Overview

Dataset statistics

Number of variables15
Number of observations26
Missing cells52
Missing cells (%)13.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory135.1 B

Variable types

Text3
DateTime2
Numeric8
Categorical2

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/506c25ea-6075-48a2-8b6d-3810db220fe8

Alerts

최초6개월표준점수 is highly overall correlated with 최초12개월개선도High correlation
최초12개월표준점수 is highly overall correlated with 최초12개월개선도High correlation
개선도최근표준점수 is highly overall correlated with 최초6개월개선도High correlation
최초6개월개선도 is highly overall correlated with 개선도최근표준점수High correlation
최초12개월개선도 is highly overall correlated with 최초6개월표준점수 and 1 other fieldsHigh correlation
개선도지수채널설명 has 3 (11.5%) missing valuesMissing
개선도채널생성일자 has 8 (30.8%) missing valuesMissing
최근6개월개선도 has 14 (53.8%) missing valuesMissing
최근12개월개선도 has 15 (57.7%) missing valuesMissing
최근개선도지수 has 6 (23.1%) missing valuesMissing
최근6개월표준점수 has 6 (23.1%) missing valuesMissing
개선도지수채널ID has unique valuesUnique
개선도지수채널명 has unique valuesUnique
개선도최근표준점수 has unique valuesUnique
최근12개월개선도 has 1 (3.8%) zerosZeros
최근개선도지수 has 8 (30.8%) zerosZeros

Reproduction

Analysis started2023-12-10 13:55:50.166580
Analysis finished2023-12-10 13:56:02.793175
Duration12.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개선도지수채널ID
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-10T22:56:03.176721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters624
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

Unique26 ?
Unique (%)100.0%

Sample

1st rowUCghjVjOyijEZe1hawS8S3tA
2nd rowUCj7mdvAJCRKvGBmcusOr9Ag
3rd rowUC-ywC1MrAfgpDe3_vytnBqw
4th rowUC0SsBKkmlkyNx6bo0SSAEpQ
5th rowUC-js9KxuyRoB0b9zlKCgsCg
ValueCountFrequency (%)
ucghjvjoyijeze1haws8s3ta 1
 
3.8%
ucj7mdvajcrkvgbmcusor9ag 1
 
3.8%
ucedpuuct-hiao3crp1q2muw 1
 
3.8%
ucdrar1owc2md4s0jletn0ma 1
 
3.8%
uc3enwze-pftcxatcuuaahgw 1
 
3.8%
ucbxfbo7tksgdt3ymphgkwqa 1
 
3.8%
ucapvyrwd-6yyx85oqgfnqcw 1
 
3.8%
uca9lr7w1otxtvit4oflnu8a 1
 
3.8%
uca7wtdf9byurw1bowr00e6g 1
 
3.8%
uc9zlv1m7qdlv991x1-p50aa 1
 
3.8%
Other values (16) 16
61.5%
2023-12-10T22:56:03.784052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 40
 
6.4%
U 36
 
5.8%
A 21
 
3.4%
g 18
 
2.9%
w 17
 
2.7%
f 15
 
2.4%
1 14
 
2.2%
W 14
 
2.2%
R 13
 
2.1%
k 12
 
1.9%
Other values (54) 424
67.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 272
43.6%
Lowercase Letter 243
38.9%
Decimal Number 92
 
14.7%
Dash Punctuation 12
 
1.9%
Connector Punctuation 5
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 40
14.7%
U 36
 
13.2%
A 21
 
7.7%
W 14
 
5.1%
R 13
 
4.8%
D 12
 
4.4%
N 11
 
4.0%
K 10
 
3.7%
S 10
 
3.7%
L 9
 
3.3%
Other values (16) 96
35.3%
Lowercase Letter
ValueCountFrequency (%)
g 18
 
7.4%
w 17
 
7.0%
f 15
 
6.2%
k 12
 
4.9%
t 12
 
4.9%
m 12
 
4.9%
q 10
 
4.1%
o 10
 
4.1%
u 10
 
4.1%
r 10
 
4.1%
Other values (16) 117
48.1%
Decimal Number
ValueCountFrequency (%)
1 14
15.2%
6 12
13.0%
9 12
13.0%
0 10
10.9%
7 10
10.9%
3 10
10.9%
5 7
7.6%
4 7
7.6%
8 5
 
5.4%
2 5
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 515
82.5%
Common 109
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 40
 
7.8%
U 36
 
7.0%
A 21
 
4.1%
g 18
 
3.5%
w 17
 
3.3%
f 15
 
2.9%
W 14
 
2.7%
R 13
 
2.5%
k 12
 
2.3%
D 12
 
2.3%
Other values (42) 317
61.6%
Common
ValueCountFrequency (%)
1 14
12.8%
- 12
11.0%
6 12
11.0%
9 12
11.0%
0 10
9.2%
7 10
9.2%
3 10
9.2%
5 7
6.4%
4 7
6.4%
8 5
 
4.6%
Other values (2) 10
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 40
 
6.4%
U 36
 
5.8%
A 21
 
3.4%
g 18
 
2.9%
w 17
 
2.7%
f 15
 
2.4%
1 14
 
2.2%
W 14
 
2.2%
R 13
 
2.1%
k 12
 
1.9%
Other values (54) 424
67.9%

개선도지수채널명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-10T22:56:04.179115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length14
Mean length9.9230769
Min length2

