Overview

Dataset statistics

Number of variables19
Number of observations27
Missing cells28
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory169.9 B

Variable types

Text3
Categorical5
DateTime1
Numeric10

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/5ad35e86-0ada-4f70-8fcc-1e2544069f31

Alerts

상호작용정도1개월 has constant value ""Constant
상호작용도1개월표준점수 is highly overall correlated with 그룹내채널수 and 2 other fieldsHigh correlation
역량별그룹할당 is highly overall correlated with 그룹내채널수 and 5 other fieldsHigh correlation
그룹내채널수 is highly overall correlated with 그룹내구독자순위 and 3 other fieldsHigh correlation
그룹내구독자순위 is highly overall correlated with 그룹내채널수 and 7 other fieldsHigh correlation
그룹내조회수순위 is highly overall correlated with 그룹내구독자순위 and 5 other fieldsHigh correlation
그룹내좋아요순위 is highly overall correlated with 그룹내구독자순위 and 4 other fieldsHigh correlation
역량평가 is highly overall correlated with 그룹내구독자순위 and 5 other fieldsHigh correlation
홍보지수 is highly overall correlated with 그룹내구독자순위 and 4 other fieldsHigh correlation
그룹내홍보지수표준점수 is highly overall correlated with 그룹내채널수 and 6 other fieldsHigh correlation
역량평가채널설명 has 6 (22.2%) missing valuesMissing
홍보지수 has 6 (22.2%) missing valuesMissing
최근6개월 has 10 (37.0%) missing valuesMissing
그룹내홍보지수표준점수 has 6 (22.2%) missing valuesMissing
역량평가채널ID has unique valuesUnique
역량평가채널명 has unique valuesUnique
역량평가채널생성일자 has unique valuesUnique
최근6개월 has 3 (11.1%) zerosZeros
최초6개월표준점수 has 1 (3.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:15:27.453107
Analysis finished2023-12-10 14:15:49.267061
Duration21.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역량평가채널ID
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-10T23:15:49.480744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters648
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

Unique27 ?
Unique (%)100.0%

Sample

1st rowUCTa6Q94LRfbhyuYKa1dS6BA
2nd rowUCoWKSz0mU15S6V6kTQ7CsMA
3rd rowUCXGh59C0ilyfsJaO59uR5Yw
4th rowUCVvyVBQF5ejhjx4l-c9RUWw
5th rowUCbAEthmNnJk8JVZP8FWXqZg
ValueCountFrequency (%)
ucta6q94lrfbhyuyka1ds6ba 1
 
3.7%
ucpl2m1nihzh9y23svg1ywmw 1
 
3.7%
ucq0ugwudycevkadtrfsjpyg 1
 
3.7%
ucct6in6njemse_ohrihybaq 1
 
3.7%
ucvf-pbl3em2iqmvyuziqmta 1
 
3.7%
ucdbuj8x8zwe1oazsyzucflg 1
 
3.7%
ucxndnglymuh1ljxsfeb7mrg 1
 
3.7%
ucsuz7amaob56wsqgpqunefa 1
 
3.7%
ucklof14rze4o9k6wvp-ccjq 1
 
3.7%
ucqvarotdadtpbo_15poxjcq 1
 
3.7%
Other values (17) 17
63.0%
2023-12-10T23:15:50.131245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 43
 
6.6%
C 35
 
5.4%
A 17
 
2.6%
w 15
 
2.3%
1 15
 
2.3%
m 14
 
2.2%
J 13
 
2.0%
Q 13
 
2.0%
h 12
 
1.9%
l 12
 
1.9%
Other values (54) 459
70.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 312
48.1%
Lowercase Letter 225
34.7%
Decimal Number 97
 
15.0%
Connector Punctuation 10
 
1.5%
Dash Punctuation 4
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 43
 
13.8%
C 35
 
11.2%
A 17
 
5.4%
J 13
 
4.2%
Q 13
 
4.2%
V 12
 
3.8%
Y 12
 
3.8%
M 12
 
3.8%
F 11
 
3.5%
S 11
 
3.5%
Other values (16) 133
42.6%
Lowercase Letter
ValueCountFrequency (%)
w 15
 
6.7%
m 14
 
6.2%
h 12
 
5.3%
l 12
 
5.3%
v 10
 
4.4%
k 10
 
4.4%
g 10
 
4.4%
d 10
 
4.4%
p 10
 
4.4%
t 9
 
4.0%
Other values (16) 113
50.2%
Decimal Number
ValueCountFrequency (%)
1 15
15.5%
0 11
11.3%
2 11
11.3%
8 11
11.3%
6 11
11.3%
7 9
9.3%
5 9
9.3%
9 8
8.2%
3 7
7.2%
4 5
 
5.2%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 537
82.9%
Common 111
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 43
 
