Overview

Dataset statistics

Number of variables44
Number of observations10000
Missing cells88255
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory382.0 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
Author금천구
URLhttps://data.seoul.go.kr/dataList/OA-18669/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
총인원 is highly imbalanced (80.1%)Imbalance
본사종업원수 is highly imbalanced (80.1%)Imbalance
공장사무직종업원수 is highly imbalanced (80.1%)Imbalance
공장판매직종업원수 is highly imbalanced (80.1%)Imbalance
공장생산직종업원수 is highly imbalanced (80.1%)Imbalance
보증액 is highly imbalanced (80.1%)Imbalance
월세액 is highly imbalanced (80.1%)Imbalance
다중이용업소여부 is highly imbalanced (86.0%)Imbalance
전통업소지정번호 is highly imbalanced (97.2%)Imbalance
전통업소주된음식 is highly imbalanced (97.5%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 2338 (23.4%) missing valuesMissing
휴업시작일자 has 10000 (100.0%) missing valuesMissing
휴업종료일자 has 10000 (100.0%) missing valuesMissing
재개업일자 has 10000 (100.0%) missing valuesMissing
전화번호 has 3396 (34.0%) missing valuesMissing
도로명주소 has 5101 (51.0%) missing valuesMissing
도로명우편번호 has 5179 (51.8%) missing valuesMissing
좌표정보(X) has 323 (3.2%) missing valuesMissing
좌표정보(Y) has 323 (3.2%) missing valuesMissing
남성종사자수 has 4276 (42.8%) missing valuesMissing
여성종사자수 has 4163 (41.6%) missing valuesMissing
건물소유구분명 has 10000 (100.0%) missing valuesMissing
다중이용업소여부 has 1531 (15.3%) missing valuesMissing
시설총규모 has 1531 (15.3%) missing valuesMissing
홈페이지 has 10000 (100.0%) missing valuesMissing
시설총규모 is highly skewed (γ1 = 82.45198409)Skewed
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물소유구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
홈페이지 is an unsupported type, check if it needs cleaning or further analysisUnsupported
남성종사자수 has 4381 (43.8%) zerosZeros
여성종사자수 has 3760 (37.6%) zerosZeros

Reproduction

Analysis started2024-05-11 07:06:54.987996
Analysis finished2024-05-11 07:07:00.042797
Duration5.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3170000
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3170000
2nd row3170000
3rd row3170000
4th row3170000
5th row3170000

Common Values

ValueCountFrequency (%)
3170000 10000
100.0%

Length

2024-05-11T07:07:00.243049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:00.536078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3170000 10000
100.0%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:07:00.924886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters220000
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row3170000-101-2007-00016
2nd row3170000-101-2020-00276
3rd row3170000-101-1995-01488
4th row3170000-101-2002-05488
5th row3170000-101-2021-00127
ValueCountFrequency (%)
3170000-101-2007-00016 1
 
< 0.1%
3170000-101-2004-00220 1
 
< 0.1%
3170000-101-2022-00250 1
 
< 0.1%
3170000-101-2011-00312 1
 
< 0.1%
3170000-101-2002-05361 1
 
< 0.1%
3170000-101-1987-05432 1
 
< 0.1%
3170000-101-2019-00288 1
 
< 0.1%
3170000-101-1988-02698 1
 
< 0.1%
3170000-101-2013-00065 1
 
< 0.1%
3170000-101-1987-06192 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T07:07:01.789521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 79189
36.0%
1 42300
19.2%
- 30000
 
13.6%
3 14880
 
6.8%
7 13389
 
6.1%
2 12936
 
5.9%
9 10184
 
4.6%
5 4488
 
2.0%
4 4323
 
2.0%
8 4268
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190000
86.4%
Dash Punctuation 30000
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79189
41.7%
1 42300
22.3%
3 14880
 
7.8%
7 13389
 
7.0%
2 12936
 
6.8%
9 10184
 
5.4%
5 4488
 
2.4%
4 4323
 
2.3%
8 4268
 
2.2%
6 4043
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79189
36.0%
1 42300
19.2%
- 30000
 
13.6%
3 14880
 
6.8%
7 13389
 
6.1%
2 12936
 
5.9%
9 10184
 
4.6%
5 4488
 
2.0%
4 4323
 
2.0%
8 4268
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79189
36.0%
1 42300
19.2%
- 30000
 
13.6%
3 14880
 
6.8%
7 13389
 
6.1%
2 12936
 
5.9%
9 10184
 
4.6%
5 4488
 
2.0%
4 4323
 
2.0%
8 4268
 
1.9%
Distinct6327
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1955-04-22 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T07:07:02.231961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:07:02.654227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
7662 
1
2338 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 7662
76.6%
1 2338
 
23.4%

Length

2024-05-11T07:07:03.070512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:03.376482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 7662
76.6%
1 2338
 
23.4%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
7662 
영업/정상
2338 

Length

Max length5
Median length2
Mean length2.7014
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 7662
76.6%
영업/정상 2338
 
23.4%

Length

2024-05-11T07:07:03.733479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:04.050337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 7662
76.6%
영업/정상 2338
 
23.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7662 
1
2338 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 7662
76.6%
1 2338
 
23.4%

Length

2024-05-11T07:07:04.396248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:04.729604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7662
76.6%
1 2338
 
23.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
7662 
영업
2338 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 7662
76.6%
영업 2338
 
23.4%

Length

2024-05-11T07:07:05.102391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:05.378098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 7662
76.6%
영업 2338
 
23.4%

폐업일자
Date

MISSING 

Distinct4329
Distinct (%)56.5%
Missing2338
Missing (%)23.4%
Memory size156.2 KiB
Minimum1986-06-20 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T07:07:05.762704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:07:06.286111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

전화번호
Text

MISSING 

Distinct5813
Distinct (%)88.0%
Missing3396
Missing (%)34.0%
Memory size156.2 KiB
2024-05-11T07:07:07.243191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.6950333
Min length2

Characters and Unicode

Total characters64026
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

Unique5616 ?
Unique (%)85.0%

Sample

1st row02 20260014
2nd row02 8911875
3rd row02 8911795
4th row02 859 8295
5th row02 8586220
ValueCountFrequency (%)
02 4698
40.5%
0200000000 152
 
1.3%
802 44
 
0.4%
00000 34
 
0.3%
808 32
 
0.3%
803 30
 
0.3%
0 28
 
0.2%
830 25
 
0.2%
858 22
 
0.2%
805 21
 
0.2%
Other values (5857) 6523
56.2%
2024-05-11T07:07:08.780905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13897
21.7%
2 10108
15.8%
8 8965
14.0%
5860
9.2%
5 4448
 