Characters and Unicode

Total characters258
Distinct characters133
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row부산시부산정보산업진흥원
2nd row엠브로 MBRO
3rd row브리랜서 브로디
4th row김쵸코
5th row이영자 채널 :LYJ CH.
ValueCountFrequency (%)
tv 2
 
3.7%
2
 
3.7%
부산시부산정보산업진흥원 1
 
1.9%
go-trip 1
 
1.9%
탱구tv 1
 
1.9%
탁주 1
 
1.9%
jefetoy 1
 
1.9%
ivan 1
 
1.9%
lam 1
 
1.9%
보물섬 1
 
1.9%
Other values (42) 42
77.8%
2023-12-10T22:56:04.905695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
10.9%
a 11
 
4.3%
T 9
 
3.5%
n 9
 
3.5%
e 5
 
1.9%
V 5
 
1.9%
B 5
 
1.9%
i 4
 
1.6%
r 4
 
1.6%
브 4
 
1.6%
Other values (123) 174
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
42.2%
Uppercase Letter 61
23.6%
Lowercase Letter 53
20.5%
Space Separator 28
 
10.9%
Dash Punctuation 3
 
1.2%
Other Punctuation 2
 
0.8%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
브 4
 
3.7%
산 3
 
2.8%
인 3
 
2.8%
업 3
 
2.8%
터 2
 
1.8%
구 2
 
1.8%
의 2
 
1.8%
기 2
 
1.8%
부 2
 
1.8%
원 2
 
1.8%
Other values (78) 84
77.1%
Uppercase Letter
ValueCountFrequency (%)
T 9
14.8%
V 5
 
8.2%
B 5
 
8.2%
A 4
 
6.6%
E 3
 
4.9%
N 3
 
4.9%
J 3
 
4.9%
I 3
 
4.9%
O 3
 
4.9%
R 3
 
4.9%
Other values (11) 20
32.8%
Lowercase Letter
ValueCountFrequency (%)
a 11
20.8%
n 9
17.0%
e 5
9.4%
i 4
 
7.5%
r 4
 
7.5%
d 2
 
3.8%
m 2
 
3.8%
l 2
 
3.8%
y 2
 
3.8%
c 2
 
3.8%
Other values (8) 10
18.9%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
: 1
50.0%
Decimal Number
ValueCountFrequency (%)
6 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 114
44.2%
Hangul 109
42.2%
Common 35
 
13.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
브 4
 
3.7%
산 3
 
2.8%
인 3
 
2.8%
업 3
 
2.8%
터 2
 
1.8%
구 2
 
1.8%
의 2
 
1.8%
기 2
 
1.8%
부 2
 
1.8%
원 2
 
1.8%
Other values (78) 84
77.1%
Latin
ValueCountFrequency (%)
a 11
 
9.6%
T 9
 
7.9%
n 9
 
7.9%
e 5
 
4.4%
V 5
 
4.4%
B 5
 
4.4%
i 4
 
3.5%
r 4
 
3.5%
A 4
 
3.5%
E 3
 
2.6%
Other values (29) 55
48.2%
Common
ValueCountFrequency (%)
28
80.0%
- 3
 
8.6%
. 1
 
2.9%
: 1
 
2.9%
6 1
 
2.9%
2 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149
57.8%
Hangul 109
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
18.8%
a 11
 
7.4%
T 9
 
6.0%
n 9
 
6.0%
e 5
 
3.4%
V 5
 
3.4%
B 5
 
3.4%
i 4
 
2.7%
r 4
 
2.7%
A 4
 
2.7%
Other values (35) 65
43.6%
Hangul
ValueCountFrequency (%)
브 4
 
3.7%
산 3
 
2.8%
인 3
 
2.8%
업 3
 
2.8%
터 2
 
1.8%
구 2
 
1.8%
의 2
 
1.8%
기 2
 
1.8%
부 2
 
1.8%
원 2
 
1.8%
Other values (78) 84
77.1%

개선도지수수집일자
Date

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
Minimum2021-06-03 00:00:00
Maximum2021-06-30 00:00:00
2023-12-10T22:56:05.149482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:05.389502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

개선도지수채널설명
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing3
Missing (%)11.5%
Memory size340.0 B
2023-12-10T22:56:05.727093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length786
Median length132
Mean length157.30435
Min length11

Characters and Unicode

Total characters3618
Distinct characters399
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row부산정보산업진흥원은 지역의 정보통신문화콘텐츠 산업을 육성; 지원하는 대표 기관입니다. 창조문화산업도시 부산 건설에 앞장서겠습니다.
2nd row브리랜서 브로디입니다 브하하하하하!!!
3rd row각종 게임을 재미있게 플레이 해 나가는 게임 크리에이터 쵸코 입니다
4th row이영자 채널 _ LEE YOUNG JA CHANNEL 이영자의 소소(小) & 대대(大)한 이야기들을 영상으로 만나실 수 있습니다. 구독 & 좋아요 & 알람 꾸욱! ^^ 비지니스 문의 및 채널 이벤트 관련 ▶ youngjachannel@gmail.com
5th row안녕하세요~ 6마리 요크셔테리어 2마리 고양이와 함께 살고 있는 판다입니다 강아지와 고양이와 함께 수제간식도 만들고~ 놀고 싸우고 하고 지내는 이야기 뉴욕에 사는 저의 이야기들을 함께 나누고 싶습니다~ 안녕 우리 자주 만나요
ValueCountFrequency (%)
37
 
