Overview

Dataset statistics

Number of variables8
Number of observations26
Missing cells7
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory71.1 B

Variable types

Text4
Categorical1
DateTime1
Numeric2

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/be2337db-014c-4c79-94fc-e3d1195ae9df

Alerts

수집일 has constant value ""Constant
키워드빈도 is highly overall correlated with 키워드중요도High correlation
키워드중요도 is highly overall correlated with 키워드빈도High correlation
설명 has 2 (7.7%) missing valuesMissing
생성일 has 5 (19.2%) missing valuesMissing
채널ID has unique valuesUnique
채널명 has unique valuesUnique
키워드중요도 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:55:13.931645
Analysis finished2023-12-10 13:55:16.269724
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

채널ID
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-10T22:55:16.596521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters624
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st rowUCr1YT5IRw3iI1oQgbBOpWrQ
2nd rowUCENyD8OH9DUCGyRj6SVU5EA
3rd rowUCOg2xzbQEVw091Ss2aeEXOA
4th rowUChpjIaEgwtDZtmWEkzFulSA
5th rowUC_fFLoUU8NzaZ0G7jlE1DQA
ValueCountFrequency (%)
ucr1yt5irw3ii1oqgbbopwrq 1
 
3.8%
ucenyd8oh9ducgyrj6svu5ea 1
 
3.8%
uciubh7vqeqxj_ejofp0cgqw 1
 
3.8%
ucb5nltxastbrmazvhyfa-wg 1
 
3.8%
ucvymrl0n9smwqaa2ohcjoka 1
 
3.8%
ucuj6rrhmtr9pipbawbamvuq 1
 
3.8%
ucnhgcri8x6xhehanhae9ipw 1
 
3.8%
ucyr69kqfuovmpyecpffksaq 1
 
3.8%
uc1hwsjkrfziggetl4mglokq 1
 
3.8%
ucsaqlcw1dx-uypozewbudzq 1
 
3.8%
Other values (16) 16
61.5%
2023-12-10T22:55:17.317941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 37
 
5.9%
C 35
 
5.6%
Q 19
 
3.0%
A 18
 
2.9%
r 16
 
2.6%
E 15
 
2.4%
m 15
 
2.4%
h 14
 
2.2%
w 13
 
2.1%
1 13
 
2.1%
Other values (54) 429
68.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 296
47.4%
Lowercase Letter 236
37.8%
Decimal Number 78
 
12.5%
Dash Punctuation 10
 
1.6%
Connector Punctuation 4
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 37
 
12.5%
C 35
 
11.8%
Q 19
 
6.4%
A 18
 
6.1%
E 15
 
5.1%
O 12
 
4.1%
G 11
 
3.7%
B 11
 
3.7%
M 11
 
3.7%
N 10
 
3.4%
Other values (16) 117
39.5%
Lowercase Letter
ValueCountFrequency (%)
r 16
 
6.8%
m 15
 
6.4%
h 14
 
5.9%
w 13
 
5.5%
a 12
 
5.1%
g 12
 
5.1%
e 11
 
4.7%
l 11
 
4.7%
i 10
 
4.2%
t 10
 
4.2%
Other values (16) 112
47.5%
Decimal Number
ValueCountFrequency (%)
1 13
16.7%
9 11
14.1%
2 9
11.5%
0 8
10.3%
7 8
10.3%
6 8
10.3%
4 7
9.0%
8 6
7.7%
3 4
 
5.1%
5 4
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 532
85.3%
Common 92
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 37
 
7.0%
C 35
 
6.6%
Q 19
 
3.6%
A 18
 
3.4%
r 16
 
3.0%
E 15
 
2.8%
m 15
 
2.8%
h 14
 
2.6%
w 13
 
2.4%
O 12
 
2.3%
Other values (42) 338
63.5%
Common
ValueCountFrequency (%)
1 13
14.1%
9 11
12.0%
- 10
10.9%
2 9
9.8%
0 8
8.7%
7 8
8.7%
6 8
8.7%
4 7
7.6%
8 6
6.5%
_ 4
 
4.3%
Other values (2) 8
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 37
 
5.9%
C 35
 
5.6%
Q 19
 
3.0%
A 18
 
2.9%
r 16
 
2.6%
E 15
 
2.4%
m 15
 
2.4%
h 14
 
2.2%
w 13
 
2.1%
1 13
 
2.1%
Other values (54) 429
68.8%

채널명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-10T22:55:17.671889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length10.230769
Min length3

