Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells385
Missing cells (%)21.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory151.3 B

Variable types

Numeric4
Text5
Categorical8
Unsupported1

Alerts

movie_ty_nm has constant value ""Constant
movie_stle_nm has constant value ""Constant
su_selng_am is highly overall correlated with sn and 8 other fieldsHigh correlation
grad_nm is highly overall correlated with su_selng_amHigh correlation
nlty_nm is highly overall correlated with su_selng_amHigh correlation
open_de is highly overall correlated with su_selng_amHigh correlation
movie_se is highly overall correlated with wnty_aude_co and 2 other fieldsHigh correlation
genre_nm is highly overall correlated with sn and 3 other fieldsHigh correlation
sn is highly overall correlated with wnty_screen_co and 4 other fieldsHigh correlation
wnty_screen_co is highly overall correlated with sn and 1 other fieldsHigh correlation
wnty_aude_co is highly overall correlated with sn and 4 other fieldsHigh correlation
su_aude_co is highly overall correlated with sn and 2 other fieldsHigh correlation
su_selng_am is highly imbalanced (80.6%)Imbalance
mng_nm has 8 (8.0%) missing valuesMissing
makr_nm has 64 (64.0%) missing valuesMissing
import_cmpny_nm has 40 (40.0%) missing valuesMissing
wnty_screen_co has 12 (12.0%) missing valuesMissing
wnty_selng_am has 100 (100.0%) missing valuesMissing
wnty_aude_co has 86 (86.0%) missing valuesMissing
su_aude_co has 75 (75.0%) missing valuesMissing
sn has unique valuesUnique
movie_nm has unique valuesUnique
wnty_selng_am is an unsupported type, check if it needs cleaning or further analysisUnsupported
su_aude_co has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:49:04.285717
Analysis finished2023-12-10 09:49:10.818311
Duration6.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

sn
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.4
Minimum1
Maximum345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:10.964759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum345
Range344
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation57.511703
Coefficient of variation (CV)0.95218051
Kurtosis16.097483
Mean60.4
Median Absolute Deviation (MAD)25.5
Skewness3.5883183
Sum6040
Variance3307.596
MonotonicityNot monotonic
2023-12-10T18:49:11.252168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
345 1
1.0%
344 1
1.0%
343 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

movie_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:49:12.045878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length17
Mean length7.69
Min length2

Characters and Unicode

Total characters769
Distinct characters263
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row모가디슈
2nd row11번가청순글래머오피스걸
3rd row싱크홀
4th row인질
5th row보스 베이비 2
ValueCountFrequency (%)
더 10
 
4.5%
2 5
 
2.2%
로드 3
 
1.3%
극장판 3
 
1.3%
킬러의 3
 
1.3%
인 2
 
0.9%
무비 2
 
0.9%
보디가드 2
 
0.9%
우리 2
 
0.9%
웬디 1
 
0.4%
Other values (190) 190
85.2%
2023-12-10T18:49:13.146167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
16.0%
이 33
 
4.3%
드 23
 
3.0%
스 20
 
2.6%
: 15
 
2.0%
더 13
 
1.7%
아 11
 
1.4%
리 11
 
1.4%
가 10
 
1.3%
의 10
 
1.3%
Other values (253) 500
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 606
78.8%
Space Separator 123
 
16.0%
Other Punctuation 19
 
2.5%
Decimal Number 14
 
1.8%
Uppercase Letter 6
 
0.8%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 33
 
5.4%
드 23
 
3.8%
스 20
 
3.3%
더 13
 
2.1%
아 11
 
1.8%
리 11
 
1.8%
가 10
 
1.7%
의 10
 
1.7%
트 9
 
1.5%
로 9
 
1.5%
Other values (237) 457
75.4%
Other Punctuation
ValueCountFrequency (%)
: 15
78.9%
; 1
 
5.3%
! 1
 
5.3%
/ 1
 
5.3%
, 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
2 5
35.7%
1 4
28.6%
3 3
21.4%
9 1
 
7.1%
0 1
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
V 2
33.3%
C 2
33.3%
T 1
16.7%
A 1
16.7%
Space Separator
ValueCountFrequency (%)
123
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 606
78.8%
Common 156
 
20.3%
Latin 7
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 33
 
5.4%
드 23
 
3.8%
스 20
 
3.3%
더 13
 
2.1%
아 11
 
1.8%
리 11
 
1.8%
가 10
 
1.7%
의 10
 
1.7%
트 9
 
1.5%
로 9
 
1.5%
Other values (237) 457
75.4%
Common
ValueCountFrequency (%)
123
78.8%
: 15
 
9.6%
2 5
 
3.2%
1 4
 
2.6%
3 3
 
1.9%
; 1
 
0.6%
! 1
 
0.6%
9 1
 
0.6%
/ 1
 
0.6%
0 1
 
0.6%
Latin
ValueCountFrequency (%)
V 2
28.6%
C 2
28.6%
Ⅰ 1
14.3%
T 1
14.3%
A 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 606
78.8%
ASCII 162
 
