.0%
Memory size3.1 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing338
Missing (%)100.0%
Memory size3.1 KiB

전화번호
Text

MISSING 

Distinct207
Distinct (%)96.7%
Missing124
Missing (%)36.7%
Memory size2.8 KiB
2024-05-11T15:51:55.530453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.7990654
Min length2

Characters and Unicode

Total characters2097
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)94.4%

Sample

1st row0226340171
2nd row02 8348900
3rd row02 26391040
4th row02 6722584
5th row02 7558261
ValueCountFrequency (%)
02 93
28.2%
031 5
 
1.5%
835 3
 
0.9%
834 3
 
0.9%
26789961 2
 
0.6%
8331774 2
 
0.6%
21652610 2
 
0.6%
070 2
 
0.6%
8312777 2
 
0.6%
028344227 2
 
0.6%
Other values (212) 214
64.8%
2024-05-11T15:51:56.107066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 388
18.5%
0 324
15.5%
6 198
9.4%
3 188
9.0%
8 179
8.5%
7 174
8.3%
4 143
 
6.8%
139
 
6.6%
1 139
 
6.6%
5 133
 
6.3%
Other values (2) 92
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1957
93.3%
Space Separator 139
 
6.6%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 388
19.8%
0 324
16.6%
6 198
10.1%
3 188
9.6%
8 179
9.1%
7 174
8.9%
4 143
 
7.3%
1 139
 
7.1%
5 133
 
6.8%
9 91
 
4.6%
Space Separator
ValueCountFrequency (%)
139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2097
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 388
18.5%
0 324
15.5%
6 198
9.4%
3 188
9.0%
8 179
8.5%
7 174
8.3%
4 143
 
6.8%
139
 
6.6%
1 139
 
6.6%
5 133
 
6.3%
Other values (2) 92
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 388
18.5%
0 324
15.5%
6 198
9.4%
3 188
9.0%
8 179
8.5%
7 174
8.3%
4 143
 
6.8%
139
 
6.6%
1 139
 
6.6%
5 133
 
6.3%
Other values (2) 92
 
4.4%

소재지면적
Text

MISSING 

Distinct184
Distinct (%)64.1%
Missing51
Missing (%)15.1%
Memory size2.8 KiB
2024-05-11T15:51:56.591689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.912892
Min length3

Characters and Unicode

Total characters1410
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

Unique148 ?
Unique (%)51.6%

Sample

1st row47.30
2nd row47.60
3rd row101.00
4th row315.00
5th row30.20
ValueCountFrequency (%)
33.00 12
 
4.2%
16.50 9
 
3.1%
10.00 9
 
3.1%
66.00 8
 
2.8%
15.00 7
 
2.4%
9.90 7
 
2.4%
40.00 6
 
2.1%
20.00 6
 
2.1%
6.60 6
 
2.1%
3.30 5
 
1.7%
Other values (174) 212
73.9%
2024-05-11T15:51:57.286734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 392
27.8%
. 287
20.4%
1 141
 
10.0%
3 109
 
7.7%
6 90
 
6.4%
2 89
 
6.3%
5 88
 
6.2%
9 63
 
4.5%
4 61
 
4.3%
8 54
 
3.8%
Other values (2) 36
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1122
79.6%
Other Punctuation 288
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 392
34.9%
1 141
 
12.6%
3 109
 
9.7%
6 90
 
8.0%
2 89
 
7.9%
5 88
 
7.8%
9 63
 
5.6%
4 61
 
5.4%
8 54
 
4.8%
7 35
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 287
99.7%
, 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 392
27.8%
. 287
20.4%
1 141
 
10.0%
3 109
 
7.7%
6 90
 
6.4%
2 89
 
6.3%
5 88
 
6.2%
9 63
 
4.5%
4 61
 
4.3%
8 54
 
3.8%
Other values (2) 36
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 392
27.8%
. 287
20.4%
1 141
 
10.0%
3 109
 
7.7%
6 90
 
6.4%
2 89
 
6.3%
5 88
 
6.2%
9 63
 
4.5%
4 61
 
4.3%
8 54
 
3.8%
Other values (2) 36
 
2.6%

소재지우편번호
Text

MISSING 

Distinct110
Distinct (%)33.4%
Missing9
Missing (%)2.7%
Memory size2.8 KiB
2024-05-11T15:51:57.698777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.100304
Min length6

Characters and Unicode

Total characters2007
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

Unique52 ?
Unique (%)15.8%

Sample

1st row150-866
2nd row150841
3rd row150034
4th row150867
5th row150094
ValueCountFrequency (%)
150899 20
 
