Overview

Dataset statistics

Number of variables15
Number of observations28
Missing cells72
Missing cells (%)17.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory134.7 B

Variable types

Text3
Categorical1
DateTime1
Numeric10

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/48edc1c0-5ff3-4e4a-9f73-dd64844082ed

Alerts

IMPRVMDGREE_IDEX_COLCT_DE has constant value ""Constant
RECENT_SIX_MONTH_IMPRVMDGREE is highly overall correlated with FRST_SIX_MONTH_IMPRVMDGREEHigh correlation
FRST_SIX_MONTH_IMPRVMDGREE is highly overall correlated with RECENT_SIX_MONTH_IMPRVMDGREE and 2 other fieldsHigh correlation
FRST_12_MONTH_IMPRVMDGREE is highly overall correlated with FRST_SIX_MONTH_IMPRVMDGREEHigh correlation
FRST_SIX_MONTH_STD_SCORE is highly overall correlated with FRST_12_MONTH_STD_SCOREHigh correlation
FRST_12_MONTH_STD_SCORE is highly overall correlated with FRST_SIX_MONTH_STD_SCORE and 1 other fieldsHigh correlation
IMPRVMDGREE_RECENT_STD_SCORE is highly overall correlated with FRST_SIX_MONTH_IMPRVMDGREE and 1 other fieldsHigh correlation
IMPRVMDGREE_IDEX_CHNNL_DC has 2 (7.1%) missing valuesMissing
RECENT_SIX_MONTH_IMPRVMDGREE has 14 (50.0%) missing valuesMissing
RECENT_12_MONTH_IMPRVMDGREE has 15 (53.6%) missing valuesMissing
FRST_SIX_MONTH_IMPRVMDGREE has 15 (53.6%) missing valuesMissing
FRST_12_MONTH_IMPRVMDGREE has 14 (50.0%) missing valuesMissing
RECENT_IMPRVMDGREE_IDEX has 11 (39.3%) missing valuesMissing
RECENT_SIX_MONTH_STD_SCORE has 1 (3.6%) missing valuesMissing
IMPRVMDGREE_IDEX_CHNNL_ID has unique valuesUnique
IMPRVMDGREE_IDEX_CHNNL_NM has unique valuesUnique
IMPRVMDGREE_CHNNL_CREAT_DE has unique valuesUnique
IMPRVMDGREE_RECENT_STD_SCORE has unique valuesUnique
RECENT_SIX_MONTH_IMPRVMDGREE has 2 (7.1%) zerosZeros
RECENT_IMPRVMDGREE_IDEX has 7 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:07:22.259582
Analysis finished2023-12-10 14:07:40.334847
Duration18.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

IMPRVMDGREE_IDEX_CHNNL_ID
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-10T23:07:40.599170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters672
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

Unique28 ?
Unique (%)100.0%

Sample

1st rowUCDBUJ8x8ZwE1OAzSyzUcFLg
2nd rowUCmiWMl8Qu6rZDA4D8dSWxFA
3rd rowUC2iVamQdGdLAWJR6ui8zowg
4th rowUCxSBWvnORMtCtJNbK6pL4YQ
5th rowUCc7v5yYC_mviB1_fLuB-GRw
ValueCountFrequency (%)
ucdbuj8x8zwe1oazsyzucflg 1
 
3.6%
ucmiwml8qu6rzda4d8dswxfa 1
 
3.6%
ucyf3owa9rnjqfyhblhzlqba 1
 
3.6%
uczvl1qnvhnqmsiay6oti0aw 1
 
3.6%
ucksyx3zivryvaowzgfvogka 1
 
3.6%
ucnce1102l7ts7k64soncjgq 1
 
3.6%
ucbvdrqetp01juovhaumo08q 1
 
3.6%
ucagvvioc5k6vfcecr5y15hw 1
 
3.6%
ucnr50abyf4e4evi-mb3c5uw 1
 
3.6%
uc2u30ljkwieslwpfznvb7mw 1
 
3.6%
Other values (18) 18
64.3%
2023-12-10T23:07:41.204286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 38
 
5.7%
U 36
 
5.4%
Q 22
 
3.3%
A 19
 
2.8%
v 17
 
2.5%
g 17
 
2.5%
D 16
 
2.4%
w 16
 
2.4%
S 13
 
1.9%
z 13
 
1.9%
Other values (54) 465
69.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 314
46.7%
Lowercase Letter 249
37.1%
Decimal Number 93
 
13.8%
Connector Punctuation 11
 
1.6%
Dash Punctuation 5
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 38
 
12.1%
U 36
 
11.5%
Q 22
 
7.0%
A 19
 
6.1%
D 16
 
5.1%
S 13
 
4.1%
V 12
 
3.8%
Y 11
 
3.5%
N 11
 
3.5%
W 11
 
3.5%
Other values (16) 125
39.8%
Lowercase Letter
ValueCountFrequency (%)
v 17
 
6.8%
g 17
 
6.8%
w 16
 
6.4%
z 13
 
5.2%
x 12
 
4.8%
r 11
 
4.4%
m 11
 
4.4%
i 11
 
4.4%
f 11
 
4.4%
b 10
 
4.0%
Other values (16) 120
48.2%
Decimal Number
ValueCountFrequency (%)
5 12
12.9%
7 10
10.8%
1 10
10.8%
3 10
10.8%
4 9
9.7%
8 9
9.7%
2 9
9.7%
6 8
8.6%
0 8
8.6%
9 8
8.6%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 563
83.8%
Common 109
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 38
 
6.7%
U 36
 
6.4%
Q 22
 
3.9%
A 19
 
3.4%
v 17
 
3.0%
g 17
 
3.0%
D 16
 
2.8%
w 16
 
2.8%
S 13
 
2.3%
z 13
 
2.3%
Other values (42) 356
63.2%
Common
ValueCountFrequency (%)
5 12
11.0%
_ 11
10.1%
7 10
9.2%
1 10
9.2%
3 10
9.2%
4 9
8.3%
8 9
8.3%
2 9
8.3%
6 8
7.3%
0 8
7.3%
Other values (2) 13
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 38
 
