Overview

Dataset statistics

Number of variables16
Number of observations53
Missing cells101
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory137.5 B

Variable types

Numeric7
Text6
Categorical2
DateTime1

Dataset

Description유통산업발전법 제8조에 따라 우리구에 등록되어있는 서울시 중구 관내 2022년 9월자 대규모 점포 현황 파일입니다.
URLhttps://www.data.go.kr/data/3078658/fileData.do

Alerts

비고 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
업태 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 직영 and 2 other fieldsHigh correlation
점포수 is highly overall correlated with 임대 and 2 other fieldsHigh correlation
임대 is highly overall correlated with 점포수 and 1 other fieldsHigh correlation
분양 is highly overall correlated with 점포수 and 3 other fieldsHigh correlation
직영 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
영업장면적 is highly overall correlated with 분양 and 2 other fieldsHigh correlation
매장면적 is highly overall correlated with 분양 and 3 other fieldsHigh correlation
임대 has 37 (69.8%) missing valuesMissing
분양 has 20 (37.7%) missing valuesMissing
직영 has 44 (83.0%) missing valuesMissing
연번 has unique valuesUnique
상호명 has unique valuesUnique
영업장면적 has unique valuesUnique
매장면적 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:12:49.865074
Analysis finished2023-12-12 03:12:57.226369
Duration7.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T12:12:57.307050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.6
Q114
median27
Q340
95-th percentile50.4
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.443445
Coefficient of variation (CV)0.57197945
Kurtosis-1.2
Mean27
Median Absolute Deviation (MAD)13
Skewness0
Sum1431
Variance238.5
MonotonicityStrictly increasing
2023-12-12T12:12:57.490070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
41 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
53 1
1.9%
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%

상호명
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T12:12:57.752683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.6981132
Min length2

Characters and Unicode

Total characters355
Distinct characters140
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

Unique53 ?
Unique (%)100.0%

Sample

1st row신세계백화점 본점
2nd row롯데쇼핑 본점 영플라자
3rd row롯데백화점 본점
4th row현대시티타워
5th row롯데아울렛 서울역점
ValueCountFrequency (%)
본점 3
 
4.3%
남산타운 2
 
2.9%
아트프라자 1
 
1.4%
혜양엘리시움 1
 
1.4%
팀204 1
 
1.4%
누죤 1
 
1.4%
선싸인 1
 
1.4%
디자이너크럽 1
 
1.4%
신세계백화점 1
 
1.4%
밀리오레 1
 
1.4%
Other values (56) 56
81.2%
2023-12-12T12:12:58.160629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
4.5%
가 11
 
3.1%
상 11
 
3.1%
시 11
 
3.1%
점 10
 
2.8%
이 9
 
2.5%
화 8
 
2.3%
장 8
 
2.3%
롯 7
 
2.0%
데 7
 
2.0%
Other values (130) 257
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 304
85.6%
Space Separator 16
 
4.5%
Uppercase Letter 15
 
4.2%
Open Punctuation 5
 
1.4%
Close Punctuation 5
 
1.4%
Decimal Number 5
 
1.4%
Lowercase Letter 5
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 11
 
3.6%
상 11
 
3.6%
시 11
 
3.6%
점 10
 
3.3%
이 9
 
3.0%
화 8
 
2.6%
장 8
 
2.6%
롯 7
 
2.3%
데 7
 
2.3%
타 7
 
2.3%
Other values (108) 215
70.7%
Uppercase Letter
ValueCountFrequency (%)
A 2
13.3%
D 2
13.3%
M 2
13.3%
L 2
13.3%
F 1
6.7%
N 1
6.7%
O 1
6.7%
I 1
6.7%
H 1
6.7%
S 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%
l 1
20.0%
p 1
20.0%
z 1
20.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 304
85.6%
Common 31
 
8.7%
Latin 20
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 11
 
3.6%
상 11
 
3.6%
시 11
 
3.6%
점 10
 
3.3%
이 9
 
3.0%
화 8
 
2.6%
장 8
 
2.6%
롯 7
 
2.3%
데 7
 
2.3%
타 7
 
2.3%
Other values (108) 215
70.7%
Latin
ValueCountFrequency (%)
A 2
 
10.0%
D 2
 
10.0%
a 2
 
10.0%
M 2
 
10.0%
L 2
 
10.0%
F 1
 
5.0%
N 1
 
5.0%
O 1
 
5.0%
I 1
 
5.0%
H 1
 
5.0%
Other values (5) 5
25.0%
Common
ValueCountFrequency (%)
16
51.6%
( 5
 
16.1%
) 5
 
16.1%
2 2
 
6.5%
5 1
 
3.2%
4 1
 
3.2%
0 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 304
85.6%
ASCII 51
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
31.4%
( 5
 
