Overview

Dataset statistics

Number of variables13
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory115.7 B

Variable types

Text6
Numeric7

Alerts

314620 is highly overall correlated with 557081High correlation
557081 is highly overall correlated with 314620 and 2 other fieldsHigh correlation
6 is highly overall correlated with 692High correlation
692 is highly overall correlated with 6High correlation
1130510100000350004 is highly overall correlated with 557081 and 1 other fieldsHigh correlation
1130510100100350004000001 is highly overall correlated with 557081 and 1 other fieldsHigh correlation
S00299 has unique valuesUnique
CGV 미아 has unique valuesUnique
2007-04-01 has unique valuesUnique
692 has unique valuesUnique
692 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-10 06:33:01.417246
Analysis finished2023-12-10 06:33:12.782818
Duration11.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

S00299
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-10T15:33:12.992196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters138
Distinct characters11
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

Unique23 ?
Unique (%)100.0%

Sample

1st rowS00286
2nd rowS00499
3rd rowS00211
4th rowS00415
5th rowS00588
ValueCountFrequency (%)
s00286 1
 
4.3%
s00224 1
 
4.3%
s00330 1
 
4.3%
s00342 1
 
4.3%
s00617 1
 
4.3%
s00509 1
 
4.3%
s00077 1
 
4.3%
s00370 1
 
4.3%
s00771 1
 
4.3%
s00482 1
 
4.3%
Other values (13) 13
56.5%
2023-12-10T15:33:13.538306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 52
37.7%
S 23
16.7%
2 12
 
8.7%
8 10
 
7.2%
3 7
 
5.1%
7 7
 
5.1%
6 6
 
4.3%
1 6
 
4.3%
5 6
 
4.3%
4 5
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115
83.3%
Uppercase Letter 23
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52
45.2%
2 12
 
10.4%
8 10
 
8.7%
3 7
 
6.1%
7 7
 
6.1%
6 6
 
5.2%
1 6
 
5.2%
5 6
 
5.2%
4 5
 
4.3%
9 4
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
S 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115
83.3%
Latin 23
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52
45.2%
2 12
 
10.4%
8 10
 
8.7%
3 7
 
6.1%
7 7
 
6.1%
6 6
 
5.2%
1 6
 
5.2%
5 6
 
5.2%
4 5
 
4.3%
9 4
 
3.5%
Latin
ValueCountFrequency (%)
S 23
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52
37.7%
S 23
16.7%
2 12
 
8.7%
8 10
 
7.2%
3 7
 
5.1%
7 7
 
5.1%
6 6
 
4.3%
1 6
 
4.3%
5 6
 
4.3%
4 5
 
3.6%

CGV 미아
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-10T15:33:13.873301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.3913043
Min length3

Characters and Unicode

Total characters193
Distinct characters81
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st rowCGV 압구정
2nd row이봄씨어터
3rd row롯데시네마 브로드웨이(신사)
4th row에어플릭스 3호점
5th row롯데시네마 가양
ValueCountFrequency (%)
롯데시네마 6
 
13.6%
cgv 5
 
11.4%
메가박스 4
 
9.1%
수유 2
 
4.5%
에어플릭스 2
 
4.5%
압구정 2
 
4.5%
de 1
 
2.3%
cine 1
 
2.3%
강남 1
 
2.3%
도곡 1
 
2.3%
Other values (19) 19
43.2%
2023-12-10T15:33:14.423905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
10.9%
C 7
 
3.6%
롯 7
 
3.6%
마 7
 
3.6%
네 7
 
3.6%
시 7
 
3.6%
스 7
 
3.6%
데 7
 
3.6%
V 5
 
2.6%
가 5
 
2.6%
Other values (71) 113
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 140
72.5%
Uppercase Letter 23
 
11.9%
Space Separator 21
 
10.9%
Open Punctuation 2
 
1.0%
Close Punctuation 2
 
1.0%
Lowercase Letter 2
 
1.0%
Decimal Number 2
 
1.0%
Connector Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
롯 7
 
5.0%
마 7
 
5.0%
네 7
 
5.0%
시 7
 
5.0%
스 7
 
5.0%
데 7
 
5.0%
가 5
 
3.6%
메 4
 
2.9%
박 4
 
2.9%
씨 3
 
2.1%
Other values (55) 82
58.6%
Uppercase Letter
ValueCountFrequency (%)
C 7
30.4%
V 5
21.7%
G 5
21.7%
E 2
 
8.7%
N 1
 
4.3%
I 1
 
4.3%
H 1
 
4.3%
F 1
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
d 1
50.0%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
3 1
50.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 140
72.5%
Common 28
 