5.7%
U 36
 
5.4%
Q 22
 
3.3%
A 19
 
2.8%
v 17
 
2.5%
g 17
 
2.5%
D 16
 
2.4%
w 16
 
2.4%
S 13
 
1.9%
z 13
 
1.9%
Other values (54) 465
69.2%

IMPRVMDGREE_IDEX_CHNNL_NM
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-10T23:07:41.703283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length13
Mean length10.107143
Min length2

Characters and Unicode

Total characters283
Distinct characters140
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row세계일주 저니맨 Journeyman
2nd rowJena Lee 제나리
3rd row청도군
4th rowsusiemeoww
5th rowMINEE EATS
ValueCountFrequency (%)
2
 
4.0%
세계일주 1
 
2.0%
hemtube햄튜브 1
 
2.0%
생방송 1
 
2.0%
다시보기 1
 
2.0%
로이어프렌즈 1
 
2.0%
변호사 1
 
2.0%
친구들 1
 
2.0%
홍개 1
 
2.0%
상해기sanghyuk 1
 
2.0%
Other values (39) 39
78.0%
2023-12-10T23:07:42.325433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
7.8%
n 13
 
4.6%
e 12
 
4.2%
o 10
 
3.5%
y 7
 
2.5%
a 6
 
2.1%
시 5
 
1.8%
기 5
 
1.8%
i 5
 
1.8%
T 5
 
1.8%
Other values (130) 193
68.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
45.2%
Lowercase Letter 83
29.3%
Uppercase Letter 40
 
14.1%
Space Separator 22
 
7.8%
Decimal Number 3
 
1.1%
Dash Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5
 
3.9%
기 5
 
3.9%
오 4
 
3.1%
니 4
 
3.1%
구 4
 
3.1%
사 3
 
2.3%
다 3
 
2.3%
이 3
 
2.3%
리 3
 
2.3%
튜 2
 
1.6%
Other values (84) 92
71.9%
Lowercase Letter
ValueCountFrequency (%)
n 13
15.7%
e 12
14.5%
o 10
12.0%
y 7
8.4%
a 6
7.2%
i 5
 
6.0%
g 5
 
6.0%
m 5
 
6.0%
u 4
 
4.8%
r 4
 
4.8%
Other values (9) 12
14.5%
Uppercase Letter
ValueCountFrequency (%)
T 5
12.5%
E 5
12.5%
M 4
10.0%
S 3
 
7.5%
K 3
 
7.5%
J 3
 
7.5%
R 2
 
5.0%
H 2
 
5.0%
N 2
 
5.0%
O 2
 
5.0%
Other values (8) 9
22.5%
Decimal Number
ValueCountFrequency (%)
5 2
66.7%
9 1
33.3%
Open Punctuation
ValueCountFrequency (%)
「 1
50.0%
[ 1
50.0%
Close Punctuation
ValueCountFrequency (%)
」 1
50.0%
] 1
50.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
45.2%
Latin 123
43.5%
Common 32
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5
 
3.9%
기 5
 
3.9%
오 4
 
3.1%
니 4
 
3.1%
구 4
 
3.1%
사 3
 
2.3%
다 3
 
2.3%
이 3
 
2.3%
리 3
 
2.3%
튜 2
 
1.6%
Other values (84) 92
71.9%
Latin
ValueCountFrequency (%)
n 13
 
10.6%
e 12
 
9.8%
o 10
 
8.1%
y 7
 
5.7%
a 6
 
4.9%
i 5
 
4.1%
T 5
 
4.1%
E 5
 
4.1%
g 5
 
4.1%
m 5
 
4.1%
Other values (27) 50
40.7%
Common
ValueCountFrequency (%)
22
68.8%
- 2
 
6.2%
5 2
 
6.2%
「 1
 
3.1%
」 1
 
3.1%
9 1
 
3.1%
: 1
 
3.1%
] 1
 
3.1%
[ 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153
54.1%
Hangul 128
45.2%
None 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
 
14.4%
n 13
 
8.5%
e 12
 
7.8%
o 10
 
6.5%
y 7
 
4.6%
a 6
 
3.9%
i 5
 
3.3%
T 5
 
3.3%
E 5
 
3.3%
g 5
 
3.3%
Other values (34) 63
41.2%
Hangul
ValueCountFrequency (%)
시 5
 
3.9%
기 5
 
3.9%
오 4
 
3.1%
니 4
 
3.1%
구 4
 
3.1%
사 3
 
2.3%
다 3
 
2.3%
이 3
 
2.3%
리 3
 
2.3%
튜 2
 
1.6%
Other values (84) 92
71.9%
None
ValueCountFrequency (%)
「 1
50.0%
」 1
50.0%

IMPRVMDGREE_IDEX_COLCT_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2020-10-01
28 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-10-01
2nd row2020-10-01
3rd row2020-10-01
4th row2020-10-01
5th row2020-10-01

Common Values

ValueCountFrequency (%)
2020-10-01 28
100.0%

Length

2023-12-10T23:07:42.523944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:07:42.672180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-10-01 28
100.0%

IMPRVMDGREE_IDEX_CHNNL_DC
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing2
Missing (%)7.1%
Memory size356.0 B
2023-12-10T23:07:43.045446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length952
Median length88
Mean length147.76923
Min length4

Characters and Unicode

Total characters3842
Distinct characters430
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row고품격 병맛감성 세계일주 [채널 연혁] 19년 12월 3일 : 채널 개설 1월 5일 : 태국 여행 1월 20일 : 첫 영상 업로드 2월 28일 : 공군 중위 전역 3월 1일 : 세계일주 시작; 남미 입성 3월 11일 : 고산병 시작 3월 16일 : 페루 국가비상사태 선포; 국경폐쇄 4월 22일 : 귀국 5월 11일 : 채널 공식 종료 (취업 공부하러 감) * 채널은 가끔 들어와 보니; 댓글 남겨주시면 소통이 가능합니다
2nd row안녕하세요. 18살 이제나에요. 인스타그램 @jenaxlee 이메일 lsj337js@hanmail.net
3rd rowKpop Dance K-Beauty Daily Vlogs
4th rowMINEE EATS Channel is all about positivity. Delicious foods; satisfying sounds; and good vibes.. I hope you have a happier day watching my videos. Lots of love to you minee stars - Minee - Instagram @minee.asmr @minee.kim TikTok @therealmineeeats Address: Minee Eats 257 Lawrence St NE #4861 Marietta; GA 30060 USA
5th row사사건건 공영방송 KBS가 새롭게 선보이는 데일리 시사 토크 프로그램; 사사건건! 날카로운 분석; 명쾌한 해설; 진실을 향한 거친 질문! [여의도 사사건건] 평론가들의 해설만으로는 현실 정치를 제대로 이해할 수 없다. 여의도 정치의 은밀한 내막; 현직 의원들이 직접 밝힌다! [사사건건 플러스 ①; ②] 매일 쏟아지는 각종 시사 이슈; 전문 패널단이 사사건건 파헤친다! 범람하는 가짜 뉴스 속에서 진짜 팩트만을 골라 명쾌하게 해설한다! 많은 관심 부탁드립니다.
ValueCountFrequency (%)
35
 
