Overview

Dataset statistics

Number of variables19
Number of observations26
Missing cells30
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory170.1 B

Variable types

Text3
DateTime2
Categorical5
Numeric9

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/9d911d78-67c6-46be-9eb7-4b17e51965de

Alerts

역량평가수집일자 has constant value ""Constant
최초6개월 is highly overall correlated with 최초6개월표준점수 and 1 other fieldsHigh correlation
그룹내채널수 is highly overall correlated with 최초6개월표준점수 and 2 other fieldsHigh correlation
상호작용정도1개월 is highly overall correlated with 홍보지수 and 4 other fieldsHigh correlation
역량별그룹할당 is highly overall correlated with 최초6개월표준점수 and 2 other fieldsHigh correlation
그룹내구독자순위 is highly overall correlated with 그룹내조회수순위 and 4 other fieldsHigh correlation
그룹내조회수순위 is highly overall correlated with 그룹내구독자순위 and 4 other fieldsHigh correlation
그룹내좋아요순위 is highly overall correlated with 그룹내구독자순위 and 4 other fieldsHigh correlation
역량평가 is highly overall correlated with 그룹내구독자순위 and 4 other fieldsHigh correlation
홍보지수 is highly overall correlated with 그룹내구독자순위 and 5 other fieldsHigh correlation
최근6개월 is highly overall correlated with 상호작용정도1개월High correlation
그룹내홍보지수표준점수 is highly overall correlated with 그룹내구독자순위 and 6 other fieldsHigh correlation
최초6개월표준점수 is highly overall correlated with 그룹내홍보지수표준점수 and 3 other fieldsHigh correlation
상호작용도1개월표준점수 is highly overall correlated with 역량별그룹할당 and 2 other fieldsHigh correlation
상호작용정도1개월 is highly imbalanced (76.5%)Imbalance
역량평가채널설명 has 6 (23.1%) missing valuesMissing
홍보지수 has 8 (30.8%) missing valuesMissing
최근6개월 has 8 (30.8%) missing valuesMissing
그룹내홍보지수표준점수 has 8 (30.8%) missing valuesMissing
역량평가채널ID has unique valuesUnique
역량평가채널명 has unique valuesUnique
역량평가채널생성일자 has unique valuesUnique
그룹내조회수순위 has unique valuesUnique
그룹내좋아요순위 has unique valuesUnique
역량평가 has unique valuesUnique
그룹내홍보지수표준점수 has 1 (3.8%) zerosZeros

Reproduction

Analysis started2023-12-10 14:20:42.986240
Analysis finished2023-12-10 14:20:52.097808
Duration9.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역량평가채널ID
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-10T23:20:52.251341image/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 rowUCWVqtkSFKE_SB_r0JqB9rBQ
2nd rowUCSOeJWyByeoBHpmrm0NnlLw
3rd rowUCSQPjdpKtG7egh3qmOcUn1w
4th rowUCM3pMyyslUgGIGXK6M6Dm-w
5th rowUCGPvcR_72yc5wPqsKefo4jg
ValueCountFrequency (%)
ucwvqtksfke_sb_r0jqb9rbq 1
 
3.8%
ucsoejwybyeobhpmrm0nnllw 1
 
3.8%
ucol4ig5vgbuxdzgtxsxasgq 1
 
3.8%
ucd4pvwa8t2spko3ml6kupkg 1
 
3.8%
ucpsfkw-1tfyy--ag7__0w5a 1
 
3.8%
ucwp8zbd9w_4jlwmad8n8hog 1
 
3.8%
uclsyz6xrgwfnrnlytj9daqw 1
 
3.8%
ucao7xcdmacgpcv0tz4mdycg 1
 
3.8%
uc1r112pr9ngcg2ntce946hq 1
 
3.8%
uclh6zbnwknday50fodg0sca 1
 
3.8%
Other values (16) 16
61.5%
2023-12-10T23:20:52.568920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 32
 
5.1%
C 32
 
5.1%
g 20
 
3.2%
A 18
 
2.9%
w 18
 
2.9%
1 15
 
2.4%
D 15
 
2.4%
y 14
 
2.2%
e 12
 
1.9%
Q 12
 
1.9%
Other values (54) 436
69.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 269
43.1%
Lowercase Letter 245
39.3%
Decimal Number 95
 
15.2%
Dash Punctuation 9
 
1.4%
Connector Punctuation 6
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 32
 
11.9%
C 32
 
11.9%
A 18
 
6.7%
D 15
 
5.6%
Q 12
 
4.5%
M 12
 
4.5%
S 12
 
4.5%
P 10
 
3.7%
T 10
 
3.7%
Z 10
 
3.7%
Other values (16) 106
39.4%
Lowercase Letter
ValueCountFrequency (%)
g 20
 
8.2%
w 18
 
7.3%
y 14
 
5.7%
e 12
 
4.9%
l 12
 
4.9%
r 11
 
4.5%
c 11
 
4.5%
p 10
 
4.1%
a 10
 
4.1%
d 10
 
4.1%
Other values (16) 117
47.8%
Decimal Number
ValueCountFrequency (%)
1 15
15.8%
7 11
11.6%
4 10
10.5%
9 10
10.5%
8 9
9.5%
3 9
9.5%
0 9
9.5%
2 8
8.4%
6 8
8.4%
5 6
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 514
82.4%
Common 110
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 32
 
6.2%
C 32
 
6.2%
g 20
 
3.9%
A 18
 
3.5%
w 18
 
3.5%
D 15
 
2.9%
y 14
 
2.7%
e 12
 
2.3%
Q 12
 
2.3%
l 12
 
2.3%
Other values (42) 329
64.0%
Common
ValueCountFrequency (%)
1 15
13.6%
7 11
10.0%
4 10
9.1%
9 10
9.1%
8 9
8.2%
- 9
8.2%
3 9
8.2%
0 9
8.2%
2 8
7.3%
6 8
7.3%
Other values (2) 12
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 32
 