6.1%
150034 15
 
4.6%
150841 14
 
4.3%
150834 13
 
4.0%
150103 11
 
3.3%
150038 10
 
3.0%
150035 10
 
3.0%
150800 9
 
2.7%
150835 7
 
2.1%
150095 7
 
2.1%
Other values (100) 213
64.7%
2024-05-11T15:51:58.326733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 510
25.4%
1 405
20.2%
5 391
19.5%
8 222
11.1%
3 129
 
6.4%
9 99
 
4.9%
4 77
 
3.8%
6 58
 
2.9%
2 47
 
2.3%
7 36
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1974
98.4%
Dash Punctuation 33
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 510
25.8%
1 405
20.5%
5 391
19.8%
8 222
11.2%
3 129
 
6.5%
9 99
 
5.0%
4 77
 
3.9%
6 58
 
2.9%
2 47
 
2.4%
7 36
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 510
25.4%
1 405
20.2%
5 391
19.5%
8 222
11.1%
3 129
 
6.4%
9 99
 
4.9%
4 77
 
3.8%
6 58
 
2.9%
2 47
 
2.3%
7 36
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 510
25.4%
1 405
20.2%
5 391
19.5%
8 222
11.1%
3 129
 
6.4%
9 99
 
4.9%
4 77
 
3.8%
6 58
 
2.9%
2 47
 
2.3%
7 36
 
1.8%

지번주소
Text

MISSING 

Distinct292
Distinct (%)88.8%
Missing9
Missing (%)2.7%
Memory size2.8 KiB
2024-05-11T15:51:58.629051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length38
Mean length26.735562
Min length19

Characters and Unicode

Total characters8796
Distinct characters193
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

Unique270 ?
Unique (%)82.1%

Sample

1st row서울특별시 영등포구 양평동4가 19
2nd row서울특별시 영등포구 신길동 255-9
3rd row서울특별시 영등포구 영등포동4가 434-5
4th row서울특별시 영등포구 양평동4가 160-2
5th row서울특별시 영등포구 문래동4가 40
ValueCountFrequency (%)
서울특별시 329
20.4%
영등포구 329
20.4%
지하1층 38
 
2.4%
대림동 37
 
2.3%
신길동 35
 
2.2%
1층 31
 
1.9%
문래동3가 29
 
1.8%
영등포동4가 24
 
1.5%
여의도동 23
 
1.4%
영등포동 21
 
1.3%
Other values (417) 716
44.4%
2024-05-11T15:51:59.112514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1549
 
17.6%
432
 
4.9%
428
 
4.9%
426
 
4.8%
1 397
 
4.5%
338
 
3.8%
334
 
3.8%
331
 
3.8%
331
 
3.8%
329
 
3.7%
Other values (183) 3901
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5254
59.7%
Decimal Number 1675
 
19.0%
Space Separator 1549
 
17.6%
Dash Punctuation 254
 
2.9%
Close Punctuation 18
 
0.2%
Open Punctuation 18
 
0.2%
Uppercase Letter 16
 
0.2%
Other Punctuation 6
 
0.1%
Lowercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
432
 
8.2%
428
 
8.1%
426
 
8.1%
338
 
6.4%
334
 
6.4%
331
 
6.3%
331
 
6.3%
329
 
6.3%
329
 
6.3%
329
 
6.3%
Other values (153) 1647
31.3%
Decimal Number
ValueCountFrequency (%)
1 397
23.7%
4 208
12.4%
2 205
12.2%
3 192
11.5%
6 160
9.6%
5 124
 
7.4%
0 109
 
6.5%
8 96
 
5.7%
9 96
 
5.7%
7 88
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
B 4
25.0%
A 3
18.8%
K 3
18.8%
M 2
12.5%
S 1
 
6.2%
V 1
 
6.2%
G 1
 
6.2%
L 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
c 1
16.7%
n 1
16.7%
t 1
16.7%
r 1
16.7%
Close Punctuation
ValueCountFrequency (%)
) 17
94.4%
] 1
 
5.6%
Open Punctuation
ValueCountFrequency (%)
( 17
94.4%
[ 1
 
5.6%
Space Separator
ValueCountFrequency (%)
1549
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 254
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5254
59.7%
Common 3520
40.0%
Latin 22
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
432
 
8.2%
428
 
8.1%
426
 
8.1%
338
 
6.4%
334
 
6.4%
331
 
6.3%
331
 
6.3%
329
 
6.3%
329
 
6.3%
329
 
6.3%
Other values (153) 1647
31.3%
Common
ValueCountFrequency (%)
1549
44.0%
1 397
 
11.3%
- 254
 
7.2%
4 208
 
5.9%
2 205
 
5.8%
3 192
 
5.5%
6 160
 
4.5%
5 124
 
3.5%
0 109
 
3.1%
8 96
 
2.7%
Other values (7) 226
 
6.4%
Latin
ValueCountFrequency (%)
B 4
18.2%
A 3
13.6%
K 3
13.6%
e 2
9.1%
M 2
9.1%
S 1
 
