Overview

Dataset statistics

Number of variables22
Number of observations28
Missing cells30
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory197.7 B

Variable types

Text3
DateTime2
Numeric17

Dataset

Description샘플 데이터
Author한양대
URLhttps://bigdata-region.kr/#/dataset/c628ab6a-7e18-4dce-98f9-e9b1ad202563

Alerts

PR_IDEX_COLCT_DE has constant value ""Constant
PR_IDEX_CHNNL_DC has 2 (7.1%) missing valuesMissing
MNTHNG_ACTVTY_EMPLYR_CO has 1 (3.6%) missing valuesMissing
PR_FDQNT_IMPRVMDGREE has 5 (17.9%) missing valuesMissing
FDQNT_IDEX has 5 (17.9%) missing valuesMissing
QL_IDEX has 1 (3.6%) missing valuesMissing
GNRLZ_PR_IDEX has 5 (17.9%) missing valuesMissing
FDQNT_IDEX_STD_SCORE has 5 (17.9%) missing valuesMissing
QL_IDEX_STD_SCORE has 1 (3.6%) missing valuesMissing
GNRLZ_PR_IDEX_STD_SCORE has 5 (17.9%) missing valuesMissing
PR_IDEX_CHNNL_ID has unique valuesUnique
PR_IDEX_CHNNL_NM has unique valuesUnique
CHNNL_CREAT_DAY has unique valuesUnique
OUTPUT_CO has unique valuesUnique
PR_IDEX_READER_CO has unique valuesUnique
TOT_RDCNT has unique valuesUnique
TOT_ILKT_CO has unique valuesUnique
TOT_NLKT_CO has unique valuesUnique
OUTPUT_CO has 1 (3.6%) zerosZeros
PR_IDEX_READER_CO has 1 (3.6%) zerosZeros
TOT_RDCNT has 1 (3.6%) zerosZeros
TOT_ILKT_CO has 1 (3.6%) zerosZeros
TOT_NLKT_CO has 1 (3.6%) zerosZeros
MNTHNG_ACTVTY_EMPLYR_CO has 1 (3.6%) zerosZeros
RECENT_RDCNT has 6 (21.4%) zerosZeros
RECENT_ILKT_CO has 7 (25.0%) zerosZeros
RECENT_NLKT_CO has 8 (28.6%) zerosZeros
CO has 7 (25.0%) zerosZeros
PR_FDQNT_IMPRVMDGREE has 3 (10.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:23:07.069139
Analysis finished2023-12-10 14:23:07.512711
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PR_IDEX_CHNNL_ID
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-10T23:23:07.653207image/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%
2023-12-10T23:23:07.973116image/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%

PR_IDEX_CHNNL_NM
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-10T23:23:08.216608image/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%
2023-12-10T23:23:08.606810image/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%

PR_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
2023-12-10T23:23:08.744955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:09.111835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PR_IDEX_CHNNL_DC
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing2
Missing (%)7.1%
Memory size356.0 B
2023-12-10T23:23:09.309055image/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%
2023-12-10T23:23:09.700794image/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%

CHNNL_CREAT_DAY
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
2023-12-10T23:23:09.838995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:09.970753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

OUTPUT_CO
Real number (ℝ)

UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.85714
Minimum0
Maximum1904
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:10.112300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.4
Q141.75
median166
Q3302.75
95-th percentile982.15
Maximum1904
Range1904
Interquartile range (IQR)261

Descriptive statistics

Standard deviation413.89583
Coefficient of variation (CV)1.4181453
Kurtosis8.1163766
Mean291.85714
Median Absolute Deviation (MAD)133
Skewness2.6605662
Sum8172
Variance171309.76
MonotonicityNot monotonic
2023-12-10T23:23:10.217384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
14 1
 