5.1%
C 32
 
5.1%
g 20
 
3.2%
A 18
 
2.9%
w 18
 
2.9%
1 15
 
2.4%
D 15
 
2.4%
y 14
 
2.2%
e 12
 
1.9%
Q 12
 
1.9%
Other values (54) 436
69.9%

역량평가채널명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-10T23:20:52.768481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14.5
Mean length9.5769231
Min length2

Characters and Unicode

Total characters249
Distinct characters123
Distinct categories5 ?
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 row해피둥이가 간다_가족여행 실전 꿀팁
2nd row겜브링 GGAM BRING
3rd row아롱다롱TV ArongDarongTV
4th row개밍순 DogMingsoon
5th row도로교통공단
ValueCountFrequency (%)
해피둥이가 1
 
2.2%
leeanfilm리안 1
 
2.2%
김소예 1
 
2.2%
얌무yammoo 1
 
2.2%
찌워니의 1
 
2.2%
삶 1
 
2.2%
감스트gamst 1
 
2.2%
갑부주방아울렛 1
 
2.2%
daiya다이야 1
 
2.2%
과학하는 1
 
2.2%
Other values (35) 35
77.8%
2023-12-10T23:20:53.078488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19
 
7.6%
o 13
 
5.2%
n 8
 
3.2%
M 7
 
2.8%
a 6
 
2.4%
i 6
 
2.4%
g 5
 
2.0%
T 5
 
2.0%
A 5
 
2.0%
D 4
 
1.6%
Other values (113) 171
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110
44.2%
Lowercase Letter 66
26.5%
Uppercase Letter 53
21.3%
Space Separator 19
 
7.6%
Connector Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 3
 
2.7%
무 3
 
2.7%
이 3
 
2.7%
가 3
 
2.7%
개 2
 
1.8%
도 2
 
1.8%
울 2
 
1.8%
단 2
 
1.8%
공 2
 
1.8%
스 2
 
1.8%
Other values (75) 86
78.2%
Lowercase Letter
ValueCountFrequency (%)
o 13
19.7%
n 8
12.1%
a 6
9.1%
i 6
9.1%
g 5
 
7.6%
l 4
 
6.1%
e 4
 
6.1%
r 3
 
4.5%
h 3
 
4.5%
y 3
 
4.5%
Other values (8) 11
16.7%
Uppercase Letter
ValueCountFrequency (%)
M 7
13.2%
T 5
9.4%
A 5
9.4%
D 4
 
7.5%
G 4
 
7.5%
R 4
 
7.5%
B 3
 
5.7%
I 3
 
5.7%
V 3
 
5.7%
E 3
 
5.7%
Other values (8) 12
22.6%
Space Separator
ValueCountFrequency (%)
19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119
47.8%
Hangul 110
44.2%
Common 20
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 3
 
2.7%
무 3
 
2.7%
이 3
 
2.7%
가 3
 
2.7%
개 2
 
1.8%
도 2
 
1.8%
울 2
 
1.8%
단 2
 
1.8%
공 2
 
1.8%
스 2
 
1.8%
Other values (75) 86
78.2%
Latin
ValueCountFrequency (%)
o 13
 
10.9%
n 8
 
6.7%
M 7
 
5.9%
a 6
 
5.0%
i 6
 
5.0%
g 5
 
4.2%
T 5
 
4.2%
A 5
 
4.2%
D 4
 
3.4%
G 4
 
3.4%
Other values (26) 56
47.1%
Common
ValueCountFrequency (%)
19
95.0%
_ 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139
55.8%
Hangul 110
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19
 
13.7%
o 13
 
9.4%
n 8
 
5.8%
M 7
 
5.0%
a 6
 
4.3%
i 6
 
4.3%
g 5
 
3.6%
T 5
 
3.6%
A 5
 
3.6%
D 4
 
2.9%
Other values (28) 61
43.9%
Hangul
ValueCountFrequency (%)
다 3
 
2.7%
무 3
 
2.7%
이 3
 
2.7%
가 3
 
2.7%
개 2
 
1.8%
도 2
 
1.8%
울 2
 
1.8%
단 2
 
1.8%
공 2
 
1.8%
스 2
 
1.8%
Other values (75) 86
78.2%

역량평가수집일자
Date

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
Minimum2021-04-11 00:00:00
Maximum2021-04-11 00:00:00
2023-12-10T23:20:53.178574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:53.258612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

역량평가채널설명
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing6
Missing (%)23.1%
Memory size340.0 B
2023-12-10T23:20:53.464984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length958
Median length158.5
Mean length203.7
Min length7

