Overview

Dataset statistics

Number of variables15
Number of observations25
Missing cells56
Missing cells (%)14.9%
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/12278763-db6d-45fa-b1f4-17b388b48f1c

Alerts

최근6개월개선도 is highly overall correlated with 최초6개월표준점수High correlation
최근12개월개선도 is highly overall correlated with 최근12개월표준점수 and 3 other fieldsHigh correlation
최근개선도지수 is highly overall correlated with 최초12개월개선도High correlation
최근12개월표준점수 is highly overall correlated with 최근12개월개선도 and 1 other fieldsHigh correlation
최초6개월표준점수 is highly overall correlated with 최근6개월개선도 and 2 other fieldsHigh correlation
최초12개월표준점수 is highly overall correlated with 최초12개월개선도High correlation
최초6개월개선도 is highly overall correlated with 최근12개월개선도High correlation
최초12개월개선도 is highly overall correlated with 최근12개월개선도 and 4 other fieldsHigh correlation
개선도지수채널설명 has 6 (24.0%) missing valuesMissing
개선도채널생성일자 has 6 (24.0%) missing valuesMissing
최근6개월개선도 has 16 (64.0%) missing valuesMissing
최근12개월개선도 has 15 (60.0%) missing valuesMissing
최근개선도지수 has 10 (40.0%) missing valuesMissing
최근6개월표준점수 has 2 (8.0%) missing valuesMissing
최근12개월표준점수 has 1 (4.0%) missing valuesMissing
개선도지수채널ID has unique valuesUnique
개선도지수채널명 has unique valuesUnique
최근12개월개선도 has 2 (8.0%) zerosZeros
최근개선도지수 has 4 (16.0%) zerosZeros
최초6개월표준점수 has 1 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:10:42.243065
Analysis finished2023-12-10 14:10:56.127773
Duration13.88 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
2023-12-10T23:10:56.364578image/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 rowUC_S4C51xOcE7ooD48jZeCKQ
2nd rowUC0ru5w57PyGpbsEKwN4LuwA
3rd rowUC0hk2D145ogyBYMKjylWkkw
4th rowUC-gWrEGYpG2jYfl8A_KIGnQ
5th rowUC1dMe0fYTM4r9mNQc9v1ziA
ValueCountFrequency (%)
uc_s4c51xoce7ood48jzeckq 1
 
4.0%
uc4qw1ue9kwui4ccax9hztxw 1
 
4.0%
uc8jolz-ya34ylqtz2tqslgw 1
 
4.0%
uc85mxewraycnlfmrhbdaalg 1
 
4.0%
uc2wzjuud_wa9ps2forco1eq 1
 
4.0%
uc7bqxkhltafctwwwhuzoouq 1
 
4.0%
uc7qzaxgxsbobtjv4ppgdsxg 1
 
4.0%
uc6dtags0tiobtojlnlwzdqw 1
 
4.0%
uc69dhld0fumlgp9bsxitbdw 1
 
4.0%
uc5oft5dvf43m2cfmhpjlvgq 1
 
4.0%
Other values (15) 15
60.0%
2023-12-10T23:10:56.942268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 34
 
5.7%
U 33
 
5.5%
Q 20
 
3.3%
w 17
 
2.8%
g 15
 
2.5%
o 14
 
2.3%
4 14
 
2.3%
Z 14
 
2.3%
A 13
 
2.2%
W 12
 
2.0%
Other values (54) 414
69.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 271
45.2%
Lowercase Letter 222
37.0%
Decimal Number 94
 
15.7%
Dash Punctuation 7
 
1.2%
Connector Punctuation 6
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 34
 
12.5%
U 33
 
12.2%
Q 20
 
7.4%
Z 14
 
5.2%
A 13
 
4.8%
W 12
 
4.4%
D 11
 
4.1%
G 11
 
4.1%
O 10
 
3.7%
Y 9
 
3.3%
Other values (16) 104
38.4%
Lowercase Letter
ValueCountFrequency (%)
w 17
 
7.7%
g 15
 
6.8%
o 14
 
6.3%
l 11
 
5.0%
d 10
 
4.5%
x 10
 
4.5%
e 10
 
4.5%
f 10
 
4.5%
k 9
 
4.1%
t 9
 
4.1%
Other values (16) 107
48.2%
Decimal Number
ValueCountFrequency (%)
4 14
14.9%
2 11
11.7%
5 11
11.7%
7 10
10.6%
9 10
10.6%
0 10
10.6%
8 8
8.5%
6 7
7.4%
3 7
7.4%
1 6
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 7
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 (%)
C 34
 
