Overview

Dataset statistics

Number of variables15
Number of observations200
Missing cells2
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 KiB
Average record size in memory126.7 B

Variable types

Text5
Numeric7
Categorical3

Alerts

RETAIL_CD is highly overall correlated with RETAIL_NM and 1 other fieldsHigh correlation
RETAIL_CLSS is highly overall correlated with SHOP_AREA and 2 other fieldsHigh correlation
RETAIL_NM is highly overall correlated with RETAIL_CD and 1 other fieldsHigh correlation
X_AXIS is highly overall correlated with Y_AXISHigh correlation
Y_AXIS is highly overall correlated with X_AXISHigh correlation
SHOP_AREA is highly overall correlated with RETAIL_CLSSHigh correlation
HOUS_ID is highly overall correlated with BLD_CDHigh correlation
BLD_CD is highly overall correlated with HOUS_IDHigh correlation
SHOP_CD has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:57:53.639598
Analysis finished2023-12-10 06:58:08.903741
Duration15.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHOP_CD
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:58:09.142453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1600
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st rowD0002389
2nd rowD0000006
3rd rowD0000008
4th rowD0000001
5th rowD0000004
ValueCountFrequency (%)
d0002389 1
 
0.5%
m0003553 1
 
0.5%
m0003673 1
 
0.5%
m0003540 1
 
0.5%
m0003541 1
 
0.5%
m0003545 1
 
0.5%
m0003546 1
 
0.5%
m0003547 1
 
0.5%
m0003548 1
 
0.5%
m0003549 1
 
0.5%
Other values (190) 190
95.0%
2023-12-10T15:58:09.628307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 704
44.0%
M 137
 
8.6%
4 122
 
7.6%
5 106
 
6.6%
3 100
 
6.2%
7 79
 
4.9%
2 76
 
4.8%
6 73
 
4.6%
D 63
 
3.9%
1 51
 
3.2%
Other values (2) 89
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
87.5%
Uppercase Letter 200
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 704
50.3%
4 122
 
8.7%
5 106
 
7.6%
3 100
 
7.1%
7 79
 
5.6%
2 76
 
5.4%
6 73
 
5.2%
1 51
 
3.6%
9 46
 
3.3%
8 43
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
M 137
68.5%
D 63
31.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1400
87.5%
Latin 200
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 704
50.3%
4 122
 
8.7%
5 106
 
7.6%
3 100
 
7.1%
7 79
 
5.6%
2 76
 
5.4%
6 73
 
5.2%
1 51
 
3.6%
9 46
 
3.3%
8 43
 
3.1%
Latin
ValueCountFrequency (%)
M 137
68.5%
D 63
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 704
44.0%
M 137
 
8.6%
4 122
 
7.6%
5 106
 
6.6%
3 100
 
6.2%
7 79
 
4.9%
2 76
 
4.8%
6 73
 
4.6%
D 63
 
3.9%
1 51
 
3.2%
Other values (2) 89
 
5.6%

SHOP_NM
Text

Distinct158
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:58:09.906449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.53
Min length2

Characters and Unicode

Total characters706
Distinct characters166
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)60.5%

Sample

1st row센터시티점
2nd row타임월드점
3rd row진주점
4th row명품관
5th row수원점
ValueCountFrequency (%)
수원점 4
 
2.0%
마산점 4
 
2.0%
원주점 3
 
1.5%
동래점 2
 
1.0%
금정점 2
 
1.0%
울산점 2
 
1.0%
전주점 2
 
1.0%
미아점 2
 
1.0%
판교점 2
 
1.0%
부평점 2
 
1.0%
Other values (148) 175
87.5%
2023-12-10T15:58:10.353242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 197
27.9%
산 23
 
3.3%
천 16
 
2.3%
주 16
 
2.3%
수 15
 
2.1%
원 10
 
1.4%
스 9
 
1.3%
이 9
 
1.3%
구 8
 
1.1%
평 8
 
1.1%
Other values (156) 395
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 704
99.7%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 197
28.0%
산 23
 
3.3%
천 16
 
2.3%
주 16
 
2.3%
수 15
 
2.1%
원 10
 
1.4%
스 9
 
1.3%
이 9
 
1.3%
구 8
 
1.1%
평 8
 
1.1%
Other values (154) 393
55.8%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
N 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 704
99.7%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 197
28.0%
산 23
 
3.3%
천 16
 
2.3%
주 16
 
2.3%
수 15
 
2.1%
원 10
 
1.4%
스 9
 
1.3%
이 9
 
1.3%
구 8
 
1.1%
평 8
 
1.1%
Other values (154) 393
55.8%
Latin
ValueCountFrequency (%)
C 1
50.0%
N 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 704
99.7%
ASCII 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 197
28.0%
산 23
 