21.1%
Number Forms 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
75.9%
: 15
 
9.3%
2 5
 
3.1%
1 4
 
2.5%
3 3
 
1.9%
V 2
 
1.2%
C 2
 
1.2%
; 1
 
0.6%
! 1
 
0.6%
T 1
 
0.6%
Other values (5) 5
 
3.1%
Hangul
ValueCountFrequency (%)
이 33
 
5.4%
드 23
 
3.8%
스 20
 
3.3%
더 13
 
2.1%
아 11
 
1.8%
리 11
 
1.8%
가 10
 
1.7%
의 10
 
1.7%
트 9
 
1.5%
로 9
 
1.5%
Other values (237) 457
75.4%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%

mng_nm
Text

MISSING 

Distinct90
Distinct (%)97.8%
Missing8
Missing (%)8.0%
Memory size932.0 B
2023-12-10T18:49:13.830959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length5.3913043
Min length2

Characters and Unicode

Total characters496
Distinct characters192
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)95.7%

Sample

1st row류승완
2nd row이희성
3rd row김지훈
4th row필감성
5th row톰 맥그라스
ValueCountFrequency (%)
패트릭 2
 
1.3%
숀 2
 
1.3%
이희성 2
 
1.3%
마이클 2
 
1.3%
휴즈 2
 
1.3%
존 2
 
1.3%
드로르 1
 
0.7%
자하비 1
 
0.7%
이정곤 1
 
0.7%
김종재 1
 
0.7%
Other values (133) 133
89.3%
2023-12-10T18:49:14.754644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57
 
11.5%
이 32
 
6.5%
김 13
 
2.6%
로 12
 
2.4%
스 11
 
2.2%
리 11
 
2.2%
라 8
 
1.6%
카 7
 
1.4%
키 7
 
1.4%
드 6
 
1.2%
Other values (182) 332
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 430
86.7%
Space Separator 57
 
11.5%
Other Punctuation 7
 
1.4%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 32
 
7.4%
김 13
 
3.0%
로 12
 
2.8%
스 11
 
2.6%
리 11
 
2.6%
라 8
 
1.9%
카 7
 
1.6%
키 7
 
1.6%
드 6
 
1.4%
자 6
 
1.4%
Other values (177) 317
73.7%
Other Punctuation
ValueCountFrequency (%)
, 5
71.4%
. 2
 
28.6%
Uppercase Letter
ValueCountFrequency (%)
D 1
50.0%
M 1
50.0%
Space Separator
ValueCountFrequency (%)
57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 430
86.7%
Common 64
 
12.9%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 32
 
7.4%
김 13
 
3.0%
로 12
 
2.8%
스 11
 
2.6%
리 11
 
2.6%
라 8
 
1.9%
카 7
 
1.6%
키 7
 
1.6%
드 6
 
1.4%
자 6
 
1.4%
Other values (177) 317
73.7%
Common
ValueCountFrequency (%)
57
89.1%
, 5
 
7.8%
. 2
 
3.1%
Latin
ValueCountFrequency (%)
D 1
50.0%
M 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 430
86.7%
ASCII 66
 
13.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57
86.4%
, 5
 
7.6%
. 2
 
3.0%
D 1
 
1.5%
M 1
 
1.5%
Hangul
ValueCountFrequency (%)
이 32
 
7.4%
김 13
 
3.0%
로 12
 
2.8%
스 11
 
2.6%
리 11
 
2.6%
라 8
 
1.9%
카 7
 
1.6%
키 7
 
1.6%
드 6
 
1.4%
자 6
 
1.4%
Other values (177) 317
73.7%

makr_nm
Text

MISSING 

Distinct36
Distinct (%)100.0%
Missing64
Missing (%)64.0%
Memory size932.0 B
2023-12-10T18:49:15.125230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length18
Mean length10.888889
Min length3

Characters and Unicode

Total characters392
Distinct characters133
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

Unique36 ?
Unique (%)100.0%

Sample

1st row(주)덱스터스튜디오,(주)외유내강,(주)필름케이
2nd row스마일컨텐츠
3rd row(주)더타워픽쳐스
4th row(주)외유내강,(주)샘컴퍼니
5th row(주)노던크로스
ValueCountFrequency (%)
주식회사 3
 
