Overview

Dataset statistics

Number of variables9
Number of observations33
Missing cells213
Missing cells (%)71.7%
Duplicate rows1
Duplicate rows (%)3.0%
Total size in memory2.6 KiB
Average record size in memory80.9 B

Variable types

Text4
Numeric4
Unsupported1

Dataset

Description샘플 데이터
AuthorMBN
URLhttps://kdx.kr/data/view/29826

Alerts

Dataset has 1 (3.0%) duplicate rowsDuplicates
play_sec is highly overall correlated with play_hourHigh correlation
play_hour is highly overall correlated with play_secHigh correlation
vod_seq_no has 5 (15.2%) missing valuesMissing
bcast_seq_no has 25 (75.8%) missing valuesMissing
play_sec has 25 (75.8%) missing valuesMissing
play_hour has 25 (75.8%) missing valuesMissing
file_size has 25 (75.8%) missing valuesMissing
vod_path has 25 (75.8%) missing valuesMissing
title has 25 (75.8%) missing valuesMissing
contents has 25 (75.8%) missing valuesMissing
Unnamed: 8 has 33 (100.0%) missing valuesMissing
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-21 10:17:09.251533
Analysis finished2024-04-21 10:17:13.722883
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

vod_seq_no
Text

MISSING 

Distinct28
Distinct (%)100.0%
Missing5
Missing (%)15.2%
Memory size392.0 B
2024-04-21T19:17:14.656834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length92
Median length52
Mean length26.071429
Min length6

Characters and Unicode

Total characters730
Distinct characters226
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

Unique28 ?
Unique (%)100.0%

Sample

1st row474477
2nd row한약재료 듬뿍넣고 고아낸 삼계탕은 기본!
3rd row힘이 불끈 느껴지는 장어,미꾸라지 추어탕과
4th row쓰러진 소도 일으킨다는 낙지 그리고 뽀얀 곰탕 한그릇!
5th row하지만 묻지마 보양식은 독이 될 수 잇다?
ValueCountFrequency (%)
맛있는 7
 
4.0%
수다에서 4
 
2.3%
족발 3
 
1.7%
먹고 2
 
1.1%
1위 2
 
1.1%
꽃게 2
 
1.1%
그리고 2
 
1.1%
푹 2
 
1.1%
수 2
 
1.1%
송송 2
 
1.1%
Other values (145) 147
84.0%
2024-04-21T19:17:15.896023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
147
 
20.1%
! 17
 
2.3%
는 15
 
2.1%
한 14
 
1.9%
4 13
 
1.8%
다 13
 
1.8%
7 12
 
1.6%
지 11
 
1.5%
맛 11
 
1.5%
에 10
 
1.4%
Other values (216) 467
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 497
68.1%
Space Separator 147
 
20.1%
Decimal Number 54
 
7.4%
Other Punctuation 28
 
3.8%
Math Symbol 2
 
0.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
는 15
 
3.0%
한 14
 
2.8%
다 13
 
2.6%
지 11
 
2.2%
맛 11
 
2.2%
에 10
 
2.0%
수 9
 
1.8%
고 9
 
1.8%
개 8
 
1.6%
국 8
 
1.6%
Other values (198) 389
78.3%
Decimal Number
ValueCountFrequency (%)
4 13
24.1%
7 12
22.2%
9 6
11.1%
1 5
 
9.3%
8 4
 
7.4%
3 4
 
7.4%
5 3
 
5.6%
0 3
 
5.6%
6 3
 
5.6%
2 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
! 17
60.7%
, 5
 
17.9%
. 5
 
17.9%
? 1
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
N 1
50.0%
O 1
50.0%
Space Separator
ValueCountFrequency (%)
147
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 497
68.1%
Common 231
31.6%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
는 15
 
3.0%
한 14
 
2.8%
다 13
 
2.6%
지 11
 
2.2%
맛 11
 
2.2%
에 10
 
2.0%
수 9
 
1.8%
고 9
 
1.8%
개 8
 
1.6%
국 8
 
1.6%
Other values (198) 389
78.3%
Common
ValueCountFrequency (%)
147
63.6%
! 17
 
7.4%
4 13
 
5.6%
7 12
 
5.2%
9 6
 
2.6%
, 5
 
2.2%
. 5
 
2.2%
1 5
 
2.2%
8 4
 
1.7%
3 4
 
1.7%
Other values (6) 13
 
5.6%
Latin
ValueCountFrequency (%)
N 1
50.0%
O 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 497
68.1%
ASCII 233
31.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147
63.1%
! 17
 
