Overview

Dataset statistics

Number of variables15
Number of observations27
Missing cells49
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory134.9 B

Variable types

Text3
Categorical4
DateTime1
Numeric7

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/39263feb-59b1-43a2-b81e-47ba199968ca

Alerts

최초6개월개선도 is highly overall correlated with 개선도지수수집일자 and 2 other fieldsHigh correlation
개선도지수수집일자 is highly overall correlated with 최근6개월개선도 and 2 other fieldsHigh correlation
최근6개월개선도 is highly overall correlated with 개선도지수수집일자 and 1 other fieldsHigh correlation
최근개선도지수 is highly overall correlated with 개선도최근표준점수High correlation
최근6개월표준점수 is highly overall correlated with 개선도최근표준점수 and 1 other fieldsHigh correlation
개선도최근표준점수 is highly overall correlated with 최근개선도지수 and 2 other fieldsHigh correlation
최근12개월개선도 is highly overall correlated with 개선도최근표준점수 and 1 other fieldsHigh correlation
최초12개월개선도 is highly overall correlated with 최근6개월개선도 and 1 other fieldsHigh correlation
개선도지수수집일자 is highly imbalanced (77.1%)Imbalance
개선도지수채널설명 has 7 (25.9%) missing valuesMissing
개선도채널생성일자 has 8 (29.6%) missing valuesMissing
최근6개월개선도 has 17 (63.0%) missing valuesMissing
최근개선도지수 has 10 (37.0%) missing valuesMissing
최근6개월표준점수 has 5 (18.5%) missing valuesMissing
최근12개월표준점수 has 1 (3.7%) missing valuesMissing
최초6개월표준점수 has 1 (3.7%) missing valuesMissing
개선도지수채널ID has unique valuesUnique
개선도지수채널명 has unique valuesUnique
최근6개월개선도 has 2 (7.4%) zerosZeros
최근개선도지수 has 6 (22.2%) zerosZeros

Reproduction

Analysis started2023-12-10 14:16:24.046398
Analysis finished2023-12-10 14:16:35.606767
Duration11.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개선도지수채널ID
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-10T23:16:35.893236image/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 rowUCFtF-qGMr8-edE8K0KVxQSA
2nd rowUC0bm8kKuMp8chJuxzlLnlnA
3rd rowUC-JZtfVAgIjmNfhapEV3zgg
4th rowUC2tGWq3BCZUDAgNh965yM-A
5th rowUC3m0s5XAQydCtbLHc8j1Uog
ValueCountFrequency (%)
ucftf-qgmr8-ede8k0kvxqsa 1
 
3.7%
uc7itqntfg_qs1ufcmbgikkw 1
 
3.7%
uccir1qib7r1mr77byzj0miq 1
 
3.7%
uccd5onp_ljxqu0us89wm-ww 1
 
3.7%
uc6ldby4j2lqwt5pdr-dfw9a 1
 
3.7%
uc9dpfivlnpbohm81mfghhsq 1
 
3.7%
uc9egnou8y9ty3zkrxfk0t_w 1
 
3.7%
uc99oela9yvqgkq9ffbvm9iq 1
 
3.7%
uc9153vuiks_neltqwdqx-6a 1
 
3.7%
uc8gcjee6ffhdhzyul6zlemq 1
 
3.7%
Other values (17) 17
63.0%
2023-12-10T23:16:36.690516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 37
 
5.7%
U 33
 
5.1%
9 20
 
3.1%
g 17
 
2.6%
A 17
 
2.6%
t 16
 
2.5%
l 16
 
2.5%
Q 15
 
2.3%
d 15
 
2.3%
8 14
 
2.2%
Other values (54) 448
69.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 275
42.4%
Lowercase Letter 249
38.4%
Decimal Number 104
 
16.0%
Dash Punctuation 10
 
1.5%
Connector Punctuation 10
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 37
 
13.5%
U 33
 
12.0%
A 17
 
6.2%
Q 15
 
5.5%
F 12
 
4.4%
Y 12
 
4.4%
M 12
 
4.4%
K 12
 
4.4%
B 10
 
3.6%
E 10
 
3.6%
Other values (16) 105
38.2%
Lowercase Letter
ValueCountFrequency (%)
g 17
 
6.8%
t 16
 
6.4%
l 16
 
6.4%
d 15
 
6.0%
w 13
 
5.2%
q 12
 
4.8%
m 12
 
4.8%
x 12
 
4.8%
f 11
 
4.4%
z 10
 
4.0%
Other values (16) 115
46.2%
Decimal Number
ValueCountFrequency (%)
9 20
19.2%
8 14
13.5%
1 12
11.5%
3 12
11.5%
7 12
11.5%
0 10
9.6%
6 8
 
7.7%
2 6
 
5.8%
4 5
 
4.8%
5 5
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 524
80.9%
Common 124
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 37
 
7.1%
U 33
 
6.3%
g 17
 
3.2%
A 17
 
3.2%
t 16
 
3.1%
l 16
 
3.1%
Q 15
 
2.9%
d 15
 
2.9%
w 13
 
2.5%
q 12
 
2.3%
Other values (42) 333
63.5%
Common
ValueCountFrequency (%)
9 20
16.1%
8 14
11.3%
1 12
9.7%
3 12
9.7%
7 12
9.7%
- 10
8.1%
_ 10
8.1%
0 10
8.1%
6 8
 
