Overview

Dataset statistics

Number of variables15
Number of observations28
Missing cells73
Missing cells (%)17.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory134.7 B

Variable types

Text3
DateTime2
Numeric10

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/6ceeba8a-fa60-4059-98af-a364c2a83328

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 1 other fieldsHigh correlation
FRST_12_MONTH_IMPRVMDGREE is highly overall correlated with FRST_SIX_MONTH_IMPRVMDGREE and 1 other fieldsHigh correlation
RECENT_IMPRVMDGREE_IDEX is highly overall correlated with FRST_12_MONTH_IMPRVMDGREEHigh correlation
RECENT_SIX_MONTH_STD_SCORE is highly overall correlated with IMPRVMDGREE_RECENT_STD_SCOREHigh 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_SCOREHigh correlation
IMPRVMDGREE_RECENT_STD_SCORE is highly overall correlated with RECENT_SIX_MONTH_STD_SCOREHigh correlation
IMPRVMDGREE_IDEX_CHNNL_DC has 2 (7.1%) missing valuesMissing
RECENT_SIX_MONTH_IMPRVMDGREE has 15 (53.6%) missing valuesMissing
RECENT_12_MONTH_IMPRVMDGREE has 14 (50.0%) missing valuesMissing
FRST_SIX_MONTH_IMPRVMDGREE has 15 (53.6%) missing valuesMissing
FRST_12_MONTH_IMPRVMDGREE has 15 (53.6%) missing valuesMissing
RECENT_IMPRVMDGREE_IDEX has 10 (35.7%) missing valuesMissing
RECENT_12_MONTH_STD_SCORE has 2 (7.1%) missing valuesMissing
IMPRVMDGREE_IDEX_CHNNL_ID has unique valuesUnique
IMPRVMDGREE_IDEX_CHNNL_NM has unique valuesUnique
IMPRVMDGREE_CHNNL_CREAT_DE has unique valuesUnique
RECENT_IMPRVMDGREE_IDEX has 9 (32.1%) zerosZeros

Reproduction

Analysis started2023-12-10 14:11:09.323613
Analysis finished2023-12-10 14:11:29.333060
Duration20.01 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
2023-12-10T23:11:29.609714image/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 rowUC2iVamQdGdLAWJR6ui8zowg
2nd rowUCmiWMl8Qu6rZDA4D8dSWxFA
3rd rowUCDBUJ8x8ZwE1OAzSyzUcFLg
4th rowUCaijie_uqaAQQCt_fCZ-5qQ
5th rowUCpVw9Y6pqUiCY0Sgc8Ae9mA
ValueCountFrequency (%)
uc2ivamqdgdlawjr6ui8zowg 1
 
3.6%
ucmiwml8qu6rzda4d8dswxfa 1
 
3.6%
ucix-qk5khufams9tci-fnyg 1
 
3.6%
ucrlfzmsowct0kyltqkawf_a 1
 
3.6%
ucdv2bsta2p7g4vhhjj7_foa 1
 
3.6%
ucc1g8zpj7bv2abuy3wxdhra 1
 
3.6%
ucc7v5yyc_mvib1_flub-grw 1
 
3.6%
uctxucszxmdanek7t6dvs21g 1
 
3.6%
ucmrnr2v0a2qxjffjgja47tw 1
 
3.6%
ucv35ktymnjtd91t_wd5p53q 1
 
3.6%
Other values (18) 18
64.3%
2023-12-10T23:11:30.252333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 39
 
5.8%
U 34
 
5.1%
Q 21
 
3.1%
A 20
 
3.0%
D 17
 
2.5%
g 17
 
2.5%
5 16
 
2.4%
S 15
 
2.2%
v 14
 
2.1%
w 13
 
1.9%
Other values (54) 466
69.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 314
46.7%
Lowercase Letter 243
36.2%
Decimal Number 98
 
14.6%
Connector Punctuation 10
 
1.5%
Dash Punctuation 7
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 39
 
12.4%
U 34
 
10.8%
Q 21
 
6.7%
A 20
 
6.4%
D 17
 
5.4%
S 15
 
4.8%
V 12
 
3.8%
N 12
 
3.8%
T 11
 
3.5%
Y 10
 
3.2%
Other values (16) 123
39.2%
Lowercase Letter
ValueCountFrequency (%)
g 17
 
7.0%
v 14
 
5.8%
w 13
 
5.3%
f 12
 
4.9%
i 12
 
4.9%
u 12
 
4.9%
x 11
 
4.5%
y 11
 
4.5%
r 11
 
4.5%
a 10
 
4.1%
Other values (16) 120
49.4%
Decimal Number
ValueCountFrequency (%)
5 16
16.3%
2 12
12.2%
7 11
11.2%
4 10
10.2%
1 10
10.2%
8 9
9.2%
6 8
8.2%
9 8
8.2%
3 7
7.1%
0 7
7.1%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 557
82.9%
Common 115
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 39
 
7.0%
U 34
 
6.1%
Q 21
 
3.8%
A 20
 
3.6%
D 17
 
3.1%
g 17
 
3.1%
S 15
 
2.7%
v 14
 
2.5%
w 13
 
2.3%
f 12
 
2.2%
Other values (42) 355
63.7%
Common
ValueCountFrequency (%)
5 16
13.9%
2 12
10.4%
7 11
9.6%
4 10
8.7%
_ 10
8.7%
1 10
8.7%
8 9
7.8%
6 8
7.0%
9 8
7.0%
3 7
6.1%
Other values (2) 14
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 39
 