4.8%
및 6
 
0.8%
and 5
 
0.7%
a 5
 
0.7%
사사건건 5
 
0.7%
입니다 4
 
0.6%
children 4
 
0.6%
i 4
 
0.6%
영상을 4
 
0.6%
minee 4
 
0.6%
Other values (536) 646
89.5%
2023-12-10T23:07:43.813342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
778
 
20.2%
e 115
 
3.0%
o 89
 
2.3%
t 83
 
2.2%
. 81
 
2.1%
i 78
 
2.0%
a 78
 
2.0%
n 77
 
2.0%
s 55
 
1.4%
이 51
 
1.3%
Other values (420) 2357
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1555
40.5%
Lowercase Letter 1043
27.1%
Space Separator 778
20.2%
Other Punctuation 201
 
5.2%
Decimal Number 113
 
2.9%
Uppercase Letter 103
 
2.7%
Open Punctuation 9
 
0.2%
Close Punctuation 9
 
0.2%
Dash Punctuation 8
 
0.2%
Other Symbol 7
 
0.2%
Other values (4) 16
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 51
 
3.3%
다 44
 
2.8%
니 39
 
2.5%
의 35
 
2.3%
는 30
 
1.9%
시 25
 
1.6%
하 24
 
1.5%
일 23
 
1.5%
사 19
 
1.2%
공 19
 
1.2%
Other values (336) 1246
80.1%
Lowercase Letter
ValueCountFrequency (%)
e 115
 
11.0%
o 89
 
8.5%
t 83
 
8.0%
i 78
 
7.5%
a 78
 
7.5%
n 77
 
7.4%
s 55
 
5.3%
r 48
 
4.6%
l 44
 
4.2%
c 43
 
4.1%
Other values (15) 333
31.9%
Uppercase Letter
ValueCountFrequency (%)
S 17
16.5%
T 11
10.7%
M 11
10.7%
A 8
 
7.8%
I 7
 
6.8%
E 7
 
6.8%
C 5
 
4.9%
D 5
 
4.9%
V 4
 
3.9%
K 4
 
3.9%
Other values (13) 24
23.3%
Other Punctuation
ValueCountFrequency (%)
. 81
40.3%
: 34
16.9%
; 33
16.4%
! 33
16.4%
@ 12
 
6.0%
& 2
 
1.0%
※ 2
 
1.0%
' 2
 
1.0%
# 1
 
0.5%
* 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 27
23.9%
2 19
16.8%
0 15
13.3%
4 14
12.4%
3 10
 
8.8%
6 8
 
7.1%
5 8
 
7.1%
8 5
 
4.4%
7 4
 
3.5%
9 3
 
2.7%
Other Symbol
ValueCountFrequency (%)
★ 5
71.4%
♥ 1
 
14.3%
ⓒ 1
 
14.3%
Other Number
ValueCountFrequency (%)
② 2
40.0%
① 2
40.0%
③ 1
20.0%
Open Punctuation
ValueCountFrequency (%)
[ 5
55.6%
( 4
44.4%
Close Punctuation
ValueCountFrequency (%)
] 5
55.6%
) 4
44.4%
Math Symbol
ValueCountFrequency (%)
+ 4
80.0%
~ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
778
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1541
40.1%
Latin 1146
29.8%
Common 1141
29.7%
Han 14
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 51
 
3.3%
다 44
 
2.9%
니 39
 
2.5%
의 35
 
2.3%
는 30
 
1.9%
시 25
 
1.6%
하 24
 
1.6%
일 23
 
1.5%
사 19
 
1.2%
공 19
 
1.2%
Other values (329) 1232
79.9%
Latin
ValueCountFrequency (%)
e 115
 
10.0%
o 89
 
7.8%
t 83
 
7.2%
i 78
 
6.8%
a 78
 
6.8%
n 77
 
6.7%
s 55
 
4.8%
r 48
 
4.2%
l 44
 
3.8%
c 43
 
3.8%
Other values (38) 436
38.0%
Common
ValueCountFrequency (%)
778
68.2%
. 81
 
7.1%
: 34
 
3.0%
; 33
 
2.9%
! 33
 
2.9%
1 27
 
2.4%
2 19
 
1.7%
0 15
 
1.3%
4 14
 
1.2%
@ 12
 
1.1%
Other values (26) 95
 
8.3%
Han
ValueCountFrequency (%)
无 2
14.3%
法 2
14.3%
阻 2
14.3%
止 2
14.3%
的 2
14.3%
兄 2
14.3%
妹 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2273
59.2%
Hangul 1533
39.9%
CJK 14
 
0.4%
Compat Jamo 8
 
0.2%
Misc Symbols 6
 
0.2%
Enclosed Alphanum 6
 
0.2%
Punctuation 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
778
34.2%
e 115
 
5.1%
o 89
 
3.9%
t 83
 
3.7%
. 81
 
3.6%
i 78
 
3.4%
a 78
 
3.4%
n 77
 
3.4%
s 55
 
2.4%
r 48
 
2.1%
Other values (67) 791
34.8%
Hangul
ValueCountFrequency (%)
이 51
 
3.3%
다 44
 
2.9%
니 39
 
2.5%
의 35
 
2.3%
는 30
 
2.0%
시 25
 
1.6%
하 24
 
1.6%
일 23
 
1.5%
사 19
 
1.2%
공 19
 
1.2%
Other values (328) 1224
79.8%
Compat Jamo
ValueCountFrequency (%)
ㅤ 8
100.0%
Misc Symbols
ValueCountFrequency (%)
★ 5
83.3%
♥ 1
 
