Overview

Dataset statistics

Number of variables26
Number of observations35
Missing cells183
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory223.8 B

Variable types

Categorical9
Numeric5
DateTime3
Unsupported3
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
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-18715/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
재개업일자 is highly imbalanced (81.3%)Imbalance
인허가취소일자 has 35 (100.0%) missing valuesMissing
폐업일자 has 29 (82.9%) missing valuesMissing
휴업시작일자 has 35 (100.0%) missing valuesMissing
휴업종료일자 has 35 (100.0%) missing valuesMissing
소재지우편번호 has 19 (54.3%) missing valuesMissing
지번주소 has 4 (11.4%) missing valuesMissing
도로명주소 has 1 (2.9%) missing valuesMissing
도로명우편번호 has 21 (60.0%) missing valuesMissing
좌표정보(X) has 2 (5.7%) missing valuesMissing
좌표정보(Y) has 2 (5.7%) missing valuesMissing
관리번호 has unique valuesUnique
최종수정일자 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
소재지면적 has 6 (17.1%) zerosZeros

Reproduction

Analysis started2024-04-06 12:09:17.012848
Analysis finished2024-04-06 12:09:17.635405
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
3130000
35 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3130000
2nd row3130000
3rd row3130000
4th row3130000
5th row3130000

Common Values

ValueCountFrequency (%)
3130000 35
100.0%

Length

2024-04-06T21:09:17.760948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:17.932479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3130000 35
100.0%

관리번호
Real number (ℝ)

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0095702 × 1018
Minimum1.977313 × 1018
Maximum2.021313 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-06T21:09:18.162418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.977313 × 1018
5-th percentile2.001313 × 1018
Q12.008813 × 1018
median2.009313 × 1018
Q32.012313 × 1018
95-th percentile2.018613 × 1018
Maximum2.021313 × 1018
Range4.4000013 × 1016
Interquartile range (IQR)3.5 × 1015

Descriptive statistics

Standard deviation7.2855353 × 1015
Coefficient of variation (CV)0.0036254197
Kurtosis10.890691
Mean2.0095702 × 1018
Median Absolute Deviation (MAD)2 × 1015
Skewness-2.4691702
Sum-3.4520209 × 1018
Variance5.3079024 × 1031
MonotonicityStrictly increasing
2024-04-06T21:09:18.427166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1977313009907500009 1
 
2.9%
2001313007907500001 1
 
2.9%
2010313011807500002 1
 
2.9%
2011313011807500001 1
 
2.9%
2011313011807500002 1
 
2.9%
2011313011807500003 1
 
2.9%
2011313011807500004 1
 
2.9%
2012313011807500001 1
 
2.9%
2012313011807500002 1
 
2.9%
2014313011807500001 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1977313009907500009 1
2.9%
2001313007907500001 1
2.9%
2001313007907500002 1
2.9%
2003313009907500001 1
2.9%
2005313009907500001 1
2.9%
2005313009907500003 1
2.9%
2005313009907500004 1
2.9%
2007313011807500001 1
2.9%
2008313011807500001 1
2.9%
2009313011807500001 1
2.9%
ValueCountFrequency (%)
2021313022507500001 1
2.9%
2019313020107500001 1
2.9%
2018313020107500002 1
2.9%
2018313020107500001 1
2.9%
2016313018007500001 1
2.9%
2014313011807500003 1
2.9%
2014313011807500002 1
2.9%
2014313011807500001 1
2.9%
2012313011807500002 1
2.9%
2012313011807500001 1
2.9%

인허가일자
Date

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
Minimum1970-07-29 00:00:00
Maximum2021-04-22 00:00:00
2024-04-06T21:09:18.671274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:09:18.955585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B

영업상태코드
Categorical

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
1
28 
3
6 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row1
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 28
80.0%
3 6
 
17.1%
2 1
 
2.9%

Length

2024-04-06T21:09:19.218954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:19.398330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28
80.0%
3 6
 
17.1%
2 1
 
2.9%

영업상태명
Categorical

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
영업/정상
28 
폐업
6 
휴업
 
1

Length

Max length5
Median length5
Mean length4.4
Min length2

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 28
80.0%
폐업 6
 
17.1%
휴업 1
 
2.9%

Length

2024-04-06T21:09:19.610533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:19.822483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 28
80.0%
폐업 6
 
17.1%
휴업 1
 
2.9%

상세영업상태코드
Categorical

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
1
27 
3
6 
2
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)5.7%

Sample

1st row1
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 27
77.1%
3 6
 
17.1%
2 1
 
2.9%
5 1
 
2.9%

Length

2024-04-06T21:09:20.032041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:20.228829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
77.1%
3 6
 
