gimi9 Pandas Profiling

Dataset statistics

Number of variables	6
Number of observations	52
Missing cells	242
Missing cells (%)	77.6%
Duplicate rows	1
Duplicate rows (%)	1.9%
Total size in memory	2.7 KiB
Average record size in memory	53.5 B

Variable types

Text	3
Unsupported	3

Dataset

Description	샘플 데이터
Author	MBN
URL	https://kdx.kr/data/view/147

Alerts

Dataset has 1 (1.9%) duplicate rows	Duplicates
`RSTRC_VID_ESSN_NO` has 22 (42.3%) missing values	Missing
`VID_SJ_CN` has 32 (61.5%) missing values	Missing
`VID_CN` has 32 (61.5%) missing values	Missing
`REG_DATE` has 52 (100.0%) missing values	Missing
`VOD_CRS_NM` has 52 (100.0%) missing values	Missing
`Unnamed: 5` has 52 (100.0%) missing values	Missing
`REG_DATE` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`VOD_CRS_NM` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Unnamed: 5` is an unsupported type, check if it needs cleaning or further analysis	Unsupported

Reproduction

Analysis started	2023-12-11 21:20:47.193462
Analysis finished	2023-12-11 21:20:47.701369
Duration	0.51 seconds
Software version	ydata-profiling vv4.5.1
Download configuration	config.json

RSTRC_VID_ESSN_NO
Text

MISSING

Distinct	30
Distinct (%)	100.0%
Missing	22
Missing (%)	42.3%
Memory size	548.0 B

Length

Max length	45
Median length	40.5
Mean length	23.633333
Min length	7

Characters and Unicode

Total characters	709
Distinct characters	189
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	30 ?
Unique (%)	100.0%

Sample

1st row	1010583
2nd row	몸속 쓰레기 축적으로 각종 질병에 취약해진다고 하는데...
3rd row	그렇다면 이 쓰레기들을 방치하면 어떻게 될까?
4th row	1010584
5th row	이 호흡법을 잘 배우면 몸속 쓰레기를 배출할 수 있다고 하는데~

Value	Count	Frequency (%)
몸속	6	3.4%
수	5	2.9%
하는데	4	2.3%
무엇일까	4	2.3%
있는	4	2.3%
이	3	1.7%
쓰레기의	2	1.1%
쓰레기를	2	1.1%
밥상에서	2	1.1%
그	2	1.1%
Other values (132)	140	80.5%

Most occurring characters

Value	Count	Frequency (%)
	144	20.3%
0	25	3.5%
는	23	3.2%
1	22	3.1%
이	20	2.8%
하	11	1.6%
?	11	1.6%
.	10	1.4%
은	9	1.3%
기	9	1.3%
Other values (179)	425	59.9%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	459	64.7%
Space Separator	144	20.3%
Decimal Number	72	10.2%
Other Punctuation	28	3.9%
Math Symbol	4	0.6%
Close Punctuation	1	0.1%
Open Punctuation	1	0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
는	23	5.0%
이	20	4.4%
하	11	2.4%
은	9	2.0%
기	9	2.0%
다	9	2.0%
데	8	1.7%
속	7	1.5%
나	7	1.5%
가	7	1.5%
Other values (160)	349	76.0%

Decimal Number

Value	Count	Frequency (%)
0	25	34.7%
1	22	30.6%
5	9	12.5%
6	6	8.3%
8	4	5.6%
9	2	2.8%
3	1	1.4%
7	1	1.4%
2	1	1.4%
4	1	1.4%

Other Punctuation

Value	Count	Frequency (%)
?	11	39.3%
.	10	35.7%
!	3	10.7%
'	2	7.1%
,	2	7.1%

Space Separator

Value	Count	Frequency (%)
	144	100.0%

Math Symbol

Value	Count	Frequency (%)
~	4	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Hangul	459	64.7%
Common	250	35.3%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
는	23	5.0%
이	20	4.4%
하	11	2.4%
은	9	2.0%
기	9	2.0%
다	9	2.0%
데	8	1.7%
속	7	1.5%
나	7	1.5%
가	7	1.5%
Other values (160)	349	76.0%

Common

Value	Count	Frequency (%)
	144	57.6%
0	25	10.0%
1	22	8.8%
?	11	4.4%
.	10	4.0%
5	9	3.6%
6	6	2.4%
~	4	1.6%
8	4	1.6%
!	3	1.2%
Other values (9)	12	4.8%

