Overview

Dataset statistics

Number of variables5
Number of observations429
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.3 KiB
Average record size in memory41.3 B

Variable types

Numeric1
Categorical1
Text2
DateTime1

Dataset

DescriptionKDI에서 발간하는 영상보고서입니다. KDI FOCUS, KDI 정책포럼 보고서를 더 이해하고 보기 쉽게 영상으로 제작해 발간하고 있습니다.
Author한국개발연구원
URLhttps://www.data.go.kr/data/15106261/fileData.do

Alerts

순번 is highly overall correlated with 채널High correlation
채널 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique
영상제목 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:24:20.889975
Analysis finished2023-12-12 15:24:21.435354
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct429
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215
Minimum1
Maximum429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:24:21.525205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.4
Q1108
median215
Q3322
95-th percentile407.6
Maximum429
Range428
Interquartile range (IQR)214

Descriptive statistics

Standard deviation123.98589
Coefficient of variation (CV)0.57667854
Kurtosis-1.2
Mean215
Median Absolute Deviation (MAD)107
Skewness0
Sum92235
Variance15372.5
MonotonicityStrictly increasing
2023-12-13T00:24:21.692088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
296 1
 
0.2%
294 1
 
0.2%
293 1
 
0.2%
292 1
 
0.2%
291 1
 
0.2%
290 1
 
0.2%
289 1
 
0.2%
288 1
 
0.2%
287 1
 
0.2%
Other values (419) 419
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
429 1
0.2%
428 1
0.2%
427 1
0.2%
426 1
0.2%
425 1
0.2%
424 1
0.2%
423 1
0.2%
422 1
0.2%
421 1
0.2%
420 1
0.2%

채널
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
유튜브
377 
네이버TV
52 

Length

Max length5
Median length3
Mean length3.2424242
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row네이버TV
2nd row네이버TV
3rd row네이버TV
4th row네이버TV
5th row네이버TV

Common Values

ValueCountFrequency (%)
유튜브 377
87.9%
네이버TV 52
 
12.1%

Length

2023-12-13T00:24:21.871361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:24:22.005266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유튜브 377
87.9%
네이버tv 52
 
12.1%

영상제목
Text

UNIQUE 

Distinct429
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-13T00:24:22.371271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length174
Median length81
Mean length35.4662
Min length3

Characters and Unicode

Total characters15215
Distinct characters509
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique429 ?
Unique (%)100.0%

Sample

1st row기업집단을 중심으로 한 우리 경제의 자원배분 효율성 하락
2nd row기혼 여성의 근로 지속 여부 및 출산 관련 요인과 정책적 시사점
3rd row법인세율 변화가 기업투자에 미치는 영향
4th row근로시간 단축이 노동생산성에 미치는 영향
5th row그룹 리스크 반영을 위한 금융회사 자기자본 규제 개선 방향
ValueCountFrequency (%)
the 73
 
2.5%
and 52
 
1.8%
of 49
 
1.7%
위한 34
 
1.2%
및 32
 
1.1%
on 28
 
1.0%
in 26
 
0.9%
시사점 26
 
0.9%
implications 20
 
0.7%
경제전망 19
 
0.7%
Other values (1576) 2545
87.6%
2023-12-13T00:24:22.988231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2480
 
16.3%
e 504
 
3.3%
n 493
 
3.2%
o 464
 
3.0%
t 403
 
2.6%
a 400
 
2.6%
i 393
 
2.6%
r 267
 
1.8%
s 256
 
1.7%
l 216
 
1.4%
Other values (499) 9339
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5748
37.8%
Lowercase Letter 4734
31.1%
Space Separator 2480
16.3%
Uppercase Letter 790
 
5.2%
Decimal Number 558
 
3.7%
Open Punctuation 231
 
1.5%
Close Punctuation 231
 
1.5%
Dash Punctuation 210
 
1.4%
Other Punctuation 210
 
1.4%
Final Punctuation 16
 
0.1%
Other values (2) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 212
 
3.7%
제 141
 
2.5%
과 140
 
2.4%
한 126
 
2.2%
정 120
 
2.1%
경 106
 
1.8%
기 105
 
1.8%
국 85
 
1.5%
사 84
 
1.5%
가 83
 
1.4%
Other values (412) 4546
79.1%
Lowercase Letter
ValueCountFrequency (%)
e 504
10.6%
n 493
10.4%
o 464
9.8%
t 403
 