16.7%
CJK
ValueCountFrequency (%)
无 2
14.3%
法 2
14.3%
阻 2
14.3%
止 2
14.3%
的 2
14.3%
兄 2
14.3%
妹 2
14.3%
Punctuation
ValueCountFrequency (%)
※ 2
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
② 2
33.3%
① 2
33.3%
③ 1
16.7%
ⓒ 1
16.7%

IMPRVMDGREE_CHNNL_CREAT_DE
Date

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
Minimum2008-03-14 00:00:00
Maximum2019-12-03 00:00:00
2023-12-10T23:07:44.015088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:44.210090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

RECENT_SIX_MONTH_IMPRVMDGREE
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)92.9%
Missing14
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean-0.62492857
Minimum-37.684
Maximum10.086
Zeros2
Zeros (%)7.1%
Negative2
Negative (%)7.1%
Memory size384.0 B
2023-12-10T23:07:44.432983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-37.684
5-th percentile-14.6545
Q10.155
median0.9335
Q33.6485
95-th percentile6.9153
Maximum10.086
Range47.77
Interquartile range (IQR)3.4935

Descriptive statistics

Standard deviation11.083957
Coefficient of variation (CV)-17.736359
Kurtosis11.579345
Mean-0.62492857
Median Absolute Deviation (MAD)1.174
Skewness-3.2411066
Sum-8.749
Variance122.85411
MonotonicityNot monotonic
2023-12-10T23:07:44.664607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 2
 
7.1%
1.763 1
 
3.6%
4.544 1
 
3.6%
5.208 1
 
3.6%
-2.254 1
 
3.6%
0.62 1
 
3.6%
10.086 1
 
3.6%
1.086 1
 
3.6%
2.348 1
 
3.6%
0.671 1
 
3.6%
Other values (3) 3
 
10.7%
(Missing) 14
50.0%
ValueCountFrequency (%)
-37.684 1
3.6%
-2.254 1
3.6%
0.0 2
7.1%
0.62 1
3.6%
0.671 1
3.6%
0.781 1
3.6%
1.086 1
3.6%
1.763 1
3.6%
2.348 1
3.6%
4.082 1
3.6%
ValueCountFrequency (%)
10.086 1
3.6%
5.208 1
3.6%
4.544 1
3.6%
4.082 1
3.6%
2.348 1
3.6%
1.763 1
3.6%
1.086 1
3.6%
0.781 1
3.6%
0.671 1
3.6%
0.62 1
3.6%

RECENT_12_MONTH_IMPRVMDGREE
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)100.0%
Missing15
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean1.0885385
Minimum-2.174
Maximum6.349
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.6%
Memory size384.0 B
2023-12-10T23:07:44.867725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.174
5-th percentile-0.683
Q10.426
median0.845
Q31.404
95-th percentile3.862
Maximum6.349
Range8.523
Interquartile range (IQR)0.978

Descriptive statistics

Standard deviation1.8737603
Coefficient of variation (CV)1.7213543
Kurtosis5.9346026
Mean1.0885385
Median Absolute Deviation (MAD)0.495
Skewness1.6588549
Sum14.151
Variance3.5109778
MonotonicityNot monotonic
2023-12-10T23:07:45.048754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.57 1
 
3.6%
6.349 1
 
3.6%
0.426 1
 
3.6%
0.845 1
 
3.6%
1.404 1
 
3.6%
0.311 1
 
3.6%
0.923 1
 
3.6%
0.936 1
 
3.6%
0.35 1
 
3.6%
2.204 1
 
3.6%
Other values (3) 3
 
10.7%
(Missing) 15
53.6%
ValueCountFrequency (%)
-2.174 1
3.6%
0.311 1
3.6%
0.35 1
3.6%
0.426 1
3.6%
0.509 1
3.6%
0.57 1
3.6%
0.845 1
3.6%
0.923 1
3.6%
0.936 1
3.6%
1.404 1
3.6%
ValueCountFrequency (%)
6.349 1
3.6%
2.204 1
3.6%
1.498 1
3.6%
1.404 1
3.6%
0.936 1
3.6%
0.923 1
3.6%
0.845 1
3.6%
0.57 1
3.6%
0.509 1
3.6%
0.426 1
3.6%

FRST_SIX_MONTH_IMPRVMDGREE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)92.3%
Missing15
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean0.36392308
Minimum-0.269
Maximum1
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.1%
Memory size384.0 B
2023-12-10T23:07:45.232311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.269
5-th percentile-0.131
Q10.027
median0.345
Q30.773
95-th percentile1
Maximum1
Range1.269
Interquartile range (IQR)0.746

Descriptive statistics

Standard deviation0.43284841
Coefficient of variation (CV)1.1893953
Kurtosis-1.3159283
Mean0.36392308
Median Absolute Deviation (MAD)0.339
Skewness0.3343999
Sum4.731
Variance0.18735774
MonotonicityNot monotonic
2023-12-10T23:07:45.435700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1.0 2
 
7.1%
0.38 1
 
3.6%
-0.039 1
 
3.6%
0.479 1
 
3.6%
0.097 1
 
3.6%
0.041 1
 
3.6%
-0.269 1
 
3.6%
0.773 1
 
3.6%
0.345 1
 
3.6%
0.891 1
 
3.6%
Other values (2) 2
 
7.1%
(Missing) 15
53.6%
ValueCountFrequency (%)
-0.269 1
3.6%
-0.039 1
3.6%
0.006 1
3.6%
0.027 1
3.6%
0.041 1
3.6%
0.097 1
3.6%
0.345 1
3.6%
0.38 1
3.6%
0.479 1
3.6%
0.773 1
3.6%
ValueCountFrequency (%)
1.0 2
7.1%
0.891 1
3.6%
0.773 1
3.6%
0.479 1
3.6%
0.38 1
3.6%
0.345 1
3.6%
0.097 1
3.6%
0.041 1
3.6%
0.027 1
3.6%
0.006 1
3.6%