5.8%
U 34
 
5.1%
Q 21
 
3.1%
A 20
 
3.0%
D 17
 
2.5%
g 17
 
2.5%
5 16
 
2.4%
S 15
 
2.2%
v 14
 
2.1%
w 13
 
1.9%
Other values (54) 466
69.3%

IMPRVMDGREE_IDEX_CHNNL_NM
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-10T23:11:30.758216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length9.6071429
Min length2

Characters and Unicode

Total characters269
Distinct characters132
Distinct categories7 ?
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 row청도군
2nd rowJena Lee 제나리
3rd row세계일주 저니맨 Journeyman
4th row통영시Tongyeong
5th row「싸꼰」사사건건
ValueCountFrequency (%)
청도군 1
 
2.2%
동네오빠엔터테인먼트 1
 
2.2%
한국동서발전 1
 
2.2%
부산시설공단 1
 
2.2%
기미티 1
 
2.2%
민자킴mj 1
 
2.2%
kim 1
 
2.2%
official 1
 
2.2%
dopa 1
 
2.2%
꽃보다유이 1
 
2.2%
Other values (36) 36
78.3%
2023-12-10T23:11:31.942881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
6.7%
n 12
 
4.5%
e 10
 
3.7%
a 9
 
3.3%
o 8
 
3.0%
i 8
 
3.0%
시 6
 
2.2%
y 6
 
2.2%
니 6
 
2.2%
g 5
 
1.9%
Other values (122) 181
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
43.9%
Lowercase Letter 87
32.3%
Uppercase Letter 41
 
15.2%
Space Separator 18
 
6.7%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
 
5.1%
니 6
 
5.1%
리 5
 
4.2%
기 5
 
4.2%
구 3
 
2.5%
사 3
 
2.5%
서 2
 
1.7%
동 2
 
1.7%
티 2
 
1.7%
공 2
 
1.7%
Other values (75) 82
69.5%
Lowercase Letter
ValueCountFrequency (%)
n 12
13.8%
e 10
11.5%
a 9
10.3%
o 8
9.2%
i 8
9.2%
y 6
 
6.9%
g 5
 
5.7%
m 5
 
5.7%
s 4
 
4.6%
u 4
 
4.6%
Other values (12) 16
18.4%
Uppercase Letter
ValueCountFrequency (%)
E 4
 
9.8%
N 4
 
9.8%
J 3
 
7.3%
T 3
 
7.3%
M 3
 
7.3%
K 3
 
7.3%
R 3
 
7.3%
S 3
 
7.3%
H 2
 
4.9%
I 2
 
4.9%
Other values (9) 11
26.8%
Close Punctuation
ValueCountFrequency (%)
」 1
50.0%
] 1
50.0%
Open Punctuation
ValueCountFrequency (%)
「 1
50.0%
[ 1
50.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128
47.6%
Hangul 118
43.9%
Common 23
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
 
5.1%
니 6
 
5.1%
리 5
 
4.2%
기 5
 
4.2%
구 3
 
2.5%
사 3
 
2.5%
서 2
 
1.7%
동 2
 
1.7%
티 2
 
1.7%
공 2
 
1.7%
Other values (75) 82
69.5%
Latin
ValueCountFrequency (%)
n 12
 
9.4%
e 10
 
7.8%
a 9
 
7.0%
o 8
 
6.2%
i 8
 
6.2%
y 6
 
4.7%
g 5
 
3.9%
m 5
 
3.9%
E 4
 
3.1%
s 4
 
3.1%
Other values (31) 57
44.5%
Common
ValueCountFrequency (%)
18
78.3%
」 1
 
4.3%
「 1
 
4.3%
- 1
 
4.3%
] 1
 
4.3%
[ 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149
55.4%
Hangul 118
43.9%
None 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
 
12.1%
n 12
 
8.1%
e 10
 
6.7%
a 9
 
6.0%
o 8
 
5.4%
i 8
 
5.4%
y 6
 
4.0%
g 5
 
3.4%
m 5
 
3.4%
E 4
 
2.7%
Other values (35) 64
43.0%
Hangul
ValueCountFrequency (%)
시 6
 
5.1%
니 6
 
5.1%
리 5
 
4.2%
기 5
 
4.2%
구 3
 
2.5%
사 3
 
2.5%
서 2
 
1.7%
동 2
 
1.7%
티 2
 
1.7%
공 2
 
1.7%
Other values (75) 82
69.5%
None
ValueCountFrequency (%)
」 1
50.0%
「 1
50.0%

IMPRVMDGREE_IDEX_COLCT_DE
Date

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
Minimum2020-09-01 00:00:00
Maximum2020-09-01 00:00:00
2023-12-10T23:11:32.194930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:32.380387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

IMPRVMDGREE_IDEX_CHNNL_DC
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing2
Missing (%)7.1%
Memory size356.0 B
2023-12-10T23:11:32.766910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length588
Median length77.5
Mean length136.65385
Min length4

Characters and Unicode

Total characters3553
Distinct characters395
Distinct categories13 ?
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 row안녕하세요. 18살 이제나에요. 인스타그램 @jenaxlee 이메일 lsj337js@hanmail.net
2nd 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
3rd row통영시에서 운영하는 공식 유튜브 채널입니다. 통영관광; 통영소식 통영과 관련된 모든 소식을 알려드려요. 언제든지 놀러오세요. 환영합니다!
4th 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.
5th row지랄견보다 더한 시바견 노리의 하루
ValueCountFrequency (%)
25
 
3.8%
to 8
 
1.2%
i 8
 
1.2%
사사건건 5
 
0.8%
for 5
 
0.8%
videos 5
 
0.8%
the 5
 
0.8%
minee 4
 
0.6%
a 4
 
0.6%
of 4
 
0.6%
Other values (490) 582
88.9%
2023-12-10T23:11:33.604579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
692
 
