Overview

Dataset statistics

Number of variables26
Number of observations24
Missing cells75
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory225.5 B

Variable types

Categorical11
Numeric4
DateTime3
Unsupported2
Text6

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),점포구분명
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-18721/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
폐업일자 is highly imbalanced (55.0%)Imbalance
휴업시작일자 is highly imbalanced (75.0%)Imbalance
휴업종료일자 is highly imbalanced (75.0%)Imbalance
인허가취소일자 has 24 (100.0%) missing valuesMissing
재개업일자 has 24 (100.0%) missing valuesMissing
소재지면적 has 1 (4.2%) missing valuesMissing
소재지우편번호 has 11 (45.8%) missing valuesMissing
지번주소 has 1 (4.2%) missing valuesMissing
도로명주소 has 3 (12.5%) missing valuesMissing
도로명우편번호 has 11 (45.8%) missing valuesMissing
관리번호 has unique valuesUnique
인허가취소일자 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
소재지면적 has 1 (4.2%) zerosZeros

Reproduction

Analysis started2024-05-11 06:10:16.279111
Analysis finished2024-05-11 06:10:16.778474
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
3190000
24 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3190000
2nd row3190000
3rd row3190000
4th row3190000
5th row3190000

Common Values

ValueCountFrequency (%)
3190000 24
100.0%

Length

2024-05-11T15:10:16.854393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:16.979878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3190000 24
100.0%

관리번호
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.011194 × 1018
Minimum1.979319 × 1018
Maximum2.023319 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-05-11T15:10:17.096538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.979319 × 1018
5-th percentile2.001769 × 1018
Q12.011319 × 1018
median2.011819 × 1018
Q32.012819 × 1018
95-th percentile2.022169 × 1018
Maximum2.023319 × 1018
Range4.4000011 × 1016
Interquartile range (IQR)1.5 × 1015

Descriptive statistics

Standard deviation8.5124611 × 1015
Coefficient of variation (CV)0.004232541
Kurtosis8.3515835
Mean2.011194 × 1018
Median Absolute Deviation (MAD)5 × 1014
Skewness-2.2414713
Sum-7.0715759 × 1018
Variance7.2461994 × 1031
MonotonicityStrictly increasing
2024-05-11T15:10:17.247806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1979319015807500001 1
 
4.2%
2012319012807500002 1
 
4.2%
2023319027107500001 1
 
4.2%
2022319021607500001 1
 
4.2%
2021319021607500001 1
 
4.2%
2018319015807500001 1
 
4.2%
2016319015807500001 1
 
4.2%
2014319012807500001 1
 
4.2%
2012319012807500006 1
 
4.2%
2012319012807500005 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1979319015807500001 1
4.2%
2001319007207500010 1
4.2%
2004319007207500001 1
4.2%
2005319007207500001 1
4.2%
2009319009907500001 1
4.2%
2011319012807500001 1
4.2%
2011319012807500002 1
4.2%
2011319012807500003 1
4.2%
2011319012807500004 1
4.2%
2011319012807500005 1
4.2%
ValueCountFrequency (%)
2023319027107500001 1
4.2%
2022319021607500001 1
4.2%
2021319021607500001 1
4.2%
2018319015807500001 1
4.2%
2016319015807500001 1
4.2%
2014319012807500001 1
4.2%
2012319012807500006 1
4.2%
2012319012807500005 1
4.2%
2012319012807500004 1
4.2%
2012319012807500003 1
4.2%

인허가일자
Date

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum1979-12-18 00:00:00
Maximum2023-11-01 00:00:00
2024-05-11T15:10:17.668197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:17.827174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing24
Missing (%)100.0%
Memory size348.0 B

영업상태코드
Categorical

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
1
16 
2
4 
3
4 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 16
66.7%
2 4
 
16.7%
3 4
 
16.7%

Length

2024-05-11T15:10:17.997906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:18.130337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 16
66.7%
2 4
 
16.7%
3 4
 
16.7%

영업상태명
Categorical

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
영업/정상
16 
휴업
4 
폐업
4 

Length

Max length5
Median length5
Mean length4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 16
66.7%
휴업 4
 
16.7%
폐업 4
 
16.7%

Length

2024-05-11T15:10:18.309640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:18.480176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 16
66.7%
휴업 4
 
16.7%
폐업 4
 
16.7%

상세영업상태코드
Categorical

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
1
15 
2
4 
3
4 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 15
62.5%
2 4
 
16.7%
3 4
 
16.7%
5 1
 
4.2%

Length

2024-05-11T15:10:18.608195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:18.753309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 15
62.5%
2 4
 
16.7%
3 4
 
16.7%
5 1
 
4.2%

상세영업상태명
Categorical

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
정상영업
15 
휴업처리
4 
폐업처리
4 
영업개시전
 