6.1%
이안필름,(주)키즈리턴 1
 
2.0%
주)돈키호테엔터테인먼트 1
 
2.0%
주)퍼니콘 1
 
2.0%
유한회사 1
 
2.0%
영화사 1
 
2.0%
반딧불 1
 
2.0%
영희야 1
 
2.0%
놀자 1
 
2.0%
주)씨네이천 1
 
2.0%
Other values (37) 37
75.5%
2023-12-10T18:49:15.777037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 31
 
7.9%
( 28
 
7.1%
) 28
 
7.1%
스 20
 
5.1%
13
 
3.3%
, 12
 
3.1%
이 11
 
2.8%
씨 7
 
1.8%
필 6
 
1.5%
름 6
 
1.5%
Other values (123) 230
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 290
74.0%
Open Punctuation 28
 
7.1%
Close Punctuation 28
 
7.1%
Space Separator 13
 
3.3%
Other Punctuation 13
 
3.3%
Uppercase Letter 11
 
2.8%
Decimal Number 9
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 31
 
10.7%
스 20
 
6.9%
이 11
 
3.8%
씨 7
 
2.4%
필 6
 
2.1%
름 6
 
2.1%
사 6
 
2.1%
오 6
 
2.1%
디 6
 
2.1%
퍼 5
 
1.7%
Other values (109) 186
64.1%
Uppercase Letter
ValueCountFrequency (%)
G 3
27.3%
D 2
18.2%
C 2
18.2%
A 2
18.2%
T 1
 
9.1%
E 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
3 4
44.4%
5 4
44.4%
6 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 12
92.3%
. 1
 
7.7%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 290
74.0%
Common 91
 
23.2%
Latin 11
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 31
 
10.7%
스 20
 
6.9%
이 11
 
3.8%
씨 7
 
2.4%
필 6
 
2.1%
름 6
 
2.1%
사 6
 
2.1%
오 6
 
2.1%
디 6
 
2.1%
퍼 5
 
1.7%
Other values (109) 186
64.1%
Common
ValueCountFrequency (%)
( 28
30.8%
) 28
30.8%
13
14.3%
, 12
13.2%
3 4
 
4.4%
5 4
 
4.4%
. 1
 
1.1%
6 1
 
1.1%
Latin
ValueCountFrequency (%)
G 3
27.3%
D 2
18.2%
C 2
18.2%
A 2
18.2%
T 1
 
9.1%
E 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 290
74.0%
ASCII 102
 
26.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 31
 
10.7%
스 20
 
6.9%
이 11
 
3.8%
씨 7
 
2.4%
필 6
 
2.1%
름 6
 
2.1%
사 6
 
2.1%
오 6
 
2.1%
디 6
 
2.1%
퍼 5
 
1.7%
Other values (109) 186
64.1%
ASCII
ValueCountFrequency (%)
( 28
27.5%
) 28
27.5%
13
12.7%
, 12
11.8%
3 4
 
3.9%
5 4
 
3.9%
G 3
 
2.9%
D 2
 
2.0%
C 2
 
2.0%
A 2
 
2.0%
Other values (4) 4
 
3.9%

import_cmpny_nm
Text

MISSING 

Distinct36
Distinct (%)60.0%
Missing40
Missing (%)40.0%
Memory size932.0 B
2023-12-10T18:49:16.174154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length10.466667
Min length2

Characters and Unicode

Total characters628
Distinct characters124
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)40.0%

Sample

1st row유니버설픽쳐스인터내셔널 코리아(유)
2nd row워너브러더스 코리아(주)
3rd row월트디즈니컴퍼니코리아 유한책임회사
4th row월트디즈니컴퍼니코리아 유한책임회사
5th row소니픽쳐스엔터테인먼트코리아주식회사극장배급지점
ValueCountFrequency (%)
워너브러더스 5
 
5.7%
코리아(주 5
 
5.7%
주식회사 5
 
5.7%
유니버설픽쳐스인터내셔널 4
 
4.5%
월트디즈니컴퍼니코리아 4
 
4.5%
유한책임회사 4
 
4.5%
유)조이앤시네마 4
 
4.5%
코리아(유 4
 
4.5%
주)영화사 4
 
4.5%
찬란 3
 
3.4%
Other values (36) 46
52.3%
2023-12-10T18:49:16.793252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 41
 
6.5%
( 41
 
6.5%
주 39
 
6.2%
28
 
4.5%
스 24
 
3.8%
리 17
 
2.7%
유 16
 
2.5%
니 15
 
2.4%
아 15
 
2.4%
사 14
 
2.2%
Other values (114) 378
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 510
81.2%
Close Punctuation 41
 