19.5%
e 113
 
3.2%
t 111
 
3.1%
o 99
 
2.8%
a 96
 
2.7%
i 95
 
2.7%
n 78
 
2.2%
s 77
 
2.2%
. 67
 
1.9%
r 55
 
1.5%
Other values (385) 2070
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1317
37.1%
Lowercase Letter 1147
32.3%
Space Separator 692
19.5%
Other Punctuation 177
 
5.0%
Decimal Number 105
 
3.0%
Uppercase Letter 86
 
2.4%
Dash Punctuation 7
 
0.2%
Close Punctuation 7
 
0.2%
Open Punctuation 7
 
0.2%
Connector Punctuation 2
 
0.1%
Other values (3) 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 40
 
3.0%
니 35
 
2.7%
공 30
 
2.3%
이 28
 
2.1%
하 25
 
1.9%
의 25
 
1.9%
시 24
 
1.8%
는 23
 
1.7%
일 19
 
1.4%
영 17
 
1.3%
Other values (305) 1051
79.8%
Lowercase Letter
ValueCountFrequency (%)
e 113
 
9.9%
t 111
 
9.7%
o 99
 
8.6%
a 96
 
8.4%
i 95
 
8.3%
n 78
 
6.8%
s 77
 
6.7%
r 55
 
4.8%
l 49
 
4.3%
m 46
 
4.0%
Other values (15) 328
28.6%
Uppercase Letter
ValueCountFrequency (%)
I 12
14.0%
A 8
 
9.3%
S 8
 
9.3%
D 7
 
8.1%
E 7
 
8.1%
M 6
 
7.0%
K 5
 
5.8%
H 4
 
4.7%
T 4
 
4.7%
U 3
 
3.5%
Other values (12) 22
25.6%
Decimal Number
ValueCountFrequency (%)
1 27
25.7%
2 20
19.0%
0 13
12.4%
4 13
12.4%
3 10
 
9.5%
8 7
 
6.7%
6 5
 
4.8%
5 4
 
3.8%
7 4
 
3.8%
9 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 67
37.9%
; 43
24.3%
: 29
16.4%
! 21
 
11.9%
@ 13
 
7.3%
' 1
 
0.6%
# 1
 
0.6%
& 1
 
0.6%
* 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 3
42.9%
] 3
42.9%
】 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
[ 3
42.9%
( 3
42.9%
【 1
 
14.3%
Other Symbol
ValueCountFrequency (%)
● 1
50.0%
♥ 1
50.0%
Other Number
ValueCountFrequency (%)
① 1
50.0%
② 1
50.0%
Space Separator
ValueCountFrequency (%)
692
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1303
36.7%
Latin 1233
34.7%
Common 1003
28.2%
Han 14
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 40
 
3.1%
니 35
 
2.7%
공 30
 
2.3%
이 28
 
2.1%
하 25
 
1.9%
의 25
 
1.9%
시 24
 
1.8%
는 23
 
1.8%
일 19
 
1.5%
영 17
 
1.3%
Other values (298) 1037
79.6%
Latin
ValueCountFrequency (%)
e 113
 
9.2%
t 111
 
9.0%
o 99
 
8.0%
a 96
 
7.8%
i 95
 
7.7%
n 78
 
6.3%
s 77
 
6.2%
r 55
 
4.5%
l 49
 
4.0%
m 46
 
3.7%
Other values (37) 414
33.6%
Common
ValueCountFrequency (%)
692
69.0%
. 67
 
6.7%
; 43
 
4.3%
: 29
 
2.9%
1 27
 
2.7%
! 21
 
2.1%
2 20
 
2.0%
0 13
 
1.3%
4 13
 
1.3%
@ 13
 
1.3%
Other values (23) 65
 
6.5%
Han
ValueCountFrequency (%)
的 2
14.3%
兄 2
14.3%
妹 2
14.3%
阻 2
14.3%
止 2
14.3%
无 2
14.3%
法 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2230
62.8%
Hangul 1303
36.7%
CJK 14
 
0.4%
None 2
 
0.1%
Enclosed Alphanum 2
 
0.1%
Geometric Shapes 1
 
< 0.1%
Misc Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
692
31.0%
e 113
 
5.1%
t 111
 
5.0%
o 99
 
4.4%
a 96
 
4.3%
i 95
 
4.3%
n 78
 
3.5%
s 77
 
3.5%
. 67
 
3.0%
r 55
 
2.5%
Other values (64) 747
33.5%
Hangul
ValueCountFrequency (%)
다 40
 
3.1%
니 35
 
2.7%
공 30
 
2.3%
이 28
 
2.1%
하 25
 
1.9%
의 25
 
1.9%
시 24
 
1.8%
는 23
 
1.8%
일 19
 
1.5%
영 17
 
1.3%
Other values (298) 1037
79.6%
CJK
ValueCountFrequency (%)
的 2
14.3%
兄 2
14.3%
妹 2
14.3%
阻 2
14.3%
止 2
14.3%
无 2
14.3%
法 2
14.3%
Geometric Shapes
ValueCountFrequency (%)
● 1
100.0%
None
ValueCountFrequency (%)
【 1
50.0%
】 1
50.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 1
50.0%
② 1
50.0%
Misc Symbols
ValueCountFrequency (%)
♥ 1
100.0%

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
2023-12-10T23:11:33.938622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:34.200295image/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 

Distinct13
Distinct (%)100.0%
Missing15
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean0.73023077
Minimum-6.853
Maximum3.026
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.1%
Memory size384.0 B
2023-12-10T23:11:34.465346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.853
5-th percentile-4.366
Q10.52
median1.531
Q32.242
95-th percentile2.8544
Maximum3.026
Range9.879
Interquartile range (IQR)1.722

