Overview

Dataset statistics

Number of variables26
Number of observations74
Missing cells251
Missing cells (%)13.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 KiB
Average record size in memory217.8 B

Variable types

Categorical11
Numeric4
DateTime5
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
Author중구
URLhttps://data.seoul.go.kr/dataList/OA-18703/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
인허가취소일자 is highly imbalanced (84.5%)Imbalance
영업상태코드 is highly imbalanced (51.0%)Imbalance
영업상태명 is highly imbalanced (51.0%)Imbalance
상세영업상태코드 is highly imbalanced (51.0%)Imbalance
상세영업상태명 is highly imbalanced (51.0%)Imbalance
휴업시작일자 is highly imbalanced (67.3%)Imbalance
휴업종료일자 is highly imbalanced (67.3%)Imbalance
폐업일자 has 64 (86.5%) missing valuesMissing
재개업일자 has 68 (91.9%) missing valuesMissing
소재지우편번호 has 52 (70.3%) missing valuesMissing
지번주소 has 38 (51.4%) missing valuesMissing
도로명주소 has 2 (2.7%) missing valuesMissing
도로명우편번호 has 21 (28.4%) missing valuesMissing
좌표정보(X) has 3 (4.1%) missing valuesMissing
좌표정보(Y) has 3 (4.1%) missing valuesMissing
관리번호 has unique valuesUnique
최종수정일자 has unique valuesUnique
소재지면적 has 2 (2.7%) zerosZeros

Reproduction

Analysis started2024-05-11 05:31:45.683176
Analysis finished2024-05-11 05:31:46.551956
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
3010000
74 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3010000
2nd row3010000
3rd row3010000
4th row3010000
5th row3010000

Common Values

ValueCountFrequency (%)
3010000 74
100.0%

Length

2024-05-11T14:31:46.729842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:46.894401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3010000 74
100.0%

관리번호
Real number (ℝ)

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9793672 × 1018
Minimum2.007301 × 1014
Maximum2.021301 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-05-11T14:31:47.072997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.007301 × 1014
5-th percentile1.970951 × 1018
Q12.002301 × 1018
median2.010301 × 1018
Q32.014301 × 1018
95-th percentile2.016651 × 1018
Maximum2.021301 × 1018
Range2.0211003 × 1018
Interquartile range (IQR)1.2 × 1016

Descriptive statistics

Standard deviation2.3353923 × 1017
Coefficient of variation (CV)0.11798681
Kurtosis73.586048
Mean1.9793672 × 1018
Median Absolute Deviation (MAD)4.000003 × 1015
Skewness-8.5669841
Sum-1.1007781 × 1018
Variance5.454057 × 1034
MonotonicityNot monotonic
2024-05-11T14:31:47.325155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1964301010007500001 1
 
1.4%
2014301013007500010 1
 
1.4%
2014301013007500008 1
 
1.4%
2014301013007500007 1
 
1.4%
2014301013007500006 1
 
1.4%
2014301013007500005 1
 
1.4%
2014301013007500004 1
 
1.4%
2014301013007500003 1
 
1.4%
2014301013007500001 1
 
1.4%
2013301013007500001 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
200730101300751 1
1.4%
1964301010007500001 1
1.4%
1968301010007500001 1
1.4%
1970301013007500001 1
1.4%
1971301007107500021 1
1.4%
1977301013007500022 1
1.4%
1980301007107500001 1
1.4%
1985301013007500003 1
1.4%
1994301010007500001 1
1.4%
1996301013007500001 1
1.4%
ValueCountFrequency (%)
2021301016607500001 1
1.4%
2019301016607500001 1
1.4%
2017301013007500002 1
1.4%
2017301013007500001 1
1.4%
2016301013007500002 1
1.4%
2016301013007500001 1
1.4%
2014301013007500025 1
1.4%
2014301013007500024 1
1.4%
2014301013007500023 1
1.4%
2014301013007500022 1
1.4%

인허가일자
Date

Distinct68
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size724.0 B
Minimum1959-02-24 00:00:00
Maximum2021-07-16 00:00:00
2024-05-11T14:31:47.595054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:31:47.856122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
71 
20190614
 
1
20161219
 
1
20141110
 
1

Length

Max length8
Median length4
Mean length4.1621622
Min length4

Unique

Unique3 ?
Unique (%)4.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 71
95.9%
20190614 1
 
1.4%
20161219 1
 
1.4%
20141110 1
 
1.4%

Length

2024-05-11T14:31:48.182956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:48.410957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 71
95.9%
20190614 1
 
1.4%
20161219 1
 
1.4%
20141110 1
 
1.4%

영업상태코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
1
59 
3
10 
4
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 59
79.7%
3 10
 
13.5%
4 3
 
4.1%
2 2
 
2.7%

Length

2024-05-11T14:31:48.625205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:48.822884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 59
79.7%
3 10
 
