Overview

Dataset statistics

Number of variables8
Number of observations25
Missing cells13
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory71.3 B

Variable types

Text4
Categorical1
DateTime1
Numeric2

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/2682e090-7864-4b2d-825f-cc705bd1bebb

Alerts

수집일 has constant value ""Constant
키워드빈도 is highly overall correlated with 키워드중요도High correlation
키워드중요도 is highly overall correlated with 키워드빈도High correlation
설명 has 2 (8.0%) missing valuesMissing
생성일 has 11 (44.0%) missing valuesMissing
채널ID has unique valuesUnique
채널명 has unique valuesUnique
키워드중요도 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:48:59.480692
Analysis finished2023-12-10 13:49:01.365338
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

채널ID
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-10T22:49:01.654977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters600
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st rowUCDcfRn7jWaPaeuJ1OJBfP4A
2nd rowUCkKP0fWXIvAJN0Tgj2Ky3iA
3rd rowUCsdQx7N2z7rgLYqCMeViw2Q
4th rowUCS1bJuQ8_1KXC86RhPvJexw
5th rowUCK7VzEaF3_lxRrvxnqHDdJw
ValueCountFrequency (%)
ucdcfrn7jwapaeuj1ojbfp4a 1
 
4.0%
ucn8cpzwkyidvlzlgd4jqgjq 1
 
4.0%
uclnlxn8cipki7rtpc9qckvq 1
 
4.0%
ucdywkqyljl1nta21avximdg 1
 
4.0%
ucd46h9pm3nlqcj7vv3pqkpa 1
 
4.0%
uccjxhyjwa91sekzyqzmvuda 1
 
4.0%
ucn8har9psgy94rd6u7rz_ig 1
 
4.0%
ucftf-qgmr8-ede8k0kvxqsa 1
 
4.0%
ucxvx64o-lj9m4co9zm6druq 1
 
4.0%
ucnv5tunovgyt-ncj0s0tptw 1
 
4.0%
Other values (15) 15
60.0%
2023-12-10T22:49:02.351629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 36
 
6.0%
U 31
 
5.2%
Q 20
 
3.3%
g 14
 
2.3%
7 14
 
2.3%
J 14
 
2.3%
P 13
 
2.2%
w 13
 
2.2%
r 12
 
2.0%
A 12
 
2.0%
Other values (54) 421
70.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 275
45.8%
Lowercase Letter 217
36.2%
Decimal Number 90
 
15.0%
Dash Punctuation 10
 
1.7%
Connector Punctuation 8
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 36
 
13.1%
U 31
 
11.3%
Q 20
 
7.3%
J 14
 
5.1%
P 13
 
4.7%
A 12
 
4.4%
V 12
 
4.4%
K 11
 
4.0%
D 10
 
3.6%
Z 10
 
3.6%
Other values (16) 106
38.5%
Lowercase Letter
ValueCountFrequency (%)
g 14
 
6.5%
w 13
 
6.0%
r 12
 
5.5%
x 11
 
5.1%
d 11
 
5.1%
j 11
 
5.1%
a 10
 
4.6%
v 10
 
4.6%
k 10
 
4.6%
n 9
 
4.1%
Other values (16) 106
48.8%
Decimal Number
ValueCountFrequency (%)
7 14
15.6%
8 11
12.2%
2 10
11.1%
6 10
11.1%
4 10
11.1%
9 9
10.0%
3 8
8.9%
1 8
8.9%
5 5
 
5.6%
0 5
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 492
82.0%
Common 108
 
18.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 36
 
7.3%
U 31
 
6.3%
Q 20
 
4.1%
g 14
 
2.8%
J 14
 
2.8%
P 13
 
2.6%
w 13
 
2.6%
r 12
 
2.4%
A 12
 
2.4%
V 12
 
2.4%
Other values (42) 315
64.0%
Common
ValueCountFrequency (%)
7 14
13.0%
8 11
10.2%
2 10
9.3%
- 10
9.3%
6 10
9.3%
4 10
9.3%
9 9
8.3%
3 8
7.4%
1 8
7.4%
_ 8
7.4%
Other values (2) 10
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 36
 
6.0%
U 31
 
5.2%
Q 20
 
3.3%
g 14
 
2.3%
7 14
 
2.3%
J 14
 
2.3%
P 13
 
2.2%
w 13
 
2.2%
r 12
 
2.0%
A 12
 
2.0%
Other values (54) 421
70.2%

채널명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-10T22:49:02.740031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length13
Mean length6.36
Min length2