6.5%
Open Punctuation 41
 
6.5%
Space Separator 28
 
4.5%
Uppercase Letter 4
 
0.6%
Other Punctuation 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 39
 
7.6%
스 24
 
4.7%
리 17
 
3.3%
유 16
 
3.1%
니 15
 
2.9%
아 15
 
2.9%
사 14
 
2.7%
코 14
 
2.7%
네 11
 
2.2%
인 11
 
2.2%
Other values (108) 334
65.5%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
& 2
50.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 510
81.2%
Common 114
 
18.2%
Latin 4
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 39
 
7.6%
스 24
 
4.7%
리 17
 
3.3%
유 16
 
3.1%
니 15
 
2.9%
아 15
 
2.9%
사 14
 
2.7%
코 14
 
2.7%
네 11
 
2.2%
인 11
 
2.2%
Other values (108) 334
65.5%
Common
ValueCountFrequency (%)
) 41
36.0%
( 41
36.0%
28
24.6%
, 2
 
1.8%
& 2
 
1.8%
Latin
ValueCountFrequency (%)
M 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 510
81.2%
ASCII 118
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 41
34.7%
( 41
34.7%
28
23.7%
M 4
 
3.4%
, 2
 
1.7%
& 2
 
1.7%
Hangul
ValueCountFrequency (%)
주 39
 
7.6%
스 24
 
4.7%
리 17
 
3.3%
유 16
 
3.1%
니 15
 
2.9%
아 15
 
2.9%
사 14
 
2.7%
코 14
 
2.7%
네 11
 
2.2%
인 11
 
2.2%
Other values (108) 334
65.5%

distb_cmpny_nm
Text

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:49:17.123417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length19
Mean length11.49
Min length2

Characters and Unicode

Total characters1149
Distinct characters166
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)42.0%

Sample

1st row롯데컬처웍스(주)롯데엔터테인먼트
2nd row스마일컨텐츠
3rd row(주)쇼박스
4th row(주)넥스트엔터테인먼트월드(NEW)
5th row유니버설픽쳐스인터내셔널 코리아(유)
ValueCountFrequency (%)
주)영화사 6
 
4.2%
워너브러더스 5
 
3.5%
코리아(주 5
 
3.5%
주식회사 5
 
3.5%
유니버설픽쳐스인터내셔널 4
 
2.8%
코리아(유 4
 
2.8%
월트디즈니컴퍼니코리아 4
 
2.8%
유한책임회사 4
 
2.8%
진진 4
 
2.8%
cgv)(주 3
 
2.1%
Other values (72) 100
69.4%
2023-12-10T18:49:17.741263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 86
 
7.5%
( 86
 
7.5%
주 84
 
7.3%
스 51
 
4.4%
44
 
3.8%
이 28
 
2.4%
인 22
 
1.9%
리 22
 
1.9%
트 21
 
1.8%
씨 20
 
1.7%
Other values (156) 685
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 862
75.0%
Close Punctuation 86
 
7.5%
Open Punctuation 86
 
7.5%
Uppercase Letter 52
 
4.5%
Space Separator 44
 
3.8%
Other Punctuation 17
 
1.5%
Other Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 84
 
9.7%
스 51
 
5.9%
이 28
 
3.2%
인 22
 
2.6%
리 22
 
2.6%
트 21
 
2.4%
씨 20
 
2.3%
디 20
 
2.3%
터 19
 
2.2%
니 18
 
2.1%
Other values (138) 557
64.6%
Uppercase Letter
ValueCountFrequency (%)
C 14
26.9%
G 5
 
9.6%
E 5
 
9.6%
V 5
 
9.6%
O 4
 
7.7%
N 4
 
7.7%
M 4
 
7.7%
T 3
 
5.8%
J 3
 
5.8%
W 2
 
3.8%
Other values (2) 3
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 15
88.2%
& 2
 
11.8%
Close Punctuation
ValueCountFrequency (%)
) 86
100.0%
Open Punctuation
ValueCountFrequency (%)
( 86
100.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 864
75.2%
Common 233
 
20.3%
Latin 52
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 84
 
9.7%
스 51
 
5.9%
이 28
 
3.2%
인 22
 
2.5%
리 22
 
2.5%
트 21
 
2.4%
씨 20
 
2.3%
디 20
 
2.3%
터 19
 
2.2%
니 18
 
2.1%
Other values (139) 559
64.7%
Latin
ValueCountFrequency (%)
C 14
26.9%
G 5
 
9.6%
E 5
 
9.6%
V 5
 
9.6%
O 4
 
7.7%
N 4
 
7.7%
M 4
 
7.7%
T 3
 
5.8%
J 3
 
5.8%
W 2
 
3.8%
Other values (2) 3
 
5.8%
Common
ValueCountFrequency (%)
) 86
36.9%
( 86
36.9%
44
18.9%
, 15
 
6.4%
& 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 862
75.0%
ASCII 285
 
24.8%
None 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 86
30.2%
( 86
30.2%
44
15.4%
, 15
 
