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
genre_nm is highly overall correlated with srchwrd_nmHigh correlation
srchwrd_nm is highly overall correlated with genre_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_coHigh correlation
pc_sccnt_value is highly overall correlated with all_kwrd_rank_co and 1 other fieldsHigh correlation
sccnt_sm_value is highly overall correlated with all_kwrd_rank_co and 1 other fieldsHigh correlation
sccnt_de is highly overall correlated with seq_noHigh correlation
seq_no has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:05:24.128968
Analysis finished2023-12-10 10:05:31.614255
Duration7.49 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%
Mean22254.5
Minimum22205
Maximum22304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:31.813996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22205
5-th percentile22209.95
Q122229.75
median22254.5
Q322279.25
95-th percentile22299.05
Maximum22304
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.0013036236
Kurtosis-1.2
Mean22254.5
Median Absolute Deviation (MAD)25
Skewness0
Sum2225450
Variance841.66667
MonotonicityNot monotonic
2023-12-10T19:05:32.131863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22296 1
 
1.0%
22241 1
 
1.0%
22234 1
 
1.0%
22233 1
 
1.0%
22232 1
 
1.0%
22227 1
 
1.0%
22226 1
 
1.0%
22237 1
 
1.0%
22236 1
 
1.0%
22235 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
22205 1
1.0%
22206 1
1.0%
22207 1
1.0%
22208 1
1.0%
22209 1
1.0%
22210 1
1.0%
22211 1
1.0%
22212 1
1.0%
22213 1
1.0%
22214 1
1.0%
ValueCountFrequency (%)
22304 1
1.0%
22303 1
1.0%
22302 1
1.0%
22301 1
1.0%
22300 1
1.0%
22299 1
1.0%
22298 1
1.0%
22297 1
1.0%
22296 1
1.0%
22295 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
2023-12-10T19:05:32.331752image/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
2023-12-10T19:05:32.508665image/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 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
뮤지컬시카고
20 
뮤지컬레베카
10 
뮤지컬빌리엘리어트
10 
뮤지컬잭더리퍼
10 
뮤지컬엑스칼리버
10 
Other values (8)
40 

Length

Max length10
Median length9
Mean length6.95
Min length5

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row뮤지컬레베카
2nd row뮤지컬팬레터
3rd row뮤지컬시카고
4th row뮤지컬빌리엘리어트
5th row뮤지컬잭더리퍼

Common Values

ValueCountFrequency (%)
뮤지컬시카고 20
20.0%
뮤지컬레베카 10
10.0%
뮤지컬빌리엘리어트 10
10.0%
뮤지컬잭더리퍼 10
10.0%
뮤지컬엑스칼리버 10
10.0%
뮤지컬하데스타운 10
10.0%
뮤지컬빨래 10
10.0%
뮤지컬팬레터 7
 
7.0%
신비아파트뮤지컬 5
 
5.0%
뮤지컬미스터쇼 4
 
4.0%
Other values (3) 4
 
4.0%

Length

2023-12-10T19:05:32.736174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
뮤지컬시카고 20
20.0%
뮤지컬레베카 10
10.0%
뮤지컬빌리엘리어트 10
10.0%
뮤지컬잭더리퍼 10
10.0%
뮤지컬엑스칼리버 10
10.0%
뮤지컬하데스타운 10
10.0%
뮤지컬빨래 10
10.0%
뮤지컬팬레터 7
 
7.0%
신비아파트뮤지컬 5
 
5.0%
뮤지컬미스터쇼 4
 
4.0%
Other values (3) 4
 
4.0%

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

2023-12-10T19:05:32.960681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:33.132333image/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
뮤지컬
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
뮤지컬 100
100.0%

Length

2023-12-10T19:05:33.300492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:33.472836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
뮤지컬 100
100.0%

genre_nm
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
라이선스
51 
창작
45 
뮤지컬
 
4

Length

Max length4
Median length4
Mean length3.06
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row창작
2nd row창작
3rd row라이선스
4th row라이선스
5th row라이선스

Common Values

ValueCountFrequency (%)
라이선스 51
51.0%
창작 45
45.0%
뮤지컬 4
 
4.0%

Length

2023-12-10T19:05:33.669529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:33.872396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
라이선스 51
51.0%
창작 45
45.0%
뮤지컬 4
 
