Overview

Dataset statistics

Number of variables15
Number of observations25
Missing cells41
Missing cells (%)10.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory135.3 B

Variable types

Text3
DateTime2
Numeric8
Categorical2

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/93949e31-0dc4-4a2e-ade4-cc0c06214d57

Alerts

최근6개월개선도 is highly overall correlated with 최초12개월개선도High correlation
최근12개월개선도 is highly overall correlated with 최근개선도지수High correlation
최근개선도지수 is highly overall correlated with 최근12개월개선도High correlation
최초6개월표준점수 is highly overall correlated with 최초6개월개선도 and 1 other fieldsHigh correlation
최초6개월개선도 is highly overall correlated with 최초6개월표준점수High correlation
최초12개월개선도 is highly overall correlated with 최근6개월개선도 and 1 other fieldsHigh correlation
개선도지수채널설명 has 2 (8.0%) missing valuesMissing
개선도채널생성일자 has 7 (28.0%) missing valuesMissing
최근6개월개선도 has 12 (48.0%) missing valuesMissing
최근12개월개선도 has 9 (36.0%) missing valuesMissing
최근개선도지수 has 8 (32.0%) missing valuesMissing
최근6개월표준점수 has 1 (4.0%) missing valuesMissing
최근12개월표준점수 has 2 (8.0%) missing valuesMissing
개선도지수채널ID has unique valuesUnique
개선도지수채널명 has unique valuesUnique
개선도최근표준점수 has unique valuesUnique
최근6개월개선도 has 1 (4.0%) zerosZeros
최근12개월개선도 has 3 (12.0%) zerosZeros
최근개선도지수 has 7 (28.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:57:31.066533
Analysis finished2023-12-10 13:57:43.291022
Duration12.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개선도지수채널ID
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-10T22:57:43.517414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters600
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

Unique25 ?
Unique (%)100.0%

Sample

1st rowUC6ldBY4j2lQWT5pdr-dFw9A
2nd rowUCqqr-GRWimMmMvgxL_yNSUw
3rd rowUC1EEpE0lA9BaArXhRTHIG6w
4th rowUC-gWrEGYpG2jYfl8A_KIGnQ
5th rowUC4XjKgCtpwpACzUCiUKOd3Q
ValueCountFrequency (%)
uc6ldby4j2lqwt5pdr-dfw9a 1
 
4.0%
ucb9e3pof1o83aa0kkaoejga 1
 
4.0%
uceyuopmy0lx5b5pgch03fsq 1
 
4.0%
uceeevbzufze0gmn0ff55h8w 1
 
4.0%
uc9gw47nqzi1x7e8qsflvuuw 1
 
4.0%
uccuurwm5jtvybnbufwj4e5q 1
 
4.0%
uccxgedotqfr2g2hmev9vsxq 1
 
4.0%
uccwwpm3zkhvckg0tmupkgaa 1
 
4.0%
ucbvdrqetp01juovhaumo08q 1
 
4.0%
ucbdkh3mg6x71y94a2wudetq 1
 
4.0%
Other values (15) 15
60.0%
2023-12-10T22:57:44.067260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 37
 
6.2%
C 34
 
5.7%
A 19
 
3.2%
G 16
 
2.7%
0 15
 
2.5%
E 14
 
2.3%
g 14
 
2.3%
w 14
 
2.3%
M 13
 
2.2%
a 12
 
2.0%
Other values (54) 412
68.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 284
47.3%
Lowercase Letter 209
34.8%
Decimal Number 93
 
15.5%
Dash Punctuation 8
 
1.3%
Connector Punctuation 6
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 37
 
13.0%
C 34
 
12.0%
A 19
 
6.7%
G 16
 
5.6%
E 14
 
4.9%
M 13
 
4.6%
W 12
 
4.2%
Q 12
 
4.2%
B 11
 
3.9%
H 10
 
3.5%
Other values (16) 106
37.3%
Lowercase Letter
ValueCountFrequency (%)
g 14
 
6.7%
w 14
 
6.7%
a 12
 
5.7%
p 12
 
5.7%
d 11
 
5.3%
l 11
 
5.3%
z 10
 
4.8%
y 10
 
4.8%
q 9
 
4.3%
u 9
 
4.3%
Other values (16) 97
46.4%
Decimal Number
ValueCountFrequency (%)
0 15
16.1%
8 10
10.8%
3 10
10.8%
5 10
10.8%
7 9
9.7%
9 9
9.7%
2 9
9.7%
6 8
8.6%
4 7
7.5%
1 6
 
6.5%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 493
82.2%
Common 107
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 37
 
7.5%
C 34
 
6.9%
A 19
 
3.9%
G 16
 
3.2%
E 14
 
2.8%
g 14
 
2.8%
w 14
 
2.8%
M 13
 
2.6%
a 12
 
2.4%
W 12
 
2.4%
Other values (42) 308
62.5%
Common
ValueCountFrequency (%)
0 15
14.0%
8 10
9.3%
3 10
9.3%
5 10
9.3%
7 9
8.4%
9 9
8.4%
2 9
8.4%
- 8
7.5%
6 8
7.5%
4 7
6.5%
Other values (2) 12
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 37
 