17.1%
2 1
 
2.9%
5 1
 
2.9%

상세영업상태명
Categorical

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
정상영업
27 
폐업처리
6 
휴업처리
 
1
영업개시전
 
1

Length

Max length5
Median length4
Mean length4.0285714
Min length4

Unique

Unique2 ?
Unique (%)5.7%

Sample

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

Common Values

ValueCountFrequency (%)
정상영업 27
77.1%
폐업처리 6
 
17.1%
휴업처리 1
 
2.9%
영업개시전 1
 
2.9%

Length

2024-04-06T21:09:20.884983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:21.092761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상영업 27
77.1%
폐업처리 6
 
17.1%
휴업처리 1
 
2.9%
영업개시전 1
 
2.9%

폐업일자
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)100.0%
Missing29
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean20167200
Minimum20140107
Maximum20220119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-06T21:09:21.291687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20140107
5-th percentile20142684
Q120150592
median20151173
Q320180468
95-th percentile20212643
Maximum20220119
Range80012
Interquartile range (IQR)29876.75

Descriptive statistics

Standard deviation31175.566
Coefficient of variation (CV)0.0015458549
Kurtosis0.32508281
Mean20167200
Median Absolute Deviation (MAD)5911.5
Skewness1.2626717
Sum1.210032 × 108
Variance9.7191592 × 108
MonotonicityNot monotonic
2024-04-06T21:09:21.506617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20151228 1
 
2.9%
20190215 1
 
2.9%
20150416 1
 
2.9%
20151118 1
 
2.9%
20140107 1
 
2.9%
20220119 1
 
2.9%
(Missing) 29
82.9%
ValueCountFrequency (%)
20140107 1
2.9%
20150416 1
2.9%
20151118 1
2.9%
20151228 1
2.9%
20190215 1
2.9%
20220119 1
2.9%
ValueCountFrequency (%)
20220119 1
2.9%
20190215 1
2.9%
20151228 1
2.9%
20151118 1
2.9%
20150416 1
2.9%
20140107 1
2.9%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B

재개업일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
34 
20070507
 
1

Length

Max length8
Median length4
Mean length4.1142857
Min length4

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 34
97.1%
20070507 1
 
2.9%

Length

2024-04-06T21:09:21.830594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:22.059777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 34
97.1%
20070507 1
 
2.9%

전화번호
Text

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-04-06T21:09:22.383957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.9428571
Min length4

Characters and Unicode

Total characters313
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)94.3%

Sample

1st row712-0687
2nd row717-7042
3rd row300-5062
4th row02-312-2080
5th row02 3005062
ValueCountFrequency (%)
02-2179-5021 2
 
5.4%
712-0076 1
 
2.7%
334-6949 1
 
2.7%
02-2006-2678 1
 
2.7%
02-701-8544 1
 
2.7%
325-5601 1
 
2.7%
701-5604 1
 
2.7%
7065602 1
 
2.7%
717-7042 1
 
2.7%
02-337-8541 1
 
2.7%
Other values (26) 26
70.3%
2024-04-06T21:09:23.129732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 47
15.0%
0 45
14.4%
- 38
12.1%
3 36
11.5%
7 28
8.9%
1 27
8.6%
5 26
8.3%
4 25
8.0%
6 18
 
5.8%
9 10
 
3.2%
Other values (3) 13
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 272
86.9%
Dash Punctuation 38
 
12.1%
Space Separator 2
 
0.6%
Close Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 47
17.3%
0 45
16.5%
3 36
13.2%
7 28
10.3%
1 27
9.9%
5 26
9.6%
4 25
9.2%
6 18
 
6.6%
9 10
 
3.7%
8 10
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 47
15.0%
0 45
14.4%
- 38
12.1%
3 36
11.5%
7 28
8.9%
1 27
8.6%
5 26
8.3%
4 25
8.0%
6 18
 
5.8%
9 10
 
3.2%
Other values (3) 13
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 47
15.0%
0 45
14.4%
- 38
12.1%
3 36
11.5%
7 28
8.9%
1 27
8.6%
5 26
8.3%
4 25
8.0%
6 18
 
5.8%
9 10
 
3.2%
Other values (3) 13
 
4.2%

소재지면적
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4186.2117
Minimum0
Maximum22471.9
Zeros6
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-06T21:09:23.469714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240.75
median1335.9
Q36140.155
95-th percentile15246.89
Maximum22471.9
Range22471.9
Interquartile range (IQR)5899.405

Descriptive statistics

Standard deviation5719.5879
Coefficient of variation (CV)1.3662921
Kurtosis2.2944091
Mean4186.2117
Median Absolute Deviation (MAD)1335.9
Skewness1.6618559
Sum146517.41
Variance32713686
MonotonicityNot monotonic
2024-04-06T21:09:23.748362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 6
 