3.6%
150 1
 
3.6%
142 1
 
3.6%
27 1
 
3.6%
99 1
 
3.6%
221 1
 
3.6%
218 1
 
3.6%
748 1
 
3.6%
563 1
 
3.6%
44 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
14 1
3.6%
18 1
3.6%
27 1
3.6%
29 1
3.6%
31 1
3.6%
35 1
3.6%
44 1
3.6%
52 1
3.6%
95 1
3.6%
ValueCountFrequency (%)
1904 1
3.6%
1021 1
3.6%
910 1
3.6%
748 1
3.6%
563 1
3.6%
338 1
3.6%
308 1
3.6%
301 1
3.6%
221 1
3.6%
218 1
3.6%

PR_IDEX_READER_CO
Real number (ℝ)

UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228145.71
Minimum0
Maximum1750000
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:10.347167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125.1
Q11842.5
median57900
Q3265250
95-th percentile1056250
Maximum1750000
Range1750000
Interquartile range (IQR)263407.5

Descriptive statistics

Standard deviation413216.49
Coefficient of variation (CV)1.8111955
Kurtosis7.4298876
Mean228145.71
Median Absolute Deviation (MAD)57126
Skewness2.6757602
Sum6388080
Variance1.7074787 × 1011
MonotonicityNot monotonic
2023-12-10T23:23:10.468938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
18 1
 
3.6%
81400 1
 
3.6%
6960 1
 
3.6%
65600 1
 
3.6%
14000 1
 
3.6%
63500 1
 
3.6%
119000 1
 
3.6%
1750000 1
 
3.6%
585000 1
 
3.6%
1090 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
18 1
3.6%
324 1
3.6%
458 1
3.6%
1090 1
3.6%
1470 1
3.6%
1490 1
3.6%
1960 1
3.6%
3310 1
3.6%
6960 1
3.6%
ValueCountFrequency (%)
1750000 1
3.6%
1310000 1
3.6%
585000 1
3.6%
566000 1
3.6%
456000 1
3.6%
414000 1
3.6%
362000 1
3.6%
233000 1
3.6%
133000 1
3.6%
119000 1
3.6%

TOT_RDCNT
Real number (ℝ)

UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50703292
Minimum0
Maximum4.6151111 × 108
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:10.594935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6338.75
Q1142371
median4731380.5
Q324964812
95-th percentile2.7794061 × 108
Maximum4.6151111 × 108
Range4.6151111 × 108
Interquartile range (IQR)24822440

Descriptive statistics

Standard deviation1.108419 × 108
Coefficient of variation (CV)2.1860889
Kurtosis7.2189695
Mean50703292
Median Absolute Deviation (MAD)4710836
Skewness2.7116613
Sum1.4196922 × 109
Variance1.2285928 × 1016
MonotonicityNot monotonic
2023-12-10T23:23:10.721114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1108 1
 
3.6%
12882426 1
 
3.6%
584886 1
 
3.6%
1201116 1
 
3.6%
2799228 1
 
3.6%
5860555 1
 
3.6%
23155464 1
 
3.6%
461511114 1
 
3.6%
194797938 1
 
3.6%
56727 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
1108 1
3.6%
16053 1
3.6%
25036 1
3.6%
56727 1
3.6%
94925 1
3.6%
100110 1
3.6%
156458 1
3.6%
252529 1
3.6%
584886 1
3.6%
ValueCountFrequency (%)
461511114 1
3.6%
315003678 1
3.6%
209109184 1
3.6%
194797938 1
3.6%
79332000 1
3.6%
36910051 1
3.6%
30392854 1
3.6%
23155464 1
3.6%
16604616 1
3.6%
12882426 1
3.6%

TOT_ILKT_CO
Real number (ℝ)

UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean776989.07
Minimum0
Maximum7859787
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:10.855866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile292
Q11295.75
median26671.5
Q3328845.75
95-th percentile4912569.8
Maximum7859787
Range7859787
Interquartile range (IQR)327550

Descriptive statistics

Standard deviation1880371.2
Coefficient of variation (CV)2.4200743
Kurtosis9.6905591
Mean776989.07
Median Absolute Deviation (MAD)25906
Skewness3.1685021
Sum21755694
Variance3.535796 × 1012
MonotonicityNot monotonic
2023-12-10T23:23:10.998363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
61 1
 
