Overview

Dataset statistics

Number of variables6
Number of observations41
Missing cells136
Missing cells (%)55.3%
Duplicate rows6
Duplicate rows (%)14.6%
Total size in memory2.1 KiB
Average record size in memory53.2 B

Variable types

Text4
Categorical1
Unsupported1

Dataset

Description샘플 데이터
AuthorMBN
URLhttps://kdx.kr/data/view/165

Alerts

Dataset has 6 (14.6%) duplicate rowsDuplicates
RSTRC_VID_ESSN_NO has 7 (17.1%) missing valuesMissing
VID_SJ_CN has 26 (63.4%) missing valuesMissing
VID_CN has 26 (63.4%) missing valuesMissing
VOD_CRS_NM has 36 (87.8%) missing valuesMissing
Unnamed: 5 has 41 (100.0%) missing valuesMissing
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 20:49:11.243471
Analysis finished2023-12-11 20:49:12.980931
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRC_VID_ESSN_NO
Text

MISSING 

Distinct29
Distinct (%)85.3%
Missing7
Missing (%)17.1%
Memory size460.0 B
2023-12-12T05:49:13.192952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length46
Mean length25.352941
Min length7

Characters and Unicode

Total characters862
Distinct characters176
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)70.6%

Sample

1st row1036380
2nd row믿고 보는 알토란의 마스코트! 임성근 조리기능장의 <새해 밥상>
3rd row“밥?국만 바꿔도 한 해가 건강하다?”
4th row건강한 <밥?국>의 새로운 표준이 밝혀진다!
5th row2018년 꼭 챙겨 먹어야 할 건강밥상의 표준! 알토란 표 <미역국>?
ValueCountFrequency (%)
밥상 8
 
3.9%
새해 7
 
3.4%
먹어야 4
 
2.0%
한 4
 
2.0%
꼭 4
 
2.0%
진한 3
 
1.5%
할 3
 
1.5%
건강 3
 
1.5%
보약 3
 
1.5%
표 3
 
1.5%
Other values (120) 162
79.4%
2023-12-12T05:49:13.690017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
170
 
19.7%
한 18
 
2.1%
6 17
 
2.0%
< 17
 
2.0%
> 17
 
2.0%
1 16
 
1.9%
의 16
 
1.9%
3 16
 
1.9%
0 16
 
1.9%
는 16
 
1.9%
Other values (166) 543
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 503
58.4%
Space Separator 170
 
19.7%
Decimal Number 87
 
10.1%
Math Symbol 35
 
4.1%
Other Punctuation 35
 
4.1%
Initial Punctuation 9
 
1.0%
Final Punctuation 9
 
1.0%
Close Punctuation 7
 
0.8%
Open Punctuation 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 18
 
3.6%
의 16
 
3.2%
는 16
 
3.2%
밥 14
 
2.8%
국 12
 
2.4%
해 12
 
2.4%
강 11
 
2.2%
하 11
 
2.2%
건 11
 
2.2%
새 9
 
1.8%
Other values (144) 373
74.2%
Decimal Number
ValueCountFrequency (%)
6 17
19.5%
1 16
18.4%
3 16
18.4%
0 16
18.4%
8 10
11.5%
2 6
 
6.9%
7 3
 
3.4%
4 2
 
2.3%
9 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
< 17
48.6%
> 17
48.6%
~ 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
! 16
45.7%
? 12
34.3%
, 7
20.0%
Initial Punctuation
ValueCountFrequency (%)
“ 5
55.6%
‘ 4
44.4%
Final Punctuation
ValueCountFrequency (%)
” 5
55.6%
’ 4
44.4%
Space Separator
ValueCountFrequency (%)
170
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 493
57.2%
Common 359
41.6%
Han 10
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 18
 
3.7%
의 16
 
3.2%
는 16
 
3.2%
밥 14
 
2.8%
국 12
 
2.4%
해 12
 
2.4%
강 11
 
2.2%
하 11
 
2.2%
건 11
 
2.2%
새 9
 
1.8%
Other values (137) 363
73.6%
Common
ValueCountFrequency (%)
170
47.4%
6 17
 
4.7%
< 17
 
4.7%
> 17
 
4.7%
1 16
 
4.5%
3 16
 
4.5%
0 16
 
4.5%
! 16
 
4.5%
? 12
 
3.3%
8 10
 
2.8%
Other values (12) 52
 
14.5%
Han
ValueCountFrequency (%)
病 2
20.0%
大 2
20.0%
家 2
20.0%
石 1
10.0%
定 1
10.0%
藥 1
10.0%
腸 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 493
57.2%
ASCII 341
39.6%
Punctuation 18
 