Characters and Unicode

Total characters4074
Distinct characters401
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row언제나 해맑은 6살둥이들 오채 오하의 잘놀고 잘먹고 잘즐기는 이야기 아이들과 어디가서 놀지? 뭐먹지? 잘 놀고 잘 노는 꿀팁 대방출!! 해피둥이가간다 무료구독 http:bit.ly2Fi6mFI 문의: hakseang@gmail.com 둥이아빠오군 : 010-5140-1475 카카오1:1채팅 : https:open.kakao.comosnSunenb 인스타그램 https:www.instagram.comhakseang08
2nd row하브!!!!!!!! 【채널 구독하기 클릭】 : https:www.youtube.comggambring 【일상 인스타그램】 : https:www.instagram.comggambringtv 【페이스북】 : https:www.facebook.comggambring 【이메일】 : ggambring@sandboxnetwork.net
3rd row아롱다롱TV는 초등학생 키즈크리에이터 남매 아롱이(유리)와 다롱이(채민)의 채널입니다. 또래 친구들이 좋아하는 놀이와 게임; 남매간의 대결이나 챌린지; 가족과 함께하는 여행이나 일상 영상으로 만들어 집니다. 유튜브와 네이버TV에 업로드 되고 있으며; 페이스북; 인스타그램 등 SNS 를 통해 구독자와 소통하고 있습니다. 아롱다롱TV 많이 사랑해주세요! - 페이스북 https:www.facebook.comarongdarongtv - 인스타그램 https:www.instagram.comarongdarongtv * 비지니스 문의 e-mail: arongdarongtv@naver.com
4th row꼬똥 드 툴레아(Coton de Tulear) 밍순이 실버 푸들 (Silver Poodle) 개순이 입양부터 평생을 기록할 강아지 유튜브 채널입니다♥ - 프로필 (profile) ♥ 첫째 (하얀애) 이름 : 밍순 MingSoon 성별 : 여 Female 생일 : 2019.02.27 견종 : 꼬똥 드 툴레아 Coton de Tulear 특징 : 눈마주치면 꼬리 흔들기 :) 별명 : 밍뚜니; 토끼; 개끼; 개끼냥이; 대걸레; 민들레홀씨; 로봇청소기; 먼지털이개; 꼬질이똥개; 하찮은먼지 (아마도 더 추가 될 예정 ㄷㄷ) ♥ 둘째 (까만애) 이름 : 개순 GaeSoon 성별 : 여 Female 생일 : 2019.07.10 견종 : 실버 푸들 Silver Poodle 특징 : 자기가 고양이 인줄 알고있음ㅋ (손을 잘씀) 별명 : 개엄살; 흑염소; 파괴왕; 양순이 (기똥찬걸로 지어주시면 추가함'ㅡ') ------------------------------------------- ♥ First born Name : MingSoon Sex : Female Birth date : 28 February 2019 Breed : Coton de Tulear Characteristics : Tail wobble ♥ Second born Name : GaeSoon Sex : Female Birth date : 10 July 2019 Breed : Silver Toy Poodle - e-mail : hyuna0124@naver.com #반려견 #강아지 #꼬똥 #꼬똥드툴레아 #CotondeTulear #밍순이 #푸들 #실버푸들 #SilverPoodle #개순이
5th row도로교통공단 공식 유튜브 채널입니다. 교통안전; 교통사고 예방을 위해 많은 관심 가져주세요!
ValueCountFrequency (%)
47
 
6.7%
the 6
 
0.9%
and 5
 
0.7%
채널입니다 4
 
0.6%
문의 4
 
0.6%
통해 4
 
0.6%
인스타그램 4
 
0.6%
수 4
 
0.6%
channel 4
 
0.6%
유튜브 4
 
0.6%
Other values (518) 617
87.8%
2023-12-10T23:20:53.825637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
756
 
18.6%
e 141
 
3.5%
o 123
 
3.0%
a 115
 
2.8%
n 102
 
2.5%
t 101
 
2.5%
i 91
 
2.2%
r 81
 
2.0%
- 67
 
1.6%
s 63
 
1.5%
Other values (391) 2434
59.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1312
32.2%
Other Letter 1232
30.2%
Space Separator 756
18.6%
Uppercase Letter 285
 
7.0%
Other Punctuation 207
 
5.1%
Decimal Number 126
 
3.1%
Dash Punctuation 67
 
1.6%
Close Punctuation 40
 
1.0%
Open Punctuation 37
 
0.9%
Other Symbol 6
 
0.1%
Other values (3) 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 43
 
3.5%
다 29
 
2.4%
니 26
 
2.1%
하 22
 
1.8%
고 21
 
1.7%
는 21
 
1.7%
의 18
 
1.5%
가 16
 
1.3%
을 16
 
1.3%
에 16
 
1.3%
Other values (310) 1004
81.5%
Lowercase Letter
ValueCountFrequency (%)
e 141
 
10.7%
o 123
 
9.4%
a 115
 
8.8%
n 102
 
7.8%
t 101
 
7.7%
i 91
 
6.9%
r 81
 
6.2%
s 63
 
4.8%
l 59
 
4.5%
m 58
 
4.4%
Other values (15) 378
28.8%
Uppercase Letter
ValueCountFrequency (%)
S 30
 
10.5%
M 25
 
8.8%
I 21
 
7.4%
T 20
 
7.0%
C 19
 
6.7%
A 19
 
6.7%
P 16
 
5.6%
N 15
 
5.3%
E 15
 
5.3%
F 13
 
4.6%
Other values (15) 92
32.3%
Other Punctuation
ValueCountFrequency (%)
. 60
29.0%
: 49
23.7%
; 40
19.3%
! 18
 
8.7%
@ 10
 
4.8%
# 10
 
4.8%
* 8
 
3.9%
' 7
 
3.4%
? 4
 
1.9%
& 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 35
27.8%
2 24
19.0%
0 24
19.0%
8 12
 
9.5%
4 9
 
7.1%
7 6
 
4.8%
9 6
 
4.8%
5 5
 
4.0%
6 3
 
2.4%
3 2
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 36
90.0%
】 4
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 33
89.2%
【 4
 
10.8%
Other Symbol
ValueCountFrequency (%)
♥ 5
83.3%
★ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
756
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1597
39.2%
Common 1245
30.6%
Hangul 1232
30.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 43
 
3.5%
다 29
 
2.4%
니 26
 
2.1%
하 22
 
1.8%
고 21
 
1.7%
는 21
 
1.7%
의 18
 
1.5%
가 16
 
1.3%
을 16
 
1.3%
에 16
 
1.3%
Other values (310) 1004
81.5%
Latin
ValueCountFrequency (%)
e 141
 
8.8%
o 123
 
7.7%
a 115
 
7.2%
n 102
 
6.4%
t 101
 
6.3%
i 91
 
5.7%
r 81
 
5.1%
s 63
 
3.9%
l 59
 
3.7%
m 58
 
3.6%
Other values (40) 663
41.5%
Common
ValueCountFrequency (%)
756
60.7%
- 67
 
5.4%
. 60
 
4.8%
: 49
 
3.9%
; 40
 
3.2%
) 36
 
2.9%
1 35
 
2.8%
( 33
 
2.7%
2 24
 
1.9%
0 24
 
1.9%
Other values (21) 121
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2828
69.4%
Hangul 1228
30.1%
None 8
 
0.2%
Misc Symbols 6
 
0.1%
Compat Jamo 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
756
26.7%
e 141
 