5.9%
and 8
 
1.3%
영상 7
 
1.1%
문의 6
 
1.0%
jefetoy 5
 
0.8%
2019 5
 
0.8%
있는 5
 
0.8%
비지니스 4
 
0.6%
관광 4
 
0.6%
홍보 4
 
0.6%
Other values (459) 540
86.4%
2023-12-10T22:56:06.399738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
734
 
20.3%
a 120
 
3.3%
e 111
 
3.1%
o 96
 
2.7%
t 90
 
2.5%
n 90
 
2.5%
r 63
 
1.7%
. 62
 
1.7%
s 53
 
1.5%
m 50
 
1.4%
Other values (389) 2149
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1241
34.3%
Lowercase Letter 1118
30.9%
Space Separator 734
20.3%
Other Punctuation 182
 
5.0%
Uppercase Letter 118
 
3.3%
Decimal Number 109
 
3.0%
Other Symbol 62
 
1.7%
Math Symbol 16
 
0.4%
Close Punctuation 11
 
0.3%
Modifier Symbol 10
 
0.3%
Other values (3) 17
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
하 43
 
3.5%
다 34
 
2.7%
니 34
 
2.7%
이 29
 
2.3%
는 26
 
2.1%
고 24
 
1.9%
의 21
 
1.7%
오 19
 
1.5%
리 18
 
1.5%
기 17
 
1.4%
Other values (305) 976
78.6%
Lowercase Letter
ValueCountFrequency (%)
a 120
 
10.7%
e 111
 
9.9%
o 96
 
8.6%
t 90
 
8.1%
n 90
 
8.1%
r 63
 
5.6%
s 53
 
4.7%
m 50
 
4.5%
l 48
 
4.3%
i 48
 
4.3%
Other values (16) 349
31.2%
Uppercase Letter
ValueCountFrequency (%)
T 11
 
9.3%
A 11
 
9.3%
S 8
 
6.8%
B 8
 
6.8%
J 8
 
6.8%
L 7
 
5.9%
N 7
 
5.9%
O 6
 
5.1%
H 6
 
5.1%
C 5
 
4.2%
Other values (14) 41
34.7%
Decimal Number
ValueCountFrequency (%)
1 27
24.8%
0 26
23.9%
2 19
17.4%
9 8
 
7.3%
6 6
 
5.5%
3 6
 
5.5%
5 6
 
5.5%
4 5
 
4.6%
7 4
 
3.7%
8 2
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 62
34.1%
: 49
26.9%
! 24
 
13.2%
' 12
 
6.6%
& 10
 
5.5%
@ 9
 
4.9%
; 9
 
4.9%
* 6
 
3.3%
% 1
 
0.5%
Other Symbol
ValueCountFrequency (%)
━ 50
80.6%
♥ 3
 
4.8%
▶ 3
 
4.8%
☞ 3
 
4.8%
☆ 2
 
3.2%
★ 1
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 8
72.7%
] 3
 
27.3%
Open Punctuation
ValueCountFrequency (%)
( 6
66.7%
[ 3
33.3%
Space Separator
ValueCountFrequency (%)
734
100.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1239
34.2%
Latin 1236
34.2%
Common 1141
31.5%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
하 43
 
3.5%
다 34
 
2.7%
니 34
 
2.7%
이 29
 
2.3%
는 26
 
2.1%
고 24
 
1.9%
의 21
 
1.7%
오 19
 
1.5%
리 18
 
1.5%
기 17
 
1.4%
Other values (303) 974
78.6%
Latin
ValueCountFrequency (%)
a 120
 
9.7%
e 111
 
9.0%
o 96
 
7.8%
t 90
 
7.3%
n 90
 
7.3%
r 63
 
5.1%
s 53
 
4.3%
m 50
 
4.0%
l 48
 
3.9%
i 48
 
3.9%
Other values (40) 467
37.8%
Common
ValueCountFrequency (%)
734
64.3%
. 62
 
5.4%
━ 50
 
4.4%
: 49
 
4.3%
1 27
 
2.4%
0 26
 
2.3%
! 24
 
2.1%
2 19
 
1.7%
~ 16
 
1.4%
' 12
 
1.1%
Other values (24) 122
 
10.7%
Han
ValueCountFrequency (%)
大 1
50.0%
小 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2315
64.0%
Hangul 1239
34.2%
Box Drawing 50
 
1.4%
Misc Symbols 9
 
0.2%
Geometric Shapes 3
 
0.1%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
734
31.7%
a 120
 
5.2%
e 111
 
4.8%
o 96
 
4.1%
t 90
 
3.9%
n 90
 
3.9%
r 63
 
2.7%
. 62
 
2.7%
s 53
 
2.3%
m 50
 
2.2%
Other values (68) 846
36.5%
Box Drawing
ValueCountFrequency (%)
━ 50
100.0%
Hangul
ValueCountFrequency (%)
하 43
 