8.0%
C 35
 
6.5%
A 17
 
3.2%
w 15
 
2.8%
m 14
 
2.6%
J 13
 
2.4%
Q 13
 
2.4%
h 12
 
2.2%
l 12
 
2.2%
V 12
 
2.2%
Other values (42) 351
65.4%
Common
ValueCountFrequency (%)
1 15
13.5%
0 11
9.9%
2 11
9.9%
8 11
9.9%
6 11
9.9%
_ 10
9.0%
7 9
8.1%
5 9
8.1%
9 8
7.2%
3 7
6.3%
Other values (2) 9
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 43
 
6.6%
C 35
 
5.4%
A 17
 
2.6%
w 15
 
2.3%
1 15
 
2.3%
m 14
 
2.2%
J 13
 
2.0%
Q 13
 
2.0%
h 12
 
1.9%
l 12
 
1.9%
Other values (54) 459
70.8%

역량평가채널명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-10T23:15:50.610273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length15
Mean length9.7407407
Min length3

Characters and Unicode

Total characters263
Distinct characters131
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row유튜브 플러스
2nd row애니먹animuk
3rd row똑똑 스튜디오
4th row경북대학교병원
5th rowdareharu달의하루
ValueCountFrequency (%)
유튜브 1
 
2.3%
플러스 1
 
2.3%
nids한국의료기기안전정보원 1
 
2.3%
남북의 1
 
2.3%
창 1
 
2.3%
김팀장의 1
 
2.3%
북한 1
 
2.3%
확대경 1
 
2.3%
priimebtchs 1
 
2.3%
female 1
 
2.3%
Other values (34) 34
77.3%
2023-12-10T23:15:51.554142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
6.5%
e 9
 
3.4%
a 7
 
2.7%
T 7
 
2.7%
r 6
 
2.3%
의 6
 
2.3%
n 5
 
1.9%
원 5
 
1.9%
I 5
 
1.9%
V 5
 
1.9%
Other values (121) 191
72.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
57.0%
Uppercase Letter 48
 
18.3%
Lowercase Letter 45
 
17.1%
Space Separator 17
 
6.5%
Other Punctuation 2
 
0.8%
Connector Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 6
 
4.0%
원 5
 
3.3%
북 4
 
2.7%
니 4
 
2.7%
기 4
 
2.7%
스 4
 
2.7%
문 4
 
2.7%
정 4
 
2.7%
대 3
 
2.0%
한 3
 
2.0%
Other values (82) 109
72.7%
Uppercase Letter
ValueCountFrequency (%)
T 7
14.6%
I 5
10.4%
V 5
10.4%
O 4
 
8.3%
D 3
 
6.2%
E 3
 
6.2%
S 3
 
6.2%
J 3
 
6.2%
C 2
 
4.2%
B 2
 
4.2%
Other values (9) 11
22.9%
Lowercase Letter
ValueCountFrequency (%)
e 9
20.0%
a 7
15.6%
r 6
13.3%
n 5
11.1%
m 3
 
6.7%
u 3
 
6.7%
l 2
 
4.4%
o 2
 
4.4%
v 1
 
2.2%
y 1
 
2.2%
Other values (6) 6
13.3%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
· 1
50.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
57.0%
Latin 93
35.4%
Common 20
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 6
 
4.0%
원 5
 
3.3%
북 4
 
2.7%
니 4
 
2.7%
기 4
 
2.7%
스 4
 
2.7%
문 4
 
2.7%
정 4
 
2.7%
대 3
 
2.0%
한 3
 
2.0%
Other values (82) 109
72.7%
Latin
ValueCountFrequency (%)
e 9
 
9.7%
a 7
 
7.5%
T 7
 
7.5%
r 6
 
6.5%
n 5
 
5.4%
I 5
 
5.4%
V 5
 
5.4%
O 4
 
4.3%
D 3
 
3.2%
E 3
 
3.2%
Other values (25) 39
41.9%
Common
ValueCountFrequency (%)
17
85.0%
_ 1
 
5.0%
' 1
 
5.0%
· 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
57.0%
ASCII 112
42.6%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
 
15.2%
e 9
 
8.0%
a 7
 
6.2%
T 7
 
6.2%
r 6
 
5.4%
n 5
 
4.5%
I 5
 
4.5%
V 5
 
4.5%
O 4
 
3.6%
D 3
 
2.7%
Other values (28) 44
39.3%
Hangul
ValueCountFrequency (%)
의 6
 
4.0%
원 5
 
3.3%
북 4
 
2.7%
니 4
 
2.7%
기 4
 
2.7%
스 4
 
2.7%
문 4
 
2.7%
정 4
 
2.7%
대 3
 
2.0%
한 3
 
2.0%
Other values (82) 109
72.7%
None
ValueCountFrequency (%)
· 1
100.0%

역량평가수집일자
Categorical

Distinct5
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size348.0 B
2020-11-08
11 
2020-11-09
8 
2020-11-07
4 
2020-11-02
3 
2020-11-04
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row2020-11-02
2nd row2020-11-02
3rd row2020-11-02
4th row2020-11-04
5th row2020-11-07