6.9%
6 4241
 
6.6%
9 4038
 
6.3%
3 3581
 
5.6%
7 3182
 
5.0%
4 2952
 
4.6%
Other values (2) 2754
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58165
90.8%
Space Separator 5860
 
9.2%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13897
23.9%
2 10108
17.4%
8 8965
15.4%
5 4448
 
7.6%
6 4241
 
7.3%
9 4038
 
6.9%
3 3581
 
6.2%
7 3182
 
5.5%
4 2952
 
5.1%
1 2753
 
4.7%
Space Separator
ValueCountFrequency (%)
5860
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64026
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13897
21.7%
2 10108
15.8%
8 8965
14.0%
5860
9.2%
5 4448
 
6.9%
6 4241
 
6.6%
9 4038
 
6.3%
3 3581
 
5.6%
7 3182
 
5.0%
4 2952
 
4.6%
Other values (2) 2754
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13897
21.7%
2 10108
15.8%
8 8965
14.0%
5860
9.2%
5 4448
 
6.9%
6 4241
 
6.6%
9 4038
 
6.3%
3 3581
 
5.6%
7 3182
 
5.0%
4 2952
 
4.6%
Other values (2) 2754
 
4.3%
Distinct5014
Distinct (%)50.6%
Missing90
Missing (%)0.9%
Memory size156.2 KiB
2024-05-11T07:07:09.815015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.127447
Min length3

Characters and Unicode

Total characters50813
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

Unique3290 ?
Unique (%)33.2%

Sample

1st row60.84
2nd row46.00
3rd row27.90
4th row55.30
5th row63.77
ValueCountFrequency (%)
33.00 77
 
0.8%
30.00 50
 
0.5%
26.40 44
 
0.4%
21.00 36
 
0.4%
28.00 30
 
0.3%
23.10 29
 
0.3%
18.00 28
 
0.3%
40.00 28
 
0.3%
16.50 27
 
0.3%
20.00 27
 
0.3%
Other values (5004) 9534
96.2%
2024-05-11T07:07:11.707842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9910
19.5%
0 6230
12.3%
2 5905
11.6%
1 4896
9.6%
3 3884
 
7.6%
4 3809
 
7.5%
5 3530
 
6.9%
6 3503
 
6.9%
8 3265
 
6.4%
7 2995
 
5.9%
Other values (2) 2886
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40888
80.5%
Other Punctuation 9925
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6230
15.2%
2 5905
14.4%
1 4896
12.0%
3 3884
9.5%
4 3809
9.3%
5 3530
8.6%
6 3503
8.6%
8 3265
8.0%
7 2995
7.3%
9 2871
7.0%
Other Punctuation
ValueCountFrequency (%)
. 9910
99.8%
, 15
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 50813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9910
19.5%
0 6230
12.3%
2 5905
11.6%
1 4896
9.6%
3 3884
 
7.6%
4 3809
 
7.5%
5 3530
 
6.9%
6 3503
 
6.9%
8 3265
 
6.4%
7 2995
 
5.9%
Other values (2) 2886
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9910
19.5%
0 6230
12.3%
2 5905
11.6%
1 4896
9.6%
3 3884
 
7.6%
4 3809
 
7.5%
5 3530
 
6.9%
6 3503
 
6.9%
8 3265
 
6.4%
7 2995
 
5.9%
Other values (2) 2886
 
5.7%
Distinct152
Distinct (%)1.5%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T07:07:12.902245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0993199
Min length6

Characters and Unicode

Total characters60981
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

Unique24 ?
Unique (%)0.2%

Sample

1st row153786
2nd row153801
3rd row153859
4th row153813
5th row153801
ValueCountFrequency (%)
153801 1097
 
11.0%
153858 664
 
6.6%
153813 450
 
4.5%
153803 417
 
4.2%
153806 346
 
3.5%
153821 343
 
3.4%
153825 334
 
3.3%
153864 311
 
3.1%
153856 283
 
2.8%
153857 249
 
2.5%
Other values (142) 5504
55.1%
2024-05-11T07:07:15.145694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13316
21.8%
5 12573
20.6%
3 12463
20.4%
8 10292
16.9%
0 4129
 
6.8%
6 2052
 
3.4%
2 2045
 
3.4%
4 1123
 
1.8%
7 1097
 
1.8%
- 993
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59988
98.4%
Dash Punctuation 993
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13316
22.2%
5 12573
21.0%
3 12463
20.8%
8 10292
17.2%
0 4129
 
6.9%
6 2052
 
3.4%
2 2045
 
3.4%
4 1123
 
1.9%
7 1097
 
1.8%
9 898
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13316
21.8%
5 12573
20.6%
3 12463
20.4%
8 10292
16.9%
0 4129
 
6.8%
6 2052
 
3.4%
2 2045
 
3.4%
4 1123
 
1.8%
7 1097
 
1.8%
- 993
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13316
21.8%
5 12573
20.6%
3 12463
20.4%
8 10292
16.9%
0 4129
 
6.8%
6 2052
 
3.4%
2 2045
 
3.4%
4 1123
 
1.8%
7 1097
 
1.8%
- 993
 
1.6%
Distinct7508
Distinct (%)75.1%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T07:07:16.554804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length58
Mean length28.09822
Min length14

Characters and Unicode

Total characters280926
Distinct characters394
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6188 ?
Unique (%)61.9%

Sample

1st row서울특별시 금천구 가산동 371-28 우림라이온스밸리 A동 214호 (가산디지털1길 71)
2nd row서울특별시 금천구 가산동 151-30
3rd row서울특별시 금천구 시흥동 920-6번지 지상1층
4th row서울특별시 금천구 독산동 296-15번지
5th row서울특별시 금천구 가산동 151-7
ValueCountFrequency (%)
금천구 10004
19.8%
서울특별시 9998
19.8%
독산동 4145
 
8.2%
시흥동 3509
 
6.9%
가산동 2346
 
4.6%
지상1층 2189
 
4.3%
1층 594
 
1.2%
지하1층 316
 
0.6%
지상2층 240
 
0.5%
984-0번지 107
 
0.2%
Other values (6184) 17073
33.8%
2024-05-11T07:07:18.652782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48674
 
17.3%
1 15205
 
5.4%
14013
 
5.0%
11099
 
4.0%
10639
 
3.8%
10279
 
3.7%
10255
 
3.7%
10173
 
3.6%
10078
 
3.6%
10026
 
3.6%
Other values (384) 130485
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 156722
55.8%
Decimal Number 59626
 