5.0%
o 123
 
4.3%
a 115
 
4.1%
n 102
 
3.6%
t 101
 
3.6%
i 91
 
3.2%
r 81
 
2.9%
- 67
 
2.4%
s 63
 
2.2%
Other values (67) 1188
42.0%
Hangul
ValueCountFrequency (%)
이 43
 
3.5%
다 29
 
2.4%
니 26
 
2.1%
하 22
 
1.8%
고 21
 
1.7%
는 21
 
1.7%
의 18
 
1.5%
가 16
 
1.3%
을 16
 
1.3%
에 16
 
1.3%
Other values (307) 1000
81.4%
Misc Symbols
ValueCountFrequency (%)
♥ 5
83.3%
★ 1
 
16.7%
None
ValueCountFrequency (%)
【 4
50.0%
】 4
50.0%
Compat Jamo
ValueCountFrequency (%)
ㄷ 2
50.0%
ㅋ 1
25.0%
ㅡ 1
25.0%

역량평가채널생성일자
Date

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
Minimum2006-08-02 00:00:00
Maximum2019-04-10 00:00:00
2023-12-10T23:20:53.950196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:54.120152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

역량별그룹할당
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
MACRO
12 
MICRO
9 
MEGA
5 

Length

Max length5
Median length5
Mean length4.8076923
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMICRO
2nd rowMEGA
3rd rowMACRO
4th rowMACRO
5th rowMACRO

Common Values

ValueCountFrequency (%)
MACRO 12
46.2%
MICRO 9
34.6%
MEGA 5
19.2%

Length

2023-12-10T23:20:54.250256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:54.364108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
macro 12
46.2%
micro 9
34.6%
mega 5
19.2%

그룹내채널수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
1466
12 
1056
9 
197
5 

Length

Max length4
Median length4
Mean length3.8076923
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1056
2nd row197
3rd row1466
4th row1466
5th row1466

Common Values

ValueCountFrequency (%)
1466 12
46.2%
1056 9
34.6%
197 5
19.2%

Length

2023-12-10T23:20:54.485459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:54.595632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1466 12
46.2%
1056 9
34.6%
197 5
19.2%

그룹내구독자순위
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean633.07692
Minimum88
Maximum1396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T23:20:54.729252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile129.25
Q1203
median731.5
Q3876
95-th percentile1296.25
Maximum1396
Range1308
Interquartile range (IQR)673

Descriptive statistics

Standard deviation390.91052
Coefficient of variation (CV)0.61747713
Kurtosis-0.91152243
Mean633.07692
Median Absolute Deviation (MAD)229
Skewness0.11270658
Sum16460
Variance152811.03
MonotonicityNot monotonic
2023-12-10T23:20:54.877319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
876 3
 
11.5%
88 1
 
3.8%
129 1
 
3.8%
925 1
 
3.8%
885 1
 
3.8%
680 1
 
3.8%
747 1
 
3.8%
786 1
 
3.8%
130 1
 
3.8%
346 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
88 1
3.8%
129 1
3.8%
130 1
3.8%
133 1
3.8%
157 1
3.8%
186 1
3.8%
192 1
3.8%
236 1
3.8%
346 1
3.8%
467 1
3.8%
ValueCountFrequency (%)
1396 1
 
3.8%
1337 1
 
3.8%
1174 1
 
3.8%
925 1
 
3.8%
899 1
 
3.8%
885 1
 
3.8%
876 3
11.5%
844 1
 
3.8%
786 1
 
3.8%
774 1
 
3.8%

그룹내조회수순위
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean574.80769
Minimum12
Maximum1407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T23:20:55.022254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile56
Q1174.25
median618.5
Q3888.25
95-th percentile1133.25
Maximum1407
Range1395
Interquartile range (IQR)714

Descriptive statistics

Standard deviation396.13281
Coefficient of variation (CV)0.68915711
Kurtosis-0.9382835
Mean574.80769
Median Absolute Deviation (MAD)309.5
Skewness0.16292913
Sum14945
Variance156921.2
MonotonicityNot monotonic
2023-12-10T23:20:55.152068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
699 1
 
3.8%
54 1
 
3.8%
110 1
 
3.8%
924 1
 
3.8%
675 1
 
3.8%
12 1
 
3.8%
921 1
 
3.8%
889 1
 
3.8%
886 1
 
3.8%
108 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
12 1
3.8%
54 1
3.8%
62 1
3.8%
100 1
3.8%
108 1
3.8%
110 1
3.8%
171 1
3.8%
184 1
3.8%
305 1
3.8%
460 1
3.8%
ValueCountFrequency (%)
1407 1
3.8%
1145 1
3.8%
1098 1
3.8%
1012 1
3.8%
924 1
3.8%
921 1
3.8%
889 1
3.8%
886 1
3.8%
722 1
3.8%
717 1
3.8%

그룹내좋아요순위
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean554.92308
Minimum14
Maximum1433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T23:20:55.268103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile69.25
Q1138.75
median603.5
Q3822.5
95-th percentile1297.5
Maximum1433
Range1419
Interquartile range (IQR)683.75

Descriptive statistics

Standard deviation411.19435
Coefficient of variation (CV)0.74099342
Kurtosis-0.5360124
Mean554.92308
Median Absolute Deviation (MAD)329.5
Skewness0.42937389
Sum14428
Variance169080.79
MonotonicityNot monotonic
2023-12-10T23:20:55.390244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
662 1
 
3.8%
68 1
 
3.8%
107 1
 
3.8%
752 1
 
3.8%
821 1
 
3.8%
14 1
 
3.8%
823 1
 
3.8%
914 1
 
3.8%
887 1
 
3.8%
144 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
14 1
3.8%
68 1
3.8%
73 1
3.8%
85 1
3.8%
107 1
3.8%
133 1
3.8%
137 1
3.8%
144 1
3.8%
160 1
3.8%
328 1
3.8%
ValueCountFrequency (%)
1433 1
3.8%
1399 1
3.8%
993 1
3.8%
952 1
3.8%
914 1
3.8%
887 1
3.8%
823 1
3.8%
821 1
3.8%
752 1
3.8%
717 1
3.8%