2.1%
CJK 10
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
170
49.9%
6 17
 
5.0%
< 17
 
5.0%
> 17
 
5.0%
1 16
 
4.7%
3 16
 
4.7%
0 16
 
4.7%
! 16
 
4.7%
? 12
 
3.5%
8 10
 
2.9%
Other values (8) 34
 
10.0%
Hangul
ValueCountFrequency (%)
한 18
 
3.7%
의 16
 
3.2%
는 16
 
3.2%
밥 14
 
2.8%
국 12
 
2.4%
해 12
 
2.4%
강 11
 
2.2%
하 11
 
2.2%
건 11
 
2.2%
새 9
 
1.8%
Other values (137) 363
73.6%
Punctuation
ValueCountFrequency (%)
“ 5
27.8%
” 5
27.8%
‘ 4
22.2%
’ 4
22.2%
CJK
ValueCountFrequency (%)
病 2
20.0%
大 2
20.0%
家 2
20.0%
石 1
10.0%
定 1
10.0%
藥 1
10.0%
腸 1
10.0%

VID_SJ_CN
Text

MISSING 

Distinct11
Distinct (%)73.3%
Missing26
Missing (%)63.4%
Memory size460.0 B
2023-12-12T05:49:13.855784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length40
Mean length22.933333
Min length8

Characters and Unicode

Total characters344
Distinct characters94
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)66.7%

Sample

1st row[미공개 영상] <2018 약(藥) 되는 ‘새해 밥상’> ‘가자미 미역국’
2nd row20180101
3rd row[미공개 영상] <2018 약(藥) 되는 ‘새해 밥상’> ‘건강 잡곡밥’
4th row20180101
5th row[미공개 영상] <2018 약(藥) 되는 ‘새해 밥상’> ‘떡국&떡국 육수’
ValueCountFrequency (%)
20180101 5
 
6.6%
영상 5
 
6.6%
2018 5
 
6.6%
약(藥 5
 
6.6%
되는 5
 
6.6%
‘새해 5
 
6.6%
밥상’ 5
 
6.6%
미공개 5
 
6.6%
‘양배추 2
 
2.6%
메주를 2
 
2.6%
Other values (32) 32
42.1%
2023-12-12T05:49:14.110852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
 
17.7%
1 20
 
5.8%
0 20
 
5.8%
2 10
 
2.9%
8 10
 
2.9%
’ 10
 
2.9%
상 10
 
2.9%
‘ 10
 
2.9%
미 7
 
2.0%
밥 6
 
1.7%
Other values (84) 180
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 165
48.0%
Space Separator 61
 
17.7%
Decimal Number 60
 
17.4%
Final Punctuation 10
 
2.9%
Initial Punctuation 10
 
2.9%
Close Punctuation 10
 
2.9%
Math Symbol 10
 
2.9%
Open Punctuation 10
 
2.9%
Other Punctuation 8
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 10
 
6.1%
미 7
 
4.2%
밥 6
 
3.6%
는 6
 
3.6%
해 5
 
3.0%
되 5
 
3.0%
새 5
 
3.0%
藥 5
 
3.0%
약 5
 
3.0%
영 5
 
3.0%
Other values (68) 106
64.2%
Decimal Number
ValueCountFrequency (%)
1 20
33.3%
0 20
33.3%
2 10
16.7%
8 10
16.7%
Other Punctuation
ValueCountFrequency (%)
? 5
62.5%
! 2
 
25.0%
& 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 5
50.0%
] 5
50.0%
Math Symbol
ValueCountFrequency (%)
> 5
50.0%
< 5
50.0%
Open Punctuation
ValueCountFrequency (%)
( 5
50.0%
[ 5
50.0%
Space Separator
ValueCountFrequency (%)
61
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 10
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 179
52.0%
Hangul 160
46.5%
Han 5
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 10
 
6.2%
미 7
 
4.4%
밥 6
 
3.8%
는 6
 
3.8%
해 5
 
3.1%
되 5
 
3.1%
새 5
 
3.1%
약 5
 
3.1%
영 5
 
3.1%
개 5
 
3.1%
Other values (67) 101
63.1%
Common
ValueCountFrequency (%)
61
34.1%
1 20
 
11.2%
0 20
 
11.2%
2 10
 
5.6%
8 10
 
5.6%
’ 10
 
5.6%
‘ 10
 
5.6%
) 5
 
2.8%
? 5
 
2.8%
> 5
 
2.8%
Other values (6) 23
 
12.8%
Han
ValueCountFrequency (%)
藥 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
46.5%
ASCII 159
46.2%
Punctuation 20
 