21.2%
Space Separator 48674
 
17.3%
Dash Punctuation 9953
 
3.5%
Open Punctuation 2031
 
0.7%
Close Punctuation 2029
 
0.7%
Uppercase Letter 1492
 
0.5%
Other Punctuation 343
 
0.1%
Lowercase Letter 36
 
< 0.1%
Math Symbol 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14013
 
8.9%
11099
 
7.1%
10639
 
6.8%
10279
 
6.6%
10255
 
6.5%
10173
 
6.5%
10078
 
6.4%
10026
 
6.4%
10021
 
6.4%
9998
 
6.4%
Other values (327) 50141
32.0%
Uppercase Letter
ValueCountFrequency (%)
B 607
40.7%
A 149
 
10.0%
T 100
 
6.7%
I 73
 
4.9%
S 72
 
4.8%
C 72
 
4.8%
L 57
 
3.8%
Y 50
 
3.4%
H 41
 
2.7%
K 38
 
2.5%
Other values (17) 233
 
15.6%
Decimal Number
ValueCountFrequency (%)
1 15205
25.5%
2 6536
11.0%
9 6244
10.5%
0 5979
 
10.0%
8 5212
 
8.7%
4 5002
 
8.4%
3 4955
 
8.3%
5 4149
 
7.0%
7 3193
 
5.4%
6 3151
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
e 12
33.3%
w 4
 
11.1%
r 4
 
11.1%
t 4
 
11.1%
s 2
 
5.6%
o 2
 
5.6%
x 2
 
5.6%
v 2
 
5.6%
u 2
 
5.6%
n 2
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 339
98.8%
/ 3
 
0.9%
. 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 2029
99.9%
[ 2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 2027
99.9%
] 2
 
0.1%
Space Separator
ValueCountFrequency (%)
48674
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9953
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 156722
55.8%
Common 122676
43.7%
Latin 1528
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14013
 
8.9%
11099
 
7.1%
10639
 
6.8%
10279
 
6.6%
10255
 
6.5%
10173
 
6.5%
10078
 
6.4%
10026
 
6.4%
10021
 
6.4%
9998
 
6.4%
Other values (327) 50141
32.0%
Latin
ValueCountFrequency (%)
B 607
39.7%
A 149
 
9.8%
T 100
 
6.5%
I 73
 
4.8%
S 72
 
4.7%
C 72
 
4.7%
L 57
 
3.7%
Y 50
 
3.3%
H 41
 
2.7%
K 38
 
2.5%
Other values (27) 269
17.6%
Common
ValueCountFrequency (%)
48674
39.7%
1 15205
 
12.4%
- 9953
 
8.1%
2 6536
 
5.3%
9 6244
 
5.1%
0 5979
 
4.9%
8 5212
 
4.2%
4 5002
 
4.1%
3 4955
 
4.0%
5 4149
 
3.4%
Other values (10) 10767
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 156722
55.8%
ASCII 124203
44.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48674
39.2%
1 15205
 
12.2%
- 9953
 
8.0%
2 6536
 
5.3%
9 6244
 
5.0%
0 5979
 
4.8%
8 5212
 
4.2%
4 5002
 
4.0%
3 4955
 
4.0%
5 4149
 
3.3%
Other values (46) 12294
 
9.9%
Hangul
ValueCountFrequency (%)
14013
 
8.9%
11099
 
7.1%
10639
 
6.8%
10279
 
6.6%
10255
 
6.5%
10173
 
6.5%
10078
 
6.4%
10026
 
6.4%
10021
 
6.4%
9998
 
6.4%
Other values (327) 50141
32.0%
None
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct4382
Distinct (%)89.4%
Missing5101
Missing (%)51.0%
Memory size156.2 KiB
2024-05-11T07:07:19.297140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length58
Mean length36.527863
Min length21

Characters and Unicode

Total characters178950
Distinct characters385
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3986 ?
Unique (%)81.4%

Sample

1st row서울특별시 금천구 가산디지털1로 168 (가산동,우림라이온스밸리 A동 214호 (가산디지털1길 71))
2nd row서울특별시 금천구 가산로 116, 지상1층 (107)호 (가산동)
3rd row서울특별시 금천구 시흥대로153길 68-29, 1층 (가산동)
4th row서울특별시 금천구 독산로 42, 지상1층 (시흥동)
5th row서울특별시 금천구 시흥대로64길 13, 지상1층 (시흥동)
ValueCountFrequency (%)
금천구 4900
 
14.9%
서울특별시 4899
 
14.9%
지상1층 1825
 
5.5%
독산동 1517
 
4.6%
가산동 1390
 
4.2%
시흥동 1138
 
3.5%
1층 780
 
2.4%
시흥대로 494
 
1.5%
독산로 446
 
1.4%
가산디지털1로 443
 
1.3%
Other values (2645) 15136
45.9%
2024-05-11T07:07:20.622028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28113
 
15.7%
1 10491
 
5.9%
7975
 
4.5%
5906
 
3.3%
, 5882
 
3.3%
( 5491
 
3.1%
) 5490
 
3.1%
5456
 
3.0%
5355
 
3.0%
5098
 
2.8%
Other values (375) 93693
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102161
57.1%
Decimal Number 29673
 
16.6%
Space Separator 28113
 
15.7%
Other Punctuation 5885
 
3.3%
Open Punctuation 5491
 
3.1%
Close Punctuation 5490
 
3.1%
Uppercase Letter 1423
 
0.8%
Dash Punctuation 655
 
0.4%
Lowercase Letter 37
 
< 0.1%
Math Symbol 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7975
 
7.8%
5906
 
5.8%
5456
 
5.3%
5355
 
5.2%
5098
 
5.0%
4990
 
4.9%
4973
 
4.9%
4953
 
4.8%
4934
 
4.8%
4906
 
4.8%
Other values (316) 47615
46.6%
Uppercase Letter
ValueCountFrequency (%)
B 508
35.7%
A 139
 
9.8%
T 100
 
7.0%
C 76
 
5.3%
S 73
 
5.1%
L 72
 
5.1%
I 70
 
4.9%
Y 58
 
4.1%
H 43
 
3.0%
G 35
 
2.5%
Other values (17) 249
17.5%
Lowercase Letter
ValueCountFrequency (%)
e 12
32.4%
r 4
 
10.8%
o 3
 
8.1%
t 3
 
8.1%
w 3
 
8.1%
n 2
 
5.4%
u 2
 
5.4%
s 2
 
5.4%
v 1
 
2.7%
x 1
 
2.7%
Other values (4) 4
 
10.8%
Decimal Number
ValueCountFrequency (%)
1 10491
35.4%
2 4047
 
13.6%
3 2686
 
9.1%
0 2526
 
8.5%
4 1870
 
6.3%
6 1809
 
6.1%
5 1766
 
6.0%
8 1612
 
5.4%
9 1566
 
5.3%
7 1300
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 5882
99.9%
. 2
 
< 0.1%
/ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
28113
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5491
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5490
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 655
100.0%
Math Symbol
ValueCountFrequency (%)
~ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102161
57.1%
Common 75329
42.1%
Latin 1460
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7975
 