6.9%
U 33
 
6.7%
Q 20
 
4.1%
w 17
 
3.4%
g 15
 
3.0%
o 14
 
2.8%
Z 14
 
2.8%
A 13
 
2.6%
W 12
 
2.4%
D 11
 
2.2%
Other values (42) 310
62.9%
Common
ValueCountFrequency (%)
4 14
13.1%
2 11
10.3%
5 11
10.3%
7 10
9.3%
9 10
9.3%
0 10
9.3%
8 8
7.5%
- 7
6.5%
6 7
6.5%
3 7
6.5%
Other values (2) 12
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 34
 
5.7%
U 33
 
5.5%
Q 20
 
3.3%
w 17
 
2.8%
g 15
 
2.5%
o 14
 
2.3%
4 14
 
2.3%
Z 14
 
2.3%
A 13
 
2.2%
W 12
 
2.0%
Other values (54) 414
69.0%

개선도지수채널명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-10T23:10:57.295420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length13
Mean length9.96
Min length3

Characters and Unicode

Total characters249
Distinct characters128
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row수줍은 먹방
2nd row재민정
3rd row안녕히_계세요
4th row[KERI]한국전기연구원
5th rowSaehyeon세현
ValueCountFrequency (%)
3
 
6.4%
수줍은 1
 
2.1%
사모장 1
 
2.1%
한국전력 1
 
2.1%
kepco 1
 
2.1%
애주가tv참pd 1
 
2.1%
잡아바 1
 
2.1%
tv 1
 
2.1%
경기도일자리재단 1
 
2.1%
ebs 1
 
2.1%
Other values (35) 35
74.5%
2023-12-10T23:10:57.874734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
8.8%
T 7
 
2.8%
e 7
 
2.8%
S 6
 
2.4%
o 6
 
2.4%
E 5
 
2.0%
V 5
 
2.0%
a 5
 
2.0%
D 4
 
1.6%
s 4
 
1.6%
Other values (118) 178
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101
40.6%
Uppercase Letter 62
24.9%
Lowercase Letter 50
20.1%
Space Separator 22
 
8.8%
Close Punctuation 3
 
1.2%
Open Punctuation 3
 
1.2%
Decimal Number 3
 
1.2%
Dash Punctuation 2
 
0.8%
Other Punctuation 2
 
0.8%
Connector Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3
 
3.0%
국 3
 
3.0%
한 3
 
3.0%
방 2
 
2.0%
선 2
 
2.0%
재 2
 
2.0%
가 2
 
2.0%
제 2
 
2.0%
김 2
 
2.0%
애 2
 
2.0%
Other values (69) 78
77.2%
Lowercase Letter
ValueCountFrequency (%)
e 7
14.0%
o 6
12.0%
a 5
10.0%
s 4
 
8.0%
f 4
 
8.0%
c 3
 
6.0%
r 3
 
6.0%
i 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%
E 5
 
8.1%
V 5
 
8.1%
D 4
 
6.5%
C 4
 
6.5%
I 3
 
4.8%
P 3
 
4.8%
N 3
 
4.8%
O 3
 
4.8%
Other values (9) 19
30.6%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
9 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
· 1
50.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112
45.0%
Hangul 101
40.6%
Common 36
 
14.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3
 
3.0%
국 3
 
3.0%
한 3
 
3.0%
방 2
 
2.0%
선 2
 
2.0%
재 2
 
2.0%
가 2
 
2.0%
제 2
 
2.0%
김 2
 
2.0%
애 2
 
2.0%
Other values (69) 78
77.2%
Latin
ValueCountFrequency (%)
T 7
 
6.2%
e 7
 
6.2%
S 6
 
5.4%
o 6
 
5.4%
E 5
 
4.5%
V 5
 
4.5%
a 5
 
4.5%
D 4
 
3.6%
s 4
 
3.6%
f 4
 
3.6%
Other values (29) 59
52.7%
Common
ValueCountFrequency (%)
22
61.1%
] 3
 
8.3%
[ 3
 
8.3%
- 2
 
5.6%
_ 1
 
2.8%
1 1
 
2.8%
. 1
 
2.8%
5 1
 
2.8%
9 1
 
2.8%
· 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147
59.0%
Hangul 101
40.6%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
 
