Overview

Dataset statistics

Number of variables19
Number of observations24
Missing cells28
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory170.5 B

Variable types

Text3
Categorical5
DateTime1
Numeric10

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/fc827ef0-a451-4652-abf3-632152ba08cb

Alerts

최초6개월 is highly overall correlated with 그룹내구독자순위 and 2 other fieldsHigh correlation
역량평가수집일자 is highly overall correlated with 최근6개월 and 2 other fieldsHigh correlation
그룹내채널수 is highly overall correlated with 최근6개월 and 3 other fieldsHigh correlation
역량별그룹할당 is highly overall correlated with 그룹내조회수순위 and 3 other fieldsHigh correlation
그룹내구독자순위 is highly overall correlated with 그룹내조회수순위 and 5 other fieldsHigh correlation
그룹내조회수순위 is highly overall correlated with 그룹내구독자순위 and 6 other fieldsHigh correlation
그룹내좋아요순위 is highly overall correlated with 그룹내구독자순위 and 5 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 역량평가수집일자 and 1 other fieldsHigh correlation
그룹내홍보지수표준점수 is highly overall correlated with 그룹내구독자순위 and 5 other fieldsHigh correlation
최근6개월표준점수 is highly overall correlated with 최초6개월High correlation
최초6개월표준점수 is highly overall correlated with 상호작용도1개월표준점수High correlation
상호작용도1개월표준점수 is highly overall correlated with 그룹내조회수순위 and 5 other fieldsHigh correlation
상호작용정도1개월 is highly overall correlated with 홍보지수 and 1 other fieldsHigh correlation
역량평가수집일자 is highly imbalanced (75.0%)Imbalance
상호작용정도1개월 is highly imbalanced (50.5%)Imbalance
역량평가채널설명 has 3 (12.5%) missing valuesMissing
홍보지수 has 7 (29.2%) missing valuesMissing
최근6개월 has 7 (29.2%) missing valuesMissing
그룹내홍보지수표준점수 has 7 (29.2%) missing valuesMissing
최근6개월표준점수 has 4 (16.7%) missing valuesMissing
역량평가채널ID has unique valuesUnique
역량평가채널명 has unique valuesUnique
역량평가채널생성일자 has unique valuesUnique
그룹내구독자순위 has unique valuesUnique
그룹내좋아요순위 has unique valuesUnique
최근6개월 has 2 (8.3%) zerosZeros
그룹내홍보지수표준점수 has 1 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-10 14:14:39.842011
Analysis finished2023-12-10 14:14:59.280052
Duration19.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-10T23:14:59.537163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters576
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

Unique24 ?
Unique (%)100.0%

Sample

1st rowUC0hk2D145ogyBYMKjylWkkw
2nd rowUC1r112Pr9Ngcg2NtcE946HQ
3rd rowUC1dMe0fYTM4r9mNQc9v1ziA
4th rowUC0ru5w57PyGpbsEKwN4LuwA
5th rowUC270ueFEsQ21S26TYI_9yVA
ValueCountFrequency (%)
uc0hk2d145ogybymkjylwkkw 1
 
4.2%
uc1r112pr9ngcg2ntce946hq 1
 
4.2%
ucepuitfwooj2o5htu65nleg 1
 
4.2%
uce1w2qzbks8hgbjhiiac0xw 1
 
4.2%
uccq9w9rg51vzxwpxlnb04mq 1
 
4.2%
uc1o0tfy6umy2rgfrgymiwiw 1
 
4.2%
uc2mgnh831kcmuykk7u8c2dg 1
 
4.2%
uca6glf0zq1sl_y-agvoeznw 1
 
4.2%
uca5agxsjoanrpcp-fkj52mg 1
 
4.2%
uc2iysriatge71sliolsxguw 1
 
4.2%
Other values (14) 14
58.3%
2023-12-10T23:15:00.096375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 29
 
5.0%
U 27
 
4.7%
g 18
 
3.1%
2 17
 
3.0%
A 15
 
2.6%
1 15
 
2.6%
w 15
 
2.6%
m 15
 
2.6%
u 13
 
2.3%
F 13
 
2.3%
Other values (54) 399
69.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 254
44.1%
Lowercase Letter 206
35.8%
Decimal Number 109
18.9%
Dash Punctuation 4
 
0.7%
Connector Punctuation 3
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 29
 
11.4%
U 27
 
10.6%
A 15
 
5.9%
F 13
 
5.1%
Y 13
 
5.1%
I 11
 
4.3%
E 10
 
3.9%
M 10
 
3.9%
G 10
 
3.9%
Q 9
 
3.5%
Other values (16) 107
42.1%
Lowercase Letter
ValueCountFrequency (%)
g 18
 
8.7%
w 15
 
7.3%
m 15
 
7.3%
u 13
 
6.3%
r 12
 
5.8%
i 10
 
4.9%
e 9
 
4.4%
z 8
 
3.9%
k 8
 
3.9%
c 8
 
3.9%
Other values (16) 90
43.7%
Decimal Number
ValueCountFrequency (%)
2 17
15.6%
1 15
13.8%
3 12
11.0%
0 11
10.1%
5 11
10.1%
7 9
8.3%
6 9
8.3%
4 9
8.3%
8 8
7.3%
9 8
7.3%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 460
79.9%
Common 116
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 29
 
6.3%
U 27
 
5.9%
g 18
 
3.9%
A 15
 
3.3%
w 15
 
3.3%
m 15
 
3.3%
u 13
 
2.8%
F 13
 
2.8%
Y 13
 
2.8%
r 12
 
2.6%
Other values (42) 290
63.0%
Common
ValueCountFrequency (%)
2 17
14.7%
1 15
12.9%
3 12
10.3%
0 11
9.5%
5 11
9.5%
7 9
7.8%
6 9
7.8%
4 9
7.8%
8 8
6.9%
9 8
6.9%
Other values (2) 7
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 29
 
