Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells3
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory70.9 B

Variable types

Text4
Categorical1
DateTime1
Numeric2

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/45268515-15b2-4a64-a610-ac34eae21061

Alerts

COLCT_DAY has constant value ""Constant
KWRD_FQ is highly overall correlated with KWRD_IPCRHigh correlation
KWRD_IPCR is highly overall correlated with KWRD_FQHigh correlation
DC has 3 (11.1%) missing valuesMissing
KWRD_IPCR has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:14:26.441729
Analysis finished2023-12-10 14:14:28.600738
Duration2.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CHNNL_ID
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-10T23:14:28.857500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters648
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 (%)92.6%

Sample

1st rowUCRH7j5GsT7MWuEFwjORNlFg
2nd rowUCF5yAKCwcLi-C6bxFkSSRTA
3rd rowUCVIs6DqCoafGiZPMETjKE-Q
4th rowUCz22EWP6u08x3b3aYSMh5BQ
5th rowUCuw1hxBo5mDVUhgMzRDk3aw
ValueCountFrequency (%)
ucshuyzg-kl3ld6_2nzo55eg 2
 
7.4%
ucrh7j5gst7mwuefwjornlfg 1
 
3.7%
uc31gc42xzclooi5gp1xipzw 1
 
3.7%
ucfq4v1dauaojnr2ryvwnysw 1
 
3.7%
ucv7hx-7ctqth4cxxt1nfrig 1
 
3.7%
ucgsffs7mfkl6yu3r_u3e-aa 1
 
3.7%
ucwa_dkk1abbogwr0ntsuz8a 1
 
3.7%
uc-l6ubdiu2z6eu3ahyv0r9a 1
 
3.7%
ucw9vtbtqxd0fra1b9qcpr7a 1
 
3.7%
uc3qntxr0q03rgrav8quza2q 1
 
3.7%
Other values (16) 16
59.3%
2023-12-10T23:14:29.469986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 39
 
6.0%
C 32
 
4.9%
Q 17
 
2.6%
A 17
 
2.6%
g 16
 
2.5%
r 16
 
2.5%
l 16
 
2.5%
3 14
 
2.2%
T 14
 
2.2%
6 14
 
2.2%
Other values (54) 453
69.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 288
44.4%
Lowercase Letter 241
37.2%
Decimal Number 100
 
15.4%
Dash Punctuation 11
 
1.7%
Connector Punctuation 8
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 39
 
13.5%
C 32
 
11.1%
Q 17
 
5.9%
A 17
 
5.9%
T 14
 
4.9%
F 12
 
4.2%
E 11
 
3.8%
Z 11
 
3.8%
D 11
 
3.8%
Y 10
 
3.5%
Other values (16) 114
39.6%
Lowercase Letter
ValueCountFrequency (%)
g 16
 
6.6%
r 16
 
6.6%
l 16
 
6.6%
a 14
 
5.8%
w 12
 
5.0%
f 12
 
5.0%
k 11
 
4.6%
s 10
 
4.1%
j 10
 
4.1%
x 10
 
4.1%
Other values (16) 114
47.3%
Decimal Number
ValueCountFrequency (%)
3 14
14.0%
6 14
14.0%
2 12
12.0%
1 11
11.0%
5 11
11.0%
0 11
11.0%
7 10
10.0%
4 7
7.0%
8 6
6.0%
9 4
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 529
81.6%
Common 119
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 39
 
7.4%
C 32
 
6.0%
Q 17
 
3.2%
A 17
 
3.2%
g 16
 
3.0%
r 16
 
3.0%
l 16
 
3.0%
T 14
 
2.6%
a 14
 
2.6%
w 12
 
2.3%
Other values (42) 336
63.5%
Common
ValueCountFrequency (%)
3 14
11.8%
6 14
11.8%
2 12
10.1%
1 11
9.2%
- 11
9.2%
5 11
9.2%
0 11
9.2%
7 10
8.4%
_ 8
6.7%
4 7
5.9%
Other values (2) 10
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 39
 
6.0%
C 32
 
4.9%
Q 17
 
2.6%
A 17
 
2.6%
g 16
 
2.5%
r 16
 
2.5%
l 16
 
2.5%
3 14
 
2.2%
T 14
 
2.2%
6 14
 
2.2%
Other values (54) 453
69.9%

CHNNL_NM
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-10T23:14:29.833516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length7.7037037
Min length3

Characters and Unicode

Total characters208
Distinct characters113
Distinct categories8 ?
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 (%)92.6%