역량평가
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean526.5
Minimum12
Maximum1364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T23:20:55.539197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile35.75
Q1125.5
median523.5
Q3818.75
95-th percentile1287
Maximum1364
Range1352
Interquartile range (IQR)693.25

Descriptive statistics

Standard deviation424.89766
Coefficient of variation (CV)0.80702309
Kurtosis-0.88884374
Mean526.5
Median Absolute Deviation (MAD)395.5
Skewness0.48644494
Sum13689
Variance180538.02
MonotonicityNot monotonic
2023-12-10T23:20:55.720650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
339 1
 
3.8%
35 1
 
3.8%
123 1
 
3.8%
687 1
 
3.8%
619 1
 
3.8%
12 1
 
3.8%
640 1
 
3.8%
937 1
 
3.8%
854 1
 
3.8%
56 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
12 1
3.8%
35 1
3.8%
38 1
3.8%
56 1
3.8%
79 1
3.8%
98 1
3.8%
123 1
3.8%
133 1
3.8%
160 1
3.8%
309 1
3.8%
ValueCountFrequency (%)
1364 1
3.8%
1338 1
3.8%
1134 1
3.8%
1059 1
3.8%
976 1
3.8%
937 1
3.8%
854 1
3.8%
713 1
3.8%
687 1
3.8%
640 1
3.8%

홍보지수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing8
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean5.91
Minimum0.39
Maximum18.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T23:20:55.832467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.39
5-th percentile0.3985
Q12.52
median4.06
Q37.3025
95-th percentile15.0735
Maximum18.55
Range18.16
Interquartile range (IQR)4.7825

Descriptive statistics

Standard deviation5.0210674
Coefficient of variation (CV)0.84958839
Kurtosis1.1922105
Mean5.91
Median Absolute Deviation (MAD)1.95
Skewness1.3218685
Sum106.38
Variance25.211118
MonotonicityNot monotonic
2023-12-10T23:20:55.965520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4.19 1
 
3.8%
3.93 1
 
3.8%
0.4 1
 
3.8%
1.9 1
 
3.8%
18.55 1
 
3.8%
4.53 1
 
3.8%
3.91 1
 
3.8%
9.12 1
 
3.8%
14.46 1
 
3.8%
5.8 1
 
3.8%
Other values (8) 8
30.8%
(Missing) 8
30.8%
ValueCountFrequency (%)
0.39 1
3.8%
0.4 1
3.8%
1.9 1
3.8%
2.42 1
3.8%
2.44 1
3.8%
2.76 1
3.8%
3.85 1
3.8%
3.91 1
3.8%
3.93 1
3.8%
4.19 1
3.8%
ValueCountFrequency (%)
18.55 1
3.8%
14.46 1
3.8%
13.24 1
3.8%
9.12 1
3.8%
7.36 1
3.8%
7.13 1
3.8%
5.8 1
3.8%
4.53 1
3.8%
4.19 1
3.8%
3.93 1
3.8%

최근6개월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)77.8%
Missing8
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean-1.1111111
Minimum-80
Maximum73
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)38.5%
Memory size366.0 B
2023-12-10T23:20:56.074666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-80
5-th percentile-19.65
Q1-3.75
median-1
Q31.75
95-th percentile17.75
Maximum73
Range153
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation26.590332
Coefficient of variation (CV)-23.931299
Kurtosis7.9426147
Mean-1.1111111
Median Absolute Deviation (MAD)3
Skewness-0.29418538
Sum-20
Variance707.04575
MonotonicityNot monotonic
2023-12-10T23:20:56.485255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 3
 
11.5%
-1 2
 
7.7%
-2 2
 
7.7%
-80 1
 
3.8%
-6 1
 
3.8%
-4 1
 
3.8%
5 1
 
3.8%
4 1
 
3.8%
2 1
 
3.8%
-3 1
 
3.8%
Other values (4) 4
15.4%
(Missing) 8
30.8%
ValueCountFrequency (%)
-80 1
 
3.8%
-9 1
 
3.8%
-7 1
 
3.8%
-6 1
 
3.8%
-4 1
 
3.8%
-3 1
 
3.8%
-2 2
7.7%
-1 2
7.7%
1 3
11.5%
2 1
 
3.8%
ValueCountFrequency (%)
73 1
 
3.8%
8 1
 
3.8%
5 1
 
3.8%
4 1
 
3.8%
2 1
 
3.8%
1 3
11.5%
-1 2
7.7%
-2 2
7.7%
-3 1
 
3.8%
-4 1
 
3.8%

최초6개월
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
14 
0
9 
-1
2 
1
 
1

Length

Max length4
Median length4
Mean length2.6923077
Min length1

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 14
53.8%
0 9
34.6%
-1 2
 
7.7%
1 1
 
3.8%

Length

2023-12-10T23:20:56.632096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:56.730657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
53.8%
0 9
34.6%
1 3
 
11.5%

상호작용정도1개월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
0
25 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
96.2%
1 1
 
3.8%

Length

2023-12-10T23:20:56.845659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:56.944958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
96.2%
1 1
 
3.8%

그룹내홍보지수표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)94.4%
Missing8
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean-0.11
Minimum-0.57
Maximum0.74
Zeros1
Zeros (%)3.8%
Negative13
Negative (%)50.0%
Memory size366.0 B
2023-12-10T23:20:57.066412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.57
5-th percentile-0.3915
Q1-0.2975
median-0.135
Q3-0.005
95-th percentile0.315
Maximum0.74
Range1.31
Interquartile range (IQR)0.2925

Descriptive statistics

Standard deviation0.28819928
Coefficient of variation (CV)-2.6199934
Kurtosis3.6475226
Mean-0.11
Median Absolute Deviation (MAD)0.15
Skewness1.45426
Sum-1.98
Variance0.083058824
MonotonicityNot monotonic
2023-12-10T23:20:57.176592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
-0.11 2
 