15.0%
T 7
 
4.8%
e 7
 
4.8%
S 6
 
4.1%
o 6
 
4.1%
E 5
 
3.4%
V 5
 
3.4%
a 5
 
3.4%
D 4
 
2.7%
s 4
 
2.7%
Other values (38) 76
51.7%
Hangul
ValueCountFrequency (%)
주 3
 
3.0%
국 3
 
3.0%
한 3
 
3.0%
방 2
 
2.0%
선 2
 
2.0%
재 2
 
2.0%
가 2
 
2.0%
제 2
 
2.0%
김 2
 
2.0%
애 2
 
2.0%
Other values (69) 78
77.2%
None
ValueCountFrequency (%)
· 1
100.0%

개선도지수수집일자
Date

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2021-08-01 00:00:00
Maximum2021-08-31 00:00:00
2023-12-10T23:10:58.172590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:58.499167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

개선도지수채널설명
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing6
Missing (%)24.0%
Memory size332.0 B
2023-12-10T23:10:58.960859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length897
Median length95
Mean length206.63158
Min length1

Characters and Unicode

Total characters3926
Distinct characters409
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
ValueCountFrequency (%)
51
 
7.8%
▶ 20
 
3.1%
월~금 8
 
1.2%
공식 6
 
0.9%
you 5
 
0.8%
있습니다 5
 
0.8%
for 5
 
0.8%
주말 4
 
0.6%
his 4
 
0.6%
09:00 4
 
0.6%
Other values (468) 543
82.9%
2023-12-10T23:10:59.706987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
711
 
18.1%
- 181
 
4.6%
o 100
 
2.5%
0 88
 
2.2%
e 83
 
2.1%
: 77
 
2.0%
. 77
 
2.0%
t 72
 
1.8%
a 63
 
1.6%
i 60
 
1.5%
Other values (399) 2414
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1397
35.6%
Lowercase Letter 937
23.9%
Space Separator 711
18.1%
Other Punctuation 217
 
5.5%
Decimal Number 207
 
5.3%
Dash Punctuation 181
 
4.6%
Uppercase Letter 105
 
2.7%
Math Symbol 68
 
1.7%
Open Punctuation 32
 
0.8%
Close 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
 
2.6%
요 28
 
2.0%
의 26
 
1.9%
는 24
 
1.7%
하 23
 
1.6%
주 21
 
1.5%
리 21
 
1.5%
일 20
 
1.4%
Other values (316) 1119
80.1%
Uppercase Letter
ValueCountFrequency (%)
T 16
15.2%
D 10
 
9.5%
S 9
 
8.6%
O 6
 
5.7%
V 6
 
5.7%
H 5
 
4.8%
B 5
 
4.8%
E 4
 
3.8%
C 4
 
3.8%
L 4
 
3.8%
Other values (16) 36
34.3%
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%
9 9
 
4.3%
5 9
 
4.3%
7 8
 
3.9%
8 7
 
3.4%
4 7
 
3.4%
3 3
 
1.4%
Other Punctuation
ValueCountFrequency (%)
: 77
35.5%
. 77
35.5%
; 21
 
9.7%
! 16
 
7.4%
? 15
 
6.9%
@ 6
 
2.8%
* 3
 
1.4%
' 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
~ 38
55.9%
= 30
44.1%
Open Punctuation
ValueCountFrequency (%)
[ 24
75.0%
( 8
 
25.0%
Close Punctuation
ValueCountFrequency (%)
] 24
75.0%
) 8
 
25.0%
Other Symbol
ValueCountFrequency (%)
▶ 21
84.0%
♥ 4
 
16.0%
Space Separator
ValueCountFrequency (%)
711
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 1487
37.9%
Hangul 1397
35.6%
Latin 1042
26.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 41
 
2.9%
니 38
 
2.7%
이 36
 
2.6%
요 28
 
2.0%
의 26
 
1.9%
는 24
 
1.7%
하 23
 
1.6%
주 21
 
1.5%
리 21
 
1.5%
일 20
 
1.4%
Other values (316) 1119
80.1%
Latin
ValueCountFrequency (%)
o 100
 
9.6%
e 83
 
8.0%
t 72
 
6.9%
a 63
 
6.0%
i 60
 
5.8%
s 56
 
5.4%
n 53
 
5.1%
h 53
 
5.1%
r 46
 
4.4%
w 43
 
4.1%
Other values (41) 413
39.6%
Common
ValueCountFrequency (%)
711
47.8%
- 181
 
12.2%
0 88
 
5.9%
: 77
 
5.2%
. 77
 
5.2%
1 39
 
2.6%
~ 38
 
2.6%
= 30
 
2.0%
[ 24
 
1.6%
] 24
 
1.6%
Other values (22) 198
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2502
63.7%
Hangul 1397
35.6%
Geometric Shapes 21
 