6.5%
2 6
 
4.8%
Other values (2) 10
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 37
 
5.7%
U 33
 
5.1%
9 20
 
3.1%
g 17
 
2.6%
A 17
 
2.6%
t 16
 
2.5%
l 16
 
2.5%
Q 15
 
2.3%
d 15
 
2.3%
8 14
 
2.2%
Other values (54) 448
69.1%

개선도지수채널명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-10T23:16:37.079878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length8.5555556
Min length2

Characters and Unicode

Total characters231
Distinct characters127
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row몽당분필
2nd row주예지 JOOYEJI
3rd row차차튜브 Chacha Tube
4th row파쇄축
5th rowKBS 한국방송
ValueCountFrequency (%)
몽당분필 1
 
2.4%
대듀tv 1
 
2.4%
tv 1
 
2.4%
재외동포재단_okf 1
 
2.4%
0zoo 1
 
2.4%
영주 1
 
2.4%
황꿀 1
 
2.4%
gguul's 1
 
2.4%
boundary 1
 
2.4%
자유분방travellog 1
 
2.4%
Other values (31) 31
75.6%
2023-12-10T23:16:37.731902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
 
6.1%
a 8
 
3.5%
u 7
 
3.0%
e 6
 
2.6%
T 6
 
2.6%
o 6
 
2.6%
g 5
 
2.2%
O 4
 
1.7%
b 4
 
1.7%
V 4
 
1.7%
Other values (117) 167
72.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
49.8%
Lowercase Letter 60
26.0%
Uppercase Letter 36
 
15.6%
Space Separator 14
 
6.1%
Other Punctuation 1
 
0.4%
Decimal Number 1
 
0.4%
Connector Punctuation 1
 
0.4%
Dash Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
재 3
 
2.6%
국 3
 
2.6%
한 3
 
2.6%
경 3
 
2.6%
의 3
 
2.6%
주 3
 
2.6%
지 3
 
2.6%
리 3
 
2.6%
방 3
 
2.6%
베 2
 
1.7%
Other values (74) 86
74.8%
Lowercase Letter
ValueCountFrequency (%)
a 8
13.3%
u 7
11.7%
e 6
10.0%
o 6
10.0%
g 5
 
8.3%
b 4
 
6.7%
s 3
 
5.0%
r 3
 
5.0%
n 3
 
5.0%
l 2
 
3.3%
Other values (10) 13
21.7%
Uppercase Letter
ValueCountFrequency (%)
T 6
16.7%
O 4
11.1%
V 4
11.1%
K 3
8.3%
D 3
8.3%
J 3
8.3%
I 3
8.3%
S 2
 
5.6%
L 1
 
2.8%
F 1
 
2.8%
Other values (6) 6
16.7%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
49.8%
Latin 96
41.6%
Common 20
 
8.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
재 3
 
2.6%
국 3
 
2.6%
한 3
 
2.6%
경 3
 
2.6%
의 3
 
2.6%
주 3
 
2.6%
지 3
 
2.6%
리 3
 
2.6%
방 3
 
2.6%
베 2
 
1.7%
Other values (74) 86
74.8%
Latin
ValueCountFrequency (%)
a 8
 
8.3%
u 7
 
7.3%
e 6
 
6.2%
T 6
 
6.2%
o 6
 
6.2%
g 5
 
5.2%
O 4
 
4.2%
b 4
 
4.2%
V 4
 
4.2%
s 3
 
3.1%
Other values (26) 43
44.8%
Common
ValueCountFrequency (%)
14
70.0%
' 1
 
5.0%
0 1
 
5.0%
_ 1
 
5.0%
- 1
 
5.0%
) 1
 
5.0%
( 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116
50.2%
Hangul 115
49.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14
 
12.1%
a 8
 
6.9%
u 7
 
6.0%
e 6
 
5.2%
T 6
 
5.2%
o 6
 
5.2%
g 5
 
4.3%
O 4
 
3.4%
b 4
 
3.4%
V 4
 
3.4%
Other values (33) 52
44.8%
Hangul
ValueCountFrequency (%)
재 3
 
2.6%
국 3
 
2.6%
한 3
 
2.6%
경 3
 
2.6%
의 3
 
2.6%
주 3
 
2.6%
지 3
 
2.6%
리 3
 
2.6%
방 3
 
2.6%
베 2
 
1.7%
Other values (74) 86
74.8%

개선도지수수집일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
2021-04-30
26 
2021-04-22
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row2021-04-22
2nd row2021-04-30
3rd row2021-04-30
4th row2021-04-30
5th row2021-04-30

Common Values

ValueCountFrequency (%)
2021-04-30 26
96.3%
2021-04-22 1
 
3.7%

Length

2023-12-10T23:16:38.063507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:38.251183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-30 26
96.3%
2021-04-22 1
 
3.7%

개선도지수채널설명
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing7
Missing (%)25.9%
Memory size348.0 B
2023-12-10T23:16:38.614922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length477
Median length89.5
Mean length156.35
Min length7