14.5%
Latin 25
 
13.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
롯 7
 
5.0%
마 7
 
5.0%
네 7
 
5.0%
시 7
 
5.0%
스 7
 
5.0%
데 7
 
5.0%
가 5
 
3.6%
메 4
 
2.9%
박 4
 
2.9%
씨 3
 
2.1%
Other values (55) 82
58.6%
Latin
ValueCountFrequency (%)
C 7
28.0%
V 5
20.0%
G 5
20.0%
E 2
 
8.0%
N 1
 
4.0%
e 1
 
4.0%
I 1
 
4.0%
d 1
 
4.0%
H 1
 
4.0%
F 1
 
4.0%
Common
ValueCountFrequency (%)
21
75.0%
( 2
 
7.1%
) 2
 
7.1%
_ 1
 
3.6%
1 1
 
3.6%
3 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 140
72.5%
ASCII 53
 
27.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
39.6%
C 7
 
13.2%
V 5
 
9.4%
G 5
 
9.4%
E 2
 
3.8%
( 2
 
3.8%
) 2
 
3.8%
N 1
 
1.9%
e 1
 
1.9%
I 1
 
1.9%
Other values (6) 6
 
11.3%
Hangul
ValueCountFrequency (%)
롯 7
 
5.0%
마 7
 
5.0%
네 7
 
5.0%
시 7
 
5.0%
스 7
 
5.0%
데 7
 
5.0%
가 5
 
3.6%
메 4
 
2.9%
박 4
 
2.9%
씨 3
 
2.1%
Other values (55) 82
58.6%

314620
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310249.04
Minimum294453
Maximum317019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-10T15:33:14.654485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294453
5-th percentile294732.9
Q1306427.5
median314403
Q3315074.5
95-th percentile316877.7
Maximum317019
Range22566
Interquartile range (IQR)8647

Descriptive statistics

Standard deviation8190.3735
Coefficient of variation (CV)0.026399351
Kurtosis-0.50351564
Mean310249.04
Median Absolute Deviation (MAD)672
Skewness-1.1782089
Sum7135728
Variance67082218
MonotonicityNot monotonic
2023-12-10T15:33:14.850398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
314403 2
 
8.7%
294453 2
 
8.7%
314039 1
 
4.3%
314106 1
 
4.3%
315916 1
 
4.3%
297252 1
 
4.3%
317019 1
 
4.3%
315080 1
 
4.3%
315075 1
 
4.3%
314574 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
294453 2
8.7%
297252 1
4.3%
297470 1
4.3%
298885 1
4.3%
299121 1
4.3%
313734 1
4.3%
313749 1
4.3%
314039 1
4.3%
314106 1
4.3%
314162 1
4.3%
ValueCountFrequency (%)
317019 1
4.3%
316968 1
4.3%
316065 1
4.3%
315916 1
4.3%
315080 1
4.3%
315075 1
4.3%
315074 1
4.3%
314909 1
4.3%
314818 1
4.3%
314574 1
4.3%

557081
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548602.17
Minimum543240
Maximum560449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-10T15:33:15.046057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum543240
5-th percentile544699.6
Q1545457.5
median547203
Q3551333
95-th percentile559015
Maximum560449
Range17209
Interquartile range (IQR)5875.5

Descriptive statistics

Standard deviation4552.6336
Coefficient of variation (CV)0.0082986067
Kurtosis1.7868684
Mean548602.17
Median Absolute Deviation (MAD)2355
Skewness1.4548029
Sum12617850
Variance20726473
MonotonicityNot monotonic
2023-12-10T15:33:15.246015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
547347 2
 
8.7%
552796 2
 
8.7%
559706 1
 
4.3%
544830 1
 
4.3%
543240 1
 
4.3%
551427 1
 
4.3%
545931 1
 
4.3%
547232 1
 
4.3%
544848 1
 
4.3%
560449 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
543240 1
4.3%
544691 1
4.3%
544777 1
4.3%
544830 1
4.3%
544848 1
4.3%
545160 1
4.3%
545755 1
4.3%
545931 1
4.3%
546433 1
4.3%
546469 1
4.3%
ValueCountFrequency (%)
560449 1
4.3%
559706 1
4.3%
552796 2
8.7%
551656 1
4.3%
551427 1
4.3%
551239 1
4.3%
549339 1
4.3%
547347 2
8.7%
547232 1
4.3%
547203 1
4.3%

220729
Real number (ℝ)

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234568.65
Minimum15231
Maximum508825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-10T15:33:15.498920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15231
5-th percentile17777.9
Q132293.5
median270733
Q3378760
95-th percentile499967.1
Maximum508825
Range493594
Interquartile range (IQR)346466.5

Descriptive statistics

Standard deviation173739.95
Coefficient of variation (CV)0.74067848
Kurtosis-1.4062166
Mean234568.65
Median Absolute Deviation (MAD)146716
Skewness-0.039950587
Sum5395079
Variance3.0185571 × 1010
MonotonicityNot monotonic
2023-12-10T15:33:15.856838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
417449 2
 