13.5%
4 3
 
4.1%
2 2
 
2.7%

영업상태명
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
영업/정상
59 
폐업
10 
취소/말소/만료/정지/중지
 
3
휴업
 
2

Length

Max length14
Median length5
Mean length4.8783784
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 59
79.7%
폐업 10
 
13.5%
취소/말소/만료/정지/중지 3
 
4.1%
휴업 2
 
2.7%

Length

2024-05-11T14:31:49.079565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:49.251939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 59
79.7%
폐업 10
 
13.5%
취소/말소/만료/정지/중지 3
 
4.1%
휴업 2
 
2.7%

상세영업상태코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
1
59 
3
10 
4
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 59
79.7%
3 10
 
13.5%
4 3
 
4.1%
2 2
 
2.7%

Length

2024-05-11T14:31:49.456748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:49.638516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 59
79.7%
3 10
 
13.5%
4 3
 
4.1%
2 2
 
2.7%

상세영업상태명
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
정상영업
59 
폐업처리
10 
직권취소
 
3
휴업처리
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상영업 59
79.7%
폐업처리 10
 
13.5%
직권취소 3
 
4.1%
휴업처리 2
 
2.7%

Length

2024-05-11T14:31:49.860284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:50.108235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상영업 59
79.7%
폐업처리 10
 
13.5%
직권취소 3
 
4.1%
휴업처리 2
 
2.7%

폐업일자
Date

MISSING 

Distinct2
Distinct (%)20.0%
Missing64
Missing (%)86.5%
Memory size724.0 B
Minimum2015-08-27 00:00:00
Maximum2023-04-20 00:00:00
2024-05-11T14:31:50.283180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:31:50.454207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

휴업시작일자
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
65 
11111111
7 
2015-05-30
 
1
2024-01-20
 
1

Length

Max length10
Median length4
Mean length4.5405405
Min length4

Unique

Unique2 ?
Unique (%)2.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 65
87.8%
11111111 7
 
9.5%
2015-05-30 1
 
1.4%
2024-01-20 1
 
1.4%

Length

2024-05-11T14:31:50.680950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:50.886116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 65
87.8%
11111111 7
 
9.5%
2015-05-30 1
 
1.4%
2024-01-20 1
 
1.4%

휴업종료일자
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
65 
11111111
7 
2015-08-14
 
1
2024-09-05
 
1

Length

Max length10
Median length4
Mean length4.5405405
Min length4

Unique

Unique2 ?
Unique (%)2.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 65
87.8%
11111111 7
 
9.5%
2015-08-14 1
 
1.4%
2024-09-05 1
 
1.4%

Length

2024-05-11T14:31:51.114888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:51.356972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 65
87.8%
11111111 7
 
9.5%
2015-08-14 1
 
1.4%
2024-09-05 1
 
1.4%

재개업일자
Date

MISSING 

Distinct5
Distinct (%)83.3%
Missing68
Missing (%)91.9%
Memory size724.0 B
Minimum1962-02-23 00:00:00
Maximum2024-09-06 00:00:00
2024-05-11T14:31:51.511217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:31:51.682434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

전화번호
Text

Distinct72
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size724.0 B
2024-05-11T14:31:52.231909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.162162
Min length8

Characters and Unicode

Total characters826
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

Unique70 ?
Unique (%)94.6%

Sample

1st row02-753-2805
2nd row02-2232-3608
3rd row02-2269-1967
4th row0222699891
5th row02-778-4611
ValueCountFrequency (%)
02-2256-3500 2
 
2.6%
02 2
 
2.6%
02-2250-2002 2
 
2.6%
02-2232-2000 1
 
1.3%
02-2267-5617 1
 
1.3%
2238-0981 1
 
1.3%
02-6366-3056 1
 
1.3%
2265-9611 1
 
1.3%
02-6365-5959 1
 
1.3%
02-2290-5853 1
 
1.3%
Other values (64) 64
83.1%
2024-05-11T14:31:53.202070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 193
23.4%
0 146
17.7%
- 120
14.5%
5 61
 
7.4%
3 61
 
7.4%
1 57
 
6.9%
7 49
 
5.9%
6 45
 
5.4%
8 34
 
4.1%
9 31
 
3.8%
Other values (2) 29
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 703
85.1%
Dash Punctuation 120
 
14.5%
Space Separator 3
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 193
27.5%
0 146
20.8%
5 61
 
8.7%
3 61
 
8.7%
1 57
 
8.1%
7 49
 
7.0%
6 45
 
6.4%
8 34
 
4.8%
9 31
 
4.4%
4 26
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 826
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 193
23.4%
0 146
17.7%
- 120
14.5%
5 61
 
7.4%
3 61
 
7.4%
1 57
 
6.9%
7 49
 
5.9%
6 45
 
5.4%
8 34
 
4.1%
9 31
 
3.8%
Other values (2) 29
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 193
23.4%
0 146
17.7%
- 120
14.5%
5 61
 