Characters and Unicode

Total characters266
Distinct characters131
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row보고싶진아
2nd rowP4pero Dance
3rd row서대문여성인력개발센터
4th rowKBS Drama
5th row고용노동부
ValueCountFrequency (%)
2
 
4.5%
보고싶진아 1
 
2.3%
배드마우스 1
 
2.3%
한국자산관리공사 1
 
2.3%
캠코tv 1
 
2.3%
조효진 1
 
2.3%
hyojin 1
 
2.3%
달려라치킨 1
 
2.3%
dalchi 1
 
2.3%
한국건설기술연구원[kict 1
 
2.3%
Other values (33) 33
75.0%
2023-12-10T22:55:18.213073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
6.8%
o 13
 
4.9%
e 12
 
4.5%
n 6
 
2.3%
P 6
 
2.3%
r 6
 
2.3%
T 5
 
1.9%
Y 5
 
1.9%
원 5
 
1.9%
한 5
 
1.9%
Other values (121) 185
69.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
46.6%
Lowercase Letter 72
27.1%
Uppercase Letter 45
 
16.9%
Space Separator 18
 
6.8%
Close Punctuation 2
 
0.8%
Open Punctuation 2
 
0.8%
Dash Punctuation 1
 
0.4%
Decimal Number 1
 
0.4%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 5
 
4.0%
한 5
 
4.0%
국 5
 
4.0%
인 4
 
3.2%
연 3
 
2.4%
기 3
 
2.4%
우 3
 
2.4%
자 2
 
1.6%
효 2
 
1.6%
술 2
 
1.6%
Other values (78) 90
72.6%
Lowercase Letter
ValueCountFrequency (%)
o 13
18.1%
e 12
16.7%
n 6
8.3%
r 6
8.3%
a 5
 
6.9%
i 4
 
5.6%
u 4
 
5.6%
c 3
 
4.2%
y 3
 
4.2%
s 3
 
4.2%
Other values (10) 13
18.1%
Uppercase Letter
ValueCountFrequency (%)
P 6
13.3%
T 5
11.1%
Y 5
11.1%
K 4
 
8.9%
O 3
 
6.7%
D 3
 
6.7%
H 3
 
6.7%
J 2
 
4.4%
N 2
 
4.4%
I 2
 
4.4%
Other values (7) 10
22.2%
Space Separator
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%
Other Punctuation
ValueCountFrequency (%)
# 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
46.6%
Latin 117
44.0%
Common 25
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 5
 
4.0%
한 5
 
4.0%
국 5
 
4.0%
인 4
 
3.2%
연 3
 
2.4%
기 3
 
2.4%
우 3
 
2.4%
자 2
 
1.6%
효 2
 
1.6%
술 2
 
1.6%
Other values (78) 90
72.6%
Latin
ValueCountFrequency (%)
o 13
 
11.1%
e 12
 
10.3%
n 6
 
5.1%
P 6
 
5.1%
r 6
 
5.1%
T 5
 
4.3%
Y 5
 
4.3%
a 5
 
4.3%
i 4
 
3.4%
u 4
 
3.4%
Other values (27) 51
43.6%
Common
ValueCountFrequency (%)
18
72.0%
] 2
 
8.0%
[ 2
 
8.0%
- 1
 
4.0%
4 1
 
4.0%
# 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
53.4%
Hangul 124
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
 
12.7%
o 13
 
9.2%
e 12
 
8.5%
n 6
 
4.2%
P 6
 
4.2%
r 6
 
4.2%
T 5
 
3.5%
Y 5
 
3.5%
a 5
 
3.5%
i 4
 
2.8%
Other values (33) 62
43.7%
Hangul
ValueCountFrequency (%)
원 5
 
4.0%
한 5
 
4.0%
국 5
 
4.0%
인 4
 
3.2%
연 3
 
2.4%
기 3
 
2.4%
우 3
 
2.4%
자 2
 
1.6%
효 2
 
1.6%
술 2
 
1.6%
Other values (78) 90
72.6%

수집일
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
2021-07-01
26 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-07-01
2nd row2021-07-01
3rd row2021-07-01
4th row2021-07-01
5th row2021-07-01

Common Values

ValueCountFrequency (%)
2021-07-01 26
100.0%

Length

2023-12-10T22:55:18.409074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:18.544443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-07-01 26
100.0%

설명
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Memory size340.0 B
2023-12-10T22:55:18.881874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length935
Median length79.5
Mean length188.125
Min length20