Common Values

ValueCountFrequency (%)
2020-11-08 11
40.7%
2020-11-09 8
29.6%
2020-11-07 4
 
14.8%
2020-11-02 3
 
11.1%
2020-11-04 1
 
3.7%

Length

2023-12-10T23:15:51.904310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:52.106527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-11-08 11
40.7%
2020-11-09 8
29.6%
2020-11-07 4
 
14.8%
2020-11-02 3
 
11.1%
2020-11-04 1
 
3.7%

역량평가채널설명
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing6
Missing (%)22.2%
Memory size348.0 B
2023-12-10T23:15:52.563950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length471
Median length112
Mean length128.85714
Min length15

Characters and Unicode

Total characters2706
Distinct characters386
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row안녕하세요 ~~ 유튜브 플러스 입니다 저희 채널은 유튜브의 지식을 플러스 하다! 라는 의미로 채널명을 선정했구요~ 유튜브교육 채널입니다!! 여러분들의 지식을 더 플러스+ 하기 위해 더욱더 노력하도록 하겠습니다!! 최대한 정확한 지식을 전해드리려고 발로 뛰고 있으니 많은 관심 부탁드립니다 ------------------------------------------------------------------------------------------------------------------------------------------------------------- 문의 : onsemedia1@gmail.com
2nd rowThis channel is the official channel of Animuk. Animuk is made for anyone to enjoy. 본 채널은 애니먹 공식 채널입니다. 애니먹 영상은 누구나 즐겁게 볼 수 있도록 제작되어집니다. Email : animuk@naver.com
3rd row복잡한 세상 똑똑하게 살자! 생활정보채널 '똑똑 스튜디오'
4th row경북대학교병원 동영상 자료실
5th row불친절한 행복과 다정한 상처를 노래해요. Music - ampstyle Vocal - 초희
ValueCountFrequency (%)
26
 
5.0%
등 8
 
1.5%
채널 6
 
1.2%
다문화가족 5
 
1.0%
공식 5
 
1.0%
채널입니다 5
 
1.0%
있습니다 4
 
0.8%
여행 4
 
0.8%
지식을 3
 
0.6%
채널은 3
 
0.6%
Other values (398) 449
86.7%
2023-12-10T23:15:53.300414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
564
 
20.8%
- 164
 
6.1%
다 47
 
1.7%
; 42
 
1.6%
니 33
 
1.2%
가 31
 
1.1%
이 29
 
1.1%
i 29
 
1.1%
. 28
 
1.0%
o 27
 
1.0%
Other values (376) 1712
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1452
53.7%
Space Separator 564
 
20.8%
Lowercase Letter 271
 
10.0%
Dash Punctuation 164
 
6.1%
Other Punctuation 129
 
4.8%
Uppercase Letter 56
 
2.1%
Decimal Number 50
 
1.8%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 47
 
3.2%
니 33
 
2.3%
가 31
 
2.1%
이 29
 
2.0%
지 24
 
1.7%
일 24
 
1.7%
는 22
 
1.5%
의 22
 
1.5%
한 22
 
1.5%
하 21
 
1.4%
Other values (308) 1177
81.1%
Lowercase Letter
ValueCountFrequency (%)
i 29
10.7%
o 27
10.0%
n 25
 
9.2%
a 23
 
8.5%
m 20
 
7.4%
s 20
 
7.4%
e 19
 
7.0%
c 15
 
5.5%
t 14
 
5.2%
l 14
 
5.2%
Other values (13) 65
24.0%
Uppercase Letter
ValueCountFrequency (%)
T 8
14.3%
V 7
12.5%
S 5
 
8.9%
C 5
 
8.9%
O 4
 
7.1%
A 3
 
5.4%
I 3
 
5.4%
D 3
 
5.4%
B 3
 
5.4%
P 2
 
3.6%
Other values (9) 13
23.2%
Decimal Number
ValueCountFrequency (%)
1 17
34.0%
2 9
18.0%
0 7
14.0%
3 6
 
12.0%
6 4
 
8.0%
5 3
 
6.0%
4 2
 
4.0%
9 1
 
2.0%
8 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
; 42
32.6%
. 28
21.7%
: 18
14.0%
! 17
13.2%
' 14
 
10.9%
@ 5
 
3.9%
· 4
 
3.1%
* 1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 5
71.4%
[ 2
 
28.6%
Close Punctuation
ValueCountFrequency (%)
) 5
71.4%
] 2
 
28.6%
Math Symbol
ValueCountFrequency (%)
~ 3
75.0%
+ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
564
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1452
53.7%
Common 927
34.3%
Latin 327
 