1

Length

Max length5
Median length4
Mean length4.0416667
Min length4

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row정상영업
2nd row휴업처리
3rd row휴업처리
4th row휴업처리
5th row정상영업

Common Values

ValueCountFrequency (%)
정상영업 15
62.5%
휴업처리 4
 
16.7%
폐업처리 4
 
16.7%
영업개시전 1
 
4.2%

Length

2024-05-11T15:10:18.897528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:19.020925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상영업 15
62.5%
휴업처리 4
 
16.7%
폐업처리 4
 
16.7%
영업개시전 1
 
4.2%

폐업일자
Categorical

IMBALANCE 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
20 
20220128
 
2
20181025
 
1
20150630
 
1

Length

Max length8
Median length4
Mean length4.6666667
Min length4

Unique

Unique2 ?
Unique (%)8.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
83.3%
20220128 2
 
8.3%
20181025 1
 
4.2%
20150630 1
 
4.2%

Length

2024-05-11T15:10:19.138639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:19.271928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
83.3%
20220128 2
 
8.3%
20181025 1
 
4.2%
20150630 1
 
4.2%

휴업시작일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
23 
20211031
 
1

Length

Max length8
Median length4
Mean length4.1666667
Min length4

Unique

Unique1 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 23
95.8%
20211031 1
 
4.2%

Length

2024-05-11T15:10:19.424389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:19.569857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
95.8%
20211031 1
 
4.2%

휴업종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
23 
20250430
 
1

Length

Max length8
Median length4
Mean length4.1666667
Min length4

Unique

Unique1 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 23
95.8%
20250430 1
 
4.2%

Length

2024-05-11T15:10:19.724439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:19.845903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
95.8%
20250430 1
 
4.2%

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing24
Missing (%)100.0%
Memory size348.0 B

전화번호
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-05-11T15:10:20.006237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10.5
Mean length9.9583333
Min length7

Characters and Unicode

Total characters239
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

Unique22 ?
Unique (%)91.7%

Sample

1st row02-823-5100
2nd row02 8468258
3rd row02 5481448
4th row02 5481448
5th row822-7387
ValueCountFrequency (%)
02 4
 
13.8%
5481448 2
 
6.9%
02-3476-2242 1
 
3.4%
8138545 1
 
3.4%
02-792-0653 1
 
3.4%
02-595-8000 1
 
3.4%
2006-2676 1
 
3.4%
02-3280-8899 1
 
3.4%
2006-2156 1
 
3.4%
3280-8545 1
 
3.4%
Other values (15) 15
51.7%
2024-05-11T15:10:20.376992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 40
16.7%
0 33
13.8%
8 31
13.0%
- 27
11.3%
5 24
10.0%
4 19
7.9%
6 19
7.9%
1 14
 
5.9%
3 14
 
5.9%
9 7
 
2.9%
Other values (2) 11
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 206
86.2%
Dash Punctuation 27
 
11.3%
Space Separator 6
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40
19.4%
0 33
16.0%
8 31
15.0%
5 24
11.7%
4 19
9.2%
6 19
9.2%
1 14
 
6.8%
3 14
 
6.8%
9 7
 
3.4%
7 5
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40
16.7%
0 33
13.8%
8 31
13.0%
- 27
11.3%
5 24
10.0%
4 19
7.9%
6 19
7.9%
1 14
 
5.9%
3 14
 
5.9%
9 7
 
2.9%
Other values (2) 11
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40
16.7%
0 33
13.8%
8 31
13.0%
- 27
11.3%
5 24
10.0%
4 19
7.9%
6 19
7.9%
1 14
 
5.9%
3 14
 
5.9%
9 7
 
2.9%
Other values (2) 11
 
4.6%

소재지면적
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)95.7%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean1945.4283
Minimum0
Maximum8995.84
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-05-11T15:10:20.541573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile197.033
Q1297.9
median539.78
Q33368.575
95-th percentile6313.302
Maximum8995.84
Range8995.84
Interquartile range (IQR)3070.675

Descriptive statistics

Standard deviation2544.9504
Coefficient of variation (CV)1.3081698
Kurtosis1.2850422
Mean1945.4283
Median Absolute Deviation (MAD)288.45
Skewness1.4909217
Sum44744.85
Variance6476772.7
MonotonicityNot monotonic
2024-05-11T15:10:20.729970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4382.15 2
 