6.2%
C 34
 
5.7%
A 19
 
3.2%
G 16
 
2.7%
0 15
 
2.5%
E 14
 
2.3%
g 14
 
2.3%
w 14
 
2.3%
M 13
 
2.2%
a 12
 
2.0%
Other values (54) 412
68.7%

개선도지수채널명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-10T22:57:44.480986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length18
Mean length11
Min length3

Characters and Unicode

Total characters275
Distinct characters124
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row지수의 일상
2nd row신기해
3rd row강과장
4th row[KERI]한국전기연구원
5th row사모장
ValueCountFrequency (%)
지수의 1
 
2.1%
한국개발연구원 1
 
2.1%
kpop 1
 
2.1%
효진hlog[아가와 1
 
2.1%
개세mr리들 1
 
2.1%
hong 1
 
2.1%
sound 1
 
2.1%
부산광역시연제구청 1
 
2.1%
예술의전당 1
 
2.1%
concert 1
 
2.1%
Other values (37) 37
78.7%
2023-12-10T22:57:45.217913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
8.0%
e 10
 
3.6%
a 10
 
3.6%
o 8
 
2.9%
S 6
 
2.2%
K 6
 
2.2%
D 5
 
1.8%
O 5
 
1.8%
y 5
 
1.8%
m 5
 
1.8%
Other values (114) 193
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105
38.2%
Lowercase Letter 76
27.6%
Uppercase Letter 66
24.0%
Space Separator 22
 
8.0%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Other Punctuation 1
 
0.4%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4
 
3.8%
피 3
 
2.9%
시 3
 
2.9%
연 3
 
2.9%
국 3
 
2.9%
한 3
 
2.9%
장 3
 
2.9%
산 2
 
1.9%
개 2
 
1.9%
디 2
 
1.9%
Other values (66) 77
73.3%
Lowercase Letter
ValueCountFrequency (%)
e 10
13.2%
a 10
13.2%
o 8
 
10.5%
y 5
 
6.6%
m 5
 
6.6%
i 4
 
5.3%
r 4
 
5.3%
n 4
 
5.3%
u 3
 
3.9%
p 3
 
3.9%
Other values (13) 20
26.3%
Uppercase Letter
ValueCountFrequency (%)
S 6
 
9.1%
K 6
 
9.1%
D 5
 
7.6%
O 5
 
7.6%
U 4
 
6.1%
H 4
 
6.1%
N 4
 
6.1%
C 4
 
6.1%
T 4
 
6.1%
P 4
 
6.1%
Other values (10) 20
30.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 142
51.6%
Hangul 105
38.2%
Common 28
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4
 
3.8%
피 3
 
2.9%
시 3
 
2.9%
연 3
 
2.9%
국 3
 
2.9%
한 3
 
2.9%
장 3
 
2.9%
산 2
 
1.9%
개 2
 
1.9%
디 2
 
1.9%
Other values (66) 77
73.3%
Latin
ValueCountFrequency (%)
e 10
 
7.0%
a 10
 
7.0%
o 8
 
5.6%
S 6
 
4.2%
K 6
 
4.2%
D 5
 
3.5%
O 5
 
3.5%
y 5
 
3.5%
m 5
 
3.5%
U 4
 
2.8%
Other values (33) 78
54.9%
Common
ValueCountFrequency (%)
22
78.6%
] 2
 
7.1%
[ 2
 
7.1%
. 1
 
3.6%
- 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170
61.8%
Hangul 105
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
 
12.9%
e 10
 
5.9%
a 10
 
5.9%
o 8
 
4.7%
S 6
 
3.5%
K 6
 
3.5%
D 5
 
2.9%
O 5
 
2.9%
y 5
 
2.9%
m 5
 
2.9%
Other values (38) 88
51.8%
Hangul
ValueCountFrequency (%)
구 4
 
3.8%
피 3
 
2.9%
시 3
 
2.9%
연 3
 
2.9%
국 3
 
2.9%
한 3
 
2.9%
장 3
 
2.9%
산 2
 
1.9%
개 2
 
1.9%
디 2
 
1.9%
Other values (66) 77
73.3%

개선도지수수집일자
Date

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2021-05-08 00:00:00
Maximum2021-05-31 00:00:00
2023-12-10T22:57:45.509883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:45.717217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

개선도지수채널설명
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing2
Missing (%)8.0%
Memory size332.0 B
2023-12-10T22:57:46.029818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length680
Median length95
Mean length155.78261
Min length7

Characters and Unicode

Total characters3583
Distinct characters387
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

