Overview

Dataset statistics

Number of variables15
Number of observations25
Missing cells39
Missing cells (%)10.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory135.3 B

Variable types

Text3
Categorical3
DateTime1
Numeric8

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/ea08b4c9-04a9-4c74-bd52-38d75c52fb5e

Alerts

최초12개월개선도 is highly overall correlated with 최근6개월개선도 and 3 other fieldsHigh correlation
최초6개월개선도 is highly overall correlated with 최근6개월표준점수 and 2 other fieldsHigh correlation
개선도지수수집일자 is highly overall correlated with 최근6개월개선도 and 4 other fieldsHigh correlation
최근6개월개선도 is highly overall correlated with 개선도지수수집일자 and 1 other fieldsHigh correlation
최근12개월개선도 is highly overall correlated with 개선도지수수집일자 and 1 other fieldsHigh correlation
최근개선도지수 is highly overall correlated with 최근6개월표준점수 and 1 other fieldsHigh correlation
최근6개월표준점수 is highly overall correlated with 최근개선도지수 and 2 other fieldsHigh correlation
최초6개월표준점수 is highly overall correlated with 최초12개월표준점수 and 1 other fieldsHigh correlation
최초12개월표준점수 is highly overall correlated with 최초6개월표준점수High correlation
개선도지수수집일자 is highly imbalanced (75.8%)Imbalance
개선도지수채널설명 has 2 (8.0%) missing valuesMissing
최근6개월개선도 has 12 (48.0%) missing valuesMissing
최근12개월개선도 has 11 (44.0%) missing valuesMissing
최근개선도지수 has 8 (32.0%) missing valuesMissing
최근6개월표준점수 has 3 (12.0%) missing valuesMissing
최근12개월표준점수 has 2 (8.0%) missing valuesMissing
최초12개월표준점수 has 1 (4.0%) missing valuesMissing
개선도지수채널ID has unique valuesUnique
개선도지수채널명 has unique valuesUnique
개선도채널생성일자 has unique valuesUnique
개선도최근표준점수 has unique valuesUnique
최근개선도지수 has 3 (12.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:07:57.835339
Analysis finished2023-12-10 14:08:10.904315
Duration13.07 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
2023-12-10T23:08:11.154403image/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 rowUCVvyVBQF5ejhjx4l-c9RUWw
2nd rowUC1EEpE0lA9BaArXhRTHIG6w
3rd rowUC-gWrEGYpG2jYfl8A_KIGnQ
4th rowUC4XjKgCtpwpACzUCiUKOd3Q
5th rowUC6qXcpjNzFg7Pe4NDRZVQDg
ValueCountFrequency (%)
ucvvyvbqf5ejhjx4l-c9ruww 1
 
4.0%
ucb9e3pof1o83aa0kkaoejga 1
 
4.0%
uceyuopmy0lx5b5pgch03fsq 1
 
4.0%
uceeevbzufze0gmn0ff55h8w 1
 
4.0%
ucecuyraqhrvumrdod6fgksw 1
 
4.0%
uc9t8wl0quwhpcksv73j5dqa 1
 
4.0%
uccwwpm3zkhvckg0tmupkgaa 1
 
4.0%
uccxgedotqfr2g2hmev9vsxq 1
 
4.0%
ucbvdrqetp01juovhaumo08q 1
 
4.0%
ucbdkh3mg6x71y94a2wudetq 1
 
4.0%
Other values (15) 15
60.0%
2023-12-10T23:08:11.758220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 36
 
6.0%
C 33
 
5.5%
A 19
 
3.2%
G 16
 
2.7%
0 15
 
2.5%
g 14
 
2.3%
w 14
 
2.3%
E 14
 
2.3%
Q 13
 
2.2%
a 12
 
2.0%
Other values (54) 414
69.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 294
49.0%
Lowercase Letter 206
34.3%
Decimal Number 88
 
14.7%
Dash Punctuation 7
 
1.2%
Connector Punctuation 5
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 36
 
12.2%
C 33
 
11.2%
A 19
 
6.5%
G 16
 
5.4%
E 14
 
4.8%
Q 13
 
4.4%
F 12
 
4.1%
H 11
 
3.7%
B 11
 
3.7%
M 10
 
3.4%
Other values (16) 119
40.5%
Lowercase Letter
ValueCountFrequency (%)
g 14
 
6.8%
w 14
 
6.8%
a 12
 
5.8%
p 12
 
5.8%
y 12
 
5.8%
z 10
 
4.9%
j 10
 
4.9%
d 10
 
4.9%
l 10
 
4.9%
e 8
 
3.9%
Other values (16) 94
45.6%
Decimal Number
ValueCountFrequency (%)
0 15
17.0%
3 10
11.4%
9 9
10.2%
2 9
10.2%
6 9
10.2%
8 9
10.2%
7 8
9.1%
5 8
9.1%
4 6
 