3.3%
천 16
 
2.3%
주 16
 
2.3%
수 15
 
2.1%
원 10
 
1.4%
스 9
 
1.3%
이 9
 
1.3%
구 8
 
1.1%
평 8
 
1.1%
Other values (154) 393
55.8%
ASCII
ValueCountFrequency (%)
C 1
50.0%
N 1
50.0%

HOUS_ADDR
Text

Distinct193
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:58:10.718918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length20.665
Min length16

Characters and Unicode

Total characters4133
Distinct characters193
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

Unique186 ?
Unique (%)93.0%

Sample

1st row충청남도 천안시 서북구 불당동 1299번지
2nd row대전광역시 서구 둔산동 1036번지
3rd row경상남도 진주시 평안동 195번지
4th row서울특별시 강남구 압구정동 494번지
5th row경기도 수원시 팔달구 인계동 1125-1번지
ValueCountFrequency (%)
경기도 50
 
5.8%
서울특별시 33
 
3.8%
경상남도 18
 
2.1%
대구광역시 15
 
1.7%
인천광역시 13
 
1.5%
부산광역시 12
 
1.4%
충청남도 12
 
1.4%
창원시 10
 
1.2%
경상북도 9
 
1.0%
수원시 8
 
0.9%
Other values (480) 684
79.2%
2023-12-10T15:58:11.144454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
664
 
16.1%
동 211
 
5.1%
지 204
 
4.9%
시 200
 
4.8%
번 200
 
4.8%
구 170
 
4.1%
1 167
 
4.0%
도 119
 
2.9%
- 92
 
2.2%
2 90
 
2.2%
Other values (183) 2016
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2637
63.8%
Decimal Number 740
 
17.9%
Space Separator 664
 
16.1%
Dash Punctuation 92
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 211
 
8.0%
지 204
 
7.7%
시 200
 
7.6%
번 200
 
7.6%
구 170
 
6.4%
도 119
 
4.5%
경 79
 
3.0%
산 66
 
2.5%
광 63
 
2.4%
서 63
 
2.4%
Other values (171) 1262
47.9%
Decimal Number
ValueCountFrequency (%)
1 167
22.6%
2 90
12.2%
3 80
10.8%
0 68
9.2%
6 62
 
8.4%
9 58
 
7.8%
5 57
 
7.7%
4 56
 
7.6%
7 55
 
7.4%
8 47
 
6.4%
Space Separator
ValueCountFrequency (%)
664
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2637
63.8%
Common 1496
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 211
 
8.0%
지 204
 
7.7%
시 200
 
7.6%
번 200
 
7.6%
구 170
 
6.4%
도 119
 
4.5%
경 79
 
3.0%
산 66
 
2.5%
광 63
 
2.4%
서 63
 
2.4%
Other values (171) 1262
47.9%
Common
ValueCountFrequency (%)
664
44.4%
1 167
 
11.2%
- 92
 
6.1%
2 90
 
6.0%
3 80
 
5.3%
0 68
 
4.5%
6 62
 
4.1%
9 58
 
3.9%
5 57
 
3.8%
4 56
 
3.7%
Other values (2) 102
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2637
63.8%
ASCII 1496
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
664
44.4%
1 167
 
11.2%
- 92
 
6.1%
2 90
 
6.0%
3 80
 
5.3%
0 68
 
4.5%
6 62
 
4.1%
9 58
 
3.9%
5 57
 
3.8%
4 56
 
3.7%
Other values (2) 102
 
6.8%
Hangul
ValueCountFrequency (%)
동 211
 
8.0%
지 204
 
7.7%
시 200
 
7.6%
번 200
 
7.6%
구 170
 
6.4%
도 119
 
4.5%
경 79
 
3.0%
산 66
 
2.5%
광 63
 
2.4%
서 63
 
2.4%
Other values (171) 1262
47.9%

ROAD_ADDR
Text

Distinct191
Distinct (%)96.5%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
2023-12-10T15:58:11.466006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23.5
Mean length18.10101
Min length13

Characters and Unicode

Total characters3584
Distinct characters207
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

Unique184 ?
Unique (%)92.9%

Sample

1st row충청남도 천안시 서북구 공원로 227
2nd row대전광역시 서구 대덕대로 211
3rd row경상남도 진주시 진주대로 1095
4th row서울특별시 강남구 압구정로 343
5th row경기도 수원시 팔달구 효원로 282
ValueCountFrequency (%)
경기도 50
 
5.8%
서울특별시 32
 
3.7%
경상남도 18
 
2.1%
대구광역시 15
 
1.8%
인천광역시 13
 
1.5%
충청남도 12
 
1.4%
부산광역시 11
 
1.3%
창원시 10
 
1.2%
경상북도 9
 
1.1%
고양시 8
 
0.9%
Other values (447) 679
79.2%
2023-12-10T15:58:11.907254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
659
 
18.4%
시 203
 
5.7%
로 191
 
5.3%
구 168
 
4.7%
도 121
 
3.4%
1 115
 
3.2%
경 88
 
2.5%
2 87
 
2.4%
대 74
 
2.1%
3 67
 
1.9%
Other values (197) 1811
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2347
65.5%
Space Separator 659
 