7.3%
4 13
 
5.6%
7 12
 
5.2%
9 6
 
2.6%
, 5
 
2.1%
. 5
 
2.1%
1 5
 
2.1%
8 4
 
1.7%
3 4
 
1.7%
Other values (8) 15
 
6.4%
Hangul
ValueCountFrequency (%)
는 15
 
3.0%
한 14
 
2.8%
다 13
 
2.6%
지 11
 
2.2%
맛 11
 
2.2%
에 10
 
2.0%
수 9
 
1.8%
고 9
 
1.8%
개 8
 
1.6%
국 8
 
1.6%
Other values (198) 389
78.3%

bcast_seq_no
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)100.0%
Missing25
Missing (%)75.8%
Infinite0
Infinite (%)0.0%
Mean1043551.4
Minimum1041505
Maximum1045588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-04-21T19:17:16.096128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1041505
5-th percentile1041628.2
Q11042316.8
median1043567.5
Q31044828.5
95-th percentile1045426.7
Maximum1045588
Range4083
Interquartile range (IQR)2511.75

Descriptive statistics

Standard deviation1571.0856
Coefficient of variation (CV)0.0015055182
Kurtosis-1.9190318
Mean1043551.4
Median Absolute Deviation (MAD)1360.5
Skewness-0.032226588
Sum8348411
Variance2468310
MonotonicityStrictly increasing
2024-04-21T19:17:16.302592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1041505 1
 
3.0%
1041857 1
 
3.0%
1042470 1
 
3.0%
1042849 1
 
3.0%
1044286 1
 
3.0%
1044729 1
 
3.0%
1045127 1
 
3.0%
1045588 1
 
3.0%
(Missing) 25
75.8%
ValueCountFrequency (%)
1041505 1
3.0%
1041857 1
3.0%
1042470 1
3.0%
1042849 1
3.0%
1044286 1
3.0%
1044729 1
3.0%
1045127 1
3.0%
1045588 1
3.0%
ValueCountFrequency (%)
1045588 1
3.0%
1045127 1
3.0%
1044729 1
3.0%
1044286 1
3.0%
1042849 1
3.0%
1042470 1
3.0%
1041857 1
3.0%
1041505 1
3.0%

play_sec
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing25
Missing (%)75.8%
Infinite0
Infinite (%)0.0%
Mean3477
Minimum3050
Maximum3670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-04-21T19:17:16.493471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3050
5-th percentile3051.05
Q13442.25
median3591.5
Q33635.5
95-th percentile3666.85
Maximum3670
Range620
Interquartile range (IQR)193.25

Descriptive statistics

Standard deviation264.87194
Coefficient of variation (CV)0.076178296
Kurtosis-0.090841372
Mean3477
Median Absolute Deviation (MAD)52.5
Skewness-1.3671394
Sum27816
Variance70157.143
MonotonicityNot monotonic
2024-04-21T19:17:16.670484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3050 1
 
3.0%
3053 1
 
3.0%
3627 1
 
3.0%
3661 1
 
3.0%
3670 1
 
3.0%
3583 1
 
3.0%
3600 1
 
3.0%
3572 1
 
3.0%
(Missing) 25
75.8%
ValueCountFrequency (%)
3050 1
3.0%
3053 1
3.0%
3572 1
3.0%
3583 1
3.0%
3600 1
3.0%
3627 1
3.0%
3661 1
3.0%
3670 1
3.0%
ValueCountFrequency (%)
3670 1
3.0%
3661 1
3.0%
3627 1
3.0%
3600 1
3.0%
3583 1
3.0%
3572 1
3.0%
3053 1
3.0%
3050 1
3.0%

play_hour
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing25
Missing (%)75.8%
Infinite0
Infinite (%)0.0%
Mean0.965825
Minimum0.8472
Maximum1.0194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-04-21T19:17:16.852734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8472
5-th percentile0.847515
Q10.956175
median0.99765
Q31.00985
95-th percentile1.018525
Maximum1.0194
Range0.1722
Interquartile range (IQR)0.053675

Descriptive statistics

Standard deviation0.073561592
Coefficient of variation (CV)0.076164515
Kurtosis-0.09056098
Mean0.965825
Median Absolute Deviation (MAD)0.01455
Skewness-1.3673724
Sum7.7266
Variance0.0054113079
MonotonicityNot monotonic
2024-04-21T19:17:17.041145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.8472 1
 