12.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 47
 
3.2%
니 33
 
2.3%
가 31
 
2.1%
이 29
 
2.0%
지 24
 
1.7%
일 24
 
1.7%
는 22
 
1.5%
의 22
 
1.5%
한 22
 
1.5%
하 21
 
1.4%
Other values (308) 1177
81.1%
Latin
ValueCountFrequency (%)
i 29
 
8.9%
o 27
 
8.3%
n 25
 
7.6%
a 23
 
7.0%
m 20
 
6.1%
s 20
 
6.1%
e 19
 
5.8%
c 15
 
4.6%
t 14
 
4.3%
l 14
 
4.3%
Other values (32) 121
37.0%
Common
ValueCountFrequency (%)
564
60.8%
- 164
 
17.7%
; 42
 
4.5%
. 28
 
3.0%
: 18
 
1.9%
1 17
 
1.8%
! 17
 
1.8%
' 14
 
1.5%
2 9
 
1.0%
0 7
 
0.8%
Other values (16) 47
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1452
53.7%
ASCII 1250
46.2%
None 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
564
45.1%
- 164
 
13.1%
; 42
 
3.4%
i 29
 
2.3%
. 28
 
2.2%
o 27
 
2.2%
n 25
 
2.0%
a 23
 
1.8%
m 20
 
1.6%
s 20
 
1.6%
Other values (57) 308
24.6%
Hangul
ValueCountFrequency (%)
다 47
 
3.2%
니 33
 
2.3%
가 31
 
2.1%
이 29
 
2.0%
지 24
 
1.7%
일 24
 
1.7%
는 22
 
1.5%
의 22
 
1.5%
한 22
 
1.5%
하 21
 
1.4%
Other values (308) 1177
81.1%
None
ValueCountFrequency (%)
· 4
100.0%

역량평가채널생성일자
Date

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2016-03-03 00:00:00
Maximum2020-08-06 00:00:00
2023-12-10T23:15:53.547126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:53.833322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

역량별그룹할당
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
MICRO
18 
MACRO
9 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MICRO 18
66.7%
MACRO 9
33.3%

Length

2023-12-10T23:15:54.101982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:54.274815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
micro 18
66.7%
macro 9
33.3%

그룹내채널수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1195.037
Minimum1052
Maximum1481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:15:54.399611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1052
5-th percentile1052
Q11052
median1052
Q31480
95-th percentile1481
Maximum1481
Range429
Interquartile range (IQR)428

Descriptive statistics

Standard deviation205.50023
Coefficient of variation (CV)0.17196139
Kurtosis-1.5599257
Mean1195.037
Median Absolute Deviation (MAD)0
Skewness0.74936644
Sum32266
Variance42230.345
MonotonicityNot monotonic
2023-12-10T23:15:54.557173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1052 15
55.6%
1480 4
 
14.8%
1481 4
 
14.8%
1055 2
 
7.4%
1478 1
 
3.7%
1054 1
 
3.7%
ValueCountFrequency (%)
1052 15
55.6%
1054 1
 
3.7%
1055 2
 
7.4%
1478 1
 
3.7%
1480 4
 
14.8%
1481 4
 
14.8%
ValueCountFrequency (%)
1481 4
 
14.8%
1480 4
 
14.8%
1478 1
 
3.7%
1055 2
 
7.4%
1054 1
 
3.7%
1052 15
55.6%

그룹내구독자순위
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.66667
Minimum36
Maximum1339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:15:54.762921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile242.9
Q1585.5
median738
Q3864
95-th percentile1243.4
Maximum1339
Range1303
Interquartile range (IQR)278.5

Descriptive statistics

Standard deviation325.29147
Coefficient of variation (CV)0.43565821
Kurtosis-0.18897501
Mean746.66667
Median Absolute Deviation (MAD)150
Skewness-0.0054537316
Sum20160
Variance105814.54
MonotonicityNot monotonic
2023-12-10T23:15:54.934365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
738 2
 
7.4%
617 1
 
3.7%
838 1
 
3.7%
837 1
 
3.7%
771 1
 
3.7%
1153 1
 
3.7%
1193 1
 
3.7%
681 1
 
3.7%
1339 1
 
3.7%
604 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
36 1
3.7%
185 1
3.7%
378 1
3.7%
426 1
3.7%
435 1
3.7%
490 1
3.7%
568 1
3.7%
603 1
3.7%
604 1
3.7%
617 1
3.7%
ValueCountFrequency (%)
1339 1
3.7%
1265 1
3.7%
1193 1
3.7%
1191 1
3.7%
1181 1
3.7%
1153 1
3.7%
888 1
3.7%
840 1
3.7%
838 1
3.7%
837 1
3.7%

그룹내조회수순위
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean911.59259
Minimum223
Maximum1418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:15:55.094466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum223
5-th percentile399.4
Q1782.5
median895
Q31020
95-th percentile1369.8
Maximum1418
Range1195
Interquartile range (IQR)237.5

Descriptive statistics

Standard deviation286.41635
Coefficient of variation (CV)0.31419338
Kurtosis0.62312229
Mean911.59259
Median Absolute Deviation (MAD)125
Skewness-0.34585023
Sum24613
Variance82034.328
MonotonicityNot monotonic
2023-12-10T23:15:55.257226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1020 3
 