8.3%
6373.0 1
 
4.2%
595.0 1
 
4.2%
539.78 1
 
4.2%
8995.84 1
 
4.2%
5776.02 1
 
4.2%
1395.05 1
 
4.2%
191.0 1
 
4.2%
251.33 1
 
4.2%
258.0 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
0.0 1
4.2%
191.0 1
4.2%
251.33 1
4.2%
258.0 1
4.2%
280.0 1
4.2%
292.5 1
4.2%
303.3 1
4.2%
304.1 1
4.2%
331.34 1
4.2%
507.4 1
4.2%
ValueCountFrequency (%)
8995.84 1
4.2%
6373.0 1
4.2%
5776.02 1
4.2%
5051.61 1
4.2%
4382.15 2
8.3%
2355.0 1
4.2%
1395.05 1
4.2%
978.3 1
4.2%
671.98 1
4.2%
595.0 1
4.2%

소재지우편번호
Text

MISSING 

Distinct11
Distinct (%)84.6%
Missing11
Missing (%)45.8%
Memory size324.0 B
2024-05-11T15:10:20.940085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2307692
Min length6

Characters and Unicode

Total characters81
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

Unique9 ?
Unique (%)69.2%

Sample

1st row156020
2nd row156070
3rd row156-843
4th row156-819
5th row156030
ValueCountFrequency (%)
156020 2
15.4%
156030 2
15.4%
156070 1
7.7%
156-843 1
7.7%
156-819 1
7.7%
156771 1
7.7%
156-020 1
7.7%
156827 1
7.7%
156718 1
7.7%
156842 1
7.7%
2024-05-11T15:10:21.368911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16
19.8%
5 13
16.0%
6 13
16.0%
0 13
16.0%
2 6
 
7.4%
7 5
 
6.2%
8 5
 
6.2%
3 3
 
3.7%
- 3
 
3.7%
4 2
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78
96.3%
Dash Punctuation 3
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
20.5%
5 13
16.7%
6 13
16.7%
0 13
16.7%
2 6
 
7.7%
7 5
 
6.4%
8 5
 
6.4%
3 3
 
3.8%
4 2
 
2.6%
9 2
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
19.8%
5 13
16.0%
6 13
16.0%
0 13
16.0%
2 6
 
7.4%
7 5
 
6.2%
8 5
 
6.2%
3 3
 
3.7%
- 3
 
3.7%
4 2
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16
19.8%
5 13
16.0%
6 13
16.0%
0 13
16.0%
2 6
 
7.4%
7 5
 
6.2%
8 5
 
6.2%
3 3
 
3.7%
- 3
 
3.7%
4 2
 
2.5%

지번주소
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Memory size324.0 B
2024-05-11T15:10:21.709801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length34
Mean length25.869565
Min length19

Characters and Unicode

Total characters595
Distinct characters71
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row서울특별시 동작구 상도동 324번지 1호
2nd row서울특별시 동작구 신대방동 565호
3rd row서울특별시 동작구 노량진동 16번지 0001호
4th row서울특별시 동작구 노량진동 13번지 0008호
5th row서울특별시 동작구 대방동 501번지
ValueCountFrequency (%)
서울특별시 23
19.5%
동작구 23
19.5%
상도동 6
 
5.1%
사당동 5
 
4.2%
대방동 3
 
2.5%
노량진동 3
 
2.5%
105번지 2
 
1.7%
1호 2
 
1.7%
19호 1
 
0.8%
1042번지 1
 
0.8%
Other values (49) 49
41.5%
2024-05-11T15:10:22.211495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
105
17.6%
동 48
 
8.1%
1 35
 
5.9%
서 23
 
3.9%
특 23
 
3.9%
별 23
 
3.9%
시 23
 
3.9%
작 23
 
3.9%
구 23
 
3.9%
울 23
 
3.9%
Other values (61) 246
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 367
61.7%
Decimal Number 118
 
19.8%
Space Separator 105
 
17.6%
Math Symbol 2
 
0.3%
Dash Punctuation 2
 
0.3%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 48
13.1%
서 23
 
6.3%
특 23
 
6.3%
별 23
 
6.3%
시 23
 
6.3%
작 23
 
6.3%
구 23
 
6.3%
울 23
 
6.3%
지 22
 
6.0%
번 19
 
5.2%
Other values (47) 117
31.9%
Decimal Number
ValueCountFrequency (%)
1 35
29.7%
0 17
14.4%
5 16
13.6%
2 14
 
11.9%
3 14
 
11.9%
4 9
 
7.6%
6 5
 
4.2%
7 3
 
2.5%
8 3
 
2.5%
9 2
 
1.7%
Space Separator
ValueCountFrequency (%)
105
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 367
61.7%
Common 228
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 48
13.1%
서 23
 