8.7%
33656 2
 
8.7%
508825 2
 
8.7%
219325 1
 
4.3%
24578 1
 
4.3%
420246 1
 
4.3%
414800 1
 
4.3%
270459 1
 
4.3%
342720 1
 
4.3%
219832 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
15231 1
4.3%
17031 1
4.3%
24500 1
4.3%
24578 1
4.3%
28975 1
4.3%
30931 1
4.3%
33656 2
8.7%
219325 1
4.3%
219832 1
4.3%
270459 1
4.3%
ValueCountFrequency (%)
508825 2
8.7%
420246 1
4.3%
417449 2
8.7%
414800 1
4.3%
342720 1
4.3%
339723 1
4.3%
282921 1
4.3%
277651 1
4.3%
275563 1
4.3%
270733 1
4.3%

서울특별시 강북구 미아동 35-4 번지 트레지오쇼핑몰 9층
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-10T15:33:16.345740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length35
Mean length27.73913
Min length20

Characters and Unicode

Total characters638
Distinct characters94
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st row서울특별시 강남구 신사동 602 번지
2nd row서울특별시 강남구 신사동 532-1 번지 대원빌딩 B2
3rd row서울특별시 강남구 논현동 3-5 번지
4th row서울특별시 강남구 역삼동 705-27 번지 화남빌딩 지하1층
5th row서울특별시 강서구 등촌동 73-1 번지 롯데시네마 가양
ValueCountFrequency (%)
서울특별시 22
 
15.3%
번지 22
 
15.3%
강남구 15
 
10.4%
강서구 6
 
4.2%
신사동 5
 
3.5%
역삼동 4
 
2.8%
지하1층 2
 
1.4%
방화동 2
 
1.4%
삼성동 2
 
1.4%
886 2
 
1.4%
Other values (57) 62
43.1%
2023-12-10T15:33:16.980824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
19.0%
서 29
 
4.5%
1 29
 
4.5%
지 27
 
4.2%
시 26
 
4.1%
동 25
 
3.9%
강 23
 
3.6%
번 23
 
3.6%
구 23
 
3.6%
울 23
 
3.6%
Other values (84) 289
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 385
60.3%
Space Separator 121
 
19.0%
Decimal Number 109
 
17.1%
Dash Punctuation 14
 
2.2%
Uppercase Letter 5
 
0.8%
Math Symbol 2
 
0.3%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 29
 
7.5%
지 27
 
7.0%
시 26
 
6.8%
동 25
 
6.5%
강 23
 
6.0%
번 23
 
6.0%
구 23
 
6.0%
울 23
 
6.0%
별 22
 
5.7%
특 22
 
5.7%
Other values (65) 142
36.9%
Decimal Number
ValueCountFrequency (%)
1 29
26.6%
6 13
11.9%
2 11
 
10.1%
4 9
 
8.3%
5 9
 
8.3%
7 8
 
7.3%
8 8
 
7.3%
3 8
 
7.3%
0 8
 
7.3%
9 6
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 2
40.0%
V 1
20.0%
C 1
20.0%
G 1
20.0%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 385
60.3%
Common 248
38.9%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 29
 
7.5%
지 27
 
7.0%
시 26
 
6.8%
동 25
 
6.5%
강 23
 
6.0%
번 23
 
6.0%
구 23
 
6.0%
울 23
 
6.0%
별 22
 
5.7%
특 22
 
5.7%
Other values (65) 142
36.9%
Common
ValueCountFrequency (%)
121
48.8%
1 29
 
11.7%
- 14
 
5.6%
6 13
 
5.2%
2 11
 
4.4%
4 9
 
3.6%
5 9
 
3.6%
7 8
 
3.2%
8 8
 
3.2%
3 8
 
3.2%
Other values (5) 18
 
7.3%
Latin
ValueCountFrequency (%)
B 2
40.0%
V 1
20.0%
C 1
20.0%
G 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 385
60.3%
ASCII 253
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
47.8%
1 29
 
11.5%
- 14
 
5.5%
6 13
 
5.1%
2 11
 
4.3%
4 9
 
3.6%
5 9
 
3.6%
7 8
 
3.2%
8 8
 
3.2%
3 8
 
3.2%
Other values (9) 23
 
9.1%
Hangul
ValueCountFrequency (%)
서 29
 
7.5%
지 27
 
7.0%
시 26
 
6.8%
동 25
 
6.5%
강 23
 
6.0%
번 23
 
6.0%
구 23
 
6.0%
울 23
 
6.0%
별 22
 
5.7%
특 22
 
5.7%
Other values (65) 142
36.9%

2007-04-01
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-10T15:33:17.337285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6086957
Min length1