18.4%
Decimal Number 575
 
16.0%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 203
 
8.6%
로 191
 
8.1%
구 168
 
7.2%
도 121
 
5.2%
경 88
 
3.7%
대 74
 
3.2%
광 64
 
2.7%
서 62
 
2.6%
남 59
 
2.5%
산 56
 
2.4%
Other values (185) 1261
53.7%
Decimal Number
ValueCountFrequency (%)
1 115
20.0%
2 87
15.1%
3 67
11.7%
7 55
9.6%
0 49
8.5%
4 44
 
7.7%
5 44
 
7.7%
6 40
 
7.0%
8 38
 
6.6%
9 36
 
6.3%
Space Separator
ValueCountFrequency (%)
659
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2347
65.5%
Common 1237
34.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 203
 
8.6%
로 191
 
8.1%
구 168
 
7.2%
도 121
 
5.2%
경 88
 
3.7%
대 74
 
3.2%
광 64
 
2.7%
서 62
 
2.6%
남 59
 
2.5%
산 56
 
2.4%
Other values (185) 1261
53.7%
Common
ValueCountFrequency (%)
659
53.3%
1 115
 
9.3%
2 87
 
7.0%
3 67
 
5.4%
7 55
 
4.4%
0 49
 
4.0%
4 44
 
3.6%
5 44
 
3.6%
6 40
 
3.2%
8 38
 
3.1%
Other values (2) 39
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2347
65.5%
ASCII 1237
34.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
659
53.3%
1 115
 
9.3%
2 87
 
7.0%
3 67
 
5.4%
7 55
 
4.4%
0 49
 
4.0%
4 44
 
3.6%
5 44
 
3.6%
6 40
 
3.2%
8 38
 
3.1%
Other values (2) 39
 
3.2%
Hangul
ValueCountFrequency (%)
시 203
 
8.6%
로 191
 
8.1%
구 168
 
7.2%
도 121
 
5.2%
경 88
 
3.7%
대 74
 
3.2%
광 64
 
2.7%
서 62
 
2.6%
남 59
 
2.5%
산 56
 
2.4%
Other values (185) 1261
53.7%

X_AXIS
Real number (ℝ)

HIGH CORRELATION 

Distinct195
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360124.16
Minimum258981
Maximum523588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:58:12.042301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum258981
5-th percentile283454.9
Q1305828.25
median321138
Q3441037.75
95-th percentile498354.35
Maximum523588
Range264607
Interquartile range (IQR)135209.5

Descriptive statistics

Standard deviation74183.597
Coefficient of variation (CV)0.2059945
Kurtosis-0.89134905
Mean360124.16
Median Absolute Deviation (MAD)29213
Skewness0.79067406
Sum72024833
Variance5.5032061 × 109
MonotonicityNot monotonic
2023-12-10T15:58:12.210750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
289385 2
 
1.0%
311258 2
 
1.0%
320802 2
 
1.0%
320465 2
 
1.0%
313265 2
 
1.0%
368035 1
 
0.5%
418031 1
 
0.5%
323486 1
 
0.5%
368299 1
 
0.5%
345228 1
 
0.5%
Other values (185) 185
92.5%
ValueCountFrequency (%)
258981 1
0.5%
260782 1
0.5%
279141 1
0.5%
279800 1
0.5%
280061 1
0.5%
280684 1
0.5%
281762 1
0.5%
282265 1
0.5%
282716 1
0.5%
282921 1
0.5%
ValueCountFrequency (%)
523588 1
0.5%
523164 1
0.5%
519589 1
0.5%
519530 1
0.5%
507419 1
0.5%
506184 1
0.5%
503230 1
0.5%
502434 1
0.5%
499527 1
0.5%
499444 1
0.5%

Y_AXIS
Real number (ℝ)

HIGH CORRELATION 

Distinct196
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451296.82
Minimum99810
Maximum584936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:58:12.362286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99810
5-th percentile281381.35
Q1361986.5
median514731
Q3545716.5
95-th percentile563594.05
Maximum584936
Range485126
Interquartile range (IQR)183730

Descriptive statistics

Standard deviation109129.34
Coefficient of variation (CV)0.24181279
Kurtosis-0.89594043
Mean451296.82
Median Absolute Deviation (MAD)48145
Skewness-0.64437098
Sum90259364
Variance1.1909214 × 1010
MonotonicityNot monotonic
2023-12-10T15:58:12.507101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
516921 2
 