3.6%
278304 1
 
3.6%
10804 1
 
3.6%
33491 1
 
3.6%
26179 1
 
3.6%
46235 1
 
3.6%
284119 1
 
3.6%
7859787 1
 
3.6%
2146877 1
 
3.6%
1298 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
61 1
3.6%
721 1
3.6%
810 1
3.6%
837 1
3.6%
875 1
3.6%
1289 1
3.6%
1298 1
3.6%
2468 1
3.6%
10804 1
3.6%
ValueCountFrequency (%)
7859787 1
3.6%
6401789 1
3.6%
2146877 1
3.6%
1538540 1
3.6%
1081813 1
3.6%
976393 1
3.6%
451026 1
3.6%
288119 1
3.6%
284119 1
3.6%
278304 1
3.6%

TOT_NLKT_CO
Real number (ℝ)

UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25524.679
Minimum0
Maximum326163
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:11.139680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.2
Q139.75
median1905
Q322374.25
95-th percentile84841.3
Maximum326163
Range326163
Interquartile range (IQR)22334.5

Descriptive statistics

Standard deviation64040.911
Coefficient of variation (CV)2.5089801
Kurtosis19.195105
Mean25524.679
Median Absolute Deviation (MAD)1889
Skewness4.151249
Sum714691
Variance4.1012382 × 109
MonotonicityNot monotonic
2023-12-10T23:23:11.252256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
4435 1
 
3.6%
352 1
 
3.6%
394 1
 
3.6%
3078 1
 
3.6%
1871 1
 
3.6%
29881 1
 
3.6%
326163 1
 
3.6%
81382 1
 
3.6%
23 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
1 1
3.6%
13 1
3.6%
19 1
3.6%
23 1
3.6%
24 1
3.6%
36 1
3.6%
41 1
3.6%
94 1
3.6%
186 1
3.6%
ValueCountFrequency (%)
326163 1
3.6%
86704 1
3.6%
81382 1
3.6%
57958 1
3.6%
47928 1
3.6%
37315 1
3.6%
29881 1
3.6%
19872 1
3.6%
6676 1
3.6%
5505 1
3.6%

MNTHNG_ACTVTY_EMPLYR_CO
Real number (ℝ)

MISSING  ZEROS 

Distinct27
Distinct (%)100.0%
Missing1
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean178.66515
Minimum0
Maximum1313.391
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:11.367940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.427
Q156.5
median92.299
Q3200.967
95-th percentile374.5693
Maximum1313.391
Range1313.391
Interquartile range (IQR)144.467

Descriptive statistics

Standard deviation250.95846
Coefficient of variation (CV)1.4046302
Kurtosis16.973621
Mean178.66515
Median Absolute Deviation (MAD)65.967
Skewness3.8067759
Sum4823.959
Variance62980.149
MonotonicityNot monotonic
2023-12-10T23:23:11.497401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
61.667 1
 
3.6%
58.251 1
 
3.6%
84.106 1
 
3.6%
18.31 1
 
3.6%
199.904 1
 
3.6%
92.299 1
 
3.6%
194.781 1
 
3.6%
263.791 1
 
3.6%
333.006 1
 
3.6%
52.074 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
0.0 1
3.6%
15.62 1
3.6%
18.31 1
3.6%
36.418 1
3.6%
49.552 1
3.6%
52.074 1
3.6%
54.749 1
3.6%
58.251 1
3.6%
61.667 1
3.6%
63.781 1
3.6%
ValueCountFrequency (%)
1313.391 1
3.6%
376.741 1
3.6%
369.502 1
3.6%
333.006 1
3.6%
263.791 1
3.6%
240.486 1
3.6%
202.03 1
3.6%
199.904 1
3.6%
194.781 1
3.6%
191.651 1
3.6%

RECENT_RDCNT
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1648687.3
Minimum0
Maximum13102051
Zeros6
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:11.644100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1429.25
median27673
Q31056277.5
95-th percentile9333136.4
Maximum13102051
Range13102051
Interquartile range (IQR)1055848.2