7.4%
3 61
 
7.4%
1 57
 
6.9%
7 49
 
5.9%
6 45
 
5.4%
8 34
 
4.1%
9 31
 
3.8%
Other values (2) 29
 
3.5%

소재지면적
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13815.688
Minimum0
Maximum75439.97
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-05-11T14:31:53.437120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile267.6055
Q16050.24
median10332.65
Q316982.715
95-th percentile45042.077
Maximum75439.97
Range75439.97
Interquartile range (IQR)10932.475

Descriptive statistics

Standard deviation13885.004
Coefficient of variation (CV)1.0050172
Kurtosis7.2244591
Mean13815.688
Median Absolute Deviation (MAD)4937.835
Skewness2.4824669
Sum1022360.9
Variance1.9279334 × 108
MonotonicityNot monotonic
2024-05-11T14:31:53.701873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15144.0 2
 
2.7%
13006.43 2
 
2.7%
0.0 2
 
2.7%
11576.19 2
 
2.7%
53845.06 1
 
1.4%
17736.37 1
 
1.4%
13793.3 1
 
1.4%
15695.63 1
 
1.4%
19056.99 1
 
1.4%
19023.64 1
 
1.4%
Other values (60) 60
81.1%
ValueCountFrequency (%)
0.0 2
2.7%
107.8 1
1.4%
251.57 1
1.4%
276.24 1
1.4%
492.56 1
1.4%
648.4 1
1.4%
754.0 1
1.4%
1210.07 1
1.4%
3613.39 1
1.4%
3778.41 1
1.4%
ValueCountFrequency (%)
75439.97 1
1.4%
59979.25 1
1.4%
56336.98 1
1.4%
53845.06 1
1.4%
40302.01 1
1.4%
33008.72 1
1.4%
27653.82 1
1.4%
24483.79 1
1.4%
21481.63 1
1.4%
20707.0 1
1.4%

소재지우편번호
Text

MISSING 

Distinct19
Distinct (%)86.4%
Missing52
Missing (%)70.3%
Memory size724.0 B
2024-05-11T14:31:54.075660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.3636364
Min length6

Characters and Unicode

Total characters140
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

Unique16 ?
Unique (%)72.7%

Sample

1st row100060
2nd row100430
3rd row100951
4th row100-451
5th row100889
ValueCountFrequency (%)
100-451 2
 
9.1%
100162 2
 
9.1%
100451 2
 
9.1%
100810 1
 
4.5%
100060 1
 
4.5%
100194 1
 
4.5%
100-196 1
 
4.5%
100-011 1
 
4.5%
100726 1
 
4.5%
100-881 1
 
4.5%
Other values (9) 9
40.9%
2024-05-11T14:31:54.878578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 50
35.7%
1 35
25.0%
4 9
 
6.4%
- 8
 
5.7%
6 7
 
5.0%
9 7
 
5.0%
2 6
 
4.3%
7 6
 
4.3%
8 6
 
4.3%
5 5
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132
94.3%
Dash Punctuation 8
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50
37.9%
1 35
26.5%
4 9
 
6.8%
6 7
 
5.3%
9 7
 
5.3%
2 6
 
4.5%
7 6
 
4.5%
8 6
 
4.5%
5 5
 
3.8%
3 1
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 50
35.7%
1 35
25.0%
4 9
 
6.4%
- 8
 
5.7%
6 7
 
5.0%
9 7
 
5.0%
2 6
 
4.3%
7 6
 
4.3%
8 6
 
4.3%
5 5
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 50
35.7%
1 35
25.0%
4 9
 
6.4%
- 8
 
5.7%
6 7
 
5.0%
9 7
 
5.0%
2 6
 
4.3%
7 6
 
4.3%
8 6
 
4.3%
5 5
 
3.6%

지번주소
Text

MISSING 

Distinct35
Distinct (%)97.2%
Missing38
Missing (%)51.4%
Memory size724.0 B
2024-05-11T14:31:55.455892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length21.555556
Min length16

Characters and Unicode

Total characters776
Distinct characters68
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

Unique34 ?
Unique (%)94.4%

Sample

1st row서울특별시 중구 남창동 49번지
2nd row서울특별시 중구 충무로4가 120번지 3 호
3rd row서울특별시 중구 신당동 776호
4th row서울특별시 중구 흥인동 3번지 4호
5th row서울특별시 중구 충무로1가 24번지 1호
ValueCountFrequency (%)
서울특별시 36
21.1%
중구 36
21.1%
을지로6가 6
 
3.5%
신당동 5
 
2.9%
1호 4
 
2.3%
신당1동 4
 
2.3%
봉래동2가 3
 
1.8%
충무로1가 3
 
1.8%
18번지 3
 
1.8%
17번지 2
 
1.2%
Other values (58) 69
40.4%
2024-05-11T14:31:56.634401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
140
18.0%
1 40
 
5.2%
지 38
 
4.9%
시 37
 
4.8%
서 36
 
4.6%
구 36
 
4.6%
울 36
 
4.6%
중 36
 
4.6%
별 36
 
4.6%
특 36
 
4.6%
Other values (58) 305
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 492
63.4%
Decimal Number 143
 