0.5%
Misc Symbols 4
 
0.1%
Punctuation 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
711
28.4%
- 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 (69) 990
39.6%
Hangul
ValueCountFrequency (%)
다 41
 
2.9%
니 38
 
2.7%
이 36
 
2.6%
요 28
 
2.0%
의 26
 
1.9%
는 24
 
1.7%
하 23
 
1.6%
주 21
 
1.5%
리 21
 
1.5%
일 20
 
1.4%
Other values (316) 1119
80.1%
Geometric Shapes
ValueCountFrequency (%)
▶ 21
100.0%
Misc Symbols
ValueCountFrequency (%)
♥ 4
100.0%
Punctuation
ValueCountFrequency (%)
‘ 1
50.0%
’ 1
50.0%

개선도채널생성일자
Date

MISSING 

Distinct19
Distinct (%)100.0%
Missing6
Missing (%)24.0%
Memory size332.0 B
Minimum2011-05-18 00:00:00
Maximum2019-01-18 00:00:00
2023-12-10T23:11:00.048342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:00.921797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

최근6개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)88.9%
Missing16
Missing (%)64.0%
Infinite0
Infinite (%)0.0%
Mean-7.4444444
Minimum-76
Maximum76
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)32.0%
Memory size357.0 B
2023-12-10T23:11:01.149338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-76
5-th percentile-59.6
Q1-14
median-4
Q3-3
95-th percentile44.8
Maximum76
Range152
Interquartile range (IQR)11

Descriptive statistics

Standard deviation39.604643
Coefficient of variation (CV)-5.3200267
Kurtosis3.1750729
Mean-7.4444444
Median Absolute Deviation (MAD)2
Skewness0.61572299
Sum-67
Variance1568.5278
MonotonicityNot monotonic
2023-12-10T23:11:01.329859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-3 2
 
8.0%
-2 1
 
4.0%
76 1
 
4.0%
-4 1
 
4.0%
-35 1
 
4.0%
-14 1
 
4.0%
-76 1
 
4.0%
-6 1
 
4.0%
(Missing) 16
64.0%
ValueCountFrequency (%)
-76 1
4.0%
-35 1
4.0%
-14 1
4.0%
-6 1
4.0%
-4 1
4.0%
-3 2
8.0%
-2 1
4.0%
76 1
4.0%
ValueCountFrequency (%)
76 1
4.0%
-2 1
4.0%
-3 2
8.0%
-4 1
4.0%
-6 1
4.0%
-14 1
4.0%
-35 1
4.0%
-76 1
4.0%

최근12개월개선도
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)70.0%
Missing15
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum-12
Maximum13
Zeros2
Zeros (%)8.0%
Negative3
Negative (%)12.0%
Memory size357.0 B
2023-12-10T23:11:01.518389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-9.3
Q1-1.5
median2.5
Q35
95-th percentile10.3
Maximum13
Range25
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation7.1063352
Coefficient of variation (CV)4.7375568
Kurtosis0.40146391
Mean1.5
Median Absolute Deviation (MAD)3.5
Skewness-0.43191136
Sum15
Variance50.5
MonotonicityNot monotonic
2023-12-10T23:11:01.703935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 3
 
12.0%
0 2
 
8.0%
-6 1
 
4.0%
-12 1
 
4.0%
-2 1
 
4.0%
7 1
 
4.0%
13 1
 
4.0%
(Missing) 15
60.0%
ValueCountFrequency (%)
-12 1
 
4.0%
-6 1
 
4.0%
-2 1
 
4.0%
0 2
8.0%
5 3
12.0%
7 1
 
4.0%
13 1
 
4.0%
ValueCountFrequency (%)
13 1
 
4.0%
7 1
 
4.0%
5 3
12.0%
0 2
8.0%
-2 1
 
4.0%
-6 1
 
4.0%
-12 1
 
4.0%

최초6개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
16 
0
5 
1
4 

Length

Max length4
Median length4
Mean length2.92
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 16
64.0%
0 5
 
20.0%
1 4
 
16.0%

Length

2023-12-10T23:11:01.903972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:11:02.085304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
64.0%
0 5
 
