Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory87.3 B

Variable types

Numeric6
Categorical4

Alerts

upper_ctgry_nm has constant value ""Constant
lwprt_ctgry_nm has constant value ""Constant
srchwrd_nm is highly overall correlated with genre_nmHigh correlation
genre_nm is highly overall correlated with srchwrd_nmHigh correlation
seq_no is highly overall correlated with sccnt_deHigh correlation
all_kwrd_rank_co is highly overall correlated with mobile_sccnt_value and 2 other fieldsHigh correlation
mobile_sccnt_value is highly overall correlated with all_kwrd_rank_co and 2 other fieldsHigh correlation
pc_sccnt_value is highly overall correlated with all_kwrd_rank_co and 2 other fieldsHigh correlation
sccnt_sm_value is highly overall correlated with all_kwrd_rank_co and 2 other fieldsHigh correlation
sccnt_de is highly overall correlated with seq_noHigh correlation
seq_no has unique valuesUnique
mobile_sccnt_value has unique valuesUnique
pc_sccnt_value has unique valuesUnique
sccnt_sm_value has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:43:05.777001
Analysis finished2023-12-10 09:43:13.276511
Duration7.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64531.5
Minimum64482
Maximum64581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:13.437212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64482
5-th percentile64486.95
Q164506.75
median64531.5
Q364556.25
95-th percentile64576.05
Maximum64581
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.00044957102
Kurtosis-1.2
Mean64531.5
Median Absolute Deviation (MAD)25
Skewness0
Sum6453150
Variance841.66667
MonotonicityNot monotonic
2023-12-10T18:43:13.732748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64573 1
 
1.0%
64518 1
 
1.0%
64511 1
 
1.0%
64510 1
 
1.0%
64509 1
 
1.0%
64504 1
 
1.0%
64503 1
 
1.0%
64514 1
 
1.0%
64513 1
 
1.0%
64512 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
64482 1
1.0%
64483 1
1.0%
64484 1
1.0%
64485 1
1.0%
64486 1
1.0%
64487 1
1.0%
64488 1
1.0%
64489 1
1.0%
64490 1
1.0%
64491 1
1.0%
ValueCountFrequency (%)
64581 1
1.0%
64580 1
1.0%
64579 1
1.0%
64578 1
1.0%
64577 1
1.0%
64576 1
1.0%
64575 1
1.0%
64574 1
1.0%
64573 1
1.0%
64572 1
1.0%

all_kwrd_rank_co
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:13.954649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5.5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8867513
Coefficient of variation (CV)0.52486388
Kurtosis-1.2252472
Mean5.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum550
Variance8.3333333
MonotonicityNot monotonic
2023-12-10T18:43:14.151947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 10
10.0%
4 10
10.0%
8 10
10.0%
3 10
10.0%
5 10
10.0%
1 10
10.0%
6 10
10.0%
7 10
10.0%
9 10
10.0%
10 10
10.0%
ValueCountFrequency (%)
1 10
10.0%
2 10
10.0%
3 10
10.0%
4 10
10.0%
5 10
10.0%
6 10
10.0%
7 10
10.0%
8 10
10.0%
9 10
10.0%
10 10
10.0%
ValueCountFrequency (%)
10 10
10.0%
9 10
10.0%
8 10
10.0%
7 10
10.0%
6 10
10.0%
5 10
10.0%
4 10
10.0%
3 10
10.0%
2 10
10.0%
1 10
10.0%

srchwrd_nm
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
이터널스
10 
베놈2
10 
모가디슈
10 
보이스
10 
인질
10 
Other values (13)
50 

Length

Max length9
Median length8
Mean length3.72
Min length2

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row이터널스
2nd row인질
3rd row해치지않아
4th row베놈2
5th row기생충

Common Values

ValueCountFrequency (%)
이터널스 10
10.0%
베놈2 10
10.0%
모가디슈 10
10.0%
보이스 10
10.0%
인질 10
10.0%
랑종 9
9.0%
해치지않아 8
8.0%
기생충 8
8.0%
싱크홀 8
8.0%
007노타임투다이 7
7.0%
Other values (8) 10
10.0%