Unique23 ?
Unique (%)100.0%

Sample

1st row별거없는 일상
2nd row신기해의 유튜브입니다. 트위치 : https:www.twitch.tv1am_shin
3rd row그저 하루하루 적당히 열심히 살아가는 직장인의 일상입니다 방문해주셔서 감사합니다 ^^ 비지니스 문의 kangmanagertv@sandboxnetwork.net (방송; 광고; 협찬 문의는 이쪽으로 부탁드립니다 ^^) 인스타그램 kangmang612 개인메일 kang0930yu@gmail.com (개인메일로 오는 협찬; 광고; 방송출연 문의는 응답이 어려울 수 있습니다 ㅜㅜ) 촬영장비 : 소니 a6400; 소니 zv1; 삼성 A80; 오즈모포켓2 편집 : 프리미어프로 녹음 : 로지텍 g430 게이밍 해드셋 택배주소 (04387) 서울시 용산구 서빙고로 17 센트럴파크타워 30층 샌드박스 강과장 2주마다 샌드박스쪽에서 취합해서 택배를 보내주시는데 냉장보관이 불가하여 상하는 식품류는 불가합니다
4th row전기전문 정부출연연구기관 한국전기연구원의 공식 유튜브 채널입니다. - 전기기술관련 연구성과 영상 - 주요 행사관련 영상 - 과학문화확산을 위한 다채로운 영상 등이 여러분께 제공되고 있습니다. 많은 관심과 참여 부탁드립니다. ============================ -홈 페 이 지 : http:www.keri.re.kr -네이버BLOG: http:blog.naver.comkeri_on -네이버POST: http:post.naver.comkeri_on -네이버 T V : http:tv.naver.comkeri -페 이 스 북 : http:www.facebook.comkeristory -인스타그램 : http:www.instagram.comkokoma_keri
5th row안녕하세요! 아주 능숙한 게임실력과 파이팅이넘치는 사모장입니다! 동영상 업로드는 매일 1개씩 오후 5시~6시에 올라갑니다!! 문의메일 : mozang11@naver.com
ValueCountFrequency (%)
39
 
6.8%
공식 7
 
1.2%
있습니다 5
 
0.9%
of 5
 
0.9%
문의 4
 
0.7%
영상 4
 
0.7%
채널 4
 
0.7%
먹방 4
 
0.7%
감사합니다 4
 
0.7%
채널입니다 3
 
0.5%
Other values (431) 498
86.3%
2023-12-10T22:57:46.619273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
613
 
17.1%
o 103
 
2.9%
t 91
 
2.5%
e 88
 
2.5%
a 76
 
2.1%
n 75
 
2.1%
. 67
 
1.9%
= 65
 
1.8%
r 58
 
1.6%
i 54
 
1.5%
Other values (377) 2293
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1349
37.7%
Lowercase Letter 1014
28.3%
Space Separator 613
17.1%
Other Punctuation 195
 
5.4%
Uppercase Letter 175
 
4.9%
Decimal Number 107
 
3.0%
Math Symbol 71
 
2.0%
Connector Punctuation 13
 
0.4%
Dash Punctuation 12
 
0.3%
Open Punctuation 12
 
0.3%
Other values (3) 22
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
는 33
 
2.4%
다 32
 
2.4%
니 30
 
2.2%
이 28
 
2.1%
하 25
 
1.9%
의 24
 
1.8%
고 19
 
1.4%
한 19
 
1.4%
운 18
 
1.3%
드 18
 
1.3%
Other values (294) 1103
81.8%
Lowercase Letter
ValueCountFrequency (%)
o 103
 
10.2%
t 91
 
9.0%
e 88
 
8.7%
a 76
 
7.5%
n 75
 
7.4%
r 58
 
5.7%
i 54
 
5.3%
m 50
 
4.9%
s 50
 
4.9%
c 47
 
4.6%
Other values (16) 322
31.8%
Uppercase Letter
ValueCountFrequency (%)
S 16
 
9.1%
I 14
 
8.0%
T 14
 
8.0%
M 12
 
6.9%
P 11
 
6.3%
A 11
 
6.3%
O 10
 
5.7%
K 10
 
5.7%
D 9
 
5.1%
B 7
 
4.0%
Other values (16) 61
34.9%
Other Punctuation
ValueCountFrequency (%)
. 67
34.4%
: 43
22.1%
; 38
19.5%
! 29
14.9%
@ 10
 
5.1%
* 3
 
1.5%
? 2
 
1.0%
& 1
 
0.5%
· 1
 
0.5%
' 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 22
20.6%
0 21
19.6%
3 13
12.1%
2 11
10.3%
7 10
9.3%
4 8
 
7.5%
5 8
 
7.5%
8 6
 
5.6%
6 4
 
3.7%
9 4
 
3.7%
Math Symbol
ValueCountFrequency (%)
= 65
91.5%
+ 5
 
7.0%
~ 1
 
1.4%
Other Symbol
ValueCountFrequency (%)
▶ 1
50.0%
★ 1
50.0%
Space Separator
ValueCountFrequency (%)
613
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1349
37.7%
Latin 1189
33.2%
Common 1045
29.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
는 33
 
2.4%
다 32
 
2.4%
니 30
 
2.2%
이 28
 
2.1%
하 25
 
1.9%
의 24
 
1.8%
고 19
 
1.4%
한 19
 
1.4%
운 18
 
1.3%
드 18
 
1.3%
Other values (294) 1103
81.8%
Latin
ValueCountFrequency (%)
o 103
 
8.7%
t 91
 
7.7%
e 88
 
7.4%
a 76
 
6.4%
n 75
 
6.3%
r 58
 
4.9%
i 54
 
4.5%
m 50
 
4.2%
s 50
 
4.2%
c 47
 
4.0%
Other values (42) 497
41.8%
Common
ValueCountFrequency (%)
613
58.7%
. 67
 
6.4%
= 65
 
6.2%
: 43
 
4.1%
; 38
 
3.6%
! 29
 
2.8%
1 22
 
2.1%
0 21
 
2.0%
3 13
 
1.2%
_ 13
 
1.2%
Other values (21) 121
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2231
62.3%
Hangul 1337
37.3%
Compat Jamo 12
 