5.0%
U 27
 
4.7%
g 18
 
3.1%
2 17
 
3.0%
A 15
 
2.6%
1 15
 
2.6%
w 15
 
2.6%
m 15
 
2.6%
u 13
 
2.3%
F 13
 
2.3%
Other values (54) 399
69.3%
Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-10T23:15:00.520610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length14.5
Mean length7.4166667
Min length2

Characters and Unicode

Total characters178
Distinct characters106
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

Unique24 ?
Unique (%)100.0%

Sample

1st row신속
2nd row맛상무
3rd rowSaehyeon세현
4th row재민정
5th row야신야덕
ValueCountFrequency (%)
신속 1
 
3.0%
낭만고양이tv 1
 
3.0%
tv 1
 
3.0%
태경 1
 
3.0%
다정하게dajung 1
 
3.0%
learning 1
 
3.0%
ebs 1
 
3.0%
changmakeup 1
 
3.0%
꽃핀 1
 
3.0%
패션센터중구 1
 
3.0%
Other values (23) 23
69.7%
2023-12-10T23:15:01.275640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10
 
5.6%
9
 
5.1%
e 7
 
3.9%
n 5
 
2.8%
u 5
 
2.8%
m 4
 
2.2%
t 4
 
2.2%
o 4
 
2.2%
i 3
 
1.7%
3
 
1.7%
Other values (96) 124
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
44.4%
Lowercase Letter 66
37.1%
Uppercase Letter 23
 
12.9%
Space Separator 9
 
5.1%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
3.8%
3
 
3.8%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (57) 57
72.2%
Lowercase Letter
ValueCountFrequency (%)
a 10
15.2%
e 7
 
10.6%
n 5
 
7.6%
u 5
 
7.6%
m 4
 
6.1%
t 4
 
6.1%
o 4
 
6.1%
i 3
 
4.5%
c 3
 
4.5%
k 3
 
4.5%
Other values (11) 18
27.3%
Uppercase Letter
ValueCountFrequency (%)
N 3
13.0%
L 2
 
8.7%
V 2
 
8.7%
T 2
 
8.7%
C 2
 
8.7%
S 2
 
8.7%
G 1
 
4.3%
U 1
 
4.3%
J 1
 
4.3%
A 1
 
4.3%
Other values (6) 6
26.1%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89
50.0%
Hangul 79
44.4%
Common 10
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
3.8%
3
 
3.8%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (57) 57
72.2%
Latin
ValueCountFrequency (%)
a 10
 
11.2%
e 7
 
7.9%
n 5
 
5.6%
u 5
 
5.6%
m 4
 
4.5%
t 4
 
4.5%
o 4
 
4.5%
i 3
 
3.4%
c 3
 
3.4%
k 3
 
3.4%
Other values (27) 41
46.1%
Common
ValueCountFrequency (%)
9
90.0%
- 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99
55.6%
Hangul 79
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10
 
10.1%
9
 
9.1%
e 7
 
7.1%
n 5
 
5.1%
u 5
 
5.1%
m 4
 
4.0%
t 4
 
4.0%
o 4
 
4.0%
i 3
 
3.0%
c 3
 
3.0%
Other values (29) 45
45.5%
Hangul
ValueCountFrequency (%)
3
 
3.8%
3
 
3.8%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (57) 57
72.2%

역량평가수집일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2021-02-28
23 
2021-02-01
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row2021-02-01
2nd row2021-02-28
3rd row2021-02-28
4th row2021-02-28
5th row2021-02-28

Common Values

ValueCountFrequency (%)
2021-02-28 23
95.8%
2021-02-01 1
 
4.2%

Length

2023-12-10T23:15:01.938761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:02.269289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-28 23
95.8%
2021-02-01 1
 
4.2%
Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2023-12-10T23:15:02.637406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length423
Median length92
Mean length115.2381
Min length10

Characters and Unicode

Total characters2420
Distinct characters337
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

Unique21 ?
Unique (%)100.0%

Sample

1st row게임에 진심인 남자
2nd row술상무 아닙니다. 대신 맛봐 드리는 맛상무 입니다. 맛있는음식; 즐거움; 감동이 있는 채널입니다
3rd row뷰티와 일상을 기록하는 유튜버 세현입니다 비즈니스 문의- saehyeon031105@gmail.com 으로 주시면 감사하겠습니다!
4th row야구 유튜브 채널! 야구 유튜버 빡코가 야구와 다양한 스포츠를 함께 다루고 있습니다:) 여러분 기상천외한 아이디어와 창의력으로 스포츠를 즐겨봅시다! 구독부탁드려요~~:) #스포츠 #운동 #야구
5th row유준호의 능력자 강산이
ValueCountFrequency (%)
16
 
3.7%
and 7
 
1.6%
음악 5
 
1.1%
있습니다 5
 
1.1%
미라지 5
 
1.1%
lululala 4
 
0.9%
i 4
 
0.9%
4
 
0.9%
you 4
 
0.9%
the 4
 
0.9%
Other values (326) 378
86.7%
2023-12-10T23:15:03.452727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
452
 
18.7%
a 80
 
3.3%
e 75
 
3.1%
t 65
 
2.7%
o 57
 
2.4%
i 53
 
2.2%
u 51
 
2.1%
l 50
 
2.1%
n 47
 
1.9%
s 40
 
1.7%
Other values (327) 1450
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 910
37.6%
Lowercase Letter 816
33.7%
Space Separator 452
18.7%
Other Punctuation 99
 