3.5%
다 34
 
2.7%
니 34
 
2.7%
이 29
 
2.3%
는 26
 
2.1%
고 24
 
1.9%
의 21
 
1.7%
오 19
 
1.5%
리 18
 
1.5%
기 17
 
1.4%
Other values (303) 974
78.6%
Misc Symbols
ValueCountFrequency (%)
♥ 3
33.3%
☞ 3
33.3%
☆ 2
22.2%
★ 1
 
11.1%
Geometric Shapes
ValueCountFrequency (%)
▶ 3
100.0%
CJK
ValueCountFrequency (%)
大 1
50.0%
小 1
50.0%

개선도채널생성일자
Date

MISSING 

Distinct18
Distinct (%)100.0%
Missing8
Missing (%)30.8%
Memory size340.0 B
Minimum2011-05-31 00:00:00
Maximum2019-06-02 00:00:00
2023-12-10T22:56:06.621731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:06.951281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

최근6개월개선도
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)66.7%
Missing14
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean-5
Minimum-36
Maximum12
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)15.4%
Memory size366.0 B
2023-12-10T22:56:07.120675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-36
5-th percentile-29.4
Q1-12.5
median1.5
Q32.25
95-th percentile7.05
Maximum12
Range48
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation13.777452
Coefficient of variation (CV)-2.7554904
Kurtosis1.0586053
Mean-5
Median Absolute Deviation (MAD)1.5
Skewness-1.2649354
Sum-60
Variance189.81818
MonotonicityNot monotonic
2023-12-10T22:56:07.390005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 3
 
11.5%
1 2
 
7.7%
3 2
 
7.7%
-12 1
 
3.8%
-36 1
 
3.8%
-14 1
 
3.8%
12 1
 
3.8%
-24 1
 
3.8%
(Missing) 14
53.8%
ValueCountFrequency (%)
-36 1
 
3.8%
-24 1
 
3.8%
-14 1
 
3.8%
-12 1
 
3.8%
1 2
7.7%
2 3
11.5%
3 2
7.7%
12 1
 
3.8%
ValueCountFrequency (%)
12 1
 
3.8%
3 2
7.7%
2 3
11.5%
1 2
7.7%
-12 1
 
3.8%
-14 1
 
3.8%
-24 1
 
3.8%
-36 1
 
3.8%

최근12개월개선도
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)72.7%
Missing15
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean0.54545455
Minimum-8
Maximum12
Zeros1
Zeros (%)3.8%
Negative4
Negative (%)15.4%
Memory size366.0 B
2023-12-10T22:56:07.578344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile-6
Q1-3
median1
Q33
95-th percentile8
Maximum12
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.2794628
Coefficient of variation (CV)9.6790151
Kurtosis1.4339478
Mean0.54545455
Median Absolute Deviation (MAD)3
Skewness0.63133619
Sum6
Variance27.872727
MonotonicityNot monotonic
2023-12-10T22:56:07.781601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-4 2
 
7.7%
4 2
 
7.7%
1 2
 
7.7%
12 1
 
3.8%
-2 1
 
3.8%
-8 1
 
3.8%
0 1
 
3.8%
2 1
 
3.8%
(Missing) 15
57.7%
ValueCountFrequency (%)
-8 1
3.8%
-4 2
7.7%
-2 1
3.8%
0 1
3.8%
1 2
7.7%
2 1
3.8%
4 2
7.7%
12 1
3.8%
ValueCountFrequency (%)
12 1
3.8%
4 2
7.7%
2 1
3.8%
1 2
7.7%
0 1
3.8%
-2 1
3.8%
-4 2
7.7%
-8 1
3.8%

최초6개월개선도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
14 
0
8 
1
4 

Length

Max length4
Median length4
Mean length2.6153846
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 14
53.8%
0 8
30.8%
1 4
 
15.4%

Length

2023-12-10T22:56:08.454157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:08.714340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
53.8%
0 8
30.8%
1 4
 
15.4%

최초12개월개선도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
14 
0
6 
1
4 
-1
2 

Length

Max length4
Median length4
Mean length2.6923077
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 14
53.8%
0 6
23.1%
1 4
 
15.4%
-1 2
 
7.7%

Length

2023-12-10T22:56:08.921854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:09.111660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
53.8%
0 6
23.1%
1 6
23.1%

최근개선도지수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)65.0%
Missing6
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean22.4925
Minimum0
Maximum151.48
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-10T22:56:09.257547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.8
Q322.655
95-th percentile89.3785
Maximum151.48
Range151.48
Interquartile range (IQR)22.655

Descriptive statistics

Standard deviation38.146429
Coefficient of variation (CV)1.6959622
Kurtosis6.608678
Mean22.4925
Median Absolute Deviation (MAD)5.8
Skewness2.4754956
Sum449.85
Variance1455.1501
MonotonicityNot monotonic
2023-12-10T22:56:09.432297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 8
30.8%
34.31 1
 