6.3%
특 23
 
6.3%
별 23
 
6.3%
시 23
 
6.3%
작 23
 
6.3%
구 23
 
6.3%
울 23
 
6.3%
지 22
 
6.0%
번 19
 
5.2%
Other values (47) 117
31.9%
Common
ValueCountFrequency (%)
105
46.1%
1 35
 
15.4%
0 17
 
7.5%
5 16
 
7.0%
2 14
 
6.1%
3 14
 
6.1%
4 9
 
3.9%
6 5
 
2.2%
7 3
 
1.3%
8 3
 
1.3%
Other values (4) 7
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 367
61.7%
ASCII 228
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105
46.1%
1 35
 
15.4%
0 17
 
7.5%
5 16
 
7.0%
2 14
 
6.1%
3 14
 
6.1%
4 9
 
3.9%
6 5
 
2.2%
7 3
 
1.3%
8 3
 
1.3%
Other values (4) 7
 
3.1%
Hangul
ValueCountFrequency (%)
동 48
13.1%
서 23
 
6.3%
특 23
 
6.3%
별 23
 
6.3%
시 23
 
6.3%
작 23
 
6.3%
구 23
 
6.3%
울 23
 
6.3%
지 22
 
6.0%
번 19
 
5.2%
Other values (47) 117
31.9%

도로명주소
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-05-11T15:10:22.533431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length40
Mean length29.238095
Min length22

Characters and Unicode

Total characters614
Distinct characters93
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row서울특별시 동작구 상도로 102 (상도동, 성대시장)
2nd row서울특별시 동작구 여의대방로 22 (신대방동)
3rd row서울특별시 동작구 서달로 150 (흑석동)
4th row서울특별시 동작구 성대로 43 (상도동)
5th row서울특별시 동작구 사당로 215 (사당동)
ValueCountFrequency (%)
서울특별시 21
17.4%
동작구 21
17.4%
상도동 8
 
6.6%
사당동 7
 
5.8%
상도로 4
 
3.3%
동작대로 3
 
2.5%
사당로 2
 
1.7%
신대방동 2
 
1.7%
여의대방로 2
 
1.7%
동작대로29길 2
 
1.7%
Other values (47) 49
40.5%
2024-05-11T15:10:23.058368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102
 
16.6%
동 51
 
8.3%
작 26
 
4.2%
시 23
 
3.7%
1 23
 
3.7%
서 22
 
3.6%
로 21
 
3.4%
울 21
 
3.4%
( 21
 
3.4%
) 21
 
3.4%
Other values (83) 283
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 386
62.9%
Space Separator 102
 
16.6%
Decimal Number 71
 
11.6%
Open Punctuation 21
 
3.4%
Close Punctuation 21
 
3.4%
Other Punctuation 11
 
1.8%
Math Symbol 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 51
 
13.2%
작 26
 
6.7%
시 23
 
6.0%
서 22
 
5.7%
로 21
 
5.4%
울 21
 
5.4%
구 21
 
5.4%
별 21
 
5.4%
특 21
 
5.4%
상 17
 
4.4%
Other values (66) 142
36.8%
Decimal Number
ValueCountFrequency (%)
1 23
32.4%
0 10
14.1%
2 10
14.1%
3 7
 
9.9%
5 5
 
7.0%
4 5
 
7.0%
9 5
 
7.0%
7 3
 
4.2%
6 2
 
2.8%
8 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 10
90.9%
/ 1
 
9.1%
Space Separator
ValueCountFrequency (%)
102
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 386
62.9%
Common 228
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 51
 
13.2%
작 26
 
6.7%
시 23
 
6.0%
서 22
 
5.7%
로 21
 
5.4%
울 21
 
5.4%
구 21
 
5.4%
별 21
 
5.4%
특 21
 
5.4%
상 17
 
4.4%
Other values (66) 142
36.8%
Common
ValueCountFrequency (%)
102
44.7%
1 23
 
10.1%
( 21
 
9.2%
) 21
 
9.2%
0 10
 
4.4%
2 10
 
4.4%
, 10
 
4.4%
3 7
 
3.1%
5 5
 
2.2%
4 5
 
2.2%
Other values (7) 14
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 386
62.9%
ASCII 228
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
102
44.7%
1 23
 
10.1%
( 21
 
9.2%
) 21
 
9.2%
0 10
 
4.4%
2 10
 
4.4%
, 10
 
4.4%
3 7
 
3.1%
5 5
 
2.2%
4 5
 
2.2%
Other values (7) 14
 
6.1%
Hangul
ValueCountFrequency (%)
동 51
 
13.2%
작 26
 
6.7%
시 23
 
6.0%
서 22
 
5.7%
로 21
 
5.4%
울 21
 
5.4%
구 21
 
5.4%
별 21
 
5.4%
특 21
 
5.4%
상 17
 
4.4%
Other values (66) 142
36.8%

도로명우편번호
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing11
Missing (%)45.8%
Memory size324.0 B
2024-05-11T15:10:23.346835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.4615385
Min length5