6.8%
1 5
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 500
83.3%
Common 100
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 36
 
7.2%
C 33
 
6.6%
A 19
 
3.8%
G 16
 
3.2%
g 14
 
2.8%
w 14
 
2.8%
E 14
 
2.8%
Q 13
 
2.6%
a 12
 
2.4%
p 12
 
2.4%
Other values (42) 317
63.4%
Common
ValueCountFrequency (%)
0 15
15.0%
3 10
10.0%
9 9
9.0%
2 9
9.0%
6 9
9.0%
8 9
9.0%
7 8
8.0%
5 8
8.0%
- 7
7.0%
4 6
 
6.0%
Other values (2) 10
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 36
 
6.0%
C 33
 
5.5%
A 19
 
3.2%
G 16
 
2.7%
0 15
 
2.5%
g 14
 
2.3%
w 14
 
2.3%
E 14
 
2.3%
Q 13
 
2.2%
a 12
 
2.0%
Other values (54) 414
69.0%

개선도지수채널명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-10T23:08:12.163656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length13
Mean length10.32
Min length3

Characters and Unicode

Total characters258
Distinct characters121
Distinct categories7 ?
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 row경북대학교병원
2nd row강과장
3rd row[KERI]한국전기연구원
4th row사모장
5th rowYoonJae윤졔
ValueCountFrequency (%)
경북대학교병원 1
 
2.4%
kdi 1
 
2.4%
mdromeda 1
 
2.4%
까꿍이와개세mr리들 1
 
2.4%
hong 1
 
2.4%
sound 1
 
2.4%
부산광역시연제구청 1
 
2.4%
예술의전당 1
 
2.4%
concert 1
 
2.4%
인천도시공사 1
 
2.4%
Other values (32) 32
76.2%
2023-12-10T23:08:12.739360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
6.6%
e 11
 
4.3%
a 11
 
4.3%
o 10
 
3.9%
i 5
 
1.9%
S 5
 
1.9%
D 5
 
1.9%
m 5
 
1.9%
N 5
 
1.9%
K 5
 
1.9%
Other values (111) 179
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
39.5%
Lowercase Letter 76
29.5%
Uppercase Letter 60
23.3%
Space Separator 17
 
6.6%
Other Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4
 
3.9%
원 3
 
2.9%
국 3
 
2.9%
연 3
 
2.9%
장 3
 
2.9%
시 3
 
2.9%
한 2
 
2.0%
울 2
 
2.0%
도 2
 
2.0%
교 2
 
2.0%
Other values (63) 75
73.5%
Lowercase Letter
ValueCountFrequency (%)
e 11
14.5%
a 11
14.5%
o 10
13.2%
i 5
 
6.6%
m 5
 
6.6%
n 5
 
6.6%
r 4
 
5.3%
y 3
 
3.9%
d 3
 
3.9%
c 2
 
2.6%
Other values (13) 17
22.4%
Uppercase Letter
ValueCountFrequency (%)
S 5
 
8.3%
D 5
 
8.3%
N 5
 
8.3%
K 5
 
8.3%
T 4
 
6.7%
U 4
 
6.7%
C 4
 
6.7%
V 3
 
5.0%
I 3
 
5.0%
O 3
 
5.0%
Other values (11) 19
31.7%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136
52.7%
Hangul 102
39.5%
Common 20
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4
 
3.9%
원 3
 
2.9%
국 3
 
2.9%
연 3
 
2.9%
장 3
 
2.9%
시 3
 
2.9%
한 2
 
2.0%
울 2
 
2.0%
도 2
 
2.0%
교 2
 
2.0%
Other values (63) 75
73.5%
Latin
ValueCountFrequency (%)
e 11
 
8.1%
a 11
 
8.1%
o 10
 
7.4%
i 5
 
3.7%
S 5
 
3.7%
D 5
 
3.7%
m 5
 
3.7%
N 5
 
3.7%
K 5
 
3.7%
n 5
 
3.7%
Other values (34) 69
50.7%
Common
ValueCountFrequency (%)
17
85.0%
. 1
 
5.0%
] 1
 
5.0%
[ 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156
60.5%
Hangul 102
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
 
10.9%
e 11
 
7.1%
a 11
 
7.1%
o 10
 
6.4%
i 5
 
3.2%
S 5
 
3.2%
D 5
 
3.2%
m 5
 
3.2%
N 5
 
3.2%
K 5
 
3.2%
Other values (38) 77
49.4%
Hangul
ValueCountFrequency (%)
구 4
 
3.9%
원 3
 
2.9%
국 3
 
2.9%
연 3
 
2.9%
장 3
 
2.9%
시 3
 
2.9%
한 2
 
2.0%
울 2
 
2.0%
도 2
 
2.0%
교 2
 
2.0%
Other values (63) 75
73.5%

개선도지수수집일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2020-11-30
24 
2020-11-04
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row2020-11-04
2nd row2020-11-30
3rd row2020-11-30
4th row2020-11-30
5th row2020-11-30