3.0%
0.8481 1
 
3.0%
1.0075 1
 
3.0%
1.0169 1
 
3.0%
1.0194 1
 
3.0%
0.9953 1
 
3.0%
1.0 1
 
3.0%
0.9922 1
 
3.0%
(Missing) 25
75.8%
ValueCountFrequency (%)
0.8472 1
3.0%
0.8481 1
3.0%
0.9922 1
3.0%
0.9953 1
3.0%
1.0 1
3.0%
1.0075 1
3.0%
1.0169 1
3.0%
1.0194 1
3.0%
ValueCountFrequency (%)
1.0194 1
3.0%
1.0169 1
3.0%
1.0075 1
3.0%
1.0 1
3.0%
0.9953 1
3.0%
0.9922 1
3.0%
0.8481 1
3.0%
0.8472 1
3.0%

file_size
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)100.0%
Missing25
Missing (%)75.8%
Infinite0
Infinite (%)0.0%
Mean5.6918165 × 108
Minimum4.7897608 × 108
Maximum6.1893271 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-04-21T19:17:17.242500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.7897608 × 108
5-th percentile4.9127706 × 108
Q15.152612 × 108
median6.0392646 × 108
Q36.0744721 × 108
95-th percentile6.1650582 × 108
Maximum6.1893271 × 108
Range1.3995663 × 108
Interquartile range (IQR)92186016

Descriptive statistics

Standard deviation56207155
Coefficient of variation (CV)0.09875082
Kurtosis-1.5373271
Mean5.6918165 × 108
Median Absolute Deviation (MAD)11539276
Skewness-0.77083219
Sum4.5534532 × 109
Variance3.1592443 × 1015
MonotonicityNot monotonic
2024-04-21T19:17:17.435160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
514121738 1
 
3.0%
515641018 1
 
3.0%
611998753 1
 
3.0%
618932708 1
 
3.0%
478976075 1
 
3.0%
605930035 1
 
3.0%
605680351 1
 
3.0%
602172559 1
 
3.0%
(Missing) 25
75.8%
ValueCountFrequency (%)
478976075 1
3.0%
514121738 1
3.0%
515641018 1
3.0%
602172559 1
3.0%
605680351 1
3.0%
605930035 1
3.0%
611998753 1
3.0%
618932708 1
3.0%
ValueCountFrequency (%)
618932708 1
3.0%
611998753 1
3.0%
605930035 1
3.0%
605680351 1
3.0%
602172559 1
3.0%
515641018 1
3.0%
514121738 1
3.0%
478976075 1
3.0%

vod_path
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing25
Missing (%)75.8%
Memory size392.0 B
2024-04-21T19:17:18.032078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length61
Mean length61
Min length61

Characters and Unicode

Total characters488
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st row/mbnvod2/600/2013/02/10/20130210100405_20_600_1041505_360.mp4
2nd row/mbnvod2/600/2013/02/17/20130217095438_20_600_1041857_360.mp4
3rd row/mbnvod2/600/2013/02/28/20130228001037_20_600_1042470_360.mp4
4th row/mbnvod2/600/2013/03/07/20130307003354_20_600_1042849_360.mp4
5th row/mbnvod2/600/2013/03/30/20130330001624_20_600_1044286_360.mp4
ValueCountFrequency (%)
mbnvod2/600/2013/02/10/20130210100405_20_600_1041505_360.mp4 1
12.5%
mbnvod2/600/2013/02/17/20130217095438_20_600_1041857_360.mp4 1
12.5%
mbnvod2/600/2013/02/28/20130228001037_20_600_1042470_360.mp4 1
12.5%
mbnvod2/600/2013/03/07/20130307003354_20_600_1042849_360.mp4 1
12.5%
mbnvod2/600/2013/03/30/20130330001624_20_600_1044286_360.mp4 1
12.5%
mbnvod2/600/2013/04/06/20130406000629_20_600_1044729_360.mp4 1
12.5%
mbnvod2/600/2013/04/13/20130413213231_20_600_1045127_360.mp4 1
12.5%
mbnvod2/600/2013/04/20/20130420212014_20_600_1045588_360.mp4 1
12.5%
2024-04-21T19:17:18.821816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 115
23.6%
2 53
10.9%
/ 48
9.8%
1 40
 
8.2%
3 38
 
7.8%
_ 32
 
6.6%
4 31
 
6.4%
6 29
 
5.9%
m 16
 
3.3%
5 9
 
1.8%
Other values (10) 77
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336
68.9%
Lowercase Letter 64
 