Characters and Unicode

Total characters221
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row2006-03-30
2nd row2017-01-11
3rd row2013-04-05
4th row2018-02-07
5th row2015-10-17
ValueCountFrequency (%)
2006-03-30 1
 
4.3%
2017-04-26 1
 
4.3%
2007-02-08 1
 
4.3%
2020-06-11 1
 
4.3%
2011-12-01 1
 
4.3%
2017-09-25 1
 
4.3%
2000-05-13 1
 
4.3%
2020-02-09 1
 
4.3%
2000-03-03 1
 
4.3%
2018-03-23 1
 
4.3%
Other values (13) 13
56.5%
2023-12-10T15:33:17.872750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 65
29.4%
- 44
19.9%
2 34
15.4%
1 33
14.9%
3 9
 
4.1%
7 9
 
4.1%
5 7
 
3.2%
9 7
 
3.2%
6 5
 
2.3%
8 5
 
2.3%
Other values (2) 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
79.6%
Dash Punctuation 44
 
19.9%
Uppercase Letter 1
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65
36.9%
2 34
19.3%
1 33
18.8%
3 9
 
5.1%
7 9
 
5.1%
5 7
 
4.0%
9 7
 
4.0%
6 5
 
2.8%
8 5
 
2.8%
4 2
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220
99.5%
Latin 1
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65
29.5%
- 44
20.0%
2 34
15.5%
1 33
15.0%
3 9
 
4.1%
7 9
 
4.1%
5 7
 
3.2%
9 7
 
3.2%
6 5
 
2.3%
8 5
 
2.3%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65
29.4%
- 44
19.9%
2 34
15.4%
1 33
14.9%
3 9
 
4.1%
7 9
 
4.1%
5 7
 
3.2%
9 7
 
3.2%
6 5
 
2.3%
8 5
 
2.3%
Other values (2) 3
 
1.4%

6
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9565217
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-10T15:33:18.081605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q36.5
95-th percentile9
Maximum19
Range18
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.2047404
Coefficient of variation (CV)0.84832482
Kurtosis4.492666
Mean4.9565217
Median Absolute Deviation (MAD)3
Skewness1.6284882
Sum114
Variance17.679842
MonotonicityNot monotonic
2023-12-10T15:33:18.292341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 8
34.8%
6 6
26.1%
9 2
 
8.7%
7 2
 
8.7%
5 1
 
4.3%
4 1
 
4.3%
8 1
 
4.3%
19 1
 
4.3%
2 1
 
4.3%
ValueCountFrequency (%)
1 8
34.8%
2 1
 
4.3%
4 1
 
4.3%
5 1
 
4.3%
6 6
26.1%
7 2
 
8.7%
8 1
 
4.3%
9 2
 
8.7%
19 1
 
4.3%
ValueCountFrequency (%)
19 1
 
4.3%
9 2
 
8.7%
8 1
 
4.3%
7 2
 
8.7%
6 6
26.1%
5 1
 
4.3%
4 1
 
4.3%
2 1
 
4.3%
1 8
34.8%

692
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean726.91304
Minimum0
Maximum3529
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-10T15:33:18.516182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.2
Q189
median719
Q3912
95-th percentile2007.2
Maximum3529
Range3529
Interquartile range (IQR)823

Descriptive statistics

Standard deviation801.3804
Coefficient of variation (CV)1.1024433
Kurtosis6.2335526
Mean726.91304
Median Absolute Deviation (MAD)498
Skewness2.1507391
Sum16719
Variance642210.54
MonotonicityNot monotonic
2023-12-10T15:33:18.706448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1075 1
 
4.3%
59 1
 
4.3%
78 1
 
4.3%
974 1
 
4.3%
847 1
 
4.3%
32 1
 
4.3%
815 1
 
4.3%
3529 1
 
4.3%
144 1
 
4.3%
0 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
30 1
4.3%
32 1
4.3%
59 1
4.3%
77 1
4.3%
78 1
4.3%
100 1
4.3%
144 1
4.3%
170 1
4.3%
570 1
4.3%
ValueCountFrequency (%)
3529 1
4.3%
2095 1
4.3%
1217 1
4.3%
1075 1
4.3%
974 1
4.3%
972 1
4.3%
852 1
4.3%
851 1
4.3%
847 1
4.3%
839 1
4.3%

1130510100000350004
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.160054 × 1018
Minimum1.1305102 × 1018
Maximum1.1680118 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-10T15:33:18.982892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305102 × 1018
5-th percentile1.1324603 × 1018
Q11.1500107 × 1018
median1.1680101 × 1018
Q31.1680107 × 1018
95-th percentile1.1680108 × 1018
Maximum1.1680118 × 1018
Range3.75016 × 1016
Interquartile range (IQR)1.8 × 1016