Descriptive statistics

Standard deviation2.709146
Coefficient of variation (CV)3.7099861
Kurtosis5.0516934
Mean0.73023077
Median Absolute Deviation (MAD)0.856
Skewness-2.202047
Sum9.493
Variance7.3394722
MonotonicityNot monotonic
2023-12-10T23:11:34.699516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2.387 1
 
3.6%
2.019 1
 
3.6%
1.303 1
 
3.6%
2.242 1
 
3.6%
1.717 1
 
3.6%
-2.708 1
 
3.6%
2.74 1
 
3.6%
-6.853 1
 
3.6%
1.531 1
 
3.6%
0.171 1
 
3.6%
Other values (3) 3
 
10.7%
(Missing) 15
53.6%
ValueCountFrequency (%)
-6.853 1
3.6%
-2.708 1
3.6%
0.171 1
3.6%
0.52 1
3.6%
1.303 1
3.6%
1.398 1
3.6%
1.531 1
3.6%
1.717 1
3.6%
2.019 1
3.6%
2.242 1
3.6%
ValueCountFrequency (%)
3.026 1
3.6%
2.74 1
3.6%
2.387 1
3.6%
2.242 1
3.6%
2.019 1
3.6%
1.717 1
3.6%
1.531 1
3.6%
1.398 1
3.6%
1.303 1
3.6%
0.52 1
3.6%

RECENT_12_MONTH_IMPRVMDGREE
Real number (ℝ)

MISSING 

Distinct14
Distinct (%)100.0%
Missing14
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean0.93378571
Minimum-2.563
Maximum6.28
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)10.7%
Memory size384.0 B
2023-12-10T23:11:34.936326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.563
5-th percentile-2.4733
Q10.23575
median0.8195
Q31.57925
95-th percentile4.46585
Maximum6.28
Range8.843
Interquartile range (IQR)1.3435

Descriptive statistics

Standard deviation2.2546322
Coefficient of variation (CV)2.4145071
Kurtosis1.6263082
Mean0.93378571
Median Absolute Deviation (MAD)0.733
Skewness0.65774454
Sum13.073
Variance5.0833663
MonotonicityNot monotonic
2023-12-10T23:11:35.318836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.57 1
 
3.6%
3.489 1
 
3.6%
0.865 1
 
3.6%
1.606 1
 
3.6%
-2.563 1
 
3.6%
6.28 1
 
3.6%
0.325 1
 
3.6%
0.206 1
 
3.6%
1.499 1
 
3.6%
-2.425 1
 
3.6%
Other values (4) 4
 
14.3%
(Missing) 14
50.0%
ValueCountFrequency (%)
-2.563 1
3.6%
-2.425 1
3.6%
-0.997 1
3.6%
0.206 1
3.6%
0.325 1
3.6%
0.57 1
3.6%
0.774 1
3.6%
0.865 1
3.6%
1.148 1
3.6%
1.499 1
3.6%
ValueCountFrequency (%)
6.28 1
3.6%
3.489 1
3.6%
2.296 1
3.6%
1.606 1
3.6%
1.499 1
3.6%
1.148 1
3.6%
0.865 1
3.6%
0.774 1
3.6%
0.57 1
3.6%
0.325 1
3.6%

FRST_SIX_MONTH_IMPRVMDGREE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)84.6%
Missing15
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean0.44215385
Minimum-0.279
Maximum1
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.1%
Memory size384.0 B
2023-12-10T23:11:35.580018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.279
5-th percentile-0.1404
Q10.088
median0.362
Q30.889
95-th percentile1
Maximum1
Range1.279
Interquartile range (IQR)0.801

Descriptive statistics

Standard deviation0.43895194
Coefficient of variation (CV)0.99275839
Kurtosis-1.299089
Mean0.44215385
Median Absolute Deviation (MAD)0.329
Skewness0.0095302262
Sum5.748
Variance0.19267881
MonotonicityNot monotonic
2023-12-10T23:11:35.967331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.0 3
 
10.7%
0.362 1
 
3.6%
-0.279 1
 
3.6%
0.475 1
 
3.6%
0.088 1
 
3.6%
0.033 1
 
3.6%
0.261 1
 
3.6%
-0.048 1
 
3.6%
0.281 1
 
3.6%
0.686 1
 
3.6%
(Missing) 15
53.6%
ValueCountFrequency (%)
-0.279 1
3.6%
-0.048 1
3.6%
0.033 1
3.6%
0.088 1
3.6%
0.261 1
3.6%
0.281 1
3.6%
0.362 1
3.6%
0.475 1
3.6%
0.686 1
3.6%
0.889 1
3.6%
ValueCountFrequency (%)
1.0 3
10.7%
0.889 1
 
3.6%
0.686 1
 
3.6%
0.475 1
 
3.6%
0.362 1
 
3.6%
0.281 1
 
3.6%
0.261 1
 
3.6%
0.088 1
 
3.6%
0.033 1
 
3.6%
-0.048 1
 
3.6%

FRST_12_MONTH_IMPRVMDGREE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing15
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean0.19115385
Minimum-0.402
Maximum0.597
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)10.7%
Memory size384.0 B
2023-12-10T23:11:36.231369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.402
5-th percentile-0.2724
Q10.025
median0.227
Q30.416
95-th percentile0.5898
Maximum0.597
Range0.999
Interquartile range (IQR)0.391

Descriptive statistics

Standard deviation0.30727508
Coefficient of variation (CV)1.6074753
Kurtosis-0.56815464
Mean0.19115385
Median Absolute Deviation (MAD)0.202
Skewness-0.35200657
Sum2.485
Variance0.094417974
MonotonicityNot monotonic
2023-12-10T23:11:36.440208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.025 1
 