0.3%
Geometric Shapes 1
 
< 0.1%
None 1
 
< 0.1%
Misc Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
613
27.5%
o 103
 
4.6%
t 91
 
4.1%
e 88
 
3.9%
a 76
 
3.4%
n 75
 
3.4%
. 67
 
3.0%
= 65
 
2.9%
r 58
 
2.6%
i 54
 
2.4%
Other values (70) 941
42.2%
Hangul
ValueCountFrequency (%)
는 33
 
2.5%
다 32
 
2.4%
니 30
 
2.2%
이 28
 
2.1%
하 25
 
1.9%
의 24
 
1.8%
고 19
 
1.4%
한 19
 
1.4%
운 18
 
1.3%
드 18
 
1.3%
Other values (291) 1091
81.6%
Compat Jamo
ValueCountFrequency (%)
ㅤ 8
66.7%
ㅜ 2
 
16.7%
ㅋ 2
 
16.7%
Geometric Shapes
ValueCountFrequency (%)
▶ 1
100.0%
None
ValueCountFrequency (%)
· 1
100.0%
Misc Symbols
ValueCountFrequency (%)
★ 1
100.0%

개선도채널생성일자
Date

MISSING 

Distinct18
Distinct (%)100.0%
Missing7
Missing (%)28.0%
Memory size332.0 B
Minimum2011-09-20 00:00:00
Maximum2019-06-13 00:00:00
2023-12-10T22:57:46.828500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:47.004629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

최근6개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)84.6%
Missing12
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean-0.076923077
Minimum-9
Maximum14
Zeros1
Zeros (%)4.0%
Negative7
Negative (%)28.0%
Memory size357.0 B
2023-12-10T22:57:47.180166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-7.2
Q1-3
median-2
Q33
95-th percentile9.2
Maximum14
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.8943693
Coefficient of variation (CV)-76.626801
Kurtosis1.6671713
Mean-0.076923077
Median Absolute Deviation (MAD)3
Skewness0.99509337
Sum-1
Variance34.74359
MonotonicityNot monotonic
2023-12-10T22:57:47.391689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-2 2
 
8.0%
-3 2
 
8.0%
14 1
 
4.0%
-4 1
 
4.0%
-9 1
 
4.0%
1 1
 
4.0%
6 1
 
4.0%
-6 1
 
4.0%
3 1
 
4.0%
0 1
 
4.0%
(Missing) 12
48.0%
ValueCountFrequency (%)
-9 1
4.0%
-6 1
4.0%
-4 1
4.0%
-3 2
8.0%
-2 2
8.0%
0 1
4.0%
1 1
4.0%
3 1
4.0%
4 1
4.0%
6 1
4.0%
ValueCountFrequency (%)
14 1
4.0%
6 1
4.0%
4 1
4.0%
3 1
4.0%
1 1
4.0%
0 1
4.0%
-2 2
8.0%
-3 2
8.0%
-4 1
4.0%
-6 1
4.0%

최근12개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)62.5%
Missing9
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean-0.625
Minimum-12
Maximum9
Zeros3
Zeros (%)12.0%
Negative6
Negative (%)24.0%
Memory size357.0 B
2023-12-10T22:57:47.576268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-9
Q1-2
median0
Q32
95-th percentile4.5
Maximum9
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7592016
Coefficient of variation (CV)-7.6147226
Kurtosis1.9204821
Mean-0.625
Median Absolute Deviation (MAD)2
Skewness-0.67786824
Sum-10
Variance22.65
MonotonicityNot monotonic
2023-12-10T22:57:47.737988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 3
 
12.0%
2 3
 
12.0%
-2 2
 
8.0%
1 2
 
8.0%
9 1
 
4.0%
-8 1
 
4.0%
-1 1
 
4.0%
-5 1
 
4.0%
3 1
 
4.0%
-12 1
 
4.0%
(Missing) 9
36.0%
ValueCountFrequency (%)
-12 1
 
4.0%
-8 1
 
4.0%
-5 1
 
4.0%
-2 2
8.0%
-1 1
 
4.0%
0 3
12.0%
1 2
8.0%
2 3
12.0%
3 1
 
4.0%
9 1
 
4.0%
ValueCountFrequency (%)
9 1
 
4.0%
3 1
 
4.0%
2 3
12.0%
1 2
8.0%
0 3
12.0%
-1 1
 
4.0%
-2 2
8.0%
-5 1
 
4.0%
-8 1
 
4.0%
-12 1
 
4.0%

최초6개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
12 
0
8 
1
5 

Length

Max length4
Median length1
Mean length2.44
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 12
48.0%
0 8
32.0%
1 5
20.0%

Length

2023-12-10T22:57:47.944772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:57:48.558519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 12
48.0%
0 8
32.0%
1 5
20.0%

최초12개월개선도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
11 
0
11 
1
2 
-1
 
1

Length

Max length4
Median length1
Mean length2.36
Min length1

Unique

Unique1 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 11
44.0%
0 11
44.0%
1 2
 