Most occurring blocks

Value	Count	Frequency (%)
Hangul	459	64.7%
ASCII	250	35.3%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	144	57.6%
0	25	10.0%
1	22	8.8%
?	11	4.4%
.	10	4.0%
5	9	3.6%
6	6	2.4%
~	4	1.6%
8	4	1.6%
!	3	1.2%
Other values (9)	12	4.8%

Hangul

Value	Count	Frequency (%)
는	23	5.0%
이	20	4.4%
하	11	2.4%
은	9	2.0%
기	9	2.0%
다	9	2.0%
데	8	1.7%
속	7	1.5%
나	7	1.5%
가	7	1.5%
Other values (160)	349	76.0%

VID_SJ_CN
Text

MISSING

Distinct	11
Distinct (%)	55.0%
Missing	32
Missing (%)	61.5%
Memory size	548.0 B

Length

Max length	39
Median length	37
Mean length	17.95
Min length	8

Characters and Unicode

Total characters	359
Distinct characters	114
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10 ?
Unique (%)	50.0%

Sample

1st row	추운 겨울철에 몸속 노폐물이 더 쌓이는 이유는?!
2nd row	20160106
3rd row	스튜디오를 몸속 쓰레기 배출 현장으로 만든 '5분 호흡법'을 배워보자!
4th row	20160106
5th row	돌발 상황! 허참의 몸속 쓰레기를 배출하는 현장 포착?!

Value	Count	Frequency (%)
20160106	10	11.8%
몸속	5	5.9%
쓰레기를	2	2.4%
5분	2	2.4%
육류	2	2.4%
쓰레기의	1	1.2%
원인이	1	1.2%
있다	1	1.2%
현대인은	1	1.2%
내	1	1.2%
Other values (59)	59	69.4%

Most occurring characters

Value	Count	Frequency (%)
	65	18.1%
0	30	8.4%
1	20	5.6%
6	20	5.6%
2	10	2.8%
!	10	2.8%
?	8	2.2%
이	6	1.7%
는	6	1.7%
몸	5	1.4%
Other values (104)	179	49.9%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	191	53.2%
Decimal Number	82	22.8%
Space Separator	65	18.1%
Other Punctuation	21	5.8%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
이	6	3.1%
는	6	3.1%
몸	5	2.6%
속	5	2.6%
법	5	2.6%
를	4	2.1%
레	4	2.1%
기	4	2.1%
배	4	2.1%
쓰	4	2.1%
Other values (94)	144	75.4%

Decimal Number

Value	Count	Frequency (%)
0	30	36.6%
1	20	24.4%
6	20	24.4%
2	10	12.2%
5	2	2.4%

Other Punctuation

Value	Count	Frequency (%)
!	10	47.6%
?	8	38.1%
'	2	9.5%
,	1	4.8%

Space Separator

Value	Count	Frequency (%)
	65	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Hangul	191	53.2%
Common	168	46.8%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
이	6	3.1%
는	6	3.1%
몸	5	2.6%
속	5	2.6%
법	5	2.6%
를	4	2.1%
레	4	2.1%
기	4	2.1%
배	4	2.1%
쓰	4	2.1%
Other values (94)	144	75.4%

Common

Value	Count	Frequency (%)
	65	38.7%
0	30	17.9%
1	20	11.9%
6	20	11.9%
2	10	6.0%
!	10	6.0%
?	8	4.8%
5	2	1.2%
'	2	1.2%
,	1	0.6%

Most occurring blocks

Value	Count	Frequency (%)
Hangul	191	53.2%
ASCII	168	46.8%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	65	38.7%
0	30	17.9%
1	20	11.9%
6	20	11.9%
2	10	6.0%
!	10	6.0%
?	8	4.8%
5	2	1.2%
'	2	1.2%
,	1	0.6%

Hangul

Value	Count	Frequency (%)
이	6	3.1%
는	6	3.1%
몸	5	2.6%
속	5	2.6%
법	5	2.6%
를	4	2.1%
레	4	2.1%
기	4	2.1%
배	4	2.1%
쓰	4	2.1%
Other values (94)	144	75.4%

VID_CN
Text

MISSING

Distinct	20
Distinct (%)	100.0%
Missing	32
Missing (%)	61.5%
Memory size	548.0 B

Length

Max length	82
Median length	58
Mean length	56
Min length	24

Characters and Unicode

Total characters	1120
Distinct characters	164
Distinct categories	10 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	100.0%

Sample

1st row	추운 겨울철, 몸속 쓰레기가 더 많이 쌓인다?!
2nd row	http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1010583
3rd row	편안하게 앉은 자세로 쉽게 따라 해볼 수 있는 5분 호흡법.
4th row	http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1010584
5th row	5분 호흡법을 배우는 도중 갑자기 신호(?)가 온 MC 허참!