9.8%
) 5
 
9.8%
A 2
 
3.9%
D 2
 
3.9%
2 2
 
3.9%
a 2
 
3.9%
M 2
 
3.9%
L 2
 
3.9%
5 1
 
2.0%
Other values (12) 12
23.5%
Hangul
ValueCountFrequency (%)
가 11
 
3.6%
상 11
 
3.6%
시 11
 
3.6%
점 10
 
3.3%
이 9
 
3.0%
화 8
 
2.6%
장 8
 
2.6%
롯 7
 
2.3%
데 7
 
2.3%
타 7
 
2.3%
Other values (108) 215
70.7%

대표자
Text

Distinct49
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T12:12:58.396484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.1886792
Min length3

Characters and Unicode

Total characters169
Distinct characters75
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)86.8%

Sample

1st row정재영
2nd row강희태
3rd row강희태
4th row윤종규
5th row박병열
ValueCountFrequency (%)
강희태 3
 
5.5%
석주형 2
 
3.6%
김화순 2
 
3.6%
주태상 1
 
1.8%
김인성 1
 
1.8%
고경열 1
 
1.8%
강종섭 1
 
1.8%
김우생 1
 
1.8%
이영동 1
 
1.8%
이근식 1
 
1.8%
Other values (41) 41
74.5%
2023-12-12T12:12:59.209899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 14
 
8.3%
이 10
 
5.9%
재 8
 
4.7%
영 6
 
3.6%
태 5
 
3.0%
석 5
 
3.0%
박 4
 
2.4%
장 4
 
2.4%
인 4
 
2.4%
강 4
 
2.4%
Other values (65) 105
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 165
97.6%
Space Separator 2
 
1.2%
Other Punctuation 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 14
 
8.5%
이 10
 
6.1%
재 8
 
4.8%
영 6
 
3.6%
태 5
 
3.0%
석 5
 
3.0%
박 4
 
2.4%
장 4
 
2.4%
인 4
 
2.4%
강 4
 
2.4%
Other values (63) 101
61.2%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 165
97.6%
Common 4
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 14
 
8.5%
이 10
 
6.1%
재 8
 
4.8%
영 6
 
3.6%
태 5
 
3.0%
석 5
 
3.0%
박 4
 
2.4%
장 4
 
2.4%
인 4
 
2.4%
강 4
 
2.4%
Other values (63) 101
61.2%
Common
ValueCountFrequency (%)
2
50.0%
, 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 165
97.6%
ASCII 4
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 14
 
8.5%
이 10
 
6.1%
재 8
 
4.8%
영 6
 
3.6%
태 5
 
3.0%
석 5
 
3.0%
박 4
 
2.4%
장 4
 
2.4%
인 4
 
2.4%
강 4
 
2.4%
Other values (63) 101
61.2%
ASCII
ValueCountFrequency (%)
2
50.0%
, 2
50.0%

업태
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size556.0 B
그 밖의 대규모점포
38 
쇼핑센터
8 
백화점
 
3
대형마트
 
2
전문점
 
2

Length

Max length10
Median length10
Mean length8.2075472
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row백화점
2nd row백화점
3rd row백화점
4th row쇼핑센터
5th row쇼핑센터

Common Values

ValueCountFrequency (%)
그 밖의 대규모점포 38
71.7%
쇼핑센터 8
 
15.1%
백화점 3
 
5.7%
대형마트 2
 
3.8%
전문점 2
 
3.8%

Length

2023-12-12T12:12:59.430838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:12:59.612705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
그 38
29.5%
밖의 38
29.5%
대규모점포 38
29.5%
쇼핑센터 8
 
6.2%
백화점 3
 
2.3%
대형마트 2
 
1.6%
전문점 2
 
1.6%

소재지
Text

Distinct51
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T12:12:59.952749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length13.660377
Min length11