18.4%
Space Separator 140
 
18.0%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 38
 
7.7%
시 37
 
7.5%
서 36
 
7.3%
구 36
 
7.3%
울 36
 
7.3%
중 36
 
7.3%
별 36
 
7.3%
특 36
 
7.3%
번 30
 
6.1%
호 26
 
5.3%
Other values (46) 145
29.5%
Decimal Number
ValueCountFrequency (%)
1 40
28.0%
2 29
20.3%
7 14
 
9.8%
3 12
 
8.4%
5 12
 
8.4%
4 9
 
6.3%
6 8
 
5.6%
8 8
 
5.6%
9 6
 
4.2%
0 5
 
3.5%
Space Separator
ValueCountFrequency (%)
140
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 492
63.4%
Common 284
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 38
 
7.7%
시 37
 
7.5%
서 36
 
7.3%
구 36
 
7.3%
울 36
 
7.3%
중 36
 
7.3%
별 36
 
7.3%
특 36
 
7.3%
번 30
 
6.1%
호 26
 
5.3%
Other values (46) 145
29.5%
Common
ValueCountFrequency (%)
140
49.3%
1 40
 
14.1%
2 29
 
10.2%
7 14
 
4.9%
3 12
 
4.2%
5 12
 
4.2%
4 9
 
3.2%
6 8
 
2.8%
8 8
 
2.8%
9 6
 
2.1%
Other values (2) 6
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 492
63.4%
ASCII 284
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
140
49.3%
1 40
 
14.1%
2 29
 
10.2%
7 14
 
4.9%
3 12
 
4.2%
5 12
 
4.2%
4 9
 
3.2%
6 8
 
2.8%
8 8
 
2.8%
9 6
 
2.1%
Other values (2) 6
 
2.1%
Hangul
ValueCountFrequency (%)
지 38
 
7.7%
시 37
 
7.5%
서 36
 
7.3%
구 36
 
7.3%
울 36
 
7.3%
중 36
 
7.3%
별 36
 
7.3%
특 36
 
7.3%
번 30
 
6.1%
호 26
 
5.3%
Other values (46) 145
29.5%

도로명주소
Text

MISSING 

Distinct67
Distinct (%)93.1%
Missing2
Missing (%)2.7%
Memory size724.0 B
2024-05-11T14:31:57.212028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length37
Mean length25.416667
Min length20

Characters and Unicode

Total characters1830
Distinct characters135
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

Unique63 ?
Unique (%)87.5%

Sample

1st row서울특별시 중구 남대문시장4길 21 (남창동)
2nd row서울특별시 중구 마장로 30 (신당동)
3rd row서울특별시 중구 청계천로 260-9 (을지로6가, 통일의류종합상가)
4th row서울특별시 중구 퇴계로 217 (충무로4가)
5th row서울특별시 중구 삼일대로 363 (장교동)
ValueCountFrequency (%)
서울특별시 72
19.1%
중구 72
19.1%
신당동 21
 
5.6%
을지로6가 10
 
2.7%
마장로 7
 
1.9%
다산로 7
 
1.9%
청계천로 7
 
1.9%
장충단로 6
 
1.6%
을지로 6
 
1.6%
마장로1길 4
 
1.1%
Other values (121) 164
43.6%
2024-05-11T14:31:58.117599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
306
 
16.7%
로 86
 
4.7%
시 77
 
4.2%
서 74
 
4.0%
중 73
 
4.0%
) 72
 
3.9%
( 72
 
3.9%
울 72
 
3.9%
구 72
 
3.9%
별 72
 
3.9%
Other values (125) 854
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1088
59.5%
Space Separator 306
 
16.7%
Decimal Number 250
 
13.7%
Close Punctuation 72
 
3.9%
Open Punctuation 72
 
3.9%
Uppercase Letter 20
 
1.1%
Other Punctuation 15
 
0.8%
Dash Punctuation 5
 
0.3%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 86
 
7.9%
시 77
 
7.1%
서 74
 
6.8%
중 73
 
6.7%
울 72
 
6.6%
구 72
 
6.6%
별 72
 
6.6%
특 72
 
6.6%
동 58
 
5.3%
가 36
 
3.3%
Other values (95) 396
36.4%
Uppercase Letter
ValueCountFrequency (%)
L 3
15.0%
A 3
15.0%
M 2
10.0%
P 2
10.0%
D 2
10.0%
C 1
 
5.0%
O 1
 
5.0%
N 1
 
5.0%
E 1
 
5.0%
I 1
 
5.0%
Other values (3) 3
15.0%
Decimal Number
ValueCountFrequency (%)
2 60
24.0%
1 36
14.4%
4 26
10.4%
6 26
10.4%
3 26
10.4%
0 25
10.0%
7 19
 