Length

2023-12-10T18:43:14.390670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이터널스 10
10.0%
베놈2 10
10.0%
모가디슈 10
10.0%
보이스 10
10.0%
인질 10
10.0%
랑종 9
9.0%
해치지않아 8
8.0%
기생충 8
8.0%
싱크홀 8
8.0%
007노타임투다이 7
7.0%
Other values (8) 10
10.0%

upper_ctgry_nm
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문화공연 100
100.0%

Length

2023-12-10T18:43:14.621731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:14.777848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화공연 100
100.0%

lwprt_ctgry_nm
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영화 100
100.0%

Length

2023-12-10T18:43:14.980258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:15.140450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영화 100
100.0%

genre_nm
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
액션
27 
코미디
16 
액션,스릴러
10 
액션,드라마
10 
범죄
10 
Other values (9)
27 

Length

Max length14
Median length11
Mean length4.68
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row액션
2nd row액션,스릴러
3rd row코미디
4th row액션
5th row드라마

Common Values

ValueCountFrequency (%)
액션 27
27.0%
코미디 16
16.0%
액션,스릴러 10
 
10.0%
액션,드라마 10
 
10.0%
범죄 10
 
10.0%
공포(호러),스릴러,드라마 9
 
9.0%
드라마 8
 
8.0%
드라마,액션 2
 
2.0%
드라마,어드벤처 2
 
2.0%
드라마,미스터리 2
 
2.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T18:43:15.323664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
액션 27
27.0%
코미디 16
16.0%
액션,스릴러 10
 
10.0%
액션,드라마 10
 
10.0%
범죄 10
 
10.0%
공포(호러),스릴러,드라마 9
 
9.0%
드라마 8
 
8.0%
드라마,액션 2
 
2.0%
드라마,어드벤처 2
 
2.0%
드라마,미스터리 2
 
2.0%
Other values (4) 4
 
4.0%

mobile_sccnt_value
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1309
Minimum210
Maximum4593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:15.660672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile361.75
Q1641
median826.5
Q31526.75
95-th percentile3990.55
Maximum4593
Range4383
Interquartile range (IQR)885.75

Descriptive statistics

Standard deviation1089.6248
Coefficient of variation (CV)0.8324101
Kurtosis1.8538745
Mean1309
Median Absolute Deviation (MAD)294
Skewness1.691439
Sum130900
Variance1187282.2
MonotonicityNot monotonic
2023-12-10T18:43:16.014400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 1
 
1.0%
656 1
 
1.0%
666 1
 
1.0%
654 1
 
1.0%
548 1
 
1.0%
1826 1
 
1.0%
2558 1
 
1.0%
1173 1
 
1.0%
2233 1
 
1.0%
3872 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
210 1
1.0%
213 1
1.0%
280 1
1.0%
304 1
1.0%
338 1
1.0%
363 1
1.0%
392 1
1.0%
476 1
1.0%
501 1
1.0%
508 1
1.0%
ValueCountFrequency (%)
4593 1
1.0%
4389 1
1.0%
4253 1
1.0%
4086 1
1.0%
4077 1
1.0%
3986 1
1.0%
3974 1
1.0%
3872 1
1.0%
3457 1
1.0%
3434 1
1.0%

pc_sccnt_value
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7896.93
Minimum2638
Maximum43459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:16.650416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2638
5-th percentile2990.8
Q13623.75
median4854.5
Q38921.75
95-th percentile20646.95
Maximum43459
Range40821
Interquartile range (IQR)5298

Descriptive statistics

Standard deviation7129.7372
Coefficient of variation (CV)0.90284923
Kurtosis6.2218472
Mean7896.93
Median Absolute Deviation (MAD)1464.5
Skewness2.2688863
Sum789693
Variance50833152
MonotonicityNot monotonic
2023-12-10T18:43:16.947818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15249 1
 