4.1%
Uppercase Letter 62
 
2.6%
Decimal Number 36
 
1.5%
Close Punctuation 13
 
0.5%
Dash Punctuation 11
 
0.5%
Open Punctuation 10
 
0.4%
Other Symbol 4
 
0.2%
Other values (3) 7
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30
 
3.3%
24
 
2.6%
22
 
2.4%
20
 
2.2%
19
 
2.1%
15
 
1.6%
15
 
1.6%
13
 
1.4%
12
 
1.3%
12
 
1.3%
Other values (253) 728
80.0%
Lowercase Letter
ValueCountFrequency (%)
a 80
 
9.8%
e 75
 
9.2%
t 65
 
8.0%
o 57
 
7.0%
i 53
 
6.5%
u 51
 
6.2%
l 50
 
6.1%
n 47
 
5.8%
s 40
 
4.9%
c 38
 
4.7%
Other values (15) 260
31.9%
Uppercase Letter
ValueCountFrequency (%)
L 8
12.9%
S 7
11.3%
T 6
 
9.7%
M 6
 
9.7%
I 6
 
9.7%
D 3
 
4.8%
O 3
 
4.8%
E 3
 
4.8%
C 3
 
4.8%
H 2
 
3.2%
Other values (11) 15
24.2%
Decimal Number
ValueCountFrequency (%)
2 8
22.2%
0 7
19.4%
1 5
13.9%
7 5
13.9%
8 3
 
8.3%
5 3
 
8.3%
3 3
 
8.3%
6 1
 
2.8%
4 1
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 37
37.4%
! 22
22.2%
; 14
 
14.1%
: 12
 
12.1%
' 6
 
6.1%
@ 4
 
4.0%
# 3
 
3.0%
* 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 12
92.3%
] 1
 
7.7%
Open Punctuation
ValueCountFrequency (%)
( 9
90.0%
[ 1
 
10.0%
Math Symbol
ValueCountFrequency (%)
~ 3
75.0%
+ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
452
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Symbol
ValueCountFrequency (%)
4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 910
37.6%
Latin 878
36.3%
Common 632
26.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
30
 
3.3%
24
 
2.6%
22
 
2.4%
20
 
2.2%
19
 
2.1%
15
 
1.6%
15
 
1.6%
13
 
1.4%
12
 
1.3%
12
 
1.3%
Other values (253) 728
80.0%
Latin
ValueCountFrequency (%)
a 80
 
9.1%
e 75
 
8.5%
t 65
 
7.4%
o 57
 
6.5%
i 53
 
6.0%
u 51
 
5.8%
l 50
 
5.7%
n 47
 
5.4%
s 40
 
4.6%
c 38
 
4.3%
Other values (36) 322
36.7%
Common
ValueCountFrequency (%)
452
71.5%
. 37
 
5.9%
! 22
 
3.5%
; 14
 
2.2%
) 12
 
1.9%
: 12
 
1.9%
- 11
 
1.7%
( 9
 
1.4%
2 8
 
1.3%
0 7
 
1.1%
Other values (18) 48
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1505
62.2%
Hangul 888
36.7%
Compat Jamo 22
 
0.9%
Geometric Shapes 4
 
0.2%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
452
30.0%
a 80
 
5.3%
e 75
 
5.0%
t 65
 
4.3%
o 57
 
3.8%
i 53
 
3.5%
u 51
 
3.4%
l 50
 
3.3%
n 47
 
3.1%
s 40
 
2.7%
Other values (62) 535
35.5%
Hangul
ValueCountFrequency (%)
30
 
3.4%
24
 
2.7%
20
 
2.3%
19
 
2.1%
15
 
1.7%
15
 
1.7%
13
 
1.5%
12
 
1.4%
12
 
1.4%
12
 
1.4%
Other values (252) 716
80.6%
Compat Jamo
ValueCountFrequency (%)
22
100.0%
Geometric Shapes
ValueCountFrequency (%)
4
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2010-03-19 00:00:00
Maximum2018-12-30 00:00:00
2023-12-10T23:15:03.803107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:04.167821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

역량별그룹할당
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
MACRO
16 
MICRO
MEGA

Length

Max length5
Median length5
Mean length4.9166667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MACRO 16
66.7%
MICRO 6
 
25.0%
MEGA 2
 
8.3%

Length

2023-12-10T23:15:04.393366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:04.564367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
macro 16
66.7%
micro 6
 
25.0%
mega 2
 
8.3%

그룹내채널수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
1467
15 
1058
193
1470
 
1

Length

Max length4
Median length4
Mean length3.9166667
Min length3

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row1470
2nd row1467
3rd row1467
4th row1058
5th row1467

Common Values

ValueCountFrequency (%)
1467 15
62.5%
1058 6
 
25.0%
193 2
 
8.3%
1470 1
 
4.2%

Length

2023-12-10T23:15:04.735664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:04.920712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1467 15
62.5%
1058 6
 
25.0%
193 2
 
8.3%
1470 1
 
4.2%

그룹내구독자순위
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean745.58333
Minimum118
Maximum1420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-10T23:15:05.081444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile136.1
Q1544.25
median765.5
Q3921.5
95-th percentile1403.55
Maximum1420
Range1302
Interquartile range (IQR)377.25

Descriptive statistics

Standard deviation408.29336
Coefficient of variation (CV)0.547616
Kurtosis-0.79936875
Mean745.58333
Median Absolute Deviation (MAD)232.5
Skewness0.062487536
Sum17894
Variance166703.47
MonotonicityNot monotonic
2023-12-10T23:15:05.299541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
869 1
 
4.2%
614 1
 
4.2%
707 1
 
4.2%
118 1
 
4.2%
1248 1
 
4.2%
643 1
 
4.2%
148 1
 
4.2%
285 1
 
4.2%
813 1
 
4.2%
755 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
118 1
4.2%
134 1
4.2%
148 1
4.2%
172 1
4.2%
285 1
4.2%
335 1
4.2%
614 1
4.2%
641 1
4.2%
643 1
4.2%
646 1
4.2%
ValueCountFrequency (%)
1420 1
4.2%
1416 1
4.2%
1333 1
4.2%
1290 1
4.2%
1248 1
4.2%
1079 1
4.2%
869 1
4.2%
864 1
4.2%
813 1
4.2%
809 1
4.2%