13.1%
Other Punctuation 56
 
11.5%
Connector Punctuation 32
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115
34.2%
2 53
15.8%
1 40
 
11.9%
3 38
 
11.3%
4 31
 
9.2%
6 29
 
8.6%
5 9
 
2.7%
7 9
 
2.7%
8 8
 
2.4%
9 4
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
m 16
25.0%
v 8
12.5%
o 8
12.5%
d 8
12.5%
n 8
12.5%
b 8
12.5%
p 8
12.5%
Other Punctuation
ValueCountFrequency (%)
/ 48
85.7%
. 8
 
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 424
86.9%
Latin 64
 
13.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 115
27.1%
2 53
12.5%
/ 48
11.3%
1 40
 
9.4%
3 38
 
9.0%
_ 32
 
7.5%
4 31
 
7.3%
6 29
 
6.8%
5 9
 
2.1%
7 9
 
2.1%
Other values (3) 20
 
4.7%
Latin
ValueCountFrequency (%)
m 16
25.0%
v 8
12.5%
o 8
12.5%
d 8
12.5%
n 8
12.5%
b 8
12.5%
p 8
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 115
23.6%
2 53
10.9%
/ 48
9.8%
1 40
 
8.2%
3 38
 
7.8%
_ 32
 
6.6%
4 31
 
6.4%
6 29
 
5.9%
m 16
 
3.3%
5 9
 
1.8%
Other values (10) 77
15.8%

title
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing25
Missing (%)75.8%
Memory size392.0 B
2024-04-21T19:17:19.482658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length28.75
Min length22

Characters and Unicode

Total characters230
Distinct characters82
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st row[맛있는 수다 1회] '묻지마 보양식'은 독이 될 수 있다?
2nd row[맛있는 수다 2회] 오늘의 메뉴 '김치찌개'
3rd row[맛있는 수다 3회] 야식 최강자 족발!
4th row[맛있는 수다 4회] 한국인이 사랑하는 최고의 면 요리 칼국수
5th row[맛있는 수다 5회] 오늘의 메뉴 한국 발효음식의 진미 된장찌개
ValueCountFrequency (%)
맛있는 8
 
13.1%
수다 8
 
13.1%
오늘의 3
 
4.9%
메뉴 2
 
3.3%
밥 1
 
1.6%
한국 1
 
1.6%
발효음식의 1
 
1.6%
진미 1
 
1.6%
된장찌개 1
 
1.6%
6회 1
 
1.6%
Other values (34) 34
55.7%
2024-04-21T19:17:20.374043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53
23.0%
다 11
 
4.8%
는 10
 
4.3%
수 10
 
4.3%
맛 9
 
3.9%
있 9
 
3.9%
[ 8
 
3.5%
회 8
 
3.5%
] 8
 
3.5%
' 8
 
3.5%
Other values (72) 96
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
62.2%
Space Separator 53
 
23.0%
Other Punctuation 10
 
4.3%
Open Punctuation 8
 
3.5%
Close Punctuation 8
 
3.5%
Decimal Number 8
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 11
 
7.7%
는 10
 
7.0%
수 10
 
7.0%
맛 9
 
6.3%
있 9
 
6.3%
회 8
 
5.6%
의 7
 
4.9%
식 3
 
2.1%
국 3
 
2.1%
오 3
 
2.1%
Other values (58) 70
49.0%
Decimal Number
ValueCountFrequency (%)
2 1
12.5%
6 1
12.5%
1 1
12.5%
7 1
12.5%
8 1
12.5%
5 1
12.5%
3 1
12.5%
4 1
12.5%
Other Punctuation
ValueCountFrequency (%)
' 8
80.0%
? 1
 
10.0%
! 1
 
10.0%
Space Separator
ValueCountFrequency (%)
53
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 8
100.0%
Close Punctuation
ValueCountFrequency (%)
] 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143
62.2%
Common 87
37.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 11
 
7.7%
는 10
 
7.0%
수 10
 
7.0%
맛 9
 
6.3%
있 9
 
6.3%
회 8
 
5.6%
의 7
 
4.9%
식 3
 
2.1%
국 3
 
2.1%
오 3
 
2.1%
Other values (58) 70
49.0%
Common
ValueCountFrequency (%)
53
60.9%
[ 8
 
9.2%
] 8
 
9.2%
' 8
 
9.2%
2 1
 
1.1%
6 1
 
1.1%
1 1
 
1.1%
7 1
 
1.1%
8 1
 
1.1%
5 1
 
1.1%
Other values (4) 4
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143
62.2%
ASCII 87
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53
60.9%
[ 8
 