4.0%

mobile_sccnt_value
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.14
Minimum12
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:34.073602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile53.75
Q1103
median146.5
Q3191.5
95-th percentile261.9
Maximum516
Range504
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation73.937049
Coefficient of variation (CV)0.48598034
Kurtosis4.8335615
Mean152.14
Median Absolute Deviation (MAD)45.5
Skewness1.3068671
Sum15214
Variance5466.6873
MonotonicityNot monotonic
2023-12-10T19:05:34.342423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176 7
 
7.0%
204 4
 
4.0%
90 3
 
3.0%
150 3
 
3.0%
64 2
 
2.0%
77 2
 
2.0%
104 2
 
2.0%
138 2
 
2.0%
215 2
 
2.0%
111 2
 
2.0%
Other values (62) 71
71.0%
ValueCountFrequency (%)
12 1
1.0%
20 1
1.0%
41 1
1.0%
48 1
1.0%
49 1
1.0%
54 1
1.0%
59 1
1.0%
61 1
1.0%
64 2
2.0%
66 1
1.0%
ValueCountFrequency (%)
516 1
1.0%
323 1
1.0%
309 1
1.0%
283 1
1.0%
279 1
1.0%
261 1
1.0%
251 1
1.0%
250 1
1.0%
245 1
1.0%
240 1
1.0%

pc_sccnt_value
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean864.1
Minimum350
Maximum4401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:34.571983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile459.7
Q1647.5
median772.5
Q3985.25
95-th percentile1396.95
Maximum4401
Range4051
Interquartile range (IQR)337.75

Descriptive statistics

Standard deviation446.70817
Coefficient of variation (CV)0.51696351
Kurtosis39.441632
Mean864.1
Median Absolute Deviation (MAD)156.5
Skewness5.1774612
Sum86410
Variance199548.19
MonotonicityNot monotonic
2023-12-10T19:05:34.813950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1036 3
 
3.0%
857 2
 
2.0%
917 2
 
2.0%
738 2
 
2.0%
703 2
 
2.0%
650 2
 
2.0%
556 2
 
2.0%
667 2
 
2.0%
1081 2
 
2.0%
620 2
 
2.0%
Other values (75) 79
79.0%
ValueCountFrequency (%)
350 1
1.0%
367 1
1.0%
412 1
1.0%
449 1
1.0%
454 1
1.0%
460 1
1.0%
469 1
1.0%
480 1
1.0%
516 1
1.0%
535 1
1.0%
ValueCountFrequency (%)
4401 1
1.0%
1493 1
1.0%
1472 1
1.0%
1463 1
1.0%
1415 1
1.0%
1396 1
1.0%
1364 1
1.0%
1362 1
1.0%
1306 1
1.0%
1303 1
1.0%

sccnt_sm_value
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1016.24
Minimum450
Maximum4917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:35.106187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum450
5-th percentile563.2
Q1775.75
median942
Q31123.5
95-th percentile1585.3
Maximum4917
Range4467
Interquartile range (IQR)347.75

Descriptive statistics

Standard deviation492.99495
Coefficient of variation (CV)0.48511666
Kurtosis39.37083
Mean1016.24
Median Absolute Deviation (MAD)171
Skewness5.1866731
Sum101624
Variance243044.02
MonotonicityNot monotonic
2023-12-10T19:05:35.346141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
942 3
 
3.0%
1166 3
 
3.0%
1468 2
 
2.0%
879 2
 
2.0%
826 2
 
2.0%
706 2
 
2.0%
978 2
 
2.0%
811 2
 
2.0%
565 2
 
2.0%
1140 2
 
2.0%
Other values (74) 78
78.0%
ValueCountFrequency (%)
450 1
1.0%
482 1
1.0%
508 1
1.0%
521 1
1.0%
529 1
1.0%
565 2
2.0%
593 1
1.0%
610 1
1.0%
663 1
1.0%
673 1
1.0%
ValueCountFrequency (%)
4917 1
1.0%
1802 1
1.0%
1713 1
1.0%
1676 1
1.0%
1629 1
1.0%
1583 1
1.0%
1579 1
1.0%
1554 1
1.0%
1545 1
1.0%
1475 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
2023-12-10T19:05:35.550674image/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
2023-12-10T19:05:35.749847image/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