Descriptive statistics

Standard deviation1.2248377 × 1016
Coefficient of variation (CV)0.010558454
Kurtosis0.90878967
Mean1.160054 × 1018
Median Absolute Deviation (MAD)5.9999837 × 1011
Skewness-1.3572445
Sum8.2344978 × 1018
Variance1.5002275 × 1032
MonotonicityNot monotonic
2023-12-10T15:33:19.183781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1168010700006020000 2
 
8.7%
1150010900008860000 2
 
8.7%
1130510200004490001 1
 
4.3%
1168010100008140006 1
 
4.3%
1168011800001740003 1
 
4.3%
1150010500007970001 1
 
4.3%
1168010500001590000 1
 
4.3%
1168010700006510019 1
 
4.3%
1168010100006790000 1
 
4.3%
1130510300001680012 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
1130510200004490001 1
4.3%
1130510300001680012 1
4.3%
1150010200000730001 1
4.3%
1150010200006390023 1
4.3%
1150010300010730010 1
4.3%
1150010500007970001 1
4.3%
1150010900008860000 2
8.7%
1168010100006420000 1
4.3%
1168010100006790000 1
4.3%
1168010100007050027 1
4.3%
ValueCountFrequency (%)
1168011800001740003 1
4.3%
1168010800000850011 1
4.3%
1168010800000030005 1
4.3%
1168010700006510021 1
4.3%
1168010700006510019 1
4.3%
1168010700006020000 2
8.7%
1168010700005320001 1
4.3%
1168010500001590009 1
4.3%
1168010500001590000 1
4.3%
1168010100008160000 1
4.3%

1130510100100350004000001
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.160054 × 1024
Minimum1.1305102 × 1024
Maximum1.1680118 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-10T15:33:19.394256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305102 × 1024
5-th percentile1.1324603 × 1024
Q11.1500107 × 1024
median1.1680101 × 1024
Q31.1680107 × 1024
95-th percentile1.1680108 × 1024
Maximum1.1680118 × 1024
Range3.75016 × 1022
Interquartile range (IQR)1.8 × 1022

Descriptive statistics

Standard deviation1.2248377 × 1022
Coefficient of variation (CV)0.010558454
Kurtosis0.90878967
Mean1.160054 × 1024
Median Absolute Deviation (MAD)5.9999837 × 1017
Skewness-1.3572445
Sum2.6681242 × 1025
Variance1.5002275 × 1044
MonotonicityNot monotonic
2023-12-10T15:33:19.603599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.16801070010602e+24 2
 
8.7%
1.16801070010651e+24 2
 
8.7%
1.16801050010159e+24 2
 
8.7%
1.15001090010686e+24 2
 
8.7%
1.13051020010449e+24 1
 
4.3%
1.16801010010814e+24 1
 
4.3%
1.16801180010174e+24 1
 
4.3%
1.15001050010797e+24 1
 
4.3%
1.16801010010679e+24 1
 
4.3%
1.13051030010168e+24 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
1.13051020010449e+24 1
4.3%
1.13051030010168e+24 1
4.3%
1.15001020010073e+24 1
4.3%
1.15001020010639e+24 1
4.3%
1.15001030011073e+24 1
4.3%
1.15001050010797e+24 1
4.3%
1.15001090010686e+24 2
8.7%
1.16801010010642e+24 1
4.3%
1.16801010010679e+24 1
4.3%
1.16801010010705e+24 1
4.3%
ValueCountFrequency (%)
1.16801180010174e+24 1
4.3%
1.16801080010085e+24 1
4.3%
1.16801080010003e+24 1
4.3%
1.16801070010651e+24 2
8.7%
1.16801070010602e+24 2
8.7%
1.16801070010532e+24 1
4.3%
1.16801050010159e+24 2
8.7%
1.16801010010816e+24 1
4.3%
1.16801010010814e+24 1
4.3%
1.16801010010705e+24 1
4.3%

서울특별시 강북구 미아동 35-4번지
Text

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-10T15:33:19.911597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length20.434783
Min length19

Characters and Unicode

Total characters470
Distinct characters40
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

Unique19 ?
Unique (%)82.6%

Sample

1st row서울특별시 강남구 신사동 602번지
2nd row서울특별시 강남구 신사동 532-1번지
3rd row서울특별시 강남구 논현동 3-5번지
4th row서울특별시 강남구 역삼동 705-27번지
5th row서울특별시 강서구 등촌동 73-1번지
ValueCountFrequency (%)
서울특별시 23
25.0%
강남구 15
16.3%
강서구 6
 
6.5%
신사동 5
 
5.4%
역삼동 5
 
5.4%
602번지 2
 
2.2%
방화동 2
 
2.2%
886번지 2
 
2.2%
강북구 2
 
2.2%
논현동 2
 
2.2%
Other values (26) 28
30.4%
2023-12-10T15:33:20.461153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69
 