17.1%
12337.24 2
 
5.7%
9747.1 2
 
5.7%
5687.96 2
 
5.7%
1689.0 1
 
2.9%
377.72 1
 
2.9%
250.8 1
 
2.9%
323.62 1
 
2.9%
230.7 1
 
2.9%
206.9 1
 
2.9%
Other values (17) 17
48.6%
ValueCountFrequency (%)
0.0 6
17.1%
158.07 1
 
2.9%
206.9 1
 
2.9%
230.7 1
 
2.9%
250.8 1
 
2.9%
264.0 1
 
2.9%
323.62 1
 
2.9%
377.72 1
 
2.9%
414.5 1
 
2.9%
750.0 1
 
2.9%
ValueCountFrequency (%)
22471.9 1
2.9%
17687.3 1
2.9%
14201.0 1
2.9%
12337.24 2
5.7%
9747.1 2
5.7%
6884.25 1
2.9%
6592.35 1
2.9%
5687.96 2
5.7%
3992.0 1
2.9%
3185.1 1
2.9%

소재지우편번호
Text

MISSING 

Distinct15
Distinct (%)93.8%
Missing19
Missing (%)54.3%
Memory size412.0 B
2024-04-06T21:09:24.018144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.125
Min length6

Characters and Unicode

Total characters98
Distinct characters10
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

Unique14 ?
Unique (%)87.5%

Sample

1st row121190
2nd row121200
3rd row121803
4th row121897
5th row121839
ValueCountFrequency (%)
121827 2
 
12.5%
121190 1
 
6.2%
121200 1
 
6.2%
121803 1
 
6.2%
121897 1
 
6.2%
121839 1
 
6.2%
121856 1
 
6.2%
121838 1
 
6.2%
121865 1
 
6.2%
121815 1
 
6.2%
Other values (5) 5
31.2%
2024-04-06T21:09:24.619818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 36
36.7%
2 21
21.4%
8 15
15.3%
0 6
 
6.1%
9 4
 
4.1%
3 4
 
4.1%
5 4
 
4.1%
7 3
 
3.1%
6 3
 
3.1%
- 2
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
98.0%
Dash Punctuation 2
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36
37.5%
2 21
21.9%
8 15
15.6%
0 6
 
6.2%
9 4
 
4.2%
3 4
 
4.2%
5 4
 
4.2%
7 3
 
3.1%
6 3
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 36
36.7%
2 21
21.4%
8 15
15.3%
0 6
 
6.1%
9 4
 
4.1%
3 4
 
4.1%
5 4
 
4.1%
7 3
 
3.1%
6 3
 
3.1%
- 2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 36
36.7%
2 21
21.4%
8 15
15.3%
0 6
 
6.1%
9 4
 
4.1%
3 4
 
4.1%
5 4
 
4.1%
7 3
 
3.1%
6 3
 
3.1%
- 2
 
2.0%

지번주소
Text

MISSING 

Distinct30
Distinct (%)96.8%
Missing4
Missing (%)11.4%
Memory size412.0 B
2024-04-06T21:09:25.032459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length22.516129
Min length18

Characters and Unicode

Total characters698
Distinct characters64
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)93.5%

Sample

1st row서울특별시 마포구 공덕동 256번지 5호 마포시장
2nd row서울특별시 마포구 염리동 36번지 249호
3rd row서울특별시 마포구 성산동 533번지 1호
4th row서울특별시 마포구 성산동 515호 월드컵경기장내
5th row서울특별시 마포구 성산동 533번지 1호
ValueCountFrequency (%)
서울특별시 31
20.8%
마포구 31
20.8%
1호 10
 
6.7%
서교동 5
 
3.4%
동교동 4
 
2.7%
성산동 3
 
2.0%
공덕동 3
 
2.0%
망원2동 2
 
1.3%
도화동 2
 
1.3%
15호 2
 
1.3%
Other values (46) 56
37.6%
2024-04-06T21:09:25.714731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
17.3%
서 36
 
5.2%
동 35
 
5.0%
마 34
 
4.9%
포 33
 
4.7%
시 32
 
4.6%
구 32
 
4.6%
특 31
 
4.4%
별 31
 
4.4%
울 31
 
4.4%
Other values (54) 282
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 454
65.0%
Decimal Number 122
 
17.5%
Space Separator 121
 
17.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 36
 
7.9%
동 35
 
7.7%
마 34
 
7.5%
포 33
 
7.3%
시 32
 
7.0%
구 32
 
7.0%
특 31
 
6.8%
별 31
 
6.8%
울 31
 
6.8%
번 28
 
6.2%
Other values (42) 131
28.9%
Decimal Number
ValueCountFrequency (%)
1 28
23.0%
5 20
16.4%
4 14
11.5%
3 13
10.7%
2 10
 