11.1%
1233 1
 
3.7%
975 1
 
3.7%
1346 1
 
3.7%
1216 1
 
3.7%
853 1
 
3.7%
1380 1
 
3.7%
785 1
 
3.7%
787 1
 
3.7%
895 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
223 1
3.7%
301 1
3.7%
629 1
3.7%
662 1
3.7%
751 1
3.7%
765 1
3.7%
780 1
3.7%
785 1
3.7%
787 1
3.7%
798 1
3.7%
ValueCountFrequency (%)
1418 1
 
3.7%
1380 1
 
3.7%
1346 1
 
3.7%
1233 1
 
3.7%
1230 1
 
3.7%
1216 1
 
3.7%
1020 3
11.1%
993 1
 
3.7%
990 1
 
3.7%
975 1
 
3.7%

그룹내좋아요순위
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean825.7037
Minimum90
Maximum1383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:15:55.403527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile226.9
Q1650.5
median863
Q31007.5
95-th percentile1308.9
Maximum1383
Range1293
Interquartile range (IQR)357

Descriptive statistics

Standard deviation326.86022
Coefficient of variation (CV)0.39585655
Kurtosis0.051179786
Mean825.7037
Median Absolute Deviation (MAD)186
Skewness-0.43763883
Sum22294
Variance106837.6
MonotonicityNot monotonic
2023-12-10T23:15:55.571554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1007 2
 
7.4%
942 1
 
3.7%
1042 1
 
3.7%
1008 1
 
3.7%
1206 1
 
3.7%
976 1
 
3.7%
684 1
 
3.7%
1323 1
 
3.7%
672 1
 
3.7%
704 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
90 1
3.7%
133 1
3.7%
446 1
3.7%
496 1
3.7%
513 1
3.7%
619 1
3.7%
629 1
3.7%
672 1
3.7%
677 1
3.7%
684 1
3.7%
ValueCountFrequency (%)
1383 1
3.7%
1323 1
3.7%
1276 1
3.7%
1206 1
3.7%
1150 1
3.7%
1042 1
3.7%
1008 1
3.7%
1007 2
7.4%
980 1
3.7%
976 1
3.7%

역량평가
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.74074
Minimum102
Maximum1384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:15:55.720709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile251.9
Q1522
median850
Q31014.5
95-th percentile1337.3
Maximum1384
Range1282
Interquartile range (IQR)492.5

Descriptive statistics

Standard deviation344.49691
Coefficient of variation (CV)0.43347081
Kurtosis-0.63889161
Mean794.74074
Median Absolute Deviation (MAD)234
Skewness-0.22895153
Sum21458
Variance118678.12
MonotonicityNot monotonic
2023-12-10T23:15:55.930832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
964 5
 
18.5%
502 1
 
3.7%
626 1
 
3.7%
1065 1
 
3.7%
1233 1
 
3.7%
1090 1
 
3.7%
431 1
 
3.7%
1382 1
 
3.7%
712 1
 
3.7%
778 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
102 1
3.7%
227 1
3.7%
310 1
3.7%
316 1
3.7%
431 1
3.7%
502 1
3.7%
516 1
3.7%
528 1
3.7%
626 1
3.7%
711 1
3.7%
ValueCountFrequency (%)
1384 1
 
3.7%
1382 1
 
3.7%
1233 1
 
3.7%
1158 1
 
3.7%
1090 1
 
3.7%
1084 1
 
3.7%
1065 1
 
3.7%
964 5
18.5%
852 1
 
3.7%
850 1
 
3.7%

홍보지수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing6
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean2.11
Minimum0.1
Maximum8.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:15:56.164664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.27
Q10.9
median1.75
Q32.63
95-th percentile4.55
Maximum8.61
Range8.51
Interquartile range (IQR)1.73

Descriptive statistics

Standard deviation1.858494
Coefficient of variation (CV)0.88080285
Kurtosis7.0657833
Mean2.11
Median Absolute Deviation (MAD)0.87
Skewness2.2878939
Sum44.31
Variance3.454
MonotonicityNot monotonic
2023-12-10T23:15:56.325056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.75 1
 
3.7%
2.63 1
 
3.7%
1.13 1
 
3.7%
1.53 1
 
3.7%
0.65 1
 
3.7%
0.9 1
 
3.7%
1.95 1
 
3.7%
2.12 1
 
3.7%
2.48 1
 
3.7%
4.55 1
 
3.7%
Other values (11) 11
40.7%
(Missing) 6
22.2%
ValueCountFrequency (%)
0.1 1
3.7%
0.27 1
3.7%
0.61 1
3.7%
0.65 1
3.7%
0.88 1
3.7%
0.9 1
3.7%
1.13 1
3.7%
1.25 1
3.7%
1.37 1
3.7%
1.53 1
3.7%
ValueCountFrequency (%)
8.61 1
3.7%
4.55 1
3.7%
3.29 1
3.7%
3.2 1
3.7%
2.64 1
3.7%
2.63 1
3.7%
2.48 1
3.7%
2.4 1
3.7%
2.12 1
3.7%
1.95 1
3.7%