1.0%
562876 2
 
1.0%
518503 2
 
1.0%
545840 2
 
1.0%
330060 1
 
0.5%
242121 1
 
0.5%
425302 1
 
0.5%
420618 1
 
0.5%
357753 1
 
0.5%
555531 1
 
0.5%
Other values (186) 186
93.0%
ValueCountFrequency (%)
99810 1
0.5%
236725 1
0.5%
240004 1
0.5%
242121 1
0.5%
251439 1
0.5%
254625 1
0.5%
263876 1
0.5%
269056 1
0.5%
276663 1
0.5%
280932 1
0.5%
ValueCountFrequency (%)
584936 1
0.5%
584633 1
0.5%
574637 1
0.5%
572344 1
0.5%
571456 1
0.5%
570965 1
0.5%
564204 1
0.5%
564188 1
0.5%
563901 1
0.5%
563880 1
0.5%

BLK_CD
Real number (ℝ)

Distinct193
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337960.94
Minimum14691
Maximum519862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:58:12.651730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14691
5-th percentile61857.45
Q1224030.25
median410649.5
Q3462246.75
95-th percentile513866.8
Maximum519862
Range505171
Interquartile range (IQR)238216.5

Descriptive statistics

Standard deviation151101.25
Coefficient of variation (CV)0.44709678
Kurtosis-0.97945847
Mean337960.94
Median Absolute Deviation (MAD)99771.5
Skewness-0.57779139
Sum67592188
Variance2.2831587 × 1010
MonotonicityNot monotonic
2023-12-10T15:58:12.816899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
432213 2
 
1.0%
519862 2
 
1.0%
294890 2
 
1.0%
155402 2
 
1.0%
510421 2
 
1.0%
237224 2
 
1.0%
433053 2
 
1.0%
446533 1
 
0.5%
473155 1
 
0.5%
271715 1
 
0.5%
Other values (183) 183
91.5%
ValueCountFrequency (%)
14691 1
0.5%
18296 1
0.5%
19929 1
0.5%
23614 1
0.5%
24500 1
0.5%
43130 1
0.5%
50680 1
0.5%
53708 1
0.5%
58780 1
0.5%
60270 1
0.5%
ValueCountFrequency (%)
519862 2
1.0%
519223 1
0.5%
518577 1
0.5%
517650 1
0.5%
516656 1
0.5%
516389 1
0.5%
516359 1
0.5%
516347 1
0.5%
516276 1
0.5%
513740 1
0.5%

RETAIL_CD
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
M005
75 
M009
57 
D025
20 
D013
9 
D015
8 
Other values (13)
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique6 ?
Unique (%)3.0%

Sample

1st rowD001
2nd rowD001
3rd rowD001
4th rowD001
5th rowD001

Common Values

ValueCountFrequency (%)
M005 75
37.5%
M009 57
28.5%
D025 20
 
10.0%
D013 9
 
4.5%
D015 8
 
4.0%
D018 7
 
3.5%
D008 5
 
2.5%
D001 5
 
2.5%
D004 2
 
1.0%
M007 2
 
1.0%
Other values (8) 10
 
5.0%

Length

2023-12-10T15:58:12.943875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m005 75
37.5%
m009 57
28.5%
d025 20
 
10.0%
d013 9
 
4.5%
d015 8
 
4.0%
d018 7
 
3.5%
d008 5
 
2.5%
d001 5
 
2.5%
m001 2
 
1.0%
d014 2
 
1.0%
Other values (8) 10
 
5.0%

RETAIL_NM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
롯데마트
75 
이마트
57 
롯데백화점/영플라자/애비뉴엘
20 
신세계백화점
9 
엔씨백화점
8 
Other values (13)
31 

Length

Max length15
Median length7
Mean length5.145
Min length3

Unique

Unique6 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
롯데마트 75
37.5%
이마트 57
28.5%
롯데백화점/영플라자/애비뉴엘 20
 
10.0%
신세계백화점 9
 
4.5%
엔씨백화점 8
 
4.0%
현대백화점 7
 
3.5%
동아백화점 5
 
2.5%
갤러리아백화점 5
 
2.5%
대구백화점 2
 
1.0%
메가마트 2
 
1.0%
Other values (8) 10
 
5.0%

Length

2023-12-10T15:58:13.055311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
롯데마트 75
37.5%
이마트 57
28.5%
롯데백화점/영플라자/애비뉴엘 20
 
10.0%
신세계백화점 9
 
4.5%
엔씨백화점 8
 
4.0%
현대백화점 7
 
3.5%
동아백화점 5
 
2.5%
갤러리아백화점 5
 
2.5%
그랜드마트 2
 
1.0%
ak플라자 2
 
1.0%
Other values (8) 10
 
5.0%

RETAIL_CLSS
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
대형마트
137 
백화점
63 

Length

Max length4
Median length4
Mean length3.685
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대형마트 137
68.5%
백화점 63
31.5%

Length

2023-12-10T15:58:13.164094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:58:13.255068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대형마트 137
68.5%
백화점 63
31.5%

ADDRESS
Text

Distinct198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:58:13.560930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length16.61
Min length12

Characters and Unicode

Total characters3322
Distinct characters212
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196 ?
Unique (%)98.0%