7.6%
5 14
 
5.6%
8 11
 
4.4%
9 7
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
p 1
50.0%
Space Separator
ValueCountFrequency (%)
306
100.0%
Close Punctuation
ValueCountFrequency (%)
) 72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 72
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1088
59.5%
Common 720
39.3%
Latin 22
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 86
 
7.9%
시 77
 
7.1%
서 74
 
6.8%
중 73
 
6.7%
울 72
 
6.6%
구 72
 
6.6%
별 72
 
6.6%
특 72
 
6.6%
동 58
 
5.3%
가 36
 
3.3%
Other values (95) 396
36.4%
Common
ValueCountFrequency (%)
306
42.5%
) 72
 
10.0%
( 72
 
10.0%
2 60
 
8.3%
1 36
 
5.0%
4 26
 
3.6%
6 26
 
3.6%
3 26
 
3.6%
0 25
 
3.5%
7 19
 
2.6%
Other values (5) 52
 
7.2%
Latin
ValueCountFrequency (%)
L 3
13.6%
A 3
13.6%
M 2
 
9.1%
P 2
 
9.1%
D 2
 
9.1%
C 1
 
4.5%
O 1
 
4.5%
N 1
 
4.5%
E 1
 
4.5%
I 1
 
4.5%
Other values (5) 5
22.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1088
59.5%
ASCII 742
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
306
41.2%
) 72
 
9.7%
( 72
 
9.7%
2 60
 
8.1%
1 36
 
4.9%
4 26
 
3.5%
6 26
 
3.5%
3 26
 
3.5%
0 25
 
3.4%
7 19
 
2.6%
Other values (20) 74
 
10.0%
Hangul
ValueCountFrequency (%)
로 86
 
7.9%
시 77
 
7.1%
서 74
 
6.8%
중 73
 
6.7%
울 72
 
6.6%
구 72
 
6.6%
별 72
 
6.6%
특 72
 
6.6%
동 58
 
5.3%
가 36
 
3.3%
Other values (95) 396
36.4%

도로명우편번호
Text

MISSING 

Distinct47
Distinct (%)88.7%
Missing21
Missing (%)28.4%
Memory size724.0 B
2024-05-11T14:31:58.513578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.9056604
Min length5

Characters and Unicode

Total characters313
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

Unique42 ?
Unique (%)79.2%

Sample

1st row100060
2nd row100886
3rd row04563
4th row100760
5th row100400
ValueCountFrequency (%)
100804 3
 
5.7%
04536 2
 
3.8%
100822 2
 
3.8%
100754 2
 
3.8%
100887 2
 
3.8%
100721 1
 
1.9%
100060 1
 
1.9%
100162 1
 
1.9%
100-450 1
 
1.9%
100762 1
 
1.9%
Other values (37) 37
69.8%
2024-05-11T14:31:59.364074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 113
36.1%
1 55
17.6%
8 28
 
8.9%
4 22
 
7.0%
5 21
 
6.7%
6 21
 
6.7%
7 16
 
5.1%
2 13
 
4.2%
3 10
 
3.2%
9 8
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 307
98.1%
Dash Punctuation 6
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 113
36.8%
1 55
17.9%
8 28
 
9.1%
4 22
 
7.2%
5 21
 
6.8%
6 21
 
6.8%
7 16
 
5.2%
2 13
 
4.2%
3 10
 
3.3%
9 8
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 113
36.1%
1 55
17.6%
8 28
 
8.9%
4 22
 
7.0%
5 21
 
6.7%
6 21
 
6.7%
7 16
 
5.1%
2 13
 
4.2%
3 10
 
3.2%
9 8
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 113
36.1%
1 55
17.6%
8 28
 
8.9%
4 22
 
7.0%
5 21
 
6.7%
6 21
 
6.7%
7 16
 
5.1%
2 13
 
4.2%
3 10
 
3.2%
9 8
 
2.6%

사업장명
Text

Distinct72
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size724.0 B
2024-05-11T14:31:59.891210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length14.5
Mean length7.0675676
Min length2

Characters and Unicode

Total characters523
Distinct characters162
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)94.6%

Sample

1st row서울남대문시장
2nd row팀204
3rd row통일상가
4th row진양상가
5th row상제리제상가
ValueCountFrequency (%)
서울역점 3
 
3.2%
통일상가 2
 
2.1%
청계천점 2
 
2.1%
롯데아울렛 2
 
2.1%
쇼핑몰 2
 
2.1%
이마트 2
 
2.1%
서울남대문시장 1
 
1.1%
남산타운점 1
 
1.1%
주)지에스리테일 1
 
1.1%
삼익패션타운 1
 
1.1%
Other values (77) 77
81.9%
2024-05-11T14:32:00.650066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
3.8%
점 16
 
3.1%
상 13
 
2.5%
가 13
 
2.5%
롯 12
 
2.3%
데 12
 
2.3%
스 12
 
2.3%
시 11
 
2.1%
타 11
 
2.1%
이 11
 
2.1%
Other values (152) 392
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 460
88.0%
Space Separator 20
 