Characters and Unicode

Total characters4515
Distinct characters422
Distinct categories16 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row일상뷰티패션개그 기타등등 진아의 라이프 서타일 채널 비즈니스 문의 : bogoshipjina@sandboxnetwork.net
2nd rowHello! We are a 4 member girl group based in Brisbane; Australia. We love to cover Kpop dances for fun :) Hope you enjoy our videos and please subscribe and support us~ ^_^
3rd row교육? 취업? 서대문센터에 多있다! 모든 여성이 자신의 능력을 발휘하여 행복한 삶을 살 수 있도록 지원합니다^^ www.workers.or.kr
4th row★ 멀리서보면푸른봄; 오월의청춘; 대박부동산; 오케이광자매 등 KBS 신작과 태양의 후예; 구르미그린달빛 등 KBS 명작 까지 모든 드라마를 즐기실 수 있는 유튜브 채널입니다 :) ★ KBS [Korean Broadcasting System; 韓國放送] Drama Official Youtube Channel ★ KBS Official Website: http:www.kbs.co.kr ★ 웨이브 바로가기 : https:www.wavve.com #KBS #드라마 #꿀잼 #KBS드라마 #KBSDRAMA #KBSDrama #믿고보는 #KBS
5th row일하는 대한민국 국민이라면 누구나! 절대 놓쳐서는 안될 정책 알려드림! 구독과 좋아요는 에헤라디야~
ValueCountFrequency (%)
35
 
4.6%
돌파 8
 
1.1%
and 8
 
1.1%
★ 8
 
1.1%
you 7
 
0.9%
kbs 6
 
0.8%
to 5
 
0.7%
♬ 5
 
0.7%
we 5
 
0.7%
수 5
 
0.7%
Other values (551) 668
87.9%
2023-12-10T22:55:19.518843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
857
 
19.0%
o 161
 
3.6%
e 150
 
3.3%
a 136
 
3.0%
t 122
 
2.7%
. 108
 
2.4%
s 102
 
2.3%
n 89
 
2.0%
r 88
 
1.9%
i 86
 
1.9%
Other values (412) 2616
57.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1655
36.7%
Other Letter 1322
29.3%
Space Separator 859
19.0%
Other Punctuation 247
 
5.5%
Uppercase Letter 164
 
3.6%
Decimal Number 138
 
3.1%
Math Symbol 34
 
0.8%
Other Symbol 25
 
0.6%
Close Punctuation 20
 
0.4%
Dash Punctuation 15
 
0.3%
Other values (6) 36
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 45
 
3.4%
니 45
 
3.4%
이 38
 
2.9%
는 25
 
1.9%
기 20
 
1.5%
드 19
 
1.4%
리 19
 
1.4%
요 19
 
1.4%
고 18
 
1.4%
자 18
 
1.4%
Other values (323) 1056
79.9%
Lowercase Letter
ValueCountFrequency (%)
o 161
 
9.7%
e 150
 
9.1%
a 136
 
8.2%
t 122
 
7.4%
s 102
 
6.2%
n 89
 
5.4%
r 88
 
5.3%
i 86
 
5.2%
m 77
 
4.7%
w 74
 
4.5%
Other values (15) 570
34.4%
Uppercase Letter
ValueCountFrequency (%)
S 21
12.8%
B 20
12.2%
K 17
 
10.4%
T 10
 
6.1%
A 9
 
5.5%
I 9
 
5.5%
D 9
 
5.5%
M 8
 
4.9%
H 7
 
4.3%
Y 6
 
3.7%
Other values (13) 48
29.3%
Other Punctuation
ValueCountFrequency (%)
. 108
43.7%
: 36
 
14.6%
; 36
 
14.6%
! 30
 
12.1%
? 9
 
3.6%
# 8
 
3.2%
* 7
 
2.8%
@ 6
 
2.4%
' 4
 
1.6%
& 3
 
1.2%
Decimal Number
ValueCountFrequency (%)
0 44
31.9%
1 35
25.4%
6 11
 
8.0%
2 9
 
6.5%
3 8
 
5.8%
9 8
 
5.8%
4 7
 
5.1%
7 6
 
4.3%
5 6
 
4.3%
8 4
 
2.9%
Other Symbol
ValueCountFrequency (%)
★ 8
32.0%
♥ 7
28.0%
♬ 5
20.0%
▶ 3
 
12.0%
☆ 1
 
4.0%
♡ 1
 
4.0%
Space Separator
ValueCountFrequency (%)
857
99.8%
　 2
 
0.2%
Math Symbol
ValueCountFrequency (%)
= 31
91.2%
~ 3
 
8.8%
Close Punctuation
ValueCountFrequency (%)
) 18
90.0%
] 2
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 12
85.7%
[ 2
 