Descriptive statistics

Standard deviation3362540.8
Coefficient of variation (CV)2.0395261
Kurtosis5.358758
Mean1648687.3
Median Absolute Deviation (MAD)27673
Skewness2.4393172
Sum46163245
Variance1.130668 × 1013
MonotonicityNot monotonic
2023-12-10T23:23:11.779424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 6
21.4%
253 1
 
3.6%
280432 1
 
3.6%
13988 1
 
3.6%
31157 1
 
3.6%
595240 1
 
3.6%
1185315 1
 
3.6%
3447403 1
 
3.6%
488 1
 
3.6%
18614 1
 
3.6%
Other values (13) 13
46.4%
ValueCountFrequency (%)
0 6
21.4%
253 1
 
3.6%
488 1
 
3.6%
5501 1
 
3.6%
5722 1
 
3.6%
9562 1
 
3.6%
13988 1
 
3.6%
18614 1
 
3.6%
24189 1
 
3.6%
31157 1
 
3.6%
ValueCountFrequency (%)
13102051 1
3.6%
9519880 1
3.6%
8986327 1
3.6%
3784903 1
3.6%
3447403 1
3.6%
2884146 1
3.6%
1185315 1
3.6%
1013265 1
3.6%
937227 1
3.6%
595240 1
3.6%

RECENT_ILKT_CO
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34471.25
Minimum0
Maximum262148
Zeros7
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:11.920927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.75
median469
Q328557.25
95-th percentile210219.65
Maximum262148
Range262148
Interquartile range (IQR)28547.5

Descriptive statistics

Standard deviation70649.483
Coefficient of variation (CV)2.049519
Kurtosis5.884654
Mean34471.25
Median Absolute Deviation (MAD)469
Skewness2.5232338
Sum965195
Variance4.9913495 × 109
MonotonicityNot monotonic
2023-12-10T23:23:12.036701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 7
25.0%
13 1
 
3.6%
5216 1
 
3.6%
212 1
 
3.6%
976 1
 
3.6%
10743 1
 
3.6%
58048 1
 
3.6%
67872 1
 
3.6%
242 1
 
3.6%
595 1
 
3.6%
Other values (12) 12
42.9%
ValueCountFrequency (%)
0 7
25.0%
13 1
 
3.6%
212 1
 
3.6%
242 1
 
3.6%
246 1
 
3.6%
339 1
 
3.6%
340 1
 
3.6%
343 1
 
3.6%
595 1
 
3.6%
683 1
 
3.6%
ValueCountFrequency (%)
262148 1
3.6%
249861 1
3.6%
136600 1
3.6%
84321 1
3.6%
67872 1
3.6%
58048 1
3.6%
46918 1
3.6%
22437 1
3.6%
17042 1
3.6%
10743 1
3.6%

RECENT_NLKT_CO
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1349.2143
Minimum0
Maximum16892
Zeros8
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:12.131305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.5
Q3625.5
95-th percentile5931.2
Maximum16892
Range16892
Interquartile range (IQR)625.5

Descriptive statistics

Standard deviation3452.736
Coefficient of variation (CV)2.5590717
Kurtosis16.028481
Mean1349.2143
Median Absolute Deviation (MAD)11.5
Skewness3.8026484
Sum37778
Variance11921386
MonotonicityNot monotonic
2023-12-10T23:23:12.237213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
28.6%
9 3
 
10.7%
106 1
 
3.6%
28 1
 
3.6%
726 1
 
3.6%
592 1
 
3.6%
1692 1
 
3.6%
250 1
 
3.6%
3755 1
 
3.6%
83 1
 
3.6%
Other values (9) 9
32.1%
ValueCountFrequency (%)
0 8
28.6%
2 1
 
3.6%
7 1
 
3.6%
8 1
 
3.6%
9 3
 
10.7%
14 1
 
3.6%
28 1
 
3.6%
83 1
 
3.6%
106 1
 
3.6%
250 1
 
3.6%
ValueCountFrequency (%)
16892 1
3.6%
7103 1
3.6%
3755 1
3.6%
3298 1
3.6%
2853 1
3.6%
1692 1
3.6%
726 1
3.6%
592 1
3.6%
342 1
3.6%
250 1
3.6%