3.8%
3.32 1
 
3.8%
17.64 1
 
3.8%
86.11 1
 
3.8%
15.97 1
 
3.8%
11.52 1
 
3.8%
18.77 1
 
3.8%
40.15 1
 
3.8%
3.85 1
 
3.8%
Other values (3) 3
 
11.5%
(Missing) 6
23.1%
ValueCountFrequency (%)
0.0 8
30.8%
3.32 1
 
3.8%
3.85 1
 
3.8%
7.75 1
 
3.8%
11.52 1
 
3.8%
15.97 1
 
3.8%
17.64 1
 
3.8%
18.77 1
 
3.8%
34.31 1
 
3.8%
40.15 1
 
3.8%
ValueCountFrequency (%)
151.48 1
3.8%
86.11 1
3.8%
58.98 1
3.8%
40.15 1
3.8%
34.31 1
3.8%
18.77 1
3.8%
17.64 1
3.8%
15.97 1
3.8%
11.52 1
3.8%
7.75 1
3.8%

최근6개월표준점수
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)100.0%
Missing6
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean0.228
Minimum-41.61
Maximum23.79
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)42.3%
Memory size366.0 B
2023-12-10T22:56:09.608518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-41.61
5-th percentile-19.9785
Q1-5.3575
median-1.99
Q312.645
95-th percentile19.249
Maximum23.79
Range65.4
Interquartile range (IQR)18.0025

Descriptive statistics

Standard deviation14.811184
Coefficient of variation (CV)64.961334
Kurtosis2.1949332
Mean0.228
Median Absolute Deviation (MAD)7.22
Skewness-0.93201215
Sum4.56
Variance219.37117
MonotonicityNot monotonic
2023-12-10T22:56:09.793745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-2.67 1
 
3.8%
12.96 1
 
3.8%
-6.25 1
 
3.8%
19.01 1
 
3.8%
13.65 1
 
3.8%
-41.61 1
 
3.8%
5.89 1
 
3.8%
-3.52 1
 
3.8%
-5.06 1
 
3.8%
17.17 1
 
3.8%
Other values (10) 10
38.5%
(Missing) 6
23.1%
ValueCountFrequency (%)
-41.61 1
3.8%
-18.84 1
3.8%
-11.18 1
3.8%
-8.55 1
3.8%
-6.25 1
3.8%
-5.06 1
3.8%
-3.52 1
3.8%
-3.15 1
3.8%
-3.04 1
3.8%
-2.67 1
3.8%
ValueCountFrequency (%)
23.79 1
3.8%
19.01 1
3.8%
17.17 1
3.8%
13.65 1
3.8%
12.96 1
3.8%
12.54 1
3.8%
5.89 1
3.8%
2.48 1
3.8%
2.25 1
3.8%
-1.31 1
3.8%

최근12개월표준점수
Real number (ℝ)

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.8738462
Minimum-10.47
Maximum24.46
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)69.2%
Memory size366.0 B
2023-12-10T22:56:09.987910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10.47
5-th percentile-9.8925
Q1-9.7375
median-7.84
Q30.2925
95-th percentile6.66
Maximum24.46
Range34.93
Interquartile range (IQR)10.03

Descriptive statistics

Standard deviation8.0266362
Coefficient of variation (CV)-2.072007
Kurtosis5.0311846
Mean-3.8738462
Median Absolute Deviation (MAD)2.355
Skewness1.9513234
Sum-100.72
Variance64.426889
MonotonicityNot monotonic
2023-12-10T22:56:10.191177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
-9.76 4
 
15.4%
6.73 1
 
3.8%
24.46 1
 
3.8%
6.45 1
 
3.8%
-2.97 1
 
3.8%
1.34 1
 
3.8%
1.3 1
 
3.8%
-8.29 1
 
3.8%
-9.81 1
 
3.8%
-10.47 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
-10.47 1
 
3.8%
-9.92 1
 
3.8%
-9.81 1
 
3.8%
-9.76 4
15.4%
-9.67 1
 
3.8%
-9.64 1
 
3.8%
-9.48 1
 
3.8%
-9.34 1
 
3.8%
-9.19 1
 
3.8%
-8.29 1
 
3.8%
ValueCountFrequency (%)
24.46 1
3.8%
6.73 1
3.8%
6.45 1
3.8%
3.09 1
3.8%
1.34 1
3.8%
1.3 1
3.8%
0.3 1
3.8%
0.27 1
3.8%
-0.99 1
3.8%
-2.97 1
3.8%

최초6개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14961538
Minimum-1.38
Maximum1.68
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)15.4%
Memory size366.0 B
2023-12-10T22:56:10.424238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.38
5-th percentile-0.3825
Q10.1425
median0.18
Q30.1875
95-th percentile0.4425
Maximum1.68
Range3.06
Interquartile range (IQR)0.045

Descriptive statistics

Standard deviation0.46441775
Coefficient of variation (CV)3.1040775
Kurtosis8.7712241
Mean0.14961538
Median Absolute Deviation (MAD)0.035
Skewness-0.089641519
Sum3.89
Variance0.21568385
MonotonicityNot monotonic
2023-12-10T22:56:10.644950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.18 9
34.6%
0.15 2
 