20.0%
1 4
 
16.0%

최초12개월개선도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
16 
0
7 
-1
 
1
1
 
1

Length

Max length4
Median length4
Mean length2.96
Min length1

Unique

Unique2 ?
Unique (%)8.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 16
64.0%
0 7
28.0%
-1 1
 
4.0%
1 1
 
4.0%

Length

2023-12-10T23:11:02.297509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:11:02.472987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
64.0%
0 7
28.0%
1 2
 
8.0%

최근개선도지수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)80.0%
Missing10
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean20.284667
Minimum0
Maximum70.15
Zeros4
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T23:11:02.622465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.275
median10.2
Q331.45
95-th percentile60.595
Maximum70.15
Range70.15
Interquartile range (IQR)30.175

Descriptive statistics

Standard deviation22.347868
Coefficient of variation (CV)1.1017123
Kurtosis0.1830662
Mean20.284667
Median Absolute Deviation (MAD)10.2
Skewness1.0154834
Sum304.27
Variance499.42718
MonotonicityNot monotonic
2023-12-10T23:11:02.799448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 4
 
16.0%
56.5 1
 
4.0%
5.47 1
 
4.0%
29.17 1
 
4.0%
29.7 1
 
4.0%
20.16 1
 
4.0%
33.2 1
 
4.0%
70.15 1
 
4.0%
10.2 1
 
4.0%
2.55 1
 
4.0%
Other values (2) 2
 
8.0%
(Missing) 10
40.0%
ValueCountFrequency (%)
0.0 4
16.0%
2.55 1
 
4.0%
5.47 1
 
4.0%
6.91 1
 
4.0%
10.2 1
 
4.0%
20.16 1
 
4.0%
29.17 1
 
4.0%
29.7 1
 
4.0%
33.2 1
 
4.0%
40.26 1
 
4.0%
ValueCountFrequency (%)
70.15 1
4.0%
56.5 1
4.0%
40.26 1
4.0%
33.2 1
4.0%
29.7 1
4.0%
29.17 1
4.0%
20.16 1
4.0%
10.2 1
4.0%
6.91 1
4.0%
5.47 1
4.0%

최근6개월표준점수
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)78.3%
Missing2
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean-19.7
Minimum-50.46
Maximum15.09
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)80.0%
Memory size357.0 B
2023-12-10T23:11:03.005913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50.46
5-th percentile-50.46
Q1-43.08
median-16.46
Q3-3.39
95-th percentile13.432
Maximum15.09
Range65.55
Interquartile range (IQR)39.69

Descriptive statistics

Standard deviation22.530547
Coefficient of variation (CV)-1.1436826
Kurtosis-1.2522153
Mean-19.7
Median Absolute Deviation (MAD)15.44
Skewness-0.1877662
Sum-453.1
Variance507.62555
MonotonicityNot monotonic
2023-12-10T23:11:03.300075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-50.46 6
24.0%
-16.46 1
 
4.0%
-0.95 1
 
4.0%
-28.17 1
 
4.0%
-6.76 1
 
4.0%
10.93 1
 
4.0%
-8.02 1
 
4.0%
-8.2 1
 
4.0%
-2.53 1
 
4.0%
-35.7 1
 
4.0%
Other values (8) 8
32.0%
(Missing) 2
 
8.0%
ValueCountFrequency (%)
-50.46 6
24.0%
-35.7 1
 
4.0%
-28.42 1
 
4.0%
-28.17 1
 
4.0%
-22.29 1
 
4.0%
-19.2 1
 
4.0%
-16.46 1
 
4.0%
-8.2 1
 
4.0%
-8.1 1
 
4.0%
-8.02 1
 
4.0%
ValueCountFrequency (%)
15.09 1
4.0%
13.71 1
4.0%
10.93 1
4.0%
-0.95 1
4.0%
-1.02 1
4.0%
-2.53 1
4.0%
-4.25 1
4.0%
-6.76 1
4.0%
-8.02 1
4.0%
-8.1 1
4.0%

최근12개월표준점수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)83.3%
Missing1
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean4.3020833
Minimum-29.46
Maximum17.06
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)12.0%
Memory size357.0 B
2023-12-10T23:11:03.516790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-29.46
5-th percentile-17.5055
Q15.655
median7.325
Q37.4875
95-th percentile12.551
Maximum17.06
Range46.52
Interquartile range (IQR)1.8325