Common Values

ValueCountFrequency (%)
2020-11-30 24
96.0%
2020-11-04 1
 
4.0%

Length

2023-12-10T23:08:12.950788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:13.111662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-11-30 24
96.0%
2020-11-04 1
 
4.0%

개선도지수채널설명
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing2
Missing (%)8.0%
Memory size332.0 B
2023-12-10T23:08:13.377460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length680
Median length95
Mean length151.86957
Min length14

Characters and Unicode

Total characters3493
Distinct characters372
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row경북대학교병원 동영상 자료실
2nd row그저 하루하루 적당히 열심히 살아가는 직장인의 일상입니다 방문해주셔서 감사합니다 ^^ 비지니스 문의 kangmanagertv@sandboxnetwork.net 인스타그램 kangmang612 개인메일 kang0930yu@gmail.com 주소 : 서울 강남구 테헤란로 518 한국섬유산업연합회 WeWork빌딩 15층 샌드박스 강과장 (보내주시는 구독자 선물은 샌드박스쪽에서 2주마다 모아서 저에게 택배가 보내지기때문에 변질되는 음식물은 택배로 받기가 어렵습니다)
3rd 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
4th row안녕하세요! 아주 능숙한 게임실력과 파이팅이넘치는 사모장입니다! 동영상 업로드는 매일 1개씩 오후 5시~6시에 올라갑니다!! 문의메일 : mozang11@naver.com
5th row구독 좋아요 문의 yoonjae.work@gmail.com
ValueCountFrequency (%)
36
 
6.5%
공식 7
 
1.3%
문의 5
 
0.9%
of 5
 
0.9%
채널 5
 
0.9%
감사합니다 4
 
0.7%
먹방 4
 
0.7%
인스타그램 4
 
0.7%
영상 4
 
0.7%
있습니다 4
 
0.7%
Other values (411) 474
85.9%
2023-12-10T23:08:13.897448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
590
 
16.9%
o 108
 
3.1%
e 92
 
2.6%
t 90
 
2.6%
a 79
 
2.3%
n 78
 
2.2%
. 65
 
1.9%
= 65
 
1.9%
r 60
 
1.7%
i 59
 
1.7%
Other values (362) 2207
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1304
37.3%
Lowercase Letter 1032
29.5%
Space Separator 590
16.9%
Other Punctuation 188
 
5.4%
Uppercase Letter 174
 
5.0%
Decimal Number 81
 
2.3%
Math Symbol 71
 
2.0%
Connector Punctuation 14
 
0.4%
Dash Punctuation 12
 
0.3%
Open Punctuation 10
 
0.3%
Other values (3) 17
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 31
 
2.4%
는 29
 
2.2%
니 28
 
2.1%
하 25
 
1.9%
이 24
 
1.8%
의 21
 
1.6%
한 19
 
1.5%
고 18
 
1.4%
운 18
 
1.4%
기 17
 
1.3%
Other values (280) 1074
82.4%
Lowercase Letter
ValueCountFrequency (%)
o 108
 
10.5%
e 92
 
8.9%
t 90
 
8.7%
a 79
 
7.7%
n 78
 
7.6%
r 60
 
5.8%
i 59
 
5.7%
m 56
 
5.4%
c 48
 
4.7%
s 47
 
4.6%
Other values (16) 315
30.5%
Uppercase Letter
ValueCountFrequency (%)
T 15
 
8.6%
S 15
 
8.6%
I 13
 
7.5%
P 13
 
7.5%
M 11
 
6.3%
O 11
 
6.3%
A 10
 
5.7%
K 9
 
5.2%
D 9
 
5.2%
B 7
 
4.0%
Other values (16) 61
35.1%
Other Punctuation
ValueCountFrequency (%)
. 65
34.6%
: 40
21.3%
! 32
17.0%
; 31
16.5%
@ 12
 
6.4%
* 3
 
1.6%
? 2
 
1.1%
· 1
 
0.5%
& 1
 
0.5%
' 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 19
23.5%
0 13
16.0%
3 10
12.3%
2 9
11.1%
7 8
9.9%
5 7
 
8.6%
9 5
 
6.2%
8 5
 
6.2%
6 3
 
3.7%
4 2
 
2.5%
Math Symbol
ValueCountFrequency (%)
= 65
91.5%
+ 5
 
7.0%
~ 1
 
1.4%
Space Separator
ValueCountFrequency (%)
590
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 6
100.0%
Other Symbol
ValueCountFrequency (%)
★ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1304
37.3%
Latin 1206
34.5%
Common 983
28.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 31
 