Characters and Unicode

Total characters3127
Distinct characters390
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks6 ?
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 row교육;짧게 쓰다! 교사가 만드는 영상기반 교육콘텐츠 제작소 몽당분필입니다. 교육에 대한 오해를 이해로 바꾸는 소통의 창구가 되길 바랍니다. 정보; 유쾌; 소통; 이해를 바탕으로 교육계 선한 영향을 주는 교사영상제작모임! 선생님들에게는 공감;힐링;자료를 학생들에게는 재미;정보를 학부모님에게는정보;상담;소통을 관심있는 재생목록을 라이브러리에 추가해 주세요. 채널 '구독'과 '좋아요'; '알람설정'누르기 잊지 마세요! 문의 e-mail : mdbftv@naver.com homepage : 몽당분필.com (https:mdbftv.tistory.com) instagram : @mdbf__ facebook : www.facebook.commdbftv 인디스쿨 : 인디모임 '몽당분필' (서울 합정 정기모임 : 매월 2째주) 카카오톡 친구추가 : '몽당분필' 검색 교사콘텐츠 공유플랫폼 쌤동네; 쌤스토리; 아이스크림 쌤블로그 : '몽당분필' 검색
2nd rowEmailchadahye@gmail.com Insta cha.dahye
3rd row철권 관련 영상 채널입니다.
4th row대한민국 대표 공영방송 KBS(Korean Broadcasting System) 의 공식 유튜브 채널 입니다. 재미있고 유익한 소식을 전하겠습니다.
5th row평화와 번영; 강원시대! 강원도의 모든 것을 전세계인과 함께 나눕니다! [강원도청 공식 유튜브] Peace and prosperity; Gangwon time! Share all the information in Gangwon Province with people from all over the world! [official YouTube channel of Gangwon Province] 페이스북 https:www.facebook.comgwdoraeyo 네이버블로그 https:blog.naver.comgwdoraeyo 인스타그램 https:www.instagram.comgangwon_official 트위터 https:twitter.comhappygangwon 카카오스토리 https:story.kakao.comchbanbiraeyo 홈페이지 http:www.provin.gangwon.krgwportal
ValueCountFrequency (%)
25
 
4.8%
미라지 5
 
1.0%
⊙ 4
 
0.8%
채널입니다 4
 
0.8%
영상 4
 
0.8%
◐ 4
 
0.8%
공식 4
 
0.8%
유튜브 4
 
0.8%
많이 4
 
0.8%
gangwon 3
 
0.6%
Other values (397) 456
88.2%
2023-12-10T23:16:39.267644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
584
 
18.7%
a 80
 
2.6%
t 80
 
2.6%
o 77
 
2.5%
. 57
 
1.8%
e 51
 
1.6%
m 47
 
1.5%
n 46
 
1.5%
r 45
 
1.4%
c 39
 
1.2%
Other values (380) 2021
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1381
44.2%
Lowercase Letter 807
25.8%
Space Separator 584
18.7%
Other Punctuation 190
 
6.1%
Decimal Number 57
 
1.8%
Uppercase Letter 49
 
1.6%
Close Punctuation 14
 
0.4%
Open Punctuation 12
 
0.4%
Other Symbol 9
 
0.3%
Dash Punctuation 6
 
0.2%
Other values (3) 18
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 34
 
2.5%
다 31
 
2.2%
니 29
 
2.1%
는 28
 
2.0%
상 24
 
1.7%
요 22
 
1.6%
ㅡ 22
 
1.6%
영 22
 
1.6%
에 20
 
1.4%
하 20
 
1.4%
Other values (303) 1129
81.8%
Lowercase Letter
ValueCountFrequency (%)
a 80
 
9.9%
t 80
 
9.9%
o 77
 
9.5%
e 51
 
6.3%
m 47
 
5.8%
n 46
 
5.7%
r 45
 
5.6%
c 39
 
4.8%
g 39
 
4.8%
w 38
 
4.7%
Other values (15) 265
32.8%
Uppercase Letter
ValueCountFrequency (%)
T 9
18.4%
V 6
12.2%
S 4
 
8.2%
Y 3
 
6.1%
I 3
 
6.1%
G 3
 
6.1%
P 3
 
6.1%
K 3
 
6.1%
B 2
 
4.1%
D 2
 
4.1%
Other values (10) 11
22.4%
Other Punctuation
ValueCountFrequency (%)
. 57
30.0%
: 36
18.9%
' 29
15.3%
; 26
13.7%
! 20
 
10.5%
# 12
 
6.3%
@ 6
 
3.2%
* 2
 
1.1%
& 1
 
0.5%
? 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 11
19.3%
2 11
19.3%
3 10
17.5%
7 7
12.3%
1 7
12.3%
4 3
 
5.3%
5 3
 
5.3%
8 2
 
3.5%
6 2
 
3.5%
9 1
 
1.8%
Close Punctuation
ValueCountFrequency (%)
) 11
78.6%
] 3
 
21.4%
Open Punctuation
ValueCountFrequency (%)
( 9
75.0%
[ 3
 
25.0%
Other Symbol
ValueCountFrequency (%)
♥ 5
55.6%
◐ 4
44.4%
Math Symbol
ValueCountFrequency (%)
⊙ 4
66.7%
~ 2
33.3%
Space Separator
ValueCountFrequency (%)
584
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1381
44.2%
Common 890
28.5%
Latin 856
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 34
 