9.2%
] 8
 
9.2%
' 8
 
9.2%
2 1
 
1.1%
6 1
 
1.1%
1 1
 
1.1%
7 1
 
1.1%
8 1
 
1.1%
5 1
 
1.1%
Other values (4) 4
 
4.6%
Hangul
ValueCountFrequency (%)
다 11
 
7.7%
는 10
 
7.0%
수 10
 
7.0%
맛 9
 
6.3%
있 9
 
6.3%
회 8
 
5.6%
의 7
 
4.9%
식 3
 
2.1%
국 3
 
2.1%
오 3
 
2.1%
Other values (58) 70
49.0%

contents
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing25
Missing (%)75.8%
Memory size392.0 B
2024-04-21T19:17:21.119245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length119
Median length23.5
Mean length30.75
Min length12

Characters and Unicode

Total characters246
Distinct characters102
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

Unique8 ?
Unique (%)100.0%

Sample

1st row<'묻지마 보양식'은 독이 될 수 있다?>
2nd row<오늘의 메뉴 '김치찌개'>
3rd row<야식 최강자 족발!>
4th row<한국인이 사랑하는 최고의 면 요리 칼국수>
5th row<오늘의 메뉴 한국 발효음식의 진미 된장찌개>
ValueCountFrequency (%)
오늘의 3
 
4.8%
메뉴 3
 
4.8%
한국인이 2
 
3.2%
묻지마 1
 
1.6%
윤기나는 1
 
1.6%
씹을수록 1
 
1.6%
톡톡 1
 
1.6%
터지는 1
 
1.6%
고소한 1
 
1.6%
맛까지 1
 
1.6%
Other values (47) 47
75.8%
2024-04-21T19:17:22.093422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54
 
22.0%
의 11
 
4.5%
는 7
 
2.8%
< 6
 
2.4%
한 6
 
2.4%
> 6
 
2.4%
이 5
 
2.0%
국 5
 
2.0%
밥 4
 
1.6%
' 4
 
1.6%
Other values (92) 138
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 169
68.7%
Space Separator 54
 
22.0%
Math Symbol 12
 
4.9%
Other Punctuation 11
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 11
 
6.5%
는 7
 
4.1%
한 6
 
3.6%
이 5
 
3.0%
국 5
 
3.0%
밥 4
 
2.4%
맛 4
 
2.4%
지 4
 
2.4%
뉴 3
 
1.8%
수 3
 
1.8%
Other values (84) 117
69.2%
Other Punctuation
ValueCountFrequency (%)
' 4
36.4%
? 3
27.3%
! 2
18.2%
. 1
 
9.1%
, 1
 
9.1%
Math Symbol
ValueCountFrequency (%)
< 6
50.0%
> 6
50.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 169
68.7%
Common 77
31.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 11
 
6.5%
는 7
 
4.1%
한 6
 
3.6%
이 5
 
3.0%
국 5
 
3.0%
밥 4
 
2.4%
맛 4
 
2.4%
지 4
 
2.4%
뉴 3
 
1.8%
수 3
 
1.8%
Other values (84) 117
69.2%
Common
ValueCountFrequency (%)
54
70.1%
< 6
 
7.8%
> 6
 
7.8%
' 4
 
5.2%
? 3
 
3.9%
! 2
 
2.6%
. 1
 
1.3%
, 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 169
68.7%
ASCII 77
31.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54
70.1%
< 6
 
7.8%
> 6
 
7.8%
' 4
 
5.2%
? 3
 
3.9%
! 2
 
2.6%
. 1
 
1.3%
, 1
 
1.3%
Hangul
ValueCountFrequency (%)
의 11
 
6.5%
는 7
 
4.1%
한 6
 
3.6%
이 5
 
3.0%
국 5
 
3.0%
밥 4
 
2.4%
맛 4
 
2.4%
지 4
 
2.4%
뉴 3
 
1.8%
수 3
 
1.8%
Other values (84) 117
69.2%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing33
Missing (%)100.0%
Memory size425.0 B