8.0%
-1 1
 
4.0%

Length

2023-12-10T22:57:49.083120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:57:49.501960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 11
44.0%
0 11
44.0%
1 3
 
12.0%

최근개선도지수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)64.7%
Missing8
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean13.158824
Minimum0
Maximum77.69
Zeros7
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T22:57:49.732359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.92
Q317.12
95-th percentile48.226
Maximum77.69
Range77.69
Interquartile range (IQR)17.12

Descriptive statistics

Standard deviation20.350537
Coefficient of variation (CV)1.5465316
Kurtosis6.0052359
Mean13.158824
Median Absolute Deviation (MAD)4.92
Skewness2.3143664
Sum223.7
Variance414.14435
MonotonicityNot monotonic
2023-12-10T22:57:50.021188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 7
28.0%
14.17 1
 
4.0%
20.99 1
 
4.0%
40.86 1
 
4.0%
4.92 1
 
4.0%
2.96 1
 
4.0%
10.84 1
 
4.0%
17.12 1
 
4.0%
7.16 1
 
4.0%
77.69 1
 
4.0%
(Missing) 8
32.0%
ValueCountFrequency (%)
0.0 7
28.0%
2.96 1
 
4.0%
4.92 1
 
4.0%
7.16 1
 
4.0%
10.84 1
 
4.0%
14.17 1
 
4.0%
17.12 1
 
4.0%
20.99 1
 
4.0%
26.99 1
 
4.0%
40.86 1
 
4.0%
ValueCountFrequency (%)
77.69 1
4.0%
40.86 1
4.0%
26.99 1
4.0%
20.99 1
4.0%
17.12 1
4.0%
14.17 1
4.0%
10.84 1
4.0%
7.16 1
4.0%
4.92 1
4.0%
2.96 1
4.0%

최근6개월표준점수
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)91.7%
Missing1
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean6.6745833
Minimum-9.25
Maximum27.6
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)20.0%
Memory size357.0 B
2023-12-10T22:57:50.295706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.25
5-th percentile-5.9625
Q12.5625
median7.485
Q310.9275
95-th percentile16.29
Maximum27.6
Range36.85
Interquartile range (IQR)8.365

Descriptive statistics

Standard deviation8.3439345
Coefficient of variation (CV)1.2501057
Kurtosis0.66400057
Mean6.6745833
Median Absolute Deviation (MAD)4.045
Skewness0.15282646
Sum160.19
Variance69.621243
MonotonicityNot monotonic
2023-12-10T22:57:50.622810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
16.29 2
 
8.0%
4.53 2
 
8.0%
27.6 1
 
4.0%
-5.97 1
 
4.0%
8.51 1
 
4.0%
15.5 1
 
4.0%
11.43 1
 
4.0%
8.86 1
 
4.0%
-9.25 1
 
4.0%
10.76 1
 
4.0%
Other values (12) 12
48.0%
ValueCountFrequency (%)
-9.25 1
4.0%
-5.97 1
4.0%
-5.92 1
4.0%
-4.4 1
4.0%
-1.57 1
4.0%
2.51 1
4.0%
2.58 1
4.0%
4.53 2
8.0%
5.86 1
4.0%
5.9 1
4.0%
ValueCountFrequency (%)
27.6 1
4.0%
16.29 2
8.0%
15.5 1
4.0%
11.63 1
4.0%
11.43 1
4.0%
10.76 1
4.0%
10.39 1
4.0%
9.16 1
4.0%
8.86 1
4.0%
8.51 1
4.0%

최근12개월표준점수
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)100.0%
Missing2
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean3.7534783
Minimum-15.27
Maximum28.77
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)28.0%
Memory size357.0 B
2023-12-10T22:57:50.933400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-15.27
5-th percentile-3.907
Q1-0.16
median2.57
Q36.855
95-th percentile16.23
Maximum28.77
Range44.04
Interquartile range (IQR)7.015

Descriptive statistics

Standard deviation8.1252377
Coefficient of variation (CV)2.1647222
Kurtosis4.2579673
Mean3.7534783
Median Absolute Deviation (MAD)3.84
Skewness0.95010592
Sum86.33
Variance66.019487
MonotonicityNot monotonic
2023-12-10T22:57:51.186659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3.75 1
 
4.0%
-0.26 1
 
4.0%
7.32 1
 
4.0%
-2.28 1
 
4.0%
17.11 1
 
4.0%
7.22 1
 
4.0%
5.94 1
 
4.0%
1.39 1
 
4.0%
4.57 1
 
4.0%
0.43 1
 
4.0%
Other values (13) 13
52.0%
(Missing) 2
 
8.0%
ValueCountFrequency (%)
-15.27 1
4.0%
-4.08 1
4.0%
-2.35 1
4.0%
-2.28 1
4.0%
-0.4 1
4.0%
-0.26 1
4.0%
-0.06 1
4.0%
0.43 1
4.0%
1.39 1
4.0%
1.45 1
4.0%
ValueCountFrequency (%)
28.77 1
4.0%
17.11 1
4.0%
8.31 1
4.0%
7.32 1
4.0%
7.22 1
4.0%
6.93 1
4.0%
6.78 1
4.0%
6.41 1
4.0%
5.94 1
4.0%
4.57 1
4.0%