3.8%
Uppercase Letter 15
 
2.9%
Close Punctuation 9
 
1.7%
Open Punctuation 9
 
1.7%
Decimal Number 5
 
1.0%
Lowercase Letter 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 16
 
3.5%
상 13
 
2.8%
가 13
 
2.8%
롯 12
 
2.6%
데 12
 
2.6%
스 12
 
2.6%
시 11
 
2.4%
타 11
 
2.4%
이 11
 
2.4%
화 9
 
2.0%
Other values (130) 340
73.9%
Uppercase Letter
ValueCountFrequency (%)
M 2
13.3%
D 2
13.3%
A 2
13.3%
L 2
13.3%
I 1
6.7%
O 1
6.7%
N 1
6.7%
H 1
6.7%
S 1
6.7%
F 1
6.7%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
5 1
20.0%
4 1
20.0%
0 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
a 2
40.0%
p 1
20.0%
l 1
20.0%
z 1
20.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 460
88.0%
Common 43
 
8.2%
Latin 20
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 16
 
3.5%
상 13
 
2.8%
가 13
 
2.8%
롯 12
 
2.6%
데 12
 
2.6%
스 12
 
2.6%
시 11
 
2.4%
타 11
 
2.4%
이 11
 
2.4%
화 9
 
2.0%
Other values (130) 340
73.9%
Latin
ValueCountFrequency (%)
M 2
 
10.0%
a 2
 
10.0%
D 2
 
10.0%
A 2
 
10.0%
L 2
 
10.0%
I 1
 
5.0%
O 1
 
5.0%
N 1
 
5.0%
H 1
 
5.0%
S 1
 
5.0%
Other values (5) 5
25.0%
Common
ValueCountFrequency (%)
20
46.5%
) 9
20.9%
( 9
20.9%
2 2
 
4.7%
5 1
 
2.3%
4 1
 
2.3%
0 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 460
88.0%
ASCII 63
 
12.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
31.7%
) 9
14.3%
( 9
14.3%
2 2
 
3.2%
M 2
 
3.2%
a 2
 
3.2%
D 2
 
3.2%
A 2
 
3.2%
L 2
 
3.2%
I 1
 
1.6%
Other values (12) 12
19.0%
Hangul
ValueCountFrequency (%)
점 16
 
3.5%
상 13
 
2.8%
가 13
 
2.8%
롯 12
 
2.6%
데 12
 
2.6%
스 12
 
2.6%
시 11
 
2.4%
타 11
 
2.4%
이 11
 
2.4%
화 9
 
2.0%
Other values (130) 340
73.9%

최종수정일자
Date

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size724.0 B
Minimum2007-07-07 11:22:15
Maximum2024-04-30 16:18:26
2024-05-11T14:32:00.918461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:32:01.154988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
U
43 
I
31 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
U 43
58.1%
I 31
41.9%

Length

2024-05-11T14:32:01.371017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:32:01.555160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
u 43
58.1%
i 31
41.9%

데이터갱신일자
Date

Distinct34
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Memory size724.0 B
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:02:00
2024-05-11T14:32:01.726428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:32:01.922806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

업태구분명
Categorical

Distinct7
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size724.0 B
그 밖의 대규모점포
52 
쇼핑센터
10 
구분없음
 
4
백화점
 
3
전문점
 
2
Other values (2)
 
3

Length

Max length10
Median length10
Mean length8.1756757
Min length3

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
그 밖의 대규모점포 52
70.3%
쇼핑센터 10
 
13.5%
구분없음 4
 
5.4%
백화점 3
 
4.1%
전문점 2
 
2.7%
복합쇼핑몰 2
 
2.7%
대형마트 1
 
1.4%

Length

2024-05-11T14:32:02.190171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:32:02.440621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
그 52
29.2%
밖의 52
29.2%
대규모점포 52
29.2%
쇼핑센터 10
 
5.6%
구분없음 4
 
2.2%
백화점 3
 
1.7%
전문점 2
 
1.1%
복합쇼핑몰 2
 
1.1%
대형마트 1
 
0.6%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct63
Distinct (%)88.7%
Missing3
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean199826.74
Minimum197037.37
Maximum202011.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-05-11T14:32:02.682418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum197037.37
5-th percentile197313.85
Q1198410.22
median200573.64
Q3200987.2
95-th percentile201535.35
Maximum202011.8
Range4974.4313
Interquartile range (IQR)2576.9787

Descriptive statistics

Standard deviation1419.9775
Coefficient of variation (CV)0.0071060436
Kurtosis-1.2735854
Mean199826.74
Median Absolute Deviation (MAD)677.23572
Skewness-0.45338017
Sum14187698
Variance2016336.1
MonotonicityNot monotonic
2024-05-11T14:32:02.931361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200673.011457453 3
 