Value	Count	Frequency (%)
5분	3	3.2%
밥상	2	2.2%
호흡법을	2	2.2%
있다	2	2.2%
수	2	2.2%
추운	1	1.1%
섭취가	1	1.1%
습관	1	1.1%
먹는	1	1.1%
빨리	1	1.1%
Other values (77)	77	82.8%

Most occurring characters

Value	Count	Frequency (%)
t	80	7.1%
n	80	7.1%
	73	6.5%
o	50	4.5%
e	50	4.5%
c	50	4.5%
.	42	3.8%
/	40	3.6%
1	32	2.9%
m	30	2.7%
Other values (154)	593	52.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	560	50.0%
Other Letter	203	18.1%
Other Punctuation	126	11.2%
Decimal Number	94	8.4%
Space Separator	73	6.5%
Connector Punctuation	30	2.7%
Math Symbol	20	1.8%
Uppercase Letter	12	1.1%
Open Punctuation	1	0.1%
Close Punctuation	1	0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
는	6	3.0%
호	6	3.0%
가	6	3.0%
법	5	2.5%
다	5	2.5%
이	5	2.5%
흡	4	2.0%
한	4	2.0%
의	4	2.0%
기	4	2.0%
Other values (111)	154	75.9%

Lowercase Letter

Value	Count	Frequency (%)
t	80	14.3%
n	80	14.3%
o	50	8.9%
e	50	8.9%
c	50	8.9%
m	30	5.4%
w	30	5.4%
d	20	3.6%
l	20	3.6%
r	20	3.6%
Other values (9)	130	23.2%

Decimal Number

Value	Count	Frequency (%)
1	32	34.0%
0	25	26.6%
2	12	12.8%
5	10	10.6%
6	6	6.4%
8	4	4.3%
9	2	2.1%
3	1	1.1%
4	1	1.1%
7	1	1.1%

Other Punctuation

Value	Count	Frequency (%)
.	42	33.3%
/	40	31.7%
?	14	11.1%
:	10	7.9%
&	10	7.9%
!	8	6.3%
,	2	1.6%

Uppercase Letter

Value	Count	Frequency (%)
C	11	91.7%
M	1	8.3%

Space Separator

Value	Count	Frequency (%)
	73	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	30	100.0%

Math Symbol

Value	Count	Frequency (%)
=	20	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	572	51.1%
Common	345	30.8%
Hangul	203	18.1%

Most frequent character per script

Hangul

Value	Count	Frequency (%)
는	6	3.0%
호	6	3.0%
가	6	3.0%
법	5	2.5%
다	5	2.5%
이	5	2.5%
흡	4	2.0%
한	4	2.0%
의	4	2.0%
기	4	2.0%
Other values (111)	154	75.9%

Common

Value	Count	Frequency (%)
	73	21.2%
.	42	12.2%
/	40	11.6%
1	32	9.3%
_	30	8.7%
0	25	7.2%
=	20	5.8%
?	14	4.1%
2	12	3.5%
:	10	2.9%
Other values (12)	47	13.6%

Latin

Value	Count	Frequency (%)
t	80	14.0%
n	80	14.0%
o	50	8.7%
e	50	8.7%
c	50	8.7%
m	30	5.2%
w	30	5.2%
d	20	3.5%
l	20	3.5%
r	20	3.5%
Other values (11)	142	24.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	917	81.9%
Hangul	203	18.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	80	8.7%
n	80	8.7%
	73	8.0%
o	50	5.5%
e	50	5.5%
c	50	5.5%
.	42	4.6%
/	40	4.4%
1	32	3.5%
m	30	3.3%
Other values (33)	390	42.5%