8.2%
7 9
 
7.4%
6 9
 
7.4%
9 8
 
6.6%
0 6
 
4.9%
8 5
 
4.1%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 454
65.0%
Common 244
35.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 36
 
7.9%
동 35
 
7.7%
마 34
 
7.5%
포 33
 
7.3%
시 32
 
7.0%
구 32
 
7.0%
특 31
 
6.8%
별 31
 
6.8%
울 31
 
6.8%
번 28
 
6.2%
Other values (42) 131
28.9%
Common
ValueCountFrequency (%)
121
49.6%
1 28
 
11.5%
5 20
 
8.2%
4 14
 
5.7%
3 13
 
5.3%
2 10
 
4.1%
7 9
 
3.7%
6 9
 
3.7%
9 8
 
3.3%
0 6
 
2.5%
Other values (2) 6
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 454
65.0%
ASCII 244
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
49.6%
1 28
 
11.5%
5 20
 
8.2%
4 14
 
5.7%
3 13
 
5.3%
2 10
 
4.1%
7 9
 
3.7%
6 9
 
3.7%
9 8
 
3.3%
0 6
 
2.5%
Other values (2) 6
 
2.5%
Hangul
ValueCountFrequency (%)
서 36
 
7.9%
동 35
 
7.7%
마 34
 
7.5%
포 33
 
7.3%
시 32
 
7.0%
구 32
 
7.0%
특 31
 
6.8%
별 31
 
6.8%
울 31
 
6.8%
번 28
 
6.2%
Other values (42) 131
28.9%

도로명주소
Text

MISSING 

Distinct32
Distinct (%)94.1%
Missing1
Missing (%)2.9%
Memory size412.0 B
2024-04-06T21:09:26.159888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length35
Mean length26.882353
Min length22

Characters and Unicode

Total characters914
Distinct characters114
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)88.2%

Sample

1st row서울특별시 마포구 마포대로6길 16, 마포시장 (공덕동)
2nd row서울특별시 마포구 숭문길 96 (염리동)
3rd row서울특별시 마포구 월드컵로 235 (성산동)
4th row서울특별시 마포구 월드컵로 240 (성산동,월드컵경기장내)
5th row서울특별시 마포구 월드컵로 235 (성산동)
ValueCountFrequency (%)
서울특별시 34
 
18.2%
마포구 34
 
18.2%
서교동 5
 
2.7%
월드컵로 4
 
2.1%
양화로 4
 
2.1%
동교동 4
 
2.1%
노고산동 3
 
1.6%
공덕동 3
 
1.6%
신촌로 3
 
1.6%
토정로 2
 
1.1%
Other values (78) 91
48.7%
2024-04-06T21:09:26.914577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
157
 
17.2%
동 41
 
4.5%
서 39
 
4.3%
마 39
 
4.3%
포 39
 
4.3%
시 35
 
3.8%
구 35
 
3.8%
( 34
 
3.7%
특 34
 
3.7%
별 34
 
3.7%
Other values (104) 427
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 575
62.9%
Space Separator 157
 
17.2%
Decimal Number 102
 
11.2%
Open Punctuation 34
 
3.7%
Close Punctuation 34
 
3.7%
Other Punctuation 9
 
1.0%
Uppercase Letter 1
 
0.1%
Dash Punctuation 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 41
 
7.1%
서 39
 
6.8%
마 39
 
6.8%
포 39
 
6.8%
시 35
 
6.1%
구 35
 
6.1%
특 34
 
5.9%
별 34
 
5.9%
울 34
 
5.9%
로 31
 
5.4%
Other values (87) 214
37.2%
Decimal Number
ValueCountFrequency (%)
1 21
20.6%
2 17
16.7%
5 11
10.8%
3 9
8.8%
8 9
8.8%
4 8
 
7.8%
9 7
 
6.9%
6 7
 
6.9%
7 7
 
6.9%
0 6
 
5.9%
Space Separator
ValueCountFrequency (%)
157
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 575
62.9%
Common 338
37.0%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 41
 
7.1%
서 39
 
6.8%
마 39
 
6.8%
포 39
 
6.8%
시 35
 
6.1%
구 35
 
6.1%
특 34
 
5.9%
별 34
 
5.9%
울 34
 
5.9%
로 31
 
5.4%
Other values (87) 214
37.2%
Common
ValueCountFrequency (%)
157
46.4%
( 34
 
10.1%
) 34
 
10.1%
1 21
 
6.2%
2 17
 
5.0%
5 11
 
3.3%
3 9
 
2.7%
, 9
 
2.7%
8 9
 
2.7%
4 8
 
2.4%
Other values (6) 29
 
8.6%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 575
62.9%
ASCII 339
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
157
46.3%
( 34
 