Sample

1st row충남 천안시 서북구 불당동 1299
2nd row대전 서구 둔산2동 1036
3rd row경남 진주시 평안동 195
4th row서울 강남구 압구정동 494 515
5th row경기 수원시 팔달구 인계동 1125-1
ValueCountFrequency (%)
경기 49
 
5.5%
서울 33
 
3.7%
경남 18
 
2.0%
대구 15
 
1.7%
인천 12
 
1.4%
충남 12
 
1.4%
부산 11
 
1.2%
경북 9
 
1.0%
고양시 8
 
0.9%
수원시 8
 
0.9%
Other values (505) 712
80.3%
2023-12-10T15:58:14.060821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
687
20.7%
1 166
 
5.0%
구 165
 
5.0%
동 120
 
3.6%
시 119
 
3.6%
로 96
 
2.9%
2 89
 
2.7%
경 83
 
2.5%
3 70
 
2.1%
서 61
 
1.8%
Other values (202) 1666
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1911
57.5%
Space Separator 687
 
20.7%
Decimal Number 672
 
20.2%
Dash Punctuation 50
 
1.5%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 165
 
8.6%
동 120
 
6.3%
시 119
 
6.2%
로 96
 
5.0%
경 83
 
4.3%
서 61
 
3.2%
산 60
 
3.1%
남 58
 
3.0%
대 57
 
3.0%
기 55
 
2.9%
Other values (188) 1037
54.3%
Decimal Number
ValueCountFrequency (%)
1 166
24.7%
2 89
13.2%
3 70
10.4%
4 56
 
8.3%
5 53
 
7.9%
0 53
 
7.9%
7 52
 
7.7%
6 49
 
7.3%
9 49
 
7.3%
8 35
 
5.2%
Space Separator
ValueCountFrequency (%)
687
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1911
57.5%
Common 1411
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 165
 
8.6%
동 120
 
6.3%
시 119
 
6.2%
로 96
 
5.0%
경 83
 
4.3%
서 61
 
3.2%
산 60
 
3.1%
남 58
 
3.0%
대 57
 
3.0%
기 55
 
2.9%
Other values (188) 1037
54.3%
Common
ValueCountFrequency (%)
687
48.7%
1 166
 
11.8%
2 89
 
6.3%
3 70
 
5.0%
4 56
 
4.0%
5 53
 
3.8%
0 53
 
3.8%
7 52
 
3.7%
- 50
 
3.5%
6 49
 
3.5%
Other values (4) 86
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1911
57.5%
ASCII 1411
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
687
48.7%
1 166
 
11.8%
2 89
 
6.3%
3 70
 
5.0%
4 56
 
4.0%
5 53
 
3.8%
0 53
 
3.8%
7 52
 
3.7%
- 50
 
3.5%
6 49
 
3.5%
Other values (4) 86
 
6.1%
Hangul
ValueCountFrequency (%)
구 165
 
8.6%
동 120
 
6.3%
시 119
 
6.2%
로 96
 
5.0%
경 83
 
4.3%
서 61
 
3.2%
산 60
 
3.1%
남 58
 
3.0%
대 57
 
3.0%
기 55
 
2.9%
Other values (188) 1037
54.3%

SHOP_AREA
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17956.39
Minimum900
Maximum93390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:58:14.225652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile3648
Q18926.5
median11784.5
Q323809.5
95-th percentile49500
Maximum93390
Range92490
Interquartile range (IQR)14883

Descriptive statistics

Standard deviation15548.195
Coefficient of variation (CV)0.86588648
Kurtosis4.9714698
Mean17956.39
Median Absolute Deviation (MAD)4168.5
Skewness2.0445976
Sum3591278
Variance2.4174638 × 108
MonotonicityNot monotonic
2023-12-10T15:58:14.377502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34650 2
 
1.0%
9825 2
 
1.0%
9190 2
 
1.0%
9900 2
 
1.0%
14520 2
 
1.0%
2856 2
 
1.0%
49500 2
 
1.0%
13686 2
 
1.0%
11820 2
 
1.0%
30360 2
 
1.0%
Other values (180) 180
90.0%
ValueCountFrequency (%)
900 1
0.5%
1058 1
0.5%
1140 1
0.5%
1719 1
0.5%
2764 1
0.5%
2856 2
1.0%
3200 1
0.5%
3319 1
0.5%
3420 1
0.5%
3660 1
0.5%
ValueCountFrequency (%)
93390 1
0.5%
90000 1
0.5%
65683 1
0.5%
65100 1
0.5%
60390 1
0.5%
60000 1
0.5%
54454 1
0.5%
52800 1
0.5%
49590 1
0.5%
49500 2
1.0%

REG_DATE
Real number (ℝ)

Distinct178
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19778537
Minimum1994
Maximum20170915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:58:14.514928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile19920189
Q120011193
median20071161
Q320111062
95-th percentile20150622
Maximum20170915
Range20168921
Interquartile range (IQR)99869.75