최근6개월
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)47.1%
Missing10
Missing (%)37.0%
Infinite0
Infinite (%)0.0%
Mean1.1176471
Minimum-4
Maximum28
Zeros3
Zeros (%)11.1%
Negative9
Negative (%)33.3%
Memory size375.0 B
2023-12-10T23:15:56.484887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile-3.2
Q1-3
median-2
Q31
95-th percentile15.2
Maximum28
Range32
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.8492225
Coefficient of variation (CV)7.0229886
Kurtosis9.2670292
Mean1.1176471
Median Absolute Deviation (MAD)2
Skewness2.9496826
Sum19
Variance61.610294
MonotonicityNot monotonic
2023-12-10T23:15:56.656154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-3 5
18.5%
-2 3
 
11.1%
0 3
 
11.1%
1 2
 
7.4%
12 1
 
3.7%
2 1
 
3.7%
-4 1
 
3.7%
28 1
 
3.7%
(Missing) 10
37.0%
ValueCountFrequency (%)
-4 1
 
3.7%
-3 5
18.5%
-2 3
11.1%
0 3
11.1%
1 2
 
7.4%
2 1
 
3.7%
12 1
 
3.7%
28 1
 
3.7%
ValueCountFrequency (%)
28 1
 
3.7%
12 1
 
3.7%
2 1
 
3.7%
1 2
 
7.4%
0 3
11.1%
-2 3
11.1%
-3 5
18.5%
-4 1
 
3.7%

최초6개월
Categorical

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
13 
1
9 
<NA>
5 

Length

Max length4
Median length1
Mean length1.5555556
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 13
48.1%
1 9
33.3%
<NA> 5
 
18.5%

Length

2023-12-10T23:15:56.843329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:57.016594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13
48.1%
1 9
33.3%
na 5
 
18.5%

상호작용정도1개월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
27 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 27
100.0%

Length

2023-12-10T23:15:57.195783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:57.349997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
100.0%

그룹내홍보지수표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)85.7%
Missing6
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean-0.30047619
Minimum-0.55
Maximum0.14
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)74.1%
Memory size375.0 B
2023-12-10T23:15:57.928188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.55
5-th percentile-0.5
Q1-0.43
median-0.27
Q3-0.21
95-th percentile-0.11
Maximum0.14
Range0.69
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.16356883
Coefficient of variation (CV)-0.54436536
Kurtosis1.149908
Mean-0.30047619
Median Absolute Deviation (MAD)0.09
Skewness0.67624659
Sum-6.31
Variance0.026754762
MonotonicityNot monotonic
2023-12-10T23:15:58.139426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-0.33 2
 
7.4%
-0.23 2
 
7.4%
-0.5 2
 
7.4%
-0.43 1
 
3.7%
-0.47 1
 
3.7%
-0.2 1
 
3.7%
-0.19 1
 
3.7%
-0.25 1
 
3.7%
-0.11 1
 
3.7%
-0.27 1
 
3.7%
Other values (8) 8
29.6%
(Missing) 6
22.2%
ValueCountFrequency (%)
-0.55 1
3.7%
-0.5 2
7.4%
-0.49 1
3.7%
-0.47 1
3.7%
-0.43 1
3.7%
-0.39 1
3.7%
-0.36 1
3.7%
-0.33 2
7.4%
-0.27 1
3.7%
-0.26 1
3.7%
ValueCountFrequency (%)
0.14 1
3.7%
-0.11 1
3.7%
-0.15 1
3.7%
-0.19 1
3.7%
-0.2 1
3.7%
-0.21 1
3.7%
-0.23 2
7.4%
-0.25 1
3.7%
-0.26 1
3.7%
-0.27 1
3.7%

최근6개월표준점수
Real number (ℝ)

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-21.344444
Minimum-181.49
Maximum12.71
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)77.8%
Memory size375.0 B
2023-12-10T23:15:58.352285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-181.49
5-th percentile-91.91
Q1-12.52
median-5
Q3-1.03
95-th percentile5.782
Maximum12.71
Range194.2
Interquartile range (IQR)11.49

Descriptive statistics

Standard deviation43.460239
Coefficient of variation (CV)-2.0361382
Kurtosis6.6127353
Mean-21.344444
Median Absolute Deviation (MAD)5.09
Skewness-2.5206266
Sum-576.3
Variance1888.7924
MonotonicityNot monotonic
2023-12-10T23:15:58.695567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-91.91 2
 