7.7%
0.19 1
 
3.8%
1.68 1
 
3.8%
0.07 1
 
3.8%
0.22 1
 
3.8%
0.42 1
 
3.8%
0.14 1
 
3.8%
0.27 1
 
3.8%
0.45 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
-1.38 1
 
3.8%
-0.47 1
 
3.8%
-0.12 1
 
3.8%
-0.03 1
 
3.8%
0.07 1
 
3.8%
0.08 1
 
3.8%
0.14 1
 
3.8%
0.15 2
 
7.7%
0.17 1
 
3.8%
0.18 9
34.6%
ValueCountFrequency (%)
1.68 1
 
3.8%
0.45 1
 
3.8%
0.42 1
 
3.8%
0.28 1
 
3.8%
0.27 1
 
3.8%
0.22 1
 
3.8%
0.19 1
 
3.8%
0.18 9
34.6%
0.17 1
 
3.8%
0.15 2
 
7.7%

최초12개월표준점수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50653846
Minimum-0.83
Maximum5.55
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)15.4%
Memory size366.0 B
2023-12-10T22:56:10.866073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.83
5-th percentile-0.1875
Q10.2175
median0.25
Q30.2825
95-th percentile2.7075
Maximum5.55
Range6.38
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation1.2400127
Coefficient of variation (CV)2.448013
Kurtosis12.222096
Mean0.50653846
Median Absolute Deviation (MAD)0.04
Skewness3.4135502
Sum13.17
Variance1.5376315
MonotonicityNot monotonic
2023-12-10T22:56:11.038825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.25 8
30.8%
0.24 2
 
7.7%
0.26 2
 
7.7%
0.48 1
 
3.8%
0.09 1
 
3.8%
-0.05 1
 
3.8%
0.45 1
 
3.8%
0.29 1
 
3.8%
0.21 1
 
3.8%
0.39 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
-0.83 1
 
3.8%
-0.2 1
 
3.8%
-0.15 1
 
3.8%
-0.05 1
 
3.8%
0.07 1
 
3.8%
0.09 1
 
3.8%
0.21 1
 
3.8%
0.24 2
 
7.7%
0.25 8
30.8%
0.26 2
 
7.7%
ValueCountFrequency (%)
5.55 1
 
3.8%
3.45 1
 
3.8%
0.48 1
 
3.8%
0.45 1
 
3.8%
0.42 1
 
3.8%
0.39 1
 
3.8%
0.29 1
 
3.8%
0.26 2
 
7.7%
0.25 8
30.8%
0.24 2
 
7.7%

개선도최근표준점수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.411154
Minimum-26.85
Maximum204.7
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.7%
Memory size366.0 B
2023-12-10T22:56:11.267875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-26.85
5-th percentile-11.18
Q117.1325
median18.65
Q322.5425
95-th percentile37.95
Maximum204.7
Range231.55
Interquartile range (IQR)5.41

Descriptive statistics

Standard deviation39.279221
Coefficient of variation (CV)1.6777995
Kurtosis19.885582
Mean23.411154
Median Absolute Deviation (MAD)3.295
Skewness4.1152829
Sum608.69
Variance1542.8572
MonotonicityNot monotonic
2023-12-10T22:56:11.498305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
13.5 1
 
3.8%
6.85 1
 
3.8%
19.61 1
 
3.8%
24.94 1
 
3.8%
204.7 1
 
3.8%
-26.85 1
 
3.8%
18.18 1
 
3.8%
23.81 1
 
3.8%
18.34 1
 
3.8%
18.8 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
-26.85 1
3.8%
-16.88 1
3.8%
5.92 1
3.8%
6.85 1
3.8%
8.58 1
3.8%
13.5 1
3.8%
17.0 1
3.8%
17.53 1
3.8%
18.15 1
3.8%
18.18 1
3.8%
ValueCountFrequency (%)
204.7 1
3.8%
39.79 1
3.8%
32.43 1
3.8%
25.57 1
3.8%
24.94 1
3.8%
23.81 1
3.8%
23.14 1
3.8%
20.75 1
3.8%
20.44 1
3.8%
19.61 1
3.8%