CO
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3809.2857
Minimum0
Maximum24482
Zeros7
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:12.350070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median132
Q35542
95-th percentile19026.7
Maximum24482
Range24482
Interquartile range (IQR)5541.25

Descriptive statistics

Standard deviation6699.5673
Coefficient of variation (CV)1.7587463
Kurtosis3.338143
Mean3809.2857
Median Absolute Deviation (MAD)132
Skewness1.9967107
Sum106660
Variance44884202
MonotonicityNot monotonic
2023-12-10T23:23:12.470368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 7
25.0%
21 2
 
7.1%
132 2
 
7.1%
1 1
 
3.6%
24482 1
 
3.6%
117 1
 
3.6%
5533 1
 
3.6%
9398 1
 
3.6%
4192 1
 
3.6%
180 1
 
3.6%
Other values (10) 10
35.7%
ValueCountFrequency (%)
0 7
25.0%
1 1
 
3.6%
18 1
 
3.6%
21 2
 
7.1%
55 1
 
3.6%
117 1
 
3.6%
132 2
 
7.1%
180 1
 
3.6%
419 1
 
3.6%
747 1
 
3.6%
ValueCountFrequency (%)
24482 1
3.6%
20559 1
3.6%
16181 1
3.6%
10892 1
3.6%
9398 1
3.6%
6766 1
3.6%
5569 1
3.6%
5533 1
3.6%
4192 1
3.6%
1245 1
3.6%

PR_FDQNT_IMPRVMDGREE
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)91.3%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.91913043
Minimum0
Maximum4.4
Zeros3
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:12.598381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4065
median0.842
Q31.036
95-th percentile2.1774
Maximum4.4
Range4.4
Interquartile range (IQR)0.6295

Descriptive statistics

Standard deviation0.93592115
Coefficient of variation (CV)1.018268
Kurtosis8.4880194
Mean0.91913043
Median Absolute Deviation (MAD)0.241
Skewness2.5014203
Sum21.14
Variance0.87594839
MonotonicityNot monotonic
2023-12-10T23:23:12.718873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 3
 
10.7%
0.876 1
 
3.6%
0.5 1
 
3.6%
1.083 1
 
3.6%
4.4 1
 
3.6%
0.026 1
 
3.6%
0.633 1
 
3.6%
0.313 1
 
3.6%
0.2 1
 
3.6%
0.932 1
 
3.6%
Other values (11) 11
39.3%
(Missing) 5
17.9%
ValueCountFrequency (%)
0.0 3
10.7%
0.026 1
 
3.6%
0.2 1
 
3.6%
0.313 1
 
3.6%
0.5 1
 
3.6%
0.633 1
 
3.6%
0.645 1
 
3.6%
0.706 1
 
3.6%
0.793 1
 
3.6%
0.842 1
 
3.6%
ValueCountFrequency (%)
4.4 1
3.6%
2.25 1
3.6%
1.524 1
3.6%
1.5 1
3.6%
1.083 1
3.6%
1.072 1
3.6%
1.0 1
3.6%
0.97 1
3.6%
0.932 1
3.6%
0.876 1
3.6%

FDQNT_IDEX
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)100.0%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.71656522
Minimum0.003
Maximum2.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:12.832624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.0111
Q10.2945
median0.658
Q30.88
95-th percentile1.5445
Maximum2.958
Range2.955
Interquartile range (IQR)0.5855

Descriptive statistics

Standard deviation0.65203638
Coefficient of variation (CV)0.90994701
Kurtosis5.5544618
Mean0.71656522
Median Absolute Deviation (MAD)0.292
Skewness1.9361638
Sum16.481
Variance0.42515144
MonotonicityNot monotonic
2023-12-10T23:23:12.957058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.658 1
 