Characters and Unicode

Total characters724
Distinct characters46
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

Unique49 ?
Unique (%)92.5%

Sample

1st row서울 중구 소공로 63
2nd row서울 중구 남대문로 67
3rd row서울 중구 남대문로 81
4th row서울 중구 장충단로13길 20
5th row서울 중구 한강대로 405
ValueCountFrequency (%)
서울 53
24.9%
중구 53
24.9%
마장로 6
 
2.8%
청계천로 6
 
2.8%
장충단로 5
 
2.3%
마장로1길 4
 
1.9%
을지로 4
 
1.9%
다산로 4
 
1.9%
30 3
 
1.4%
남대문로 3
 
1.4%
Other values (59) 72
33.8%
2023-12-12T12:13:00.537164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160
22.1%
서 53
 
7.3%
중 53
 
7.3%
구 53
 
7.3%
울 53
 
7.3%
로 47
 
6.5%
2 33
 
4.6%
3 23
 
3.2%
장 20
 
2.8%
1 20
 
2.8%
Other values (36) 209
28.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 412
56.9%
Space Separator 160
 
22.1%
Decimal Number 150
 
20.7%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 53
12.9%
중 53
12.9%
구 53
12.9%
울 53
12.9%
로 47
11.4%
장 20
 
4.9%
길 17
 
4.1%
마 10
 
2.4%
대 9
 
2.2%
계 9
 
2.2%
Other values (24) 88
21.4%
Decimal Number
ValueCountFrequency (%)
2 33
22.0%
3 23
15.3%
1 20
13.3%
4 16
10.7%
0 15
10.0%
7 13
 
8.7%
6 11
 
7.3%
8 8
 
5.3%
5 8
 
5.3%
9 3
 
2.0%
Space Separator
ValueCountFrequency (%)
160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 412
56.9%
Common 312
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 53
12.9%
중 53
12.9%
구 53
12.9%
울 53
12.9%
로 47
11.4%
장 20
 
4.9%
길 17
 
4.1%
마 10
 
2.4%
대 9
 
2.2%
계 9
 
2.2%
Other values (24) 88
21.4%
Common
ValueCountFrequency (%)
160
51.3%
2 33
 
10.6%
3 23
 
7.4%
1 20
 
6.4%
4 16
 
5.1%
0 15
 
4.8%
7 13
 
4.2%
6 11
 
3.5%
8 8
 
2.6%
5 8
 
2.6%
Other values (2) 5
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 412
56.9%
ASCII 312
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
160
51.3%
2 33
 
10.6%
3 23
 
7.4%
1 20
 
6.4%
4 16
 
5.1%
0 15
 
4.8%
7 13
 
4.2%
6 11
 
3.5%
8 8
 
2.6%
5 8
 
2.6%
Other values (2) 5
 
1.6%
Hangul
ValueCountFrequency (%)
서 53
12.9%
중 53
12.9%
구 53
12.9%
울 53
12.9%
로 47
11.4%
장 20
 
4.9%
길 17
 
4.1%
마 10
 
2.4%
대 9
 
2.2%
계 9
 
2.2%
Other values (24) 88
21.4%

점포수
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean961.39623
Minimum1
Maximum5400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T12:13:00.768666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.2
Q1263
median680
Q31169
95-th percentile2885.6
Maximum5400
Range5399
Interquartile range (IQR)906

Descriptive statistics

Standard deviation1077.8904
Coefficient of variation (CV)1.1211719
Kurtosis6.5549095
Mean961.39623
Median Absolute Deviation (MAD)428
Skewness2.3556842
Sum50954
Variance1161847.8
MonotonicityNot monotonic
2023-12-12T12:13:00.974723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
776 2
 
3.8%
36 2
 
3.8%
1 2
 
3.8%
956 1
 
1.9%
379 1
 
1.9%
592 1
 
1.9%
711 1
 
1.9%
1108 1
 
1.9%
1403 1
 
1.9%
175 1
 
1.9%
Other values (40) 40
75.5%
ValueCountFrequency (%)
1 2
3.8%
9 1
1.9%
36 2
3.8%
38 1
1.9%
110 1
1.9%
118 1
1.9%
160 1
1.9%
175 1
1.9%
192 1
1.9%
231 1
1.9%
ValueCountFrequency (%)
5400 1
1.9%
4563 1
1.9%
3233 1
1.9%
2654 1
1.9%
2524 1
1.9%
2069 1
1.9%
2003 1
1.9%
1666 1
1.9%
1545 1
1.9%
1520 1
1.9%