7.8%
5906
 
5.8%
5456
 
5.3%
5355
 
5.2%
5098
 
5.0%
4990
 
4.9%
4973
 
4.9%
4953
 
4.8%
4934
 
4.8%
4906
 
4.8%
Other values (316) 47615
46.6%
Latin
ValueCountFrequency (%)
B 508
34.8%
A 139
 
9.5%
T 100
 
6.8%
C 76
 
5.2%
S 73
 
5.0%
L 72
 
4.9%
I 70
 
4.8%
Y 58
 
4.0%
H 43
 
2.9%
G 35
 
2.4%
Other values (31) 286
19.6%
Common
ValueCountFrequency (%)
28113
37.3%
1 10491
 
13.9%
, 5882
 
7.8%
( 5491
 
7.3%
) 5490
 
7.3%
2 4047
 
5.4%
3 2686
 
3.6%
0 2526
 
3.4%
4 1870
 
2.5%
6 1809
 
2.4%
Other values (8) 6924
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102161
57.1%
ASCII 76788
42.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28113
36.6%
1 10491
 
13.7%
, 5882
 
7.7%
( 5491
 
7.2%
) 5490
 
7.1%
2 4047
 
5.3%
3 2686
 
3.5%
0 2526
 
3.3%
4 1870
 
2.4%
6 1809
 
2.4%
Other values (48) 8383
 
10.9%
Hangul
ValueCountFrequency (%)
7975
 
7.8%
5906
 
5.8%
5456
 
5.3%
5355
 
5.2%
5098
 
5.0%
4990
 
4.9%
4973
 
4.9%
4953
 
4.8%
4934
 
4.8%
4906
 
4.8%
Other values (316) 47615
46.6%
None
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct152
Distinct (%)3.2%
Missing5179
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean8569.0832
Minimum8500
Maximum8657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:07:21.138911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8500
5-th percentile8504
Q18523
median8569
Q38617
95-th percentile8643
Maximum8657
Range157
Interquartile range (IQR)94

Descriptive statistics

Standard deviation48.309565
Coefficient of variation (CV)0.0056376585
Kurtosis-1.3944399
Mean8569.0832
Median Absolute Deviation (MAD)47
Skewness0.13376152
Sum41311550
Variance2333.814
MonotonicityNot monotonic
2024-05-11T07:07:21.641336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8628 145
 
1.5%
8511 134
 
1.3%
8608 119
 
1.2%
8584 111
 
1.1%
8507 107
 
1.1%
8510 95
 
0.9%
8629 86
 
0.9%
8506 85
 
0.9%
8503 82
 
0.8%
8512 79
 
0.8%
Other values (142) 3778
37.8%
(Missing) 5179
51.8%
ValueCountFrequency (%)
8500 6
 
0.1%
8501 55
0.5%
8502 63
0.6%
8503 82
0.8%
8504 58
0.6%
8505 42
 
0.4%
8506 85
0.9%
8507 107
1.1%
8508 7
 
0.1%
8509 74
0.7%
ValueCountFrequency (%)
8657 22
0.2%
8656 18
0.2%
8655 5
 
0.1%
8654 25
0.2%
8653 2
 
< 0.1%
8652 29
0.3%
8651 27
0.3%
8650 1
 
< 0.1%
8649 23
0.2%
8648 3
 
< 0.1%
Distinct8330
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:07:22.533928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length5.4454
Min length1

Characters and Unicode

Total characters54454
Distinct characters1088
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7427 ?
Unique (%)74.3%

Sample

1st row얌샘 가산디지털단지역점
2nd row이건형 수제분식
3rd row나락호프
4th row윤가네칼국수
5th row조가네
ValueCountFrequency (%)
가산점 98
 
0.8%
독산점 87
 
0.7%
금천점 46
 
0.4%
가산디지털점 30
 
0.3%
실내포장마차 29
 
0.2%
전주식당 28
 
0.2%
호남식당 24
 
0.2%
가산디지털단지점 21
 
0.2%
시흥점 21
 
0.2%
금천시흥점 20
 
0.2%
Other values (8899) 11555
96.6%
2024-05-11T07:07:24.027835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1963
 
3.6%
1474
 
2.7%
1085
 
2.0%
970
 
1.8%
935
 
1.7%
840
 
1.5%
800
 
1.5%
764
 
1.4%
718
 
1.3%
711
 
1.3%
Other values (1078) 44194
81.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49715
91.3%
Space Separator 1963
 
3.6%
Uppercase Letter 789
 
1.4%
Lowercase Letter 629
 
1.2%
Decimal Number 493
 
0.9%
Close Punctuation 342
 
0.6%
Open Punctuation 340
 
0.6%
Other Punctuation 163
 
0.3%
Dash Punctuation 15
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1474
 
3.0%
1085
 
2.2%
970
 
2.0%
935
 
1.9%
840
 
1.7%
800
 
1.6%
764
 
1.5%
718
 
1.4%
711
 
1.4%
650
 
1.3%
Other values (998) 40768
82.0%
Uppercase Letter
ValueCountFrequency (%)
O 74
 
9.4%
C 69
 
8.7%
B 59
 
7.5%
A 56
 
7.1%
H 48
 
6.1%
E 48
 
6.1%
S 41
 
5.2%
N 36
 
4.6%
T 32
 
4.1%
P 29
 
3.7%
Other values (16) 297
37.6%
Lowercase Letter
ValueCountFrequency (%)
e 89
14.1%
a 80
12.7%
o 60
 
9.5%
n 45
 
7.2%
i 44
 
7.0%
t 37
 
5.9%
c 33
 
5.2%
f 31
 
4.9%
s 31
 
4.9%
r 27
 
4.3%
Other values (15) 152
24.2%
Other Punctuation
ValueCountFrequency (%)
& 57
35.0%
. 43
26.4%
, 18
 
11.0%
' 10
 
6.1%
! 10
 
6.1%
? 10
 
6.1%
# 4
 
2.5%
4
 
2.5%
: 3
 
1.8%
/ 2
 
1.2%
Other values (2) 2
 
1.2%
Decimal Number
ValueCountFrequency (%)
2 98
19.9%
1 92
18.7%
0 76
15.4%
3 42
8.5%
5 38
 