4.1%
198129.435542814 2
 
2.7%
198418.741655644 2
 
2.7%
200999.356221857 2
 
2.7%
198259.65357739 2
 
2.7%
201823.908977364 2
 
2.7%
200984.713668049 2
 
2.7%
198079.694295498 1
 
1.4%
200664.582936542 1
 
1.4%
200750.455125653 1
 
1.4%
Other values (53) 53
71.6%
(Missing) 3
 
4.1%
ValueCountFrequency (%)
197037.368787577 1
1.4%
197230.206089772 1
1.4%
197242.994816102 1
1.4%
197263.068324911 1
1.4%
197364.626754215 1
1.4%
197831.002724157 1
1.4%
197843.820719318 1
1.4%
197951.150595295 1
1.4%
198079.694295498 1
1.4%
198091.202136386 1
1.4%
ValueCountFrequency (%)
202011.800096 1
1.4%
201823.908977364 2
2.7%
201680.067548359 1
1.4%
201390.623455546 1
1.4%
201308.536482314 1
1.4%
201250.876993355 1
1.4%
201236.031514508 1
1.4%
201096.681322159 1
1.4%
201092.281213446 1
1.4%
201075.606676645 1
1.4%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct63
Distinct (%)88.7%
Missing3
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean451330.99
Minimum449638.82
Maximum452413.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-05-11T14:32:03.165253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum449638.82
5-th percentile450050.3
Q1450967.94
median451443.28
Q3451789.94
95-th percentile451908.74
Maximum452413.41
Range2774.5886
Interquartile range (IQR)821.99606

Descriptive statistics

Standard deviation593.37841
Coefficient of variation (CV)0.00131473
Kurtosis1.3102245
Mean451330.99
Median Absolute Deviation (MAD)374.23547
Skewness-1.1518397
Sum32044500
Variance352097.94
MonotonicityNot monotonic
2024-05-11T14:32:03.479248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451443.279894262 3
 
4.1%
450900.239718295 2
 
2.7%
451237.168596152 2
 
2.7%
451654.418167327 2
 
2.7%
451392.198218657 2
 
2.7%
452076.818664092 2
 
2.7%
449703.239755874 2
 
2.7%
450952.174489406 1
 
1.4%
451781.8683954 1
 
1.4%
449638.824308081 1
 
1.4%
Other values (53) 53
71.6%
(Missing) 3
 
4.1%
ValueCountFrequency (%)
449638.824308081 1
1.4%
449687.143213423 1
1.4%
449703.239755874 2
2.7%
450397.369597089 1
1.4%
450446.684395506 1
1.4%
450610.61087631 1
1.4%
450658.947895548 1
1.4%
450717.353642797 1
1.4%
450771.087810153 1
1.4%
450794.31483169 1
1.4%
ValueCountFrequency (%)
452413.412894 1
1.4%
452076.818664092 2
2.7%
451908.773811626 1
1.4%
451908.700024524 1
1.4%
451888.040346722 1
1.4%
451877.080833985 1
1.4%
451874.637536958 1
1.4%
451872.409388295 1
1.4%
451852.149537542 1
1.4%
451852.142613352 1
1.4%

점포구분명
Categorical

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size724.0 B
대규모점포
46 
<NA>
27 
준대규모점포
 
1

Length

Max length6
Median length5
Mean length4.6486486
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
대규모점포 46
62.2%
<NA> 27
36.5%
준대규모점포 1
 
1.4%

Length

2024-05-11T14:32:03.705596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:32:03.891744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대규모점포 46
62.2%
na 27
36.5%
준대규모점포 1
 