14.3%
Final Punctuation
ValueCountFrequency (%)
” 2
66.7%
’ 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 2
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1819
40.3%
Common 1374
30.4%
Hangul 1317
29.2%
Han 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 45
 
3.4%
니 45
 
3.4%
이 38
 
2.9%
는 25
 
1.9%
기 20
 
1.5%
드 19
 
1.4%
리 19
 
1.4%
요 19
 
1.4%
고 18
 
1.4%
자 18
 
1.4%
Other values (318) 1051
79.8%
Latin
ValueCountFrequency (%)
o 161
 
8.9%
e 150
 
8.2%
a 136
 
7.5%
t 122
 
6.7%
s 102
 
5.6%
n 89
 
4.9%
r 88
 
4.8%
i 86
 
4.7%
m 77
 
4.2%
w 74
 
4.1%
Other values (38) 734
40.4%
Common
ValueCountFrequency (%)
857
62.4%
. 108
 
7.9%
0 44
 
3.2%
: 36
 
2.6%
; 36
 
2.6%
1 35
 
2.5%
= 31
 
2.3%
! 30
 
2.2%
) 18
 
1.3%
- 15
 
1.1%
Other values (31) 164
 
11.9%
Han
ValueCountFrequency (%)
國 1
20.0%
放 1
20.0%
送 1
20.0%
韓 1
20.0%
多 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3161
70.0%
Hangul 1315
29.1%
Misc Symbols 22
 
0.5%
Punctuation 5
 
0.1%
CJK 5
 
0.1%
Geometric Shapes 3
 
0.1%
Compat Jamo 2
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
857
27.1%
o 161
 
5.1%
e 150
 
4.7%
a 136
 
4.3%
t 122
 
3.9%
. 108
 
3.4%
s 102
 
3.2%
n 89
 
2.8%
r 88
 
2.8%
i 86
 
2.7%
Other values (69) 1262
39.9%
Hangul
ValueCountFrequency (%)
다 45
 
3.4%
니 45
 
3.4%
이 38
 
2.9%
는 25
 
1.9%
기 20
 
1.5%
드 19
 
1.4%
리 19
 
1.4%
요 19
 
1.4%
고 18
 
1.4%
자 18
 
1.4%
Other values (317) 1049
79.8%
Misc Symbols
ValueCountFrequency (%)
★ 8
36.4%
♥ 7
31.8%
♬ 5
22.7%
☆ 1
 
4.5%
♡ 1
 
4.5%
Geometric Shapes
ValueCountFrequency (%)
▶ 3
100.0%
Punctuation
ValueCountFrequency (%)
“ 2
40.0%
” 2
40.0%
’ 1
20.0%
Compat Jamo
ValueCountFrequency (%)
ㅠ 2
100.0%
None
ValueCountFrequency (%)
　 2
100.0%
CJK
ValueCountFrequency (%)
國 1
20.0%
放 1
20.0%
送 1
20.0%
韓 1
20.0%
多 1
20.0%

생성일
Date

MISSING 

Distinct21
Distinct (%)100.0%
Missing5
Missing (%)19.2%
Memory size340.0 B
Minimum2011-05-19 00:00:00
Maximum2019-02-26 00:00:00
2023-12-10T22:55:19.745608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:19.935920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

키워드
Text

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-10T22:55:20.291877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1923077
Min length1

Characters and Unicode

Total characters57
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)23.1%

Sample

1st row데이트
2nd row먹방
3rd row창업
4th row행사
5th row축제
ValueCountFrequency (%)
먹방 3
11.5%
카페 3
11.5%
데이트 2
 
7.7%
행사 2
 
7.7%
축제 2
 
7.7%
관광 2
 
7.7%
캠핑 2
 
7.7%
일자리 2
 
7.7%
교육 2
 
7.7%
창업 1
 
3.8%
Other values (5) 5
19.2%
2023-12-10T22:55:20.790071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
먹 3
 
5.3%
카 3
 
5.3%
페 3
 
5.3%
사 3
 
5.3%
방 3
 
5.3%
캠 2
 
3.5%
업 2
 
3.5%
육 2
 
3.5%
교 2
 
3.5%
리 2
 
3.5%
Other values (21) 32
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
먹 3
 
5.3%
카 3
 
5.3%
페 3
 
5.3%
사 3
 
5.3%
방 3
 
5.3%
캠 2
 
3.5%
업 2
 
3.5%
육 2
 
3.5%
교 2
 
3.5%
리 2
 
3.5%
Other values (21) 32
56.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
먹 3
 