Descriptive statistics

Standard deviation2285514.3
Coefficient of variation (CV)0.11555527
Kurtosis63.865262
Mean19778537
Median Absolute Deviation (MAD)49752
Skewness-8.0602352
Sum3.9557074 × 109
Variance5.2235755 × 1012
MonotonicityNot monotonic
2023-12-10T15:58:14.674604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20100601 12
 
6.0%
20141127 3
 
1.5%
20000831 2
 
1.0%
20121213 2
 
1.0%
20150618 2
 
1.0%
20120913 2
 
1.0%
20071221 2
 
1.0%
20081223 2
 
1.0%
20131219 2
 
1.0%
20150226 2
 
1.0%
Other values (168) 169
84.5%
ValueCountFrequency (%)
1994 1
0.5%
2000616 1
0.5%
2010603 1
0.5%
19691226 1
0.5%
19720917 1
0.5%
19841215 1
0.5%
19851201 1
0.5%
19881112 1
0.5%
19900512 1
0.5%
19900901 1
0.5%
ValueCountFrequency (%)
20170915 1
0.5%
20170427 1
0.5%
20161215 1
0.5%
20161209 1
0.5%
20160909 1
0.5%
20160623 1
0.5%
20151203 1
0.5%
20150919 1
0.5%
20150821 1
0.5%
20150701 1
0.5%

HOUS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct193
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4343096 × 1018
Minimum1.1200107 × 1018
Maximum5.0110122 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:58:14.825459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1200107 × 1018
5-th percentile1.1348606 × 1018
Q12.7140114 × 1018
median4.1172103 × 1018
Q34.4133102 × 1018
95-th percentile4.8127112 × 1018
Maximum5.0110122 × 1018
Range3.8910015 × 1018
Interquartile range (IQR)1.6992988 × 1018

Descriptive statistics

Standard deviation1.2409856 × 1018
Coefficient of variation (CV)0.36134936
Kurtosis-0.73625694
Mean3.4343096 × 1018
Median Absolute Deviation (MAD)6.95202 × 1017
Skewness-0.76082359
Sum4.3323894 × 1018
Variance1.5400452 × 1036
MonotonicityNot monotonic
2023-12-10T15:58:14.985374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4111313100003810000 2
 
1.0%
4128710400027030000 2
 
1.0%
4128710200000220000 2
 
1.0%
1171010100000400001 2
 
1.0%
4213011000011230000 2
 
1.0%
2626010800005020003 2
 
1.0%
4111313700011890000 2
 
1.0%
4413310800012990000 1
 
0.5%
3120012100007050000 1
 
0.5%
3020013200005190000 1
 
0.5%
Other values (183) 183
91.5%
ValueCountFrequency (%)
1120010700003460000 1
0.5%
1121510500002270007 1
0.5%
1121510500002270342 1
0.5%
1123011000002920010 1
0.5%
1126010400001700001 1
0.5%
1129013400000200001 1
0.5%
1129013400000250002 1
0.5%
1129013600002300000 1
0.5%
1130510100007770003 1
0.5%
1132010600007170006 1
0.5%
ValueCountFrequency (%)
5011012200009190000 1
0.5%
4833011400010110000 1
0.5%
4831010600003080000 1
0.5%
4825012900003000000 1
0.5%
4825010900012640000 1
0.5%
4825010300010410000 1
0.5%
4822011100010690000 1
0.5%
4817011000001950000 1
0.5%
4817010800000030000 1
0.5%
4812725028000250002 1
0.5%

BLD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct193
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4343363 × 1024
Minimum1.1200107 × 1024
Maximum5.0110122 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:58:15.140563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1200107 × 1024
5-th percentile1.1348606 × 1024
Q12.7140114 × 1024
median4.1172103 × 1024
Q34.4133103 × 1024
95-th percentile4.8127112 × 1024
Maximum5.0110122 × 1024
Range3.8910015 × 1024
Interquartile range (IQR)1.6992989 × 1024

Descriptive statistics

Standard deviation1.2410054 × 1024
Coefficient of variation (CV)0.36135231
Kurtosis-0.73631476
Mean3.4343363 × 1024
Median Absolute Deviation (MAD)6.95202 × 1023
Skewness-0.76082826
Sum6.8686727 × 1026
Variance1.5400943 × 1048
MonotonicityNot monotonic
2023-12-10T15:58:15.297062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.11131310010296e+24 2
 