Characters and Unicode

Total characters71
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

Unique13 ?
Unique (%)100.0%

Sample

1st row07041
2nd row156070
3rd row07013
4th row156771
5th row156-020
ValueCountFrequency (%)
07041 1
 
7.7%
156070 1
 
7.7%
07013 1
 
7.7%
156771 1
 
7.7%
156-020 1
 
7.7%
06990 1
 
7.7%
156842 1
 
7.7%
07039 1
 
7.7%
07064 1
 
7.7%
156092 1
 
7.7%
Other values (3) 3
23.1%
2024-05-11T15:10:23.840117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22
31.0%
6 10
14.1%
7 8
 
11.3%
1 8
 
11.3%
9 6
 
8.5%
5 5
 
7.0%
4 4
 
5.6%
2 3
 
4.2%
3 2
 
2.8%
8 2
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
98.6%
Dash Punctuation 1
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22
31.4%
6 10
14.3%
7 8
 
11.4%
1 8
 
11.4%
9 6
 
8.6%
5 5
 
7.1%
4 4
 
5.7%
2 3
 
4.3%
3 2
 
2.9%
8 2
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22
31.0%
6 10
14.1%
7 8
 
11.3%
1 8
 
11.3%
9 6
 
8.5%
5 5
 
7.0%
4 4
 
5.6%
2 3
 
4.2%
3 2
 
2.8%
8 2
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22
31.0%
6 10
14.1%
7 8
 
11.3%
1 8
 
11.3%
9 6
 
8.5%
5 5
 
7.0%
4 4
 
5.6%
2 3
 
4.2%
3 2
 
2.8%
8 2
 
2.8%

사업장명
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-05-11T15:10:24.119909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16.5
Mean length12.958333
Min length5

Characters and Unicode

Total characters311
Distinct characters78
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)91.7%

Sample

1st row성대종합시장
2nd row신대방우성상가
3rd row서울노량진종합시장
4th row서울노량진종합시장
5th row대림쇼핑타운
ValueCountFrequency (%)
주)이마트에브리데이 4
 
7.7%
익스프레스 4
 
7.7%
서울노량진종합시장 2
 
3.8%
홈플러스 2
 
3.8%
홈플러스(주 2
 
3.8%
사당점 2
 
3.8%
gs수퍼 2
 
3.8%
주)지에스리테일 2
 
3.8%
gs리테일 1
 
1.9%
롯대슈펴 1
 
1.9%
Other values (30) 30
57.7%
2024-05-11T15:10:24.651821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
9.0%
점 17
 
5.5%
스 16
 
5.1%
( 12
 
3.9%
) 12
 
3.9%
주 12
 
3.9%
이 9
 
2.9%
데 8
 
2.6%
대 7
 
2.3%
리 7
 
2.3%
Other values (68) 183
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 240
77.2%
Space Separator 28
 
9.0%
Uppercase Letter 16
 
5.1%
Open Punctuation 12
 
3.9%
Close Punctuation 12
 
3.9%
Decimal Number 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 17
 
7.1%
스 16
 
6.7%
주 12
 
5.0%
이 9
 
3.8%
데 8
 
3.3%
대 7
 
2.9%
리 7
 
2.9%
상 7
 
2.9%
도 6
 
2.5%
에 6
 
2.5%
Other values (56) 145
60.4%
Uppercase Letter
ValueCountFrequency (%)
S 5
31.2%
G 4
25.0%
H 2
 
12.5%
E 2
 
12.5%
R 1
 
6.2%
F 1
 
6.2%
T 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
3 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 240
77.2%
Common 55
 
17.7%
Latin 16
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 17
 
7.1%
스 16
 
6.7%
주 12
 
5.0%
이 9
 
3.8%
데 8
 
3.3%
대 7
 
2.9%
리 7
 
2.9%
상 7
 
2.9%
도 6
 
2.5%
에 6
 
2.5%
Other values (56) 145
60.4%
Latin
ValueCountFrequency (%)
S 5
31.2%
G 4
25.0%
H 2
 
12.5%
E 2
 
12.5%
R 1
 
6.2%
F 1
 
6.2%
T 1
 
6.2%
Common
ValueCountFrequency (%)
28
50.9%
( 12
21.8%
) 12
21.8%
3 2
 
3.6%
2 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 240
77.2%
ASCII 71
 
22.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
39.4%
( 12
16.9%
) 12
16.9%
S 5
 