Sample

1st rowSINCOOK - 신쿡
2nd row락동민
3rd row대구도시철도 DTRO
4th row의왕시청
5th rowTV CHOSUN
ValueCountFrequency (%)
핫한영월 2
 
5.1%
채널a 2
 
5.1%
채채chaechae 1
 
2.6%
락동민 1
 
2.6%
야식tv]야매식당 1
 
2.6%
저작권tv 1
 
2.6%
mbn 1
 
2.6%
entertainment 1
 
2.6%
jtbc4 1
 
2.6%
증평군 1
 
2.6%
Other values (27) 27
69.2%
2023-12-10T23:14:30.556571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
5.8%
T 9
 
4.3%
C 8
 
3.8%
V 7
 
3.4%
e 7
 
3.4%
O 5
 
2.4%
S 4
 
1.9%
t 4
 
1.9%
채 4
 
1.9%
n 4
 
1.9%
Other values (103) 144
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
44.7%
Uppercase Letter 58
27.9%
Lowercase Letter 41
19.7%
Space Separator 12
 
5.8%
Close Punctuation 1
 
0.5%
Decimal Number 1
 
0.5%
Open Punctuation 1
 
0.5%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
채 4
 
4.3%
한 4
 
4.3%
심 2
 
2.2%
어 2
 
2.2%
핫 2
 
2.2%
공 2
 
2.2%
권 2
 
2.2%
야 2
 
2.2%
기 2
 
2.2%
식 2
 
2.2%
Other values (61) 69
74.2%
Uppercase Letter
ValueCountFrequency (%)
T 9
15.5%
C 8
13.8%
V 7
12.1%
O 5
8.6%
S 4
 
6.9%
B 3
 
5.2%
J 3
 
5.2%
N 3
 
5.2%
E 2
 
3.4%
M 2
 
3.4%
Other values (9) 12
20.7%
Lowercase Letter
ValueCountFrequency (%)
e 7
17.1%
t 4
9.8%
n 4
9.8%
a 4
9.8%
l 3
 
7.3%
r 3
 
7.3%
h 2
 
4.9%
o 2
 
4.9%
u 2
 
4.9%
i 2
 
4.9%
Other values (8) 8
19.5%
Space Separator
ValueCountFrequency (%)
12
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 99
47.6%
Hangul 93
44.7%
Common 16
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
채 4
 
4.3%
한 4
 
4.3%
심 2
 
2.2%
어 2
 
2.2%
핫 2
 
2.2%
공 2
 
2.2%
권 2
 
2.2%
야 2
 
2.2%
기 2
 
2.2%
식 2
 
2.2%
Other values (61) 69
74.2%
Latin
ValueCountFrequency (%)
T 9
 
9.1%
C 8
 
8.1%
V 7
 
7.1%
e 7
 
7.1%
O 5
 
5.1%
S 4
 
4.0%
t 4
 
4.0%
n 4
 
4.0%
a 4
 
4.0%
B 3
 
3.0%
Other values (27) 44
44.4%
Common
ValueCountFrequency (%)
12
75.0%
] 1
 
6.2%
4 1
 
6.2%
[ 1
 
6.2%
- 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115
55.3%
Hangul 93
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
 
10.4%
T 9
 
7.8%
C 8
 
7.0%
V 7
 
6.1%
e 7
 
6.1%
O 5
 
4.3%
S 4
 
3.5%
t 4
 
3.5%
n 4
 
3.5%
a 4
 
3.5%
Other values (32) 51
44.3%
Hangul
ValueCountFrequency (%)
채 4
 
4.3%
한 4
 
4.3%
심 2
 
2.2%
어 2
 
2.2%
핫 2
 
2.2%
공 2
 
2.2%
권 2
 
2.2%
야 2
 
2.2%
기 2
 
2.2%
식 2
 
2.2%
Other values (61) 69
74.2%

COLCT_DAY
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2020-09-01
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-01
2nd row2020-09-01
3rd row2020-09-01
4th row2020-09-01
5th row2020-09-01

Common Values

ValueCountFrequency (%)
2020-09-01 27
100.0%

Length

2023-12-10T23:14:30.791079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:30.946793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-01 27
100.0%

DC
Text

MISSING 

Distinct23
Distinct (%)95.8%
Missing3
Missing (%)11.1%
Memory size348.0 B
2023-12-10T23:14:31.291830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length777
Median length78.5
Mean length110.45833
Min length14

Characters and Unicode

Total characters2651
Distinct characters371
Distinct categories15 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)91.7%