3.6%
-0.06 1
 
3.6%
0.362 1
 
3.6%
0.227 1
 
3.6%
0.597 1
 
3.6%
0.117 1
 
3.6%
0.034 1
 
3.6%
-0.402 1
 
3.6%
0.24 1
 
3.6%
-0.186 1
 
3.6%
Other values (3) 3
 
10.7%
(Missing) 15
53.6%
ValueCountFrequency (%)
-0.402 1
3.6%
-0.186 1
3.6%
-0.06 1
3.6%
0.025 1
3.6%
0.034 1
3.6%
0.117 1
3.6%
0.227 1
3.6%
0.24 1
3.6%
0.362 1
3.6%
0.416 1
3.6%
ValueCountFrequency (%)
0.597 1
3.6%
0.585 1
3.6%
0.53 1
3.6%
0.416 1
3.6%
0.362 1
3.6%
0.24 1
3.6%
0.227 1
3.6%
0.117 1
3.6%
0.034 1
3.6%
0.025 1
3.6%

RECENT_IMPRVMDGREE_IDEX
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)55.6%
Missing10
Missing (%)35.7%
Infinite0
Infinite (%)0.0%
Mean5.991
Minimum0
Maximum22.939
Zeros9
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:11:36.658925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2775
Q312.542
95-th percentile17.98435
Maximum22.939
Range22.939
Interquartile range (IQR)12.542

Descriptive statistics

Standard deviation7.5829767
Coefficient of variation (CV)1.265728
Kurtosis-0.42440603
Mean5.991
Median Absolute Deviation (MAD)1.2775
Skewness0.93818463
Sum107.838
Variance57.501536
MonotonicityNot monotonic
2023-12-10T23:11:36.900706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 9
32.1%
13.399 1
 
3.6%
14.895 1
 
3.6%
5.787 1
 
3.6%
15.033 1
 
3.6%
9.971 1
 
3.6%
2.555 1
 
3.6%
22.939 1
 
3.6%
6.149 1
 
3.6%
17.11 1
 
3.6%
(Missing) 10
35.7%
ValueCountFrequency (%)
0.0 9
32.1%
2.555 1
 
3.6%
5.787 1
 
3.6%
6.149 1
 
3.6%
9.971 1
 
3.6%
13.399 1
 
3.6%
14.895 1
 
3.6%
15.033 1
 
3.6%
17.11 1
 
3.6%
22.939 1
 
3.6%
ValueCountFrequency (%)
22.939 1
 
3.6%
17.11 1
 
3.6%
15.033 1
 
3.6%
14.895 1
 
3.6%
13.399 1
 
3.6%
9.971 1
 
3.6%
6.149 1
 
3.6%
5.787 1
 
3.6%
2.555 1
 
3.6%
0.0 9
32.1%

RECENT_SIX_MONTH_STD_SCORE
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6205357
Minimum-5.117
Maximum44.077
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)35.7%
Memory size384.0 B
2023-12-10T23:11:37.096724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.117
5-th percentile-3.97165
Q1-0.66275
median4.076
Q38.867
95-th percentile18.46165
Maximum44.077
Range49.194
Interquartile range (IQR)9.52975

Descriptive statistics

Standard deviation9.7221704
Coefficient of variation (CV)1.7297587
Kurtosis8.5694479
Mean5.6205357
Median Absolute Deviation (MAD)4.7675
Skewness2.4446264
Sum157.375
Variance94.520596
MonotonicityNot monotonic
2023-12-10T23:11:37.321102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8.867 3
 
10.7%
7.857 1
 
3.6%
6.668 1
 
3.6%
-3.083 1
 
3.6%
11.287 1
 
3.6%
8.414 1
 
3.6%
4.191 1
 
3.6%
-1.322 1
 
3.6%
-3.503 1
 
3.6%
-2.048 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
-5.117 1
3.6%
-4.224 1
3.6%
-3.503 1
3.6%
-3.083 1
3.6%
-2.048 1
3.6%
-1.322 1
3.6%
-0.668 1
3.6%
-0.661 1
3.6%
-0.179 1
3.6%
-0.02 1
3.6%
ValueCountFrequency (%)
44.077 1
 
3.6%
21.647 1
 
3.6%
12.546 1
 
3.6%
11.287 1
 
3.6%
9.875 1
 
3.6%
8.867 3
10.7%
8.767 1
 
3.6%
8.414 1
 
3.6%
7.857 1
 
3.6%
6.668 1
 
3.6%

RECENT_12_MONTH_STD_SCORE
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)92.3%
Missing2
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean10.609923
Minimum-0.267
Maximum45.695
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.6%
Memory size384.0 B
2023-12-10T23:11:37.669552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.267
5-th percentile1.4915
Q14.8655
median12.5915
Q312.937
95-th percentile14.4685
Maximum45.695
Range45.962
Interquartile range (IQR)8.0715

Descriptive statistics

Standard deviation8.6918354
Coefficient of variation (CV)0.81921757
Kurtosis10.521326
Mean10.609923
Median Absolute Deviation (MAD)1.116
Skewness2.5331772
Sum275.858
Variance75.548003
MonotonicityNot monotonic
2023-12-10T23:11:37.997307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
12.937 3
 
10.7%
11.57 1
 
3.6%
11.381 1
 
3.6%
13.082 1
 
3.6%
14.843 1
 
3.6%
1.434 1
 
3.6%
-0.267 1
 
3.6%
2.339 1
 
3.6%
12.587 1
 
3.6%
12.929 1
 
3.6%
Other values (14) 14
50.0%
(Missing) 2
 
7.1%
ValueCountFrequency (%)
-0.267 1
3.6%
1.434 1
3.6%
1.664 1
3.6%
1.798 1
3.6%
2.037 1
3.6%
2.339 1
3.6%
4.432 1
3.6%
6.166 1
3.6%
6.891 1
3.6%
10.888 1
3.6%
ValueCountFrequency (%)
45.695 1
 