Interactions

2023-12-10T22:56:00.567716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.444995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.820625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.271934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.492890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:57.067284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.169537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:59.276918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:00.709439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.577251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.946518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.392473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.633520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:57.192045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.284363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:59.456912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:00.906879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.767172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.114118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.541432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.790754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:57.332618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.432339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:59.640325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:01.119098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.964525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.267723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.680797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.929629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:57.468306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.560442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:59.793205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:01.251814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.227272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.657118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.831531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:56.063998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:57.605681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.675335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:59.970570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:01.409624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.362186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.840345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.024578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:56.553195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:57.763006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.822859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:00.140697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:01.548679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.503488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.976687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.224029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:56.757405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:57.903937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.959045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:00.304630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:01.683923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.679362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.139718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.357846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:56.891742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:58.043619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:59.116139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:00.443443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:56:11.738726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
개선도지수채널ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도지수채널명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도지수수집일자1.0001.0001.0001.0001.000NaNNaNNaNNaNNaN0.0000.8850.0000.0000.000
개선도지수채널설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개선도채널생성일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최근6개월개선도1.0001.000NaN1.0001.0001.0000.7000.0000.0000.7180.7980.5130.0000.0000.675
최근12개월개선도1.0001.000NaN1.0001.0000.7001.0001.0001.0000.0000.0000.6550.3010.0000.760
최초6개월개선도1.0001.000NaN1.0001.0000.0001.0001.0000.3350.1200.1010.0000.2930.0000.402
최초12개월개선도1.0001.000NaN1.0001.0000.0001.0000.3351.0000.0000.3850.4870.7260.6470.751
최근개선도지수1.0001.000NaN1.0001.0000.7180.0000.1200.0001.0000.6870.2770.0000.0000.763
최근6개월표준점수1.0001.0000.0001.0001.0000.7980.0000.1010.3850.6871.0000.0000.3450.4490.062
최근12개월표준점수1.0001.0000.8851.0001.0000.5130.6550.0000.4870.2770.0001.0000.0000.2250.000
최초6개월표준점수1.0001.0000.0001.0001.0000.0000.3010.2930.7260.0000.3450.0001.0000.9820.000
최초12개월표준점수1.0001.0000.0001.0001.0000.0000.0000.0000.6470.0000.4490.2250.9821.0000.000
개선도최근표준점수1.0001.0000.0001.0001.0000.6750.7600.4020.7510.7630.0620.0000.0000.0001.000
2023-12-10T22:56:12.151332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초12개월개선도최초6개월개선도
최초12개월개선도1.0000.497
최초6개월개선도0.4971.000
2023-12-10T22:56:12.367983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최근6개월개선도최근12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수최초6개월개선도최초12개월개선도
최근6개월개선도1.000-0.076-0.202-0.115-0.435-0.185-0.1540.2010.0000.000
최근12개월개선도-0.0761.000-0.4150.4510.3250.379-0.150-0.2750.0000.347
최근개선도지수-0.202-0.4151.0000.1500.3840.2640.176-0.2780.1260.000
최근6개월표준점수-0.1150.4510.1501.0000.1770.1580.176-0.1250.0000.000
최근12개월표준점수-0.4350.3250.3840.1771.000-0.083-0.449-0.1570.0000.327
최초6개월표준점수-0.1850.3790.2640.158-0.0831.0000.087-0.3130.2460.619
최초12개월표준점수-0.154-0.1500.1760.176-0.4490.0871.000-0.1780.0000.518
개선도최근표준점수0.201-0.275-0.278-0.125-0.157-0.313-0.1781.0000.5920.389
최초6개월개선도0.0000.0000.1260.0000.0000.2460.0000.5921.0000.497
최초12개월개선도0.0000.3470.0000.0000.3270.6190.5180.3890.4971.000