2.4%
는 29
 
2.2%
니 28
 
2.1%
하 25
 
1.9%
이 24
 
1.8%
의 21
 
1.6%
한 19
 
1.5%
고 18
 
1.4%
운 18
 
1.4%
기 17
 
1.3%
Other values (280) 1074
82.4%
Latin
ValueCountFrequency (%)
o 108
 
9.0%
e 92
 
7.6%
t 90
 
7.5%
a 79
 
6.6%
n 78
 
6.5%
r 60
 
5.0%
i 59
 
4.9%
m 56
 
4.6%
c 48
 
4.0%
s 47
 
3.9%
Other values (42) 489
40.5%
Common
ValueCountFrequency (%)
590
60.0%
. 65
 
6.6%
= 65
 
6.6%
: 40
 
4.1%
! 32
 
3.3%
; 31
 
3.2%
1 19
 
1.9%
_ 14
 
1.4%
0 13
 
1.3%
- 12
 
1.2%
Other values (20) 102
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2187
62.6%
Hangul 1292
37.0%
Compat Jamo 12
 
0.3%
None 1
 
< 0.1%
Misc Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
590
27.0%
o 108
 
4.9%
e 92
 
4.2%
t 90
 
4.1%
a 79
 
3.6%
n 78
 
3.6%
. 65
 
3.0%
= 65
 
3.0%
r 60
 
2.7%
i 59
 
2.7%
Other values (70) 901
41.2%
Hangul
ValueCountFrequency (%)
다 31
 
2.4%
는 29
 
2.2%
니 28
 
2.2%
하 25
 
1.9%
이 24
 
1.9%
의 21
 
1.6%
한 19
 
1.5%
고 18
 
1.4%
운 18
 
1.4%
기 17
 
1.3%
Other values (278) 1062
82.2%
Compat Jamo
ValueCountFrequency (%)
ㅤ 8
66.7%
ㅋ 4
33.3%
None
ValueCountFrequency (%)
· 1
100.0%
Misc Symbols
ValueCountFrequency (%)
★ 1
100.0%

개선도채널생성일자
Date

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2011-09-20 00:00:00
Maximum2019-06-13 00:00:00
2023-12-10T23:08:14.097782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:14.276949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

최근6개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)61.5%
Missing12
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean-5.1538462
Minimum-73
Maximum20
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)40.0%
Memory size357.0 B
2023-12-10T23:08:14.590241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-73
5-th percentile-32.2
Q1-3
median-2
Q3-1
95-th percentile11.6
Maximum20
Range93
Interquartile range (IQR)2

Descriptive statistics

Standard deviation21.43924
Coefficient of variation (CV)-4.1598526
Kurtosis10.17725
Mean-5.1538462
Median Absolute Deviation (MAD)1
Skewness-2.9381914
Sum-67
Variance459.64103
MonotonicityNot monotonic
2023-12-10T23:08:14.794218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-2 5
20.0%
-5 2
 
8.0%
-73 1
 
4.0%
20 1
 
4.0%
-1 1
 
4.0%
6 1
 
4.0%
4 1
 
4.0%
-3 1
 
4.0%
(Missing) 12
48.0%
ValueCountFrequency (%)
-73 1
 
4.0%
-5 2
 
8.0%
-3 1
 
4.0%
-2 5
20.0%
-1 1
 
4.0%
4 1
 
4.0%
6 1
 
4.0%
20 1
 
4.0%
ValueCountFrequency (%)
20 1
 
4.0%
6 1
 
4.0%
4 1
 
4.0%
-1 1
 
4.0%
-2 5
20.0%
-3 1
 
4.0%
-5 2
 
8.0%
-73 1
 
4.0%

최근12개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)50.0%
Missing11
Missing (%)44.0%
Infinite0
Infinite (%)0.0%
Mean1.9285714
Minimum-3
Maximum7
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)8.0%
Memory size357.0 B
2023-12-10T23:08:15.006093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile-1.7
Q11
median1
Q33
95-th percentile5.7
Maximum7
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5559669
Coefficient of variation (CV)1.3253162
Kurtosis0.48928736
Mean1.9285714
Median Absolute Deviation (MAD)1.5
Skewness0.22145117
Sum27
Variance6.532967
MonotonicityNot monotonic
2023-12-10T23:08:15.237903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 6
24.0%
5 2
 
