Overview

Dataset statistics

Number of variables15
Number of observations24
Missing cells68
Missing cells (%)18.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory135.5 B

Variable types

Text3
Categorical3
DateTime1
Numeric8

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/07a1ff75-9ffe-4712-ad20-37981f470ffc

Alerts

개선도지수수집일자 has constant value ""Constant
최근6개월개선도 is highly overall correlated with 최근12개월표준점수 and 2 other fieldsHigh correlation
최근12개월개선도 is highly overall correlated with 최초6개월개선도 and 1 other fieldsHigh correlation
최근개선도지수 is highly overall correlated with 최근6개월표준점수 and 1 other fieldsHigh correlation
최근6개월표준점수 is highly overall correlated with 최근개선도지수High correlation
최근12개월표준점수 is highly overall correlated with 최근6개월개선도 and 1 other fieldsHigh correlation
최초6개월표준점수 is highly overall correlated with 최초12개월개선도High correlation
최초12개월표준점수 is highly overall correlated with 최초12개월개선도High correlation
최초6개월개선도 is highly overall correlated with 최근6개월개선도 and 2 other fieldsHigh correlation
최초12개월개선도 is highly overall correlated with 최근6개월개선도 and 4 other fieldsHigh correlation
개선도지수채널설명 has 5 (20.8%) missing valuesMissing
개선도채널생성일자 has 6 (25.0%) missing valuesMissing
최근6개월개선도 has 16 (66.7%) missing valuesMissing
최근12개월개선도 has 14 (58.3%) missing valuesMissing
최근개선도지수 has 11 (45.8%) missing valuesMissing
최근6개월표준점수 has 16 (66.7%) missing valuesMissing
개선도지수채널ID has unique valuesUnique
개선도지수채널명 has unique valuesUnique
최근6개월개선도 has 1 (4.2%) zerosZeros
최근12개월개선도 has 1 (4.2%) zerosZeros
최근개선도지수 has 2 (8.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:24:58.819810
Analysis finished2023-12-10 14:25:06.831755
Duration8.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개선도지수채널ID
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-10T23:25:07.029602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters576
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st rowUC0ru5w57PyGpbsEKwN4LuwA
2nd rowUC0hk2D145ogyBYMKjylWkkw
3rd rowUC-gWrEGYpG2jYfl8A_KIGnQ
4th rowUC1dMe0fYTM4r9mNQc9v1ziA
5th rowUC28lbdOHnj-leok6tHIx7ew
ValueCountFrequency (%)
uc0ru5w57pygpbsekwn4luwa 1
 
4.2%
uc0hk2d145ogybymkjylwkkw 1
 
4.2%
uc8jolz-ya34ylqtz2tqslgw 1
 
4.2%
uc85mxewraycnlfmrhbdaalg 1
 
4.2%
uc2wzjuud_wa9ps2forco1eq 1
 
4.2%
uc7bqxkhltafctwwwhuzoouq 1
 
4.2%
uc7qzaxgxsbobtjv4ppgdsxg 1
 
4.2%
uc6dtags0tiobtojlnlwzdqw 1
 
4.2%
uc69dhld0fumlgp9bsxitbdw 1
 
4.2%
uc5oft5dvf43m2cfmhpjlvgq 1
 
4.2%
Other values (14) 14
58.3%
2023-12-10T23:25:07.382390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 32
 
5.6%
C 31
 
5.4%
Q 19
 
3.3%
w 17
 
3.0%
g 15
 
2.6%
Z 13
 
2.3%
A 13
 
2.3%
4 12
 
2.1%
o 12
 
2.1%
W 12
 
2.1%
Other values (54) 400
69.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 260
45.1%
Lowercase Letter 216
37.5%
Decimal Number 88
 
15.3%
Dash Punctuation 7
 
1.2%
Connector Punctuation 5
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 32
 
12.3%
C 31
 
11.9%
Q 19
 
7.3%
Z 13
 
5.0%
A 13
 
5.0%
W 12
 
4.6%
G 11
 
4.2%
D 10
 
3.8%
Y 9
 
3.5%
B 9
 
3.5%
Other values (16) 101
38.8%
Lowercase Letter
ValueCountFrequency (%)
w 17
 
7.9%
g 15
 
6.9%
o 12
 
5.6%
l 11
 
5.1%
d 10
 
4.6%
f 10
 
4.6%
x 9
 
4.2%
e 9
 
4.2%
t 9
 
4.2%
k 9
 
4.2%
Other values (16) 105
48.6%
Decimal Number
ValueCountFrequency (%)
4 12
13.6%
2 11
12.5%
0 10
11.4%
9 10
11.4%
5 10
11.4%
7 9
10.2%
8 7
8.0%
6 7
8.0%
3 7
8.0%
1 5
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 476
82.6%
Common 100
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 32
 