10.0%
) 34
 
10.0%
1 21
 
6.2%
2 17
 
5.0%
5 11
 
3.2%
3 9
 
2.7%
, 9
 
2.7%
8 9
 
2.7%
4 8
 
2.4%
Other values (7) 30
 
8.8%
Hangul
ValueCountFrequency (%)
동 41
 
7.1%
서 39
 
6.8%
마 39
 
6.8%
포 39
 
6.8%
시 35
 
6.1%
구 35
 
6.1%
특 34
 
5.9%
별 34
 
5.9%
울 34
 
5.9%
로 31
 
5.4%
Other values (87) 214
37.2%

도로명우편번호
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing21
Missing (%)60.0%
Memory size412.0 B
2024-04-06T21:09:27.322819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.5714286
Min length5

Characters and Unicode

Total characters78
Distinct characters10
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

Unique14 ?
Unique (%)100.0%

Sample

1st row04214
2nd row121-030
3rd row121852
4th row04036
5th row04180
ValueCountFrequency (%)
04214 1
 
7.1%
121-030 1
 
7.1%
121852 1
 
7.1%
04036 1
 
7.1%
04180 1
 
7.1%
03989 1
 
7.1%
121-916 1
 
7.1%
121-811 1
 
7.1%
121806 1
 
7.1%
04163 1
 
7.1%
Other values (4) 4
28.6%
2024-04-06T21:09:28.077570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18
23.1%
0 17
21.8%
4 11
14.1%
2 9
11.5%
8 5
 
6.4%
3 4
 
5.1%
6 4
 
5.1%
9 4
 
5.1%
- 3
 
3.8%
5 3
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75
96.2%
Dash Punctuation 3
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
24.0%
0 17
22.7%
4 11
14.7%
2 9
12.0%
8 5
 
6.7%
3 4
 
5.3%
6 4
 
5.3%
9 4
 
5.3%
5 3
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18
23.1%
0 17
21.8%
4 11
14.1%
2 9
11.5%
8 5
 
6.4%
3 4
 
5.1%
6 4
 
5.1%
9 4
 
5.1%
- 3
 
3.8%
5 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18
23.1%
0 17
21.8%
4 11
14.1%
2 9
11.5%
8 5
 
6.4%
3 4
 
5.1%
6 4
 
5.1%
9 4
 
5.1%
- 3
 
3.8%
5 3
 
3.8%

사업장명
Text

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-04-06T21:09:28.510471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length9.2285714
Min length4

Characters and Unicode

Total characters323
Distinct characters104
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

Unique31 ?
Unique (%)88.6%

Sample

1st row마포시장
2nd row염리시장
3rd row마포농수산물시장
4th row홈플러스(주)월드컵점
5th row마포농수산물시장
ValueCountFrequency (%)
홈플러스익스프레스 4
 
6.8%
롯데슈퍼 3
 
5.1%
마포점 3
 
5.1%
주)이마트 2
 
3.4%
fresh 2
 
3.4%
gs 2
 
3.4%
the 2
 
3.4%
마포농수산물시장 2
 
3.4%
스타피카소 1
 
1.7%
아울렛 1
 
1.7%
Other values (37) 37
62.7%
2024-04-06T21:09:29.159534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
 
7.4%
스 17
 
5.3%
점 15
 
4.6%
시 14
 
4.3%
마 13
 
4.0%
장 13
 
4.0%
포 10
 
3.1%
( 6
 
1.9%
러 6
 
1.9%
홈 6
 
1.9%
Other values (94) 199
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 254
78.6%
Uppercase Letter 30
 
9.3%
Space Separator 24
 
7.4%
Open Punctuation 6
 
1.9%
Close Punctuation 6
 
1.9%
Lowercase Letter 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 17
 
6.7%
점 15
 
5.9%
시 14
 
5.5%
마 13
 
5.1%
장 13
 
5.1%
포 10
 
3.9%
러 6
 
2.4%
홈 6
 
2.4%
플 6
 
2.4%
프 6
 
2.4%
Other values (75) 148
58.3%
Uppercase Letter
ValueCountFrequency (%)
S 4
13.3%
H 4
13.3%
E 4
13.3%
A 3
10.0%
G 2
6.7%
F 2
6.7%
R 2
6.7%
T 2
6.7%
P 2
6.7%
Z 2
6.7%
Other values (3) 3
10.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
33.3%
a 1
33.3%
r 1
33.3%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 254
78.6%
Common 36
 
11.1%
Latin 33
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 17
 
6.7%
점 15
 
5.9%
시 14
 
5.5%
마 13
 
5.1%
장 13
 
5.1%
포 10
 
3.9%
러 6
 
2.4%
홈 6
 
2.4%
플 6
 
2.4%
프 6
 
2.4%
Other values (75) 148
58.3%
Latin
ValueCountFrequency (%)
S 4
12.1%
H 4
12.1%
E 4
12.1%
A 3
9.1%
G 2
 