임대
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing37
Missing (%)69.8%
Infinite0
Infinite (%)0.0%
Mean702.875
Minimum9
Maximum3233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T12:13:01.148015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile29.25
Q1116
median527
Q3864.75
95-th percentile2309
Maximum3233
Range3224
Interquartile range (IQR)748.75

Descriptive statistics

Standard deviation847.07582
Coefficient of variation (CV)1.2051586
Kurtosis4.9577415
Mean702.875
Median Absolute Deviation (MAD)376
Skewness2.1163444
Sum11246
Variance717537.45
MonotonicityNot monotonic
2023-12-12T12:13:01.292762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
526 1
 
1.9%
320 1
 
1.9%
776 1
 
1.9%
36 1
 
1.9%
989 1
 
1.9%
2001 1
 
1.9%
863 1
 
1.9%
3233 1
 
1.9%
118 1
 
1.9%
38 1
 
1.9%
Other values (6) 6
 
11.3%
(Missing) 37
69.8%
ValueCountFrequency (%)
9 1
1.9%
36 1
1.9%
38 1
1.9%
110 1
1.9%
118 1
1.9%
192 1
1.9%
320 1
1.9%
526 1
1.9%
528 1
1.9%
637 1
1.9%
ValueCountFrequency (%)
3233 1
1.9%
2001 1
1.9%
989 1
1.9%
870 1
1.9%
863 1
1.9%
776 1
1.9%
637 1
1.9%
528 1
1.9%
526 1
1.9%
320 1
1.9%

분양
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing20
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean1061.0303
Minimum23
Maximum5400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T12:13:01.480030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile61.4
Q1349
median687
Q31346
95-th percentile3417.6
Maximum5400
Range5377
Interquartile range (IQR)997

Descriptive statistics

Standard deviation1188.2469
Coefficient of variation (CV)1.1198991
Kurtosis6.5145448
Mean1061.0303
Median Absolute Deviation (MAD)424
Skewness2.4344214
Sum35014
Variance1411930.6
MonotonicityNot monotonic
2023-12-12T12:13:01.692065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1660 1
 
1.9%
711 1
 
1.9%
1108 1
 
1.9%
1403 1
 
1.9%
175 1
 
1.9%
494 1
 
1.9%
379 1
 
1.9%
56 1
 
1.9%
569 1
 
1.9%
65 1
 
1.9%
Other values (23) 23
43.4%
(Missing) 20
37.7%
ValueCountFrequency (%)
23 1
1.9%
56 1
1.9%
65 1
1.9%
160 1
1.9%
175 1
1.9%
262 1
1.9%
263 1
1.9%
273 1
1.9%
349 1
1.9%
379 1
1.9%
ValueCountFrequency (%)
5400 1
1.9%
4563 1
1.9%
2654 1
1.9%
2069 1
1.9%
1666 1
1.9%
1660 1
1.9%
1545 1
1.9%
1403 1
1.9%
1346 1
1.9%
1169 1
1.9%

직영
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing44
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean444.55556
Minimum1
Maximum1520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T12:13:01.844119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q192
median231
Q3609
95-th percentile1294.4
Maximum1520
Range1519
Interquartile range (IQR)517

Descriptive statistics

Standard deviation507.89765
Coefficient of variation (CV)1.1424841
Kurtosis1.4826305
Mean444.55556
Median Absolute Deviation (MAD)229
Skewness1.4018639
Sum4001
Variance257960.03
MonotonicityNot monotonic
2023-12-12T12:13:02.007018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
956 1
 
1.9%
231 1
 
1.9%
1520 1
 
1.9%
609 1
 
1.9%
1 1
 
1.9%
220 1
 
1.9%
370 1
 
1.9%
2 1
 
1.9%
92 1
 
1.9%
(Missing) 44
83.0%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
92 1
1.9%
220 1
1.9%
231 1
1.9%
370 1
1.9%
609 1
1.9%
956 1
1.9%
1520 1
1.9%
ValueCountFrequency (%)
1520 1
1.9%
956 1
1.9%
609 1
1.9%
370 1
1.9%
231 1
1.9%
220 1
1.9%
92 1
1.9%
2 1
1.9%
1 1
1.9%