1.0%
4.1287104001270305e+24 2
 
1.0%
4.12871020010022e+24 2
 
1.0%
1.1710101001004e+24 2
 
1.0%
4.21301100011123e+24 2
 
1.0%
2.62601080010502e+24 2
 
1.0%
4.11131370011189e+24 2
 
1.0%
4.41331080011299e+24 1
 
0.5%
3.12001210010705e+24 1
 
0.5%
3.02001320010519e+24 1
 
0.5%
Other values (183) 183
91.5%
ValueCountFrequency (%)
1.12001070010346e+24 1
0.5%
1.12151050010227e+24 1
0.5%
1.1215105001022704e+24 1
0.5%
1.12301100010292e+24 1
0.5%
1.1260104001017e+24 1
0.5%
1.1290134001002e+24 1
0.5%
1.12901340010025e+24 1
0.5%
1.1290136001008804e+24 1
0.5%
1.13051010010777e+24 1
0.5%
1.13201060010717e+24 1
0.5%
ValueCountFrequency (%)
5.011012200109191e+24 1
0.5%
4.8330114001067903e+24 1
0.5%
4.8310106001024096e+24 1
0.5%
4.825031027103e+24 1
0.5%
4.82501090011264e+24 1
0.5%
4.8250103001096e+24 1
0.5%
4.82201110011069e+24 1
0.5%
4.8170110001019504e+24 1
0.5%
4.8170108001000305e+24 1
0.5%
4.81272502810002e+24 1
0.5%

Interactions

2023-12-10T15:58:03.112952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:54.392374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:55.777542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:57.076620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:58.655426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:59.992666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:01.409600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:03.687170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:54.469340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:55.857911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:57.173242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:58.739259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:00.095371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:01.500774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:04.268780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:54.548978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:55.931956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:57.252405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:58.819945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:00.194274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:01.602172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:04.845347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:54.631687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:56.012569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:57.327952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:58.896439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:00.283205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:01.696244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:05.748648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:54.713491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:56.086440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:57.402498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:58.968907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:00.377703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:01.786126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:06.342500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:54.804748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:56.179541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:57.493785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:59.051728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:00.482055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:01.888836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:06.965288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:54.899921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:56.273889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:57.583975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:59.138046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:00.575036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:58:02.270227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:58:15.405740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X_AXISY_AXISBLK_CDRETAIL_CDRETAIL_NMRETAIL_CLSSSHOP_AREAREG_DATEHOUS_IDBLD_CD
X_AXIS1.0000.7900.5040.0000.0000.1410.0000.0000.7380.738
Y_AXIS0.7901.0000.5490.1940.1940.0730.0000.0000.8080.808
BLK_CD0.5040.5491.0000.1740.1740.1960.1300.0000.5250.525
RETAIL_CD0.0000.1940.1741.0001.0001.0000.8060.0000.3740.374
RETAIL_NM0.0000.1940.1741.0001.0001.0000.8060.0000.3740.374
RETAIL_CLSS0.1410.0730.1961.0001.0001.0000.8480.0970.1410.141
SHOP_AREA0.0000.0000.1300.8060.8060.8481.0000.2390.1070.107
REG_DATE0.0000.0000.0000.0000.0000.0970.2391.0000.1490.149
HOUS_ID0.7380.8080.5250.3740.3740.1410.1070.1491.0001.000
BLD_CD0.7380.8080.5250.3740.3740.1410.1070.1491.0001.000
2023-12-10T15:58:15.910480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RETAIL_CDRETAIL_CLSSRETAIL_NM
RETAIL_CD1.0000.9591.000
RETAIL_CLSS0.9591.0000.959
RETAIL_NM1.0000.9591.000
2023-12-10T15:58:16.011700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X_AXISY_AXISBLK_CDSHOP_AREAREG_DATEHOUS_IDBLD_CDRETAIL_CDRETAIL_NMRETAIL_CLSS
X_AXIS1.000-0.6100.0470.006-0.1700.2070.2070.0000.0000.111
Y_AXIS-0.6101.000-0.0850.0270.168-0.395-0.3950.0000.0000.008
BLK_CD0.047-0.0851.0000.0040.2200.2170.2170.0540.0540.163
SHOP_AREA0.0060.0270.0041.000-0.167-0.163-0.1630.3940.3940.852
REG_DATE-0.1700.1680.220-0.1671.0000.1350.1350.3580.3580.000
HOUS_ID0.207-0.3950.217-0.1630.1351.0001.0000.1680.1680.139
BLD_CD0.207-0.3950.217-0.1630.1351.0001.0000.0000.0000.000
RETAIL_CD0.0000.0000.0540.3940.3580.1680.0001.0001.0000.959
RETAIL_NM0.0000.0000.0540.3940.3580.1680.0001.0001.0000.959
RETAIL_CLSS0.1110.0080.1630.8520.0000.1390.0000.9590.9591.000

Missing values

2023-12-10T15:58:08.626833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:58:08.828512image/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.