6.7%
C 31
 
6.5%
Q 19
 
4.0%
w 17
 
3.6%
g 15
 
3.2%
Z 13
 
2.7%
A 13
 
2.7%
o 12
 
2.5%
W 12
 
2.5%
G 11
 
2.3%
Other values (42) 301
63.2%
Common
ValueCountFrequency (%)
4 12
12.0%
2 11
11.0%
0 10
10.0%
9 10
10.0%
5 10
10.0%
7 9
9.0%
8 7
7.0%
6 7
7.0%
3 7
7.0%
- 7
7.0%
Other values (2) 10
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 32
 
5.6%
C 31
 
5.4%
Q 19
 
3.3%
w 17
 
3.0%
g 15
 
2.6%
Z 13
 
2.3%
A 13
 
2.3%
4 12
 
2.1%
o 12
 
2.1%
W 12
 
2.1%
Other values (54) 400
69.4%

개선도지수채널명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-10T23:25:07.640135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length14
Mean length10.291667
Min length3

Characters and Unicode

Total characters247
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

Unique24 ?
Unique (%)100.0%

Sample

1st row재민정
2nd row안녕히_계세요
3rd row[KERI]한국전기연구원
4th rowSaehyeon세현
5th row고양이와 소소한생활
ValueCountFrequency (%)
3
 
6.7%
재민정 1
 
2.2%
발달장애 1
 
2.2%
한국전력 1
 
2.2%
kepco 1
 
2.2%
애주가tv참pd 1
 
2.2%
잡아바 1
 
2.2%
tv 1
 
2.2%
경기도일자리재단 1
 
2.2%
ebs 1
 
2.2%
Other values (33) 33
73.3%
2023-12-10T23:25:08.052403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
8.5%
e 7
 
2.8%
T 7
 
2.8%
o 6
 
2.4%
S 6
 
2.4%
a 5
 
2.0%
V 5
 
2.0%
E 5
 
2.0%
[ 4
 
1.6%
] 4
 
1.6%
Other values (117) 177
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
40.1%
Uppercase Letter 62
25.1%
Lowercase Letter 50
20.2%
Space Separator 21
 
8.5%
Open Punctuation 4
 
1.6%
Close Punctuation 4
 
1.6%
Decimal Number 3
 
1.2%
Dash Punctuation 2
 
0.8%
Connector Punctuation 1
 
0.4%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3
 
3.0%
국 3
 
3.0%
한 3
 
3.0%
기 2
 
2.0%
브 2
 
2.0%
자 2
 
2.0%
선 2
 
2.0%
애 2
 
2.0%
히 2
 
2.0%
가 2
 
2.0%
Other values (69) 76
76.8%
Lowercase Letter
ValueCountFrequency (%)
e 7
14.0%
o 6
12.0%
a 5
10.0%
s 4
 
8.0%
f 4
 
8.0%
i 3
 
6.0%
r 3
 
6.0%
c 3
 
6.0%
m 2
 
4.0%
k 2
 
4.0%
Other values (10) 11
22.0%
Uppercase Letter
ValueCountFrequency (%)
T 7
 
11.3%
S 6
 
9.7%
V 5
 
8.1%
E 5
 
8.1%
C 4
 
6.5%
D 4
 
6.5%
O 3
 
4.8%
P 3
 
4.8%
N 3
 
4.8%
H 3
 
4.8%
Other values (9) 19
30.6%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
21
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112
45.3%
Hangul 99
40.1%
Common 36
 
14.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3
 
3.0%
국 3
 
3.0%
한 3
 
3.0%
기 2
 
2.0%
브 2
 
2.0%
자 2
 
2.0%
선 2
 
2.0%
애 2
 
2.0%
히 2
 
2.0%
가 2
 
2.0%
Other values (69) 76
76.8%
Latin
ValueCountFrequency (%)
e 7
 
6.2%
T 7
 
6.2%
o 6
 
5.4%
S 6
 
5.4%
a 5
 
4.5%
V 5
 
4.5%
E 5
 
4.5%
s 4
 
3.6%
f 4
 
3.6%
C 4
 
3.6%
Other values (29) 59
52.7%
Common
ValueCountFrequency (%)
21
58.3%
[ 4
 
11.1%
] 4
 
11.1%
- 2
 
5.6%
_ 1
 
2.8%
1 1
 
2.8%
. 1
 
2.8%
5 1
 
2.8%
9 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148
59.9%
Hangul 99
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
 
14.2%
e 7
 
4.7%
T 7
 
4.7%
o 6
 
4.1%
S 6
 
4.1%
a 5
 
3.4%
V 5
 
3.4%
E 5
 
3.4%
[ 4
 
2.7%
] 4
 
2.7%
Other values (38) 78
52.7%
Hangul
ValueCountFrequency (%)
주 3
 
3.0%
국 3
 
3.0%
한 3
 
3.0%
기 2
 
2.0%
브 2
 
2.0%
자 2
 
2.0%
선 2
 
2.0%
애 2
 
2.0%
히 2
 
2.0%
가 2
 
2.0%
Other values (69) 76
76.8%

개선도지수수집일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2021-10-31
24 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-31
2nd row2021-10-31
3rd row2021-10-31
4th row2021-10-31
5th row2021-10-31

Common Values

ValueCountFrequency (%)
2021-10-31 24
100.0%

Length

2023-12-10T23:25:08.199258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:08.296654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-10-31 24
100.0%