2.5%
다 31
 
2.2%
니 29
 
2.1%
는 28
 
2.0%
상 24
 
1.7%
요 22
 
1.6%
ㅡ 22
 
1.6%
영 22
 
1.6%
에 20
 
1.4%
하 20
 
1.4%
Other values (303) 1129
81.8%
Latin
ValueCountFrequency (%)
a 80
 
9.3%
t 80
 
9.3%
o 77
 
9.0%
e 51
 
6.0%
m 47
 
5.5%
n 46
 
5.4%
r 45
 
5.3%
c 39
 
4.6%
g 39
 
4.6%
w 38
 
4.4%
Other values (35) 314
36.7%
Common
ValueCountFrequency (%)
584
65.6%
. 57
 
6.4%
: 36
 
4.0%
' 29
 
3.3%
; 26
 
2.9%
! 20
 
2.2%
# 12
 
1.3%
) 11
 
1.2%
0 11
 
1.2%
2 11
 
1.2%
Other values (22) 93
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1733
55.4%
Hangul 1357
43.4%
Compat Jamo 24
 
0.8%
Misc Symbols 5
 
0.2%
Math Operators 4
 
0.1%
Geometric Shapes 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
584
33.7%
a 80
 
4.6%
t 80
 
4.6%
o 77
 
4.4%
. 57
 
3.3%
e 51
 
2.9%
m 47
 
2.7%
n 46
 
2.7%
r 45
 
2.6%
c 39
 
2.3%
Other values (64) 627
36.2%
Hangul
ValueCountFrequency (%)
이 34
 
2.5%
다 31
 
2.3%
니 29
 
2.1%
는 28
 
2.1%
상 24
 
1.8%
요 22
 
1.6%
영 22
 
1.6%
에 20
 
1.5%
하 20
 
1.5%
기 18
 
1.3%
Other values (301) 1109
81.7%
Compat Jamo
ValueCountFrequency (%)
ㅡ 22
91.7%
ㅠ 2
 
8.3%
Misc Symbols
ValueCountFrequency (%)
♥ 5
100.0%
Math Operators
ValueCountFrequency (%)
⊙ 4
100.0%
Geometric Shapes
ValueCountFrequency (%)
◐ 4
100.0%

개선도채널생성일자
Date

MISSING 

Distinct19
Distinct (%)100.0%
Missing8
Missing (%)29.6%
Memory size348.0 B
Minimum2011-02-10 00:00:00
Maximum2019-07-23 00:00:00
2023-12-10T23:16:39.484666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:39.693885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

최근6개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)90.0%
Missing17
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean2.9
Minimum-10
Maximum30
Zeros2
Zeros (%)7.4%
Negative4
Negative (%)14.8%
Memory size375.0 B
2023-12-10T23:16:39.974602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-8.2
Q1-4.5
median0
Q37.5
95-th percentile21.45
Maximum30
Range40
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.532081
Coefficient of variation (CV)3.9765796
Kurtosis2.8707777
Mean2.9
Median Absolute Deviation (MAD)5.5
Skewness1.5451706
Sum29
Variance132.98889
MonotonicityNot monotonic
2023-12-10T23:16:40.237931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2
 
7.4%
9 1
 
3.7%
-6 1
 
3.7%
11 1
 
3.7%
30 1
 
3.7%
-5 1
 
3.7%
3 1
 
3.7%
-3 1
 
3.7%
-10 1
 
3.7%
(Missing) 17
63.0%
ValueCountFrequency (%)
-10 1
3.7%
-6 1
3.7%
-5 1
3.7%
-3 1
3.7%
0 2
7.4%
3 1
3.7%
9 1
3.7%
11 1
3.7%
30 1
3.7%
ValueCountFrequency (%)
30 1
3.7%
11 1
3.7%
9 1
3.7%
3 1
3.7%
0 2
7.4%
-3 1
3.7%
-5 1
3.7%
-6 1
3.7%
-10 1
3.7%

최근12개월개선도
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size348.0 B
<NA>
15 
-1
5 
0
3 
1
2 
-15
 
1

Length

Max length4
Median length4
Mean length2.9259259
Min length1

Unique

Unique2 ?
Unique (%)7.4%

Sample

1st row1
2nd row0
3rd row<NA>
4th row<NA>
5th row-1

Common Values

ValueCountFrequency (%)
<NA> 15
55.6%
-1 5
 
18.5%
0 3
 
11.1%
1 2
 
7.4%
-15 1
 
3.7%
5 1
 
3.7%

Length

2023-12-10T23:16:40.542342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:40.856869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 15
55.6%
1 7
25.9%
0 3
 
11.1%
15 1
 
3.7%
5 1
 
3.7%

최초6개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
<NA>
12 
0
11 
1
4 

Length

Max length4
Median length1
Mean length2.3333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 12
44.4%
0 11
40.7%
1 4
 
14.8%

Length

2023-12-10T23:16:41.177430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:41.484870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 12
44.4%
0 11
40.7%
1 4
 
14.8%

최초12개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
12 
<NA>
11 
1
4 

Length

Max length4
Median length1
Mean length2.2222222
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row<NA>
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
0 12
44.4%
<NA> 11
40.7%
1 4
 
14.8%

Length

2023-12-10T23:16:41.835028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:42.074337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12
44.4%
na 11
40.7%
1 4
 
14.8%

최근개선도지수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)70.6%
Missing10
Missing (%)37.0%
Infinite0
Infinite (%)0.0%
Mean32.401765
Minimum0
Maximum208.75
Zeros6
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:16:42.334546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14.65
Q328.96
95-th percentile131.038
Maximum208.75
Range208.75
Interquartile range (IQR)28.96