FRST_12_MONTH_IMPRVMDGREE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing14
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean0.21285714
Minimum-0.395
Maximum0.601
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.1%
Memory size384.0 B
2023-12-10T23:07:45.714975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.395
5-th percentile-0.25655
Q10.1135
median0.2475
Q30.36
95-th percentile0.58995
Maximum0.601
Range0.996
Interquartile range (IQR)0.2465

Descriptive statistics

Standard deviation0.27070474
Coefficient of variation (CV)1.2717672
Kurtosis0.85845353
Mean0.21285714
Median Absolute Deviation (MAD)0.1315
Skewness-0.77302025
Sum2.98
Variance0.073281055
MonotonicityNot monotonic
2023-12-10T23:07:45.908248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.028 1
 
3.6%
-0.182 1
 
3.6%
0.363 1
 
3.6%
0.121 1
 
3.6%
-0.395 1
 
3.6%
0.111 1
 
3.6%
0.351 1
 
3.6%
0.601 1
 
3.6%
0.584 1
 
3.6%
0.235 1
 
3.6%
Other values (4) 4
 
14.3%
(Missing) 14
50.0%
ValueCountFrequency (%)
-0.395 1
3.6%
-0.182 1
3.6%
0.028 1
3.6%
0.111 1
3.6%
0.121 1
3.6%
0.228 1
3.6%
0.235 1
3.6%
0.26 1
3.6%
0.266 1
3.6%
0.351 1
3.6%
ValueCountFrequency (%)
0.601 1
3.6%
0.584 1
3.6%
0.409 1
3.6%
0.363 1
3.6%
0.351 1
3.6%
0.266 1
3.6%
0.26 1
3.6%
0.235 1
3.6%
0.228 1
3.6%
0.121 1
3.6%

RECENT_IMPRVMDGREE_IDEX
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)64.7%
Missing11
Missing (%)39.3%
Infinite0
Infinite (%)0.0%
Mean16.592941
Minimum0
Maximum74.072
Zeros7
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:07:46.077553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.138
Q324.789
95-th percentile47.3336
Maximum74.072
Range74.072
Interquartile range (IQR)24.789

Descriptive statistics

Standard deviation20.36243
Coefficient of variation (CV)1.2271743
Kurtosis2.7412702
Mean16.592941
Median Absolute Deviation (MAD)12.138
Skewness1.5587223
Sum282.08
Variance414.62857
MonotonicityNot monotonic
2023-12-10T23:07:46.262303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 7
25.0%
13.541 1
 
3.6%
26.772 1
 
3.6%
14.408 1
 
3.6%
40.649 1
 
3.6%
24.789 1
 
3.6%
12.138 1
 
3.6%
39.525 1
 
3.6%
24.769 1
 
3.6%
11.417 1
 
3.6%
(Missing) 11
39.3%
ValueCountFrequency (%)
0.0 7
25.0%
11.417 1
 
3.6%
12.138 1
 
3.6%
13.541 1
 
3.6%
14.408 1
 
3.6%
24.769 1
 
3.6%
24.789 1
 
3.6%
26.772 1
 
3.6%
39.525 1
 
3.6%
40.649 1
 
3.6%
ValueCountFrequency (%)
74.072 1
3.6%
40.649 1
3.6%
39.525 1
3.6%
26.772 1
3.6%
24.789 1
3.6%
24.769 1
3.6%
14.408 1
3.6%
13.541 1
3.6%
12.138 1
3.6%
11.417 1
3.6%

RECENT_SIX_MONTH_STD_SCORE
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)88.9%
Missing1
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean6.3937778
Minimum-38.516
Maximum50.804
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)32.1%
Memory size384.0 B
2023-12-10T23:07:46.491678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-38.516
5-th percentile-30.0442
Q1-3.3295
median1.678
Q37.473
95-th percentile50.804
Maximum50.804
Range89.32
Interquartile range (IQR)10.8025

Descriptive statistics

Standard deviation24.34386
Coefficient of variation (CV)3.8074298
Kurtosis0.37068806
Mean6.3937778
Median Absolute Deviation (MAD)6.214
Skewness0.69026362
Sum172.632
Variance592.62354
MonotonicityNot monotonic
2023-12-10T23:07:46.741982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
50.804 4
 
14.3%
4.863 1
 
3.6%
0.493 1
 
3.6%
2.996 1
 
3.6%
7.951 1
 
3.6%
1.494 1
 
3.6%
-11.22 1
 
3.6%
6.995 1
 
3.6%
4.728 1
 
3.6%
-1.249 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
-38.516 1
3.6%
-36.196 1
3.6%
-15.69 1
3.6%
-11.901 1
3.6%
-11.22 1
3.6%
-7.244 1
3.6%
-4.536 1
3.6%
-2.123 1
3.6%
-1.249 1
3.6%
0.493 1
3.6%
ValueCountFrequency (%)
50.804 4
14.3%
50.417 1
 
3.6%
8.007 1
 
3.6%
7.951 1
 
3.6%
6.995 1
 
3.6%
4.863 1
 
3.6%
4.728 1
 
3.6%
3.661 1
 
3.6%
2.996 1
 
3.6%
2.684 1
 
3.6%

RECENT_12_MONTH_STD_SCORE
Real number (ℝ)

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5817143
Minimum-17.793
Maximum18.854
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)10.7%
Memory size384.0 B
2023-12-10T23:07:46.928172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-17.793
5-th percentile-0.59165
Q14.728
median7.494
Q37.7705
95-th percentile11.52095
Maximum18.854
Range36.647
Interquartile range (IQR)3.0425

Descriptive statistics

Standard deviation6.0832685
Coefficient of variation (CV)1.0898567
Kurtosis8.0314258
Mean5.5817143
Median Absolute Deviation (MAD)1.399
Skewness-1.9001983
Sum156.288
Variance37.006156
MonotonicityNot monotonic
2023-12-10T23:07:47.199549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7.598 4
 
14.3%
8.469 1
 
3.6%
7.627 1
 
3.6%
2.436 1
 
3.6%
7.963 1
 
3.6%
7.92 1
 
3.6%
6.628 1
 
3.6%
7.808 1
 
3.6%
5.35 1
 
3.6%
5.671 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
-17.793 1
3.6%
-0.627 1
3.6%
-0.526 1
3.6%
0.132 1
3.6%
0.232 1
3.6%
2.436 1
3.6%
2.862 1
3.6%
5.35 1
3.6%
5.45 1
3.6%
5.514 1
3.6%
ValueCountFrequency (%)
18.854 1
 