7.7%
-0.29 1
 
3.8%
-0.21 1
 
3.8%
-0.36 1
 
3.8%
0.74 1
 
3.8%
-0.16 1
 
3.8%
-0.08 1
 
3.8%
0.0 1
 
3.8%
0.24 1
 
3.8%
-0.02 1
 
3.8%
Other values (7) 7
26.9%
(Missing) 8
30.8%
ValueCountFrequency (%)
-0.57 1
3.8%
-0.36 1
3.8%
-0.34 1
3.8%
-0.32 1
3.8%
-0.3 1
3.8%
-0.29 1
3.8%
-0.24 1
3.8%
-0.21 1
3.8%
-0.16 1
3.8%
-0.11 2
7.7%
ValueCountFrequency (%)
0.74 1
3.8%
0.24 1
3.8%
0.14 1
3.8%
0.01 1
3.8%
0.0 1
3.8%
-0.02 1
3.8%
-0.08 1
3.8%
-0.11 2
7.7%
-0.16 1
3.8%
-0.21 1
3.8%

최근6개월표준점수
Real number (ℝ)

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1923077
Minimum-28.78
Maximum35.64
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)15.4%
Memory size366.0 B
2023-12-10T23:20:57.314596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-28.78
5-th percentile-9.4675
Q13.19
median9.9
Q314.87
95-th percentile26.1375
Maximum35.64
Range64.42
Interquartile range (IQR)11.68

Descriptive statistics

Standard deviation12.97315
Coefficient of variation (CV)1.411305
Kurtosis2.0383669
Mean9.1923077
Median Absolute Deviation (MAD)6.46
Skewness-0.71385813
Sum239
Variance168.30262
MonotonicityNot monotonic
2023-12-10T23:20:57.444054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
12.9 3
 
11.5%
10.05 1
 
3.8%
24.93 1
 
3.8%
26.54 1
 
3.8%
2.77 1
 
3.8%
2.94 1
 
3.8%
9.75 1
 
3.8%
11.57 1
 
3.8%
7.4 1
 
3.8%
-5.8 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
-28.78 1
3.8%
-10.69 1
3.8%
-5.8 1
3.8%
-3.66 1
3.8%
2.09 1
3.8%
2.77 1
3.8%
2.94 1
3.8%
3.94 1
3.8%
4.93 1
3.8%
5.29 1
3.8%
ValueCountFrequency (%)
35.64 1
 
3.8%
26.54 1
 
3.8%
24.93 1
 
3.8%
21.92 1
 
3.8%
21.37 1
 
3.8%
20.14 1
 
3.8%
15.14 1
 
3.8%
14.06 1
 
3.8%
12.9 3
11.5%
11.57 1
 
3.8%

최초6개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27307692
Minimum-1.05
Maximum4.74
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.7%
Memory size366.0 B
2023-12-10T23:20:57.547554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.05
5-th percentile-0.05
Q10.08
median0.12
Q30.14
95-th percentile0.5725
Maximum4.74
Range5.79
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.94954629
Coefficient of variation (CV)3.4772118
Kurtosis21.711771
Mean0.27307692
Median Absolute Deviation (MAD)0.04
Skewness4.3899166
Sum7.1
Variance0.90163815
MonotonicityNot monotonic
2023-12-10T23:20:57.656050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.08 6
23.1%
0.12 6
23.1%
0.14 2
 
7.7%
-1.05 1
 
3.8%
0.11 1
 
3.8%
0.24 1
 
3.8%
0.27 1
 
3.8%
0.67 1
 
3.8%
0.23 1
 
3.8%
0.28 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
-1.05 1
 
3.8%
-0.08 1
 
3.8%
0.04 1
 
3.8%
0.07 1
 
3.8%
0.08 6
23.1%
0.1 1
 
3.8%
0.11 1
 
3.8%
0.12 6
23.1%
0.14 2
 
7.7%
0.23 1
 
3.8%
ValueCountFrequency (%)
4.74 1
 
3.8%
0.67 1
 
3.8%
0.28 1
 
3.8%
0.27 1
 
3.8%
0.24 1
 
3.8%
0.23 1
 
3.8%
0.14 2
 
7.7%
0.12 6
23.1%
0.11 1
 
3.8%
0.1 1
 
3.8%

상호작용도1개월표준점수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
-0.14
9 
-0.04
8 
-0.11
6 
-0.03
2 
-0.12
1 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row-0.04
2nd row-0.11
3rd row-0.14
4th row-0.11
5th row-0.14

Common Values

ValueCountFrequency (%)
-0.14 9
34.6%
-0.04 8
30.8%
-0.11 6
23.1%
-0.03 2
 
7.7%
-0.12 1
 
3.8%

Length

2023-12-10T23:20:57.777504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:57.886875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.14 9
34.6%
0.04 8
30.8%
0.11 6
23.1%
0.03 2
 