5.8%
CJK 5
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
38.4%
1 20
 
12.6%
0 20
 
12.6%
2 10
 
6.3%
8 10
 
6.3%
) 5
 
3.1%
? 5
 
3.1%
> 5
 
3.1%
( 5
 
3.1%
< 5
 
3.1%
Other values (4) 13
 
8.2%
Punctuation
ValueCountFrequency (%)
’ 10
50.0%
‘ 10
50.0%
Hangul
ValueCountFrequency (%)
상 10
 
6.2%
미 7
 
4.4%
밥 6
 
3.8%
는 6
 
3.8%
해 5
 
3.1%
되 5
 
3.1%
새 5
 
3.1%
약 5
 
3.1%
영 5
 
3.1%
개 5
 
3.1%
Other values (67) 101
63.1%
CJK
ValueCountFrequency (%)
藥 5
100.0%

VID_CN
Text

MISSING 

Distinct11
Distinct (%)73.3%
Missing26
Missing (%)63.4%
Memory size460.0 B
2023-12-12T05:49:14.264490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length37
Mean length39.933333
Min length12

Characters and Unicode

Total characters599
Distinct characters115
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)66.7%

Sample

1st row159회 알토란 레시피
2nd rowhttp://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036380
3rd row159회 알토란 레시피
4th rowhttp://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036381
5th row159회 알토란 레시피
ValueCountFrequency (%)
159회 5
 
9.4%
레시피 5
 
9.4%
알토란 5
 
9.4%
꼭 2
 
3.8%
메주의 2
 
3.8%
넣은 1
 
1.9%
국간장 1
 
1.9%
비법 1
 
1.9%
알짜 1
 
1.9%
높인다 1
 
1.9%
Other values (29) 29
54.7%
2023-12-12T05:49:14.524156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 40
 
6.7%
n 40
 
6.7%
38
 
6.3%
e 25
 
4.2%
o 25
 
4.2%
c 25
 
4.2%
/ 20
 
3.3%
. 20
 
3.3%
1 16
 
2.7%
_ 15
 
2.5%
Other values (105) 335
55.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 280
46.7%
Other Letter 127
21.2%
Other Punctuation 61
 
10.2%
Decimal Number 60
 
10.0%
Space Separator 38
 
6.3%
Connector Punctuation 15
 
2.5%
Math Symbol 13
 
2.2%
Uppercase Letter 5
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
알 7
 
5.5%
시 5
 
3.9%
회 5
 
3.9%
토 5
 
3.9%
란 5
 
3.9%
레 5
 
3.9%
피 5
 
3.9%
주 4
 
3.1%
장 4
 
3.1%
메 3
 
2.4%
Other values (66) 79
62.2%
Lowercase Letter
ValueCountFrequency (%)
t 40
14.3%
n 40
14.3%
e 25
 
8.9%
o 25
 
8.9%
c 25
 
8.9%
w 15
 
5.4%
m 15
 
5.4%
b 10
 
3.6%
s 10
 
3.6%
p 10
 
3.6%
Other values (9) 65
23.2%
Decimal Number
ValueCountFrequency (%)
1 16
26.7%
3 11
18.3%
2 6
 
10.0%
0 6
 
10.0%
6 5
 
8.3%
5 5
 
8.3%
8 5
 
8.3%
9 5
 
8.3%
4 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 20
32.8%
. 20
32.8%
& 6
 
9.8%
! 5
 
8.2%
? 5
 
8.2%
: 5
 
8.2%
Math Symbol
ValueCountFrequency (%)
= 10
76.9%
~ 3
 
23.1%
Space Separator
ValueCountFrequency (%)
38
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 15
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 285
47.6%
Common 187
31.2%
Hangul 127
21.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
알 7
 
5.5%
시 5
 
3.9%
회 5
 
3.9%
토 5
 
3.9%
란 5
 
3.9%
레 5
 
3.9%
피 5
 
3.9%
주 4
 
3.1%
장 4
 
3.1%
메 3
 
2.4%
Other values (66) 79
62.2%
Latin
ValueCountFrequency (%)
t 40
14.0%
n 40
14.0%
e 25
 