Descriptive statistics

Standard deviation54.541046
Coefficient of variation (CV)1.6832739
Kurtosis6.8120409
Mean32.401765
Median Absolute Deviation (MAD)14.65
Skewness2.5334796
Sum550.83
Variance2974.7257
MonotonicityNot monotonic
2023-12-10T23:16:42.938046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 6
22.2%
10.46 1
 
3.7%
39.59 1
 
3.7%
14.65 1
 
3.7%
111.61 1
 
3.7%
74.08 1
 
3.7%
17.42 1
 
3.7%
19.98 1
 
3.7%
21.85 1
 
3.7%
3.48 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 10
37.0%
ValueCountFrequency (%)
0.0 6
22.2%
3.48 1
 
3.7%
10.46 1
 
3.7%
14.65 1
 
3.7%
17.42 1
 
3.7%
19.98 1
 
3.7%
21.85 1
 
3.7%
28.96 1
 
3.7%
39.59 1
 
3.7%
74.08 1
 
3.7%
ValueCountFrequency (%)
208.75 1
3.7%
111.61 1
3.7%
74.08 1
3.7%
39.59 1
3.7%
28.96 1
3.7%
21.85 1
3.7%
19.98 1
3.7%
17.42 1
3.7%
14.65 1
3.7%
10.46 1
3.7%

최근6개월표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)77.3%
Missing5
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean8.5468182
Minimum-35.74
Maximum42.17
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)14.8%
Memory size375.0 B
2023-12-10T23:16:43.113568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35.74
5-th percentile-21.1685
Q12.535
median14.3
Q315.0875
95-th percentile18.992
Maximum42.17
Range77.91
Interquartile range (IQR)12.5525

Descriptive statistics

Standard deviation15.303581
Coefficient of variation (CV)1.7905588
Kurtosis3.5511927
Mean8.5468182
Median Absolute Deviation (MAD)4.17
Skewness-1.1025386
Sum188.03
Variance234.19958
MonotonicityNot monotonic
2023-12-10T23:16:43.346622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
14.3 6
22.2%
-0.05 1
 
3.7%
17.89 1
 
3.7%
6.11 1
 
3.7%
17.02 1
 
3.7%
2.76 1
 
3.7%
7.05 1
 
3.7%
15.35 1
 
3.7%
42.17 1
 
3.7%
-35.74 1
 
3.7%
Other values (7) 7
25.9%
(Missing) 5
18.5%
ValueCountFrequency (%)
-35.74 1
3.7%
-22.28 1
3.7%
-0.05 1
3.7%
-0.03 1
3.7%
1.55 1
3.7%
2.46 1
3.7%
2.76 1
3.7%
6.11 1
3.7%
7.05 1
3.7%
11.57 1
3.7%
ValueCountFrequency (%)
42.17 1
 
3.7%
19.05 1
 
3.7%
17.89 1
 
3.7%
17.35 1
 
3.7%
17.02 1
 
3.7%
15.35 1
 
3.7%
14.3 6
22.2%
11.57 1
 
3.7%
7.05 1
 
3.7%
6.11 1
 
3.7%

최근12개월표준점수
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)84.6%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean0.21192308
Minimum-6.01
Maximum4.83
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)25.9%
Memory size375.0 B
2023-12-10T23:16:43.656136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.01
5-th percentile-2.4625
Q1-0.2675
median0.07
Q30.885
95-th percentile3.695
Maximum4.83
Range10.84
Interquartile range (IQR)1.1525

Descriptive statistics

Standard deviation2.1449756
Coefficient of variation (CV)10.121482
Kurtosis2.3131416
Mean0.21192308
Median Absolute Deviation (MAD)0.495
Skewness-0.45150964
Sum5.51
Variance4.6009202
MonotonicityNot monotonic
2023-12-10T23:16:43.962994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.07 5
18.5%
3.98 1
 
3.7%
0.05 1
 
3.7%
-0.36 1
 
3.7%
0.01 1
 
3.7%
0.11 1
 
3.7%
1.02 1
 
3.7%
-0.49 1
 
3.7%
0.13 1
 
3.7%
-6.01 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
-6.01 1
 
3.7%
-2.49 1
 
3.7%
-2.38 1
 
3.7%
-2.16 1
 
3.7%
-1.09 1
 
3.7%
-0.49 1
 
3.7%
-0.36 1
 
3.7%
0.01 1
 
3.7%
0.05 1
 
3.7%
0.07 5
18.5%
ValueCountFrequency (%)
4.83 1
3.7%
3.98 1
3.7%
2.84 1
3.7%
2.29 1
3.7%
2.02 1
3.7%
2.0 1
3.7%
1.02 1
3.7%
0.48 1
3.7%
0.3 1
3.7%
0.13 1
3.7%

최초6개월표준점수
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)61.5%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean-0.0026923077
Minimum-1.4
Maximum0.93
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)25.9%
Memory size375.0 B
2023-12-10T23:16:44.246162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.4
5-th percentile-0.8175
Q1-0.1125
median0.17
Q30.17
95-th percentile0.465
Maximum0.93
Range2.33
Interquartile range (IQR)0.2825

Descriptive statistics

Standard deviation0.46502093
Coefficient of variation (CV)-172.72206
Kurtosis2.6480403
Mean-0.0026923077
Median Absolute Deviation (MAD)0.035
Skewness-1.238235
Sum-0.07
Variance0.21624446
MonotonicityNot monotonic
2023-12-10T23:16:44.550919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.17 11
40.7%
0.21 1
 