영업장면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21603.083
Minimum4097.54
Maximum109306.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T12:13:02.218769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4097.54
5-th percentile5998.232
Q19316.94
median13941.72
Q322988.33
95-th percentile75866.562
Maximum109306.07
Range105208.53
Interquartile range (IQR)13671.39

Descriptive statistics

Standard deviation21881.511
Coefficient of variation (CV)1.0128883
Kurtosis5.6893935
Mean21603.083
Median Absolute Deviation (MAD)6401.72
Skewness2.4125155
Sum1144963.4
Variance4.7880052 × 108
MonotonicityNot monotonic
2023-12-12T12:13:02.472361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76506.45 1
 
1.9%
22988.33 1
 
1.9%
6119.0 1
 
1.9%
9629.48 1
 
1.9%
12600.28 1
 
1.9%
13793.3 1
 
1.9%
15431.66 1
 
1.9%
8433.0 1
 
1.9%
8863.74 1
 
1.9%
6913.0 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
4097.54 1
1.9%
4203.03 1
1.9%
5817.08 1
1.9%
6119.0 1
1.9%
6752.63 1
1.9%
6913.0 1
1.9%
7087.24 1
1.9%
7540.0 1
1.9%
7996.47 1
1.9%
8433.0 1
1.9%
ValueCountFrequency (%)
109306.07 1
1.9%
80260.14 1
1.9%
76506.45 1
1.9%
75439.97 1
1.9%
64612.86 1
1.9%
56298.64 1
1.9%
36779.75 1
1.9%
27186.9 1
1.9%
26315.58 1
1.9%
25746.49 1
1.9%

매장면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15404.931
Minimum3613.39
Maximum59979.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T12:13:02.710845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3613.39
5-th percentile4160.834
Q16954.85
median12340
Q318500.54
95-th percentile45719.206
Maximum59979.25
Range56365.86
Interquartile range (IQR)11545.69

Descriptive statistics

Standard deviation12653.535
Coefficient of variation (CV)0.82139509
Kurtosis4.6278988
Mean15404.931
Median Absolute Deviation (MAD)5564.7
Skewness2.1116227
Sum816461.36
Variance1.6011195 × 108
MonotonicityNot monotonic
2023-12-12T12:13:02.868639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54975.36 1
 
1.9%
17736.37 1
 
1.9%
6020.0 1
 
1.9%
9629.48 1
 
1.9%
12340.0 1
 
1.9%
13793.3 1
 
1.9%
9578.0 1
 
1.9%
4449.0 1
 
1.9%
7281.04 1
 
1.9%
3613.39 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
3613.39 1
1.9%
3778.41 1
1.9%
4097.54 1
1.9%
4203.03 1
1.9%
4449.0 1
1.9%
4944.51 1
1.9%
5270.33 1
1.9%
5590.74 1
1.9%
5835.93 1
1.9%
6020.0 1
1.9%
ValueCountFrequency (%)
59979.25 1
1.9%
54975.36 1
1.9%
53845.0 1
1.9%
40302.01 1
1.9%
33008.72 1
1.9%
27653.82 1
1.9%
24483.79 1
1.9%
23435.25 1
1.9%
21481.63 1
1.9%
20707.22 1
1.9%

건물연면적(제곱미터)
Text

Distinct51
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T12:13:03.188500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.6603774
Min length1

Characters and Unicode

Total characters353
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

Unique49 ?
Unique (%)92.5%

Sample

1st row131663.2
2nd row14448
3rd row111094.72
4th row124111.61
5th row95171.69
ValueCountFrequency (%)
560876 2
 
3.8%
382536.05 2
 
3.8%
23727.75 1
 
1.9%
23653 1
 
1.9%
67130 1
 
1.9%
36325 1
 
1.9%
131663.2 1
 
1.9%
9907.77 1
 
1.9%
4378.74 1
 
1.9%
45478 1
 
1.9%
Other values (41) 41
77.4%
2023-12-12T12:13:03.717396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 43
12.2%
2 40
11.3%
6 37
10.5%
1 35
9.9%
9 33
9.3%
7 31
8.8%
. 31
8.8%
4 30
8.5%
8 26
7.4%
5 25
7.1%
Other values (2) 22
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321
90.9%
Other Punctuation 31
 