1.0%
3497 1
 
1.0%
2968 1
 
1.0%
3082 1
 
1.0%
3435 1
 
1.0%
16102 1
 
1.0%
15949 1
 
1.0%
5285 1
 
1.0%
13328 1
 
1.0%
17053 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2638 1
1.0%
2686 1
1.0%
2845 1
1.0%
2888 1
1.0%
2968 1
1.0%
2992 1
1.0%
3082 1
1.0%
3092 1
1.0%
3095 1
1.0%
3175 1
1.0%
ValueCountFrequency (%)
43459 1
1.0%
28413 1
1.0%
26336 1
1.0%
26202 1
1.0%
24978 1
1.0%
20419 1
1.0%
19976 1
1.0%
19905 1
1.0%
18443 1
1.0%
18355 1
1.0%

sccnt_sm_value
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9205.93
Minimum3146
Maximum48052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:17.294454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3146
5-th percentile3633.75
Q14252.25
median5719.5
Q310411
95-th percentile24259.8
Maximum48052
Range44906
Interquartile range (IQR)6158.75

Descriptive statistics

Standard deviation8068.3282
Coefficient of variation (CV)0.87642728
Kurtosis5.2899631
Mean9205.93
Median Absolute Deviation (MAD)1719
Skewness2.143139
Sum920593
Variance65097919
MonotonicityNot monotonic
2023-12-10T18:43:17.587644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19502 1
 
1.0%
4153 1
 
1.0%
3634 1
 
1.0%
3736 1
 
1.0%
3983 1
 
1.0%
17928 1
 
1.0%
18507 1
 
1.0%
6458 1
 
1.0%
15561 1
 
1.0%
20925 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
3146 1
1.0%
3249 1
1.0%
3593 1
1.0%
3596 1
1.0%
3629 1
1.0%
3634 1
1.0%
3665 1
1.0%
3715 1
1.0%
3719 1
1.0%
3736 1
1.0%
ValueCountFrequency (%)
48052 1
1.0%
31678 1
1.0%
30591 1
1.0%
28194 1
1.0%
28018 1
1.0%
24062 1
1.0%
23891 1
1.0%
23876 1
1.0%
22417 1
1.0%
22025 1
1.0%

sccnt_de
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20211210
Minimum20211206
Maximum20211215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:17.797457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20211206
5-th percentile20211206
Q120211208
median20211210
Q320211213
95-th percentile20211215
Maximum20211215
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8867513
Coefficient of variation (CV)1.4282922 × 10-7
Kurtosis-1.2252472
Mean20211210
Median Absolute Deviation (MAD)2.5
Skewness0
Sum2.021121 × 109
Variance8.3333333
MonotonicityDecreasing
2023-12-10T18:43:18.159741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20211215 10
10.0%
20211214 10
10.0%
20211213 10
10.0%
20211212 10
10.0%
20211211 10
10.0%
20211210 10
10.0%
20211209 10
10.0%
20211208 10
10.0%
20211207 10
10.0%
20211206 10
10.0%
ValueCountFrequency (%)
20211206 10
10.0%
20211207 10
10.0%
20211208 10
10.0%
20211209 10
10.0%
20211210 10
10.0%
20211211 10
10.0%
20211212 10
10.0%
20211213 10
10.0%
20211214 10
10.0%
20211215 10
10.0%
ValueCountFrequency (%)
20211215 10
10.0%
20211214 10
10.0%
20211213 10
10.0%
20211212 10
10.0%
20211211 10
10.0%
20211210 10
10.0%
20211209 10
10.0%
20211208 10
10.0%
20211207 10
10.0%
20211206 10
10.0%