7.0%
G 4
 
5.6%
H 2
 
2.8%
E 2
 
2.8%
3 2
 
2.8%
R 1
 
1.4%
F 1
 
1.4%
Other values (2) 2
 
2.8%
Hangul
ValueCountFrequency (%)
점 17
 
7.1%
스 16
 
6.7%
주 12
 
5.0%
이 9
 
3.8%
데 8
 
3.3%
대 7
 
2.9%
리 7
 
2.9%
상 7
 
2.9%
도 6
 
2.5%
에 6
 
2.5%
Other values (56) 145
60.4%

최종수정일자
Date

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2007-07-07 15:19:10
Maximum2024-03-20 14:21:50
2024-05-11T15:10:24.837654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:25.006861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

데이터갱신구분
Categorical

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
U
13 
I
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
U 13
54.2%
I 11
45.8%

Length

2024-05-11T15:10:25.227222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:25.386137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
u 13
54.2%
i 11
45.8%

데이터갱신일자
Date

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2018-08-31 23:59:59
Maximum2023-12-03 00:09:00
2024-05-11T15:10:25.520880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:25.715655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

업태구분명
Categorical

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
그 밖의 대규모점포
11 
구분없음
6 
복합쇼핑몰
4 
시장
 
1
쇼핑센터
 
1

Length

Max length10
Median length5
Mean length6.7916667
Min length2

Unique

Unique3 ?
Unique (%)12.5%

Sample

1st row그 밖의 대규모점포
2nd row그 밖의 대규모점포
3rd row그 밖의 대규모점포
4th row그 밖의 대규모점포
5th row시장

Common Values

ValueCountFrequency (%)
그 밖의 대규모점포 11
45.8%
구분없음 6
25.0%
복합쇼핑몰 4
 
16.7%
시장 1
 
4.2%
쇼핑센터 1
 
4.2%
백화점 1
 
4.2%

Length

2024-05-11T15:10:25.904435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:26.048163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
그 11
23.9%
밖의 11
23.9%
대규모점포 11
23.9%
구분없음 6
13.0%
복합쇼핑몰 4
 
8.7%
시장 1
 
2.2%
쇼핑센터 1
 
2.2%
백화점 1
 
2.2%

좌표정보(X)
Real number (ℝ)

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195392.88
Minimum192113.2
Maximum198303.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-05-11T15:10:26.174487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum192113.2
5-th percentile192335.66
Q1194022.84
median194905.71
Q3197581.39
95-th percentile198296.85
Maximum198303.53
Range6190.3265
Interquartile range (IQR)3558.5478

Descriptive statistics

Standard deviation1986.5751
Coefficient of variation (CV)0.01016708
Kurtosis-1.1085308
Mean195392.88
Median Absolute Deviation (MAD)1359.4593
Skewness0.20720424
Sum4689429.1
Variance3946480.7
MonotonicityNot monotonic
2024-05-11T15:10:26.327550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
197802.054854046 2
 
8.3%
194571.152376388 2
 
8.3%
198303.527071078 2
 
8.3%
193896.384765772 1
 
4.2%
194870.38438442 1
 
4.2%
192170.155815215 1
 
4.2%
195585.914045797 1
 
4.2%
194941.030627361 1
 
4.2%
195489.423430508 1
 
4.2%
198259.042471994 1
 
4.2%
Other values (11) 11
45.8%
ValueCountFrequency (%)
192113.200600983 1
4.2%
192170.155815215 1
4.2%
193273.484014052 1
4.2%
193393.306385172 1
4.2%
193699.189949205 1
4.2%
193896.384765772 1
4.2%
194064.994071226 1
4.2%
194250.710026163 1
4.2%
194571.152376388 2
8.3%
194807.845295028 1
4.2%
ValueCountFrequency (%)
198303.527071078 2
8.3%
198259.042471994 1
4.2%
198202.472533419 1
4.2%
197802.054854046 2
8.3%
197507.834447319 1
4.2%
196587.030630143 1
4.2%
195585.914045797 1
4.2%
195489.423430508 1
4.2%
194963.274671696 1
4.2%
194941.030627361 1
4.2%

좌표정보(Y)
Real number (ℝ)

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444051.77
Minimum441696.15
Maximum445901.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-05-11T15:10:26.504617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441696.15
5-th percentile442470.98
Q1443049.47
median444140.34
Q3444953.51
95-th percentile445790.55
Maximum445901.41
Range4205.2678
Interquartile range (IQR)1904.039

Descriptive statistics

Standard deviation1189.6798
Coefficient of variation (CV)0.0026791465
Kurtosis-0.90461026
Mean444051.77
Median Absolute Deviation (MAD)863.12142
Skewness-0.1207922
Sum10657243
Variance1415337.9
MonotonicityNot monotonic
2024-05-11T15:10:26.670164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
443049.47147487 2
 