7.4%
-45.22 1
 
3.7%
-3.63 1
 
3.7%
-91.03 1
 
3.7%
2.68 1
 
3.7%
2.8 1
 
3.7%
-11.93 1
 
3.7%
12.71 1
 
3.7%
-3.83 1
 
3.7%
-1.8 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
-181.49 1
3.7%
-91.91 2
7.4%
-91.03 1
3.7%
-45.22 1
3.7%
-19.39 1
3.7%
-13.11 1
3.7%
-11.93 1
3.7%
-8.1 1
3.7%
-7.22 1
3.7%
-6.42 1
3.7%
ValueCountFrequency (%)
12.71 1
3.7%
7.06 1
3.7%
2.8 1
3.7%
2.7 1
3.7%
2.68 1
3.7%
0.09 1
3.7%
-0.26 1
3.7%
-1.8 1
3.7%
-2.73 1
3.7%
-3.63 1
3.7%

최초6개월표준점수
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13666667
Minimum-9.5
Maximum4.4
Zeros1
Zeros (%)3.7%
Negative6
Negative (%)22.2%
Memory size375.0 B
2023-12-10T23:15:59.252963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.5
5-th percentile-0.381
Q10.045
median0.26
Q30.505
95-th percentile1.93
Maximum4.4
Range13.9
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation2.1401132
Coefficient of variation (CV)15.659365
Kurtosis17.35598
Mean0.13666667
Median Absolute Deviation (MAD)0.26
Skewness-3.4162085
Sum3.69
Variance4.5800846
MonotonicityNot monotonic
2023-12-10T23:15:59.654337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.15 4
 
14.8%
0.26 3
 
11.1%
1.23 1
 
3.7%
0.69 1
 
3.7%
-0.22 1
 
3.7%
-0.04 1
 
3.7%
0.47 1
 
3.7%
-0.08 1
 
3.7%
0.43 1
 
3.7%
0.09 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
-9.5 1
 
3.7%
-0.45 1
 
3.7%
-0.22 1
 
3.7%
-0.08 1
 
3.7%
-0.07 1
 
3.7%
-0.04 1
 
3.7%
0.0 1
 
3.7%
0.09 1
 
3.7%
0.15 4
14.8%
0.17 1
 
3.7%
ValueCountFrequency (%)
4.4 1
3.7%
2.23 1
3.7%
1.23 1
3.7%
0.94 1
3.7%
0.76 1
3.7%
0.69 1
3.7%
0.54 1
3.7%
0.47 1
3.7%
0.43 1
3.7%
0.4 1
3.7%

상호작용도1개월표준점수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
-0.03
17 
-0.05
7 
-0.04
2 
0.01
 
1

Length

Max length5
Median length5
Mean length4.962963
Min length4

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row-0.03
2nd row-0.05
3rd row-0.03
4th row-0.03
5th row-0.04

Common Values

ValueCountFrequency (%)
-0.03 17
63.0%
-0.05 7
25.9%
-0.04 2
 
7.4%
0.01 1
 
3.7%

Length

2023-12-10T23:16:00.070042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:00.276050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03 17
63.0%
0.05 7
25.9%
0.04 2
 