3.7%
-0.37 1
 
3.7%
-0.69 1
 
3.7%
0.19 1
 
3.7%
0.1 1
 
3.7%
0.54 1
 
3.7%
-1.4 1
 
3.7%
0.14 1
 
3.7%
0.93 1
 
3.7%
Other values (6) 6
22.2%
ValueCountFrequency (%)
-1.4 1
3.7%
-0.86 1
3.7%
-0.69 1
3.7%
-0.55 1
3.7%
-0.37 1
3.7%
-0.33 1
3.7%
-0.18 1
3.7%
0.09 1
3.7%
0.1 1
3.7%
0.14 1
3.7%
ValueCountFrequency (%)
0.93 1
 
3.7%
0.54 1
 
3.7%
0.24 1
 
3.7%
0.21 1
 
3.7%
0.19 1
 
3.7%
0.17 11
40.7%
0.14 1
 
3.7%
0.1 1
 
3.7%
0.09 1
 
3.7%
-0.18 1
 
3.7%

최초12개월표준점수
Real number (ℝ)

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35555556
Minimum-0.23
Maximum1.2
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.7%
Memory size375.0 B
2023-12-10T23:16:44.863027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.23
5-th percentile0.08
Q10.24
median0.24
Q30.34
95-th percentile0.998
Maximum1.2
Range1.43
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.30527835
Coefficient of variation (CV)0.85859536
Kurtosis2.0898772
Mean0.35555556
Median Absolute Deviation (MAD)0.07
Skewness1.2893075
Sum9.6
Variance0.093194872
MonotonicityNot monotonic
2023-12-10T23:16:45.165174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.24 9
33.3%
0.3 2
 
7.4%
0.08 2
 
7.4%
0.34 2
 
7.4%
0.18 1
 
3.7%
0.56 1
 
3.7%
0.83 1
 
3.7%
0.72 1
 
3.7%
0.23 1
 
3.7%
0.31 1
 
3.7%
Other values (6) 6
22.2%
ValueCountFrequency (%)
-0.23 1
 
3.7%
0.08 2
 
7.4%
0.15 1
 
3.7%
0.18 1
 
3.7%
0.23 1
 
3.7%
0.24 9
33.3%
0.3 2
 
7.4%
0.31 1
 
3.7%
0.33 1
 
3.7%
0.34 2
 
7.4%
ValueCountFrequency (%)
1.2 1
3.7%
1.07 1
3.7%
0.83 1
3.7%
0.72 1
3.7%
0.65 1
3.7%
0.56 1
3.7%
0.34 2
7.4%
0.33 1
3.7%
0.31 1
3.7%
0.3 2
7.4%

개선도최근표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.005556
Minimum-59.17
Maximum106.59
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.7%
Memory size375.0 B
2023-12-10T23:16:45.476211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-59.17
5-th percentile1.457
Q116.49
median17.6
Q318.02
95-th percentile30.355
Maximum106.59
Range165.76
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation23.892155
Coefficient of variation (CV)1.4049617
Kurtosis11.234387
Mean17.005556
Median Absolute Deviation (MAD)1
Skewness0.83100107
Sum459.15
Variance570.83507
MonotonicityNot monotonic
2023-12-10T23:16:45.796417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
17.6 3
 
11.1%
17.8 1
 
3.7%
17.9 1
 
3.7%
16.46 1
 
3.7%
17.38 1
 
3.7%
16.6 1
 
3.7%
18.03 1
 
3.7%
17.25 1
 
3.7%
17.74 1
 
3.7%
13.42 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
-59.17 1
3.7%
0.53 1
3.7%
3.62 1
3.7%
5.49 1
3.7%
10.02 1
3.7%
13.42 1
3.7%
16.46 1
3.7%
16.52 1
3.7%
16.6 1
3.7%
17.25 1
3.7%
ValueCountFrequency (%)
106.59 1
3.7%
30.46 1
3.7%
30.11 1
3.7%
19.05 1
3.7%
18.68 1
3.7%
18.41 1
3.7%
18.03 1
3.7%
18.01 1
3.7%
17.9 1
3.7%
17.82 1
3.7%