4.5%
V 1
 
4.5%
c 1
 
4.5%
n 1
 
4.5%
t 1
 
4.5%
Other values (3) 3
13.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5254
59.7%
ASCII 3542
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1549
43.7%
1 397
 
11.2%
- 254
 
7.2%
4 208
 
5.9%
2 205
 
5.8%
3 192
 
5.4%
6 160
 
4.5%
5 124
 
3.5%
0 109
 
3.1%
8 96
 
2.7%
Other values (20) 248
 
7.0%
Hangul
ValueCountFrequency (%)
432
 
8.2%
428
 
8.1%
426
 
8.1%
338
 
6.4%
334
 
6.4%
331
 
6.3%
331
 
6.3%
329
 
6.3%
329
 
6.3%
329
 
6.3%
Other values (153) 1647
31.3%

도로명주소
Text

MISSING 

Distinct199
Distinct (%)96.1%
Missing131
Missing (%)38.8%
Memory size2.8 KiB
2024-05-11T15:51:59.497205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length45
Mean length34.642512
Min length23

Characters and Unicode

Total characters7171
Distinct characters195
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)93.7%

Sample

1st row서울특별시 영등포구 양평로21길 25 (양평동4가)
2nd row서울특별시 영등포구 가마산로69가길 17 (신길동)
3rd row서울특별시 영등포구 경인로 846, 영등포 민자역사 (영등포동)
4th row서울특별시 영등포구 국제금융로7길 15 (여의도동)
5th row서울특별시 영등포구 양평로 47 (당산동5가)
ValueCountFrequency (%)
서울특별시 207
 
16.1%
영등포구 207
 
16.1%
1층 44
 
3.4%
신길동 21
 
1.6%
문래동3가 16
 
1.2%
지하1층 15
 
1.2%
10 14
 
1.1%
대림동 14
 
1.1%
여의도동 13
 
1.0%
양산로 11
 
0.9%
Other values (421) 722
56.2%
2024-05-11T15:52:00.129842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1078
 
15.0%
1 314
 
4.4%
310
 
4.3%
281
 
3.9%
280
 
3.9%
226
 
3.2%
216
 
3.0%
( 214
 
3.0%
) 214
 
3.0%
212
 
3.0%
Other values (185) 3826
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4261
59.4%
Decimal Number 1150
 
16.0%
Space Separator 1078
 
15.0%
Open Punctuation 214
 
3.0%
Close Punctuation 214
 
3.0%
Other Punctuation 194
 
2.7%
Dash Punctuation 36
 
0.5%
Uppercase Letter 17
 
0.2%
Lowercase Letter 6
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
310
 
7.3%
281
 
6.6%
280
 
6.6%
226
 
5.3%
216
 
5.1%
212
 
5.0%
208
 
4.9%
207
 
4.9%
207
 
4.9%
207
 
4.9%
Other values (154) 1907
44.8%
Decimal Number
ValueCountFrequency (%)
1 314
27.3%
2 178
15.5%
3 131
11.4%
0 111
 
9.7%
4 87
 
7.6%
5 83
 
7.2%
7 78
 
6.8%
6 77
 
6.7%
8 52
 
4.5%
9 39
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
K 3
17.6%
A 3
17.6%
B 3
17.6%
M 2
11.8%
R 1
 
5.9%
H 1
 
5.9%
V 1
 
5.9%
S 1
 
5.9%
G 1
 
5.9%
L 1
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
r 1
16.7%
n 1
16.7%
c 1
16.7%
t 1
16.7%
Space Separator
ValueCountFrequency (%)
1078
100.0%
Open Punctuation
ValueCountFrequency (%)
( 214
100.0%
Close Punctuation
ValueCountFrequency (%)
) 214
100.0%
Other Punctuation
ValueCountFrequency (%)
, 194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4261
59.4%
Common 2887
40.3%
Latin 23
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
310
 
7.3%
281
 
6.6%
280
 
6.6%
226
 
5.3%
216
 
5.1%
212
 
5.0%
208
 
4.9%
207
 
4.9%
207
 
4.9%
207
 
4.9%
Other values (154) 1907
44.8%
Common
ValueCountFrequency (%)
1078
37.3%
1 314
 
10.9%
( 214
 
7.4%
) 214
 
7.4%
, 194
 
6.7%
2 178
 
6.2%
3 131
 
4.5%
0 111
 
3.8%
4 87
 
3.0%
5 83
 
2.9%
Other values (6) 283
 
9.8%
Latin
ValueCountFrequency (%)
K 3
13.0%
A 3
13.0%
B 3
13.0%
e 2
 
8.7%
M 2
 
8.7%
R 1
 
4.3%
H 1
 
4.3%
r 1
 
4.3%
n 1
 
4.3%
c 1
 
4.3%
Other values (5) 5
21.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4261
59.4%
ASCII 2910
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1078
37.0%
1 314
 