7.4%
0.01 1
 
3.7%

Interactions

2023-12-10T23:15:46.675636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:29.093378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:31.545311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:33.402742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:35.279757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:36.872445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:38.408479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:40.238848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:42.262568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:44.597073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:46.823488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:29.228263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:31.698804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:33.544725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:35.414246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:37.033595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:38.545602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:40.377516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:42.443648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:44.753833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:46.983235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:29.472263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:31.881362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:33.709308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:35.563252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:37.189710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:38.735942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:40.523591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:42.611956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:44.938317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:47.203360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:29.866691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:32.070141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:33.905954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:35.703913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:37.349748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:38.963431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:40.705935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:42.790071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:45.133092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:47.428103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:30.133745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:32.244043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:34.055344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:35.884035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:37.492800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:39.256190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:40.849976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:43.138644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:45.335641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:47.598081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:30.443008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:32.481217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:34.204221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:36.032766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:37.635089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:39.409217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:41.003354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:43.370261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:45.490887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:47.738235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:30.615375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:32.701620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:34.361947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:36.184660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:37.759607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:39.556648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:41.207166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:43.674701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:45.641232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:47.905941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:30.796436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:32.855550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:34.522166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:36.360425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:37.903084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:39.726987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:41.475423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:43.946143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:46.177067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:48.071280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:31.141797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:33.005031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:34.979761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:36.519614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:38.059497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:39.880359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:41.774139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:44.179838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:46.344782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:48.246513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:31.351868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:33.228346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:35.131076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:36.690827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:38.250254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:40.073498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:42.018690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:44.402943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:46.501791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:16:00.436612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역량평가채널ID역량평가채널명역량평가수집일자역량평가채널설명역량평가채널생성일자역량별그룹할당그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월최초6개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수
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
역량평가수집일자1.0001.0001.0001.0001.0000.0000.6190.0000.0000.4430.0000.2680.0000.0000.5420.0000.0000.000
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
역량별그룹할당1.0001.0000.0001.0001.0001.0000.9260.6530.6650.5650.9660.6340.0000.0000.9310.2720.0001.000
그룹내채널수1.0001.0000.6191.0001.0000.9261.0000.6660.4940.8621.0000.3350.0000.0000.5730.0000.0001.000
그룹내구독자순위1.0001.0000.0001.0001.0000.6530.6661.0000.8560.9340.8170.9030.0000.1990.7970.0000.3170.781
그룹내조회수순위1.0001.0000.0001.0001.0000.6650.4940.8561.0000.8380.9170.5960.0000.4340.5730.5030.0000.800
그룹내좋아요순위1.0001.0000.4431.0001.0000.5650.8620.9340.8381.0000.8220.7790.2550.0000.6890.5750.0000.592
역량평가1.0001.0000.0001.0001.0000.9661.0000.8170.9170.8221.0000.8160.5260.0000.8510.5500.0000.768
홍보지수1.0001.0000.2681.0001.0000.6340.3350.9030.5960.7790.8161.0000.0000.0000.5400.0000.0000.231
최근6개월1.0001.0000.0001.0001.0000.0000.0000.0000.0000.2550.5260.0001.0000.0000.0000.0000.0000.846
최초6개월1.0001.0000.0001.0001.0000.0000.0000.1990.4340.0000.0000.0000.0001.0000.3060.0000.0000.000
그룹내홍보지수표준점수1.0001.0000.5421.0001.0000.9310.5730.7970.5730.6890.8510.5400.0000.3061.0000.5990.2590.504
최근6개월표준점수1.0001.0000.0001.0001.0000.2720.0000.0000.5030.5750.5500.0000.0000.0000.5991.0000.0980.000
최초6개월표준점수1.0001.0000.0001.0001.0000.0000.0000.3170.0000.0000.0000.0000.0000.0000.2590.0981.0000.000
상호작용도1개월표준점수1.0001.0000.0001.0001.0001.0001.0000.7810.8000.5920.7680.2310.8460.0000.5040.0000.0001.000
2023-12-10T23:16:00.774181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역량평가수집일자최초6개월상호작용도1개월표준점수역량별그룹할당
역량평가수집일자1.0000.0000.0000.000
최초6개월0.0001.0000.0000.000
상호작용도1개월표준점수0.0000.0001.0000.959
역량별그룹할당0.0000.0000.9591.000
2023-12-10T23:16:00.997605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수역량평가수집일자역량별그룹할당최초6개월상호작용도1개월표준점수
그룹내채널수1.0000.5160.3890.4280.474-0.0450.139-0.6780.4720.0310.0000.9130.0000.959
그룹내구독자순위0.5161.0000.8500.8810.887-0.6600.006-0.8850.1640.0370.0000.5540.0910.555
그룹내조회수순위0.3890.8501.0000.9260.925-0.5910.131-0.8520.075-0.1790.0000.6480.3220.476
그룹내좋아요순위0.4280.8810.9261.0000.968-0.6530.142-0.8860.117-0.0510.2220.4740.0000.360
역량평가0.4740.8870.9250.9681.000-0.5750.106-0.8480.192-0.1070.0000.6910.0000.490
홍보지수-0.045-0.660-0.591-0.653-0.5751.000-0.1480.6790.0300.2380.1170.3990.0000.000
최근6개월0.1390.0060.1310.1420.106-0.1481.000-0.312-0.2920.1920.0000.0000.0000.489
그룹내홍보지수표준점수-0.678-0.885-0.852-0.886-0.8480.679-0.3121.000-0.3430.2210.1900.6330.1170.288
최근6개월표준점수0.4720.1640.0750.1170.1920.030-0.292-0.3431.0000.0230.4150.0850.0000.000
최초6개월표준점수0.0310.037-0.179-0.051-0.1070.2380.1920.2210.0231.0000.0000.0000.0000.000
역량평가수집일자0.0000.0000.0000.2220.0000.1170.0000.1900.4150.0001.0000.0000.0000.000
역량별그룹할당0.9130.5540.6480.4740.6910.3990.0000.6330.0850.0000.0001.0000.0000.959
최초6개월0.0000.0910.3220.0000.0000.0000.0000.1170.0000.0000.0000.0001.0000.000
상호작용도1개월표준점수0.9590.5550.4760.3600.4900.0000.4890.2880.0000.0000.0000.9590.0001.000

Missing values

2023-12-10T23:15:48.521405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:15:48.933662image/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.
2023-12-10T23:15:49.180708image/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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역량평가채널ID역량평가채널명역량평가수집일자역량평가채널설명역량평가채널생성일자역량별그룹할당그룹내채널수그룹내구독자순위그룹내조회수순위그룹내좋아요순위역량평가홍보지수최근6개월최초6개월상호작용정도1개월그룹내홍보지수표준점수최근6개월표준점수최초6개월표준점수상호작용도1개월표준점수
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