8.8%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 43
13.4%
2 40
12.5%
6 37
11.5%
1 35
10.9%
9 33
10.3%
7 31
9.7%
4 30
9.3%
8 26
8.1%
5 25
7.8%
0 21
6.5%
Other Punctuation
ValueCountFrequency (%)
. 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 43
12.2%
2 40
11.3%
6 37
10.5%
1 35
9.9%
9 33
9.3%
7 31
8.8%
. 31
8.8%
4 30
8.5%
8 26
7.4%
5 25
7.1%
Other values (2) 22
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 43
12.2%
2 40
11.3%
6 37
10.5%
1 35
9.9%
9 33
9.3%
7 31
8.8%
. 31
8.8%
4 30
8.5%
8 26
7.4%
5 25
7.1%
Other values (2) 22
6.2%

전화번호
Text

Distinct51
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T12:13:04.003003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length12
Mean length12.018868
Min length11

Characters and Unicode

Total characters637
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)92.5%

Sample

1st row02-310-1041~3
2nd row02-772-3871
3rd row02-772-3872
4th row02-2073-5111
5th row02-390-4000
ValueCountFrequency (%)
02-2238-4352 2
 
3.6%
02-2256-3500 2
 
3.6%
02-2048-5100 1
 
1.8%
02-2237-2503 1
 
1.8%
02-3405-4040 1
 
1.8%
02-2250-1115 1
 
1.8%
02-3455-3721 1
 
1.8%
1
 
1.8%
02-779-2951 1
 
1.8%
02-2252-6744 1
 
1.8%
Other values (43) 43
78.2%
2023-12-12T12:13:04.496377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 139
21.8%
0 108
17.0%
- 108
17.0%
3 48
 
7.5%
1 46
 
7.2%
5 43
 
6.8%
7 42
 
6.6%
6 30
 
4.7%
9 26
 
4.1%
8 25
 
3.9%
Other values (4) 22
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 525
82.4%
Dash Punctuation 108
 
17.0%
Space Separator 2
 
0.3%
Other Punctuation 1
 
0.2%
Math Symbol 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 139
26.5%
0 108
20.6%
3 48
 
9.1%
1 46
 
8.8%
5 43
 
8.2%
7 42
 
8.0%
6 30
 
5.7%
9 26
 
5.0%
8 25
 
4.8%
4 18
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 637
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 139
21.8%
0 108
17.0%
- 108
17.0%
3 48
 
7.5%
1 46
 
7.2%
5 43
 
6.8%
7 42
 
6.6%
6 30
 
4.7%
9 26
 
4.1%
8 25
 
3.9%
Other values (4) 22
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 139
21.8%
0 108
17.0%
- 108
17.0%
3 48
 
7.5%
1 46
 
7.2%
5 43
 
6.8%
7 42
 
6.6%
6 30
 
4.7%
9 26
 
4.1%
8 25
 
3.9%
Other values (4) 22
 
3.5%

팩스
Text

Distinct51
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T12:13:04.854074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.716981
Min length11

Characters and Unicode

Total characters621
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

Unique49 ?
Unique (%)92.5%

Sample

1st row02-310-1090
2nd row02-319-6522
3rd row02-319-6522
4th row02-6952-7013
5th row02-390-4004
ValueCountFrequency (%)
02-319-6522 2
 
3.8%
02-2256-6655 2
 
3.8%
02-2117-8084 1
 
1.9%
02-2079-0660 1
 
1.9%
02-2237-5355 1
 
1.9%
02-2048-5101 1
 
1.9%
02-2232-3613 1
 
1.9%
02-2238-8838 1
 
1.9%
02-754-3353 1
 
1.9%
02-2235-4459 1
 
1.9%
Other values (41) 41
77.4%
2023-12-12T12:13:05.285697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 136
21.9%
- 106
17.1%
0 96
15.5%
3 46
 
7.4%
9 40
 
6.4%
6 40
 
6.4%
5 40
 
6.4%
7 36
 
5.8%
1 34
 
5.5%
8 27
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 515
82.9%
Dash Punctuation 106
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 136
26.4%
0 96
18.6%
3 46
 