6.1%
F 2
 
6.1%
R 2
 
6.1%
T 2
 
6.1%
P 2
 
6.1%
Z 2
 
6.1%
Other values (6) 6
18.2%
Common
ValueCountFrequency (%)
24
66.7%
( 6
 
16.7%
) 6
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 254
78.6%
ASCII 69
 
21.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24
34.8%
( 6
 
8.7%
) 6
 
8.7%
S 4
 
5.8%
H 4
 
5.8%
E 4
 
5.8%
A 3
 
4.3%
G 2
 
2.9%
F 2
 
2.9%
R 2
 
2.9%
Other values (9) 12
17.4%
Hangul
ValueCountFrequency (%)
스 17
 
6.7%
점 15
 
5.9%
시 14
 
5.5%
마 13
 
5.1%
장 13
 
5.1%
포 10
 
3.9%
러 6
 
2.4%
홈 6
 
2.4%
플 6
 
2.4%
프 6
 
2.4%
Other values (75) 148
58.3%

최종수정일자
Date

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
Minimum2007-06-30 10:17:05
Maximum2024-03-05 10:20:33
2024-04-06T21:09:29.385226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:09:29.598866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

데이터갱신구분
Categorical

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
U
24 
I
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
U 24
68.6%
I 11
31.4%

Length

2024-04-06T21:09:29.920369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:30.116009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
u 24
68.6%
i 11
31.4%

데이터갱신일자
Date

Distinct23
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
Minimum2018-08-31 23:59:59
Maximum2023-12-03 00:07:00
2024-04-06T21:09:30.292130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:09:30.517652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

업태구분명
Categorical

Distinct6
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
시장
11 
그 밖의 대규모점포
9 
구분없음
7 
대형마트
4 
전문점
2 

Length

Max length10
Median length4
Mean length4.8571429
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시장 11
31.4%
그 밖의 대규모점포 9
25.7%
구분없음 7
20.0%
대형마트 4
 
11.4%
전문점 2
 
5.7%
쇼핑센터 2
 
5.7%

Length

2024-04-06T21:09:30.768018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:30.977381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시장 11
20.8%
그 9
17.0%
밖의 9
17.0%
대규모점포 9
17.0%
구분없음 7
13.2%
대형마트 4
 
7.5%
전문점 2
 
3.8%
쇼핑센터 2
 
3.8%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)93.9%
Missing2
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean193823.58
Minimum190931
Maximum195810.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-06T21:09:31.211489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum190931
5-th percentile191363.32
Q1192899.61
median193981.31
Q3195236.1
95-th percentile195808.9
Maximum195810.08
Range4879.0786
Interquartile range (IQR)2336.4949

Descriptive statistics

Standard deviation1443.5037
Coefficient of variation (CV)0.0074475136
Kurtosis-0.7758839
Mean193823.58
Median Absolute Deviation (MAD)1254.788
Skewness-0.29591869
Sum6396178
Variance2083703
MonotonicityNot monotonic
2024-04-06T21:09:31.465242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
195808.904523588 2
 
5.7%
190931.000875925 2
 
5.7%
194158.369649874 1
 
2.9%
193296.791432042 1
 
2.9%
195243.086998177 1
 
2.9%
194158.70131685 1
 
2.9%
193565.900355472 1
 
2.9%
193285.549735897 1
 
2.9%
194695.797060075 1
 
2.9%
194219.19139038 1
 
2.9%
Other values (21) 21
60.0%
(Missing) 2
 
5.7%
ValueCountFrequency (%)
190931.000875925 2
5.7%
191651.529315787 1
2.9%
192018.039124439 1
2.9%
192214.380853693 1
2.9%
192257.393762526 1
2.9%
192311.642580307 1
2.9%
192577.990805265 1
2.9%
192899.605749281 1
2.9%
193009.20810189 1
2.9%
193282.130799848 1
2.9%
ValueCountFrequency (%)
195810.079475465 1
2.9%
195808.904523588 2
5.7%
195804.563731799 1
2.9%
195669.217619713 1
2.9%
195564.375757848 1
2.9%
195266.07314617 1
2.9%
195243.086998177 1
2.9%
195236.100682981 1
2.9%
194695.797060075 1
2.9%
194648.759340948 1
2.9%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)93.9%
Missing2
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean449958.01
Minimum448393.66
Maximum451427.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-06T21:09:31.704095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum448393.66
5-th percentile448811.24
Q1449239.31
median450136.91
Q3450549.28
95-th percentile451387.49
Maximum451427.72
Range3034.0635
Interquartile range (IQR)1309.9655