개선도지수채널설명
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing5
Missing (%)20.8%
Memory size324.0 B
2023-12-10T23:25:08.522187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length897
Median length99
Mean length208.73684
Min length1

Characters and Unicode

Total characters3966
Distinct characters410
Distinct categories15 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row.
2nd 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
3rd row뷰티와 일상을 기록하는 유튜버 세현입니다 비즈니스 문의- saehyeon031105@gmail.com 으로 주시면 감사하겠습니다!
4th row인스타 @jj_d_m_pp 고양이를 좋아하는 사람의 소소한채널; [고양이와 소소한생활] 입니다. 16년 3월 25일; 길냥이였던 쫄냥이를 만났고; 출산이 임박해보이는 커다란 배를 보고 멸치라도 줄까 집에 들인 이후 쭉; 집사의 길을 걸어가고 있습니다. 초보 집사로 좌충우돌 해 가며 현재 반려묘 네마리; 쫄냥까망단추뿌꾸를 키우고 있습니다. 고양이로 인해 많이 웃게되는 나날을 함께하고 싶습니다~^^
5th row김제시 공식 유튜브 채널 '김제지평선TV' 입니다. https:www.youtube.comgimjecity #김제 #지평선TV #김제지평선TV #김제시 #김제시공식유튜브 #지평선
ValueCountFrequency (%)
51
 
7.7%
▶ 20
 
3.0%
월~금 8
 
1.2%
공식 6
 
0.9%
for 5
 
0.8%
you 5
 
0.8%
있습니다 5
 
0.8%
09:00 4
 
0.6%
매일 4
 
0.6%
유튜브 4
 
0.6%
Other values (472) 549
83.1%
2023-12-10T23:25:08.861189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
717
 
18.1%
- 181
 
4.6%
o 100
 
2.5%
0 88
 
2.2%
e 83
 
2.1%
. 77
 
1.9%
: 77
 
1.9%
t 72
 
1.8%
a 63
 
1.6%
i 60
 
1.5%
Other values (400) 2448
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1421
35.8%
Lowercase Letter 937
23.6%
Space Separator 717
18.1%
Other Punctuation 223
 
5.6%
Decimal Number 207
 
5.2%
Dash Punctuation 181
 
4.6%
Uppercase Letter 109
 
2.7%
Math Symbol 68
 
1.7%
Close Punctuation 32
 
0.8%
Open Punctuation 32
 
0.8%
Other values (5) 39
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 41
 
2.9%
니 38
 
2.7%
이 36
 
2.5%
요 28
 
2.0%
의 26
 
1.8%
는 24
 
1.7%
하 23
 
1.6%
리 21
 
1.5%
주 21
 
1.5%
일 20
 
1.4%
Other values (316) 1143
80.4%
Uppercase Letter
ValueCountFrequency (%)
T 18
16.5%
D 10
 
9.2%
S 9
 
8.3%
V 8
 
7.3%
O 6
 
5.5%
B 5
 
4.6%
H 5
 
4.6%
N 4
 
3.7%
L 4
 
3.7%
P 4
 
3.7%
Other values (16) 36
33.0%
Lowercase Letter
ValueCountFrequency (%)
o 100
 
10.7%
e 83
 
8.9%
t 72
 
7.7%
a 63
 
6.7%
i 60
 
6.4%
s 56
 
6.0%
n 53
 
5.7%
h 53
 
5.7%
r 46
 
4.9%
w 43
 
4.6%
Other values (15) 308
32.9%
Decimal Number
ValueCountFrequency (%)
0 88
42.5%
1 39
18.8%
2 19
 
9.2%
6 18
 
8.7%
5 9
 
4.3%
9 9
 
4.3%
7 8
 
3.9%
4 7
 
3.4%
8 7
 
3.4%
3 3
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 77
34.5%
: 77
34.5%
; 21
 
9.4%
! 16
 
7.2%
? 15
 
6.7%
@ 6
 
2.7%
# 6
 
2.7%
* 3
 
1.3%
' 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
~ 38
55.9%
= 30
44.1%
Close Punctuation
ValueCountFrequency (%)
] 24
75.0%
) 8
 
25.0%
Open Punctuation
ValueCountFrequency (%)
[ 24
75.0%
( 8
 
25.0%
Other Symbol
ValueCountFrequency (%)
▶ 21
84.0%
♥ 4
 
16.0%
Space Separator
ValueCountFrequency (%)
717
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 181
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 4
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1499
37.8%
Hangul 1421
35.8%
Latin 1046
26.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 41
 