그룹내조회수순위
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean703.45833
Minimum32
Maximum1447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-10T23:15:05.500317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile107.3
Q1326.25
median734.5
Q31001
95-th percentile1424.55
Maximum1447
Range1415
Interquartile range (IQR)674.75

Descriptive statistics

Standard deviation453.04064
Coefficient of variation (CV)0.64401916
Kurtosis-1.1059021
Mean703.45833
Median Absolute Deviation (MAD)325.5
Skewness0.15264273
Sum16883
Variance205245.82
MonotonicityNot monotonic
2023-12-10T23:15:05.846017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
487 2
 
8.3%
109 1
 
4.2%
816 1
 
4.2%
32 1
 
4.2%
1338 1
 
4.2%
170 1
 
4.2%
122 1
 
4.2%
985 1
 
4.2%
868 1
 
4.2%
478 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
32 1
4.2%
107 1
4.2%
109 1
4.2%
122 1
4.2%
170 1
4.2%
276 1
4.2%
343 1
4.2%
478 1
4.2%
487 2
8.3%
548 1
4.2%
ValueCountFrequency (%)
1447 1
4.2%
1428 1
4.2%
1405 1
4.2%
1338 1
4.2%
1071 1
4.2%
1049 1
4.2%
985 1
4.2%
929 1
4.2%
908 1
4.2%
868 1
4.2%

그룹내좋아요순위
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean675.33333
Minimum38
Maximum1436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-10T23:15:06.064949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile82.25
Q1153.25
median729.5
Q3987
95-th percentile1411.45
Maximum1436
Range1398
Interquartile range (IQR)833.75

Descriptive statistics

Standard deviation466.8325
Coefficient of variation (CV)0.69126233
Kurtosis-1.2892096
Mean675.33333
Median Absolute Deviation (MAD)423
Skewness0.070328167
Sum16208
Variance217932.58
MonotonicityNot monotonic
2023-12-10T23:15:06.271270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
533 1
 
4.2%
682 1
 
4.2%
777 1
 
4.2%
38 1
 
4.2%
1195 1
 
4.2%
865 1
 
4.2%
141 1
 
4.2%
74 1
 
4.2%
977 1
 
4.2%
824 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
38 1
4.2%
74 1
4.2%
129 1
4.2%
132 1
4.2%
141 1
4.2%
142 1
4.2%
157 1
4.2%
159 1
4.2%
533 1
4.2%
561 1
4.2%
ValueCountFrequency (%)
1436 1
4.2%
1417 1
4.2%
1380 1
4.2%
1195 1
4.2%
1110 1
4.2%
1017 1
4.2%
977 1
4.2%
954 1
4.2%
930 1
4.2%
865 1
4.2%

역량평가
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean647
Minimum11
Maximum1431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-10T23:15:06.477481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile62
Q1231.5
median677.5
Q3974
95-th percentile1407.9
Maximum1431
Range1420
Interquartile range (IQR)742.5

Descriptive statistics

Standard deviation462.06615
Coefficient of variation (CV)0.71416716
Kurtosis-1.1218083
Mean647
Median Absolute Deviation (MAD)400
Skewness0.29697362
Sum15528
Variance213505.13
MonotonicityNot monotonic
2023-12-10T23:15:06.847411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
974 2
 
8.3%
250 1
 
4.2%
719 1
 
4.2%
789 1
 
4.2%
11 1
 
4.2%
1257 1
 
4.2%
696 1
 
4.2%
134 1
 
4.2%
96 1
 
4.2%
684 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
11 1
4.2%
56 1
4.2%
96 1
4.2%
115 1
4.2%
134 1
4.2%
176 1
4.2%
250 1
4.2%
302 1
4.2%
387 1
4.2%
462 1
4.2%
ValueCountFrequency (%)
1431 1
4.2%
1410 1
4.2%
1396 1
4.2%
1257 1
4.2%
1102 1
4.2%
974 2
8.3%
959 1
4.2%
789 1
4.2%
719 1
4.2%
696 1
4.2%

홍보지수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing7
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean7.9464706
Minimum0.13
Maximum20.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-10T23:15:07.109570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.762
Q12.13
median6.53
Q311.17
95-th percentile18.956
Maximum20.5
Range20.37
Interquartile range (IQR)9.04

Descriptive statistics

Standard deviation6.4520306
Coefficient of variation (CV)0.81193664
Kurtosis-0.587589
Mean7.9464706
Median Absolute Deviation (MAD)4.5
Skewness0.75640052
Sum135.09
Variance41.628699
MonotonicityNot monotonic
2023-12-10T23:15:07.398026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
18.57 1
 
4.2%
1.54 1
 
4.2%
20.5 1
 
4.2%
0.92 1
 
4.2%
6.53 1
 
4.2%
2.13 1
 
4.2%
17.04 1
 
4.2%
0.13 1
 
4.2%
5.97 1
 
4.2%
4.68 1
 
4.2%
Other values (7) 7
29.2%
(Missing) 7
29.2%
ValueCountFrequency (%)
0.13 1
4.2%
0.92 1
4.2%
1.54 1
4.2%
2.03 1
4.2%
2.13 1
4.2%
4.68 1
4.2%
5.64 1
4.2%
5.97 1
4.2%
6.53 1
4.2%
6.96 1
4.2%
ValueCountFrequency (%)
20.5 1
4.2%
18.57 1
4.2%
17.04 1
4.2%
15.64 1
4.2%
11.17 1
4.2%
8.02 1
4.2%
7.62 1
4.2%
6.96 1
4.2%
6.53 1
4.2%
5.97 1
4.2%