5.3%
C 14
 
4.9%
G 5
 
1.8%
E 5
 
1.8%
V 5
 
1.8%
O 4
 
1.4%
N 4
 
1.4%
Other values (7) 17
 
6.0%
Hangul
ValueCountFrequency (%)
주 84
 
9.7%
스 51
 
5.9%
이 28
 
3.2%
인 22
 
2.6%
리 22
 
2.6%
트 21
 
2.4%
씨 20
 
2.3%
디 20
 
2.3%
터 19
 
2.2%
니 18
 
2.1%
Other values (138) 557
64.6%
None
ValueCountFrequency (%)
㈜ 2
100.0%

open_de
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-07-
37 
2021-08-
32 
2021-06-
23 
2021-04-
 
3
2021-05-
 
2
Other values (2)
 
3

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2021-07-
2nd row2021-07-
3rd row2021-08-
4th row2021-08-
5th row2021-07-

Common Values

ValueCountFrequency (%)
2021-07- 37
37.0%
2021-08- 32
32.0%
2021-06- 23
23.0%
2021-04- 3
 
3.0%
2021-05- 2
 
2.0%
2021-03- 2
 
2.0%
2021-01- 1
 
1.0%

Length

2023-12-10T18:49:17.970897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:18.168403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-07 37
37.0%
2021-08 32
32.0%
2021-06 23
23.0%
2021-04 3
 
3.0%
2021-05 2
 
2.0%
2021-03 2
 
2.0%
2021-01 1
 
1.0%

movie_ty_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
개봉영화
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개봉영화 100
100.0%

Length

2023-12-10T18:49:18.566180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:18.726616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개봉영화 100
100.0%

movie_stle_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
장편
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장편 100
100.0%

Length

2023-12-10T18:49:18.898805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:19.111705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장편 100
100.0%

nlty_nm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한국
36 
미국
31 
일본
9 
영국
4 
중국
 
3
Other values (11)
17 

Length

Max length5
Median length2
Mean length2.14
Min length2

Unique

Unique7 ?
Unique (%)7.0%

Sample

1st row한국
2nd row한국
3rd row한국
4th row한국
5th row미국

Common Values

ValueCountFrequency (%)
한국 36
36.0%
미국 31
31.0%
일본 9
 
9.0%
영국 4
 
4.0%
중국 3
 
3.0%
프랑스 3
 
3.0%
기타 3
 
3.0%
독일 2
 
2.0%
스페인 2
 
2.0%
대만 1
 
1.0%
Other values (6) 6
 
6.0%

Length

2023-12-10T18:49:19.313663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한국 36
36.0%
미국 31
31.0%
일본 9
 
9.0%
영국 4
 
4.0%
중국 3
 
3.0%
프랑스 3
 
3.0%
기타 3
 
3.0%
독일 2
 
2.0%
스페인 2
 
2.0%
대만 1
 
1.0%
Other values (6) 6
 
6.0%

wnty_screen_co
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)84.1%
Missing12
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean191.88636
Minimum1
Maximum975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:19.659372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.4
Q134
median81
Q3215.5
95-th percentile742.3
Maximum975
Range974
Interquartile range (IQR)181.5

Descriptive statistics

Standard deviation243.10087
Coefficient of variation (CV)1.2669002
Kurtosis1.7463485
Mean191.88636
Median Absolute Deviation (MAD)61
Skewness1.6690029
Sum16886
Variance59098.033
MonotonicityNot monotonic
2023-12-10T18:49:20.084286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
4.0%
19 2
 
2.0%
23 2
 
2.0%
45 2
 
2.0%
54 2
 
2.0%
79 2
 
2.0%
34 2
 
2.0%
181 2
 
2.0%
12 2
 
2.0%
48 2
 
2.0%
Other values (64) 66
66.0%
(Missing) 12
 
12.0%
ValueCountFrequency (%)
1 4
4.0%
5 1
 
1.0%
9 1
 
1.0%
12 2
2.0%
18 1
 
1.0%
19 2
2.0%
21 1
 
1.0%
23 2
2.0%
26 1
 
1.0%
28 1
 
1.0%
ValueCountFrequency (%)
975 1
1.0%
856 1
1.0%
838 1
1.0%
831 1
1.0%
750 1
1.0%
728 1
1.0%
693 1
1.0%
692 1
1.0%
649 1
1.0%
603 1
1.0%

wnty_selng_am
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

wnty_aude_co
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)85.7%
Missing86
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean659
Minimum1
Maximum951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:20.315605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1739
median798.5
Q3876.5
95-th percentile931.5
Maximum951
Range950
Interquartile range (IQR)137.5