10.8%
( 214
 
7.4%
) 214
 
7.4%
, 194
 
6.7%
2 178
 
6.1%
3 131
 
4.5%
0 111
 
3.8%
4 87
 
3.0%
5 83
 
2.9%
Other values (21) 306
 
10.5%
Hangul
ValueCountFrequency (%)
310
 
7.3%
281
 
6.6%
280
 
6.6%
226
 
5.3%
216
 
5.1%
212
 
5.0%
208
 
4.9%
207
 
4.9%
207
 
4.9%
207
 
4.9%
Other values (154) 1907
44.8%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct103
Distinct (%)51.5%
Missing138
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean7297.63
Minimum7201
Maximum7448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T15:52:00.336436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7201
5-th percentile7208.85
Q17255
median7289
Q37328.5
95-th percentile7413
Maximum7448
Range247
Interquartile range (IQR)73.5

Descriptive statistics

Standard deviation58.219245
Coefficient of variation (CV)0.0079778291
Kurtosis-0.19258779
Mean7297.63
Median Absolute Deviation (MAD)35
Skewness0.61766278
Sum1459526
Variance3389.4805
MonotonicityNot monotonic
2024-05-11T15:52:00.551736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7250 10
 
3.0%
7306 7
 
2.1%
7305 7
 
2.1%
7300 7
 
2.1%
7255 6
 
1.8%
7251 5
 
1.5%
7261 4
 
1.2%
7275 4
 
1.2%
7270 4
 
1.2%
7267 4
 
1.2%
Other values (93) 142
42.0%
(Missing) 138
40.8%
ValueCountFrequency (%)
7201 2
0.6%
7202 2
0.6%
7205 3
0.9%
7206 3
0.9%
7209 2
0.6%
7212 1
 
0.3%
7213 1
 
0.3%
7214 1
 
0.3%
7220 2
0.6%
7222 1
 
0.3%
ValueCountFrequency (%)
7448 1
 
0.3%
7442 2
0.6%
7440 1
 
0.3%
7428 1
 
0.3%
7420 1
 
0.3%
7417 1
 
0.3%
7414 1
 
0.3%
7413 3
0.9%
7411 2
0.6%
7406 1
 
0.3%
Distinct321
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-05-11T15:52:00.847887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length17
Mean length6.3639053
Min length2

Characters and Unicode

Total characters2151
Distinct characters363
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)90.5%

Sample

1st row롯데웰푸드(주)
2nd row(주)사러가
3rd row(주)신세계(영등포점)
4th row부성식품
5th row주식회사 조흥
ValueCountFrequency (%)
주식회사 10
 
2.7%
주)다믄촌 3
 
0.8%
신세계영등포궁실점 3
 
0.8%
연미향 2
 
0.5%
화성식품 2
 
0.5%
행복한건어물 2
 
0.5%
기흥할인마트 2
 
0.5%
jf&b 2
 
0.5%
서울지앤비 2
 
0.5%
일리카페 2
 
0.5%
Other values (336) 343
92.0%
2024-05-11T15:52:01.296155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
 
5.5%
) 105
 
4.9%
( 104
 
4.8%
49
 
2.3%
44
 
2.0%
37
 
1.7%
36
 
1.7%
35
 
1.6%
34
 
1.6%
32
 
1.5%
Other values (353) 1557
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1857
86.3%
Close Punctuation 105
 
4.9%
Open Punctuation 104
 
4.8%
Space Separator 35
 
1.6%
Uppercase Letter 32
 
1.5%
Lowercase Letter 11
 
0.5%
Decimal Number 4
 
0.2%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
118
 
6.4%
49
 
2.6%
44
 
2.4%
37
 
2.0%
36
 
1.9%
34
 
1.8%
32
 
1.7%
32
 
1.7%
30
 
1.6%
30
 
1.6%
Other values (320) 1415
76.2%
Uppercase Letter
ValueCountFrequency (%)
B 5
15.6%
F 5
15.6%
S 3
 
9.4%
E 2
 
6.2%
A 2
 
6.2%
H 2
 
6.2%
J 2
 
6.2%
P 1
 
3.1%
L 1
 
3.1%
Y 1
 
3.1%
Other values (8) 8
25.0%
Lowercase Letter
ValueCountFrequency (%)
o 3
27.3%
k 2
18.2%
d 1
 
9.1%
e 1
 
9.1%
r 1
 
9.1%
t 1
 
9.1%
c 1
 
9.1%
a 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
1 1
25.0%
2 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1857
86.3%
Common 251
 