8.5%
a 400
 
8.4%
i 393
 
8.3%
r 267
 
5.6%
s 256
 
5.4%
l 216
 
4.6%
c 204
 
4.3%
Other values (16) 1134
24.0%
Uppercase Letter
ValueCountFrequency (%)
I 103
13.0%
C 70
 
8.9%
E 67
 
8.5%
P 61
 
7.7%
D 60
 
7.6%
S 58
 
7.3%
R 56
 
7.1%
K 45
 
5.7%
T 38
 
4.8%
M 31
 
3.9%
Other values (15) 201
25.4%
Other Punctuation
ValueCountFrequency (%)
: 76
36.2%
, 53
25.2%
? 38
18.1%
. 13
 
6.2%
! 8
 
3.8%
· 7
 
3.3%
& 5
 
2.4%
% 3
 
1.4%
/ 2
 
1.0%
# 2
 
1.0%
Other values (2) 3
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 179
32.1%
2 169
30.3%
3 68
 
12.2%
0 48
 
8.6%
4 30
 
5.4%
9 24
 
4.3%
5 20
 
3.6%
6 10
 
1.8%
8 6
 
1.1%
7 4
 
0.7%
Open Punctuation
ValueCountFrequency (%)
[ 201
87.0%
( 29
 
12.6%
「 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
] 201
87.0%
) 29
 
12.6%
」 1
 
0.4%
Final Punctuation
ValueCountFrequency (%)
’ 15
93.8%
” 1
 
6.2%
Math Symbol
ValueCountFrequency (%)
~ 3
75.0%
↑ 1
 
25.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 2
66.7%
“ 1
33.3%
Space Separator
ValueCountFrequency (%)
2480
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5747
37.8%
Latin 5524
36.3%
Common 3943
25.9%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 212
 
3.7%
제 141
 
2.5%
과 140
 
2.4%
한 126
 
2.2%
정 120
 
2.1%
경 106
 
1.8%
기 105
 
1.8%
국 85
 
1.5%
사 84
 
1.5%
가 83
 
1.4%
Other values (411) 4545
79.1%
Latin
ValueCountFrequency (%)
e 504
 
9.1%
n 493
 
8.9%
o 464
 
8.4%
t 403
 
7.3%
a 400
 
7.2%
i 393
 
7.1%
r 267
 
4.8%
s 256
 
4.6%
l 216
 
3.9%
c 204
 
3.7%
Other values (41) 1924
34.8%
Common
ValueCountFrequency (%)
2480
62.9%
- 210
 
5.3%
[ 201
 
5.1%
] 201
 
5.1%
1 179
 
4.5%
2 169
 
4.3%
: 76
 
1.9%
3 68
 
1.7%
, 53
 
1.3%
0 48
 
1.2%
Other values (26) 258
 
6.5%
Han
ValueCountFrequency (%)
對 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9438
62.0%
Hangul 5747
37.8%
Punctuation 19
 
0.1%
None 9
 
0.1%
Arrows 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2480
26.3%
e 504
 
5.3%
n 493
 
5.2%
o 464
 
4.9%
t 403
 
4.3%
a 400
 
4.2%
i 393
 
4.2%
r 267
 
2.8%
s 256
 
2.7%
l 216
 
2.3%
Other values (69) 3562
37.7%
Hangul
ValueCountFrequency (%)
의 212
 
3.7%
제 141
 
2.5%
과 140
 
2.4%
한 126
 
2.2%
정 120
 
2.1%
경 106
 
1.8%
기 105
 
1.8%
국 85
 
1.5%
사 84
 
1.5%
가 83
 
1.4%
Other values (411) 4545
79.1%
Punctuation
ValueCountFrequency (%)
’ 15
78.9%
‘ 2
 
10.5%
“ 1
 
5.3%
” 1
 
5.3%
None
ValueCountFrequency (%)
· 7
77.8%
「 1
 
11.1%
」 1
 
11.1%
Arrows
ValueCountFrequency (%)
↑ 1
100.0%
CJK
ValueCountFrequency (%)
對 1
100.0%