2023-12-10T19:05:30.153772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.702682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:25.701298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:26.907980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:27.829167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:28.889777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:30.290516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.845315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:25.926310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:27.053724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:28.009056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:29.036806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:30.538903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.983135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:26.067029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:27.196524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:28.153265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:29.164809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:30.703007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:25.136184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:26.234938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:27.345331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:28.339780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:29.312766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:30.884859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:25.323354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:26.555464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:27.530495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:28.562056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:29.498035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:31.016261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:25.474486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:26.773724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:27.685126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:28.734490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:29.643291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:05:35.902063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noall_kwrd_rank_cosrchwrd_nmgenre_nmmobile_sccnt_valuepc_sccnt_valuesccnt_sm_valuesccnt_de
seq_no1.0000.0000.0000.0000.3940.2160.1741.000
all_kwrd_rank_co0.0001.0000.7200.6110.5490.7420.8080.000
srchwrd_nm0.0000.7201.0001.0000.6600.8140.6170.000
genre_nm0.0000.6111.0001.0000.4510.3490.4250.000
mobile_sccnt_value0.3940.5490.6600.4511.0000.9420.7790.390
pc_sccnt_value0.2160.7420.8140.3490.9421.0000.9470.230
sccnt_sm_value0.1740.8080.6170.4250.7790.9471.0000.160
sccnt_de1.0000.0000.0000.0000.3900.2300.1601.000
2023-12-10T19:05:36.098811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
genre_nmsrchwrd_nm
genre_nm1.0000.947
srchwrd_nm0.9471.000
2023-12-10T19:05:36.267706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noall_kwrd_rank_comobile_sccnt_valuepc_sccnt_valuesccnt_sm_valuesccnt_desrchwrd_nmgenre_nm
seq_no1.0000.100-0.277-0.207-0.2500.9950.0000.000
all_kwrd_rank_co0.1001.000-0.506-0.846-0.8980.0000.3930.438
mobile_sccnt_value-0.277-0.5061.0000.2500.417-0.2280.3610.313
pc_sccnt_value-0.207-0.8460.2501.0000.974-0.1230.4750.333
sccnt_sm_value-0.250-0.8980.4170.9741.000-0.1620.3740.347
sccnt_de0.9950.000-0.228-0.123-0.1621.0000.0000.000
srchwrd_nm0.0000.3930.3610.4750.3740.0001.0000.947
genre_nm0.0000.4380.3130.3330.3470.0000.9471.000

Missing values

2023-12-10T19:05:31.207693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:05:31.494102image/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
0222962뮤지컬레베카문화공연뮤지컬창작2301238146820211215
1222984뮤지컬팬레터문화공연뮤지컬창작18281299420211215
2223028뮤지컬시카고문화공연뮤지컬라이선스17658275820211215
3222973뮤지컬빌리엘리어트문화공연뮤지컬라이선스176873104920211215
4222995뮤지컬잭더리퍼문화공연뮤지컬라이선스12984397220211215
5222951뮤지컬엑스칼리버문화공연뮤지컬창작2791189146820211215
6223006뮤지컬하데스타운문화공연뮤지컬라이선스10970881720211215
7223017뮤지컬시카고문화공연뮤지컬라이선스17658275820211215
8223039뮤지컬빨래문화공연뮤지컬창작13457070420211215
92230410신비아파트뮤지컬문화공연뮤지컬창작4948052920211215
seq_noall_kwrd_rank_cosrchwrd_nmupper_ctgry_nmlwprt_ctgry_nmgenre_nmmobile_sccnt_valuepc_sccnt_valuesccnt_sm_valuesccnt_de
90222051뮤지컬엑스칼리버문화공연뮤지컬창작5164401491720211206
91222062뮤지컬레베카문화공연뮤지컬창작2511303155420211206
92222073뮤지컬하데스타운문화공연뮤지컬라이선스1711036120720211206
93222084뮤지컬시카고문화공연뮤지컬라이선스204857106120211206
94222095뮤지컬시카고문화공연뮤지컬라이선스204857106120211206
95222106뮤지컬빌리엘리어트문화공연뮤지컬라이선스193817101020211206
96222117뮤지컬빨래문화공연뮤지컬창작21476497820211206
97222128뮤지컬잭더리퍼문화공연뮤지컬라이선스14077591520211206
98222139뮤지컬미스터쇼문화공연뮤지컬뮤지컬13853567320211206
992221410뮤지컬어쩌면해피엔딩문화공연뮤지컬창작9057366320211206