Descriptive statistics

Standard deviation361.81721
Coefficient of variation (CV)0.54903977
Kurtosis0.32820823
Mean659
Median Absolute Deviation (MAD)79
Skewness-1.4485696
Sum9226
Variance130911.69
MonotonicityNot monotonic
2023-12-10T18:49:20.568331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 3
 
3.0%
951 1
 
1.0%
921 1
 
1.0%
900 1
 
1.0%
884 1
 
1.0%
854 1
 
1.0%
832 1
 
1.0%
808 1
 
1.0%
789 1
 
1.0%
780 1
 
1.0%
Other values (2) 2
 
2.0%
(Missing) 86
86.0%
ValueCountFrequency (%)
1 3
3.0%
726 1
 
1.0%
778 1
 
1.0%
780 1
 
1.0%
789 1
 
1.0%
808 1
 
1.0%
832 1
 
1.0%
854 1
 
1.0%
884 1
 
1.0%
900 1
 
1.0%
ValueCountFrequency (%)
951 1
1.0%
921 1
1.0%
900 1
1.0%
884 1
1.0%
854 1
1.0%
832 1
1.0%
808 1
1.0%
789 1
1.0%
780 1
1.0%
778 1
1.0%

su_selng_am
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
97 
0
 
3

Length

Max length4
Median length4
Mean length3.91
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 97
97.0%
0 3
 
3.0%

Length

2023-12-10T18:49:20.915867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:21.147885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 97
97.0%
0 3
 
3.0%

su_aude_co
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)92.0%
Missing75
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean493.56
Minimum0
Maximum991
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:21.471394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1269
median462
Q3728
95-th percentile909.8
Maximum991
Range991
Interquartile range (IQR)459

Descriptive statistics

Standard deviation294.36869
Coefficient of variation (CV)0.59641925
Kurtosis-0.96799543
Mean493.56
Median Absolute Deviation (MAD)233
Skewness-0.14336575
Sum12339
Variance86652.923
MonotonicityNot monotonic
2023-12-10T18:49:21.872405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 3
 
3.0%
841 1
 
1.0%
462 1
 
1.0%
282 1
 
1.0%
778 1
 
1.0%
242 1
 
1.0%
488 1
 
1.0%
183 1
 
1.0%
252 1
 
1.0%
422 1
 
1.0%
Other values (13) 13
 
13.0%
(Missing) 75
75.0%
ValueCountFrequency (%)
0 3
3.0%
183 1
 
1.0%
242 1
 
1.0%
252 1
 
1.0%
269 1
 
1.0%
282 1
 
1.0%
368 1
 
1.0%
407 1
 
1.0%
422 1
 
1.0%
439 1
 
1.0%
ValueCountFrequency (%)
991 1
1.0%
927 1
1.0%
841 1
1.0%
840 1
1.0%
778 1
1.0%
743 1
1.0%
728 1
1.0%
699 1
1.0%
695 1
1.0%
677 1
1.0%

genre_nm
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
드라마
27 
액션
14 
애니메이션
12 
공포(호러)
10 
다큐멘터리
8 
Other values (12)
29 

Length

Max length7
Median length6
Mean length3.71
Min length2

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row액션
2nd row성인물(에로)
3rd row코미디
4th row액션
5th row애니메이션

Common Values

ValueCountFrequency (%)
드라마 27
27.0%
액션 14
14.0%
애니메이션 12
12.0%
공포(호러) 10
 
10.0%
다큐멘터리 8
 
8.0%
코미디 7
 
7.0%
스릴러 5
 
5.0%
미스터리 3
 
3.0%
멜로/로맨스 3
 
3.0%
성인물(에로) 2
 
2.0%
Other values (7) 9
 
9.0%

Length

2023-12-10T18:49:22.144222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
드라마 27
27.0%
액션 14
14.0%
애니메이션 12
12.0%
공포(호러 10
 
10.0%
다큐멘터리 8
 
8.0%
코미디 7
 
7.0%
스릴러 5
 
5.0%
멜로/로맨스 3
 
3.0%
미스터리 3
 
3.0%
성인물(에로 2
 
2.0%
Other values (7) 9
 
9.0%

grad_nm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
12세이상관람가
38 
15세이상관람가
37 
전체관람가
14 
청소년관람불가
10 
연소자관람불가,청소년관람불가
 
1

Length

Max length15
Median length8
Mean length7.55
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row15세이상관람가
2nd row청소년관람불가
3rd row12세이상관람가
4th row15세이상관람가
5th row전체관람가

Common Values

ValueCountFrequency (%)
12세이상관람가 38
38.0%
15세이상관람가 37
37.0%
전체관람가 14
 
14.0%
청소년관람불가 10
 
10.0%
연소자관람불가,청소년관람불가 1
 
1.0%

Length

2023-12-10T18:49:22.435938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:22.748846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12세이상관람가 38
38.0%
15세이상관람가 37
37.0%
전체관람가 14
 