8.8%
o 25
 
8.8%
c 25
 
8.8%
w 15
 
5.3%
m 15
 
5.3%
b 10
 
3.5%
s 10
 
3.5%
p 10
 
3.5%
Other values (10) 70
24.6%
Common
ValueCountFrequency (%)
38
20.3%
/ 20
10.7%
. 20
10.7%
1 16
8.6%
_ 15
 
8.0%
3 11
 
5.9%
= 10
 
5.3%
& 6
 
3.2%
2 6
 
3.2%
0 6
 
3.2%
Other values (9) 39
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 472
78.8%
Hangul 127
 
21.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 40
 
8.5%
n 40
 
8.5%
38
 
8.1%
e 25
 
5.3%
o 25
 
5.3%
c 25
 
5.3%
/ 20
 
4.2%
. 20
 
4.2%
1 16
 
3.4%
_ 15
 
3.2%
Other values (29) 208
44.1%
Hangul
ValueCountFrequency (%)
알 7
 
5.5%
시 5
 
3.9%
회 5
 
3.9%
토 5
 
3.9%
란 5
 
3.9%
레 5
 
3.9%
피 5
 
3.9%
주 4
 
3.1%
장 4
 
3.1%
메 3
 
2.4%
Other values (66) 79
62.2%

REG_DATE
Categorical

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size460.0 B
<NA>
36 
20180108
5 

Length

Max length8
Median length4
Mean length4.4878049
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 36
87.8%
20180108 5
 
12.2%

Length

2023-12-12T05:49:14.634587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:49:14.723954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 36
87.8%
20180108 5
 
12.2%

VOD_CRS_NM
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing36
Missing (%)87.8%
Memory size460.0 B
2023-12-12T05:49:14.890030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length82
Mean length82
Min length82

Characters and Unicode

Total characters410
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowhttp://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036668
2nd rowhttp://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036669
3rd rowhttp://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036670
4th rowhttp://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036671
5th rowhttp://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036672
ValueCountFrequency (%)
http://www.mbn.co.kr/player/moviecontents.mbn?content_cls_cd=21&content_id=1036668 1
20.0%
http://www.mbn.co.kr/player/moviecontents.mbn?content_cls_cd=21&content_id=1036669 1
20.0%
http://www.mbn.co.kr/player/moviecontents.mbn?content_cls_cd=21&content_id=1036670 1
20.0%
http://www.mbn.co.kr/player/moviecontents.mbn?content_cls_cd=21&content_id=1036671 1
20.0%
http://www.mbn.co.kr/player/moviecontents.mbn?content_cls_cd=21&content_id=1036672 1
20.0%
2023-12-12T05:49:15.158009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 40
 
9.8%
t 40
 
9.8%
e 25
 
6.1%
o 25
 
6.1%
c 25
 
6.1%
/ 20
 
4.9%
. 20
 
4.9%
_ 15
 
3.7%
m 15
 
3.7%
w 15
 
3.7%
Other values (25) 170
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 280
68.3%
Other Punctuation 55
 
13.4%
Decimal Number 45
 
11.0%
Connector Punctuation 15
 
3.7%
Math Symbol 10
 
2.4%
Uppercase Letter 5
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 40
14.3%
t 40
14.3%
e 25
 
8.9%
o 25
 
8.9%
c 25
 
8.9%
m 15
 
5.4%
w 15
 
5.4%
b 10
 
3.6%
d 10
 
3.6%
r 10
 
3.6%
Other values (9) 65
23.2%
Decimal Number
ValueCountFrequency (%)
6 12
26.7%
1 11
24.4%
0 6
13.3%
2 6
13.3%
3 5
11.1%
7 3
 
6.7%
8 1
 
2.2%
9 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 20
36.4%
. 20
36.4%
& 5
 
9.1%
? 5
 
9.1%
: 5
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 15
100.0%
Math Symbol
ValueCountFrequency (%)
= 10
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 285
69.5%
Common 125
30.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 40
14.0%
t 40
14.0%
e 25
 
8.8%
o 25
 
8.8%
c 25
 
8.8%
m 15
 
5.3%
w 15
 
5.3%
b 10
 
3.5%
d 10
 
3.5%
r 10
 
3.5%
Other values (10) 70
24.6%
Common
ValueCountFrequency (%)
/ 20
16.0%
. 20
16.0%
_ 15
12.0%
6 12
9.6%
1 11
8.8%
= 10
8.0%
0 6
 
4.8%
2 6
 
4.8%
& 5
 
4.0%
3 5
 
4.0%
Other values (5) 15
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 40
 