최초6개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum-0.63
Maximum4.88
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)12.0%
Memory size357.0 B
2023-12-10T22:57:51.424944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.63
5-th percentile-0.546
Q10.11
median0.17
Q30.18
95-th percentile3.942
Maximum4.88
Range5.51
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation1.3218831
Coefficient of variation (CV)2.6437663
Kurtosis7.8381689
Mean0.5
Median Absolute Deviation (MAD)0.03
Skewness2.8906235
Sum12.5
Variance1.747375
MonotonicityNot monotonic
2023-12-10T22:57:51.735286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.17 7
28.0%
0.11 2
 
8.0%
0.18 2
 
8.0%
-0.63 2
 
8.0%
0.16 2
 
8.0%
4.55 1
 
4.0%
0.2 1
 
4.0%
4.88 1
 
4.0%
0.01 1
 
4.0%
1.51 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
-0.63 2
 
8.0%
-0.21 1
 
4.0%
0.01 1
 
4.0%
0.07 1
 
4.0%
0.11 2
 
8.0%
0.14 1
 
4.0%
0.16 2
 
8.0%
0.17 7
28.0%
0.18 2
 
8.0%
0.19 1
 
4.0%
ValueCountFrequency (%)
4.88 1
 
4.0%
4.55 1
 
4.0%
1.51 1
 
4.0%
0.33 1
 
4.0%
0.2 1
 
4.0%
0.19 1
 
4.0%
0.18 2
 
8.0%
0.17 7
28.0%
0.16 2
 
8.0%
0.14 1
 
4.0%

최초12개월표준점수
Real number (ℝ)

Distinct16
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0728
Minimum-5.15
Maximum0.74
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)16.0%
Memory size357.0 B
2023-12-10T22:57:51.932984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.15
5-th percentile-1.37
Q10.2
median0.25
Q30.27
95-th percentile0.608
Maximum0.74
Range5.89
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation1.1610144
Coefficient of variation (CV)-15.947999
Kurtosis16.337687
Mean-0.0728
Median Absolute Deviation (MAD)0.02
Skewness-3.8534878
Sum-1.82
Variance1.3479543
MonotonicityNot monotonic
2023-12-10T22:57:52.227814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.26 4
16.0%
0.25 4
16.0%
0.24 2
 
8.0%
0.27 2
 
8.0%
0.13 2
 
8.0%
-1.25 1
 
4.0%
-5.15 1
 
4.0%
0.44 1
 
4.0%
0.2 1
 
4.0%
-0.32 1
 
4.0%
Other values (6) 6
24.0%
ValueCountFrequency (%)
-5.15 1
 
4.0%
-1.4 1
 
4.0%
-1.25 1
 
4.0%
-0.32 1
 
4.0%
0.13 2
8.0%
0.2 1
 
4.0%
0.23 1
 
4.0%
0.24 2
8.0%
0.25 4
16.0%
0.26 4
16.0%
ValueCountFrequency (%)
0.74 1
 
4.0%
0.65 1
 
4.0%
0.44 1
 
4.0%
0.41 1
 
4.0%
0.31 1
 
4.0%
0.27 2
8.0%
0.26 4
16.0%
0.25 4
16.0%
0.24 2
8.0%
0.23 1
 
4.0%

개선도최근표준점수
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5036
Minimum-24.04
Maximum113.57
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)12.0%
Memory size357.0 B
2023-12-10T22:57:52.483788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-24.04
5-th percentile-10.132
Q112.32
median14.2
Q316.65
95-th percentile26.624
Maximum113.57
Range137.61
Interquartile range (IQR)4.33

Descriptive statistics

Standard deviation23.011607
Coefficient of variation (CV)1.4842751
Kurtosis14.84637
Mean15.5036
Median Absolute Deviation (MAD)2.45
Skewness3.1743417
Sum387.59
Variance529.53405
MonotonicityNot monotonic
2023-12-10T22:57:52.700101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
22.12 1
 
4.0%
12.7 1
 
4.0%
14.25 1
 
4.0%
13.64 1
 
4.0%
16.4 1
 
4.0%
113.57 1
 
4.0%
16.65 1
 
4.0%
14.09 1
 
4.0%
6.1 1
 
4.0%
17.65 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
-24.04 1
4.0%
-11.4 1
4.0%
-5.06 1
4.0%
6.1 1
4.0%
9.92 1
4.0%
11.24 1
4.0%
12.32 1
4.0%
12.6 1
4.0%
12.7 1
4.0%
13.64 1
4.0%
ValueCountFrequency (%)
113.57 1
4.0%
27.75 1
4.0%
22.12 1
4.0%
17.65 1
4.0%
17.06 1
4.0%
17.03 1
4.0%
16.65 1
4.0%
16.4 1
4.0%
15.14 1
4.0%
14.96 1
4.0%