Sample

1st row요리;실험;일상등 여러가지 재미난 영상을 여러분들께 보여드리기 위해 노력하는 신쿡이 되겠습니다 영상은 매주 수;금 6이후! 일요일 2시이후! 나머지 추가 영상은 랜덤입니다!! 신쿡 에게 메일보내는곳 : gkbsgk@naver.com 신쿡에게 선물보내는곳 : 전주시 완산구 삼천동2가 215-6번지
2nd row게임전문 해설가 락동민 입니다! 다양한 방송국에서 게임방송 진행과 해설로 활동 하고 있습니다~ 구독하기; 좋아요 눌러주시면 저에게 힘이 됩니다 ^^ ----------------------------------------------------------------------------------------- 경력 2012년 - InterColo Cup 스트리트파이터 vs 철권 대회 현장진행 - InterColo Cup 블레이블루 대회 방송 진행 - Dead or alive 5 일본 대회 한국예선 방송 진행. 2013년 - 나이스게임 TV WCG2013 스트리트파이터4 한국예선 해설 - 나이스게임 TV 신규bj 프로젝트 멜티블러드 교육방송 진행 - 나이스게임 TV 신규bj 프로젝트 스트리트파이터4 교육방송 진행 2015년 - 캡콤프로투어 스트리트파이터 4 한국예선 캐스터 - 길티기어 더블엘리미네이션 대회 캐스터 - 헝그리앱tv gg배 진행자 - 넥슨 마비노기 듀얼 토너먼트 예선 진행자 - 스포tv 마비노기 듀얼 해설 - 넥슨 마비노기 듀얼 팀 토너먼트 예선; 본선 진행 2016년 - 인벤방송국 수습캐스터 (롤 브론즈 응원 방송; 박동민의 무한도장 개인방송) - 룬 미디어(게임원정대 콘솔게임 방송; 가두쟁패전 진행) - PlayX4플레이 엑스포 게임쇼 FIFA 16 대학 리그 게임 캐스터 - PS아레나 사이드 토너먼트 캐스터 및 진행MC 트위치 주소 : http:twitch.tvclmovie E-mail 문의 : gamjavas7@naver.com
3rd row대구도시철도 공식 유튜브 채널입니다. 페이스북 https:www.facebook.comOKDTRO 블로그 https:blog.naver.comdtroblog
4th row종합편성채널 TV조선 공식 유튜브입니다
5th rowAn ordinary VLOG of a Korean international student in New york
ValueCountFrequency (%)
28
 
5.9%
공식 6
 
1.3%
진행 6
 
1.3%
mbn 5
 
1.1%
방송 5
 
1.1%
캐스터 4
 
0.8%
대회 4
 
0.8%
채널 4
 
0.8%
tv 4
 
0.8%
of 3
 
0.6%
Other values (346) 404
85.4%
2023-12-10T23:14:32.025944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
489
 
18.4%
- 110
 
4.1%
t 61
 
2.3%
e 56
 
2.1%
o 55
 
2.1%
a 51
 
1.9%
n 45
 
1.7%
; 32
 
1.2%
i 32
 
1.2%
. 31
 
1.2%
Other values (361) 1689
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1158
43.7%
Lowercase Letter 580
21.9%
Space Separator 489
18.4%
Other Punctuation 117
 
4.4%
Uppercase Letter 116
 
4.4%
Dash Punctuation 110
 
4.1%
Decimal Number 45
 
1.7%
Close Punctuation 8
 
0.3%
Math Symbol 6
 
0.2%
Modifier Symbol 6
 
0.2%
Other values (5) 16
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 31
 
2.7%
는 22
 
1.9%
다 20
 
1.7%
로 20
 
1.7%
스 19
 
1.6%
니 18
 
1.6%
한 18
 
1.6%
채 17
 
1.5%
기 17
 
1.5%
하 16
 
1.4%
Other values (285) 960
82.9%
Lowercase Letter
ValueCountFrequency (%)
t 61
 
10.5%
e 56
 
9.7%
o 55
 
9.5%
a 51
 
8.8%
n 45
 
7.8%
i 32
 
5.5%
r 30
 
5.2%
c 28
 
4.8%
m 26
 
4.5%
h 22
 
3.8%
Other values (16) 174
30.0%
Uppercase Letter
ValueCountFrequency (%)
T 17
14.7%
C 14
12.1%
B 10
 