2.9%
니 38
 
2.7%
이 36
 
2.5%
요 28
 
2.0%
의 26
 
1.8%
는 24
 
1.7%
하 23
 
1.6%
리 21
 
1.5%
주 21
 
1.5%
일 20
 
1.4%
Other values (316) 1143
80.4%
Latin
ValueCountFrequency (%)
o 100
 
9.6%
e 83
 
7.9%
t 72
 
6.9%
a 63
 
6.0%
i 60
 
5.7%
s 56
 
5.4%
n 53
 
5.1%
h 53
 
5.1%
r 46
 
4.4%
w 43
 
4.1%
Other values (41) 417
39.9%
Common
ValueCountFrequency (%)
717
47.8%
- 181
 
12.1%
0 88
 
5.9%
. 77
 
5.1%
: 77
 
5.1%
1 39
 
2.6%
~ 38
 
2.5%
= 30
 
2.0%
] 24
 
1.6%
[ 24
 
1.6%
Other values (23) 204
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2518
63.5%
Hangul 1421
35.8%
Geometric Shapes 21
 
0.5%
Misc Symbols 4
 
0.1%
Punctuation 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
717
28.5%
- 181
 
7.2%
o 100
 
4.0%
0 88
 
3.5%
e 83
 
3.3%
. 77
 
3.1%
: 77
 
3.1%
t 72
 
2.9%
a 63
 
2.5%
i 60
 
2.4%
Other values (70) 1000
39.7%
Hangul
ValueCountFrequency (%)
다 41
 
2.9%
니 38
 
2.7%
이 36
 
2.5%
요 28
 
2.0%
의 26
 
1.8%
는 24
 
1.7%
하 23
 
1.6%
리 21
 
1.5%
주 21
 
1.5%
일 20
 
1.4%
Other values (316) 1143
80.4%
Geometric Shapes
ValueCountFrequency (%)
▶ 21
100.0%
Misc Symbols
ValueCountFrequency (%)
♥ 4
100.0%
Punctuation
ValueCountFrequency (%)
‘ 1
50.0%
’ 1
50.0%

개선도채널생성일자
Date

MISSING 

Distinct18
Distinct (%)100.0%
Missing6
Missing (%)25.0%
Memory size324.0 B
Minimum2011-05-18 00:00:00
Maximum2019-01-18 00:00:00
2023-12-10T23:25:08.978248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:09.111888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

최근6개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)75.0%
Missing16
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean13.75
Minimum-23
Maximum74
Zeros1
Zeros (%)4.2%
Negative4
Negative (%)16.7%
Memory size348.0 B
2023-12-10T23:25:09.202462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23
5-th percentile-15.65
Q1-2
median-1
Q317.25
95-th percentile70.15
Maximum74
Range97
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation34.800452
Coefficient of variation (CV)2.5309419
Kurtosis0.038017941
Mean13.75
Median Absolute Deviation (MAD)2
Skewness1.2319774
Sum110
Variance1211.0714
MonotonicityNot monotonic
2023-12-10T23:25:09.295959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-2 3
 
12.5%
0 1
 
4.2%
-23 1
 
4.2%
74 1
 
4.2%
2 1
 
4.2%
63 1
 
4.2%
(Missing) 16
66.7%
ValueCountFrequency (%)
-23 1
 
4.2%
-2 3
12.5%
0 1
 
4.2%
2 1
 
4.2%
63 1
 
4.2%
74 1
 
4.2%
ValueCountFrequency (%)
74 1
 
4.2%
63 1
 
4.2%
2 1
 
4.2%
0 1
 
4.2%
-2 3
12.5%
-23 1
 
4.2%

최근12개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)80.0%
Missing14
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean-2.9
Minimum-31
Maximum9
Zeros1
Zeros (%)4.2%
Negative5
Negative (%)20.8%
Memory size348.0 B
2023-12-10T23:25:09.412778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-31
5-th percentile-21.1
Q1-4.75
median-0.5
Q33
95-th percentile6.75
Maximum9
Range40
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation11.110056
Coefficient of variation (CV)-3.8310536
Kurtosis4.9691731
Mean-2.9
Median Absolute Deviation (MAD)4
Skewness-2.0237826
Sum-29
Variance123.43333
MonotonicityNot monotonic
2023-12-10T23:25:09.548963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 2
 
8.3%
-1 2
 
8.3%
0 1
 
4.2%
4 1
 
4.2%
-6 1
 
4.2%
9 1
 
4.2%
-31 1
 
4.2%
-9 1
 
4.2%
(Missing) 14
58.3%
ValueCountFrequency (%)
-31 1
4.2%
-9 1
4.2%
-6 1
4.2%
-1 2
8.3%
0 1
4.2%
3 2
8.3%
4 1
4.2%
9 1
4.2%
ValueCountFrequency (%)
9 1
4.2%
4 1
4.2%
3 2
8.3%
0 1
4.2%
-1 2
8.3%
-6 1
4.2%
-9 1
4.2%
-31 1
4.2%

최초6개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
17 
0
4 
1
3 

Length

Max length4
Median length4
Mean length3.125
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
70.8%
0 4
 
16.7%
1 3
 
12.5%

Length

2023-12-10T23:25:09.717607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:09.827898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
70.8%
0 4
 
16.7%
1 3
 
12.5%

최초12개월개선도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
17 
0
5 
-1
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.1666667
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
70.8%
0 5
 
20.8%
-1 1
 
4.2%
1 1
 
4.2%

Length

2023-12-10T23:25:09.944761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:10.048481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
70.8%
0 5
 