Interactions

2023-12-10T22:57:40.847607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:32.116392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.145891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:34.152416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:35.354333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:36.535565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:38.231064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:39.577554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:40.985704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:32.236170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.278060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:34.278976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:35.555778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:36.670053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:38.362197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:39.776329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:41.107849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:32.381988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.384750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:34.431965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:35.678263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:36.784856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:38.504873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:39.947690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:41.256619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:32.517876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.530602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:34.601547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:35.815734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:37.447757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:38.702167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:40.125172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:41.382256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:32.643893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.649768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:34.731204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:35.959077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:37.587009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:38.928555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:40.291426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:41.516322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:32.758968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.771385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:34.909369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:36.085610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:37.747170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:39.106786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:40.410708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:41.715961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:32.875673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.888117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:35.054006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:36.269759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:37.856121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:39.250676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:40.536329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:42.032315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:33.009954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:34.013044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:35.212737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:36.402684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:37.995558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:39.409227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:40.677763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:57:52.861363image/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
개선도지수수집일자1.0001.0001.0001.000NaNNaN1.000NaNNaNNaN1.0000.0000.0000.0000.000
개선도지수채널설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도채널생성일자1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최근6개월개선도1.0001.000NaN1.0001.0001.0000.0000.5311.0000.0000.0000.3960.7260.0000.390
최근12개월개선도1.0001.0001.0001.0001.0000.0001.0000.3030.0000.7500.7600.0000.0000.0000.300
최초6개월개선도1.0001.000NaN1.0001.0000.5310.3031.0000.0900.0000.3090.0000.8040.3790.000
최초12개월개선도1.0001.000NaN1.0001.0001.0000.0000.0901.0000.0000.2820.0000.6380.0000.000
최근개선도지수1.0001.000NaN1.0001.0000.0000.7500.0000.0001.0000.6670.3600.2900.0000.065
최근6개월표준점수1.0001.0001.0001.0001.0000.0000.7600.3090.2820.6671.0000.0000.0000.0000.000
최근12개월표준점수1.0001.0000.0001.0001.0000.3960.0000.0000.0000.3600.0001.0000.5420.3920.000
최초6개월표준점수1.0001.0000.0001.0001.0000.7260.0000.8040.6380.2900.0000.5421.0000.1340.000
최초12개월표준점수1.0001.0000.0001.0001.0000.0000.0000.3790.0000.0000.0000.3920.1341.0000.766
개선도최근표준점수1.0001.0000.0001.0001.0000.3900.3000.0000.0000.0650.0000.0000.0000.7661.000
2023-12-10T22:57:53.220671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초6개월개선도최초12개월개선도
최초6개월개선도1.0000.081
최초12개월개선도0.0811.000
2023-12-10T22:57:53.364802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최근6개월개선도최근12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수최초6개월개선도최초12개월개선도
최근6개월개선도1.000-0.2080.0600.033-0.3100.0170.327-0.3420.1690.577
최근12개월개선도-0.2081.000-0.548-0.084-0.051-0.344-0.2010.1560.0000.000
최근개선도지수0.060-0.5481.000-0.1740.0200.2020.111-0.4670.0000.000
최근6개월표준점수0.033-0.084-0.1741.0000.153-0.320-0.0760.3930.0000.000
최근12개월표준점수-0.310-0.0510.0200.1531.0000.0840.068-0.1050.0000.000
최초6개월표준점수0.017-0.3440.202-0.3200.0841.0000.339-0.3400.5270.621
최초12개월표준점수0.327-0.2010.111-0.0760.0680.3391.000-0.0850.1820.000
개선도최근표준점수-0.3420.156-0.4670.393-0.105-0.340-0.0851.0000.0000.000
최초6개월개선도0.1690.0000.0000.0000.0000.5270.1820.0001.0000.081
최초12개월개선도0.5770.0000.0000.0000.0000.6210.0000.0000.0811.000