3.6%
0.366 1
 
3.6%
0.01 1
 
3.6%
0.766 1
 
3.6%
2.958 1
 
3.6%
0.093 1
 
3.6%
0.522 1
 
3.6%
0.223 1
 
3.6%
0.021 1
 
3.6%
0.138 1
 
3.6%
Other values (13) 13
46.4%
(Missing) 5
 
17.9%
ValueCountFrequency (%)
0.003 1
3.6%
0.01 1
3.6%
0.021 1
3.6%
0.093 1
3.6%
0.138 1
3.6%
0.223 1
3.6%
0.366 1
3.6%
0.497 1
3.6%
0.522 1
3.6%
0.579 1
3.6%
ValueCountFrequency (%)
2.958 1
3.6%
1.546 1
3.6%
1.531 1
3.6%
1.094 1
3.6%
0.99 1
3.6%
0.898 1
3.6%
0.862 1
3.6%
0.766 1
3.6%
0.743 1
3.6%
0.699 1
3.6%

QL_IDEX
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)96.3%
Missing1
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean0.3167037
Minimum0.017
Maximum1.378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:13.082373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.0429
Q10.0655
median0.141
Q30.346
95-th percentile1.2893
Maximum1.378
Range1.361
Interquartile range (IQR)0.2805

Descriptive statistics

Standard deviation0.39288145
Coefficient of variation (CV)1.2405332
Kurtosis2.6573354
Mean0.3167037
Median Absolute Deviation (MAD)0.084
Skewness1.8757605
Sum8.551
Variance0.15435583
MonotonicityNot monotonic
2023-12-10T23:23:13.196285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.119 2
 
7.1%
0.064 1
 
3.6%
0.208 1
 
3.6%
0.091 1
 
3.6%
0.039 1
 
3.6%
0.215 1
 
3.6%
0.261 1
 
3.6%
1.373 1
 
3.6%
0.7 1
 
3.6%
0.055 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
0.017 1
3.6%
0.039 1
3.6%
0.052 1
3.6%
0.055 1
3.6%
0.057 1
3.6%
0.063 1
3.6%
0.064 1
3.6%
0.067 1
3.6%
0.073 1
3.6%
0.091 1
3.6%
ValueCountFrequency (%)
1.378 1
3.6%
1.373 1
3.6%
1.094 1
3.6%
0.7 1
3.6%
0.669 1
3.6%
0.46 1
3.6%
0.404 1
3.6%
0.288 1
3.6%
0.261 1
3.6%
0.223 1
3.6%

GNRLZ_PR_IDEX
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)95.7%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.46056522
Minimum0.038
Maximum1.043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:13.303216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.038
5-th percentile0.1092
Q10.261
median0.349
Q30.6865
95-th percentile1.0002
Maximum1.043
Range1.005
Interquartile range (IQR)0.4255

Descriptive statistics

Standard deviation0.30979698
Coefficient of variation (CV)0.67264518
Kurtosis-0.68147193
Mean0.46056522
Median Absolute Deviation (MAD)0.178
Skewness0.74945307
Sum10.593
Variance0.095974166
MonotonicityNot monotonic
2023-12-10T23:23:13.425635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.171 2
 
7.1%
0.419 1
 
3.6%
0.156 1
 
3.6%
0.307 1
 
3.6%
1.043 1
 
3.6%
1.002 1
 
3.6%
0.648 1
 
3.6%
0.104 1
 
3.6%
0.984 1
 
3.6%
0.725 1
 
3.6%
Other values (12) 12
42.9%
(Missing) 5
17.9%
ValueCountFrequency (%)
0.038 1
3.6%
0.104 1
3.6%
0.156 1
3.6%
0.171 2
7.1%
0.243 1
3.6%
0.279 1
3.6%
0.299 1
3.6%
0.307 1
3.6%
0.316 1
3.6%
0.333 1
3.6%
ValueCountFrequency (%)
1.043 1
3.6%
1.002 1
3.6%
0.984 1
3.6%
0.955 1
3.6%
0.775 1
3.6%
0.725 1
3.6%
0.648 1
3.6%
0.456 1
3.6%
0.455 1
3.6%
0.419 1
3.6%

FDQNT_IDEX_STD_SCORE
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)100.0%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.71656522
Minimum0.003
Maximum2.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:13.533199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.0111
Q10.2945
median0.658
Q30.88
95-th percentile1.5445
Maximum2.958
Range2.955
Interquartile range (IQR)0.5855