Interactions

2023-12-10T23:16:33.404705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:25.313993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:26.357230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:27.731115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:29.133662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:30.911054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:32.255994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:33.584405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:25.477307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:26.673901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:27.966501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:29.780019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:31.129778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:32.423822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:33.728081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:25.604742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:26.814865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:28.116982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:29.918214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:31.324023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:32.573159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:33.893376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:25.742041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:27.021528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:28.277951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:30.078639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:31.484612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:32.731069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:34.030277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:25.876609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:27.185628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:28.436606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:30.239923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:31.623549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:32.875387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:34.242667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:26.027450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:27.343982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:28.757640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:30.403585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:31.825550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:33.012528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:34.429495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:26.180899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:27.590635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:28.954808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:30.653958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:32.086761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:33.251655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:16:46.076671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
개선도지수채널ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도지수채널명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도지수수집일자1.0001.0001.0001.0001.000NaN0.350NaN0.0000.000NaN0.7030.0000.0000.000
개선도지수채널설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도채널생성일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최근6개월개선도1.0001.000NaN1.0001.0001.0000.0000.0001.0000.8171.0000.8920.8460.5040.516
최근12개월개선도1.0001.0000.3501.0001.0000.0001.0001.0000.5360.0000.6620.0000.8810.8650.892
최초6개월개선도1.0001.000NaN1.0001.0000.0001.0001.0000.8150.0000.0000.0000.5850.0000.126
최초12개월개선도1.0001.0000.0001.0001.0001.0000.5360.8151.0000.0000.4060.6950.1610.4170.142
최근개선도지수1.0001.0000.0001.0001.0000.8170.0000.0000.0001.0000.3920.0000.9230.2530.000
최근6개월표준점수1.0001.000NaN1.0001.0001.0000.6620.0000.4060.3921.0000.5370.8410.6650.644
최근12개월표준점수1.0001.0000.7031.0001.0000.8920.0000.0000.6950.0000.5371.0000.6050.0000.212
최초6개월표준점수1.0001.0000.0001.0001.0000.8460.8810.5850.1610.9230.8410.6051.0000.7110.863
최초12개월표준점수1.0001.0000.0001.0001.0000.5040.8650.0000.4170.2530.6650.0000.7111.0000.388
개선도최근표준점수1.0001.0000.0001.0001.0000.5160.8920.1260.1420.0000.6440.2120.8630.3881.000
2023-12-10T23:16:46.427408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초6개월개선도최근12개월개선도최초12개월개선도개선도지수수집일자
최초6개월개선도1.0000.7070.6041.000
최근12개월개선도0.7071.0000.4520.316
최초12개월개선도0.6040.4521.0000.000
개선도지수수집일자1.0000.3160.0001.000
2023-12-10T23:16:46.704862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최근6개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수개선도지수수집일자최근12개월개선도최초6개월개선도최초12개월개선도
최근6개월개선도1.000-0.413-0.4290.092-0.340-0.131-0.0061.0000.0000.0000.632
최근개선도지수-0.4131.000-0.494-0.334-0.1590.132-0.6060.0000.0000.0000.000
최근6개월표준점수-0.429-0.4941.0000.397-0.1150.0460.6041.0000.3540.0000.115
최근12개월표준점수0.092-0.3340.3971.000-0.035-0.1260.2540.4560.0000.0000.412
최초6개월표준점수-0.340-0.159-0.115-0.0351.000-0.2900.1480.0000.4560.3370.000
최초12개월표준점수-0.1310.1320.046-0.126-0.2901.000-0.2230.0000.4720.0000.327
개선도최근표준점수-0.006-0.6060.6040.2540.148-0.2231.0000.0000.5230.0000.081
개선도지수수집일자1.0000.0001.0000.4560.0000.0000.0001.0000.3161.0000.000
최근12개월개선도0.0000.0000.3540.0000.4560.4720.5230.3161.0000.7070.452
최초6개월개선도0.0000.0000.0000.0000.3370.0000.0001.0000.7071.0000.604
최초12개월개선도0.6320.0000.1150.4120.0000.3270.0810.0000.4520.6041.000

Missing values

2023-12-10T23:16:34.692817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:16:35.016556image/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.
2023-12-10T23:16:35.321685image/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