3.6%
12.656 1
 
3.6%
9.413 1
 
3.6%
8.469 1
 
3.6%
7.963 1
 
3.6%
7.92 1
 
3.6%
7.808 1
 
3.6%
7.758 1
 
3.6%
7.627 1
 
3.6%
7.598 4
14.3%

FRST_SIX_MONTH_STD_SCORE
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11385714
Minimum-1.348
Maximum1.893
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)17.9%
Memory size384.0 B
2023-12-10T23:07:47.414696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.348
5-th percentile-0.6444
Q10.0625
median0.171
Q30.171
95-th percentile0.7542
Maximum1.893
Range3.241
Interquartile range (IQR)0.1085

Descriptive statistics

Standard deviation0.52995225
Coefficient of variation (CV)4.6545367
Kurtosis5.8524817
Mean0.11385714
Median Absolute Deviation (MAD)0.027
Skewness0.5600554
Sum3.188
Variance0.28084939
MonotonicityNot monotonic
2023-12-10T23:07:47.595258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.171 11
39.3%
0.055 1
 
3.6%
-0.671 1
 
3.6%
-1.348 1
 
3.6%
-0.595 1
 
3.6%
0.007 1
 
3.6%
0.219 1
 
3.6%
0.145 1
 
3.6%
0.065 1
 
3.6%
1.893 1
 
3.6%
Other values (8) 8
28.6%
ValueCountFrequency (%)
-1.348 1
3.6%
-0.671 1
3.6%
-0.595 1
3.6%
-0.375 1
3.6%
-0.168 1
3.6%
0.007 1
3.6%
0.055 1
3.6%
0.065 1
3.6%
0.143 1
3.6%
0.145 1
3.6%
ValueCountFrequency (%)
1.893 1
 
3.6%
0.918 1
 
3.6%
0.45 1
 
3.6%
0.219 1
 
3.6%
0.206 1
 
3.6%
0.194 1
 
3.6%
0.171 11
39.3%
0.169 1
 
3.6%
0.145 1
 
3.6%
0.143 1
 
3.6%

FRST_12_MONTH_STD_SCORE
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17871429
Minimum-0.482
Maximum0.43
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)10.7%
Memory size384.0 B
2023-12-10T23:07:47.810367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.482
5-th percentile-0.3153
Q10.15025
median0.245
Q30.257
95-th percentile0.38335
Maximum0.43
Range0.912
Interquartile range (IQR)0.10675

Descriptive statistics

Standard deviation0.20906119
Coefficient of variation (CV)1.1698068
Kurtosis4.2133387
Mean0.17871429
Median Absolute Deviation (MAD)0.029
Skewness-2.0678524
Sum5.004
Variance0.043706582
MonotonicityNot monotonic
2023-12-10T23:07:48.058783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.245 10
35.7%
0.358 1
 
3.6%
0.107 1
 
3.6%
0.06 1
 
3.6%
-0.365 1
 
3.6%
-0.482 1
 
3.6%
0.269 1
 
3.6%
0.231 1
 
3.6%
0.115 1
 
3.6%
0.43 1
 
3.6%
Other values (9) 9
32.1%
ValueCountFrequency (%)
-0.482 1
3.6%
-0.365 1
3.6%
-0.223 1
3.6%
0.06 1
3.6%
0.094 1
3.6%
0.107 1
3.6%
0.115 1
3.6%
0.162 1
3.6%
0.231 1
3.6%
0.238 1
3.6%
ValueCountFrequency (%)
0.43 1
 
3.6%
0.397 1
 
3.6%
0.358 1
 
3.6%
0.343 1
 
3.6%
0.288 1
 
3.6%
0.279 1
 
3.6%
0.269 1
 
3.6%
0.253 1
 
3.6%
0.245 10
35.7%
0.238 1
 
3.6%

IMPRVMDGREE_RECENT_STD_SCORE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.256643
Minimum-5.467
Maximum38.074
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.1%
Memory size384.0 B
2023-12-10T23:07:48.306900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.467
5-th percentile0.15715
Q113.2365
median15.8285
Q316.62575
95-th percentile32.72845
Maximum38.074
Range43.541
Interquartile range (IQR)3.38925

Descriptive statistics

Standard deviation9.0319037
Coefficient of variation (CV)0.59199811
Kurtosis2.0969739
Mean15.256643
Median Absolute Deviation (MAD)1.386
Skewness0.38241697
Sum427.186
Variance81.575284
MonotonicityNot monotonic
2023-12-10T23:07:48.481014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
15.871 1
 
3.6%
15.174 1
 
3.6%
-5.467 1
 
3.6%
10.58 1
 
3.6%
15.425 1
 
3.6%
14.457 1
 
3.6%
15.893 1
 
3.6%
7.522 1
 
3.6%
16.068 1
 
3.6%
8.936 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
-5.467 1
3.6%
-0.245 1
3.6%
0.904 1
3.6%
7.522 1
3.6%
8.936 1
3.6%
10.58 1
3.6%
12.005 1
3.6%
13.647 1
3.6%
14.428 1
3.6%
14.457 1
3.6%
ValueCountFrequency (%)
38.074 1
3.6%
36.513 1
3.6%
25.7 1
3.6%
24.688 1
3.6%
18.322 1
3.6%
16.957 1
3.6%
16.649 1
3.6%
16.618 1
3.6%
16.242 1
3.6%
16.068 1
3.6%