7.7%
0.12 1
 
3.8%

Interactions

2023-12-10T23:20:50.809987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:43.953001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.732300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.767339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.507259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.175185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.897439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.987954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.815721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.885175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.032299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.819901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.844517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.579688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.248807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.085224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.085130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.911305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.975248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.119410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.946193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.939035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.650787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.327662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.202203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.176190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.991306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:51.074380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.204492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.046681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.021037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.724337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.399529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.293036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.256177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.075411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:51.160634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.284750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.124035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.102511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.794016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.471951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.383536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.347852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.173167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:51.239412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.368986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.215591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.184843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.867385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.548774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.478235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.437443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.486455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:51.325419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.465082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.300137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.274038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.941658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.630354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.621156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.532858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.570076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:51.418270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.559740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.376893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.351477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.025887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.710218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.763523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.619486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.654303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:51.508694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:44.656822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:45.459502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:46.433539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.103492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:47.789925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:48.885676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:49.714113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:50.730416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:20:57.987638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역량평가채널ID역량평가채널명역량평가채널설명역량평가채널생성일자역량별그룹할당그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월최초6개월상호작용정도1개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수
역량평가채널ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량평가채널명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량평가채널설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량평가채널생성일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량별그룹할당1.0001.0001.0001.0001.0001.0000.8250.5960.5860.7010.0000.0000.0000.0000.4650.3750.5850.856
그룹내채널수1.0001.0001.0001.0001.0001.0000.8250.5960.5860.7010.0000.0000.0000.0000.4650.3750.5850.856
그룹내구독자순위1.0001.0001.0001.0000.8250.8251.0000.7230.7460.8810.0000.0000.8900.0000.0000.6030.0000.727
그룹내조회수순위1.0001.0001.0001.0000.5960.5960.7231.0000.8360.9620.0000.5120.6060.0000.1610.0000.0000.679
그룹내좋아요순위1.0001.0001.0001.0000.5860.5860.7460.8361.0000.8460.0000.0000.0000.0000.8110.5160.0000.375
역량평가1.0001.0001.0001.0000.7010.7010.8810.9620.8461.0000.0000.6960.6070.0000.0000.0000.0000.799
홍보지수1.0001.0001.0001.0000.0000.0000.0000.0000.0000.0001.0000.5930.0001.0000.6780.6350.5500.486
최근6개월1.0001.0001.0001.0000.0000.0000.0000.5120.0000.6960.5931.0000.0001.0000.1550.1420.0000.369
최초6개월1.0001.0001.0001.0000.0000.0000.8900.6060.0000.6070.0000.0001.000NaN0.0000.6140.6270.427
상호작용정도1개월1.0001.0001.0001.0000.0000.0000.0000.0000.0000.0001.0001.000NaN1.0001.0000.4970.0000.512
그룹내홍보지수표준점수1.0001.0001.0001.0000.4650.4650.0000.1610.8110.0000.6780.1550.0001.0001.0000.0000.8840.654
최근6개월표준점수1.0001.0001.0001.0000.3750.3750.6030.0000.5160.0000.6350.1420.6140.4970.0001.0000.0000.256
최초6개월표준점수1.0001.0001.0001.0000.5850.5850.0000.0000.0000.0000.5500.0000.6270.0000.8840.0001.0000.393
상호작용도1개월표준점수1.0001.0001.0001.0000.8560.8560.7270.6790.3750.7990.4860.3690.4270.5120.6540.2560.3931.000
2023-12-10T23:20:58.228498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초6개월그룹내채널수상호작용도1개월표준점수상호작용정도1개월역량별그룹할당
최초6개월1.0000.0000.3651.0000.000
그룹내채널수0.0001.0000.8560.0001.000
상호작용도1개월표준점수0.3650.8561.0000.5770.856
상호작용정도1개월1.0000.0000.5771.0000.000
역량별그룹할당0.0001.0000.8560.0001.000
2023-12-10T23:20:58.379811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수역량별그룹할당그룹내채널수최초6개월상호작용정도1개월상호작용도1개월표준점수
그룹내구독자순위1.0000.6830.6620.602-0.763-0.062-0.656-0.2520.3340.4500.4500.4630.0000.467
그룹내조회수순위0.6831.0000.9520.953-0.849-0.144-0.729-0.1240.3590.3500.3500.3120.0000.278
그룹내좋아요순위0.6620.9521.0000.910-0.841-0.148-0.731-0.1420.3310.3890.3890.0000.0000.194
역량평가0.6020.9530.9101.000-0.810-0.166-0.695-0.1120.3810.4550.4550.1470.0000.380
홍보지수-0.763-0.849-0.841-0.8101.0000.3350.883-0.044-0.3070.0000.0000.0000.8290.272
최근6개월-0.062-0.144-0.148-0.1660.3351.0000.350-0.365-0.1110.0000.0000.0000.9350.272
그룹내홍보지수표준점수-0.656-0.729-0.731-0.6950.8830.3501.000-0.234-0.5460.2270.2270.0000.7910.391
최근6개월표준점수-0.252-0.124-0.142-0.112-0.044-0.365-0.2341.0000.0740.1030.1030.3210.4080.064
최초6개월표준점수0.3340.3590.3310.381-0.307-0.111-0.5460.0741.0000.5800.5800.5960.0000.313
역량별그룹할당0.4500.3500.3890.4550.0000.0000.2270.1030.5801.0001.0000.0000.0000.856
그룹내채널수0.4500.3500.3890.4550.0000.0000.2270.1030.5801.0001.0000.0000.0000.856
최초6개월0.4630.3120.0000.1470.0000.0000.0000.3210.5960.0000.0001.0001.0000.365
상호작용정도1개월0.0000.0000.0000.0000.8290.9350.7910.4080.0000.0000.0001.0001.0000.577
상호작용도1개월표준점수0.4670.2780.1940.3800.2720.2720.3910.0640.3130.8560.8560.3650.5771.000

Missing values

2023-12-10T23:20:51.639263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:20:51.853820image/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.
2023-12-10T23:20:52.021134image/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