5.3%
카 3
 
5.3%
페 3
 
5.3%
사 3
 
5.3%
방 3
 
5.3%
캠 2
 
3.5%
업 2
 
3.5%
육 2
 
3.5%
교 2
 
3.5%
리 2
 
3.5%
Other values (21) 32
56.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
먹 3
 
5.3%
카 3
 
5.3%
페 3
 
5.3%
사 3
 
5.3%
방 3
 
5.3%
캠 2
 
3.5%
업 2
 
3.5%
육 2
 
3.5%
교 2
 
3.5%
리 2
 
3.5%
Other values (21) 32
56.1%

키워드빈도
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.769231
Minimum1
Maximum755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T22:55:20.980952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6.5
Q319.25
95-th percentile520.25
Maximum755
Range754
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation209.83066
Coefficient of variation (CV)2.1910028
Kurtosis3.7890599
Mean95.769231
Median Absolute Deviation (MAD)5.5
Skewness2.2297819
Sum2490
Variance44028.905
MonotonicityNot monotonic
2023-12-10T22:55:21.148444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 5
19.2%
2 4
15.4%
4 2
 
7.7%
13 2
 
7.7%
21 1
 
3.8%
755 1
 
3.8%
7 1
 
3.8%
506 1
 
3.8%
525 1
 
3.8%
26 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
1 5
19.2%
2 4
15.4%
3 1
 
3.8%
4 2
 
7.7%
6 1
 
3.8%
7 1
 
3.8%
8 1
 
3.8%
10 1
 
3.8%
13 2
 
7.7%
14 1
 
3.8%
ValueCountFrequency (%)
755 1
3.8%
525 1
3.8%
506 1
3.8%
478 1
3.8%
84 1
3.8%
26 1
3.8%
21 1
3.8%
14 1
3.8%
13 2
7.7%
10 1
3.8%

키워드중요도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.325615
Minimum0.786
Maximum396.716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T22:55:21.718146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.786
5-th percentile1.15375
Q12.33125
median4.6055
Q315.952
95-th percentile275.95875
Maximum396.716
Range395.93
Interquartile range (IQR)13.62075

Descriptive statistics

Standard deviation110.65989
Coefficient of variation (CV)2.0751733
Kurtosis3.6034188
Mean53.325615
Median Absolute Deviation (MAD)3.4045
Skewness2.1944139
Sum1386.466
Variance12245.612
MonotonicityNot monotonic
2023-12-10T22:55:21.912712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
16.875 1
 
3.8%
3.125 1
 
3.8%
396.716 1
 
3.8%
2.299 1
 
3.8%
5.625 1
 
3.8%
256.565 1
 
3.8%
275.862 1
 
3.8%
2.31 1
 
3.8%
1.58 1
 
3.8%
12.414 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
0.786 1
3.8%
1.051 1
3.8%
1.462 1
3.8%
1.58 1
3.8%
1.854 1
3.8%
2.299 1
3.8%
2.31 1
3.8%
2.395 1
3.8%
2.498 1
3.8%
2.78 1
3.8%
ValueCountFrequency (%)
396.716 1
3.8%
275.991 1
3.8%
275.862 1
3.8%
256.565 1
3.8%
56.212 1
3.8%
22.114 1
3.8%
16.875 1
3.8%
13.183 1
3.8%
12.414 1
3.8%
9.581 1
3.8%

Interactions

2023-12-10T22:55:15.182805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:14.853503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:15.348011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:15.017226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:55:22.077667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채널ID채널명설명생성일키워드키워드빈도키워드중요도
채널ID1.0001.0001.0001.0001.0001.0001.000
채널명1.0001.0001.0001.0001.0001.0001.000
설명1.0001.0001.0001.0001.0001.0001.000
생성일1.0001.0001.0001.0001.0001.0001.000
키워드1.0001.0001.0001.0001.0000.0000.000
키워드빈도1.0001.0001.0001.0000.0001.0001.000
키워드중요도1.0001.0001.0001.0000.0001.0001.000
2023-12-10T22:55:22.265405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
키워드빈도키워드중요도
키워드빈도1.0000.937
키워드중요도0.9371.000

Missing values

2023-12-10T22:55:15.599071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:55:15.917454image/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.
2023-12-10T22:55:16.097055image/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

채널ID채널명수집일설명생성일키워드키워드빈도키워드중요도
0UCr1YT5IRw3iI1oQgbBOpWrQ보고싶진아2021-07-01일상뷰티패션개그 기타등등 진아의 라이프 서타일 채널 비즈니스 문의 : bogoshipjina@sandboxnetwork.net2016-12-15데이트2116.875
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채널ID채널명수집일설명생성일키워드키워드빈도키워드중요도
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