Sample

SHOP_CDSHOP_NMHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDRETAIL_CDRETAIL_NMRETAIL_CLSSADDRESSSHOP_AREAREG_DATEHOUS_IDBLD_CD
0D0002389센터시티점충청남도 천안시 서북구 불당동 1299번지충청남도 천안시 서북구 공원로 227320299466952446533D001갤러리아백화점백화점충남 천안시 서북구 불당동 1299495902010120144133108000129900004413310800112990000000001
1D0000006타임월드점대전광역시 서구 둔산동 1036번지대전광역시 서구 대덕대로 211344418417031500795D001갤러리아백화점백화점대전 서구 둔산2동 1036544541997090130170112000103600003017011200110370000019242
2D0000008진주점경상남도 진주시 평안동 195번지경상남도 진주시 진주대로 1095407697288642341982D001갤러리아백화점백화점경남 진주시 평안동 195213372007080148170110000019500004817011000101950000412453
3D0000001명품관서울특별시 강남구 압구정동 494번지서울특별시 강남구 압구정로 343315490547738509392D001갤러리아백화점백화점서울 강남구 압구정동 494 515259731990090111680110000049400001168011000104940000004966
4D0000004수원점경기도 수원시 팔달구 인계동 1125-1번지경기도 수원시 팔달구 효원로 282314352518112127999D001갤러리아백화점백화점경기 수원시 팔달구 인계동 1125-1274051995080141115141000112500014111514100111250001011558
5D0000009일산점경기도 고양시 일산서구 주엽동 22번지경기도 고양시 일산서구 중앙로 1436290907563901294890D003그랜드백화점백화점경기 고양시 일산구 주엽2동 22274861996103141287102000002200004128710200100220001002058
6D0000011프라자점대구광역시 중구 대봉동 214번지대구광역시 중구 명덕로 333454969361985466284D004대구백화점백화점대구 중구 대봉동 214257531993091527110157000021400002711015700102140000018442
7D0000012대동백화점경상남도 창원시 성산구 상남동 44-1번지경상남도 창원시 성산구 동산로 115462838291390318740D005대동백화점백화점경남 창원시 상남동 44-1120001995050348123127000004400014812312700100440001022226
8D0004763구미점경상북도 구미시 송정동 60번지경상북도 구미시 송원동로 28431577391130366862D008동아백화점백화점경북 구미시 송정동 6083581990051247190110000006000004719011000100600000027976
9D0004765강북점대구광역시 북구 읍내동 1343-6번지대구광역시 북구 칠곡중앙대로 41644973637046887699D008동아백화점백화점대구 북구 읍내동 1343-6336751997070127230118000134300062723011800113430006041980
SHOP_CDSHOP_NMHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDRETAIL_CDRETAIL_NMRETAIL_CLSSADDRESSSHOP_AREAREG_DATEHOUS_IDBLD_CD
190M0003662마포공덕점서울특별시 마포구 신공덕동 173번지서울특별시 마포구 백범로 212307704549426416256M009이마트대형마트서울 마포구 신공덕동 백범로 21259802012011011440103000017300001144010300100690002000001
191M0003663펜타포트점충청남도 천안시 서북구 불당동 1289번지충청남도 천안시 서북구 공원로 196320039466781446194M009이마트대형마트충남 천안시 서북구 불당동 1289 펜타포트 주상복합상가 내100802012040544133108000128900004413310800112890000000001
192M0003667천안서북점충청남도 천안시 서북구 백석동 1047번지충청남도 천안시 서북구 삼성대로 20321914471163442484M009이마트대형마트충남 천안시 서북구 백석동 1047152002012121344133103000104700004413310500110470000000001
193M0003668의정부점경기도 의정부시 민락동 849번지경기도 의정부시 민락로 210320972571456405122M009이마트대형마트경기 의정부시 민락로 210145202013072541150106000084900004115010600104750001000001
194M0003669별내점경기도 남양주시 별내동 989번지경기도 남양주시 순화궁로 167323061560411409063M009이마트대형마트경기 남양주시 순화궁로 167122312013080841360111000098900004136011100109890000000001
195M0003670풍산점경기도 고양시 일산동구 중산동 1809번지경기도 고양시 일산동구 무궁화로 237293203564188408189M009이마트대형마트경기 고양시 일산동구 무궁화로 237145202014072441285102000180900004128510200118090000000001
196D0005439본점대구광역시 중구 동성로2가 166-1번지대구광역시 중구 동성로 30453999363424253603D004대구백화점백화점대구 중구 동성로 30187341969122627110123000016600012711012300101660001008137
197D0004922쇼핑점대구광역시 중구 덕산동 53-3번지대구광역시 중구 달구벌대로 2085453644363092248837D008동아백화점백화점대구 중구 덕산동 53-3354831984121527110112000005300032711011200100530003006516
198D0004764수성점대구광역시 수성구 범물동 1273번지대구광역시 수성구 지범로 191458059358098467804D008동아백화점백화점대구 수성구 범물동 1273478281996012727260113000127300002726011300112730000004828
199D0005474본점대구광역시 중구 동문동 20-4번지대구광역시 중구 경상감영길 171454098363787253584D008동아백화점백화점대구 중구 경상감영길 17198751972091727110120000002000042711012000100200011014252