14.7%
서 29
 
6.2%
번 24
 
5.1%
구 23
 
4.9%
울 23
 
4.9%
지 23
 
4.9%
동 23
 
4.9%
강 23
 
4.9%
시 23
 
4.9%
별 23
 
4.9%
Other values (30) 187
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
63.4%
Decimal Number 88
 
18.7%
Space Separator 69
 
14.7%
Dash Punctuation 15
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 29
9.7%
번 24
 
8.1%
구 23
 
7.7%
울 23
 
7.7%
지 23
 
7.7%
동 23
 
7.7%
강 23
 
7.7%
시 23
 
7.7%
별 23
 
7.7%
특 23
 
7.7%
Other values (18) 61
20.5%
Decimal Number
ValueCountFrequency (%)
1 19
21.6%
6 12
13.6%
9 8
9.1%
7 8
9.1%
2 8
9.1%
5 8
9.1%
8 8
9.1%
3 7
 
8.0%
4 5
 
5.7%
0 5
 
5.7%
Space Separator
ValueCountFrequency (%)
69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
63.4%
Common 172
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 29
9.7%
번 24
 
8.1%
구 23
 
7.7%
울 23
 
7.7%
지 23
 
7.7%
동 23
 
7.7%
강 23
 
7.7%
시 23
 
7.7%
별 23
 
7.7%
특 23
 
7.7%
Other values (18) 61
20.5%
Common
ValueCountFrequency (%)
69
40.1%
1 19
 
11.0%
- 15
 
8.7%
6 12
 
7.0%
9 8
 
4.7%
7 8
 
4.7%
2 8
 
4.7%
5 8
 
4.7%
8 8
 
4.7%
3 7
 
4.1%
Other values (2) 10
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
63.4%
ASCII 172
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69
40.1%
1 19
 
11.0%
- 15
 
8.7%
6 12
 
7.0%
9 8
 
4.7%
7 8
 
4.7%
2 8
 
4.7%
5 8
 
4.7%
8 8
 
4.7%
3 7
 
4.1%
Other values (2) 10
 
5.8%
Hangul
ValueCountFrequency (%)
서 29
9.7%
번 24
 
8.1%
구 23
 
7.7%
울 23
 
7.7%
지 23
 
7.7%
동 23
 
7.7%
강 23
 
7.7%
시 23
 
7.7%
별 23
 
7.7%
특 23
 
7.7%
Other values (18) 61
20.5%

서울특별시 강북구 도봉로 34
Text

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-10T15:33:20.788176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length18.304348
Min length16

Characters and Unicode

Total characters421
Distinct characters48
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

Unique19 ?
Unique (%)82.6%

Sample

1st row서울특별시 강남구 압구정로30길 45
2nd row서울특별시 강남구 압구정로10길 9
3rd row서울특별시 강남구 도산대로8길 8
4th row서울특별시 강남구 테헤란로 337
5th row서울특별시 강서구 양천로 476
ValueCountFrequency (%)
서울특별시 23
25.0%
강남구 15
16.3%
강서구 6
 
6.5%
강남대로 2
 
2.2%
9 2
 
2.2%
도봉로 2
 
2.2%
강북구 2
 
2.2%
38 2
 
2.2%
압구정로30길 2
 
2.2%
45 2
 
2.2%
Other values (33) 34
37.0%
2023-12-10T15:33:21.404127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69
16.4%
서 29
 
6.9%
구 26
 
6.2%
강 25
 
5.9%
울 23
 
5.5%
시 23
 
5.5%
특 23
 
5.5%
별 23
 
5.5%
로 21
 
5.0%
남 18
 
4.3%
Other values (38) 141
33.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 278
66.0%
Decimal Number 74
 
17.6%
Space Separator 69
 
16.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 29
10.4%
구 26
9.4%
강 25
9.0%
울 23
8.3%
시 23
8.3%
특 23
8.3%
별 23
8.3%
로 21
 
7.6%
남 18
 
6.5%
길 11
 
4.0%
Other values (27) 56
20.1%
Decimal Number
ValueCountFrequency (%)
3 17
23.0%
4 9
12.2%
8 8
10.8%
1 7
9.5%
5 7
9.5%
2 7
9.5%
0 6
 
8.1%
9 5
 
6.8%
7 5
 
6.8%
6 3
 
4.1%
Space Separator
ValueCountFrequency (%)
69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 278
66.0%
Common 143
34.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 29
10.4%
구 26
9.4%
강 25
9.0%
울 23
8.3%
시 23
8.3%
특 23
8.3%
별 23
8.3%
로 21
 
7.6%
남 18
 
6.5%
길 11
 
4.0%
Other values (27) 56
20.1%
Common
ValueCountFrequency (%)
69
48.3%
3 17
 
11.9%
4 9
 
6.3%
8 8
 
5.6%
1 7
 
4.9%
5 7
 
4.9%
2 7
 
4.9%
0 6
 
4.2%
9 5
 
3.5%
7 5
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 278
66.0%
ASCII 143
34.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69
48.3%
3 17
 