Interactions

2023-12-10T23:07:37.462448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:23.087485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:24.900568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:26.385650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:27.791843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:29.717086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:31.463167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:33.179582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:34.578435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:36.048005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:37.602271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:23.288397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:25.044892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:26.518667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:27.936856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:29.858508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:31.608586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:33.334700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:34.743190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:36.180243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:37.745504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:23.419500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:25.172442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:26.661832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:28.076034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:30.021318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:31.768625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:33.470811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:34.885242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:36.345950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:37.889164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:23.567131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:25.336474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:26.803186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:28.213832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:30.241018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:31.928879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:33.604102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:35.069203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:36.477684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:38.046136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:23.775149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:25.505795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:26.954323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:28.362042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:30.530139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:32.116773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:33.725555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:35.239583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:36.603622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:38.189285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:23.934009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:25.660949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:27.087762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:28.882364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:30.714225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:32.321406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:33.883465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:35.386566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:36.769085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:38.334241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:24.067295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:25.803642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:27.227586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:29.025526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:30.841627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:32.482190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:34.021164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:35.531393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:36.910833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:38.498061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:24.330809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:25.936140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:27.359278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:29.179494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:30.994560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:32.645255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:34.158148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:35.666910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:37.078478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:39.009222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:24.457731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:26.069665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:27.500898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:29.309116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:31.150528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:32.833613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:34.296630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:35.803162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:37.225776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:39.256368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:24.659228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:26.218234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:27.654926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:29.548792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:31.295046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:33.037898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:34.420779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:35.921867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:37.342057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:07:48.604265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IMPRVMDGREE_IDEX_CHNNL_IDIMPRVMDGREE_IDEX_CHNNL_NMIMPRVMDGREE_IDEX_CHNNL_DCIMPRVMDGREE_CHNNL_CREAT_DERECENT_SIX_MONTH_IMPRVMDGREERECENT_12_MONTH_IMPRVMDGREEFRST_SIX_MONTH_IMPRVMDGREEFRST_12_MONTH_IMPRVMDGREERECENT_IMPRVMDGREE_IDEXRECENT_SIX_MONTH_STD_SCORERECENT_12_MONTH_STD_SCOREFRST_SIX_MONTH_STD_SCOREFRST_12_MONTH_STD_SCOREIMPRVMDGREE_RECENT_STD_SCORE
IMPRVMDGREE_IDEX_CHNNL_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMPRVMDGREE_IDEX_CHNNL_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMPRVMDGREE_IDEX_CHNNL_DC1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMPRVMDGREE_CHNNL_CREAT_DE1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
RECENT_SIX_MONTH_IMPRVMDGREE1.0001.0001.0001.0001.0000.5990.0000.7520.6550.0000.1490.7980.7220.000
RECENT_12_MONTH_IMPRVMDGREE1.0001.0001.0001.0000.5991.0000.9510.5320.0000.0000.5550.0000.0000.557
FRST_SIX_MONTH_IMPRVMDGREE1.0001.0001.0001.0000.0000.9511.0000.4010.0000.0000.0000.5550.0000.843
FRST_12_MONTH_IMPRVMDGREE1.0001.0001.0001.0000.7520.5320.4011.0000.0000.0000.5040.0000.6830.000
RECENT_IMPRVMDGREE_IDEX1.0001.0001.0001.0000.6550.0000.0000.0001.0000.0000.0000.8740.6280.688
RECENT_SIX_MONTH_STD_SCORE1.0001.0001.0001.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
RECENT_12_MONTH_STD_SCORE1.0001.0001.0001.0000.1490.5550.0000.5040.0000.0001.0000.0000.0000.665
FRST_SIX_MONTH_STD_SCORE1.0001.0001.0001.0000.7980.0000.5550.0000.8740.0000.0001.0000.8370.496
FRST_12_MONTH_STD_SCORE1.0001.0001.0001.0000.7220.0000.0000.6830.6280.0000.0000.8371.0000.452
IMPRVMDGREE_RECENT_STD_SCORE1.0001.0001.0001.0000.0000.5570.8430.0000.6880.0000.6650.4960.4521.000
2023-12-10T23:07:48.829068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RECENT_SIX_MONTH_IMPRVMDGREERECENT_12_MONTH_IMPRVMDGREEFRST_SIX_MONTH_IMPRVMDGREEFRST_12_MONTH_IMPRVMDGREERECENT_IMPRVMDGREE_IDEXRECENT_SIX_MONTH_STD_SCORERECENT_12_MONTH_STD_SCOREFRST_SIX_MONTH_STD_SCOREFRST_12_MONTH_STD_SCOREIMPRVMDGREE_RECENT_STD_SCORE
RECENT_SIX_MONTH_IMPRVMDGREE1.0000.1820.5190.4940.033-0.0180.1030.2450.0500.161
RECENT_12_MONTH_IMPRVMDGREE0.1821.000-0.190-0.119-0.146-0.016-0.4010.2430.0450.077
FRST_SIX_MONTH_IMPRVMDGREE0.519-0.1901.0000.5910.0170.1720.173-0.0190.3490.671
FRST_12_MONTH_IMPRVMDGREE0.494-0.1190.5911.0000.4750.3030.048-0.1470.0420.411
RECENT_IMPRVMDGREE_IDEX0.033-0.1460.0170.4751.000-0.086-0.305-0.040-0.192-0.366
RECENT_SIX_MONTH_STD_SCORE-0.018-0.0160.1720.303-0.0861.0000.3690.2670.1420.131
RECENT_12_MONTH_STD_SCORE0.103-0.4010.1730.048-0.3050.3691.000-0.0210.0040.084
FRST_SIX_MONTH_STD_SCORE0.2450.243-0.019-0.147-0.0400.267-0.0211.0000.5280.345
FRST_12_MONTH_STD_SCORE0.0500.0450.3490.042-0.1920.1420.0040.5281.0000.516
IMPRVMDGREE_RECENT_STD_SCORE0.1610.0770.6710.411-0.3660.1310.0840.3450.5161.000

Missing values

2023-12-10T23:07:39.469056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:07:39.797852image/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.
2023-12-10T23:07:40.100974image/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