Characters and Unicode

Total characters159
Distinct characters105
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row예산군
2nd row한국어촌어항공단
3rd rowDaey 데이
4th row쥬니뚜뗄라 JuneyTutela
5th row예스잼미
ValueCountFrequency (%)
예산군 1
 
2.9%
몽당분필 1
 
2.9%
korea 1
 
2.9%
grandma 1
 
2.9%
원주시 1
 
2.9%
후투브 1
 
2.9%
제이 1
 
2.9%
예소리ssol 1
 
2.9%
인간젤리 1
 
2.9%
박막례 1
 
2.9%
Other values (25) 25
71.4%
2023-12-10T22:49:03.372388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
6.3%
u 5
 
3.1%
a 5
 
3.1%
T 4
 
2.5%
e 4
 
2.5%
예 3
 
1.9%
R 3
 
1.9%
n 3
 
1.9%
분 3
 
1.9%
리 3
 
1.9%
Other values (95) 116
73.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
56.0%
Uppercase Letter 29
 
18.2%
Lowercase Letter 27
 
17.0%
Space Separator 10
 
6.3%
Decimal Number 3
 
1.9%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
예 3
 
3.4%
분 3
 
3.4%
리 3
 
3.4%
사 2
 
2.2%
왁 2
 
2.2%
나 2
 
2.2%
이 2
 
2.2%
살 2
 
2.2%
한 2
 
2.2%
국 2
 
2.2%
Other values (63) 66
74.2%
Uppercase Letter
ValueCountFrequency (%)
T 4
13.8%
R 3
10.3%
O 3
10.3%
S 3
10.3%
K 2
 
6.9%
B 2
 
6.9%
L 2
 
6.9%
D 2
 
6.9%
G 1
 
3.4%
E 1
 
3.4%
Other values (6) 6
20.7%
Lowercase Letter
ValueCountFrequency (%)
u 5
18.5%
a 5
18.5%
e 4
14.8%
n 3
11.1%
r 2
 
7.4%
y 2
 
7.4%
o 1
 
3.7%
m 1
 
3.7%
d 1
 
3.7%
l 1
 
3.7%
Other values (2) 2
 
7.4%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
; 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
56.0%
Latin 56
35.2%
Common 14
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
예 3
 
3.4%
분 3
 
3.4%
리 3
 
3.4%
사 2
 
2.2%
왁 2
 
2.2%
나 2
 
2.2%
이 2
 
2.2%
살 2
 
2.2%
한 2
 
2.2%
국 2
 
2.2%
Other values (63) 66
74.2%
Latin
ValueCountFrequency (%)
u 5
 
8.9%
a 5
 
8.9%
T 4
 
7.1%
e 4
 
7.1%
R 3
 
5.4%
n 3
 
5.4%
O 3
 
5.4%
S 3
 
5.4%
r 2
 
3.6%
K 2
 
3.6%
Other values (18) 22
39.3%
Common
ValueCountFrequency (%)
10
71.4%
0 2
 
14.3%
1 1
 
7.1%
; 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
56.0%
ASCII 70
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10
 
14.3%
u 5
 
7.1%
a 5
 
7.1%
T 4
 
5.7%
e 4
 
5.7%
R 3
 
4.3%
n 3
 
4.3%
O 3
 
4.3%
S 3
 
4.3%
r 2
 
2.9%
Other values (22) 28
40.0%
Hangul
ValueCountFrequency (%)
예 3
 
3.4%
분 3
 
3.4%
리 3
 
3.4%
사 2
 
2.2%
왁 2
 
2.2%
나 2
 
2.2%
이 2
 
2.2%
살 2
 
2.2%
한 2
 
2.2%
국 2
 
2.2%
Other values (63) 66
74.2%

수집일
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2021-10-01
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-01
2nd row2021-10-01
3rd row2021-10-01
4th row2021-10-01
5th row2021-10-01

Common Values

ValueCountFrequency (%)
2021-10-01 25
100.0%

Length

2023-12-10T22:49:03.605190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:03.766718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-10-01 25
100.0%

설명
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing2
Missing (%)8.0%
Memory size332.0 B
2023-12-10T22:49:04.089418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length593
Median length80
Mean length138.65217
Min length13