Interactions

2024-04-21T19:17:12.164237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:09.847293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:10.878758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:11.509835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:12.335806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:10.125229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:11.046519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:11.686052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:12.495305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:10.387849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:11.191648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:11.841364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:12.664804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:10.660399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:11.352001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:17:11.999238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T19:17:22.254998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontents
vod_seq_no1.0001.0001.0001.0001.0001.0001.0001.000
bcast_seq_no1.0001.0001.0001.0001.0001.0001.0001.000
play_sec1.0001.0001.0001.0000.8981.0001.0001.000
play_hour1.0001.0001.0001.0000.9161.0001.0001.000
file_size1.0001.0000.8980.9161.0001.0001.0001.000
vod_path1.0001.0001.0001.0001.0001.0001.0001.000
title1.0001.0001.0001.0001.0001.0001.0001.000
contents1.0001.0001.0001.0001.0001.0001.0001.000
2024-04-21T19:17:22.449533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
bcast_seq_noplay_secplay_hourfile_size
bcast_seq_no1.0000.2860.2860.167
play_sec0.2861.0001.0000.310
play_hour0.2861.0001.0000.310
file_size0.1670.3100.3101.000

Missing values

2024-04-21T19:17:12.881348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T19:17:13.333461image/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.
2024-04-21T19:17:13.556750image/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

vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontentsUnnamed: 8
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1474477104150530500.8472514121738/mbnvod2/600/2013/02/10/20130210100405_20_600_1041505_360.mp4[맛있는 수다 1회] '묻지마 보양식'은 독이 될 수 있다?<'묻지마 보양식'은 독이 될 수 있다?><NA>
2<NA><NA><NA><NA><NA><NA><NA><NA><NA>
3한약재료 듬뿍넣고 고아낸 삼계탕은 기본!<NA><NA><NA><NA><NA><NA><NA><NA>
4힘이 불끈 느껴지는 장어,미꾸라지 추어탕과<NA><NA><NA><NA><NA><NA><NA><NA>
5쓰러진 소도 일으킨다는 낙지 그리고 뽀얀 곰탕 한그릇!<NA><NA><NA><NA><NA><NA><NA><NA>
6하지만 묻지마 보양식은 독이 될 수 잇다?<NA><NA><NA><NA><NA><NA><NA><NA>
7보양식에 대한 오해와 진실부터 밥상 위 진짜 보양식까지<NA><NA><NA><NA><NA><NA><NA><NA>
8맛있는 수다에서 밝혀드립니다!<NA><NA><NA><NA><NA><NA><NA><NA>
9475147104185730530.8481515641018/mbnvod2/600/2013/02/17/20130217095438_20_600_1041857_360.mp4[맛있는 수다 2회] 오늘의 메뉴 '김치찌개'<오늘의 메뉴 '김치찌개'><NA>
vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontentsUnnamed: 8
23480739104472935830.9953605930035/mbnvod2/600/2013/04/06/20130406000629_20_600_1044729_360.mp4[맛있는 수다 6회] 오늘의 주제 힘의 원천 '밥'오늘의 메뉴 한국인이 말하는 힘의 원천 밥! 입안에 착착 달라붙는 쫀득한 맛 부터 씹을수록 톡톡 터지는 고소한 맛까지 한국의 맛있는 밥의 이야기가 공개됩니다. 촉촉 윤기나는 진밥, 꼬들꼬들 된밥 여러분의 선택은??<NA>
24481495104512736001.0605680351/mbnvod2/600/2013/04/13/20130413213231_20_600_1045127_360.mp4[맛있는 수다 7회] 봄 바다의 맛객 꽃게<봄 바다의 맛객 꽃게><NA>
25대한민국 NO.1 밥도둑 꽃게!<NA><NA><NA><NA><NA><NA><NA><NA>
26게딱지 속 야들야들 속살, 바다 향이 풍기는 육즙 가득한 게살.<NA><NA><NA><NA><NA><NA><NA><NA>
27맛있는 수다에서 꽃게의 효능과 여러가지 꽃게 요리를 소개합니다.<NA><NA><NA><NA><NA><NA><NA><NA>
28482306104558835720.9922602172559/mbnvod2/600/2013/04/20/20130420212014_20_600_1045588_360.mp4[맛있는 수다 8회] 밥을 약으로 먹는다 '산채비빔밥'봄의 기운이 느껴지는 산나물<NA>
29산나물과 함께 돌아온 입맛!<NA><NA><NA><NA><NA><NA><NA><NA>
30맛있는 수다에서 산채비빔밥의 맛있는 유혹이 시작됩니다.<NA><NA><NA><NA><NA><NA><NA><NA>
31<NA><NA><NA><NA><NA><NA><NA><NA><NA>
32<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontents# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>5