11.9%
4 9
 
6.3%
8 8
 
5.6%
1 7
 
4.9%
5 7
 
4.9%
2 7
 
4.9%
0 6
 
4.2%
9 5
 
3.5%
7 5
 
3.5%
Hangul
ValueCountFrequency (%)
서 29
10.4%
구 26
9.4%
강 25
9.0%
울 23
8.3%
시 23
8.3%
특 23
8.3%
별 23
8.3%
로 21
 
7.6%
남 18
 
6.5%
길 11
 
4.0%
Other values (27) 56
20.1%

Interactions

2023-12-10T15:33:10.436309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:02.132077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:03.515076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:04.946602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:06.166603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:07.437967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:08.615638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:10.662544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:02.265468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:03.679260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:05.087410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:06.302438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:07.557777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:08.769818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:10.881602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:02.419841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:03.855466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:05.302624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:06.445001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:07.697273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:08.924622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:11.095121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:02.552579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:04.017800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:05.434512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:06.575631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:07.855468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:09.071963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:11.357785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:02.714853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:04.247701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:05.560219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:06.817613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:08.023963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:09.577690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:11.612927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:02.865181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:04.407976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:05.692225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:06.994692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:08.197075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:09.791595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:11.848562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:03.203520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:04.662729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:05.829175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:07.197288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:08.365511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:33:10.102359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:33:21.579068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
S00299CGV 미아314620557081220729서울특별시 강북구 미아동 35-4 번지 트레지오쇼핑몰 9층2007-04-01669211305101000003500041130510100100350004000001서울특별시 강북구 미아동 35-4번지서울특별시 강북구 도봉로 34
S002991.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
CGV 미아1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
3146201.0001.0001.0000.6910.0001.0001.0000.4620.4870.6540.6541.0001.000
5570811.0001.0000.6911.0000.8191.0001.0000.0000.3101.0001.0001.0001.000
2207291.0001.0000.0000.8191.0001.0001.0000.0000.4940.9610.9611.0001.000
서울특별시 강북구 미아동 35-4 번지 트레지오쇼핑몰 9층1.0001.0001.0001.0001.0001.0001.0000.9590.8141.0001.0001.0001.000
2007-04-011.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
61.0001.0000.4620.0000.0000.9591.0001.0000.9370.7220.7220.8450.845
6921.0001.0000.4870.3100.4940.8141.0000.9371.0000.0000.0000.0000.000
11305101000003500041.0001.0000.6541.0000.9611.0001.0000.7220.0001.0001.0001.0001.000
11305101001003500040000011.0001.0000.6541.0000.9611.0001.0000.7220.0001.0001.0001.0001.000
서울특별시 강북구 미아동 35-4번지1.0001.0001.0001.0001.0001.0001.0000.8450.0001.0001.0001.0001.000
서울특별시 강북구 도봉로 341.0001.0001.0001.0001.0001.0001.0000.8450.0001.0001.0001.0001.000
2023-12-10T15:33:21.833659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
314620557081220729669211305101000003500041130510100100350004000001
3146201.000-0.6270.006-0.194-0.2070.4930.493
557081-0.6271.0000.0020.2820.318-0.586-0.586
2207290.0060.0021.0000.047-0.045-0.000-0.000
6-0.1940.2820.0471.0000.901-0.234-0.234
692-0.2070.318-0.0450.9011.000-0.247-0.247
11305101000003500040.493-0.586-0.000-0.234-0.2471.0001.000
11305101001003500040000010.493-0.586-0.000-0.234-0.2471.0001.000

Missing values

2023-12-10T15:33:12.289902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:33:12.661616image/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