Missing values

2023-12-10T22:56:01.858025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:56:02.207553image/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.
2023-12-10T22:56:02.523120image/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
0UCghjVjOyijEZe1hawS8S3tA부산시부산정보산업진흥원2021-06-03부산정보산업진흥원은 지역의 정보통신문화콘텐츠 산업을 육성; 지원하는 대표 기관입니다. 창조문화산업도시 부산 건설에 앞장서겠습니다.2016-10-26<NA><NA><NA><NA><NA><NA>6.730.190.2513.5
1UCj7mdvAJCRKvGBmcusOr9Ag엠브로 MBRO2021-06-07<NA>2015-04-12<NA><NA><NA><NA><NA>17.17-3.880.180.2618.87
2UC-ywC1MrAfgpDe3_vytnBqw브리랜서 브로디2021-06-30브리랜서 브로디입니다 브하하하하하!!!2011-05-31-12-4<NA><NA>34.31<NA>-9.190.180.2517.0
3UC0SsBKkmlkyNx6bo0SSAEpQ김쵸코2021-06-30각종 게임을 재미있게 플레이 해 나가는 게임 크리에이터 쵸코 입니다<NA>14003.322.48-9.340.280.07-16.88
4UC-js9KxuyRoB0b9zlKCgsCg이영자 채널 :LYJ CH.2021-06-30이영자 채널 _ LEE YOUNG JA CHANNEL 이영자의 소소(小) & 대대(大)한 이야기들을 영상으로 만나실 수 있습니다. 구독 & 좋아요 & 알람 꾸욱! ^^ 비지니스 문의 및 채널 이벤트 관련 ▶ youngjachannel@gmail.com2019-02-28<NA><NA>0-10.0<NA>-9.76-0.473.4518.64
5UC1kL6gpiIEmRFTfGkCrQkCw뉴욕판다와 6멍2냥이네NEWYORK PANDA2021-06-30안녕하세요~ 6마리 요크셔테리어 2마리 고양이와 함께 살고 있는 판다입니다 강아지와 고양이와 함께 수제간식도 만들고~ 놀고 싸우고 하고 지내는 이야기 뉴욕에 사는 저의 이야기들을 함께 나누고 싶습니다~ 안녕 우리 자주 만나요2019-06-02<NA><NA><NA><NA><NA>-3.04-9.920.150.4218.22
6UC2SUmtLkL-oFvDR_hqn6kqw헤드헌터 윤재홍 - 세상의 모든 직업2021-06-30'헤드헌터 윤재홍의 난JOB한 이야기'는 매회 다른 직업을 가진 게스트를 모시고 재미있게 이야기 나누는 방송입니다. ━━━━━━━━━━━━━━━━━━━━━━━━━ 출연신청 및 비지니스 문의 : nanjobstory@gmail.com 1:1 컨설팅 : https:taling.meTalentDetail35620 ━━━━━━━━━━━━━━━━━━━━━━━━━<NA>3<NA><NA><NA>17.64-1.31-9.640.180.2517.53
7UC2XRTuTf0tnqcNBcWeaCVFgHale In Ocean 정혜일2021-06-30세상이란 바다 속에 살고있는 Hale In Ocean 정혜일입니다. 노래를 만들고; 노래를 부릅니다.2017-05-1331110.0-3.150.27-0.03-0.1525.57
8UC3Ax4Vmwbf7EMwBfjyjz7NA하오TV2021-06-30안녕하세요~하오의 철없는 집사 하오아빠 개그맨 손민혁 입니다. 하오TV를 찾아 주셔서 너무나 감사드립니다^^ 강아지탈을 쓰고 있는 하오와 철없는 아빠의 소소한 이야기를 담은 채널입니다~ 구독자 분들과 하오와 함께 잼나고 힐링되는 채널이 되도록 노력하겠습니다^^ 그럼 하트님들 오늘 하루도 하오하게~~~~~ ♥Profile 견종 : 골든리트리버 이름 : 하오 성별 : 수컷 생년월일 : 2016.11.25 성격 :귀차니즘의 대명사 하지만 양보와 미덕의 진정한 천사견 리트리버의 표본. 단점은 먹을거 앞에서는 모든사람이 주인!! ♥Profile Name : Hao Type : Golden Retriever Sex : male Birth : 25.11.2016 하오 유튜브 : https:www.youtube.comchannelUC3Ax4Vmwbf7EMwBfjyjz7NA 하오 인스타그램 : https:www.instagram.com_hao.tv 하오 페이스북 : https:www.facebook.compeople손민혁100001919830600 하오굿즈몰:https:smartstore.naver.comhaono1 하오와 아빠의 영상 구독해 주셔서 진심으로 감사하오!!하오!!하오!! 비지니스 문의 : ha5tv@naver.com<NA>-36-4<NA><NA>86.11-18.840.30.180.255.92
9UC3Ge0wod-JQdP-HkRT1Z_ug몽까TV2021-06-30말하기 바쁜 채널 몽까! ::이메일 kho02210@naver.com::<NA><NA><NA>100.0<NA>-9.760.150.2420.75
개선도지수채널ID개선도지수채널명개선도지수수집일자개선도지수채널설명개선도채널생성일자최근6개월개선도최근12개월개선도최초6개월개선도최초12개월개선도최근개선도지수최근6개월표준점수최근12개월표준점수최초6개월표준점수최초12개월표준점수개선도최근표준점수
16UC9HUPC-qo7mUfW4ofckWWNwIvan Lam2021-06-30Welcome to the visuals of my mind. You are welcome here! Lover of skincare; makeup; and everything self care. Sending love to you from me and my cat. Based in Los Angeles. For business inquiries contact me at : contactivanlam@gmail.com2012-05-162-2<NA><NA>18.77-2.67-7.390.180.2520.44
17UC9ZLv1m7QDLv991X1-p50AA보물섬2021-06-30아룡하세요~~ 허잇 매번 다양하고 새로운 웃음을 시도하는 삼인조 유머 크리에이터 보물섬입니다 Hellow~~ huh it We are three-member humor creator Treasure Island who always tries various and new laugh 비즈니스 문의 : bomulsum@sandbox.co.kr<NA>12-8<NA><NA>40.15-5.06-4.580.180.2523.14
18UCA7WtDf9bYuRW1BowR00e6g고뎅2021-06-30크리에이터 고뎅입니다! 1. 리그오브레전드(LOL) 서포터 플레이 2. 배틀그라운드 플레이 3. 배그 & 롤 방송 진행 구독 꼭꼭꼭 트로피카나2016-10-09<NA><NA>000.0<NA>-9.761.680.4818.8
19UCA9lR7W1otxtVIT4OFlNU8AJTBC Brand Design2021-06-30JTBC Design2014-05-28<NA><NA>003.85-3.52-10.470.450.3918.34
20UCAPVYRwD-6yYx85oqgFnqCwRacing Park2021-06-30<NA>2014-01-07<NA><NA><NA><NA>7.755.89-9.810.270.2123.81
21UCBxfbo7tKSgDt3ymphgkWQA장애인기업종합지원센터2021-06-30장애인기업의 육성과 장애인에게 경제적 자립 기회를 제공하고 있는 (재)장애인기업종합지원센터입니다.2019-04-10<NA><NA><NA><NA><NA>-41.61-8.290.140.2918.18
22UC3eNWzE-pftCXatCuUAaHGw김구라의 뻐꾸기 골프 TV2021-06-30뻐꾸기 적통 '그렇joe' 김구라와 그의 절친 '초롱좌' 박노준; 그리고 그의 친구들! 서로를 물고 뜯는 현실 반영 100% '뻐꾸기' 골프 예능! 제작 : SH enter & company(http:sh-tv.co.kr) 비즈니스 문의 : guragolf@sandbox.co.kr 샌드박스<NA>2400151.4813.651.30.420.45-26.85
23UCDrAR1OWC2MD4s0JLetN0MA각별2021-06-30'별'짓 다 합니다.<NA>-240<NA><NA>58.9819.011.340.180.25204.7
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