Missing values

2023-12-10T22:57:42.474826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:57:42.814126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T22:57:43.098888image/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
0UC6ldBY4j2lQWT5pdr-dFw9A지수의 일상2021-05-08별거없는 일상<NA><NA><NA><NA><NA><NA>27.6-0.40.170.2422.12
1UCqqr-GRWimMmMvgxL_yNSUw신기해2021-05-20신기해의 유튜브입니다. 트위치 : https:www.twitch.tv1am_shin<NA><NA>9<NA><NA><NA><NA>3.750.110.2712.7
2UC1EEpE0lA9BaArXhRTHIG6w강과장2021-05-31그저 하루하루 적당히 열심히 살아가는 직장인의 일상입니다 방문해주셔서 감사합니다 ^^ 비지니스 문의 kangmanagertv@sandboxnetwork.net (방송; 광고; 협찬 문의는 이쪽으로 부탁드립니다 ^^) 인스타그램 kangmang612 개인메일 kang0930yu@gmail.com (개인메일로 오는 협찬; 광고; 방송출연 문의는 응답이 어려울 수 있습니다 ㅜㅜ) 촬영장비 : 소니 a6400; 소니 zv1; 삼성 A80; 오즈모포켓2 편집 : 프리미어프로 녹음 : 로지텍 g430 게이밍 해드셋 택배주소 (04387) 서울시 용산구 서빙고로 17 센트럴파크타워 30층 샌드박스 강과장 2주마다 샌드박스쪽에서 취합해서 택배를 보내주시는데 냉장보관이 불가하여 상하는 식품류는 불가합니다2018-09-16-2-81014.17-4.42.084.550.3117.03
3UC-gWrEGYpG2jYfl8A_KIGnQ[KERI]한국전기연구원2021-05-31전기전문 정부출연연구기관 한국전기연구원의 공식 유튜브 채널입니다. - 전기기술관련 연구성과 영상 - 주요 행사관련 영상 - 과학문화확산을 위한 다채로운 영상 등이 여러분께 제공되고 있습니다. 많은 관심과 참여 부탁드립니다. ============================ -홈 페 이 지 : http:www.keri.re.kr -네이버BLOG: http:blog.naver.comkeri_on -네이버POST: http:post.naver.comkeri_on -네이버 T V : http:tv.naver.comkeri -페 이 스 북 : http:www.facebook.comkeristory -인스타그램 : http:www.instagram.comkokoma_keri2012-03-07<NA><NA><NA><NA><NA>-1.576.780.170.2614.2
4UC4XjKgCtpwpACzUCiUKOd3Q사모장2021-05-31안녕하세요! 아주 능숙한 게임실력과 파이팅이넘치는 사모장입니다! 동영상 업로드는 매일 1개씩 오후 5시~6시에 올라갑니다!! 문의메일 : mozang11@naver.com2013-12-081400020.995.91.450.20.27-11.4
5UC85mXeWRaycnlFmrhBdaAlg남자커피 Namja Coffee2021-05-31안녕하세요 커피와 음료를 사랑하는 남자커피 입니다!! 집에서 쉽게 만들수있는 음료 레시피 소개와 재미있는 커피상식들을 알려 드리고 싶어 운영하는 채널입니다. 부족할수있지만 재미있게 봐주세요!! 문의 - bakasa0817@gmail.com2018-08-18-4-21040.862.586.414.880.4112.32
6UC8JOLZ-YA34ylQTz2tqSlGwKAIST2021-05-31한국과학기술원(KAIST)의 공식 유튜브 채널입니다. This is the official YouTube channel of KAIST.2013-07-28<NA><NA><NA><NA><NA>5.862.570.180.269.92
7UC9G7V6-HU_Cg7-yWLO1rH6wPURE.D퓨어디2021-05-31teenpopk@gmail.com2016-12-21-92114.9210.39-0.060.01-1.427.75
8UC9T8wL0qUwhpcKSv73J5dqA모터피디2021-05-31영상으로 만나는 자동차 매거진 - Auto Magazine www.motorpd.com Facebook: facebook.commotorpd Twitter: twitter.commotorpd1 Instagram: instagrammotorpd1 Google+:plus.google.com113593086973172778755about<NA><NA>-2<NA>12.9611.638.310.170.2314.96
9UC2Zi06YjNBM37g8d0IkHPMATVCHOSUN PLUS - TV조선 플러스2021-05-31TV조선 방송 채널에; 더한 클립과; 더한 영상들을 모아둔 더한 채널 TV조선 플러스2019-01-181-10-10.07.38-4.081.510.7414.64
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
15UCBIoXzDldCnpbM_7uyG0_TgHONG SOUND2021-05-31취미로 유튜브를 시작한 품절남 아재 홍사운드 에요! 지금은 푸드크리에이터로서 그 동안 경험해보지 못한 다양하고 맛있는 음식들을 먹으며 여러분들과 공유하는 채널을 운영하고 있습니다! 감사합니다 ^^ 인스타그램(instargram) : https:www.instagram.comhong_sound 홍사운드(HONG SOUND) 채널은? 취미로 유튜브를 시작한 품절남 아재 홍사운드 에요! 지금은 푸드크리에이터로서 그 동안 경험해보지 못한 다양하고 맛있는 음식들을 먹으며 여러분들과 공유하는 채널을 운영하고 있습니다! 감사합니다 ^^ 1.리얼사운드 먹방 : 실감나는 먹방; 독특한 오프닝송과 맛 설명도 함께 있어요. *No talking ASMR 이팅사운드 : 오직 먹는 소리만 있는 영상 (편안하고 부드러운 화면) *노토킹 ASMR 이팅사운드 영상은 먹는 소리만 듣고 싶어하는 구독자분들을 위해 제작하고 있습니다. 2.미분일기(맛을 나누는 일기) : 그날의 행복한 추억과 음식의 맛을 공유하는 영상일기. 3.긍정부스터(생방송 리얼사운드 먹방) : 매주 수요일 저녁 11시 30분! 홍사운드와 함께 음식도 먹고 즐거운 담소도 나누며 긍정긍정의 힘을 받아가세요! 광고 문의 : 이메일 인스타그램 : https:www.instagram.comhong_sound 이메일 : dkagmr12@naver.com2015-12-1133000.0-5.970.430.33-0.32-5.06
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17UCBvDRQETp01JUOVHaumO08Q예술의전당 Concert2021-05-31Since 2012.03.28 Seoul Arts Center 예술의전당ㅤㅤㅤㅤ ㅤㅤㅤ Presents_ Seoul Arts Center Exacutive Producer_ Jeon Seongjin VideoAudio Filming & Editing_ Members of the Stage Sound Part ㅤ 제작 : 예술의전당 책임 프로듀서 : 전성진 녹화; 편집 : 예술의전당 무대운영부 음향파트_ 곽동렬; 문성욱; 양승모; 이강진; 전성진; 조민제; 조희명; 주원국; 한국란; 한혜림2012-03-28<NA>0000.09.161.390.070.1317.65
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