8.9%
9 40
 
7.8%
6 40
 
7.8%
5 40
 
7.8%
7 36
 
7.0%
1 34
 
6.6%
8 27
 
5.2%
4 20
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 621
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 136
21.9%
- 106
17.1%
0 96
15.5%
3 46
 
7.4%
9 40
 
6.4%
6 40
 
6.4%
5 40
 
6.4%
7 36
 
5.8%
1 34
 
5.5%
8 27
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 136
21.9%
- 106
17.1%
0 96
15.5%
3 46
 
7.4%
9 40
 
6.4%
6 40
 
6.4%
5 40
 
6.4%
7 36
 
5.8%
1 34
 
5.5%
8 27
 
4.3%

개설일
Date

Distinct51
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size556.0 B
Minimum1959-02-04 00:00:00
Maximum2017-10-17 00:00:00
2023-12-12T12:13:05.455415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:05.644108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

비고
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
<NA>
38 
전통시장
15 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 38
71.7%
전통시장 15
 
28.3%

Length

2023-12-12T12:13:05.804604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:13:05.922425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 38
71.7%
전통시장 15
 
28.3%

Interactions

2023-12-12T12:12:56.015112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.136850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.273614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.022706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.856571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.590326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.342139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.126094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.237675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.389979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.135176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.971260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.690295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.446584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.223969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.348273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.481211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.254435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.063654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.799652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.535937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.334622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.472757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.589818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.374197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.182662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.899657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.624724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.432146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.931430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.683041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.493507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.294193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.989062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.725950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.530310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.049613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.816096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.608617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.404419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.100701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.835667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.629417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.172481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.918412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.722392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.503649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.232877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.925179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:13:06.033241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호명대표자업태소재지점포수임대분양직영영업장면적매장면적건물연면적(제곱미터)전화번호팩스개설일
연번1.0001.0000.9420.9330.9720.0000.0000.0000.0000.4030.3890.9721.0001.0000.972
상호명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대표자0.9421.0001.0000.0000.9950.9801.0001.0000.7040.0000.9250.9950.9951.0000.995
업태0.9331.0000.0001.0000.0000.1710.5670.6030.5600.4180.5350.0001.0001.0001.000
소재지0.9721.0000.9950.0001.0000.9781.0001.0001.0000.0000.0001.0000.9990.9990.999
점포수0.0001.0000.9800.1710.9781.0000.9710.9960.0000.8190.8480.9780.9780.9650.894
임대0.0001.0001.0000.5671.0000.9711.000NaN1.0000.7390.6241.0001.0001.0001.000
분양0.0001.0001.0000.6031.0000.996NaN1.000NaN0.7530.9041.0000.9841.0001.000
직영0.0001.0000.7040.5601.0000.0001.000NaN1.0000.9090.5111.0001.0000.7711.000
영업장면적0.4031.0000.0000.4180.0000.8190.7390.7530.9091.0000.9780.0001.0000.0001.000
매장면적0.3891.0000.9250.5350.0000.8480.6240.9040.5110.9781.0000.0000.9930.9110.993
건물연면적(제곱미터)0.9721.0000.9950.0001.0000.9781.0001.0001.0000.0000.0001.0000.9990.9990.999
전화번호1.0001.0000.9951.0000.9990.9781.0000.9841.0001.0000.9930.9991.0000.9990.999
팩스1.0001.0001.0001.0000.9990.9651.0001.0000.7710.0000.9110.9990.9991.0000.999
개설일0.9721.0000.9951.0000.9990.8941.0001.0001.0001.0000.9930.9990.9990.9991.000
2023-12-12T12:13:06.221386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고업태
비고1.0001.000
업태1.0001.000
2023-12-12T12:13:06.351987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번점포수임대분양직영영업장면적매장면적업태비고
연번1.0000.0730.165-0.026-0.633-0.334-0.3440.6161.000
점포수0.0731.0000.9760.9180.2170.4690.4610.0881.000
임대0.1650.9761.000NaN-0.8000.4440.3820.3190.000
분양-0.0260.918NaN1.000NaN0.6240.6130.4571.000
직영-0.6330.217-0.800NaN1.0000.4330.6830.0000.000
영업장면적-0.3340.4690.4440.6240.4331.0000.9220.2241.000
매장면적-0.3440.4610.3820.6130.6830.9221.0000.3311.000
업태0.6160.0880.3190.4570.0000.2240.3311.0001.000
비고1.0001.0000.0001.0000.0001.0001.0001.0001.000