개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
0UCFtF-qGMr8-edE8K0KVxQSA몽당분필2021-04-22교육;짧게 쓰다! 교사가 만드는 영상기반 교육콘텐츠 제작소 몽당분필입니다. 교육에 대한 오해를 이해로 바꾸는 소통의 창구가 되길 바랍니다. 정보; 유쾌; 소통; 이해를 바탕으로 교육계 선한 영향을 주는 교사영상제작모임! 선생님들에게는 공감;힐링;자료를 학생들에게는 재미;정보를 학부모님에게는정보;상담;소통을 관심있는 재생목록을 라이브러리에 추가해 주세요. 채널 '구독'과 '좋아요'; '알람설정'누르기 잊지 마세요! 문의 e-mail : mdbftv@naver.com homepage : 몽당분필.com (https:mdbftv.tistory.com) instagram : @mdbf__ facebook : www.facebook.commdbftv 인디스쿨 : 인디모임 '몽당분필' (서울 합정 정기모임 : 매월 2째주) 카카오톡 친구추가 : '몽당분필' 검색 교사콘텐츠 공유플랫폼 쌤동네; 쌤스토리; 아이스크림 쌤블로그 : '몽당분필' 검색2016-07-18<NA>1<NA>110.46<NA>3.980.170.1817.8
1UC0bm8kKuMp8chJuxzlLnlnA주예지 JOOYEJI2021-04-30<NA><NA><NA>0000.042.172.840.930.3318.01
2UC-JZtfVAgIjmNfhapEV3zgg차차튜브 Chacha Tube2021-04-30Emailchadahye@gmail.com Insta cha.dahye2015-10-23<NA><NA><NA><NA>0.014.30.30.170.2417.82
3UC2tGWq3BCZUDAgNh965yM-A파쇄축2021-04-30철권 관련 영상 채널입니다.2013-08-19<NA><NA>000.019.050.080.090.1530.46
4UC3m0s5XAQydCtbLHc8j1UogKBS 한국방송2021-04-30대한민국 대표 공영방송 KBS(Korean Broadcasting System) 의 공식 유튜브 채널 입니다. 재미있고 유익한 소식을 전하겠습니다.2011-08-240-1<NA><NA>39.59<NA>-2.160.170.2430.11
5UC499dzcb2Fx9RD39Vqpz-lg강원도 - Gangwon2021-04-30평화와 번영; 강원시대! 강원도의 모든 것을 전세계인과 함께 나눕니다! [강원도청 공식 유튜브] Peace and prosperity; Gangwon time! Share all the information in Gangwon Province with people from all over the world! [official YouTube channel of Gangwon Province] 페이스북 https:www.facebook.comgwdoraeyo 네이버블로그 https:blog.naver.comgwdoraeyo 인스타그램 https:www.instagram.comgangwon_official 트위터 https:twitter.comhappygangwon 카카오스토리 https:story.kakao.comchbanbiraeyo 홈페이지 http:www.provin.gangwon.krgwportal2014-05-159-1<NA><NA>14.65-22.28-1.09-0.551.0716.52
6UC4KEOaKK3hYA8sAHogi1bAg리얼베어TV2021-04-30사진과 캠핑을 즐기고 있는 3교대 직장인의 공간 입니다2012-02-12<NA><NA><NA><NA><NA>14.30.070.170.2417.63
7UC69l_rtlCQ7M4Mz2RCS80BA미야옹철의 냥냥펀치2021-04-30반려묘 행동 전문 수의사 김명철이 들려주는 현실 집사 이야기 Cat president's Cat talk ♥ 업로드 : 화금 오후 7시 ♥ Upload : TueFri at 7pm ♥ Instagram : http:instagram.comgrrvet http:instagram.comcat_samonim http:instagram.comcat_babyc2018-11-23-6-100111.612.462.0-0.330.310.02
8UC6Jl3MrfGBvRYxXgdDxXzVADOJIN도진이2021-04-30<NA>2019-07-231100074.08<NA>0.48-0.180.083.62
9UC1dK7oMUSR9Rnk1BSpOKZng정선호2021-04-30<NA><NA>30-1<NA><NA>17.421.55<NA>0.170.245.49
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
17UC8YB7__KHYn1IfqMMl00XUw자유분방TravelLog2021-04-30http:blog.naver.combk32167 여행영상; 타임랩스 영상 등<NA><NA><NA><NA><NA><NA>15.35-6.010.170.2419.05
18UC8gCJEe6FFHdhZyul6zLeMQ생방송심야토론2021-04-30<NA>2018-06-07-10-100208.757.050.130.540.3413.42
19UC9153vUIKS_nEltqwdQX-6A세경2021-04-30안녕하세요 구독자여러분들! 다양한 요리 영상과 먹방 영상; 일상영상등을 업로드 중 이에요♥ 구독하기& 좋아요 많이 부탁드려요♥ Instagram _ 33wannabe332015-06-25<NA><NA><NA><NA><NA>14.30.070.170.2417.74
20UC99OELa9yvqgkq9ffBvm9iQ갑수목장gabsupasture2021-04-30갑수목장에 오신 것을 환영합니다. 좋아요와 구독 진심으로 감사드립니다. pgs3620@naver.com<NA><NA><NA><NA><NA><NA>2.76-0.490.170.2417.6
21UC9EgNOu8Y9tY3zkrXFk0T_w경기신용보증재단2021-04-30'중소기업과 소상공인의 희망을 함께하는 신용파트너'; 경기신용보증재단은 사업성과 기술력은 있지만; 담보력이 부족해 금융기관으로부터 자금조달이 어려운 중소기업과 소상공인에게 실질적인 자금을 지원하는 비영리 공공법인입니다. 앞으로 유튜브를 통해 재단의 소식과 보증상품들을 소개하며 경기도 기업인들에게 더욱 가까이 다가겠습니다. 많은 관심 부탁드립니다.2019-02-12<NA><NA>00<NA>17.021.020.10.3117.25
22UC9dpfiVlNpBoHm81mfghhSQ주랄라2021-04-30'주랄라와 룰루랄라 놀아보자' 라는 의미의 채널입니다.<NA><NA><NA>000.014.30.070.190.318.03
23UC6ldBY4j2lQWT5pdr-dFw9A지수의 일상2021-04-30별거없는 일상<NA><NA><NA><NA><NA><NA>14.30.070.170.2417.6
24UCCD5onP_ljXqu0Us89Wm-WwKIDS한국의약품안전관리원2021-04-30<NA>2017-01-18<NA><NA><NA><NA><NA>6.110.110.170.2316.6
25UCCIR1qib7R1mR77byZJ0MiQ블루베리TV2021-04-30블루베리TV에 방문해 주셔서 감사합니다^^^ 저는 전남 고흥에서 베리드림이라는 상호로 블루베리농장을 10년이상 운영하고 있습니다. 저는 이 채널을 통하여 첫째 블루베리재배에 방법에 관한 이론과 현장 실습; 그리고 저희 농장에 여러분들을 직접 모셔서 현장감 있는 정보공유; 둘째 농업에 관한 기본적인 이론; 세째 그리고 저희 농장일상과 저의 취미생활 등을 공유하여 여러분과 소통하고자 합니다. 구독과 좋아요 그리고 알람 설정까지 여러분들의 많은 응원 ; 부탁드립니다^^^<NA><NA><NA>1128.9617.890.01-0.690.7217.38
26UCCflwTdJf1fQxKilMVAuW2wbexco2021-04-30<NA>2019-04-08<NA><NA>00<NA>-0.05-0.36-0.370.8316.46