7.7%
4 35
 
7.1%
9 35
 
7.1%
8 30
 
6.1%
6 24
 
4.9%
7 23
 
4.7%
Space Separator
ValueCountFrequency (%)
1963
100.0%
Close Punctuation
ValueCountFrequency (%)
) 342
100.0%
Open Punctuation
ValueCountFrequency (%)
( 340
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49656
91.2%
Common 3320
 
6.1%
Latin 1419
 
2.6%
Han 59
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1474
 
3.0%
1085
 
2.2%
970
 
2.0%
935
 
1.9%
840
 
1.7%
800
 
1.6%
764
 
1.5%
718
 
1.4%
711
 
1.4%
650
 
1.3%
Other values (949) 40709
82.0%
Latin
ValueCountFrequency (%)
e 89
 
6.3%
a 80
 
5.6%
O 74
 
5.2%
C 69
 
4.9%
o 60
 
4.2%
B 59
 
4.2%
A 56
 
3.9%
H 48
 
3.4%
E 48
 
3.4%
n 45
 
3.2%
Other values (42) 791
55.7%
Han
ValueCountFrequency (%)
3
 
5.1%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
1
 
1.7%
Other values (39) 39
66.1%
Common
ValueCountFrequency (%)
1963
59.1%
) 342
 
10.3%
( 340
 
10.2%
2 98
 
3.0%
1 92
 
2.8%
0 76
 
2.3%
& 57
 
1.7%
. 43
 
1.3%
3 42
 
1.3%
5 38
 
1.1%
Other values (18) 229
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49656
91.2%
ASCII 4733
 
8.7%
CJK 56
 
0.1%
None 5
 
< 0.1%
CJK Compat Ideographs 3
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1963
41.5%
) 342
 
7.2%
( 340
 
7.2%
2 98
 
2.1%
1 92
 
1.9%
e 89
 
1.9%
a 80
 
1.7%
0 76
 
1.6%
O 74
 
1.6%
C 69
 
1.5%
Other values (67) 1510
31.9%
Hangul
ValueCountFrequency (%)
1474
 
3.0%
1085
 
2.2%
970
 
2.0%
935
 
1.9%
840
 
1.7%
800
 
1.6%
764
 
1.5%
718
 
1.4%
711
 
1.4%
650
 
1.3%
Other values (949) 40709
82.0%
None
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
CJK
ValueCountFrequency (%)
3
 
5.4%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
1
 
1.8%
Other values (36) 36
64.3%
CJK Compat Ideographs
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct7320
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1999-01-05 00:00:00
Maximum2024-05-09 15:44:10
2024-05-11T07:07:24.574407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:07:25.169021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
7556 
U
2444 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 7556
75.6%
U 2444
 
24.4%

Length

2024-05-11T07:07:25.793741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:26.161024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7556
75.6%
u 2444
 
24.4%
Distinct1239
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T07:07:26.498858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:07:27.087694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
한식
4478 
분식
1855 
호프/통닭
1090 
정종/대포집/소주방
499 
중국식
478 
Other values (20)
1600 

Length

Max length15
Median length2
Mean length3.035
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row분식
2nd row분식
3rd row호프/통닭
4th row한식
5th row한식

Common Values

ValueCountFrequency (%)
한식 4478
44.8%
분식 1855
18.6%
호프/통닭 1090
 
10.9%
정종/대포집/소주방 499
 
5.0%
중국식 478
 
4.8%
기타 384
 
3.8%
경양식 307
 
3.1%
일식 268
 
2.7%
통닭(치킨) 226
 
2.3%
식육(숯불구이) 114
 
1.1%
Other values (15) 301
 
3.0%

Length

2024-05-11T07:07:27.719657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 4478
44.8%
분식 1855
18.6%
호프/통닭 1090
 
10.9%
정종/대포집/소주방 499
 
5.0%
중국식 478
 
4.8%
기타 384
 
3.8%
경양식 307
 
3.1%
일식 268
 
2.7%
통닭(치킨 226
 
2.3%
식육(숯불구이 114
 
1.1%
Other values (15) 301
 
3.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct2763
Distinct (%)28.6%
Missing323
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean190925.24
Minimum188830.03
Maximum192754.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:07:28.216144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum188830.03
5-th percentile189519.39
Q1190523.08
median191117.76
Q3191402.73
95-th percentile191838.48
Maximum192754.35
Range3924.316
Interquartile range (IQR)879.65936

Descriptive statistics

Standard deviation706.51243
Coefficient of variation (CV)0.0037004663
Kurtosis-0.036043946
Mean190925.24
Median Absolute Deviation (MAD)347.1304
Skewness-0.76746881
Sum1.8475836 × 109
Variance499159.81
MonotonicityNot monotonic
2024-05-11T07:07:28.681420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191226.287379467 133
 
1.3%
189538.020935968 85
 
0.9%
189662.959175776 77
 
0.8%
190858.649482406 59
 
0.6%
190119.463375612 49
 
0.5%
190694.880295092 47
 
0.5%
189452.382474246 46
 
0.5%
189982.344533404 41
 
0.4%
189055.138252216 40
 
0.4%
190771.595589599 38
 
0.4%
Other values (2753) 9062
90.6%
(Missing) 323
 
3.2%
ValueCountFrequency (%)
188830.030176986 1
 
< 0.1%
188838.724807964 2
< 0.1%
188870.636085641 2
< 0.1%
188887.197646973 1
 
< 0.1%
188974.800556597 4
< 0.1%
188979.225789508 1
 
< 0.1%
189016.465808265 1
 
< 0.1%
189030.107416961 1
 
< 0.1%
189031.670933573 1
 
< 0.1%
189045.768055551 1
 
< 0.1%
ValueCountFrequency (%)
192754.34619252 6
0.1%
192742.326147617 9
0.1%
192653.790840875 1
 
< 0.1%
192443.734325211 1
 
< 0.1%
192434.31669691 1
 
< 0.1%
192421.424581433 1
 
< 0.1%
192414.837596879 2
 
< 0.1%
192400.624991299 1
 
< 0.1%
192394.502820184 1
 
< 0.1%
192394.491668961 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct2761
Distinct (%)28.5%
Missing323
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean440406.03
Minimum436888.77
Maximum442636.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:07:29.183497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum436888.77
5-th percentile438358.37
Q1439144.88
median440712.59
Q3441560.38
95-th percentile442037.87
Maximum442636.32
Range5747.5466
Interquartile range (IQR)2415.4993

Descriptive statistics

Standard deviation1298.936
Coefficient of variation (CV)0.0029494055
Kurtosis-0.95285685
Mean440406.03
Median Absolute Deviation (MAD)1041.3199
Skewness-0.41688087
Sum4.2618092 × 109
Variance1687234.7
MonotonicityNot monotonic
2024-05-11T07:07:29.732174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437914.06299827 133
 