8.0%
3 2
 
8.0%
-1 1
 
4.0%
2 1
 
4.0%
-3 1
 
4.0%
7 1
 
4.0%
(Missing) 11
44.0%
ValueCountFrequency (%)
-3 1
 
4.0%
-1 1
 
4.0%
1 6
24.0%
2 1
 
4.0%
3 2
 
8.0%
5 2
 
8.0%
7 1
 
4.0%
ValueCountFrequency (%)
7 1
 
4.0%
5 2
 
8.0%
3 2
 
8.0%
2 1
 
4.0%
1 6
24.0%
-1 1
 
4.0%
-3 1
 
4.0%

최초6개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
14 
0
8 
1
3 

Length

Max length4
Median length4
Mean length2.68
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 14
56.0%
0 8
32.0%
1 3
 
12.0%

Length

2023-12-10T23:08:15.661541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:15.903374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
56.0%
0 8
32.0%
1 3
 
12.0%

최초12개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
12 
0
11 
1
2 

Length

Max length4
Median length1
Mean length2.44
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
48.0%
0 11
44.0%
1 2
 
8.0%

Length

2023-12-10T23:08:16.195257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:16.376788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 12
48.0%
0 11
44.0%
1 2
 
8.0%

최근개선도지수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)88.2%
Missing8
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean38.571176
Minimum0
Maximum200.86
Zeros3
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T23:08:16.539273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.83
median24.67
Q342.67
95-th percentile127.636
Maximum200.86
Range200.86
Interquartile range (IQR)31.84

Descriptive statistics

Standard deviation50.684819
Coefficient of variation (CV)1.3140595
Kurtosis6.4144797
Mean38.571176
Median Absolute Deviation (MAD)13.91
Skewness2.3960029
Sum655.71
Variance2568.9509
MonotonicityNot monotonic
2023-12-10T23:08:16.775223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 3
 
12.0%
24.67 1
 
4.0%
34.51 1
 
4.0%
34.12 1
 
4.0%
10.83 1
 
4.0%
75.07 1
 
4.0%
109.33 1
 
4.0%
10.76 1
 
4.0%
11.23 1
 
4.0%
42.67 1
 
4.0%
Other values (5) 5
20.0%
(Missing) 8
32.0%
ValueCountFrequency (%)
0.0 3
12.0%
10.76 1
 
4.0%
10.83 1
 
4.0%
11.23 1
 
4.0%
11.53 1
 
4.0%
14.08 1
 
4.0%
24.67 1
 
4.0%
31.14 1
 
4.0%
34.12 1
 
4.0%
34.51 1
 
4.0%
ValueCountFrequency (%)
200.86 1
4.0%
109.33 1
4.0%
75.07 1
4.0%
44.91 1
4.0%
42.67 1
4.0%
34.51 1
4.0%
34.12 1
4.0%
31.14 1
4.0%
24.67 1
4.0%
14.08 1
4.0%

최근6개월표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing3
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean-1.8854545
Minimum-19.38
Maximum27.45
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)64.0%
Memory size357.0 B
2023-12-10T23:08:16.982709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19.38
5-th percentile-11.9545
Q1-4.4575
median-2.86
Q30.9125
95-th percentile2.4675
Maximum27.45
Range46.83
Interquartile range (IQR)5.37

Descriptive statistics

Standard deviation8.2209359
Coefficient of variation (CV)-4.3601878
Kurtosis8.2865828
Mean-1.8854545
Median Absolute Deviation (MAD)2.155
Skewness1.7789991
Sum-41.48
Variance67.583788
MonotonicityNot monotonic
2023-12-10T23:08:17.162851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-4.56 2
 
8.0%
-1.76 1
 
4.0%
27.45 1
 
4.0%
-12.26 1
 
4.0%
1.28 1
 
4.0%
-4.15 1
 
4.0%
2.48 1
 
4.0%
-6.15 1
 
4.0%
-3.59 1
 
4.0%
-1.35 1
 
4.0%
Other values (11) 11
44.0%
(Missing) 3
 
12.0%
ValueCountFrequency (%)
-19.38 1
4.0%
-12.26 1
4.0%
-6.15 1
4.0%
-5.47 1
4.0%
-4.56 2
8.0%
-4.15 1
4.0%
-4.0 1
4.0%
-3.97 1
4.0%
-3.92 1
4.0%
-3.59 1
4.0%
ValueCountFrequency (%)
27.45 1
4.0%
2.48 1
4.0%
2.23 1
4.0%
1.98 1
4.0%
1.88 1
4.0%
1.28 1
4.0%
-0.19 1
4.0%
-1.34 1
4.0%
-1.35 1
4.0%
-1.76 1
4.0%

최근12개월표준점수
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)95.7%
Missing2
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean3.7169565
Minimum-3.11
Maximum9.23
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)16.0%
Memory size357.0 B
2023-12-10T23:08:17.339550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.11
5-th percentile-1.774
Q12.77
median4.68
Q35.26
95-th percentile7.971
Maximum9.23
Range12.34
Interquartile range (IQR)2.49