링크
Text

Distinct399
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-13T00:24:23.309319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length28
Mean length35.5338
Min length28

Characters and Unicode

Total characters15244
Distinct characters69
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)88.6%

Sample

1st rowhttps://tv.naver.com/v/6quSMDFGsko
2nd rowhttps://tv.naver.com/v/DDA_wHFwg9A
3rd rowhttps://tv.naver.com/v/gSxqxFdroxY
4th rowhttps://tv.naver.com/v/ujd1V3XI-G4
5th rowhttps://tv.naver.com/v/3iC0-Q6G6bs
ValueCountFrequency (%)
https://youtu.be/pbpbs-dg7ja 5
 
1.2%
https://youtu.be/fhbjv2foezu 4
 
0.9%
https://youtu.be/7unq6f2wdmi?list=pltq65dzkdssk3mmlmkr8jekraaxa8hbs7 3
 
0.7%
https://youtu.be/jyoq8wd33ve 3
 
0.7%
https://youtu.be/qrbbigalbva 3
 
0.7%
https://youtu.be/ewxlx77sa_u 3
 
0.7%
https://youtu.be/mdbjlkneajk 3
 
0.7%
https://youtu.be/sozyenadyvm 3
 
0.7%
https://youtu.be/r348vqgeopg 2
 
0.5%
https://youtu.be/7ginqaznbtc 2
 
0.5%
Other values (389) 398
92.8%
2023-12-13T00:24:23.785286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1409
 
9.2%
/ 1339
 
8.8%
u 803
 
5.3%
s 723
 
4.7%
o 544
 
3.6%
e 535
 
3.5%
p 517
 
3.4%
h 501
 
3.3%
b 499
 
3.3%
y 484
 
3.2%
Other values (59) 7890
51.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8386
55.0%
Uppercase Letter 3148
 
20.7%
Other Punctuation 2322
 
15.2%
Decimal Number 1127
 
7.4%
Connector Punctuation 107
 
0.7%
Dash Punctuation 81
 
0.5%
Math Symbol 73
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1409
16.8%
u 803
 
9.6%
s 723
 
8.6%
o 544
 
6.5%
e 535
 
6.4%
p 517
 
6.2%
h 501
 
6.0%
b 499
 
6.0%
y 484
 
5.8%
v 236
 
2.8%
Other values (16) 2135
25.5%
Uppercase Letter
ValueCountFrequency (%)
S 215
 
6.8%
L 181
 
5.7%
Q 173
 
5.5%
T 160
 
5.1%
P 158
 
5.0%
M 142
 
4.5%
D 137
 
4.4%
J 131
 
4.2%
W 131
 
4.2%
U 127
 
4.0%
Other values (16) 1593
50.6%
Decimal Number
ValueCountFrequency (%)
6 160
14.2%
5 157
13.9%
8 139
12.3%
7 118
10.5%
2 109
9.7%
4 107
9.5%
0 101
9.0%
9 83
7.4%
3 81
7.2%
1 72
6.4%
Other Punctuation
ValueCountFrequency (%)
/ 1339
57.7%
. 481
 
20.7%
: 429
 
18.5%
? 73
 
3.1%
Connector Punctuation
ValueCountFrequency (%)
_ 107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%
Math Symbol
ValueCountFrequency (%)
= 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11534
75.7%
Common 3710
 
24.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1409
 
12.2%
u 803
 
7.0%
s 723
 
6.3%
o 544
 
4.7%
e 535
 
4.6%
p 517
 
4.5%
h 501
 
4.3%
b 499
 
4.3%
y 484
 
4.2%
v 236
 
2.0%
Other values (42) 5283
45.8%
Common
ValueCountFrequency (%)
/ 1339
36.1%
. 481
 
13.0%
: 429
 
11.6%
6 160
 
4.3%
5 157
 
4.2%
8 139
 
3.7%
7 118
 
3.2%
2 109
 
2.9%
4 107
 
2.9%
_ 107
 
2.9%
Other values (7) 564
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1409
 