20.8%
1 2
 
8.3%

최근개선도지수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)92.3%
Missing11
Missing (%)45.8%
Infinite0
Infinite (%)0.0%
Mean24.159231
Minimum0
Maximum118.7
Zeros2
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-10T23:25:10.143507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.42
median12.93
Q340.41
95-th percentile75.266
Maximum118.7
Range118.7
Interquartile range (IQR)35.99

Descriptive statistics

Standard deviation32.716563
Coefficient of variation (CV)1.3542055
Kurtosis5.8831053
Mean24.159231
Median Absolute Deviation (MAD)10.5
Skewness2.275808
Sum314.07
Variance1070.3735
MonotonicityNot monotonic
2023-12-10T23:25:10.320770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 2
 
8.3%
6.53 1
 
4.2%
118.7 1
 
4.2%
13.23 1
 
4.2%
12.15 1
 
4.2%
40.41 1
 
4.2%
46.31 1
 
4.2%
12.93 1
 
4.2%
2.43 1
 
4.2%
4.42 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 11
45.8%
ValueCountFrequency (%)
0.0 2
8.3%
2.43 1
4.2%
4.42 1
4.2%
6.53 1
4.2%
12.15 1
4.2%
12.93 1
4.2%
13.23 1
4.2%
14.61 1
4.2%
40.41 1
4.2%
42.35 1
4.2%
ValueCountFrequency (%)
118.7 1
4.2%
46.31 1
4.2%
42.35 1
4.2%
40.41 1
4.2%
14.61 1
4.2%
13.23 1
4.2%
12.93 1
4.2%
12.15 1
4.2%
6.53 1
4.2%
4.42 1
4.2%

최근6개월표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing16
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean-11.87125
Minimum-32.36
Maximum2.49
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)25.0%
Memory size348.0 B
2023-12-10T23:25:10.457771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-32.36
5-th percentile-31.1315
Q1-27.32
median-4.12
Q3-1.315
95-th percentile1.909
Maximum2.49
Range34.85
Interquartile range (IQR)26.005

Descriptive statistics

Standard deviation14.716338
Coefficient of variation (CV)-1.2396621
Kurtosis-2.0172754
Mean-11.87125
Median Absolute Deviation (MAD)5.78
Skewness-0.5934416
Sum-94.97
Variance216.57061
MonotonicityNot monotonic
2023-12-10T23:25:10.565391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-28.85 1
 
4.2%
2.49 1
 
4.2%
-26.81 1
 
4.2%
0.83 1
 
4.2%
-2.03 1
 
4.2%
-4.42 1
 
4.2%
-32.36 1
 
4.2%
-3.82 1
 
4.2%
(Missing) 16
66.7%
ValueCountFrequency (%)
-32.36 1
4.2%
-28.85 1
4.2%
-26.81 1
4.2%
-4.42 1
4.2%
-3.82 1
4.2%
-2.03 1
4.2%
0.83 1
4.2%
2.49 1
4.2%
ValueCountFrequency (%)
2.49 1
4.2%
0.83 1
4.2%
-2.03 1
4.2%
-3.82 1
4.2%
-4.42 1
4.2%
-26.81 1
4.2%
-28.85 1
4.2%
-32.36 1
4.2%

최근12개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2041667
Minimum-8.18
Maximum12.17
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)16.7%
Memory size348.0 B
2023-12-10T23:25:10.673101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.18
5-th percentile-5.718
Q12.3575
median7.81
Q38.12
95-th percentile9.0555
Maximum12.17
Range20.35
Interquartile range (IQR)5.7625

Descriptive statistics

Standard deviation5.1112016
Coefficient of variation (CV)0.98213642
Kurtosis1.3477005
Mean5.2041667
Median Absolute Deviation (MAD)0.945
Skewness-1.4002146
Sum124.9
Variance26.124382
MonotonicityNot monotonic
2023-12-10T23:25:10.797134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8.12 6
25.0%
7.72 1
 
4.2%
8.69 1
 
4.2%
2.67 1
 
4.2%
5.48 1
 
4.2%
1.42 1
 
4.2%
7.26 1
 
4.2%
8.03 1
 
4.2%
-8.18 1
 
4.2%
-0.56 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
-8.18 1
4.2%
-6.45 1
4.2%
-1.57 1
4.2%
-0.56 1
4.2%
0.85 1
4.2%
1.42 1
4.2%
2.67 1
4.2%
5.48 1
4.2%
6.45 1
4.2%
6.8 1
4.2%
ValueCountFrequency (%)
12.17 1
 
4.2%
9.12 1
 
4.2%
8.69 1
 
4.2%
8.38 1
 
4.2%
8.12 6
25.0%
8.03 1
 
4.2%
7.9 1
 
4.2%
7.72 1
 
4.2%
7.26 1
 
4.2%
6.8 1
 
4.2%

최초6개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13625
Minimum-3.45
Maximum2.76
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)12.5%
Memory size348.0 B
2023-12-10T23:25:10.911135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.45
5-th percentile-1.3335
Q10.17
median0.17
Q30.1825
95-th percentile1.5
Maximum2.76
Range6.21
Interquartile range (IQR)0.0125