IMPRVMDGREE_IDEX_CHNNL_IDIMPRVMDGREE_IDEX_CHNNL_NMIMPRVMDGREE_IDEX_COLCT_DEIMPRVMDGREE_IDEX_CHNNL_DCIMPRVMDGREE_CHNNL_CREAT_DERECENT_SIX_MONTH_IMPRVMDGREERECENT_12_MONTH_IMPRVMDGREEFRST_SIX_MONTH_IMPRVMDGREEFRST_12_MONTH_IMPRVMDGREERECENT_IMPRVMDGREE_IDEXRECENT_SIX_MONTH_STD_SCORERECENT_12_MONTH_STD_SCOREFRST_SIX_MONTH_STD_SCOREFRST_12_MONTH_STD_SCOREIMPRVMDGREE_RECENT_STD_SCORE
0UCDBUJ8x8ZwE1OAzSyzUcFLg세계일주 저니맨 Journeyman2020-10-01고품격 병맛감성 세계일주 [채널 연혁] 19년 12월 3일 : 채널 개설 1월 5일 : 태국 여행 1월 20일 : 첫 영상 업로드 2월 28일 : 공군 중위 전역 3월 1일 : 세계일주 시작; 남미 입성 3월 11일 : 고산병 시작 3월 16일 : 페루 국가비상사태 선포; 국경폐쇄 4월 22일 : 귀국 5월 11일 : 채널 공식 종료 (취업 공부하러 감) * 채널은 가끔 들어와 보니; 댓글 남겨주시면 소통이 가능합니다2019-12-03<NA><NA><NA><NA><NA>1.6788.4690.0550.35815.871
1UCmiWMl8Qu6rZDA4D8dSWxFAJena Lee 제나리2020-10-01안녕하세요. 18살 이제나에요. 인스타그램 @jenaxlee 이메일 lsj337js@hanmail.net2018-11-010.00.570.380.0280.0-15.699.4130.1430.27915.979
2UC2iVamQdGdLAWJR6ui8zowg청도군2020-10-01<NA>2018-11-29<NA><NA><NA><NA><NA>-36.1967.4440.1940.25315.85
3UCxSBWvnORMtCtJNbK6pL4YQsusiemeoww2020-10-01Kpop Dance K-Beauty Daily Vlogs2014-11-111.7636.349<NA><NA>13.541-7.244-0.5260.1710.24525.7
4UCc7v5yYC_mviB1_fLuB-GRwMINEE EATS2020-10-01MINEE EATS Channel is all about positivity. Delicious foods; satisfying sounds; and good vibes.. I hope you have a happier day watching my videos. Lots of love to you minee stars - Minee - Instagram @minee.asmr @minee.kim TikTok @therealmineeeats Address: Minee Eats 257 Lawrence St NE #4861 Marietta; GA 30060 USA2017-03-110.00.426-0.039-0.1820.0-2.123-0.627-0.3750.0940.904
5UCpVw9Y6pqUiCY0Sgc8Ae9mA「싸꼰」사사건건2020-10-01사사건건 공영방송 KBS가 새롭게 선보이는 데일리 시사 토크 프로그램; 사사건건! 날카로운 분석; 명쾌한 해설; 진실을 향한 거친 질문! [여의도 사사건건] 평론가들의 해설만으로는 현실 정치를 제대로 이해할 수 없다. 여의도 정치의 은밀한 내막; 현직 의원들이 직접 밝힌다! [사사건건 플러스 ①; ②] 매일 쏟아지는 각종 시사 이슈; 전문 패널단이 사사건건 파헤친다! 범람하는 가짜 뉴스 속에서 진짜 팩트만을 골라 명쾌하게 해설한다! 많은 관심 부탁드립니다.2018-10-304.5440.8450.4790.36326.7723.6612.8620.9180.39738.074
6UCN8CPzwkYiDVLZlgD4JQgJQ박막례 할머니 Korea Grandma2020-10-0173세 박막례 할머니의 무한도전 인생은 아름다워! 할머니와 즐거운 하루 하루를 보내고; 기록 합니다. 이 채널의 방향은 할머니의 행복 입니다.2017-01-305.2081.4040.0970.12114.408-11.9010.2320.1690.162-0.245
7UCOl4IG5vgBuXDZGTxSxaSgQ민자킴MJ Kim2020-10-01<NA>2013-03-25<NA>0.311<NA><NA>0.050.41718.8540.1710.24518.322
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IMPRVMDGREE_IDEX_CHNNL_IDIMPRVMDGREE_IDEX_CHNNL_NMIMPRVMDGREE_IDEX_COLCT_DEIMPRVMDGREE_IDEX_CHNNL_DCIMPRVMDGREE_CHNNL_CREAT_DERECENT_SIX_MONTH_IMPRVMDGREERECENT_12_MONTH_IMPRVMDGREEFRST_SIX_MONTH_IMPRVMDGREEFRST_12_MONTH_IMPRVMDGREERECENT_IMPRVMDGREE_IDEXRECENT_SIX_MONTH_STD_SCORERECENT_12_MONTH_STD_SCOREFRST_SIX_MONTH_STD_SCOREFRST_12_MONTH_STD_SCOREIMPRVMDGREE_RECENT_STD_SCORE
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19UC2U30ljkWIEsLWpFznvB7mwMinSeongToy 민성토이2020-10-01MinSeongTOY 채널 소개!! 안녕하세요! 민성이의 장난감 세상 민성토이 입니다. 민성토이는 아이들이 장난감을 재미나게 가지고 놀며 아이들의 창의력과 재미를 느낄수 있게 도움을 주는 재미있고 유익한 동영상을 제작 하고있습니다. 많은 친구들이 민성토이를 통해서 재미있게 놀고 배울수 있는 그런 시간이 되었으면 합니다. 즐거운 시간 되세요!!! ※ 2016. 08. 16 구독자 1000명 돌파!! ※ 업로드 시간 : 월~일 오후4시 ★민성이★에게 도움이 되는 부분들: ① 구독 + 좋아요 + 댓글 + 공유는 민성이에게 큰 도움이 됩니다. ② 구독자 + 시청자 여러분 및 친구들이 영상을 재미있게 봐주시면됩니다. ③민성이와 함께하는 삼촌들 및 박사님의 유튜브도 많이 봐주세요!!!! ★ 민성이와 함께하는 젤리언 박사님 : https:goo.glM13YY5 [문의 및 이벤트 이메일]skulover@naver.com Hi; MinSeongToy. produces exciting video clips which help children to learn number and alphabet playing with and drawing toys with children I hope that this channel would be a cozy space that many children could enjoy and learn a lot through the fun video clips. MinSeongToy channel are uploaded every day ★MinSeongToy[민성토이]구독!! Subscribe our channel!!★ Copyright ⓒ 2016 MinSeong Toy All Rights Reserved. 해당 영상의 저작권은 주식회사 유비크리에이티브 에게 있습니다. 이 영상을 공유하는 것은 가능하나 허가 없이 변경배포는 불가합니다.2015-10-28<NA><NA><NA>0.584<NA>50.8047.5981.8930.4316.242
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