14.0%
청소년관람불가 10
 
10.0%
연소자관람불가,청소년관람불가 1
 
1.0%

movie_se
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
일반영화
51 
독립/예술영화
49 

Length

Max length7
Median length4
Mean length5.47
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반영화 51
51.0%
독립/예술영화 49
49.0%

Length

2023-12-10T18:49:23.132741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:23.381713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반영화 51
51.0%
독립/예술영화 49
49.0%

Interactions

2023-12-10T18:49:09.135758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:06.877601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:07.619599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:08.144035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:09.276575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:07.107692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:07.768208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:08.267620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:09.416556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:07.336003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:07.886178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:08.795161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:09.555811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:07.471506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:08.014496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:08.965117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:49:23.912545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
snmovie_nmmng_nmmakr_nmimport_cmpny_nmdistb_cmpny_nmopen_denlty_nmwnty_screen_cownty_aude_cosu_aude_cogenre_nmgrad_nmmovie_se
sn1.0001.0000.9621.0000.7080.9250.1450.0000.5231.0000.9320.7770.3330.694
movie_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
mng_nm0.9621.0001.0001.0001.0000.9940.9661.0000.0001.0001.0000.0000.9181.000
makr_nm1.0001.0001.0001.000NaN1.0001.000NaN1.0001.0001.0001.0001.0001.000
import_cmpny_nm0.7081.0001.000NaN1.0000.9980.0000.9430.0001.0001.0000.0000.0000.978
distb_cmpny_nm0.9251.0000.9941.0000.9981.0000.0000.9540.0001.0000.8980.7780.8190.891
open_de0.1451.0000.9661.0000.0000.0001.0000.0000.3590.0000.7170.2970.0000.000
nlty_nm0.0001.0001.000NaN0.9430.9540.0001.0000.0000.0000.0000.0000.0000.475
wnty_screen_co0.5231.0000.0001.0000.0000.0000.3590.0001.0000.0000.0000.6680.0000.513
wnty_aude_co1.0001.0001.0001.0001.0001.0000.0000.0000.0001.0000.6860.8560.8390.947
su_aude_co0.9321.0001.0001.0001.0000.8980.7170.0000.0000.6861.0000.0000.6970.240
genre_nm0.7771.0000.0001.0000.0000.7780.2970.0000.6680.8560.0001.0000.6090.756
grad_nm0.3331.0000.9181.0000.0000.8190.0000.0000.0000.8390.6970.6091.0000.250
movie_se0.6941.0001.0001.0000.9780.8910.0000.4750.5130.9470.2400.7560.2501.000
2023-12-10T18:49:24.202860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
su_selng_amgrad_nmnlty_nmopen_demovie_segenre_nm
su_selng_am1.0001.0001.0001.0001.0001.000
grad_nm1.0001.0000.0000.0000.3000.343
nlty_nm1.0000.0001.0000.0000.3440.000
open_de1.0000.0000.0001.0000.0000.124
movie_se1.0000.3000.3440.0001.0000.642
genre_nm1.0000.3430.0000.1240.6421.000
2023-12-10T18:49:24.419493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
snwnty_screen_cownty_aude_cosu_aude_coopen_denlty_nmsu_selng_amgenre_nmgrad_nmmovie_se
sn1.000-0.740-0.996-0.6810.0950.0001.0000.5200.2750.487
wnty_screen_co-0.7401.0000.3560.3630.1810.0001.0000.3320.0000.492
wnty_aude_co-0.9960.3561.0000.5880.0000.0001.0000.6330.4730.719
su_aude_co-0.6810.3630.5881.0000.4010.0001.0000.0000.4510.164
open_de0.0950.1810.0000.4011.0000.0001.0000.1240.0000.000
nlty_nm0.0000.0000.0000.0000.0001.0001.0000.0000.0000.344
su_selng_am1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
genre_nm0.5200.3320.6330.0000.1240.0001.0001.0000.3430.642
grad_nm0.2750.0000.4730.4510.0000.0001.0000.3431.0000.300
movie_se0.4870.4920.7190.1640.0000.3441.0000.6420.3001.000