최근6개월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)70.6%
Missing7
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean4.7647059
Minimum-11
Maximum39
Zeros2
Zeros (%)8.3%
Negative6
Negative (%)25.0%
Memory size348.0 B
2023-12-10T23:15:07.647799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile-8.6
Q1-3
median2
Q33
95-th percentile30.2
Maximum39
Range50
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.51633
Coefficient of variation (CV)2.8367606
Kurtosis1.8368129
Mean4.7647059
Median Absolute Deviation (MAD)3
Skewness1.5941512
Sum81
Variance182.69118
MonotonicityNot monotonic
2023-12-10T23:15:07.867737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 3
12.5%
-3 2
 
8.3%
0 2
 
8.3%
3 2
 
8.3%
39 1
 
4.2%
27 1
 
4.2%
-4 1
 
4.2%
-8 1
 
4.2%
28 1
 
4.2%
-11 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 7
29.2%
ValueCountFrequency (%)
-11 1
 
4.2%
-8 1
 
4.2%
-4 1
 
4.2%
-3 2
8.3%
-1 1
 
4.2%
0 2
8.3%
2 3
12.5%
3 2
8.3%
5 1
 
4.2%
27 1
 
4.2%
ValueCountFrequency (%)
39 1
 
4.2%
28 1
 
4.2%
27 1
 
4.2%
5 1
 
4.2%
3 2
8.3%
2 3
12.5%
0 2
8.3%
-1 1
 
4.2%
-3 2
8.3%
-4 1
 
4.2%

최초6개월
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
1
0
-1

Length

Max length4
Median length2
Mean length2.2916667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9
37.5%
1 6
25.0%
0 5
20.8%
-1 4
16.7%

Length

2023-12-10T23:15:08.080390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:08.250853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10
41.7%
na 9
37.5%
0 5
20.8%

상호작용정도1개월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
20 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
83.3%
1 3
 
12.5%
3 1
 
4.2%

Length

2023-12-10T23:15:08.405213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:08.550397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
83.3%
1 3
 
12.5%
3 1
 
4.2%

그룹내홍보지수표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)94.1%
Missing7
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean0.041764706
Minimum-0.53
Maximum0.79
Zeros1
Zeros (%)4.2%
Negative9
Negative (%)37.5%
Memory size348.0 B
2023-12-10T23:15:08.677509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.53
5-th percentile-0.506
Q1-0.29
median-0.01
Q30.28
95-th percentile0.742
Maximum0.79
Range1.32
Interquartile range (IQR)0.57

Descriptive statistics

Standard deviation0.43396767
Coefficient of variation (CV)10.390775
Kurtosis-0.82823387
Mean0.041764706
Median Absolute Deviation (MAD)0.29
Skewness0.51592221
Sum0.71
Variance0.18832794
MonotonicityNot monotonic
2023-12-10T23:15:08.831321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.73 2
 
8.3%
-0.38 1
 
4.2%
-0.23 1
 
4.2%
0.5 1
 
4.2%
-0.53 1
 
4.2%
0.05 1
 
4.2%
-0.5 1
 
4.2%
0.79 1
 
4.2%
-0.01 1
 
4.2%
-0.29 1
 
4.2%
Other values (6) 6
25.0%
(Missing) 7
29.2%
ValueCountFrequency (%)
-0.53 1
4.2%
-0.5 1
4.2%
-0.43 1
4.2%
-0.38 1
4.2%
-0.29 1
4.2%
-0.23 1
4.2%
-0.1 1
4.2%
-0.02 1
4.2%
-0.01 1
4.2%
0.0 1
4.2%
ValueCountFrequency (%)
0.79 1
4.2%
0.73 2
8.3%
0.5 1
4.2%
0.28 1
4.2%
0.12 1
4.2%
0.05 1
4.2%
0.0 1
4.2%
-0.01 1
4.2%
-0.02 1
4.2%
-0.1 1
4.2%

최근6개월표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)90.0%
Missing4
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean4.0715
Minimum-24.53
Maximum25.53
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)33.3%
Memory size348.0 B
2023-12-10T23:15:08.999081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-24.53
5-th percentile-10.185
Q1-3.415
median1.415
Q310.5425
95-th percentile25.53
Maximum25.53
Range50.06
Interquartile range (IQR)13.9575

Descriptive statistics

Standard deviation13.061282
Coefficient of variation (CV)3.207978
Kurtosis0.054561676
Mean4.0715
Median Absolute Deviation (MAD)6.425
Skewness0.11520999
Sum81.43
Variance170.5971
MonotonicityNot monotonic
2023-12-10T23:15:09.187305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
25.53 3
 
12.5%
3.33 1
 
4.2%
-9.43 1
 
4.2%
16.64 1
 
4.2%
2.33 1
 
4.2%
8.51 1
 
4.2%
5.77 1
 
4.2%
-3.97 1
 
4.2%
-3.23 1
 
4.2%
7.83 1
 
4.2%
Other values (8) 8
33.3%
(Missing) 4
16.7%
ValueCountFrequency (%)
-24.53 1
4.2%
-9.43 1
4.2%
-8.83 1
4.2%
-5.02 1
4.2%
-3.97 1
4.2%
-3.23 1
4.2%
-2.16 1
4.2%
-1.49 1
4.2%
0.43 1
4.2%
0.5 1
4.2%
ValueCountFrequency (%)
25.53 3
12.5%
18.16 1
 