Descriptive statistics

Standard deviation832.05378
Coefficient of variation (CV)0.0018491809
Kurtosis-0.92145629
Mean449958.01
Median Absolute Deviation (MAD)671.66066
Skewness0.061458532
Sum14848614
Variance692313.49
MonotonicityNot monotonic
2024-04-06T21:09:31.969599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
448910.741283532 2
 
5.7%
451427.722144609 2
 
5.7%
450273.194140715 1
 
2.9%
450562.931242819 1
 
2.9%
450223.927419359 1
 
2.9%
449853.982367014 1
 
2.9%
450545.668460987 1
 
2.9%
450808.571641119 1
 
2.9%
448818.967990515 1
 
2.9%
450346.609802096 1
 
2.9%
Other values (21) 21
60.0%
(Missing) 2
 
5.7%
ValueCountFrequency (%)
448393.658663222 1
2.9%
448799.641804029 1
2.9%
448818.967990515 1
2.9%
448910.741283532 2
5.7%
449173.775965909 1
2.9%
449186.933273231 1
2.9%
449216.311329217 1
2.9%
449239.314703653 1
2.9%
449243.162717684 1
2.9%
449301.692398118 1
2.9%
ValueCountFrequency (%)
451427.722144609 2
5.7%
451360.660991625 1
2.9%
451125.467333415 1
2.9%
450808.571641119 1
2.9%
450694.803472189 1
2.9%
450569.670545853 1
2.9%
450562.931242819 1
2.9%
450549.280167182 1
2.9%
450545.668460987 1
2.9%
450449.030804033 1
2.9%

점포구분명
Categorical

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
15 
대규모점포
13 
준대규모점포
7 

Length

Max length6
Median length5
Mean length4.7714286
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 15
42.9%
대규모점포 13
37.1%
준대규모점포 7
20.0%