Descriptive statistics

Standard deviation3.0371569
Coefficient of variation (CV)0.81710854
Kurtosis0.44412438
Mean3.7169565
Median Absolute Deviation (MAD)0.85
Skewness-0.74220996
Sum85.49
Variance9.2243221
MonotonicityNot monotonic
2023-12-10T23:08:17.543370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5.17 2
 
8.0%
2.41 1
 
4.0%
-3.11 1
 
4.0%
5.5 1
 
4.0%
8.23 1
 
4.0%
-1.85 1
 
4.0%
4.68 1
 
4.0%
5.23 1
 
4.0%
4.82 1
 
4.0%
3.71 1
 
4.0%
Other values (12) 12
48.0%
(Missing) 2
 
8.0%
ValueCountFrequency (%)
-3.11 1
4.0%
-1.85 1
4.0%
-1.09 1
4.0%
-0.94 1
4.0%
1.65 1
4.0%
2.41 1
4.0%
3.13 1
4.0%
3.71 1
4.0%
3.83 1
4.0%
3.92 1
4.0%
ValueCountFrequency (%)
9.23 1
4.0%
8.23 1
4.0%
5.64 1
4.0%
5.5 1
4.0%
5.4 1
4.0%
5.28 1
4.0%
5.24 1
4.0%
5.23 1
4.0%
5.17 2
8.0%
4.82 1
4.0%

최초6개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3964
Minimum-1.63
Maximum4.58
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)8.0%
Memory size357.0 B
2023-12-10T23:08:17.801643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.63
5-th percentile0.006
Q10.16
median0.17
Q30.2
95-th percentile2.138
Maximum4.58
Range6.21
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation1.0652929
Coefficient of variation (CV)2.6874191
Kurtosis10.804821
Mean0.3964
Median Absolute Deviation (MAD)0.02
Skewness2.7608715
Sum9.91
Variance1.134849
MonotonicityNot monotonic
2023-12-10T23:08:18.497409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.17 7
28.0%
0.2 3
12.0%
0.19 2
 
8.0%
0.15 2
 
8.0%
4.58 1
 
4.0%
2.48 1
 
4.0%
-0.01 1
 
4.0%
0.4 1
 
4.0%
-1.63 1
 
4.0%
0.1 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
-1.63 1
 
4.0%
-0.01 1
 
4.0%
0.07 1
 
4.0%
0.1 1
 
4.0%
0.15 2
 
8.0%
0.16 1
 
4.0%
0.17 7
28.0%
0.18 1
 
4.0%
0.19 2
 
8.0%
0.2 3
12.0%
ValueCountFrequency (%)
4.58 1
 
4.0%
2.48 1
 
4.0%
0.77 1
 
4.0%
0.4 1
 
4.0%
0.34 1
 
4.0%
0.2 3
12.0%
0.19 2
 
8.0%
0.18 1
 
4.0%
0.17 7
28.0%
0.16 1
 
4.0%

최초12개월표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)58.3%
Missing1
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean0.17083333
Minimum-1.48
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)8.0%
Memory size357.0 B
2023-12-10T23:08:18.832938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.48
5-th percentile-0.407
Q10.23
median0.25
Q30.2725
95-th percentile0.3885
Maximum0.65
Range2.13
Interquartile range (IQR)0.0425

Descriptive statistics

Standard deviation0.39857263
Coefficient of variation (CV)2.3331081
Kurtosis13.767831
Mean0.17083333
Median Absolute Deviation (MAD)0.02
Skewness-3.4864225
Sum4.1
Variance0.15886014
MonotonicityNot monotonic
2023-12-10T23:08:19.038451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.25 7
28.0%
0.26 3
12.0%
0.23 2
 
8.0%
0.12 2
 
8.0%
0.33 1
 
4.0%
0.28 1
 
4.0%
0.27 1
 
4.0%
0.39 1
 
4.0%
0.38 1
 
4.0%
0.65 1
 
4.0%
Other values (4) 4
16.0%
ValueCountFrequency (%)
-1.48 1
 
4.0%
-0.5 1
 
4.0%
0.12 2
 
8.0%
0.21 1
 
4.0%
0.23 2
 
8.0%
0.25 7
28.0%
0.26 3
12.0%
0.27 1
 
4.0%
0.28 1
 
4.0%
0.33 1
 
4.0%
ValueCountFrequency (%)
0.65 1
 
4.0%
0.39 1
 
4.0%
0.38 1
 
4.0%
0.34 1
 
4.0%
0.33 1
 
4.0%
0.28 1
 
4.0%
0.27 1
 
4.0%
0.26 3
12.0%
0.25 7
28.0%
0.23 2
 
8.0%

개선도최근표준점수
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.7492
Minimum-30.6
Maximum138.01
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)12.0%
Memory size357.0 B
2023-12-10T23:08:19.199053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-30.6
5-th percentile-19.966
Q17.97
median15.94
Q316.68
95-th percentile29.6
Maximum138.01
Range168.61
Interquartile range (IQR)8.71