3.6%
14.843 1
 
3.6%
13.345 1
 
3.6%
13.244 1
 
3.6%
13.082 1
 
3.6%
12.937 3
10.7%
12.929 1
 
3.6%
12.887 1
 
3.6%
12.835 1
 
3.6%
12.671 1
 
3.6%

FRST_SIX_MONTH_STD_SCORE
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14357143
Minimum-0.731
Maximum0.88
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)10.7%
Memory size384.0 B
2023-12-10T23:11:38.177975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.731
5-th percentile-0.25655
Q10.141
median0.167
Q30.168
95-th percentile0.4266
Maximum0.88
Range1.611
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.25868191
Coefficient of variation (CV)1.8017645
Kurtosis6.7036041
Mean0.14357143
Median Absolute Deviation (MAD)0.021
Skewness-0.88250759
Sum4.02
Variance0.066916328
MonotonicityNot monotonic
2023-12-10T23:11:38.508300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.167 11
39.3%
0.192 1
 
3.6%
0.077 1
 
3.6%
-0.005 1
 
3.6%
0.094 1
 
3.6%
0.15 1
 
3.6%
-0.392 1
 
3.6%
0.411 1
 
3.6%
0.206 1
 
3.6%
0.171 1
 
3.6%
Other values (8) 8
28.6%
ValueCountFrequency (%)
-0.731 1
3.6%
-0.392 1
3.6%
-0.005 1
3.6%
0.048 1
3.6%
0.077 1
3.6%
0.094 1
3.6%
0.138 1
3.6%
0.142 1
3.6%
0.15 1
3.6%
0.153 1
3.6%
ValueCountFrequency (%)
0.88 1
 
3.6%
0.435 1
 
3.6%
0.411 1
 
3.6%
0.214 1
 
3.6%
0.206 1
 
3.6%
0.192 1
 
3.6%
0.171 1
 
3.6%
0.167 11
39.3%
0.153 1
 
3.6%
0.15 1
 
3.6%

FRST_12_MONTH_STD_SCORE
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30675
Minimum-0.474
Maximum2.945
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.6%
Memory size384.0 B
2023-12-10T23:11:38.853357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.474
5-th percentile0.0611
Q10.22675
median0.244
Q30.26575
95-th percentile0.3777
Maximum2.945
Range3.419
Interquartile range (IQR)0.039

Descriptive statistics

Standard deviation0.53822931
Coefficient of variation (CV)1.7546188
Kurtosis23.554895
Mean0.30675
Median Absolute Deviation (MAD)0.0225
Skewness4.565422
Sum8.589
Variance0.28969079
MonotonicityNot monotonic
2023-12-10T23:11:39.097513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.244 9
32.1%
0.153 2
 
7.1%
0.268 2
 
7.1%
0.252 1
 
3.6%
0.265 1
 
3.6%
-0.474 1
 
3.6%
0.192 1
 
3.6%
0.045 1
 
3.6%
2.945 1
 
3.6%
0.284 1
 
3.6%
Other values (8) 8
28.6%
ValueCountFrequency (%)
-0.474 1
 
3.6%
0.045 1
 
3.6%
0.091 1
 
3.6%
0.153 2
 
7.1%
0.192 1
 
3.6%
0.22 1
 
3.6%
0.229 1
 
3.6%
0.236 1
 
3.6%
0.244 9
32.1%
0.245 1
 
3.6%
ValueCountFrequency (%)
2.945 1
 
3.6%
0.391 1
 
3.6%
0.353 1
 
3.6%
0.284 1
 
3.6%
0.277 1
 
3.6%
0.268 2
 
7.1%
0.265 1
 
3.6%
0.252 1
 
3.6%
0.245 1
 
3.6%
0.244 9
32.1%

IMPRVMDGREE_RECENT_STD_SCORE
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0288571
Minimum-25.019
Maximum28.456
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.6%
Memory size384.0 B
2023-12-10T23:11:39.394662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-25.019
5-th percentile2.0888
Q17.38775
median8.985
Q39.3215
95-th percentile17.36535
Maximum28.456
Range53.475
Interquartile range (IQR)1.93375

Descriptive statistics

Standard deviation8.1813426
Coefficient of variation (CV)1.0189922
Kurtosis10.975995
Mean8.0288571
Median Absolute Deviation (MAD)0.8715
Skewness-1.9280108
Sum224.808
Variance66.934367
MonotonicityNot monotonic
2023-12-10T23:11:39.705361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
9.008 2
 
7.1%
8.97 1
 
3.6%
10.311 1
 
3.6%
7.925 1
 
3.6%
9.105 1
 
3.6%
9.269 1
 
3.6%
8.911 1
 
3.6%
7.427 1
 
3.6%
2.647 1
 
3.6%
-25.019 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
-25.019 1
3.6%
2.023 1
3.6%
2.211 1
3.6%
2.647 1
3.6%
4.663 1
3.6%
7.232 1
3.6%
7.27 1
3.6%
7.427 1
3.6%
7.925 1
3.6%
8.45 1
3.6%
ValueCountFrequency (%)
28.456 1
3.6%
19.514 1
3.6%
13.375 1
3.6%
10.311 1
3.6%
10.19 1
3.6%
9.668 1
3.6%
9.479 1
3.6%
9.269 1
3.6%
9.148 1
3.6%
9.105 1
3.6%