8.6%
V 9
 
7.8%
N 8
 
6.9%
M 8
 
6.9%
I 7
 
6.0%
E 6
 
5.2%
A 5
 
4.3%
K 5
 
4.3%
Other values (12) 27
23.3%
Decimal Number
ValueCountFrequency (%)
2 11
24.4%
1 8
17.8%
0 5
11.1%
4 5
11.1%
5 4
 
8.9%
6 4
 
8.9%
8 3
 
6.7%
3 3
 
6.7%
7 2
 
4.4%
Other Punctuation
ValueCountFrequency (%)
; 32
27.4%
. 31
26.5%
! 20
17.1%
: 17
14.5%
@ 7
 
6.0%
' 7
 
6.0%
& 2
 
1.7%
* 1
 
0.9%
Other Symbol
ValueCountFrequency (%)
▶ 3
75.0%
♥ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
489
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 3
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1158
43.7%
Common 797
30.1%
Latin 696
26.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 31
 
2.7%
는 22
 
1.9%
다 20
 
1.7%
로 20
 
1.7%
스 19
 
1.6%
니 18
 
1.6%
한 18
 
1.6%
채 17
 
1.5%
기 17
 
1.5%
하 16
 
1.4%
Other values (285) 960
82.9%
Latin
ValueCountFrequency (%)
t 61
 
8.8%
e 56
 
8.0%
o 55
 
7.9%
a 51
 
7.3%
n 45
 
6.5%
i 32
 
4.6%
r 30
 
4.3%
c 28
 
4.0%
m 26
 
3.7%
h 22
 
3.2%
Other values (38) 290
41.7%
Common
ValueCountFrequency (%)
489
61.4%
- 110
 
13.8%
; 32
 
4.0%
. 31
 
3.9%
! 20
 
2.5%
: 17
 
2.1%
2 11
 
1.4%
1 8
 
1.0%
) 8
 
1.0%
@ 7
 
0.9%
Other values (18) 64
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1483
55.9%
Hangul 1158
43.7%
Punctuation 6
 
0.2%
Geometric Shapes 3
 
0.1%
Misc Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
489
33.0%
- 110
 
7.4%
t 61
 
4.1%
e 56
 
3.8%
o 55
 
3.7%
a 51
 
3.4%
n 45
 
3.0%
; 32
 
2.2%
i 32
 
2.2%
. 31
 
2.1%
Other values (62) 521
35.1%
Hangul
ValueCountFrequency (%)
이 31
 
2.7%
는 22
 
1.9%
다 20
 
1.7%
로 20
 
1.7%
스 19
 
1.6%
니 18
 
1.6%
한 18
 
1.6%
채 17
 
1.5%
기 17
 
1.5%
하 16
 
1.4%
Other values (285) 960
82.9%
Geometric Shapes
ValueCountFrequency (%)
▶ 3
100.0%
Punctuation
ValueCountFrequency (%)
’ 3
50.0%
‘ 3
50.0%
Misc Symbols
ValueCountFrequency (%)
♥ 1
100.0%

CREAT_DAY
Date

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2007-04-27 00:00:00
Maximum2019-06-26 00:00:00
2023-12-10T23:14:32.271838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:32.502484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

KWRD
Text

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-10T23:14:32.844490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.4444444
Min length2

Characters and Unicode

Total characters66
Distinct characters52
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

Unique23 ?
Unique (%)85.2%

Sample

1st row페스티벌
2nd row먹방
3rd row주식
4th row관광
5th row교육
ValueCountFrequency (%)
교육 2
 
7.4%
카페 2
 
7.4%
야경 1
 
3.7%
페스티벌 1
 
3.7%
드라이브 1
 
3.7%
고양시 1
 
3.7%
등산 1
 
3.7%
상권 1
 
3.7%
행사 1
 
3.7%
캠핑 1
 
3.7%
Other values (15) 15
55.6%
2023-12-10T23:14:33.446151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5
 
7.6%
페 3
 
4.5%
이 3
 
4.5%
교 2
 
3.0%
카 2
 
3.0%
육 2
 
3.0%
행 2
 
3.0%
방 2
 
3.0%
사 2
 
3.0%
트 1
 
1.5%
Other values (42) 42
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5
 
7.6%
페 3
 
4.5%
이 3
 
4.5%
교 2
 
3.0%
카 2
 
3.0%
육 2
 
3.0%
행 2
 
3.0%
방 2
 
3.0%
사 2
 
3.0%
트 1
 
1.5%
Other values (42) 42
63.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5
 
7.6%
페 3
 
4.5%
이 3
 
4.5%
교 2
 
3.0%
카 2
 
3.0%
육 2
 
3.0%
행 2
 
3.0%
방 2
 
3.0%
사 2
 
3.0%
트 1
 
1.5%
Other values (42) 42
63.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5
 