Descriptive statistics

Standard deviation28.852694
Coefficient of variation (CV)1.8320101
Kurtosis14.404755
Mean15.7492
Median Absolute Deviation (MAD)3.35
Skewness3.1222365
Sum393.73
Variance832.47795
MonotonicityNot monotonic
2023-12-10T23:08:19.492096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
24.0 1
 
4.0%
17.77 1
 
4.0%
-22.94 1
 
4.0%
13.55 1
 
4.0%
15.4 1
 
4.0%
2.83 1
 
4.0%
15.22 1
 
4.0%
16.26 1
 
4.0%
15.97 1
 
4.0%
20.87 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
-30.6 1
4.0%
-22.94 1
4.0%
-8.07 1
4.0%
2.83 1
4.0%
4.29 1
4.0%
7.78 1
4.0%
7.97 1
4.0%
8.99 1
4.0%
13.55 1
4.0%
14.88 1
4.0%
ValueCountFrequency (%)
138.01 1
4.0%
31.0 1
4.0%
24.0 1
4.0%
20.87 1
4.0%
19.29 1
4.0%
17.77 1
4.0%
16.68 1
4.0%
16.32 1
4.0%
16.26 1
4.0%
16.21 1
4.0%

Interactions

2023-12-10T23:08:08.914500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:59.173158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:00.522382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:01.626650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:03.044529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:04.287368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:06.081856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:07.802957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:09.043451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:59.343142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:00.680448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:01.769857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:03.203562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:04.528304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:06.210832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:07.963188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:09.183455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:59.500340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:00.817791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:01.923370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:03.339811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:04.776479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:06.719326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:08.106454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:09.360243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:59.641918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:00.964871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:02.081025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:03.480395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:04.967494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:06.885808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:08.262474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:09.508117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:59.804755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:01.082051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:02.227582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:03.688850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:05.236993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:07.142602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:08.393387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:09.694704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:00.012530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:01.232722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:02.446094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:03.873907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:05.434142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:07.353748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:08.525720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:09.833090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:00.235124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:01.373305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:02.580217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:04.038550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:05.678358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:07.519676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:08.660730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:09.957153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:00.369018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:01.489456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:02.786162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:04.161500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:05.910284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:07.648512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:08.776331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:08:19.656560image/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
개선도지수수집일자1.0001.0001.0001.0001.000NaNNaNNaNNaNNaN0.0000.0000.0000.0000.341
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
최근6개월개선도1.0001.000NaN1.0001.0001.0000.0000.8071.0000.0000.2290.0000.7770.0000.443
최근12개월개선도1.0001.000NaN1.0001.0000.0001.0000.0000.5730.8500.8020.0000.0000.5530.691
최초6개월개선도1.0001.000NaN1.0001.0000.8070.0001.0000.0000.0720.3870.0000.5380.0000.000
최초12개월개선도1.0001.000NaN1.0001.0001.0000.5730.0001.0000.0000.4020.0000.0000.0000.000
최근개선도지수1.0001.000NaN1.0001.0000.0000.8500.0720.0001.0000.5680.0600.5290.7430.732
최근6개월표준점수1.0001.0000.0001.0001.0000.2290.8020.3870.4020.5681.0000.7170.0000.0000.427
최근12개월표준점수1.0001.0000.0001.0001.0000.0000.0000.0000.0000.0600.7171.0000.0000.0000.585
최초6개월표준점수1.0001.0000.0001.0001.0000.7770.0000.5380.0000.5290.0000.0001.0000.8240.000
최초12개월표준점수1.0001.0000.0001.0001.0000.0000.5530.0000.0000.7430.0000.0000.8241.0000.765
개선도최근표준점수1.0001.0000.3411.0001.0000.4430.6910.0000.0000.7320.4270.5850.0000.7651.000
2023-12-10T23:08:20.207571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초12개월개선도최초6개월개선도개선도지수수집일자
최초12개월개선도1.0000.0001.000
최초6개월개선도0.0001.0001.000
개선도지수수집일자1.0001.0001.000
2023-12-10T23:08:20.379455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최근6개월개선도최근12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수개선도지수수집일자최초6개월개선도최초12개월개선도
최근6개월개선도1.0000.128-0.271-0.163-0.120-0.123-0.0240.0111.0000.4470.816
최근12개월개선도0.1281.0000.0230.334-0.075-0.2600.415-0.2911.0000.0000.523
최근개선도지수-0.2710.0231.0000.550-0.369-0.1610.079-0.4181.0000.0000.000
최근6개월표준점수-0.1630.3340.5501.000-0.369-0.212-0.106-0.4770.0000.5590.592
최근12개월표준점수-0.120-0.075-0.369-0.3691.0000.2890.3390.2000.0000.0000.000
최초6개월표준점수-0.123-0.260-0.161-0.2120.2891.0000.531-0.1460.0000.5090.000
최초12개월표준점수-0.0240.4150.079-0.1060.3390.5311.000-0.2160.0000.0000.000
개선도최근표준점수0.011-0.291-0.418-0.4770.200-0.146-0.2161.0000.3810.0000.000
개선도지수수집일자1.0001.0001.0000.0000.0000.0000.0000.3811.0001.0001.000
최초6개월개선도0.4470.0000.0000.5590.0000.5090.0000.0001.0001.0000.000
최초12개월개선도0.8160.5230.0000.5920.0000.0000.0000.0001.0000.0001.000