Characters and Unicode

Total characters3189
Distinct characters395
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks7 ?
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 row충남의 중심; 역동하는 예산! 사과와 온천의 고장; 예산군 공식 유튜브입니다.
2nd row해양수산부 산하 공공기관; 한국어촌어항공단 공식 유튜브 쾌적한 어항! 역동하는 어장! 활력있는 어촌! 안전하고 친환경 수산업기반을 선도하는 어업인의 파트너 FIPA 한국어촌어항공단
3rd rowAppetizing stories from a cooking addict who believes everyone can be friends by sharing a food! 음식으로 모두가 친구가 될 수 있다고 믿는 요리덕후의 먹음직스러운 이야기!
4th row안녕하세요!! 쥬니의 일상을 담은 유튜브 채널에 오신걸 환영합니다!! 쥬니의 아들 우태와 신랑구리도 함께 하는 영상 재미있게 봐주세요~ 그리고 좋아요와 구독은 사랑입니당 오늘도 행복한 하루 되세용! [쥬니뚜뗄라 공식 유튜브 채널] 제작(주)샌드박스네트워크 업로드매주 금요일 오후 5시 비즈니스 이메일juneytutela@sandbox.co.kr Instagram @juneytutela TikTok @juneytutela
5th row트위치 스트리머 잼미님의 유튜브 채널입니다. https:www.twitch.tvjammi95 -비지니스 문의- Jammi95@naver.com
ValueCountFrequency (%)
20
 
3.6%
유튜브 10
 
1.8%
한국관광공사 8
 
1.5%
채널입니다 7
 
1.3%
○ 7
 
1.3%
공식 6
 
1.1%
안녕하세요 5
 
0.9%
문의 5
 
0.9%
채널 5
 
0.9%
정보 5
 
0.9%
Other values (411) 470
85.8%
2023-12-10T22:49:04.698906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
565
 
17.7%
a 88
 
2.8%
t 84
 
2.6%
o 83
 
2.6%
e 77
 
2.4%
. 73
 
2.3%
n 68
 
2.1%
i 62
 
1.9%
r 56
 
1.8%
s 52
 
1.6%
Other values (385) 1981
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1300
40.8%
Lowercase Letter 953
29.9%
Space Separator 565
17.7%
Other Punctuation 168
 
5.3%
Uppercase Letter 77
 
2.4%
Decimal Number 42
 
1.3%
Other Symbol 32
 
1.0%
Close Punctuation 15
 
0.5%
Open Punctuation 14
 
0.4%
Math Symbol 9
 
0.3%
Other values (3) 14
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
니 40
 
3.1%
다 32
 
2.5%
하 27
 
2.1%
의 26
 
2.0%
한 23
 
1.8%
는 23
 
1.8%
공 22
 
1.7%
국 19
 
1.5%
사 18
 
1.4%
고 18
 
1.4%
Other values (293) 1052
80.9%
Lowercase Letter
ValueCountFrequency (%)
a 88
 
9.2%
t 84
 
8.8%
o 83
 
8.7%
e 77
 
8.1%
n 68
 
7.1%
i 62
 
6.5%
r 56
 
5.9%
s 52
 
5.5%
m 47
 
4.9%
l 44
 
4.6%
Other values (16) 292
30.6%
Uppercase Letter
ValueCountFrequency (%)
I 10
13.0%
T 9
11.7%
A 7
 
9.1%
K 6
 
7.8%
S 5
 
6.5%
B 4
 
5.2%
C 4
 
5.2%
R 4
 
5.2%
P 3
 
3.9%
V 3
 
3.9%
Other values (13) 22
28.6%
Other Symbol
ValueCountFrequency (%)
○ 7
21.9%
▶ 5
15.6%
☆ 4
12.5%
★ 4
12.5%
◎ 2
 
6.2%
♩ 2
 
6.2%
● 1
 
3.1%
♣ 1
 
3.1%
◇ 1
 
3.1%
☞ 1
 
3.1%
Other values (4) 4
12.5%
Other Punctuation
ValueCountFrequency (%)
. 73
43.5%
: 23
 
13.7%
! 21
 
12.5%
' 17
 
10.1%
; 12
 
7.1%
@ 10
 
6.0%
& 5
 
3.0%
* 5
 
3.0%
※ 2
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 9
21.4%
0 8
19.0%
5 7
16.7%
3 7
16.7%
9 4
9.5%
4 3
 