1.3%
441982.427934953 85
 
0.9%
442139.514492212 77
 
0.8%
440808.541334678 59
 
0.6%
441716.684586176 49
 
0.5%
440764.426277932 47
 
0.5%
442205.867457803 46
 
0.5%
441928.69040241 41
 
0.4%
441958.334400683 40
 
0.4%
440515.66903538 38
 
0.4%
Other values (2751) 9062
90.6%
(Missing) 323
 
3.2%
ValueCountFrequency (%)
436888.773525926 7
0.1%
436893.312157815 6
 
0.1%
436897.466167682 6
 
0.1%
436909.870493711 14
0.1%
436910.345233026 5
 
0.1%
436911.774494656 6
 
0.1%
436936.771355438 1
 
< 0.1%
436946.358720615 16
0.2%
436953.782824253 5
 
0.1%
436985.862177293 1
 
< 0.1%
ValueCountFrequency (%)
442636.320100968 9
0.1%
442585.933234852 5
 
0.1%
442569.300676147 15
0.1%
442562.722742062 9
0.1%
442553.141302572 2
 
< 0.1%
442542.727581637 2
 
< 0.1%
442538.866901281 1
 
< 0.1%
442506.205501847 2
 
< 0.1%
442498.987321026 2
 
< 0.1%
442494.887441936 3
 
< 0.1%

위생업태명
Categorical

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
한식
3740 
분식
1786 
<NA>
1531 
호프/통닭
924 
정종/대포집/소주방
466 
Other values (19)
1553 

Length

Max length15
Median length2
Mean length3.195
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row분식
2nd row분식
3rd row호프/통닭
4th row한식
5th row<NA>

Common Values

ValueCountFrequency (%)
한식 3740
37.4%
분식 1786
17.9%
<NA> 1531
15.3%
호프/통닭 924
 
9.2%
정종/대포집/소주방 466
 
4.7%
중국식 356
 
3.6%
경양식 251
 
2.5%
일식 210
 
2.1%
통닭(치킨) 208
 
2.1%
기타 200
 
2.0%
Other values (14) 328
 
3.3%

Length

2024-05-11T07:07:30.228091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 3740
37.4%
분식 1786
17.9%
na 1531
15.3%
호프/통닭 924
 
9.2%
정종/대포집/소주방 466
 
4.7%
중국식 356
 
3.6%
경양식 251
 
2.5%
일식 210
 
2.1%
통닭(치킨 208
 
2.1%
기타 200
 
2.0%
Other values (14) 328
 
3.3%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.2%
Missing4276
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean0.30520615
Minimum0
Maximum20
Zeros4381
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:07:30.608434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69819228
Coefficient of variation (CV)2.2876088
Kurtosis129.64371
Mean0.30520615
Median Absolute Deviation (MAD)0
Skewness6.8047862
Sum1747
Variance0.48747245
MonotonicityNot monotonic
2024-05-11T07:07:31.010411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4381
43.8%
1 1069
 
10.7%
2 206
 
2.1%
3 46
 
0.5%
4 9
 
0.1%
5 8
 
0.1%
10 2
 
< 0.1%
6 2
 
< 0.1%
20 1
 
< 0.1%
(Missing) 4276
42.8%
ValueCountFrequency (%)
0 4381
43.8%
1 1069
 
10.7%
2 206
 
2.1%
3 46
 
0.5%
4 9
 
0.1%
5 8
 
0.1%
6 2
 
< 0.1%
10 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
10 2
 
< 0.1%
6 2
 
< 0.1%
5 8
 
0.1%
4 9
 
0.1%
3 46
 
0.5%
2 206
 
2.1%
1 1069
 
10.7%
0 4381
43.8%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)0.2%
Missing4163
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean0.52886757
Minimum0
Maximum18
Zeros3760
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:07:31.478878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91727629
Coefficient of variation (CV)1.7344158
Kurtosis34.731353
Mean0.52886757
Median Absolute Deviation (MAD)0
Skewness3.7006606
Sum3087
Variance0.84139579
MonotonicityNot monotonic
2024-05-11T07:07:31.859849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3760
37.6%
1 1384
 
13.8%
2 527
 
5.3%
3 97
 
1.0%
4 35
 
0.4%
5 19
 
0.2%
6 5
 
0.1%
9 3
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 4163
41.6%
ValueCountFrequency (%)
0 3760
37.6%
1 1384
 
13.8%
2 527
 
5.3%
3 97
 
1.0%
4 35
 
0.4%
5 19
 
0.2%
6 5
 
0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
11 1
 
< 0.1%
9 3
 
< 0.1%
8 2
 
< 0.1%
7 3
 
< 0.1%
6 5
 
0.1%
5 19
 
0.2%
4 35
 
0.4%
3 97
 
1.0%
2 527
5.3%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5561 
기타
2207 
주택가주변
1969 
유흥업소밀집지역
 
147
아파트지역
 
90
Other values (3)
 
26

Length

Max length8
Median length4
Mean length3.8322
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5561
55.6%
기타 2207
 
22.1%
주택가주변 1969
 
19.7%
유흥업소밀집지역 147
 
1.5%
아파트지역 90
 
0.9%
결혼예식장주변 15
 
0.1%
학교정화(절대) 6
 
0.1%
학교정화(상대) 5
 
0.1%

Length

2024-05-11T07:07:32.289191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:32.719490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5561
55.6%
기타 2207
 
22.1%
주택가주변 1969
 
19.7%
유흥업소밀집지역 147
 
1.5%
아파트지역 90
 
0.9%
결혼예식장주변 15
 
0.1%
학교정화(절대 6
 
0.1%
학교정화(상대 5
 
< 0.1%

등급구분명
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5692 
2177 
지도
696 
기타
642 
자율
 
416
Other values (2)
 
377

Length

Max length4
Median length4
Mean length2.9023
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5692
56.9%
2177
 
21.8%
지도 696
 
7.0%
기타 642
 
6.4%
자율 416
 
4.2%
우수 193
 
1.9%
184
 
1.8%

Length

2024-05-11T07:07:33.157465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:33.493808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5692
56.9%
2177
 
21.8%
지도 696
 
7.0%
기타 642
 
6.4%
자율 416
 
4.2%
우수 193
 
1.9%
184
 
1.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
상수도전용
6761 
<NA>
3228 
상수도(음용)지하수(주방용)겸용
 
11

Length

Max length17
Median length5
Mean length4.6904
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 6761
67.6%
<NA> 3228
32.3%
상수도(음용)지하수(주방용)겸용 11
 