9.8%
t 40
 
9.8%
e 25
 
6.1%
o 25
 
6.1%
c 25
 
6.1%
/ 20
 
4.9%
. 20
 
4.9%
_ 15
 
3.7%
m 15
 
3.7%
w 15
 
3.7%
Other values (25) 170
41.5%

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing41
Missing (%)100.0%
Memory size501.0 B

Correlations

2023-12-12T05:49:15.225835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRC_VID_ESSN_NOVID_SJ_CNVID_CNVOD_CRS_NM
RSTRC_VID_ESSN_NO1.0001.0001.0001.000
VID_SJ_CN1.0001.0000.0001.000
VID_CN1.0000.0001.0001.000
VOD_CRS_NM1.0001.0001.0001.000

Missing values

2023-12-12T05:49:12.725114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:49:12.830594image/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.
2023-12-12T05:49:12.928765image/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

RSTRC_VID_ESSN_NOVID_SJ_CNVID_CNREG_DATEVOD_CRS_NMUnnamed: 5
0<NA><NA><NA><NA><NA><NA>
11036380[미공개 영상] <2018 약(藥) 되는 ‘새해 밥상’> ‘가자미 미역국’159회 알토란 레시피<NA><NA><NA>
2<NA><NA><NA><NA><NA><NA>
3믿고 보는 알토란의 마스코트! 임성근 조리기능장의 <새해 밥상><NA><NA><NA><NA><NA>
4“밥?국만 바꿔도 한 해가 건강하다?”<NA><NA><NA><NA><NA>
5건강한 <밥?국>의 새로운 표준이 밝혀진다!<NA><NA><NA><NA><NA>
62018년 꼭 챙겨 먹어야 할 건강밥상의 표준! 알토란 표 <미역국>?<NA><NA><NA><NA><NA>
7사골보다 진한 <미역국> 끓이는 비법?20180101http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036380<NA><NA><NA>
81036381[미공개 영상] <2018 약(藥) 되는 ‘새해 밥상’> ‘건강 잡곡밥’159회 알토란 레시피<NA><NA><NA>
9<NA><NA><NA><NA><NA><NA>
RSTRC_VID_ESSN_NOVID_SJ_CNVID_CNREG_DATEVOD_CRS_NMUnnamed: 5
31“2018년, 꼭 먹어야 할 건강 식재료?”<NA><NA><NA><NA><NA>
32올 한 해, 병(病) 없이 건강하고 싶다면 밥상 위의 보약, <양배추>에 주목하라!<NA><NA><NA><NA><NA>
33알토란이 선정한 최고의 식재료 ‘양배추’로 차리는 보약 밥상!<NA><NA><NA><NA><NA>
34영양 가득한 <양배추 제육볶음> 만드는 법?20180101http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036384<NA><NA><NA>
351036668발효 음식! 겨울에 꼭 챙겨 먹어야 하는 이유?발효 음식의 모든 것! 절대 놓치지 마세요~20180108http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036668<NA>
361036669완성된 메주를 믹서에 갈아주기?!막장용 콩알메주는 꼭 굵직하게 갈아주세요~20180108http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036669<NA>
371036670메주를 베란다에서 저온 숙성 시켜라?메주의 날내를 완벽 제거&다른 재료와 배합할 때 숙성도가 높아진다!20180108http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036670<NA>
381036671막장에 들어갈 고추씨의 효능은?고추씨! 메주의 저장성을 높인다!20180108http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036671<NA>
391036672막장을 만들 때 국간장을 넣어라?알짜 비법 국간장 넣은 막장! 꼭 따라 해보세요~20180108http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1036672<NA>
40<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

RSTRC_VID_ESSN_NOVID_SJ_CNVID_CNREG_DATEVOD_CRS_NM# duplicates
5<NA><NA><NA><NA><NA>7
0“2018년, 꼭 먹어야 할 건강 식재료?”<NA><NA><NA><NA>2
1“밥?국만 바꿔도 한 해가 건강하다?”<NA><NA><NA><NA>2
2떠오르는 한식의 대가(大家)! 원승식 셰프가 공개하는 <새해 밥상><NA><NA><NA><NA>2
3믿고 보는 알토란의 마스코트! 임성근 조리기능장의 <새해 밥상><NA><NA><NA><NA>2
4올 한 해, 병(病) 없이 건강하고 싶다면 밥상 위의 보약, <양배추>에 주목하라!<NA><NA><NA><NA>2