11.7%
Latin 43
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
118
 
6.4%
49
 
2.6%
44
 
2.4%
37
 
2.0%
36
 
1.9%
34
 
1.8%
32
 
1.7%
32
 
1.7%
30
 
1.6%
30
 
1.6%
Other values (320) 1415
76.2%
Latin
ValueCountFrequency (%)
B 5
 
11.6%
F 5
 
11.6%
o 3
 
7.0%
S 3
 
7.0%
E 2
 
4.7%
A 2
 
4.7%
H 2
 
4.7%
J 2
 
4.7%
k 2
 
4.7%
d 1
 
2.3%
Other values (16) 16
37.2%
Common
ValueCountFrequency (%)
) 105
41.8%
( 104
41.4%
35
 
13.9%
& 3
 
1.2%
0 2
 
0.8%
1 1
 
0.4%
2 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1857
86.3%
ASCII 294
 
13.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
118
 
6.4%
49
 
2.6%
44
 
2.4%
37
 
2.0%
36
 
1.9%
34
 
1.8%
32
 
1.7%
32
 
1.7%
30
 
1.6%
30
 
1.6%
Other values (320) 1415
76.2%
ASCII
ValueCountFrequency (%)
) 105
35.7%
( 104
35.4%
35
 
11.9%
B 5
 
1.7%
F 5
 
1.7%
& 3
 
1.0%
o 3
 
1.0%
S 3
 
1.0%
E 2
 
0.7%
A 2
 
0.7%
Other values (23) 27
 
9.2%
Distinct315
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum1999-10-04 00:00:00
Maximum2024-05-08 14:55:37
2024-05-11T15:52:01.493544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:52:01.661932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
I
262 
U
76 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 262
77.5%
U 76
 
22.5%

Length

2024-05-11T15:52:01.818811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:01.940492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 262
77.5%
u 76
 
22.5%
Distinct93
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:05:00
2024-05-11T15:52:02.098427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:52:02.343813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
식품소분업
338 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 338
100.0%

Length

2024-05-11T15:52:02.547719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:02.702516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 338
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct224
Distinct (%)69.1%
Missing14
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean191310.94
Minimum189667.64
Maximum194561.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T15:52:02.823216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189667.64
5-th percentile189921.67
Q1190712.92
median191177.58
Q3191741.35
95-th percentile193383.17
Maximum194561.75
Range4894.1062
Interquartile range (IQR)1028.421

Descriptive statistics

Standard deviation954.60395
Coefficient of variation (CV)0.0049898032
Kurtosis0.99421303
Mean191310.94
Median Absolute Deviation (MAD)563.76137
Skewness0.82107476
Sum61984745
Variance911268.69
MonotonicityNot monotonic
2024-05-11T15:52:02.982332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191741.345847708 20
 
5.9%
191581.500265536 9
 
2.7%
191503.386111551 9
 
2.7%
190729.506453391 8
 
2.4%
191153.090126809 7
 
2.1%
191385.057392247 5
 
1.5%
191800.728214995 5
 
1.5%
193393.096553574 4
 
1.2%
189667.63984485 3
 
0.9%
191177.584475692 3
 
0.9%
Other values (214) 251
74.3%
(Missing) 14
 
4.1%
ValueCountFrequency (%)
189667.63984485 3
0.9%
189682.022243843 2
0.6%
189700.355755718 2
0.6%
189708.67445487 1
 
0.3%
189713.985064015 1
 
0.3%
189734.544016734 1
 
0.3%
189748.379334427 1
 
0.3%
189784.908763399 1
 
0.3%
189825.743146319 1
 
0.3%
189859.071768836 2
0.6%
ValueCountFrequency (%)
194561.746032498 2
0.6%
194530.535390096 1
0.3%
194124.497455922 1
0.3%
193882.109246282 1
0.3%
193844.169062846 1
0.3%
193809.01252005 1
0.3%
193711.992981014 1
0.3%
193592.000380036 2
0.6%
193488.889001013 1
0.3%
193421.628256074 1
0.3%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct224
Distinct (%)69.1%
Missing14
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean446167.09
Minimum442751.47
Maximum449025.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T15:52:03.126374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442751.47
5-th percentile443696.62
Q1445777.76
median446227.08
Q3446894.24
95-th percentile448182.29
Maximum449025.25
Range6273.7748
Interquartile range (IQR)1116.4865

Descriptive statistics

Standard deviation1233.5446
Coefficient of variation (CV)0.0027647593
Kurtosis0.39745283
Mean446167.09
Median Absolute Deviation (MAD)653.75607
Skewness-0.56083656
Sum1.4455814 × 108
Variance1521632.3
MonotonicityNot monotonic
2024-05-11T15:52:03.603382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
445970.307641467 20
 