0.1%

Length

2024-05-11T07:07:33.891316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:34.243776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 6761
67.6%
na 3228
32.3%
상수도(음용)지하수(주방용)겸용 11
 
0.1%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9691 
0
 
309

Length

Max length4
Median length4
Mean length3.9073
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> 9691
96.9%
0 309
 
3.1%

Length

2024-05-11T07:07:34.789970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:35.252687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9691
96.9%
0 309
 
3.1%

본사종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9691 
0
 
309

Length

Max length4
Median length4
Mean length3.9073
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> 9691
96.9%
0 309
 
3.1%

Length

2024-05-11T07:07:35.626257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:36.068929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9691
96.9%
0 309
 
3.1%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9691 
0
 
309

Length

Max length4
Median length4
Mean length3.9073
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> 9691
96.9%
0 309
 
3.1%

Length

2024-05-11T07:07:36.637556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:37.107481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9691
96.9%
0 309
 
3.1%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9691 
0
 
309

Length

Max length4
Median length4
Mean length3.9073
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> 9691
96.9%
0 309
 
3.1%

Length

2024-05-11T07:07:37.603717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:38.026532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9691
96.9%
0 309
 
3.1%

공장생산직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9691 
0
 
309

Length

Max length4
Median length4
Mean length3.9073
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> 9691
96.9%
0 309
 
3.1%

Length

2024-05-11T07:07:38.940345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:39.409647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9691
96.9%
0 309
 
3.1%

건물소유구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9691 
0
 
309

Length

Max length4
Median length4
Mean length3.9073
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> 9691
96.9%
0 309
 
3.1%

Length

2024-05-11T07:07:39.895627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:40.298371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9691
96.9%
0 309
 
3.1%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9691 
0
 
309

Length

Max length4
Median length4
Mean length3.9073
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> 9691
96.9%
0 309
 
3.1%

Length

2024-05-11T07:07:40.851930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:41.296817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9691
96.9%
0 309
 
3.1%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1531
Missing (%)15.3%
Memory size97.7 KiB
False
8302 
True
 
167
(Missing)
1531 
ValueCountFrequency (%)
False 8302
83.0%
True 167
 
1.7%
(Missing) 1531
 
15.3%
2024-05-11T07:07:41.743970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  SKEWED 

Distinct4449
Distinct (%)52.5%
Missing1531
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean63.320458
Minimum0
Maximum40079.98
Zeros86
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:07:42.332072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.37
Q123.1
median33
Q365.9
95-th percentile154.996
Maximum40079.98
Range40079.98
Interquartile range (IQR)42.8

Descriptive statistics

Standard deviation452.20516
Coefficient of variation (CV)7.1415332
Kurtosis7251.1323
Mean63.320458
Median Absolute Deviation (MAD)13.65
Skewness82.451984
Sum536260.96
Variance204489.5
MonotonicityNot monotonic
2024-05-11T07:07:43.004302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 86
 
0.9%
33.0 41
 
0.4%
26.4 35
 
0.4%
30.0 30
 
0.3%
21.0 28
 
0.3%
23.1 26
 
0.3%
28.0 25
 
0.2%
18.0 22
 
0.2%
25.2 22
 
0.2%
25.9 21
 
0.2%
Other values (4439) 8133
81.3%
(Missing) 1531
 
15.3%
ValueCountFrequency (%)
0.0 86
0.9%
3.3 1
 
< 0.1%
3.75 1
 
< 0.1%
4.89 1
 
< 0.1%
5.0 1
 
< 0.1%
5.27 1
 
< 0.1%
6.0 1
 
< 0.1%
6.6 1
 
< 0.1%
6.75 1
 
< 0.1%
6.8 1
 
< 0.1%
ValueCountFrequency (%)
40079.98 1
< 0.1%
6019.15 1
< 0.1%
6016.7 1
< 0.1%
2523.6 1
< 0.1%
1800.0 1
< 0.1%
1508.59 1
< 0.1%
1488.57 1
< 0.1%
1262.78 1
< 0.1%
1045.08 1
< 0.1%
1034.77 1
< 0.1%

전통업소지정번호
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9932 
00023
 
56
00074
 
10
0
 
1
+
 
1

Length

Max length5
Median length4
Mean length4.006
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9932
99.3%
00023 56
 
0.6%
00074 10
 
0.1%
0 1
 
< 0.1%
+ 1
 
< 0.1%

Length

2024-05-11T07:07:43.686432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:44.160499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9932
99.3%
00023 56
 
0.6%
00074 10
 
0.1%
0 1
 
< 0.1%
1
 
< 0.1%

전통업소주된음식
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9931 
팔보채
 
56
회초밥
 
10
0
 
1
00000000000000
 
1

Length

Max length14
Median length4
Mean length3.9939
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9931
99.3%
팔보채 56
 
0.6%
회초밥 10
 
0.1%
0 1
 
< 0.1%
00000000000000 1
 
< 0.1%
족발 1
 
< 0.1%

Length

2024-05-11T07:07:44.704272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:07:45.180864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9931
99.3%
팔보채 56
 