7.1%
7 2
 
4.8%
8 1
 
2.4%
2 1
 
2.4%
Math Symbol
ValueCountFrequency (%)
~ 6
66.7%
∮ 2
 
22.2%
+ 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 13
86.7%
] 2
 
13.3%
Open Punctuation
ValueCountFrequency (%)
( 12
85.7%
[ 2
 
14.3%
Space Separator
ValueCountFrequency (%)
565
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1300
40.8%
Latin 1030
32.3%
Common 859
26.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
니 40
 
3.1%
다 32
 
2.5%
하 27
 
2.1%
의 26
 
2.0%
한 23
 
1.8%
는 23
 
1.8%
공 22
 
1.7%
국 19
 
1.5%
사 18
 
1.4%
고 18
 
1.4%
Other values (293) 1052
80.9%
Latin
ValueCountFrequency (%)
a 88
 
8.5%
t 84
 
8.2%
o 83
 
8.1%
e 77
 
7.5%
n 68
 
6.6%
i 62
 
6.0%
r 56
 
5.4%
s 52
 
5.0%
m 47
 
4.6%
l 44
 
4.3%
Other values (39) 369
35.8%
Common
ValueCountFrequency (%)
565
65.8%
. 73
 
8.5%
: 23
 
2.7%
! 21
 
2.4%
' 17
 
2.0%
) 13
 
1.5%
( 12
 
1.4%
; 12
 
1.4%
@ 10
 
1.2%
1 9
 
1.0%
Other values (33) 104
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1853
58.1%
Hangul 1296
40.6%
Geometric Shapes 17
 
0.5%
Misc Symbols 15
 
0.5%
Compat Jamo 4
 
0.1%
Punctuation 2
 
0.1%
Math Operators 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
565
30.5%
a 88
 
4.7%
t 84
 
4.5%
o 83
 
4.5%
e 77
 
4.2%
. 73
 
3.9%
n 68
 
3.7%
i 62
 
3.3%
r 56
 
3.0%
s 52
 
2.8%
Other values (66) 645
34.8%
Hangul
ValueCountFrequency (%)
니 40
 
3.1%
다 32
 
2.5%
하 27
 
2.1%
의 26
 
2.0%
한 23
 
1.8%
는 23
 
1.8%
공 22
 
1.7%
국 19
 
1.5%
사 18
 
1.4%
고 18
 
1.4%
Other values (292) 1048
80.9%
Geometric Shapes
ValueCountFrequency (%)
○ 7
41.2%
▶ 5
29.4%
◎ 2
 
11.8%
● 1
 
5.9%
◇ 1
 
5.9%
◆ 1
 
5.9%
Misc Symbols
ValueCountFrequency (%)
☆ 4
26.7%
★ 4
26.7%
♩ 2
13.3%
♣ 1
 
6.7%
☞ 1
 
6.7%
☜ 1
 
6.7%
♪ 1
 
6.7%
♬ 1
 
6.7%
Compat Jamo
ValueCountFrequency (%)
ㅎ 4
100.0%
Punctuation
ValueCountFrequency (%)
※ 2
100.0%
Math Operators
ValueCountFrequency (%)
∮ 2
100.0%

생성일
Date

MISSING 

Distinct14
Distinct (%)100.0%
Missing11
Missing (%)44.0%
Memory size332.0 B
Minimum2009-10-13 00:00:00
Maximum2019-10-04 00:00:00
2023-12-10T22:49:04.902610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:05.132661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)

키워드
Text

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-10T22:49:05.468573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.4
Min length1

Characters and Unicode

Total characters60
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)72.0%

Sample

1st row자금
2nd row여행
3rd row먹방
4th row먹방
5th row사용법
ValueCountFrequency (%)
먹방 3
 
12.0%
데이트 2
 
8.0%
여행 2
 
8.0%
낚시 1
 
4.0%
자금 1
 
4.0%
맛집 1
 
4.0%
카페 1
 
4.0%
드라이브 1
 
4.0%
시흥시 1
 
4.0%
교육 1
 
4.0%
Other values (11) 11
44.0%
2023-12-10T22:49:06.001162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 4
 
6.7%
시 4
 
6.7%
먹 3
 
5.0%
여 3
 
5.0%
방 3
 
5.0%
행 2
 
3.3%
데 2
 
3.3%
트 2
 
3.3%
사 2
 
3.3%
라 1
 
1.7%
Other values (34) 34
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 4
 
6.7%
시 4
 
6.7%
먹 3
 
5.0%
여 3
 
5.0%
방 3
 
5.0%
행 2
 
3.3%
데 2
 
3.3%
트 2
 
3.3%
사 2
 
3.3%
라 1
 
1.7%
Other values (34) 34
56.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 4
 