5.9%
446108.807192753 9
 
2.7%
447289.548355449 9
 
2.7%
446227.07504062 8
 
2.4%
445991.628006216 7
 
2.1%
446098.555926507 5
 
1.5%
443809.773854649 5
 
1.5%
446218.443828439 4
 
1.2%
446140.637415622 3
 
0.9%
447965.645513185 3
 
0.9%
Other values (214) 251
74.3%
(Missing) 14
 
4.1%
ValueCountFrequency (%)
442751.471769958 1
0.3%
442905.116903156 1
0.3%
442995.593971893 1
0.3%
443049.32018912 1
0.3%
443228.349006844 2
0.6%
443236.05842825 2
0.6%
443290.587615549 1
0.3%
443313.571300724 1
0.3%
443410.445545064 1
0.3%
443539.870126759 2
0.6%
ValueCountFrequency (%)
449025.246614702 1
0.3%
448980.024828564 1
0.3%
448953.434292828 1
0.3%
448900.040843925 1
0.3%
448642.112414847 2
0.6%
448515.143175418 1
0.3%
448499.498140808 1
0.3%
448497.695424055 1
0.3%
448389.839806194 1
0.3%
448366.233223909 1
0.3%

위생업태명
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
식품소분업
288 
<NA>
50 

Length

Max length5
Median length5
Mean length4.852071
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 288
85.2%
<NA> 50
 
14.8%

Length

2024-05-11T15:52:03.769911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:03.925438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 288
85.2%
na 50
 
14.8%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
286 
<NA>
50 
1
 
2

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 286
84.6%
<NA> 50
 
14.8%
1 2
 
0.6%

Length

2024-05-11T15:52:04.095343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:04.236952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 286
84.6%
na 50
 
14.8%
1 2
 
0.6%

여성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
286 
<NA>
50 
1
 
2

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 286
84.6%
<NA> 50
 
14.8%
1 2
 
0.6%

Length

2024-05-11T15:52:04.378207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:04.551471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 286
84.6%
na 50
 
14.8%
1 2
 
0.6%

영업장주변구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
<NA>
307 
기타
 
29
주택가주변
 
2

Length

Max length5
Median length4
Mean length3.8343195
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 307
90.8%
기타 29
 
8.6%
주택가주변 2
 
0.6%

Length

2024-05-11T15:52:04.745690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:04.911488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 307
90.8%
기타 29
 
8.6%
주택가주변 2
 
0.6%

등급구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
<NA>
307 
기타
 
18
자율
 
12
우수
 
1

Length

Max length4
Median length4
Mean length3.816568
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 307
90.8%
기타 18
 
5.3%
자율 12
 
3.6%
우수 1
 
0.3%

Length

2024-05-11T15:52:05.056757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:05.192842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 307
90.8%
기타 18
 
5.3%
자율 12
 
3.6%
우수 1
 
0.3%

급수시설구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
<NA>
328 
상수도전용
 
10

Length

Max length5
Median length4
Mean length4.0295858
Min length4

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> 328
97.0%
상수도전용 10
 
3.0%

Length

2024-05-11T15:52:05.352192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:05.483311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 328
97.0%
상수도전용 10
 
3.0%

총인원
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
288 
<NA>
50 

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 288
85.2%
<NA> 50
 
14.8%

Length

2024-05-11T15:52:05.670938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:05.848856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 288
85.2%
na 50
 
14.8%

본사종업원수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
287 
<NA>
50 
5
 
1

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 287
84.9%
<NA> 50
 
14.8%
5 1
 
0.3%

Length

2024-05-11T15:52:06.001026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:06.160923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 287
84.9%
na 50
 
14.8%
5 1
 
0.3%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
288 
<NA>
50 

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 288
85.2%
<NA> 50
 
14.8%

Length

2024-05-11T15:52:06.329758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:06.478028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 288
85.2%
na 50
 
14.8%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
287 
<NA>
50 
2
 
1

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 287
84.9%
<NA> 50
 
14.8%
2 1
 
0.3%

Length

2024-05-11T15:52:06.639965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:06.805525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 287
84.9%
na 50
 
14.8%
2 1
 
0.3%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
288 
<NA>
50 

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 288
85.2%
<NA> 50
 
14.8%

Length

2024-05-11T15:52:06.976447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:07.153725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 288
85.2%
na 50
 
14.8%
Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
<NA>
187 
자가
82 
임대
69 

Length

Max length4
Median length4
Mean length3.1065089
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> 187
55.3%
자가 82
24.3%
임대 69
 
20.4%

Length

2024-05-11T15:52:07.344042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:07.522642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 187
55.3%
자가 82
24.3%
임대 69
 
20.4%

보증액
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
288 
<NA>
50 

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 288
85.2%
<NA> 50
 
14.8%

Length

2024-05-11T15:52:07.705992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:07.858048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 288
85.2%
na 50
 