0.6%
회초밥 10
 
0.1%
0 1
 
< 0.1%
00000000000000 1
 
< 0.1%
족발 1
 
< 0.1%

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
772431700003170000-101-2007-0001620070130<NA>1영업/정상1영업<NA><NA><NA><NA>02 2026001460.84153786서울특별시 금천구 가산동 371-28 우림라이온스밸리 A동 214호 (가산디지털1길 71)서울특별시 금천구 가산디지털1로 168 (가산동,우림라이온스밸리 A동 214호 (가산디지털1길 71))8507얌샘 가산디지털단지역점2021-07-15 11:22:30U2021-07-17 02:40:00.0분식189538.020936441982.427935분식00<NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N60.84<NA><NA><NA>
1197331700003170000-101-2020-0027620201214<NA>1영업/정상1영업<NA><NA><NA><NA><NA>46.00153801서울특별시 금천구 가산동 151-30서울특별시 금천구 가산로 116, 지상1층 (107)호 (가산동)8529이건형 수제분식2020-12-14 13:26:56I2020-12-16 00:23:06.0분식190481.24245441556.382012분식<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N46.0<NA><NA><NA>
335531700003170000-101-1995-0148819951229<NA>3폐업2폐업20090316<NA><NA><NA>02 891187527.90153859서울특별시 금천구 시흥동 920-6번지 지상1층<NA><NA>나락호프2009-03-16 10:52:35I2018-08-31 23:59:59.0호프/통닭191718.708882438735.401063호프/통닭00주택가주변상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N27.9<NA><NA><NA>
612931700003170000-101-2002-0548820020705<NA>3폐업2폐업20070502<NA><NA><NA>02 891179555.30153813서울특별시 금천구 독산동 296-15번지<NA><NA>윤가네칼국수2004-05-06 00:00:00I2018-08-31 23:59:59.0한식190779.171484441043.729719한식00<NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N55.3<NA><NA><NA>
1211131700003170000-101-2021-0012720210511<NA>3폐업2폐업20220727<NA><NA><NA><NA>63.77153801서울특별시 금천구 가산동 151-7서울특별시 금천구 시흥대로153길 68-29, 1층 (가산동)8529조가네2022-07-27 13:08:08U2021-12-06 22:09:00.0한식190588.969447441664.808001<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
999131700003170000-101-2013-0029020131216<NA>3폐업2폐업20140617<NA><NA><NA><NA>21.88153859서울특별시 금천구 시흥동 908-24번지 지상1층서울특별시 금천구 독산로 42, 지상1층 (시흥동)8642장군이네2014-03-14 09:57:19I2018-08-31 23:59:59.0한식191623.918088438766.981064한식<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N21.88<NA><NA><NA>
1038431700003170000-101-2015-0008320150420<NA>1영업/정상1영업<NA><NA><NA><NA><NA>27.72153857서울특별시 금천구 시흥동 882-41 지상1층서울특별시 금천구 시흥대로64길 13, 지상1층 (시흥동)8626고향집2022-08-04 13:17:56U2021-12-08 00:07:00.0호프/통닭191165.584568439286.523513<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
845631700003170000-101-2009-0011820090601<NA>3폐업2폐업20100730<NA><NA><NA>02 859 829537.17153803서울특별시 금천구 가산동 371-42번지 승일벤처타워 104호<NA><NA>토스피아2009-06-16 09:56:26I2018-08-31 23:59:59.0분식189285.310104441997.330701분식<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N37.17<NA><NA><NA>
161031700003170000-101-1990-0356519901128<NA>3폐업2폐업20040915<NA><NA><NA>02 858622028.00153010서울특별시 금천구 독산동 974-24번지<NA><NA>사랑방 퓨전포차2004-09-15 00:00:00I2018-08-31 23:59:59.0한식191411.122114441479.888912한식11주택가주변상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N28.0<NA><NA><NA>
1306531700003170000-101-2024-000522024-02-26<NA>1영업/정상1영업<NA><NA><NA><NA><NA>23.45153-855서울특별시 금천구 시흥동 823-68서울특별시 금천구 독산로22길 41, 101호 (시흥동)8572너구리 요리 연구소2024-02-26 13:16:52I2023-12-01 22:08:00.0호프/통닭191843.488741439028.44505<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
885531700003170000-101-2010-0017920100713<NA>1영업/정상1영업<NA><NA><NA><NA>02 869 229228.83153821서울특별시 금천구 독산동 1018-0번지 지상1층서울특별시 금천구 독산로75길 39, 지상1층 (독산동)8578대구막창2016-04-28 12:14:41I2018-08-31 23:59:59.0식육(숯불구이)191101.965552440774.463707식육(숯불구이)<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N28.83<NA><NA><NA>
1253731700003170000-101-2022-001772022-08-09<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.90153-864서울특별시 금천구 시흥동 999-45 1층서울특별시 금천구 금하로 617-2, 1층 (시흥동)8614자밥2023-06-08 10:20:09U2022-12-05 23:00:00.0한식191074.032252439185.621294<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1060931700003170000-101-2016-0000520160106<NA>3폐업2폐업20191213<NA><NA><NA>02 401 588540.59153769서울특별시 금천구 가산동 60-19번지 SJ테크노빌 지상3층서울특별시 금천구 벚꽃로 278, 지상3층 (가산동, SJ테크노빌)8511포보스 롯데가산점2019-12-27 15:34:58U2019-12-29 02:40:00.0외국음식전문점(인도,태국등)189722.178532441920.97277외국음식전문점(인도,태국등)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N40.59<NA><NA><NA>
4131700003170000-101-1978-0126119780901<NA>3폐업2폐업19900123<NA><NA><NA>020866022528.22153823서울특별시 금천구 독산동 967-10번지<NA><NA>낙양2002-01-11 00:00:00I2018-08-31 23:59:59.0정종/대포집/소주방191000.581021441543.623608정종/대포집/소주방01주택가주변지도상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N28.22<NA><NA><NA>
981631700003170000-101-2013-0011520130430<NA>3폐업2폐업20140825<NA><NA><NA>02 20810920161.00153801서울특별시 금천구 가산동 60-27번지 w몰서울특별시 금천구 디지털로 188, 지상9층 (가산동, w몰)8513비바폴로 구로W몰점2013-08-27 17:21:23I2018-08-31 23:59:59.0경양식189997.455663441687.841172경양식<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N161.0<NA><NA><NA>
609931700003170000-101-2002-0545820020605<NA>3폐업2폐업20030805<NA><NA><NA>02 868124323.37153821서울특별시 금천구 독산동 1018-12번지<NA><NA>순천식당2002-06-05 00:00:00I2018-08-31 23:59:59.0한식191134.522543440769.182321한식00<NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N23.37<NA><NA><NA>
282231700003170000-101-1994-0099319941223<NA>3폐업2폐업20060419<NA><NA><NA>02 893663627.87153858서울특별시 금천구 시흥동 901-0번지<NA><NA>와한잔여기다2005-07-18 00:00:00I2018-08-31 23:59:59.0정종/대포집/소주방191341.215586438801.067792정종/대포집/소주방00기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N27.87<NA><NA><NA>
559831700003170000-101-2001-0668020010321<NA>1영업/정상1영업<NA><NA><NA><NA>0226.46153823서울특별시 금천구 독산동 959-12번지 지상1층서울특별시 금천구 독산로 353, 지상1층 (독산동)8537추억의 책가방2016-10-20 14:15:49I2018-08-31 23:59:59.0한식191400.600073441801.751902한식01기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N26.46<NA><NA><NA>
380331700003170000-101-1996-0148919960109<NA>3폐업2폐업20000801<NA><NA><NA>0235.33153841서울특별시 금천구 시흥동 836-1번지<NA><NA>명가호프2000-08-01 00:00:00I2018-08-31 23:59:59.0분식191708.81563438868.754344분식00주택가주변상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N35.33<NA><NA><NA>
1142331700003170000-101-2019-0005120190227<NA>1영업/정상1영업<NA><NA><NA><NA><NA>34.36153801서울특별시 금천구 가산동 150-2 신평대일타운서울특별시 금천구 시흥대로153길 99, 신평대일타운 1층 (가산동)8532한끼2022-12-09 16:45:02U2021-11-01 23:01:00.0기타190498.327387441516.527946<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>