Missing values

2023-12-10T18:49:09.806266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:49:10.251044image/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.
2023-12-10T18:49:10.609711image/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

snmovie_nmmng_nmmakr_nmimport_cmpny_nmdistb_cmpny_nmopen_demovie_ty_nmmovie_stle_nmnlty_nmwnty_screen_cownty_selng_amwnty_aude_cosu_selng_amsu_aude_cogenre_nmgrad_nmmovie_se
01모가디슈류승완(주)덱스터스튜디오,(주)외유내강,(주)필름케이<NA>롯데컬처웍스(주)롯데엔터테인먼트2021-07-개봉영화장편한국<NA><NA><NA><NA><NA>액션15세이상관람가일반영화
134311번가청순글래머오피스걸이희성스마일컨텐츠<NA>스마일컨텐츠2021-07-개봉영화장편한국1<NA>100성인물(에로)청소년관람불가일반영화
23싱크홀김지훈(주)더타워픽쳐스<NA>(주)쇼박스2021-08-개봉영화장편한국<NA><NA><NA><NA><NA>코미디12세이상관람가일반영화
34인질필감성(주)외유내강,(주)샘컴퍼니<NA>(주)넥스트엔터테인먼트월드(NEW)2021-08-개봉영화장편한국<NA><NA><NA><NA><NA>액션15세이상관람가일반영화
45보스 베이비 2톰 맥그라스<NA>유니버설픽쳐스인터내셔널 코리아(유)유니버설픽쳐스인터내셔널 코리아(유)2021-07-개봉영화장편미국<NA><NA><NA><NA><NA>애니메이션전체관람가일반영화
56랑종반종 피산다나쿤(주)노던크로스<NA>(주)쇼박스2021-07-개봉영화장편한국<NA><NA><NA><NA><NA>공포(호러)청소년관람불가일반영화
67발신제한김창주(주)티피에스컴퍼니,(주)씨제이이엔엠<NA>(주)씨제이이엔엠2021-06-개봉영화장편한국<NA><NA><NA><NA><NA>스릴러15세이상관람가일반영화
734419번가물많은여자친구이희성스마일픽쳐스<NA>스마일픽쳐스2021-08-개봉영화장편한국1<NA>100멜로/로맨스청소년관람불가일반영화
89더 수어사이드 스쿼드제임스 건<NA>워너브러더스 코리아(주)워너브러더스 코리아(주)2021-08-개봉영화장편미국<NA><NA><NA><NA><NA>액션청소년관람불가일반영화
910프리 가이숀 레비<NA>월트디즈니컴퍼니코리아 유한책임회사월트디즈니컴퍼니코리아 유한책임회사2021-08-개봉영화장편미국750<NA><NA><NA><NA>액션12세이상관람가일반영화
snmovie_nmmng_nmmakr_nmimport_cmpny_nmdistb_cmpny_nmopen_demovie_ty_nmmovie_stle_nmnlty_nmwnty_screen_cownty_selng_amwnty_aude_cosu_selng_amsu_aude_cogenre_nmgrad_nmmovie_se
9091북샵이자벨 코이젯트<NA>(주)콘텐츠판다(주)콘텐츠판다2021-06-개봉영화장편스페인9<NA>921<NA>439드라마전체관람가독립/예술영화
9192아담마리암 투자니<NA>시네마 뉴원시네마 뉴원2021-08-개봉영화장편모로코29<NA>900<NA>606드라마12세이상관람가독립/예술영화
9293샤먼 로드최상진강 컨텐츠<NA>강 컨텐츠2021-07-개봉영화장편한국5<NA>884<NA>407다큐멘터리12세이상관람가독립/예술영화
9394사랑하고 사랑받고 차고 차이고미키 타카히로<NA>(주)도키엔터테인먼트,와이드 릴리즈(주)주식회사 컨텐츠썬2021-06-개봉영화장편일본155<NA>854<NA>422드라마12세이상관람가독립/예술영화
9495레일로드 워장성<NA>(주)엔케이컨텐츠(주)디스테이션2021-06-개봉영화장편중국45<NA>832<NA>252액션12세이상관람가독립/예술영화
9596흩어진 밤이지형,김솔(주)타이거시네마,DGC<NA>영화배급협동조합 씨네소파2021-06-개봉영화장편한국49<NA>808<NA>183드라마전체관람가독립/예술영화
9697자메이카의 소울: 이나 데 야드피터 웨버<NA>(주)엣나인필름(주)엣나인필름2021-07-개봉영화장편프랑스12<NA>789<NA>488다큐멘터리12세이상관람가독립/예술영화
9798피어 오브 레인<NA><NA>(주)누리픽쳐스(주)누리픽쳐스2021-07-개봉영화장편미국47<NA>780<NA>242드라마15세이상관람가일반영화
9899어른들은 몰라요이환(주)돈키호테엔터테인먼트<NA>(주)리틀빅픽쳐스2021-04-개봉영화장편한국209<NA>778<NA>778드라마청소년관람불가독립/예술영화
99100좀비크러쉬: 헤이리장현상GATE6<NA>필름다빈2021-06-개봉영화장편한국23<NA>726<NA>282코미디15세이상관람가독립/예술영화