9.2%
/ 1339
 
8.8%
u 803
 
5.3%
s 723
 
4.7%
o 544
 
3.6%
e 535
 
3.5%
p 517
 
3.4%
h 501
 
3.3%
b 499
 
3.3%
y 484
 
3.2%
Other values (59) 7890
51.8%

발행일
Date

Distinct147
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2010-02-17 00:00:00
Maximum2022-08-10 00:00:00
2023-12-13T00:24:24.008487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:24.576605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T00:24:21.200187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:24:24.705411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번채널
순번1.0000.991
채널0.9911.000
2023-12-13T00:24:24.831487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번채널
순번1.0000.908
채널0.9081.000

Missing values

2023-12-13T00:24:21.307778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:24:21.393211image/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

순번채널영상제목링크발행일
01네이버TV기업집단을 중심으로 한 우리 경제의 자원배분 효율성 하락https://tv.naver.com/v/6quSMDFGsko2018-04-19
12네이버TV기혼 여성의 근로 지속 여부 및 출산 관련 요인과 정책적 시사점https://tv.naver.com/v/DDA_wHFwg9A2018-02-01
23네이버TV법인세율 변화가 기업투자에 미치는 영향https://tv.naver.com/v/gSxqxFdroxY2016-11-28
34네이버TV근로시간 단축이 노동생산성에 미치는 영향https://tv.naver.com/v/ujd1V3XI-G42017-11-01
45네이버TV그룹 리스크 반영을 위한 금융회사 자기자본 규제 개선 방향https://tv.naver.com/v/3iC0-Q6G6bs2017-08-25
56네이버TV한국 성인역량의 현황과 개선방향: 문제해결 스킬을 중심으로https://tv.naver.com/v/rcRVy1Sb8Kw2017-07-03
67네이버TV비정규직 사용규제가 기업의 고용 결정에 미친 영향https://tv.naver.com/v/aosLOh4l9S42018-11-19
78네이버TV중소기업 적합업종 지정이 포장두부시장에 미친 영향https://tv.naver.com/v/TG0MrABQE-U2015-11-16
89네이버TV거시건전성 관리에 있어 단기성과 중심 정책결정의 위험성: 가계부채에 대한 논의를 중심으로https://tv.naver.com/v/lBLKyBdgM4s2019-02-19
910네이버TVKDI 경제전망, 2016 하반기https://tv.naver.com/v/Gn_-YaKMv2Y2016-12-07
순번채널영상제목링크발행일
419420유튜브[2-1] What are Global Stablecoins(GSCs) and Central Bank Digital Currency(CBDC)?https://youtu.be/VoLqbZDu_2Y2021-09-07
420421유튜브[1-2] 창조경제: 고용 없는 성장의 극복https://youtu.be/FFtoH6OaGmg?list=PLTQ65DzkdsSkOaJhYF5g1CG1XNzpXwXVq2013-07-04
421422유튜브[4-1] 영유아 보육·교육 질 개선, 무엇을 어떻게 할 것인가?https://youtu.be/HSoEbVo8MDw2019-04-03
422423유튜브[1-2] 바이든 정부의 조세재정정책 및 시사점https://youtu.be/y7iXCsJt2sU2021-08-25
423424유튜브[1-4] 일자리 확대와 질적 개선: 최근 경험의 교훈https://youtu.be/Oqmum5flNxI?list=PLTQ65DzkdsSkOaJhYF5g1CG1XNzpXwXVq2013-07-04
424425유튜브발표 1. 중소기업 생산성 향상 제약 및 개선 방안https://youtu.be/BhyC9dM9H6w2020-09-17
425426유튜브[2-1] 바이든 정부의 대외경제정책과 대응과제https://youtu.be/b6xRKLDWmMg2021-08-25
426427유튜브[1-3] 초광역 지역경제와 신지역시대https://youtu.be/mDBjLkneaJk2021-07-06
427428유튜브우리 지역 여건에 맞는 도로가 생기려면?!https://youtu.be/fzpOswWNHo42022-06-02
428429유튜브[5-1] 병원 중심 의료로부터 만성질환 관리를 위한 일차의료로https://youtu.be/mq40GVMBXDQ?list=PLTQ65DzkdsSnRLPNXPF7ceBzyOB_G_Kq52013-02-05