Descriptive statistics

Standard deviation0.65203638
Coefficient of variation (CV)0.90994701
Kurtosis5.5544618
Mean0.71656522
Median Absolute Deviation (MAD)0.292
Skewness1.9361638
Sum16.481
Variance0.42515144
MonotonicityNot monotonic
2023-12-10T23:23:13.627761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.658 1
 
3.6%
0.366 1
 
3.6%
0.01 1
 
3.6%
0.766 1
 
3.6%
2.958 1
 
3.6%
0.093 1
 
3.6%
0.522 1
 
3.6%
0.223 1
 
3.6%
0.021 1
 
3.6%
0.138 1
 
3.6%
Other values (13) 13
46.4%
(Missing) 5
 
17.9%
ValueCountFrequency (%)
0.003 1
3.6%
0.01 1
3.6%
0.021 1
3.6%
0.093 1
3.6%
0.138 1
3.6%
0.223 1
3.6%
0.366 1
3.6%
0.497 1
3.6%
0.522 1
3.6%
0.579 1
3.6%
ValueCountFrequency (%)
2.958 1
3.6%
1.546 1
3.6%
1.531 1
3.6%
1.094 1
3.6%
0.99 1
3.6%
0.898 1
3.6%
0.862 1
3.6%
0.766 1
3.6%
0.743 1
3.6%
0.699 1
3.6%

QL_IDEX_STD_SCORE
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)96.3%
Missing1
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean0.3167037
Minimum0.017
Maximum1.378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:13.728830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.0429
Q10.0655
median0.141
Q30.346
95-th percentile1.2893
Maximum1.378
Range1.361
Interquartile range (IQR)0.2805

Descriptive statistics

Standard deviation0.39288145
Coefficient of variation (CV)1.2405332
Kurtosis2.6573354
Mean0.3167037
Median Absolute Deviation (MAD)0.084
Skewness1.8757605
Sum8.551
Variance0.15435583
MonotonicityNot monotonic
2023-12-10T23:23:13.829571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.119 2
 
7.1%
0.064 1
 
3.6%
0.208 1
 
3.6%
0.091 1
 
3.6%
0.039 1
 
3.6%
0.215 1
 
3.6%
0.261 1
 
3.6%
1.373 1
 
3.6%
0.7 1
 
3.6%
0.055 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
0.017 1
3.6%
0.039 1
3.6%
0.052 1
3.6%
0.055 1
3.6%
0.057 1
3.6%
0.063 1
3.6%
0.064 1
3.6%
0.067 1
3.6%
0.073 1
3.6%
0.091 1
3.6%
ValueCountFrequency (%)
1.378 1
3.6%
1.373 1
3.6%
1.094 1
3.6%
0.7 1
3.6%
0.669 1
3.6%
0.46 1
3.6%
0.404 1
3.6%
0.288 1
3.6%
0.261 1
3.6%
0.223 1
3.6%

GNRLZ_PR_IDEX_STD_SCORE
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)95.7%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.46056522
Minimum0.038
Maximum1.043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T23:23:13.929608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.038
5-th percentile0.1092
Q10.261
median0.349
Q30.6865
95-th percentile1.0002
Maximum1.043
Range1.005
Interquartile range (IQR)0.4255

Descriptive statistics

Standard deviation0.30979698
Coefficient of variation (CV)0.67264518
Kurtosis-0.68147193
Mean0.46056522
Median Absolute Deviation (MAD)0.178
Skewness0.74945307
Sum10.593
Variance0.095974166
MonotonicityNot monotonic
2023-12-10T23:23:14.032650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.171 2
 
7.1%
0.419 1
 
3.6%
0.156 1
 
3.6%
0.307 1
 
3.6%
1.043 1
 
3.6%
1.002 1
 
3.6%
0.648 1
 
3.6%
0.104 1
 
3.6%
0.984 1
 
3.6%
0.725 1
 
3.6%
Other values (12) 12
42.9%
(Missing) 5
17.9%
ValueCountFrequency (%)
0.038 1
3.6%
0.104 1
3.6%
0.156 1
3.6%
0.171 2
7.1%
0.243 1
3.6%
0.279 1
3.6%
0.299 1
3.6%
0.307 1
3.6%
0.316 1
3.6%
0.333 1
3.6%
ValueCountFrequency (%)
1.043 1
3.6%
1.002 1
3.6%
0.984 1
3.6%
0.955 1
3.6%
0.775 1
3.6%
0.725 1
3.6%
0.648 1
3.6%
0.456 1
3.6%
0.455 1
3.6%
0.419 1
3.6%