Hangul

Value	Count	Frequency (%)
는	6	3.0%
호	6	3.0%
가	6	3.0%
법	5	2.5%
다	5	2.5%
이	5	2.5%
흡	4	2.0%
한	4	2.0%
의	4	2.0%
기	4	2.0%
Other values (111)	154	75.9%

REG_DATE
Unsupported

MISSING REJECTED UNSUPPORTED

Missing	52
Missing (%)	100.0%
Memory size	600.0 B

VOD_CRS_NM
Unsupported

MISSING REJECTED UNSUPPORTED

Missing	52
Missing (%)	100.0%
Memory size	600.0 B

Unnamed: 5
Unsupported

MISSING REJECTED UNSUPPORTED

Missing	52
Missing (%)	100.0%
Memory size	600.0 B

Phik (φk)

Heatmap
Table

	RSTRC_VID_ESSN_NO	VID_SJ_CN	VID_CN
RSTRC_VID_ESSN_NO	1.000	1.000	1.000
VID_SJ_CN	1.000	1.000	1.000
VID_CN	1.000	1.000	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	RSTRC_VID_ESSN_NO	VID_SJ_CN	VID_CN	REG_DATE	VOD_CRS_NM	Unnamed: 5
0	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
1	1010583	추운 겨울철에 몸속 노폐물이 더 쌓이는 이유는?!	추운 겨울철, 몸속 쓰레기가 더 많이 쌓인다?!	<NA>	<NA>	<NA>
2	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
3	몸속 쓰레기 축적으로 각종 질병에 취약해진다고 하는데...	<NA>	<NA>	<NA>	<NA>	<NA>
4	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
5	그렇다면 이 쓰레기들을 방치하면 어떻게 될까?	20160106	http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1010583	<NA>	<NA>	<NA>
6	1010584	스튜디오를 몸속 쓰레기 배출 현장으로 만든 '5분 호흡법'을 배워보자!	편안하게 앉은 자세로 쉽게 따라 해볼 수 있는 5분 호흡법.	<NA>	<NA>	<NA>
7	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
8	이 호흡법을 잘 배우면 몸속 쓰레기를 배출할 수 있다고 하는데~	<NA>	<NA>	<NA>	<NA>	<NA>
9	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>

	RSTRC_VID_ESSN_NO	VID_SJ_CN	VID_CN	REG_DATE	VOD_CRS_NM	Unnamed: 5
42	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
43	그러나 밥상만큼이나 중요한 '무언가'가 있다고 하는데?	<NA>	<NA>	<NA>	<NA>	<NA>
44	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
45	우리 몸속 쓰레기를 줄일 수 있는 건강한 식사법은 무엇일까?	20160106	http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1010606	<NA>	<NA>	<NA>
46	1010607	물 없이 족욕, 반신욕의 효과를 누리는 방법?!	자면서 물도 없이 족욕과 반신욕의 효과를 내는 방법이 있다?!	<NA>	<NA>	<NA>
47	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
48	그 비밀은 바로 현미로 만든 찜질팩?!	<NA>	<NA>	<NA>	<NA>	<NA>
49	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>
50	긴 시간 동안 지속되는 반신욕 효과를 낼 수 있는 현미 찜질팩 만드는 법은 과연?	20160106	http://www.mbn.co.kr/player/movieContents.mbn?content_cls_cd=21&content_id=1010607	<NA>	<NA>	<NA>
51	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>

Most frequently occurring

	RSTRC_VID_ESSN_NO	VID_SJ_CN	VID_CN	# duplicates
0	<NA>	<NA>	<NA>	22

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Decimal Number

Other Punctuation

Space Separator

Math Symbol

Close Punctuation

Open Punctuation

Most occurring scripts

Most frequent character per script

Hangul

Common

Most occurring blocks

Most frequent character per block

ASCII

Hangul

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Decimal Number

Other Punctuation

Space Separator

Most occurring scripts

Most frequent character per script

Hangul

Common

Most occurring blocks

Most frequent character per block

ASCII

Hangul

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Lowercase Letter

Decimal Number

Other Punctuation

Uppercase Letter

Space Separator

Connector Punctuation

Math Symbol

Open Punctuation

Close Punctuation

Most occurring scripts

Most frequent character per script

Hangul

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Hangul

Correlations

Missing values

Sample

Duplicate rows

Most frequently occurring