Descriptive statistics

Standard deviation9.8613184
Coefficient of variation (CV)2.2922193
Kurtosis6.7315339
Mean4.3020833
Median Absolute Deviation (MAD)0.8
Skewness-2.4863767
Sum103.25
Variance97.2456
MonotonicityNot monotonic
2023-12-10T23:11:03.742395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7.37 5
20.0%
-29.46 1
 
4.0%
0.08 1
 
4.0%
7.31 1
 
4.0%
6.56 1
 
4.0%
3.15 1
 
4.0%
0.53 1
 
4.0%
6.68 1
 
4.0%
7.25 1
 
4.0%
10.46 1
 
4.0%
Other values (10) 10
40.0%
ValueCountFrequency (%)
-29.46 1
4.0%
-20.3 1
4.0%
-1.67 1
4.0%
0.08 1
4.0%
0.53 1
4.0%
3.15 1
4.0%
6.49 1
4.0%
6.56 1
4.0%
6.68 1
4.0%
6.7 1
4.0%
ValueCountFrequency (%)
17.06 1
 
4.0%
12.92 1
 
4.0%
10.46 1
 
4.0%
9.21 1
 
4.0%
8.25 1
 
4.0%
7.84 1
 
4.0%
7.37 5
20.0%
7.34 1
 
4.0%
7.31 1
 
4.0%
7.25 1
 
4.0%

최초6개월표준점수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1748
Minimum-2.09
Maximum2.06
Zeros1
Zeros (%)4.0%
Negative3
Negative (%)12.0%
Memory size357.0 B
2023-12-10T23:11:03.943992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.09
5-th percentile-1.392
Q10.18
median0.18
Q30.21
95-th percentile1.498
Maximum2.06
Range4.15
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.79514737
Coefficient of variation (CV)4.548898
Kurtosis4.2159565
Mean0.1748
Median Absolute Deviation (MAD)0.01
Skewness-0.74363866
Sum4.37
Variance0.63225933
MonotonicityNot monotonic
2023-12-10T23:11:04.125750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.18 11
44.0%
0.31 1
 
4.0%
0.29 1
 
4.0%
-2.09 1
 
4.0%
-0.04 1
 
4.0%
0.22 1
 
4.0%
0.2 1
 
4.0%
1.63 1
 
4.0%
-1.73 1
 
4.0%
0.0 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
-2.09 1
 
4.0%
-1.73 1
 
4.0%
-0.04 1
 
4.0%
0.0 1
 
4.0%
0.17 1
 
4.0%
0.18 11
44.0%
0.19 1
 
4.0%
0.2 1
 
4.0%
0.21 1
 
4.0%
0.22 1
 
4.0%
ValueCountFrequency (%)
2.06 1
 
4.0%
1.63 1
 
4.0%
0.97 1
 
4.0%
0.31 1
 
4.0%
0.29 1
 
4.0%
0.22 1
 
4.0%
0.21 1
 
4.0%
0.2 1
 
4.0%
0.19 1
 
4.0%
0.18 11
44.0%

최초12개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.212
Minimum-1.64
Maximum2.32
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)16.0%
Memory size357.0 B
2023-12-10T23:11:04.325280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.64
5-th percentile-0.752
Q10.25
median0.25
Q30.29
95-th percentile0.632
Maximum2.32
Range3.96
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.6419177
Coefficient of variation (CV)3.0279137
Kurtosis6.9733285
Mean0.212
Median Absolute Deviation (MAD)0.02
Skewness0.28836957
Sum5.3
Variance0.41205833
MonotonicityNot monotonic
2023-12-10T23:11:04.523736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.25 11
44.0%
0.27 3
 
12.0%
-0.84 1
 
4.0%
0.31 1
 
4.0%
2.32 1
 
4.0%
-0.19 1
 
4.0%
0.29 1
 
4.0%
0.4 1
 
4.0%
0.68 1
 
4.0%
0.44 1
 
4.0%
Other values (3) 3
 
12.0%
ValueCountFrequency (%)
-1.64 1
 
4.0%
-0.84 1
 
4.0%
-0.4 1
 
4.0%
-0.19 1
 
4.0%
0.25 11
44.0%
0.27 3
 
12.0%
0.29 1
 
4.0%
0.31 1
 
4.0%
0.37 1
 
4.0%
0.4 1
 
4.0%
ValueCountFrequency (%)
2.32 1
 
4.0%
0.68 1
 
4.0%
0.44 1
 
4.0%
0.4 1
 
4.0%
0.37 1
 
4.0%
0.31 1
 
4.0%
0.29 1
 
4.0%
0.27 3
 
12.0%
0.25 11
44.0%
-0.19 1
 
4.0%

개선도최근표준점수
Real number (ℝ)