8.3%
445790.549860682 2
 
8.3%
442742.422887932 2
 
8.3%
444196.484790728 1
 
4.2%
444631.112679216 1
 
4.2%
443599.41901313 1
 
4.2%
443690.595413251 1
 
4.2%
443848.078951741 1
 
4.2%
444995.675592494 1
 
4.2%
441696.145605791 1
 
4.2%
Other values (11) 11
45.8%
ValueCountFrequency (%)
441696.145605791 1
4.2%
442470.92961062 1
4.2%
442471.241038085 1
4.2%
442742.422887932 2
8.3%
443049.47147487 2
8.3%
443344.373525343 1
4.2%
443599.41901313 1
4.2%
443690.595413251 1
4.2%
443848.078951741 1
4.2%
444089.838269412 1
4.2%
ValueCountFrequency (%)
445901.413432497 1
4.2%
445790.549860682 2
8.3%
445577.513247441 1
4.2%
445011.250115029 1
4.2%
444995.675592494 1
4.2%
444939.455414947 1
4.2%
444866.845573417 1
4.2%
444631.112679216 1
4.2%
444556.425220128 1
4.2%
444196.484790728 1
4.2%

점포구분명
Categorical

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
준대규모점포
11 
<NA>
10 
대규모점포
3 

Length

Max length6
Median length5
Mean length5.0416667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
준대규모점포 11
45.8%
<NA> 10
41.7%
대규모점포 3
 
12.5%

Length

2024-05-11T15:10:26.855743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:10:27.001061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준대규모점포 11
45.8%
na 10
41.7%
대규모점포 3
 