Interactions

2023-12-10T23:11:26.924114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:10.341758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:12.286855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:13.859315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:16.004096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:17.513717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:19.085521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:20.680729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:22.217691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:24.524059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:27.160245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:10.533940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:12.427640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:14.062306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:16.161531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:17.677133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:19.218718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:20.843803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:22.354780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:24.737831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:27.296233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:10.707942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:12.611635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:14.316042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:16.314869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:17.808282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:19.347087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:21.004857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:22.628872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:24.915847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:27.457148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:10.905885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:12.760073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:14.595678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:16.475949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:17.958609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:19.523364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:21.246670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:22.936444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:25.160233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:27.605100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:11.092796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:12.918016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:14.740735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:16.614542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:18.130992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:19.685752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:21.360630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:23.137409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:25.407276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:27.727799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:11.261675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:13.061454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:14.915808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:16.780533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:18.274311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:19.829950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:21.498837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:23.313251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:25.562547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:27.851479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:11.389304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:13.202970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:15.047696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:16.910519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:18.454124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:19.971174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:21.644745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:23.606791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:25.697783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:27.996295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:11.655198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:13.323269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:15.533393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:17.039800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:18.576038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:20.097464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:21.765353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:23.921163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:25.847140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:28.141753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:11.894123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:13.504184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:15.676912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:17.188174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:18.755662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:20.287654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:21.911054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:24.089396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:26.457868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:28.274298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:12.131893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:13.661236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:15.854434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:17.335880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:18.942684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:20.497921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:22.083525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:24.272075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:26.718961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:11:40.396086image/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_IMPRVMDGREE1.0001.0001.0001.0001.0000.0000.0000.6710.0000.5590.6460.0000.0000.000
RECENT_12_MONTH_IMPRVMDGREE1.0001.0001.0001.0000.0001.0000.0000.6660.4220.4260.3740.6550.7200.872
FRST_SIX_MONTH_IMPRVMDGREE1.0001.0001.0001.0000.0000.0001.0000.6450.0000.5410.0000.3850.0000.000
FRST_12_MONTH_IMPRVMDGREE1.0001.0001.0001.0000.6710.6660.6451.0000.4950.0000.0000.8730.8280.722
RECENT_IMPRVMDGREE_IDEX1.0001.0001.0001.0000.0000.4220.0000.4951.0000.0000.2440.6670.7400.356
RECENT_SIX_MONTH_STD_SCORE1.0001.0001.0001.0000.5590.4260.5410.0000.0001.0000.4720.7810.0000.000
RECENT_12_MONTH_STD_SCORE1.0001.0001.0001.0000.6460.3740.0000.0000.2440.4721.0000.0000.0000.338
FRST_SIX_MONTH_STD_SCORE1.0001.0001.0001.0000.0000.6550.3850.8730.6670.7810.0001.0000.7280.837
FRST_12_MONTH_STD_SCORE1.0001.0001.0001.0000.0000.7200.0000.8280.7400.0000.0000.7281.0000.666
IMPRVMDGREE_RECENT_STD_SCORE1.0001.0001.0001.0000.0000.8720.0000.7220.3560.0000.3380.8370.6661.000
2023-12-10T23:11:40.806832image/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_IMPRVMDGREE1.0000.4620.5030.3810.3500.0000.2640.0170.3170.198
RECENT_12_MONTH_IMPRVMDGREE0.4621.0000.2280.4290.194-0.134-0.322-0.155-0.1600.037
FRST_SIX_MONTH_IMPRVMDGREE0.5030.2281.0000.6130.4830.3430.130-0.481-0.0970.414
FRST_12_MONTH_IMPRVMDGREE0.3810.4290.6131.0000.5380.4620.091-0.242-0.2860.379
RECENT_IMPRVMDGREE_IDEX0.3500.1940.4830.5381.000-0.125-0.240-0.103-0.178-0.149
RECENT_SIX_MONTH_STD_SCORE0.000-0.1340.3430.462-0.1251.0000.3680.030-0.1100.520
RECENT_12_MONTH_STD_SCORE0.264-0.3220.1300.091-0.2400.3681.000-0.0330.0360.203
FRST_SIX_MONTH_STD_SCORE0.017-0.155-0.481-0.242-0.1030.030-0.0331.0000.5420.175
FRST_12_MONTH_STD_SCORE0.317-0.160-0.097-0.286-0.178-0.1100.0360.5421.0000.179
IMPRVMDGREE_RECENT_STD_SCORE0.1980.0370.4140.379-0.1490.5200.2030.1750.1791.000