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.844
Minimum-13.97
Maximum91.41
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)4.0%
Memory size357.0 B
2023-12-10T23:11:04.746009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13.97
5-th percentile5.68
Q115.77
median16.16
Q316.9
95-th percentile70.102
Maximum91.41
Range105.38
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation21.018268
Coefficient of variation (CV)1.0083606
Kurtosis7.4507129
Mean20.844
Median Absolute Deviation (MAD)0.74
Skewness2.5446492
Sum521.1
Variance441.7676
MonotonicityNot monotonic
2023-12-10T23:11:04.963348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16.02 2
 
8.0%
25.79 1
 
4.0%
-13.97 1
 
4.0%
16.9 1
 
4.0%
13.88 1
 
4.0%
19.05 1
 
4.0%
24.84 1
 
4.0%
15.15 1
 
4.0%
19.22 1
 
4.0%
16.17 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
-13.97 1
4.0%
3.63 1
4.0%
13.88 1
4.0%
14.96 1
4.0%
14.99 1
4.0%
15.15 1
4.0%
15.77 1
4.0%
16.0 1
4.0%
16.02 2
8.0%
16.1 1
4.0%
ValueCountFrequency (%)
91.41 1
4.0%
81.18 1
4.0%
25.79 1
4.0%
24.84 1
4.0%
19.22 1
4.0%
19.05 1
4.0%
16.9 1
4.0%
16.55 1
4.0%
16.54 1
4.0%
16.33 1
4.0%

Interactions

2023-12-10T23:10:53.922133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:43.902333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:45.493280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:47.287791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:48.860464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:49.883020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:51.235275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:52.660957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:54.099197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:44.165933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:45.719754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:47.504933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:48.996011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:50.038428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:51.411738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:52.816377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:54.225961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:44.339415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:45.906477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:47.642894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:49.112853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:50.222400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:51.537858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:52.981212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:54.356463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:44.550595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:46.136925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:47.814600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:49.253025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:50.374329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:51.709631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:53.142415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:54.486387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:44.770102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:46.318810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:47.978221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:49.376187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:50.502260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:51.868365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:53.315788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:54.658119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:44.956574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:46.503696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:48.125970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:49.508868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:50.624854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:52.035028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:53.447319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:54.818025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:45.127143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:46.726076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:48.244004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:49.634596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:50.811609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:52.183619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:53.603443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:54.989614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:45.334942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:46.983495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:48.422358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:49.776539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:50.966644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:52.518390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:10:53.776575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:11:05.161147image/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
개선도지수수집일자1.0001.0001.000NaN1.000NaNNaN0.0000.0000.000NaN1.0000.0001.0000.216
개선도지수채널설명1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도채널생성일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최근6개월개선도1.0001.000NaN1.0001.0001.0000.6900.0000.0000.7970.5000.8760.5310.5680.000
최근12개월개선도1.0001.000NaN1.0001.0000.6901.0001.0001.0000.8920.0000.0000.6660.6790.545
최초6개월개선도1.0001.0000.0001.0001.0000.0001.0001.0000.0540.1380.0000.3000.4890.0000.222
최초12개월개선도1.0001.0000.0001.0001.0000.0001.0000.0541.0000.8880.0001.0001.0001.0000.000
최근개선도지수1.0001.0000.0001.0001.0000.7970.8920.1380.8881.0000.5320.8660.0000.0000.701
최근6개월표준점수1.0001.000NaN1.0001.0000.5000.0000.0000.0000.5321.0000.5080.0000.0000.514
최근12개월표준점수1.0001.0001.0001.0001.0000.8760.0000.3001.0000.8660.5081.0000.0000.6130.827
최초6개월표준점수1.0001.0000.0001.0001.0000.5310.6660.4891.0000.0000.0000.0001.0000.9330.000
최초12개월표준점수1.0001.0001.0001.0001.0000.5680.6790.0001.0000.0000.0000.6130.9331.0000.000
개선도최근표준점수1.0001.0000.2161.0001.0000.0000.5450.2220.0000.7010.5140.8270.0000.0001.000
2023-12-10T23:11:05.516048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초12개월개선도최초6개월개선도
최초12개월개선도1.0000.000
최초6개월개선도0.0001.000
2023-12-10T23:11:05.746868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최근6개월개선도최근12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수최초6개월개선도최초12개월개선도
최근6개월개선도1.000-0.1080.0670.377-0.067-0.5330.3500.1000.0000.000
최근12개월개선도-0.1081.000-0.0370.322-0.6950.521-0.0700.0741.0001.000
최근개선도지수0.067-0.0371.0000.0990.164-0.092-0.193-0.2680.0610.565
최근6개월표준점수0.3770.3220.0991.000-0.479-0.037-0.0320.3300.0000.000
최근12개월표준점수-0.067-0.6950.164-0.4791.000-0.3550.190-0.0950.0000.816
최초6개월표준점수-0.5330.521-0.092-0.037-0.3551.000-0.1200.0710.4000.816
최초12개월표준점수0.350-0.070-0.193-0.0320.190-0.1201.000-0.1560.0000.707
개선도최근표준점수0.1000.074-0.2680.330-0.0950.071-0.1561.0000.2930.000
최초6개월개선도0.0001.0000.0610.0000.0000.4000.0000.2931.0000.000
최초12개월개선도0.0001.0000.5650.0000.8160.8160.7070.0000.0001.000