Descriptive statistics

Standard deviation1.0538534
Coefficient of variation (CV)7.7347041
Kurtosis6.9144414
Mean0.13625
Median Absolute Deviation (MAD)0.005
Skewness-1.1855268
Sum3.27
Variance1.1106071
MonotonicityNot monotonic
2023-12-10T23:25:11.019455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.17 12
50.0%
-3.45 1
 
4.2%
-0.05 1
 
4.2%
0.21 1
 
4.2%
0.2 1
 
4.2%
1.59 1
 
4.2%
-1.56 1
 
4.2%
0.01 1
 
4.2%
0.16 1
 
4.2%
0.99 1
 
4.2%
Other values (3) 3
 
12.5%
ValueCountFrequency (%)
-3.45 1
 
4.2%
-1.56 1
 
4.2%
-0.05 1
 
4.2%
0.01 1
 
4.2%
0.16 1
 
4.2%
0.17 12
50.0%
0.18 1
 
4.2%
0.19 1
 
4.2%
0.2 1
 
4.2%
0.21 1
 
4.2%
ValueCountFrequency (%)
2.76 1
 
4.2%
1.59 1
 
4.2%
0.99 1
 
4.2%
0.21 1
 
4.2%
0.2 1
 
4.2%
0.19 1
 
4.2%
0.18 1
 
4.2%
0.17 12
50.0%
0.16 1
 
4.2%
0.01 1
 
4.2%

최초12개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23
Minimum-2.14
Maximum2.39
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)12.5%
Memory size348.0 B
2023-12-10T23:25:11.152236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.14
5-th percentile-0.4115
Q10.25
median0.25
Q30.275
95-th percentile0.6635
Maximum2.39
Range4.53
Interquartile range (IQR)0.025

Descriptive statistics

Standard deviation0.70035395
Coefficient of variation (CV)3.0450172
Kurtosis9.1511549
Mean0.23
Median Absolute Deviation (MAD)0.01
Skewness-0.48633034
Sum5.52
Variance0.49049565
MonotonicityNot monotonic
2023-12-10T23:25:11.269741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.25 11
45.8%
0.27 2
 
8.3%
2.39 1
 
4.2%
-0.25 1
 
4.2%
0.29 1
 
4.2%
0.39 1
 
4.2%
0.71 1
 
4.2%
0.4 1
 
4.2%
0.24 1
 
4.2%
-2.14 1
 
4.2%
Other values (3) 3
 
12.5%
ValueCountFrequency (%)
-2.14 1
 
4.2%
-0.44 1
 
4.2%
-0.25 1
 
4.2%
0.24 1
 
4.2%
0.25 11
45.8%
0.26 1
 
4.2%
0.27 2
 
8.3%
0.29 1
 
4.2%
0.38 1
 
4.2%
0.39 1
 
4.2%
ValueCountFrequency (%)
2.39 1
 
4.2%
0.71 1
 
4.2%
0.4 1
 
4.2%
0.39 1
 
4.2%
0.38 1
 
4.2%
0.29 1
 
4.2%
0.27 2
 
8.3%
0.26 1
 
4.2%
0.25 11
45.8%
0.24 1
 
4.2%

개선도최근표준점수
Real number (ℝ)

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.024167
Minimum-86.11
Maximum200.08
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)8.3%
Memory size348.0 B
2023-12-10T23:25:11.411265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-86.11
5-th percentile-5.554
Q121.1625
median22.945
Q323.5575
95-th percentile27.2685
Maximum200.08
Range286.19
Interquartile range (IQR)2.395

Descriptive statistics

Standard deviation44.226322
Coefficient of variation (CV)1.9208653
Kurtosis13.178422
Mean23.024167
Median Absolute Deviation (MAD)1
Skewness2.3086706
Sum552.58
Variance1955.9675
MonotonicityNot monotonic
2023-12-10T23:25:11.588733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
22.87 2
 
8.3%
22.86 2
 
8.3%
23.0 1
 
4.2%
24.22 1
 
4.2%
20.0 1
 
4.2%
26.75 1
 
4.2%
27.36 1
 
4.2%
8.96 1
 
4.2%
26.5 1
 
4.2%
23.03 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
-86.11 1
4.2%
-7.03 1
4.2%
2.81 1
4.2%
8.96 1
4.2%
10.37 1
4.2%
20.0 1
4.2%
21.55 1
4.2%
22.86 2
8.3%
22.87 2
8.3%
22.92 1
4.2%
ValueCountFrequency (%)
200.08 1
4.2%
27.36 1
4.2%
26.75 1
4.2%
26.5 1
4.2%
24.22 1
4.2%
23.67 1
4.2%
23.52 1
4.2%
23.34 1
4.2%
23.21 1
4.2%
23.03 1
4.2%