6.7%
시 4
 
6.7%
먹 3
 
5.0%
여 3
 
5.0%
방 3
 
5.0%
행 2
 
3.3%
데 2
 
3.3%
트 2
 
3.3%
사 2
 
3.3%
라 1
 
1.7%
Other values (34) 34
56.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 4
 
6.7%
시 4
 
6.7%
먹 3
 
5.0%
여 3
 
5.0%
방 3
 
5.0%
행 2
 
3.3%
데 2
 
3.3%
트 2
 
3.3%
사 2
 
3.3%
라 1
 
1.7%
Other values (34) 34
56.7%

키워드빈도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.76
Minimum1
Maximum306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T22:49:06.205517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile129.2
Maximum306
Range305
Interquartile range (IQR)5

Descriptive statistics

Standard deviation66.128839
Coefficient of variation (CV)2.7832003
Kurtosis15.113478
Mean23.76
Median Absolute Deviation (MAD)3
Skewness3.8237137
Sum594
Variance4373.0233
MonotonicityNot monotonic
2023-12-10T22:49:06.424277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 6
24.0%
3 3
12.0%
2 3
12.0%
5 3
12.0%
4 3
12.0%
38 1
 
4.0%
12 1
 
4.0%
152 1
 
4.0%
306 1
 
4.0%
7 1
 
4.0%
Other values (2) 2
 
8.0%
ValueCountFrequency (%)
1 6
24.0%
2 3
12.0%
3 3
12.0%
4 3
12.0%
5 3
12.0%
7 1
 
4.0%
11 1
 
4.0%
12 1
 
4.0%
20 1
 
4.0%
38 1
 
4.0%
ValueCountFrequency (%)
306 1
 
4.0%
152 1
 
4.0%
38 1
 
4.0%
20 1
 
4.0%
12 1
 
4.0%
11 1
 
4.0%
7 1
 
4.0%
5 3
12.0%
4 3
12.0%
3 3
12.0%

키워드중요도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.54884
Minimum0.334
Maximum240.688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T22:49:06.709411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.334
5-th percentile0.5478
Q11.656
median4.592
Q36.352
95-th percentile66.6296
Maximum240.688
Range240.354
Interquartile range (IQR)4.696

Descriptive statistics

Standard deviation48.992789
Coefficient of variation (CV)2.7917964
Kurtosis19.749593
Mean17.54884
Median Absolute Deviation (MAD)2.79
Skewness4.3405723
Sum438.721
Variance2400.2933
MonotonicityNot monotonic
2023-12-10T22:49:06.906600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5.197 1
 
4.0%
1.003 1
 
4.0%
21.024 1
 
4.0%
0.488 1
 
4.0%
2.645 1
 
4.0%
0.787 1
 
4.0%
6.352 1
 
4.0%
6.234 1
 
4.0%
6.23 1
 
4.0%
4.592 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0.334 1
4.0%
0.488 1
4.0%
0.787 1
4.0%
0.874 1
4.0%
0.895 1
4.0%
1.003 1
4.0%
1.656 1
4.0%
2.453 1
4.0%
2.59 1
4.0%
2.645 1
4.0%
ValueCountFrequency (%)
240.688 1
4.0%
78.031 1
4.0%
21.024 1
4.0%
19.508 1
4.0%
11.319 1
4.0%
7.382 1
4.0%
6.352 1
4.0%
6.234 1
4.0%
6.23 1
4.0%
6.16 1
4.0%

Interactions

2023-12-10T22:49:00.527990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:00.136048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:00.674726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:00.307227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:49:07.184232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채널ID채널명설명생성일키워드키워드빈도키워드중요도
채널ID1.0001.0001.0001.0001.0001.0001.000
채널명1.0001.0001.0001.0001.0001.0001.000
설명1.0001.0001.0001.0001.0001.0001.000
생성일1.0001.0001.0001.0001.0001.0001.000
키워드1.0001.0001.0001.0001.0000.0000.000
키워드빈도1.0001.0001.0001.0000.0001.0001.000
키워드중요도1.0001.0001.0001.0000.0001.0001.000
2023-12-10T22:49:07.355388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
키워드빈도키워드중요도
키워드빈도1.0000.848
키워드중요도0.8481.000

Missing values

2023-12-10T22:49:00.925801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:49:01.115474image/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.
2023-12-10T22:49:01.285064image/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

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