Missing values

2023-12-10T23:10:55.337391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:10:55.681492image/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.
2023-12-10T23:10:55.950259image/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
0UC_S4C51xOcE7ooD48jZeCKQ수줍은 먹방2021-08-01<NA>2017-08-18<NA><NA>100.0<NA>-29.460.31-0.8425.79
1UC0ru5w57PyGpbsEKwN4LuwA재민정2021-08-31<NA>2013-05-13<NA><NA><NA><NA><NA>-50.467.370.180.2516.02
2UC0hk2D145ogyBYMKjylWkkw안녕히_계세요2021-08-31.2013-03-16<NA><NA><NA><NA><NA>-50.467.370.180.2516.0
3UC-gWrEGYpG2jYfl8A_KIGnQ[KERI]한국전기연구원2021-08-31전기전문 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_keri2012-03-07-2<NA><NA><NA>56.5-4.257.340.180.2714.99
4UC1dMe0fYTM4r9mNQc9v1ziASaehyeon세현2021-08-31뷰티와 일상을 기록하는 유튜버 세현입니다 비즈니스 문의- saehyeon031105@gmail.com 으로 주시면 감사하겠습니다!<NA><NA><NA>000.0-50.467.370.290.3116.16
5UC28lbdOHnj-leok6tHIx7ew고양이와 소소한생활2021-08-31인스타 @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>-50.467.370.180.2516.1
6UC2BoyzFAwgfRFc5aJrYsGZg후투브2021-08-31<NA>2012-09-09<NA><NA><NA><NA><NA>-50.467.370.180.2516.11
7UC2d79S4T-z8gTY2ceICWhMg김제시·김제지평선TV2021-08-31김제시 공식 유튜브 채널 '김제지평선TV' 입니다. https:www.youtube.comgimjecity2016-03-16<NA><NA><NA><NA><NA>-16.469.210.180.2516.3
8UC2Zi06YjNBM37g8d0IkHPMATVCHOSUN PLUS - TV조선 플러스2021-08-31TV조선 방송 채널에; 더한 클립과; 더한 영상들을 모아둔 더한 채널 TV조선 플러스2019-01-18<NA>00-15.47-8.1<NA>-2.092.3216.55
9UC0sfSZeoSUeWxys7OKkTelQTBS fm 95.1MHz2021-08-31시민의 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.kr2016-09-19765<NA><NA>29.1713.71-20.30.180.2581.18
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
15UC5UYeBQdzHjOh_vBoDQDkDQ한국전력 KEPCO2021-08-31한국전력공사 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.2012-07-05-3-2<NA><NA>70.15-35.77.840.20.2914.96
16UC5oft5dVf43M2cFmhpJLVGQ애주가TV참PD2021-08-31세상 모든 안주를 리뷰하는 애주가TV 참PD입니다. 각종문의 이메일주소 : ilovechampd@gmail.com 인스타그램 : ilovechampd2011-05-18<NA>5<NA><NA>10.2-0.950.080.180.253.63
17UC69DhLD0FUmLgP9BsxitbDw잡아바 TV [경기도일자리재단]2021-08-31안녕하세요; 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>0<NA><NA>2.55<NA>6.71.630.416.54
18UC6DTags0tiObToJLnLWZdQwEBS 디딤돌 발달장애2021-08-31<NA><NA><NA><NA><NA><NA><NA>-50.4610.46-1.730.6816.17
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