14.8%

월세액
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
288 
<NA>
50 

Length

Max length4
Median length1
Mean length1.443787
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 288
85.2%
<NA> 50
 
14.8%

Length

2024-05-11T15:52:07.997748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:08.160052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 288
85.2%
na 50
 
14.8%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing50
Missing (%)14.8%
Memory size808.0 B
False
288 
(Missing)
50 
ValueCountFrequency (%)
False 288
85.2%
(Missing) 50
 
14.8%
2024-05-11T15:52:08.280677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0.0
285 
<NA>
50 
110.39
 
1
8.0
 
1
4.74
 
1

Length

Max length6
Median length3
Mean length3.1597633
Min length3

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row<NA>
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 285
84.3%
<NA> 50
 
14.8%
110.39 1
 
0.3%
8.0 1
 
0.3%
4.74 1
 
0.3%

Length

2024-05-11T15:52:08.430906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:52:08.575999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 285
84.3%
na 50
 
14.8%
110.39 1
 
0.3%
8.0 1
 
0.3%
4.74 1
 
0.3%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing338
Missing (%)100.0%
Memory size3.1 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing338
Missing (%)100.0%
Memory size3.1 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing338
Missing (%)100.0%
Memory size3.1 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031800003180000-109-1981-000941981-10-08<NA>1영업/정상1영업<NA><NA><NA><NA>022634017147.30150-866서울특별시 영등포구 양평동4가 19서울특별시 영등포구 양평로21길 25 (양평동4가)7209롯데웰푸드(주)2023-04-17 15:03:18U2022-12-03 23:09:00.0식품소분업190508.589813448196.897432<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
131800003180000-109-1989-0009519890120<NA>3폐업2폐업20190416<NA><NA><NA>02 834890047.60150841서울특별시 영등포구 신길동 255-9서울특별시 영등포구 가마산로69가길 17 (신길동)7387(주)사러가2019-04-16 15:35:04U2019-04-18 02:40:00.0식품소분업191994.505087444933.158732식품소분업00기타자율<NA>00000<NA>00N0.0<NA><NA><NA>
231800003180000-109-1989-0009619890228<NA>3폐업2폐업20050330<NA><NA><NA>02 26391040101.00150034서울특별시 영등포구 영등포동4가 434-5<NA><NA>(주)신세계(영등포점)2005-03-28 00:00:00I2018-08-31 23:59:59.0식품소분업191581.500266446108.807193식품소분업00<NA><NA><NA>00000<NA>00N0.0<NA><NA><NA>
331800003180000-109-1990-0009619901112<NA>3폐업2폐업20100218<NA><NA><NA>02 6722584315.00150867서울특별시 영등포구 양평동4가 160-2<NA><NA>부성식품2002-06-11 00:00:00I2018-08-31 23:59:59.0식품소분업190907.518211448278.496649식품소분업00기타자율<NA>00000<NA>00N0.0<NA><NA><NA>
431800003180000-109-1990-0009719900220<NA>3폐업2폐업20091030<NA><NA><NA>02 7558261<NA>150094서울특별시 영등포구 문래동4가 40<NA><NA>주식회사 조흥2008-04-07 14:44:59I2018-08-31 23:59:59.0식품소분업190023.489433445809.666851식품소분업00<NA><NA><NA>00000<NA>00N0.0<NA><NA><NA>
531800003180000-109-1990-0009819901010<NA>3폐업2폐업20011116<NA><NA><NA><NA><NA>150812서울특별시 영등포구 대림동 644-22<NA><NA>거림교역2001-11-16 00:00:00I2018-08-31 23:59:59.0식품소분업<NA><NA>식품소분업00<NA><NA><NA>00000<NA>00N0.0<NA><NA><NA>
631800003180000-109-1991-000971991-04-29<NA>1영업/정상1영업<NA><NA><NA><NA>02 670800030.20150-899서울특별시 영등포구 영등포동 618-496 영등포 민자역사서울특별시 영등포구 경인로 846, 영등포 민자역사 (영등포동)7306롯데백화점영등포점2024-05-03 14:53:27U2023-12-05 00:05:00.0식품소분업191741.345848445970.307641<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
731800003180000-109-1994-0009819940521<NA>3폐업2폐업19991005<NA><NA><NA>02831-251739.70150832서울특별시 영등포구 도림동 231-6<NA><NA>계룡식품2001-08-02 00:00:00I2018-08-31 23:59:59.0식품소분업<NA><NA>식품소분업00기타자율<NA>00000<NA>00N0.0<NA><NA><NA>