Missing values

2023-12-10T23:08:10.160459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:08:10.465678image/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.
2023-12-10T23:08:10.736083image/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
0UCVvyVBQF5ejhjx4l-c9RUWw경북대학교병원2020-11-04경북대학교병원 동영상 자료실2016-03-03<NA><NA><NA><NA><NA>-2.135.40.190.2524.0
1UC1EEpE0lA9BaArXhRTHIG6w강과장2020-11-30그저 하루하루 적당히 열심히 살아가는 직장인의 일상입니다 방문해주셔서 감사합니다 ^^ 비지니스 문의 kangmanagertv@sandboxnetwork.net 인스타그램 kangmang612 개인메일 kang0930yu@gmail.com 주소 : 서울 강남구 테헤란로 518 한국섬유산업연합회 WeWork빌딩 15층 샌드박스 강과장 (보내주시는 구독자 선물은 샌드박스쪽에서 2주마다 모아서 저에게 택배가 보내지기때문에 변질되는 음식물은 택배로 받기가 어렵습니다)2018-09-16-7311024.67-4.03.134.580.3317.77
2UC-gWrEGYpG2jYfl8A_KIGnQ[KERI]한국전기연구원2020-11-30전기전문 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>-3.975.280.20.2816.21
3UC4XjKgCtpwpACzUCiUKOd3Q사모장2020-11-30안녕하세요! 아주 능숙한 게임실력과 파이팅이넘치는 사모장입니다! 동영상 업로드는 매일 1개씩 오후 5시~6시에 올라갑니다!! 문의메일 : mozang11@naver.com2013-12-08-210034.511.981.650.20.27-8.07
4UC6qXcpjNzFg7Pe4NDRZVQDgYoonJae윤졔2020-11-30구독 좋아요 문의 yoonjae.work@gmail.com2017-05-04-51<NA><NA>0.0-3.925.640.170.2316.68
5UC85mXeWRaycnlFmrhBdaAlg남자커피 Namja Coffee2020-11-30안녕하세요 커피와 음료를 사랑하는 남자커피 입니다!! 집에서 쉽게 만들수있는 음료 레시피 소개와 재미있는 커피상식들을 알려 드리고 싶어 운영하는 채널입니다. 부족할수있지만 재미있게 봐주세요!! 문의 - bakasa0817@gmail.com2018-08-18-211034.12<NA>9.232.480.3914.88
6UC9G7V6-HU_Cg7-yWLO1rH6wPURE.D퓨어디2020-11-30teenpopk@gmail.com2016-12-212051110.83-19.38<NA>-0.01<NA>31.0
7UC8JOLZ-YA34ylQTz2tqSlGwKAIST2020-11-30한국과학기술원(KAIST)의 공식 유튜브 채널입니다! This is the official YouTube channel of KAIST!2013-07-28<NA><NA><NA><NA><NA>-5.475.240.190.2616.11
8UC2Zi06YjNBM37g8d0IkHPMATV CHOSUN PLUS2020-11-30TV조선 방송 채널에; 더한 클립과; 더한 영상들을 모아둔 더한 채널 TV조선 플러스2019-01-18<NA><NA><NA><NA><NA>-0.193.920.170.25-30.6
9UC9mVBtjHNwSltQKTyLF2KXw여니네일 YeoniNail2020-11-30간단하고 쉬운 네일아트 방법과 뷰티를 공유하고싶은 Yeoni입니다! 네일아트 뷰티 리뷰 DIY 비지니스 문의 : yeonime0@gmail.com 인스타그램 : https:www.instagram.comyeoni_nailart @yeoni_nailart2014-11-09<NA><NA>000.0-4.565.170.40.3816.32
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
15UCBIoXzDldCnpbM_7uyG0_TgHONG SOUND2020-11-30취미로 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.com2015-12-11610011.23-1.76-1.090.34-0.54.29
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