4.2%
16.64 1
 
4.2%
8.51 1
 
4.2%
7.83 1
 
4.2%
5.77 1
 
4.2%
3.33 1
 
4.2%
2.33 1
 
4.2%
0.5 1
 
4.2%
0.43 1
 
4.2%

최초6개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21083333
Minimum-8.63
Maximum8.46
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)16.7%
Memory size348.0 B
2023-12-10T23:15:09.351192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.63
5-th percentile-0.4305
Q10.01
median0.12
Q30.2275
95-th percentile2.4725
Maximum8.46
Range17.09
Interquartile range (IQR)0.2175

Descriptive statistics

Standard deviation2.5902945
Coefficient of variation (CV)12.285982
Kurtosis10.085946
Mean0.21083333
Median Absolute Deviation (MAD)0.11
Skewness-0.31031553
Sum5.06
Variance6.7096254
MonotonicityNot monotonic
2023-12-10T23:15:09.563874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.12 4
16.7%
0.01 3
 
12.5%
0.16 1
 
4.2%
2.72 1
 
4.2%
0.03 1
 
4.2%
-0.45 1
 
4.2%
0.1 1
 
4.2%
1.07 1
 
4.2%
0.09 1
 
4.2%
-0.03 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
-8.63 1
 
4.2%
-0.45 1
 
4.2%
-0.32 1
 
4.2%
-0.03 1
 
4.2%
0.01 3
12.5%
0.03 1
 
4.2%
0.05 1
 
4.2%
0.09 1
 
4.2%
0.1 1
 
4.2%
0.12 4
16.7%
ValueCountFrequency (%)
8.46 1
 
4.2%
2.72 1
 
4.2%
1.07 1
 
4.2%
0.43 1
 
4.2%
0.27 1
 
4.2%
0.25 1
 
4.2%
0.22 1
 
4.2%
0.16 1
 
4.2%
0.13 1
 
4.2%
0.12 4
16.7%

상호작용도1개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.086666667
Minimum-0.17
Maximum0.43
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)95.8%
Memory size348.0 B
2023-12-10T23:15:09.774128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.17
5-th percentile-0.17
Q1-0.17
median-0.11
Q3-0.04
95-th percentile-0.023
Maximum0.43
Range0.6
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.12440001
Coefficient of variation (CV)-1.4353847
Kurtosis13.484957
Mean-0.086666667
Median Absolute Deviation (MAD)0.06
Skewness3.2838006
Sum-2.08
Variance0.015475362
MonotonicityNot monotonic
2023-12-10T23:15:09.949882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
-0.17 9
37.5%
-0.04 6
25.0%
-0.14 2
 