Interactions

2023-12-10T23:25:05.100338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.438265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.276687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.050447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.803477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.488508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.444492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.318653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.189585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.505817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.375093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.120837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.902460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.582074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.531734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.415709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.297917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.591874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.487501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.212169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.989358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.689018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.645981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.525758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.399954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.662685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.580013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.302529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.075506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.803501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.769437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.618733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.500745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.734930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.667616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.394366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.161380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.969612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.873728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.713915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.603774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.809794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.758702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.485531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.248655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.102022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.996023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.819860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.704626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.889675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.868458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.630976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.329994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.228011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.125521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.919223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.829436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:59.965624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:00.957766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:01.704879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:02.406255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:03.335272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:04.227684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:05.002483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:25:11.712430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개선도지수채널ID개선도지수채널명개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
개선도지수채널ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도지수채널명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도지수채널설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도채널생성일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최근6개월개선도1.0001.0001.0001.0001.0000.7061.0001.0000.0001.0000.5190.5041.0000.336
최근12개월개선도1.0001.0001.0001.0000.7061.0001.0001.0000.4060.0000.8320.6660.8160.841
최초6개월개선도1.0001.0001.0001.0001.0001.0001.0000.0850.4230.0000.0000.6850.2490.000
최초12개월개선도1.0001.0001.0001.0001.0001.0000.0851.0000.0000.0001.0001.0001.0000.000
최근개선도지수1.0001.0001.0001.0000.0000.4060.4230.0001.0000.4500.0000.0000.0000.891
최근6개월표준점수1.0001.0001.0001.0001.0000.0000.0000.0000.4501.0000.3650.0000.4350.270
최근12개월표준점수1.0001.0001.0001.0000.5190.8320.0001.0000.0000.3651.0000.7570.0000.611
최초6개월표준점수1.0001.0001.0001.0000.5040.6660.6851.0000.0000.0000.7571.0000.9600.000
최초12개월표준점수1.0001.0001.0001.0001.0000.8160.2491.0000.0000.4350.0000.9601.0000.000
개선도최근표준점수1.0001.0001.0001.0000.3360.8410.0000.0000.8910.2700.6110.0000.0001.000
2023-12-10T23:25:11.869788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초6개월개선도최초12개월개선도
최초6개월개선도1.0000.000
최초12개월개선도0.0001.000
2023-12-10T23:25:11.991201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최근6개월개선도최근12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수최초6개월개선도최초12개월개선도
최근6개월개선도1.0000.2330.366-0.410-0.7560.299-0.2470.0241.0001.000
최근12개월개선도0.2331.0000.024-0.018-0.043-0.4590.013-0.2501.0001.000
최근개선도지수0.3660.0241.0000.679-0.1650.269-0.2220.1070.5300.000
최근6개월표준점수-0.410-0.0180.6791.0000.2140.287-0.1200.3570.0000.000
최근12개월표준점수-0.756-0.043-0.1650.2141.000-0.272-0.348-0.0690.0000.632
최초6개월표준점수0.299-0.4590.2690.287-0.2721.000-0.0240.0620.2740.866
최초12개월표준점수-0.2470.013-0.222-0.120-0.348-0.0241.0000.1540.0000.707
개선도최근표준점수0.024-0.2500.1070.357-0.0690.0620.1541.0000.0000.000
최초6개월개선도1.0001.0000.5300.0000.0000.2740.0000.0001.0000.000
최초12개월개선도1.0001.0000.0000.0000.6320.8660.7070.0000.0001.000