Interactions

2023-12-10T18:43:11.771935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:06.285607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:07.377179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:08.391620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:09.692611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:10.759257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:11.919884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:06.465999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:07.546053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:08.537967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:09.865571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:10.931547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:12.055478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:06.635262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:07.708444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:08.896944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:10.037005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:11.101979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:12.215273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:06.785245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:07.877373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:09.124677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:10.197720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:11.253016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:12.413409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:07.031193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:08.077027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:09.350860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:10.411044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:11.446557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:12.564290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:07.213503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:08.236156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:09.509381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:10.597850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:11.602086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:43:18.349173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noall_kwrd_rank_cosrchwrd_nmgenre_nmmobile_sccnt_valuepc_sccnt_valuesccnt_sm_valuesccnt_de
seq_no1.0000.0000.0000.0000.0000.0000.0001.000
all_kwrd_rank_co0.0001.0000.7560.6310.7980.5810.5850.000
srchwrd_nm0.0000.7561.0001.0000.7600.4520.6240.000
genre_nm0.0000.6311.0001.0000.5770.0000.3270.000
mobile_sccnt_value0.0000.7980.7600.5771.0000.8100.8240.000
pc_sccnt_value0.0000.5810.4520.0000.8101.0000.9920.000
sccnt_sm_value0.0000.5850.6240.3270.8240.9921.0000.000
sccnt_de1.0000.0000.0000.0000.0000.0000.0001.000
2023-12-10T18:43:18.601445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
srchwrd_nmgenre_nm
srchwrd_nm1.0000.976
genre_nm0.9761.000
2023-12-10T18:43:18.832670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noall_kwrd_rank_comobile_sccnt_valuepc_sccnt_valuesccnt_sm_valuesccnt_desrchwrd_nmgenre_nm
seq_no1.0000.100-0.237-0.228-0.2510.9950.0000.000
all_kwrd_rank_co0.1001.000-0.751-0.910-0.9200.0000.3910.305
mobile_sccnt_value-0.237-0.7511.0000.7440.802-0.1630.3940.260
pc_sccnt_value-0.228-0.9100.7441.0000.992-0.1390.1970.000
sccnt_sm_value-0.251-0.9200.8020.9921.000-0.1600.3030.147
sccnt_de0.9950.000-0.163-0.139-0.1601.0000.0000.000
srchwrd_nm0.0000.3910.3940.1970.3030.0001.0000.976
genre_nm0.0000.3050.2600.0000.1470.0000.9761.000

Missing values

2023-12-10T18:43:12.824449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:43:13.147303image/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

seq_noall_kwrd_rank_cosrchwrd_nmupper_ctgry_nmlwprt_ctgry_nmgenre_nmmobile_sccnt_valuepc_sccnt_valuesccnt_sm_valuesccnt_de
0645732이터널스문화공연영화액션4253152491950220211215
1645754인질문화공연영화액션,스릴러9465431637720211215
2645798해치지않아문화공연영화코미디6433401404420211215
3645743베놈2문화공연영화액션16586903856120211215
4645765기생충문화공연영화드라마9343742467620211215
5645721모가디슈문화공연영화액션,드라마4593434594805220211215
6645776히트맨문화공연영화코미디,액션2104338454820211215
7645787반도문화공연영화드라마,액션3043907421120211215
8645809보이스문화공연영화범죄5613187374820211215
96458110백두산문화공연영화드라마,어드벤처3923323371520211215
seq_noall_kwrd_rank_cosrchwrd_nmupper_ctgry_nmlwprt_ctgry_nmgenre_nmmobile_sccnt_valuepc_sccnt_valuesccnt_sm_valuesccnt_de
90644821인질문화공연영화액션,스릴러4389262023059120211206
91644832이터널스문화공연영화액션4086199762406220211206
92644843다만악에서구하소서문화공연영화액션,범죄613140911470420211206
93644854모가디슈문화공연영화액션,드라마15176853837020211206
94644865베놈2문화공연영화액션15946144773820211206
95644876해치지않아문화공연영화코미디5405168570820211206
96644887보이스문화공연영화범죄7794610538920211206
97644898싱크홀문화공연영화코미디8724465533720211206
98644909랑종문화공연영화공포(호러),스릴러,드라마8094170497920211206
996449110007노타임투다이문화공연영화액션7823738452020211206