8.3%
-0.09 2
 
8.3%
-0.05 1
 
4.2%
-0.02 1
 
4.2%
-0.13 1
 
4.2%
-0.08 1
 
4.2%
0.43 1
 
4.2%
ValueCountFrequency (%)
-0.17 9
37.5%
-0.14 2
 
8.3%
-0.13 1
 
4.2%
-0.09 2
 
8.3%
-0.08 1
 
4.2%
-0.05 1
 
4.2%
-0.04 6
25.0%
-0.02 1
 
4.2%
0.43 1
 
4.2%
ValueCountFrequency (%)
0.43 1
 
4.2%
-0.02 1
 
4.2%
-0.04 6
25.0%
-0.05 1
 
4.2%
-0.08 1
 
4.2%
-0.09 2
 
8.3%
-0.13 1
 
4.2%
-0.14 2
 
8.3%
-0.17 9
37.5%

Interactions

2023-12-10T23:14:56.706665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:41.427142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.115028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.940801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:46.800725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:48.647780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:50.392020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:52.316390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:53.825095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:55.191802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:56.825458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:41.541635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.285035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.090479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:47.043295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:48.781780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:50.582033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:52.467583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:53.960373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:55.344197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:56.995842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:41.696706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.464500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.255230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:47.276098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:49.010812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:50.752128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:52.631554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:54.068990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:55.533995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:57.180464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:41.844911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.616679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.444315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:47.417569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:49.214790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:50.917904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:52.785182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:54.212327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:55.690503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:57.321210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:41.980335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.916535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.594443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:47.571650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:49.477921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:51.083286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:52.937790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:54.347285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:55.850464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:57.464766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:42.433015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.105912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.912687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:47.782812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:49.656378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:51.264055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:53.095595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:54.472266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:56.000504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:57.617982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:42.560956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.247476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:46.099090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:47.932017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:49.788221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:51.385969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:53.232378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:54.562171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:56.138703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:57.771777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:42.681492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.471339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:46.283788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:48.107260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:49.934204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:51.535016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:53.398300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:54.687237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:56.283446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:57.972706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:42.801558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.632953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:46.438385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:48.259073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:50.074289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:51.663623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:53.530734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:54.861361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:56.425961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:58.170031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:42.950536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.782840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:46.588018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:48.469839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:50.237904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:52.164116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:53.688764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:55.026460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:56.573177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:15:10.142256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역량평가채널ID역량평가채널명역량평가수집일자역량평가채널설명역량평가채널생성일자역량별그룹할당그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월최초6개월상호작용정도1개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수
역량평가채널ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량평가채널명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량평가수집일자1.0001.0001.0001.0001.0000.0001.0000.0000.0000.1780.5270.0001.000NaN0.0000.0000.0000.0000.000
역량평가채널설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량평가채널생성일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
역량별그룹할당1.0001.0000.0001.0001.0001.0001.0000.4240.8860.5550.6340.3850.3630.3670.0000.7850.6170.0000.725
그룹내채널수1.0001.0001.0001.0001.0001.0001.0000.0000.5960.6460.6430.1010.7490.3670.0000.8250.5730.0000.903
그룹내구독자순위1.0001.0000.0001.0001.0000.4240.0001.0000.7300.9680.8320.7510.0000.7950.4460.4480.5290.7860.683
그룹내조회수순위1.0001.0000.0001.0001.0000.8860.5960.7301.0000.8710.9020.0000.0000.4880.0000.7680.4340.6980.464
그룹내좋아요순위1.0001.0000.1781.0001.0000.5550.6460.9680.8711.0000.9260.0000.0000.6810.0000.3860.7580.6890.000
역량평가1.0001.0000.5271.0001.0000.6340.6430.8320.9020.9261.0000.1480.0000.6600.0000.8620.6500.0000.284
홍보지수1.0001.0000.0001.0001.0000.3850.1010.7510.0000.0000.1481.0000.5470.0000.8280.7940.0000.0000.732
최근6개월1.0001.0001.0001.0001.0000.3630.7490.0000.0000.0000.0000.5471.0000.5960.0000.6640.0000.6110.000
최초6개월1.0001.000NaN1.0001.0000.3670.3670.7950.4880.6810.6600.0000.5961.0000.0630.0000.7320.0000.000
상호작용정도1개월1.0001.0000.0001.0001.0000.0000.0000.4460.0000.0000.0000.8280.0000.0631.0000.1710.0000.0000.691
그룹내홍보지수표준점수1.0001.0000.0001.0001.0000.7850.8250.4480.7680.3860.8620.7940.6640.0000.1711.0000.5950.5760.639
최근6개월표준점수1.0001.0000.0001.0001.0000.6170.5730.5290.4340.7580.6500.0000.0000.7320.0000.5951.0000.0430.000
최초6개월표준점수1.0001.0000.0001.0001.0000.0000.0000.7860.6980.6890.0000.0000.6110.0000.0000.5760.0431.0000.000
상호작용도1개월표준점수1.0001.0000.0001.0001.0000.7250.9030.6830.4640.0000.2840.7320.0000.0000.6910.6390.0000.0001.000
2023-12-10T23:15:10.533790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호작용정도1개월최초6개월역량평가수집일자그룹내채널수역량별그룹할당
상호작용정도1개월1.0000.0000.0000.0000.000
최초6개월0.0001.0001.0000.0780.078
역량평가수집일자0.0001.0001.0000.9530.000
그룹내채널수0.0000.0780.9531.0000.976
역량별그룹할당0.0000.0780.0000.9761.000
2023-12-10T23:15:10.753721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수역량평가수집일자역량별그룹할당그룹내채널수최초6개월상호작용정도1개월
그룹내구독자순위1.0000.8940.8760.804-0.7210.004-0.6130.3670.345-0.4910.0000.2350.0000.5300.254
그룹내조회수순위0.8941.0000.9440.943-0.838-0.131-0.7790.3550.289-0.5030.0000.5110.3510.2560.000
그룹내좋아요순위0.8760.9441.0000.932-0.826-0.167-0.7260.4370.251-0.5270.0000.3510.2770.4590.000
역량평가0.8040.9430.9321.000-0.797-0.303-0.6920.3640.164-0.4610.3020.3740.3510.3750.000
홍보지수-0.721-0.838-0.826-0.7971.0000.1430.9270.088-0.1670.3090.0000.1830.0000.0000.664
최근6개월0.004-0.131-0.167-0.3030.1431.0000.1800.1440.1210.0750.8560.0000.5160.4550.000
그룹내홍보지수표준점수-0.613-0.779-0.726-0.6920.9270.1801.0000.024-0.2090.4920.0000.5490.3810.0000.000
최근6개월표준점수0.3670.3550.4370.3640.0880.1440.0241.0000.186-0.4710.0000.4340.3710.5020.000
최초6개월표준점수0.3450.2890.2510.164-0.1670.121-0.2090.1861.000-0.6520.0000.0000.0000.0000.000
상호작용도1개월표준점수-0.491-0.503-0.527-0.4610.3090.0750.492-0.471-0.6521.0000.0000.7480.5880.0000.705
역량평가수집일자0.0000.0000.0000.3020.0000.8560.0000.0000.0000.0001.0000.0000.9531.0000.000
역량별그룹할당0.2350.5110.3510.3740.1830.0000.5490.4340.0000.7480.0001.0000.9760.0780.000
그룹내채널수0.0000.3510.2770.3510.0000.5160.3810.3710.0000.5880.9530.9761.0000.0780.000
최초6개월0.5300.2560.4590.3750.0000.4550.0000.5020.0000.0001.0000.0780.0781.0000.000
상호작용정도1개월0.2540.0000.0000.0000.6640.0000.0000.0000.0000.7050.0000.0000.0000.0001.000