Sample

PR_IDEX_CHNNL_IDPR_IDEX_CHNNL_NMPR_IDEX_COLCT_DEPR_IDEX_CHNNL_DCCHNNL_CREAT_DAYOUTPUT_COPR_IDEX_READER_COTOT_RDCNTTOT_ILKT_COTOT_NLKT_COMNTHNG_ACTVTY_EMPLYR_CORECENT_RDCNTRECENT_ILKT_CORECENT_NLKT_COCOPR_FDQNT_IMPRVMDGREEFDQNT_IDEXQL_IDEXGNRLZ_PR_IDEXFDQNT_IDEX_STD_SCOREQL_IDEX_STD_SCOREGNRLZ_PR_IDEX_STD_SCORE
0UC2iVamQdGdLAWJR6ui8zowg청도군2020-09-01<NA>2018-11-291418110861161.66725313011.50.990.0640.3330.990.0640.333
1UCmiWMl8Qu6rZDA4D8dSWxFAJena Lee 제나리2020-09-01안녕하세요. 18살 이제나에요. 인스타그램 @jenaxlee 이메일 lsj337js@hanmail.net2018-11-011837100216080419094193958.251610246831061321.00.6580.0730.2430.6580.0730.243
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 가능합니다2019-12-0329324160538102449.55200000.00.0030.0520.0380.0030.0520.038
3UCaijie_uqaAQQCt_fCZ-5qQ통영시Tongyeong2020-09-01통영시에서 운영하는 공식 유튜브 채널입니다. 통영관광; 통영소식 통영과 관련된 모든 소식을 알려드려요. 언제든지 놀러오세요. 환영합니다!2019-09-259514909492524683663.78195623397211.5241.0940.0670.3651.0940.0670.365
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.2018-10-301904980003691005145102637315376.7412884146469182853108921.0721.5460.460.7751.5460.460.775
5UCNcE1102l7TS7k64SonCJGQ시바x노리NORI2020-09-01지랄견보다 더한 시바견 노리의 하루2017-01-18350100110837186<NA>0000<NA><NA><NA><NA><NA><NA><NA>
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♥2018-08-0821013300010936568247330550582.2839372272243734255690.970.7430.1410.3160.7430.1410.316
7UCN8CPzwkYiDVLZlgD4JQgJQ박막례 할머니 Korea Grandma2020-09-0173세 박막례 할머니의 무한도전 인생은 아름다워! 할머니와 즐거운 하루 하루를 보내고; 기록 합니다. 이 채널의 방향은 할머니의 행복 입니다.2017-01-303381310000315003678640178986704240.48689863272498617103161810.7930.6151.0940.9550.6151.0940.955
8UCxSBWvnORMtCtJNbK6pL4YQsusiemeoww2020-09-01Kpop Dance K-Beauty Daily Vlogs2014-11-112013620003039285415385401987283.9763784903262148329867660.6450.4970.2880.3490.4970.2880.349
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PR_IDEX_CHNNL_IDPR_IDEX_CHNNL_NMPR_IDEX_COLCT_DEPR_IDEX_CHNNL_DCCHNNL_CREAT_DAYOUTPUT_COPR_IDEX_READER_COTOT_RDCNTTOT_ILKT_COTOT_NLKT_COMNTHNG_ACTVTY_EMPLYR_CORECENT_RDCNTRECENT_ILKT_CORECENT_NLKT_COCOPR_FDQNT_IMPRVMDGREEFDQNT_IDEXQL_IDEXGNRLZ_PR_IDEXFDQNT_IDEX_STD_SCOREQL_IDEX_STD_SCOREGNRLZ_PR_IDEX_STD_SCORE
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