역량평가채널ID역량평가채널명역량평가수집일자역량평가채널설명역량평가채널생성일자역량별그룹할당그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월최초6개월상호작용정도1개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수
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2UCSQPjdpKtG7egh3qmOcUn1w아롱다롱TV ArongDarongTV2021-04-11아롱다롱TV는 초등학생 키즈크리에이터 남매 아롱이(유리)와 다롱이(채민)의 채널입니다. 또래 친구들이 좋아하는 놀이와 게임; 남매간의 대결이나 챌린지; 가족과 함께하는 여행이나 일상 영상으로 만들어 집니다. 유튜브와 네이버TV에 업로드 되고 있으며; 페이스북; 인스타그램 등 SNS 를 통해 구독자와 소통하고 있습니다. 아롱다롱TV 많이 사랑해주세요! - 페이스북 https:www.facebook.comarongdarongtv - 인스타그램 https:www.instagram.comarongdarongtv * 비지니스 문의 e-mail: arongdarongtv@naver.com2015-12-07MACRO14668444606143095.8100-0.024.930.11-0.14
3UCM3pMyyslUgGIGXK6M6Dm-w개밍순 DogMingsoon2021-04-11꼬똥 드 툴레아(Coton de Tulear) 밍순이 실버 푸들 (Silver Poodle) 개순이 입양부터 평생을 기록할 강아지 유튜브 채널입니다♥ - 프로필 (profile) ♥ 첫째 (하얀애) 이름 : 밍순 MingSoon 성별 : 여 Female 생일 : 2019.02.27 견종 : 꼬똥 드 툴레아 Coton de Tulear 특징 : 눈마주치면 꼬리 흔들기 :) 별명 : 밍뚜니; 토끼; 개끼; 개끼냥이; 대걸레; 민들레홀씨; 로봇청소기; 먼지털이개; 꼬질이똥개; 하찮은먼지 (아마도 더 추가 될 예정 ㄷㄷ) ♥ 둘째 (까만애) 이름 : 개순 GaeSoon 성별 : 여 Female 생일 : 2019.07.10 견종 : 실버 푸들 Silver Poodle 특징 : 자기가 고양이 인줄 알고있음ㅋ (손을 잘씀) 별명 : 개엄살; 흑염소; 파괴왕; 양순이 (기똥찬걸로 지어주시면 추가함'ㅡ') ------------------------------------------- ♥ First born Name : MingSoon Sex : Female Birth date : 28 February 2019 Breed : Coton de Tulear Characteristics : Tail wobble ♥ Second born Name : GaeSoon Sex : Female Birth date : 10 July 2019 Breed : Silver Toy Poodle - e-mail : hyuna0124@naver.com #반려견 #강아지 #꼬똥 #꼬똥드툴레아 #CotondeTulear #밍순이 #푸들 #실버푸들 #SilverPoodle #개순이2019-04-10MACRO14668997226387133.85-8000-0.245.290.14-0.11
4UCGPvcR_72yc5wPqsKefo4jg도로교통공단2021-04-11도로교통공단 공식 유튜브 채널입니다. 교통안전; 교통사고 예방을 위해 많은 관심 가져주세요!2016-03-03MACRO146613961145143313642.42-1<NA>0-0.315.140.24-0.14
5UCdTDdygpZKdDew2s1s419iwShoot for Love 슛포러브2021-04-11축구가 과연 세상을 바꿀 수 있을까요? 슛포러브는 1. 최고의 스포츠 콘텐츠를 만들어 시청자들에게 즐거움을 선사합니다 2. 단순히 즐거움 전달에 그치지 않고; 저희가 만든 콘텐츠를 통해 누군가에게 실질적인 도움이 될 수 있도록 합니다. 우리 모두가 함께 마음을 모은다면 분명 조금씩 바뀌어나갈 것이라고 믿습니다. Can football change the world? 문의 : 070-8280-09892014-06-08MEGA197157100133797.13-600-0.1114.060.08-0.11
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8UCLx9C8hve7K2mmabMqaxcCQBP마스 Channel2021-04-11여러 일본 노래를 한국어로 개사해 녹음하는 활동을 합니다. 트위치에서 '블파마스'라는 이름으로 스트리밍도 하고 있어요! 녹음러; 크리에이터; 작가로서 활동하고 있는 사람의 개인 채널입니다. 유튜브엔 주로 노래 업로드에 비중을 두고 일상적인 영상; 게임; 더빙 등의 영상등이 업로드 됩니다. 트위터 (트위터는 유튜브 커뮤니티보다 더 사적인 내용이 담기는 혼잣말을 늘어 놓곤 합니다!) 문의 Twitter @nightview218 E-Mail dlwotjs218@naver.com commission: http:bpmix.creatorlink.net2014-01-07MICRO10562363051371607.365<NA>00.01-3.660.08-0.04
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역량평가채널ID역량평가채널명역량평가수집일자역량평가채널설명역량평가채널생성일자역량별그룹할당그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월최초6개월상호작용정도1개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수
16UCPtO09ZhqgCYo1bDrtEbtZADaiya다이야2021-04-11<NA>2017-07-15MACRO146677410989521059<NA><NA>10<NA>12.94.74-0.14
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19UCAo7xCDMAcgPcv0tZ4MDycg무안군2021-04-11<NA>2015-07-22MICRO10567868868878541.9<NA><NA>0-0.117.40.04-0.04
20UCLSyz6xRgwFnrnlyTj9Daqw서울시여성가족재단2021-04-11<NA>2019-01-30MICRO10567478899149370.4<NA>00-0.3611.570.1-0.04
21UCWp8ZBD9w_4JlwMAd8N8Hog골목대장의 하루2021-04-11<NA>2012-12-16MICRO1056680921823640<NA><NA><NA>0<NA>9.750.08-0.04
22UCpSFkW-1TFYy--Ag7__0w5A블개2021-04-11게임하는 블개입니다.2013-06-29MICRO1056876121412<NA>8<NA>0<NA>2.940.08-0.03
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25UCMQ0kUQ13dTJi3026OF8sFADaily Busking2021-04-11Welcome to Daily Busking official Youtube! We create original; new K-POP live performance and street live clips with various artists every week. Subscribe the channel and watch the latest video clips from Daily Busking. This channel is being operated by INPLAY Co.; Ltd. All rights reserved (c) INPLAY Co.; Ltd. * CJ ENM DiaTV Music Partnership * Please do not re-upload our contents without proper permission. * Our specialists provide English; Japanese; Korean; Indonesian; Vietnamese; Portuguese translations and captions for most clips. * Contact Information: contact@inplayteam.com * Some Equipment In INPLAY AMP Roland Cube Street EX(2) MIC SM-58(2) RECORDER SONY PCM D-100(1); ZOOM H6(1); Canon DM E-1(1); SONY ECM XYST1M(1) LightsFOMEX BL-2280(2) CAM SONY A7 III(3) LENS SIGMA A 85mm F1.4(1); SIGMA A 24mm F1.4(1); SIGMA A 12-24mm F4(1); Canon 24-70mm F2.8 L II USM(1); SEL50M28(1) GIMBAL DJI RONIN MX(1) Beholder EC1(1) Power HONDA EU10i(1kW)2013-08-07MEGA197129110107123<NA>-2<NA>0<NA>26.540.14-0.11