Missing values

2023-12-12T12:12:56.827581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:12:57.031848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T12:12:57.163617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번상호명대표자업태소재지점포수임대분양직영영업장면적매장면적건물연면적(제곱미터)전화번호팩스개설일비고
01신세계백화점 본점정재영백화점서울 중구 소공로 63956<NA><NA>95676506.4554975.36131663.202-310-1041~302-310-10901973-04-10<NA>
12롯데쇼핑 본점 영플라자강희태백화점서울 중구 남대문로 67231<NA><NA>23114065.4913877.911444802-772-387102-319-65221973-10-30<NA>
23롯데백화점 본점강희태백화점서울 중구 남대문로 811520<NA><NA>152080260.1459979.25111094.7202-772-387202-319-65221979-12-27<NA>
34현대시티타워윤종규쇼핑센터서울 중구 장충단로13길 2032333233<NA><NA>75439.9733008.72124111.6102-2073-511102-6952-70131996-08-31<NA>
45롯데아울렛 서울역점박병열쇼핑센터서울 중구 한강대로 405118118<NA><NA>15772.1611576.1995171.6902-390-400002-390-40041999-09-01<NA>
56신세계백화점메사점장재영쇼핑센터서울 중구 남대문시장10길 2632<NA>2360913217.7412585.1346789.5602-727-147002-727-14792000-08-21<NA>
67올레오한재일쇼핑센터서울 중구 마장로1길 22569<NA>569<NA>10623.818119.481717302-2230-700002-2230-78002006-10-23<NA>
78엠플라자(M plaza)핑캠웡쇼핑센터서울 중구 명동8길 2799<NA><NA>9227.555835.9327421.2302-727-310002-727-31032008-08-27<NA>
89굿모닝시티 쇼핑몰조준영쇼핑센터서울 중구 장충단로 2474563<NA>4563<NA>56298.6440302.0192206.0302-2118-870002-2118-87362008-09-16<NA>
910롯데패션몰이광영쇼핑센터서울 중구 을지로 264192192<NA><NA>26315.5823435.2539326.3202-6262-450002-6262-49992012-08-31<NA>
연번상호명대표자업태소재지점포수임대분양직영영업장면적매장면적건물연면적(제곱미터)전화번호팩스개설일비고
4344남산타운 2상가김화순그 밖의 대규모점포서울 중구 다산로 3236<NA>56<NA>7540.05270.3356087602-2256-350002-2256-66552000-07-07<NA>
4445남산타운 5상가김화순그 밖의 대규모점포서울 중구 다산로 32776<NA>65<NA>9731.446954.8556087602-2256-350002-2256-66552000-07-07<NA>
4546눈스퀘어김대영그 밖의 대규모점포서울 중구 명동길 143636<NA><NA>21481.6321481.632383402-3783-520102-3783-52002002-01-15<NA>
4647남정종합상가조명배그 밖의 대규모점포서울 중구 소월로 9776776<NA><NA>6752.636752.63-02-757-354102-755-50522002-07-22<NA>
4748헬로우에이피엠김방진그 밖의 대규모점포서울 중구 장충단로 2531666<NA>1666<NA>13941.7213941.722365302-6388-118002-6388-11812002-07-22<NA>
4849하이해리엇안병국그 밖의 대규모점포서울 중구 퇴계로 1231169<NA>1169<NA>10635.434944.5123727.7502-2079-095002-2079-06602006-03-17<NA>
4950디오트김의성그 밖의 대규모점포서울 중구 다산로 2931346<NA>1346<NA>20365.016771.513632502-2117-888802-2117-80842006-04-14<NA>
5051베네치아 메가몰 지원센터주태상그 밖의 대규모점포서울 중구 청계천로 400714<NA>714<NA>109306.0727653.82382536.0502-2048-510002-2048-51012008-07-18<NA>
5152DDP FASHION MALL이지윤그 밖의 대규모점포서울 중구 마장로 22687<NA>687<NA>13006.4313006.436713002-3405-404002-2237-53552017-03-22<NA>
5253청계상가김장기그 밖의 대규모점포서울 중구 청계천로 160320320<NA><NA>5817.083778.4113043.2702-2264-851102-2264-85132017-10-17<NA>