12.5%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
03190000197931901580750000119791218<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-823-51006373.0<NA>서울특별시 동작구 상도동 324번지 1호서울특별시 동작구 상도로 102 (상도동, 성대시장)07041성대종합시장2017-07-06 17:46:35I2018-08-31 23:59:59.0그 밖의 대규모점포193896.384766444196.484791대규모점포
13190000200131900720750001019891201<NA>2휴업2휴업처리<NA><NA><NA><NA>02 8468258<NA><NA>서울특별시 동작구 신대방동 565호서울특별시 동작구 여의대방로 22 (신대방동)<NA>신대방우성상가2007-07-07 15:19:10I2018-08-31 23:59:59.0그 밖의 대규모점포192113.200601443344.373525<NA>
23190000200431900720750000120041218<NA>2휴업2휴업처리<NA><NA><NA><NA>02 54814484382.15<NA>서울특별시 동작구 노량진동 16번지 0001호<NA><NA>서울노량진종합시장2007-07-07 15:19:10I2018-08-31 23:59:59.0그 밖의 대규모점포194571.152376445790.549861<NA>
33190000200531900720750000120050119<NA>2휴업2휴업처리<NA><NA><NA><NA>02 54814484382.15<NA>서울특별시 동작구 노량진동 13번지 0008호<NA><NA>서울노량진종합시장2007-07-07 15:19:10I2018-08-31 23:59:59.0그 밖의 대규모점포194807.845295445901.413432<NA>
43190000200931900990750000119931130<NA>1영업/정상1정상영업<NA><NA><NA><NA>822-73870.0156020서울특별시 동작구 대방동 501번지<NA><NA>대림쇼핑타운2009-04-27 16:30:36I2018-08-31 23:59:59.0시장193273.484014445011.250115<NA>
53190000201131901280750000120110713<NA>1영업/정상1정상영업<NA><NA><NA><NA>821-21665051.61156070서울특별시 동작구 흑석동 332번지서울특별시 동작구 서달로 150 (흑석동)156070해가든프라자2021-08-24 17:11:01U2021-08-26 02:40:00.0그 밖의 대규모점포196587.03063444939.455415대규모점포
6319000020113190128075000022011-07-14<NA>1영업/정상1정상영업<NA><NA><NA><NA>8268545304.1156-843서울특별시 동작구 상도4동 255번지 4호서울특별시 동작구 성대로 43 (상도동)<NA>홈플러스(주) 익스프레스 상도점2024-03-20 14:21:50U2023-12-02 22:02:00.0쇼핑센터194064.994071444089.838269<NA>
7319000020113190128075000032011-08-26<NA>1영업/정상1정상영업<NA><NA><NA><NA>34818542978.3156-819서울특별시 동작구 사당3동 252번지 22호서울특별시 동작구 사당로 215 (사당동)<NA>홈플러스(주) 익스프레스 남성점2024-03-20 14:18:08U2023-12-02 22:02:00.0복합쇼핑몰197507.834447442470.929611<NA>
83190000201131901280750000420111004<NA>3폐업3폐업처리20181025<NA><NA><NA>02-812-5601530.0156030서울특별시 동작구 상도동 188번지 14호서울특별시 동작구 상도로 147 (상도동)<NA>롯데쇼핑(주) 롯데슈퍼 상도3동점2018-10-25 16:33:02U2018-10-27 02:37:43.0복합쇼핑몰194250.710026444556.42522준대규모점포
93190000201131901280750000520111004<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-841-3561331.34156020서울특별시 동작구 대방동 335번지 17호서울특별시 동작구 상도로 81 (대방동)<NA>(주)이마트에브리데이 대방동점2021-05-12 10:19:57U2021-05-14 02:40:00.0복합쇼핑몰193699.189949444190.844599준대규모점포
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
143190000201231901280750000320120406<NA>3폐업3폐업처리20220128<NA><NA><NA>02-3476-2242595.0<NA>서울특별시 동작구 사당동 105번지 우성아파트3단지 제비101호서울특별시 동작구 동작대로29길 119 (사당동)06990(주)지에스리테일 GS수퍼 사당점2022-02-07 08:00:27U2022-02-09 02:40:00.0구분없음197802.054854443049.471475준대규모점포
153190000201231901280750000420120413<NA>3폐업3폐업처리20150630<NA><NA><NA>22905853303.3156827서울특별시 동작구 사당1동 1042번지 19호서울특별시 동작구 동작대로 9 (사당동)<NA>롯데쇼핑(주)롯데슈퍼 사당점2015-07-08 14:12:48I2018-08-31 23:59:59.0그 밖의 대규모점포198259.042472441696.145606준대규모점포
163190000201231901280750000520120424<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-816-8546507.4156718서울특별시 동작구 상도2동 414번지서울특별시 동작구 만양로 26 (상도동)<NA>홈플러스 익스프레스 상도2점2014-10-08 09:09:37I2018-08-31 23:59:59.0구분없음195489.423431444995.675592준대규모점포
173190000201231901280750000620120424<NA>1영업/정상1정상영업<NA><NA><NA><NA>3280-8545258.0156842서울특별시 동작구 상도동 530번지서울특별시 동작구 양녕로 170 (상도동)156842홈플러스 익스프레스 상도3점2015-01-13 14:52:00I2018-08-31 23:59:59.0구분없음194941.030627443848.078952준대규모점포
183190000201431901280750000120140905<NA>1영업/정상1정상영업<NA><NA><NA><NA>2006-2156251.33<NA>서울특별시 동작구 상도동 531번지 상가2동 지상1층 101~105호서울특별시 동작구 상도로 346-1 (상도동, 상도엠코타운 센트럴파크)07039(주)지에스리테일 상도 엠코타운점2016-03-02 13:33:05I2018-08-31 23:59:59.0구분없음195585.914046443690.595413준대규모점포
193190000201631901580750000120160603<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-3280-8899191.0<NA><NA>서울특별시 동작구 여의대방로 30, 에이동동 101호 (신대방동, 현대상가)07064롯대슈펴 신대방점2018-07-06 13:15:06I2018-08-31 23:59:59.0그 밖의 대규모점포192170.155815443599.419013준대규모점포
203190000201831901580750000120180827<NA>3폐업3폐업처리20220128<NA><NA><NA>2006-26761395.05156092서울특별시 동작구 사당동 136번지 1호서울특별시 동작구 동작대로 115 (사당동)156092(주) GS리테일 GS수퍼 사당태평점2022-02-07 08:11:26U2022-02-09 02:40:00.0구분없음198303.527071442742.422888준대규모점포
213190000202131902160750000119951020<NA>2휴업2휴업처리<NA>2021103120250430<NA>02-595-80005776.02<NA>서울특별시 동작구 사당동 136-1 태평백화점서울특별시 동작구 동작대로 115, 태평백화점 (사당동)07008태평백화점2021-11-17 13:27:01U2021-11-19 02:40:00.0백화점198303.527071442742.422888대규모점포
22319000020223190216075000012022-07-22<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-792-06538995.84<NA>서울특별시 동작구 노량진동 16-1 노량진 드림스퀘어 복합빌딩서울특별시 동작구 노들로2길 7, 노량진 드림스퀘어 복합빌딩 (노량진동)06900노량진 드림스퀘어2024-03-07 09:42:42U2023-12-03 00:09:00.0그 밖의 대규모점포194571.152376445790.549861<NA>
23319000020233190271075000012023-11-01<NA>1영업/정상5영업개시전<NA><NA><NA><NA>02-2006-2364539.78<NA>서울특별시 동작구 상도동 535 상도2차두산위브트레지움아파트서울특별시 동작구 상도로30길 40, 판매/근생시설동 1층 125~131호 (상도동, 상도2차두산위브트레지움아파트)06964GS THE FRESH 동작구청점2023-11-01 16:27:36I2022-11-01 00:03:00.0구분없음194870.384384444631.112679<NA>