Missing values

2023-12-10T23:14:58.410866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:14:58.820537image/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.
2023-12-10T23:14:59.126468image/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
0UC0hk2D145ogyBYMKjylWkkw신속2021-02-01게임에 진심인 남자2013-03-16MACRO14708694875332505.9739<NA>0-0.017.830.16-0.14
1UC1r112Pr9Ngcg2NtcE946HQ맛상무2021-02-28술상무 아닙니다. 대신 맛봐 드리는 맛상무 입니다. 맛있는음식; 즐거움; 감동이 있는 채널입니다2015-12-22MACRO14671341091425618.5727<NA>10.730.50.12-0.05
2UC1dMe0fYTM4r9mNQc9v1ziASaehyeon세현2021-02-28뷰티와 일상을 기록하는 유튜버 세현입니다 비즈니스 문의- saehyeon031105@gmail.com 으로 주시면 감사하겠습니다!2018-12-30MACRO14671416140513801396<NA><NA>-10<NA>25.530.27-0.17
3UC0ru5w57PyGpbsEKwN4LuwA재민정2021-02-28<NA>2013-05-13MICRO1058864908954974<NA><NA><NA>0<NA><NA>0.01-0.04
4UC270ueFEsQ21S26TYI_9yVA야신야덕2021-02-28야구 유튜브 채널! 야구 유튜버 빡코가 야구와 다양한 스포츠를 함께 다루고 있습니다:) 여러분 기상천외한 아이디어와 창의력으로 스포츠를 즐겨봅시다! 구독부탁드려요~~:) #스포츠 #운동 #야구2018-06-27MACRO14676412765614628.02-3000.12-2.16-8.63-0.14
5UC1LYenGeHKq2IgfWgMivHYA황금통로2021-02-28유준호의 능력자 강산이2013-09-29MACRO14671333144714361431<NA><NA><NA>0<NA>25.530.12-0.17
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7UC3kgosidfK3i_47Q2otmmGw룰루랄라 뮤직-Lululala Music2021-02-28일상이 즐거워지는 오늘의 음악 룰루랄라 뮤직(Lululala Music)은 스튜디오 룰루랄라(Studio Lululala)의 음악 채널입니다. 구독하면 룰루랄라 뮤직의 콘텐츠를 가장 먼저! 확인할 수 있습니다 :D NOW On-Air [히든트랙2] 매주 목요일 6시! 익숙하지만 새로운 즐거움; JTBC 디지털 스튜디오 룰루랄라 (studio lululala) * 비즈니스 문의: lululala.ad@jtbc.co.kr Today’s playlist that makes my day. LuluLala Music is the music channel by Studio LuluLala. Set up a subscription + alarm and be the first to meet the contents of LuluLala Music :)2018-07-02MACRO14676465481594777.62010-0.02-24.530.22-0.17
8UC5DM0obdv6w2xXFQFDPra8ANektwork2021-02-28넥티즌 멤버십에 가입해 보세요! https:www.youtube.comchannelUC5DM... 넥트워크 음악 채널에 오신것을 환영합니다! 이 채널은 넥트워크가 손수 만든 음악이 올라오는 채널로 EDM 장르 위주로 공개되는 음악을 기준으로 뻗어나가는 다양한 음악 컨텐츠를 만나 볼 수 있습니다. 이 채널과 음악이 마음에 드신다면 '구독하기' 와 '알람' 버튼을 눌러주세요! 새로운 음악 컨텐츠를 누구보다 빠르게 접하실 수 있습니다. YouTube youtube.comcnektwork Soundcloud soundcloud.comnektwork2015-02-04MACRO14671290107111101102<NA>-4-10<NA>18.160.25-0.17
9UC60Z87qoAFcpZME6mPGpIFQ다정다감2021-02-28안녕하세요! 다정다감이에요 영상은 매주 일요일에 업로드 됩니다! . . Instagram(인스타그램) : dajeong_423_ E-mail(이메일) : dajeong8205@naver.com2018-12-18MACRO14671420142814171410<NA><NA>-10<NA>25.530.13-0.17
역량평가채널ID역량평가채널명역량평가수집일자역량평가채널설명역량평가채널생성일자역량별그룹할당그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월최초6개월상호작용정도1개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수
14UC83l21B4HarJD4X9Bq3zmmw전략물자관리원2021-02-28국민에게 신뢰받는 무역안보 플랫폼 전략물자관리원 공식 채널입니다.2015-02-02MICRO1058776929930959<NA><NA><NA>0<NA><NA>0.01-0.04
15UC2IYsRiATGe71sLIOlSXGuw흑열전구2021-02-28게임의 모든 것; 흑열전구 채널 입니다;2012-12-02MACRO14677794785781765.64-1<NA>1-0.1-3.230.12-0.08
16UCA5AgXSjOanrPcp-FKJ52mg개주부2021-02-283마리의 반려견과 함께하는 개주부들의 일상2018-11-28MICRO1058755868824684<NA><NA>10<NA><NA>-0.32-0.04
17UCA6GLF0zq1Sl_Y-AgVOEzNw패션센터중구2021-02-28서울의 중심 패션의 중심 중구의류패션지원센터 'JFC'는 중구패션산업 활성화와 글로벌 패션인의 육성을 위해 공용재단실 운영; 브랜드 협업; 환경조성 사업; 워크숍; 등 지원을 제공하고 있습니다.2018-09-28MICRO10588139859779740.13<NA><NA>0-0.38<NA>-0.03-0.04
18UC2MgNh831KCMuYkK7u8C2dg꽃핀2021-02-28<NA>2013-08-30MACRO1467285122749617.043<NA>30.79-3.970.120.43
19UC1O0TFY6UMY2RgfrGymiwIwChangmakeup2021-02-28Hiiii ~ I am Trang Ngo and I am Vietnamese. I have a huge love for lipsticks and I really want to share it with you guys by uploading swatch and review videos. Hope it helps! If you love lipsticks; makeup and beauty tips then you are at the right place. Please subscribe my channel if you want to see more; weeheee2010-03-19MEGA1931481701411342.130-10-0.55.770.09-0.09
20UCCq9w9rG51vZxWPxLNB04mQEBS Learning2021-02-28- Supplement school education for college entrance exam - Supplement primary and middle school education and job training - English education 대한민국 국가대표 수능강의를 비롯하여 공교육보완을 위한 초중학 교육 콘텐츠를 제공합니다.2012-11-19MACRO14676434878656966.533100.058.511.07-0.17
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22UCEPuItFWOOJ2o5hTu65NlEg태경 TV2021-02-2817살 남고생 태경 18살 여고생 쁘허 평일 2시 주말 2시!!! (변경) 일요일은 쉽니당!!! 태쁘 데이트의 날 태쁘커플 노는곳입니다 태둥이 태싹이 모두 안녕2014-05-29MEGA19311832381120.55000.516.64-0.45-0.09
23UCEeyt5KFgG4P-Qu3SAmbYHg금산군2021-02-28<NA>2018-03-28MICRO10587078167777891.54210-0.23-9.430.03-0.04