Missing values

2023-12-10T23:11:28.487630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:11:28.875931image/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.
2023-12-10T23:11:29.166898image/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
0UC2iVamQdGdLAWJR6ui8zowg청도군2020-09-01<NA>2018-11-29<NA><NA><NA><NA><NA>7.85712.8870.1920.2528.97
1UCmiWMl8Qu6rZDA4D8dSWxFAJena Lee 제나리2020-09-01안녕하세요. 18살 이제나에요. 인스타그램 @jenaxlee 이메일 lsj337js@hanmail.net2018-11-01<NA>0.570.3620.0250.08.76745.6950.1380.2779.063
2UCDBUJ8x8ZwE1OAzSyzUcFLg세계일주 저니맨 Journeyman2020-09-01고품격 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>0.49512.8350.0480.3539.0
3UCaijie_uqaAQQCt_fCZ-5qQ통영시Tongyeong2020-09-01통영시에서 운영하는 공식 유튜브 채널입니다. 통영관광; 통영소식 통영과 관련된 모든 소식을 알려드려요. 언제든지 놀러오세요. 환영합니다!2019-09-25<NA><NA>-0.279-0.06<NA>3.96113.3450.4350.227.27
4UCpVw9Y6pqUiCY0Sgc8Ae9mA「싸꼰」사사건건2020-09-01사사건건 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.2018-10-302.3873.4890.4750.36213.39912.5462.0370.880.39128.456
5UCNcE1102l7TS7k64SonCJGQ시바x노리NORI2020-09-01지랄견보다 더한 시바견 노리의 하루2017-01-18<NA><NA>1.00.227<NA>8.86712.9370.1420.2299.008
6UC2VDsgZ343N9hKnEXQWKnag로이어프렌즈 - 변호사 친구들2020-09-01일상과 멀리 떨어져 있다고 느꼈던 법률! 알고 보면 우리 생활의 많은 부분들이 법과 함께 하고 있습니다. 친구같은 변호사들이 들려주는 쉽고 재미있는 법률 이야기. 걱정 없고 평안한 사회를 위해 노력합니다. -로이어프렌즈- 문의 : lawyerfriends.kr@gmail.com 구독은 무죄입니다♥2018-08-082.0190.8651.00.59714.89521.6476.166-0.7310.0918.552
7UCN8CPzwkYiDVLZlgD4JQgJQ박막례 할머니 Korea Grandma2020-09-0173세 박막례 할머니의 무한도전 인생은 아름다워! 할머니와 즐거운 하루 하루를 보내고; 기록 합니다. 이 채널의 방향은 할머니의 행복 입니다.2017-01-301.3031.6060.0880.1175.787-5.1171.6640.1530.1532.211
8UCxSBWvnORMtCtJNbK6pL4YQsusiemeoww2020-09-01Kpop Dance K-Beauty Daily Vlogs2014-11-112.242-2.563<NA><NA>15.033-0.024.4320.1670.24413.375
9UCPVENfTGbgD-CUuvrN7rRGQ상해기SangHyuk2020-09-01먹방하는 상해기2013-05-161.7176.28<NA><NA>0.0-0.6681.7980.1670.24419.514
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
18UCEhFkcfgPosN5RtYn92LV0Q인천서구시설관리공단2020-09-01고객과 함께 행복한 미래를 창조하는 1등 공기업; 인천광역시서구시설관리공단입니다. 우리 공단은 2000년 11월에 설립되어 검단복지회관; 국민체육센터; 청라복합문화센터; 청소년수련시설; 장애인직업재활시설; 구립도서관; 노인여가복지시설; 공영주차장; 공원녹지 등 공공시설의 효율적으로 관리 운영을 통한 구민의 복리증진에 최선을 다하고 있습니다. 그 동안 행정안전부; 여성가족부; 국민권익위원회; 인천광역시의 주요한 평가 등에서 우수한 공기업임을 인정받아 2012년 국무총리 기관표창과 2019년에는 지방공기업 발전 유공으로 대통령표창을 수상하였습니다. 서구시설관리공단은 이에 만족하지 않고; 보다 나은 양질의 서비스 확대로 행복한 주민; 신뢰받는 1등 공기업이 되도록 노력하겠습니다.2019-05-31<NA><NA><NA><NA><NA>3.65212.6710.1710.2689.008
19UCv35kTyMNjTD91T_wd5p53Q동네오빠엔터테인먼트2020-09-01나만 알고 있는 유튜브 동네오빠entertainment2010-03-17<NA><NA><NA><NA>0.09.87512.9290.1670.2369.479
20UCMrnR2v0a2QxJffjgjA47Tw경기연구원2020-09-01대한민국의 중심 경기도의 싱크탱크; 경기연구원 공식 유튜브입니다. 경기연구원이 만들고 제공하는 경기도 이슈를 만나보세요!2019-03-07<NA><NA>0.033-0.402<NA>-2.04812.5870.2060.2848.45
21UCTXuCSZxMDaneK7t6DVs21gsini시니2020-09-01안녕하세요 시니입니다! 본 영상은 무단 다운로드 공개 또는 배포를 엄격히 금지합니다. Hello this is Sini! :0 Just enjoy watching this video! Unauthorized communication or distribution of all videos is strictly prohibited.2015-04-241.531-0.9970.2610.240.0-3.5032.3390.4112.945-25.019
22UCc7v5yYC_mviB1_fLuB-GRwMINEE EATS2020-09-01MINEE 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 USA2017-03-110.1710.774-0.048-0.1860.0-1.322-0.267-0.3920.0452.647
23UCc1G8zPj7bv2Abuy3WxDhrA김기쁨2020-09-01구독과 좋아요는 사랑입니다 비지니스계정 kimddunddanbo@gmail.com2015-05-081.3981.148<NA><NA>22.9394.1911.4340.1670.2447.427
24UCDv2bsTA2P7g4VhHjj7_FOABENNY베니2020-09-01lnstagram: @benny8282_nail E-mail: benny828253@naver.com2013-10-243.0262.2960.2810.536.1498.41414.8430.150.1928.911
25UCrlFZMsoWCt0kYlTqkaWF_AUsang ASMR2020-09-01안녕하세요! 한국에서 ASMR영상을 제작하고 있는 유상ASMR입니다!2015-01-08<NA><NA>0.6860.5850.011.28713.0820.0940.1539.269
26UCiX-qK5KHuFAmS9tcI-fNyg리치2020-09-01우리아빠는 조섭이개 【Instagram】 http:instagram.comrich_04122016-01-09<NA><NA><NA><NA>0.08.86712.9370.1670.2449.105
27UCzVL1QNvHNQmsiAy6Oti0AwHoyong PK2020-09-01퀄리티 중심의 여행; 노하우; 리뷰; 일상 영상을 공유하는 채널입니다. 영상 전공자로서 영상 제작에 있어서의 지식과 팁을 공유합니다. 소비자로서 제품; 서비스 등; 상품의 주관적 리뷰를 공유합니다. 마지막으로 김호용으로서 여행 및 일상에서 나오는 스토리를 공유합니다.2018-06-260.52<NA>0.8890.41617.11-3.08311.381-0.005-0.4747.925