831800003180000-109-1994-0009919940826<NA>3폐업2폐업20080307<NA><NA><NA>022630600045.10150034서울특별시 영등포구 영등포동4가 441-21<NA><NA>(주)경방유통경방필백화점2003-06-04 00:00:00I2018-08-31 23:59:59.0식품소분업<NA><NA>식품소분업00기타자율<NA>00000<NA>00N0.0<NA><NA><NA>
931800003180000-109-1995-0010019950829<NA>3폐업2폐업19980330<NA><NA><NA>02 6797654106.00150862서울특별시 영등포구 양평동1가 89-10<NA><NA>삼진물산2001-08-02 00:00:00I2018-08-31 23:59:59.0식품소분업190337.683883446859.551553식품소분업00기타자율<NA>00000<NA>00N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
32831800003180000-109-2023-000022023-04-10<NA>1영업/정상1영업<NA><NA><NA><NA><NA>5.00150-102서울특별시 영등포구 양평동2가 37-2서울특별시 영등포구 영등포로 21, 1층 (양평동2가)7275대흥할인마트2023-04-10 13:41:49I2022-12-03 23:02:00.0식품소분업189682.022244446823.857632<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32931800003180000-109-2023-000032023-06-13<NA>1영업/정상1영업<NA><NA><NA><NA>02680147556.60150-829서울특별시 영등포구 도림동 32-11 진우빌딩 102호서울특별시 영등포구 신길로 215, 진우빌딩 1층 102호 (도림동)7367(주)정현마트2023-06-13 13:58:27I2022-12-05 23:05:00.0식품소분업191949.346963445461.550056<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33031800003180000-109-2023-000042023-07-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>58.08150-105서울특별시 영등포구 양평동5가 88-4서울특별시 영등포구 양평로22길 11, 3, 5층 (양평동5가)7205레오폴트스튜디오2023-07-24 16:08:09I2022-12-06 22:06:00.0식품소분업190602.467924448515.143175<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33131800003180000-109-2023-000052023-08-04<NA>1영업/정상1영업<NA><NA><NA><NA><NA>44.37150-036서울특별시 영등포구 영등포동6가 75-2 대흥빌딩서울특별시 영등포구 영신로38길 14, 대흥빌딩 1층 (영등포동6가)7251주식회사 프레시웰2023-08-04 14:54:32I2022-12-08 00:06:00.0식품소분업191333.485036446507.772345<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33231800003180000-109-2023-000062023-09-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>15.00150-037서울특별시 영등포구 영등포동7가 94-323서울특별시 영등포구 버드나루로 60, 3층 302호 (영등포동7가)7248영일비전2023-09-13 13:25:06I2022-12-08 23:05:00.0식품소분업192034.939489446846.809466<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33331800003180000-109-2023-000072023-11-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>10.00150-094서울특별시 영등포구 문래동4가 67 리버뷰 신안인스빌서울특별시 영등포구 경인로77길 49, 109동 2층 201-468호 (문래동4가, 리버뷰 신안인스빌)7287밤짠2023-11-16 14:32:08I2022-10-31 23:08:00.0식품소분업190079.219797445778.895202<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33431800003180000-109-2023-000082023-11-28<NA>1영업/정상1영업<NA><NA><NA><NA>023460496027.00150-936서울특별시 영등포구 여의도동 36 롯데캐슬엠파이어서울특별시 영등포구 의사당대로 127, 1층 1-107호 (여의도동, 롯데캐슬엠파이어)7331일리카페 여의도점2023-11-28 09:28:06I2022-10-31 21:00:00.0식품소분업193421.628256446448.157557<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33531800003180000-109-2024-000012024-03-11<NA>1영업/정상1영업<NA><NA><NA><NA>022088575731.17150-800서울특별시 영등포구 당산동1가 186-60서울특별시 영등포구 영신로45길 11-1, 1층 (당산동1가)7267차마요2024-03-11 15:57:34I2023-12-02 23:03:00.0식품소분업191115.11703446727.072912<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33631800003180000-109-2024-000022024-04-19<NA>1영업/정상1영업<NA><NA><NA><NA>02785128253.98150-884서울특별시 영등포구 여의도동 34-3 미래에셋증권빌딩서울특별시 영등포구 국제금융로 56, 미래에셋증권빌딩 1층 (여의도동)7330일리카페 미래에셋 대우점2024-04-19 09:24:25I2023-12-03 22:01:00.0식품소분업193711.992981446672.448983<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33731800003180000-109-2024-000032024-05-08<NA>1영업/정상1영업<NA><NA><NA><NA>023395600046.36150-875서울특별시 영등포구 여의도동 22 파크원서울특별시 영등포구 여의대로 108, 파크원호텔 지하2층 (여의도동)7335페어몬트 앰배서더 서울2024-05-08 14:55:37I2023-12-04 23:00:00.0식품소분업193592.00038447092.629433<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>