Missing values

2023-12-10T23:25:06.290985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:25:06.541758image/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.
2023-12-10T23:25:06.707059image/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
0UC0ru5w57PyGpbsEKwN4LuwA재민정2021-10-31<NA>2013-05-13<NA><NA><NA><NA><NA><NA>8.120.170.2522.87
1UC0hk2D145ogyBYMKjylWkkw안녕히_계세요2021-10-31.2013-03-16<NA><NA><NA><NA><NA><NA>8.120.170.2522.86
2UC-gWrEGYpG2jYfl8A_KIGnQ[KERI]한국전기연구원2021-10-31전기전문 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>0.0<NA>7.720.170.2723.21
3UC1dMe0fYTM4r9mNQc9v1ziASaehyeon세현2021-10-31뷰티와 일상을 기록하는 유튜버 세현입니다 비즈니스 문의- saehyeon031105@gmail.com 으로 주시면 감사하겠습니다!<NA><NA><NA><NA><NA><NA><NA>8.120.170.2522.86
4UC28lbdOHnj-leok6tHIx7ew고양이와 소소한생활2021-10-31인스타 @jj_d_m_pp 고양이를 좋아하는 사람의 소소한채널; [고양이와 소소한생활] 입니다. 16년 3월 25일; 길냥이였던 쫄냥이를 만났고; 출산이 임박해보이는 커다란 배를 보고 멸치라도 줄까 집에 들인 이후 쭉; 집사의 길을 걸어가고 있습니다. 초보 집사로 좌충우돌 해 가며 현재 반려묘 네마리; 쫄냥까망단추뿌꾸를 키우고 있습니다. 고양이로 인해 많이 웃게되는 나날을 함께하고 싶습니다~^^2013-09-30<NA><NA><NA><NA><NA><NA>8.120.170.2522.92
5UC2BoyzFAwgfRFc5aJrYsGZg후투브2021-10-31<NA>2012-09-09<NA><NA><NA><NA><NA><NA>8.120.170.2522.97
6UC2d79S4T-z8gTY2ceICWhMg[지평선TV]김제시공식유튜브2021-10-31김제시 공식 유튜브 채널 '김제지평선TV' 입니다. https:www.youtube.comgimjecity #김제 #지평선TV #김제지평선TV #김제시 #김제시공식유튜브 #지평선2016-03-16<NA><NA><NA><NA><NA><NA>8.380.170.2521.55
7UC2Zi06YjNBM37g8d0IkHPMATVCHOSUN PLUS - TV조선 플러스2021-10-31TV조선 방송 채널에; 더한 클립과; 더한 영상들을 모아둔 더한 채널 TV조선 플러스2019-01-18<NA>00-16.53-28.85-6.45-3.452.3923.67
8UC0sfSZeoSUeWxys7OKkTelQTBS fm 95.1MHz2021-10-31시민의 눈으로 한걸음 더 시민의 방송 TBS FM입니다. [평일] ▶ 김어준의 뉴스공장 [월~금 07:06 ~ 09:00] ▶ 경제발전소 박연미입니다 [월~금 09:00 ~ 09:57] ▶ 이은미와 함께라면 [월~금 10:06 ~ 12:00] ▶ DJ SHOW 9595 [매일 12:11 ~ 14:00] ▶ 최일구의 허리케인라디오 [매일 14:06 ~ 16:00] ▶ 함춘호의 포크송 [월~금 16:06 ~ 17:00] ▶ 김기욱의 라쿠카라차 [월~금 17:00 ~ 18:00] ▶ 신장식의 신장개업 [월~금 18:11 ~ 20:00] ▶ 아닌 밤중에 주진우입니다 [월~금 20:06 ~ 21:00] ▶ 이가희의 러브레터 [월~금 21:00 ~ 21:43] ▶ 달콤한 밤 황진하입니다 [매일 22:06 ~ 24:00] ▶ 라디오를 켜라 정연주입니다 [월~토 05:00 ~ 07:00] [주말] ▶ 뉴스공장 주말특근 [토 07:00 ~ 09:00] ▶ TBS 아고라 [토 09:00 ~ 10:00] ▶ 오늘도 읽음 [일 08:06 ~ 10:00] ▶ 기분좋은 토;일요일 조현아입니다 [주말 10:00 ~ 12:00] ▶ 박성호의 4X6=24 [주말16:06 ~ 18:00] ▶ 웅산의 스윗멜로디 [주말 18:06 ~ 20:00] ▶ 주말이 좋다 나선홍입니다 [주말 20:06 ~ 22:00] ▶ 일요클래식 최영옥입니다 [일 05:00 ~ 08:00] ------------------------------------------------------------------------------------------------------------------------------------------------------------------- ▶TBS 홈페이지 http:tbs.seoul.kr2016-09-1904<NA><NA>118.72.496.450.170.25200.08
9UC3WJ-rb5w_ShKQw06rNZfuwDiscovery Music Records2021-10-31<NA>2013-09-14<NA><NA>10<NA><NA>7.9-0.05-0.2523.34
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
14UC5UYeBQdzHjOh_vBoDQDkDQ한국전력 KEPCO2021-10-31한국전력공사 공식 유튜브 채널입니다. 안전하고 깨끗한 에너지 세상을 만들기 위한 한국전력의 노력; 한전 직무 소개; 채용 정보; 메세나 활동 등 다양하고 재미있는 콘텐츠를 만나실 수 있습니다.2012-07-05-2-1<NA><NA>46.310.836.80.20.2923.0
15UC5oft5dVf43M2cFmhpJLVGQ애주가TV참PD2021-10-31세상 모든 안주를 리뷰하는 애주가TV 참PD입니다. 각종문의 이메일주소 : ilovechampd@gmail.com 인스타그램 : ilovechampd2011-05-1823<NA><NA>12.93-2.03-0.560.170.252.81
16UC69DhLD0FUmLgP9BsxitbDw잡아바 TV [경기도일자리재단]2021-10-31안녕하세요; 경기도일자리재단 공식 유튜브입니다. 자주 방문 및 구독신청하셔서 유용한 정보 얻어가세요! 일자리플랫폼 잡아바(www.jobaba.net) 페이스북(https:www.facebook.comjobabanet) 인스타그램(https:www.instagram.comjobabanet) 네이버포스트(http:post.naver.comgjf_job) 유튜브(https:www.youtube.comchannelUC69DhLD0FUmLgP9BsxitbDw) 잡아바 앱 다운로드 : 구글플레이스토어나 앱스토어 등에서 ‘경기도일자리재단’ 검색 * 안드로이드 : https:goo.glNLD4CX * IOS : https:goo.glJ5Q1ZP2016-10-17<NA>-1<NA><NA>2.43-4.42-8.181.590.3923.52
17UC6DTags0tiObToJLnLWZdQwEBS 디딤돌 발달장애2021-10-31<NA><NA><NA><NA><NA><NA><NA><NA>8.12-1.560.7123.03
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