7.6%
페 3
 
4.5%
이 3
 
4.5%
교 2
 
3.0%
카 2
 
3.0%
육 2
 
3.0%
행 2
 
3.0%
방 2
 
3.0%
사 2
 
3.0%
트 1
 
1.5%
Other values (42) 42
63.6%

KWRD_FQ
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.259259
Minimum1
Maximum1687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:14:33.697502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q317.5
95-th percentile150.7
Maximum1687
Range1686
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation322.93886
Coefficient of variation (CV)3.832681
Kurtosis26.025563
Mean84.259259
Median Absolute Deviation (MAD)3
Skewness5.0654666
Sum2275
Variance104289.51
MonotonicityNot monotonic
2023-12-10T23:14:33.886168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 5
18.5%
1 5
18.5%
3 3
11.1%
4 2
 
7.4%
101 1
 
3.7%
12 1
 
3.7%
5 1
 
3.7%
7 1
 
3.7%
18 1
 
3.7%
17 1
 
3.7%
Other values (6) 6
22.2%
ValueCountFrequency (%)
1 5
18.5%
2 5
18.5%
3 3
11.1%
4 2
 
7.4%
5 1
 
3.7%
7 1
 
3.7%
9 1
 
3.7%
12 1
 
3.7%
17 1
 
3.7%
18 1
 
3.7%
ValueCountFrequency (%)
1687 1
3.7%
172 1
3.7%
101 1
3.7%
95 1
3.7%
71 1
3.7%
49 1
3.7%
18 1
3.7%
17 1
3.7%
12 1
3.7%
9 1
3.7%

KWRD_IPCR
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.725741
Minimum0.586
Maximum648.687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-10T23:14:34.087707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.586
5-th percentile0.9057
Q11.776
median5.683
Q319.215
95-th percentile265.3064
Maximum648.687
Range648.101
Interquartile range (IQR)17.439

Descriptive statistics

Standard deviation138.22536
Coefficient of variation (CV)2.4367307
Kurtosis13.440934
Mean56.725741
Median Absolute Deviation (MAD)4.151
Skewness3.5043402
Sum1531.595
Variance19106.249
MonotonicityNot monotonic
2023-12-10T23:14:34.285098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3.228 1
 
3.7%
1.143 1
 
3.7%
1.564 1
 
3.7%
263.359 1
 
3.7%
120.46 1
 
3.7%
5.101 1
 
3.7%
1.532 1
 
3.7%
5.594 1
 
3.7%
1.366 1
 
3.7%
648.687 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.586 1
3.7%
0.804 1
3.7%
1.143 1
3.7%
1.366 1
3.7%
1.532 1
3.7%
1.564 1
3.7%
1.719 1
3.7%
1.833 1
3.7%
3.228 1
3.7%
3.619 1
3.7%
ValueCountFrequency (%)
648.687 1
3.7%
266.141 1
3.7%
263.359 1
3.7%
120.46 1
3.7%
67.184 1
3.7%
48.963 1
3.7%
19.265 1
3.7%
19.165 1
3.7%
12.277 1
3.7%
8.194 1
3.7%

Interactions

2023-12-10T23:14:27.966915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:27.603692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:28.106201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:27.801821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:14:34.460165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CHNNL_IDCHNNL_NMDCCREAT_DAYKWRDKWRD_FQKWRD_IPCR
CHNNL_ID1.0001.0001.0001.0000.9640.0000.000
CHNNL_NM1.0001.0001.0001.0000.9640.0000.000
DC1.0001.0001.0001.0000.9860.0000.000
CREAT_DAY1.0001.0001.0001.0000.9570.0000.000
KWRD0.9640.9640.9860.9571.0001.0000.674
KWRD_FQ0.0000.0000.0000.0001.0001.0000.783
KWRD_IPCR0.0000.0000.0000.0000.6740.7831.000
2023-12-10T23:14:34.641877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
KWRD_FQKWRD_IPCR
KWRD_FQ1.0000.923
KWRD_IPCR0.9231.000

Missing values

2023-12-10T23:14:28.297375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:14:28.518295image/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.

Sample

CHNNL_IDCHNNL_NMCOLCT_DAYDCCREAT_DAYKWRDKWRD_FQKWRD_IPCR
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CHNNL_IDCHNNL_NMCOLCT_DAYDCCREAT_DAYKWRDKWRD_FQKWRD_IPCR
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