1.4%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
03010000196430101000750000119641013<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-753-280553845.06100060서울특별시 중구 남창동 49번지서울특별시 중구 남대문시장4길 21 (남창동)100060서울남대문시장2018-09-04 10:11:22U2018-09-04 23:59:59.0그 밖의 대규모점포197951.150595450771.08781대규모점포
13010000196830101000750000119960704<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2232-36086140.0<NA><NA>서울특별시 중구 마장로 30 (신당동)100886팀2042019-05-14 08:46:48U2019-05-16 02:40:00.0그 밖의 대규모점포201096.681322451715.830448대규모점포
2301000019703010130075000011970-01-29<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2269-196715144.0<NA><NA>서울특별시 중구 청계천로 260-9 (을지로6가, 통일의류종합상가)04563통일상가2024-04-03 17:07:49U2023-12-04 00:05:00.0그 밖의 대규모점포200530.360089451852.149538<NA>
33010000197130100710750002119711230<NA>1영업/정상1정상영업<NA><NA><NA><NA>022269989110269.89<NA>서울특별시 중구 충무로4가 120번지 3 호서울특별시 중구 퇴계로 217 (충무로4가)<NA>진양상가2016-04-10 15:47:35I2018-08-31 23:59:59.0그 밖의 대규모점포199571.59947451124.851388대규모점포
43010000197730101300750002219770623<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-778-46119578.0<NA><NA>서울특별시 중구 삼일대로 363 (장교동)100760상제리제상가2020-04-30 16:07:24U2020-05-02 02:40:00.0그 밖의 대규모점포198744.433229451677.065126대규모점포
53010000198030100710750000119801016<NA>1영업/정상1정상영업<NA><NA><NA><NA>02223849144203.03<NA>서울특별시 중구 신당동 776호서울특별시 중구 마장로 19 (신당동)<NA>(주)에리어식스2015-01-30 15:14:43I2018-08-31 23:59:59.0그 밖의 대규모점포200989.686611451762.261509대규모점포
63010000198530101300750000319851109<NA>3폐업3폐업처리201508271111111111111111<NA>02227399090.0<NA><NA>서울특별시 중구 마른내로 140 (쌍림동)100400(주)심풍유통2015-08-27 14:10:35I2018-08-31 23:59:59.0그 밖의 대규모점포200247.445073451352.512043대규모점포
73010000199430101000750000120090908<NA>1영업/정상1정상영업<NA><NA><NA><NA>2236-80274097.54100430서울특별시 중구 흥인동 3번지 4호서울특별시 중구 다산로 274 (흥인동)<NA>우일타운2016-04-20 17:57:02I2018-08-31 23:59:59.0전문점201390.623456451682.924382대규모점포
83010000199630101300750000119960831<NA>3폐업3폐업처리20150827<NA><NA><NA>02-2048-480075439.97<NA><NA>서울특별시 중구 남대문로 84 (남대문로2가)100092케레스타2015-08-27 09:23:01I2018-08-31 23:59:59.0그 밖의 대규모점포198421.647678451369.54591대규모점포
93010000199730101300750000419970822<NA>3폐업3폐업처리201508271111111111111111<NA>02-3782-01140.0<NA><NA>서울특별시 중구 세종대로9길 58-12 (서소문동)100737대한통운마트2015-08-27 14:08:44I2018-08-31 23:59:59.0그 밖의 대규모점포<NA><NA>대규모점포
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
643010000201430101300750002219940831<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2237-25037281.04<NA><NA>서울특별시 중구 을지로45길 72 (신당동)100822디자이너크럽2020-04-30 15:46:04U2020-05-02 02:40:00.0그 밖의 대규모점포201075.606677451683.01781대규모점포
653010000201430101300750002319950401<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-757-34213613.39<NA><NA>서울특별시 중구 명동8가길 27 (충무로2가)100860선싸인2020-04-30 15:43:19U2020-05-02 02:40:00.0그 밖의 대규모점포198737.55377451081.894648대규모점포
663010000201430101300750002419970224<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2250-11155590.74<NA><NA>서울특별시 중구 마장로1길 18 (신당동)100887혜양엘리시움2020-04-30 15:40:11U2020-05-02 02:40:00.0그 밖의 대규모점포201047.899695451814.350447대규모점포
673010000201430101300750002520020722<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-757-35416752.63<NA><NA>서울특별시 중구 소월로 9 (남창동)100804남정종합상가2020-04-30 15:37:57U2020-05-02 02:40:00.0그 밖의 대규모점포197843.820719450717.353643대규모점포
68301000020163010130075000012016-03-07<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-777-11639272.66<NA><NA>서울특별시 중구 을지로 276 (을지로7가, apMPLACE)04565에이피엠플레이스2024-04-30 16:18:26U2023-12-05 00:02:00.0쇼핑센터200673.011457451443.279894<NA>
69301000020163010130075000022016-10-07<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2236-8543251.57<NA><NA>서울특별시 중구 다산로 222 (신당동)04586홈플러스익스프레스 신당점2024-03-22 09:36:23U2023-12-02 22:04:00.0구분없음201308.536482451174.365486<NA>
703010000201730101300750000120170323<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2290-712513006.43<NA><NA>서울특별시 중구 마장로 22 (신당동, DDP FASHION MALL)04566DDP FASHION MALL2022-08-18 15:00:40U2021-12-07 22:00:00.0그 밖의 대규모점포200999.356222451654.418167<NA>
713010000201730101300750000220171031<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2006-3215276.24<NA><NA>서울특별시 중구 만리재로 175, 401동 9,10,11호 (만리동2가)04500지에스수퍼마켓 중구만리점2018-01-23 13:25:04I2018-08-31 23:59:59.0구분없음<NA><NA>준대규모점포
723010000201930101660750000120191017<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2264-85113778.41<NA>서울특별시 중구 산림동 207번지 1호 세운청계상가서울특별시 중구 청계천로 160, 세운청계상가 (산림동)04545청계상가2019-10-17 16:09:13I2019-10-19 00:22:53.0그 밖의 대규모점포199521.200809451751.424133대규모점포
733010000202130101660750000120210716<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-3452-23798767.97<NA>서울특별시 중구 남대문로2가 9-1<NA><NA>센터포인트 명동2022-05-10 10:26:39U2021-12-04 23:02:00.0쇼핑센터198401.701306451374.312213<NA>