Length

2024-04-06T21:09:32.235775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:09:32.454604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 15
42.9%
대규모점포 13
37.1%
준대규모점포 7
20.0%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
03130000197731300990750000919770715<NA>1영업/정상1정상영업<NA><NA><NA><NA>712-06871689.0<NA>서울특별시 마포구 공덕동 256번지 5호 마포시장서울특별시 마포구 마포대로6길 16, 마포시장 (공덕동)04214마포시장2020-06-22 09:32:40U2020-06-24 02:40:00.0시장195804.563732449216.311329대규모점포
13130000200131300790750000119760116<NA>3폐업3폐업처리20151228<NA><NA><NA>717-70421077.0<NA>서울특별시 마포구 염리동 36번지 249호서울특별시 마포구 숭문길 96 (염리동)<NA>염리시장2021-09-29 10:56:35U2021-10-01 02:40:00.0시장195236.100683449819.81638대규모점포
2313000020013130079075000021998-07-18<NA>1영업/정상1정상영업<NA><NA><NA><NA>300-506212337.24<NA>서울특별시 마포구 성산동 533번지 1호서울특별시 마포구 월드컵로 235 (성산동)<NA>마포농수산물시장2023-05-18 17:40:16U2022-12-04 22:00:00.0시장190931.000876451427.722145<NA>
3313000020033130099075000012003-05-21<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-312-208017687.3<NA>서울특별시 마포구 성산동 515호 월드컵경기장내서울특별시 마포구 월드컵로 240 (성산동,월드컵경기장내)<NA>홈플러스(주)월드컵점2024-03-05 10:20:33U2023-12-03 00:07:00.0대형마트<NA><NA><NA>
43130000200531300990750000120050224<NA>2휴업2휴업처리<NA><NA><NA><NA>02 300506212337.24<NA>서울특별시 마포구 성산동 533번지 1호서울특별시 마포구 월드컵로 235 (성산동)<NA>마포농수산물시장2007-06-30 10:17:05I2018-08-31 23:59:59.0그 밖의 대규모점포190931.000876451427.722145<NA>
53130000200531300990750000319710520<NA>3폐업3폐업처리20190215<NA><NA><NA>334-54000.0<NA>서울특별시 마포구 노고산동 49번지 55호서울특별시 마포구 신촌로 86 (노고산동)<NA>신촌상가2019-02-15 11:34:21U2019-02-17 02:40:00.0시장194158.36965450273.194141대규모점포
63130000200531300990750000420051007<NA>3폐업3폐업처리20150416<NA><NA><NA>322 45313166.75<NA>서울특별시 마포구 서교동 487호서울특별시 마포구 월드컵북로5나길 18 (서교동)<NA>신교시장2015-04-16 15:08:27I2018-08-31 23:59:59.0그 밖의 대규모점포192577.990805450569.670546대규모점포
73130000200731301180750000120070507<NA>1영업/정상1정상영업<NA><NA><NA>200705073141-52623992.0121190서울특별시 마포구 창전동 442번지서울특별시 마포구 창전로 45 (창전동)<NA>마포종합시장(서강한화오벨리스크)2020-04-22 13:38:55U2020-04-24 02:40:00.0그 밖의 대규모점포193905.437192449372.4965대규모점포
83130000200831301180750000120080331<NA>1영업/정상1정상영업<NA><NA><NA><NA>02)333-36666884.25121200서울특별시 마포구 동교동 166번지 14호서울특별시 마포구 양화로 176 (동교동)<NA>스타피카소 아울렛2008-04-03 18:10:02I2018-08-31 23:59:59.0전문점193282.1308450549.280167<NA>
93130000200931301180750000119740414<NA>1영업/정상1정상영업<NA><NA><NA><NA>712-0076750.0121803서울특별시 마포구 공덕동 256번지 10호서울특별시 마포구 만리재로 19 (공덕동)<NA>공덕시장2019-05-07 10:40:20U2019-05-09 02:40:00.0시장195810.079475449173.775966대규모점포
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
25313000020123130118075000012012-03-22<NA>1영업/정상1정상영업<NA><NA><NA><NA>706-72721561.5121-130서울특별시 마포구 구수동 61번지서울특별시 마포구 토정로 193 (구수동)<NA>롯데슈퍼 마포점2023-03-16 14:56:57U2022-12-02 23:08:00.0그 밖의 대규모점포193981.312706449301.692398<NA>
263130000201231301180750000220120417<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-337-8541158.07<NA>서울특별시 마포구 연남동 573번지 코오롱아파트서울특별시 마포구 연남로 30 (연남동, 코오롱하늘채아파트)03989홈플러스익스프레스 연남점2021-06-16 11:39:04U2021-06-18 02:40:00.0그 밖의 대규모점포192899.605749451360.660992준대규모점포
27313000020143130118075000012009-05-29<NA>1영업/정상1정상영업<NA><NA><NA><NA>70656021335.9<NA><NA>서울특별시 마포구 마포대로 109 (공덕동, 롯데캐슬프레지던트)121-916롯데프리미엄푸드마켓 공덕점2023-05-19 09:26:25U2022-12-04 22:06:00.0구분없음195564.375758449186.933273<NA>
28313000020143130118075000022008-08-12<NA>1영업/정상1정상영업<NA><NA><NA><NA>701-5604414.5<NA><NA>서울특별시 마포구 큰우물로 3 (대흥동)121-811롯데슈퍼 대흥점2023-05-19 09:18:51U2022-12-04 22:06:00.0구분없음194648.759341449239.314704<NA>
293130000201431301180750000320090618<NA>3폐업3폐업처리20220119<NA><NA><NA>325-5601206.9<NA><NA>서울특별시 마포구 신촌로 88 (노고산동)121806롯데슈퍼 신촌점2022-01-19 14:59:49U2022-01-21 02:40:00.0구분없음194219.19139450346.609802준대규모점포
303130000201631301800750000120111206<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-701-8544230.7<NA><NA>서울특별시 마포구 토정로 282 (용강동)04163홈플러스익스프레스 용강점2021-11-17 09:31:51U2021-11-19 02:40:00.0구분없음194695.79706448818.967991준대규모점포
31313000020183130201075000012018-08-14<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2179-50219747.1<NA>서울특별시 마포구 동교동 190번지 1호서울특별시 마포구 양화로 188 (동교동)04051AK PLAZA 홍대2023-04-03 14:56:00U2022-12-04 00:05:00.0쇼핑센터193285.549736450808.571641<NA>
323130000201831302010750000220180813<NA>1영업/정상5영업개시전<NA><NA><NA><NA>02-2179-50219747.1<NA>서울특별시 마포구 동교동 190번지 1호서울특별시 마포구 와우산로35길 50-4 (동교동)04052홍대복합역사 상업시설2018-08-17 09:33:16I2018-08-31 23:59:59.0쇼핑센터193565.900355450545.668461대규모점포
333130000201931302010750000120190724<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2006-2678323.62<NA>서울특별시 마포구 신수동 93번지 102호 신촌숲아이파크서울특별시 마포구 광성로 17, 상가동 127호 (신수동, 신촌숲아이파크)04094GS THE FRESH 신촌숲아이파크점2019-11-06 16:54:16U2019-11-08 02:40:00.0구분없음194158.701317449853.982367준대규모점포
343130000202131302250750000120210422<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-707-1372250.8<NA>서울특별시 마포구 염리동 14-149서울특별시 마포구 대흥로24길 24, 마포 프레스티지 자이 아파트 단지 내 상가 A동 115~119호 (염리동)04124GS THE FRESH 마포염리점2021-09-07 09:25:29U2021-09-09 02:40:00.0구분없음195243.086998450223.927419준대규모점포