S00299CGV 미아314620557081220729서울특별시 강북구 미아동 35-4 번지 트레지오쇼핑몰 9층2007-04-01669211305101000003500041130510100100350004000001서울특별시 강북구 미아동 35-4번지서울특별시 강북구 도봉로 34
0S00286CGV 압구정314403547347417449서울특별시 강남구 신사동 602 번지2006-03-306107511680107000060200001168010700106020000000001서울특별시 강남구 신사동 602번지서울특별시 강남구 압구정로30길 45
1S00499이봄씨어터31374954720324500서울특별시 강남구 신사동 532-1 번지 대원빌딩 B22017-01-1115911680107000053200011168010700105320001010750서울특별시 강남구 신사동 532-1번지서울특별시 강남구 압구정로10길 9
2S00211롯데시네마 브로드웨이(신사)31373454646928975서울특별시 강남구 논현동 3-5 번지2013-04-05657011680108000000300051168010800100030005006173서울특별시 강남구 논현동 3-5번지서울특별시 강남구 도산대로8길 8
3S00415에어플릭스 3호점316065545160275563서울특별시 강남구 역삼동 705-27 번지 화남빌딩 지하1층2018-02-0717711680101000070500271168010100107050027022387서울특별시 강남구 역삼동 705-27번지서울특별시 강남구 테헤란로 337
4S00588롯데시네마 가양29888555165615231서울특별시 강서구 등촌동 73-1 번지 롯데시네마 가양2015-10-17683911500102000007300011150010200100730001000001서울특별시 강서구 등촌동 73-1번지서울특별시 강서구 양천로 476
5S00288CGV 청담씨네시티31507454717933656서울특별시 강남구 신사동 651-21 번지2011-10-09667411680107000065100211168010700106510021027860서울특별시 강남구 신사동 651-21번지서울특별시 강남구 도산대로 323
6S00885아이파크316968545755270733서울시 강남구 삼성동 도심공항타워 15층X110011680105000015900091168010500101590009016088서울특별시 강남구 삼성동 159-9번지서울특별시 강남구 테헤란로87길 36
7S00620에어플릭스 1호점31481854643330931서울특별시 강남구 논현동 85-11 번지 플랫폼엘 컨템포러리 아트센터2018-01-17117011680108000008500111168010800100850011006271서울특별시 강남구 논현동 85-11번지서울특별시 강남구 언주로133길 11
8S00218롯데시네마 김포공항294453552796508825서울특별시 강서구 방화동 886 번지 외6필지2011-12-099209511500109000088600001150010900106860001003098서울특별시 강서구 방화동 886번지서울특별시 강서구 하늘길 38
9S00526메가박스 화곡29747054933917031서울특별시 강서구 화곡동 1073-10 번지 401 501호(화곡동)2015-05-29585111500103000107300101150010300110730010012777서울특별시 강서구 화곡동 1073-10번지서울특별시 강서구 화곡로 142
S00299CGV 미아314620557081220729서울특별시 강북구 미아동 35-4 번지 트레지오쇼핑몰 9층2007-04-01669211305101000003500041130510100100350004000001서울특별시 강북구 미아동 35-4번지서울특별시 강북구 도봉로 34
13S00296CGV 등촌299121551239282921서울특별시 강서구 등촌동 639-23 번지 브릭시티몰 3층 CGV 등촌2019-01-308121711500102000063900231150010200106390023027415서울특별시 강서구 등촌동 639-23번지서울특별시 강서구 공항대로45길 63
14S00482CGV 수유314574560449219832서울특별시 강북구 수유동 168-12 번지2018-03-23997211305103000016800121130510300101680012000001서울특별시 강북구 수유동 168-12번지서울특별시 강북구 도봉로 399
15S00771엘지아트센터315075544848342720서울특별시 강남구 역삼1동 679 번지2000-03-031011680101000067900001168010100106790001026822서울특별시 강남구 역삼동 679번지서울특별시 강남구 논현로 508
16S00370배식당31508054723233656서울특별시 강남구 신사동 651-19 번지 지하1층 지하2층2020-02-09114411680107000065100191168010700106510019010601서울특별시 강남구 신사동 651-19번지서울특별시 강남구 도산대로49길 9
17S00077메가박스 코엑스317019545931270459서울특별시 강남구 삼성동 159 번지2000-05-1319352911680105000015900001168010500101590009016086서울특별시 강남구 삼성동 159번지서울특별시 강남구 영동대로 513
18S00509메가박스 마곡297252551427414800서울특별시 강서구 마곡동 797-1 번지 퀸즈파크나인 B동 4층 메가박스 마곡지점2017-09-25681511500105000079700011150010500107970001000001서울특별시 강서구 마곡동 797-1번지서울특별시 강서구 공항대로 247
19S00617롯데시네마 김포공항_샤롯데294453552796508825서울특별시 강서구 방화동 886 번지2011-12-0113211500109000088600001150010900106860001003098서울특별시 강서구 방화동 886번지서울특별시 강서구 하늘길 38
20S00342롯데시네마 도곡315916543240420246서울특별시 강남구 도곡동 174-3 번지 롯데시네마 도곡2020-06-11784711680118000017400031168011800101740003000001서울특별시 강남구 도곡동 174-3번지서울특별시 강남구 남부순환로 2753
21S00330CGV 강남31410654483024578서울특별시 강남구 역삼동 814-6 번지 스타플렉스 4층~11층2007-02-08797411680101000081400061168010100108140006023360서울특별시 강남구 역삼동 814-6번지서울특별시 강남구 강남대로 438
22S00287CINE de CHEF 압구정314403547347417449서울특별시 강남구 신사동 602 번지2007-08-0127811680107000060200001168010700106020000000001서울특별시 강남구 신사동 602번지서울특별시 강남구 압구정로30길 45