Overview

Dataset statistics

Number of variables9
Number of observations82
Missing cells514
Missing cells (%)69.6%
Duplicate rows10
Duplicate rows (%)12.2%
Total size in memory6.3 KiB
Average record size in memory78.6 B

Variable types

Categorical2
Numeric4
Text2
Unsupported1

Dataset

Description샘플 데이터
AuthorMBN
URLhttps://kdx.kr/data/view/29834

Alerts

Dataset has 10 (12.2%) duplicate rowsDuplicates
vod_seq_no is highly overall correlated with bcast_seq_no and 4 other fieldsHigh correlation
contents is highly overall correlated with bcast_seq_no and 4 other fieldsHigh correlation
bcast_seq_no is highly overall correlated with vod_seq_no and 1 other fieldsHigh correlation
play_sec is highly overall correlated with play_hour and 3 other fieldsHigh correlation
play_hour is highly overall correlated with play_sec and 3 other fieldsHigh correlation
file_size is highly overall correlated with play_sec and 3 other fieldsHigh correlation
bcast_seq_no has 72 (87.8%) missing valuesMissing
play_sec has 72 (87.8%) missing valuesMissing
play_hour has 72 (87.8%) missing valuesMissing
file_size has 72 (87.8%) missing valuesMissing
vod_path has 72 (87.8%) missing valuesMissing
title has 72 (87.8%) missing valuesMissing
Unnamed: 8 has 82 (100.0%) missing valuesMissing
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-11 03:25:00.146463
Analysis finished2024-03-11 03:25:03.361316
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

vod_seq_no
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
<NA>
12 
<출연>
10 
유호정 기자
10 
김형오 기자
10 
윤경호 매일경제 논설위원
6 
Other values (15)
34 

Length

Max length13
Median length7
Mean length6.4634146
Min length4

Unique

Unique10 ?
Unique (%)12.2%

Sample

1st row<NA>
2nd row908493
3rd row김형오 기자
4th row<NA>
5th row<출연>

Common Values

ValueCountFrequency (%)
<NA> 12
14.6%
<출연> 10
12.2%
유호정 기자 10
12.2%
김형오 기자 10
12.2%
윤경호 매일경제 논설위원 6
7.3%
김태일 기자 6
7.3%
김근희 기자 6
7.3%
김근희 기자 4
 
4.9%
김태일 기자 4
 
4.9%
윤영걸 전 매경닷컴 대표 4
 
4.9%
Other values (10) 10
12.2%

Length

2024-03-11T12:25:03.460074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기자 40
27.4%
na 12
 
8.2%
출연 10
 
6.8%
유호정 10
 
6.8%
김형오 10
 
6.8%
김태일 10
 
6.8%
김근희 10
 
6.8%
윤경호 6
 
4.1%
매일경제 6
 
4.1%
논설위원 6
 
4.1%
Other values (14) 26
17.8%

bcast_seq_no
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing72
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean1223543.5
Minimum1223503
Maximum1223602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-03-11T12:25:03.568279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1223503
5-th percentile1223503.4
Q11223505.2
median1223507.5
Q31223599.8
95-th percentile1223601.6
Maximum1223602
Range99
Interquartile range (IQR)94.5

Descriptive statistics

Standard deviation49.083263
Coefficient of variation (CV)4.0115666 × 10-5
Kurtosis-2.2716937
Mean1223543.5
Median Absolute Deviation (MAD)4
Skewness0.48203058
Sum12235435
Variance2409.1667
MonotonicityStrictly increasing
2024-03-11T12:25:03.653168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1223503 1
 
1.2%
1223504 1
 
1.2%
1223505 1
 
1.2%
1223506 1
 
1.2%
1223507 1
 
1.2%
1223508 1
 
1.2%
1223599 1
 
1.2%
1223600 1
 
1.2%
1223601 1
 
1.2%
1223602 1
 
1.2%
(Missing) 72
87.8%
ValueCountFrequency (%)
1223503 1
1.2%
1223504 1
1.2%
1223505 1
1.2%
1223506 1
1.2%
1223507 1
1.2%
1223508 1
1.2%
1223599 1
1.2%
1223600 1
1.2%
1223601 1
1.2%
1223602 1
1.2%
ValueCountFrequency (%)
1223602 1
1.2%
1223601 1
1.2%
1223600 1
1.2%
1223599 1
1.2%
1223508 1
1.2%
1223507 1
1.2%
1223506 1
1.2%
1223505 1
1.2%
1223504 1
1.2%
1223503 1
1.2%

play_sec
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing72
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean653
Minimum231
Maximum1272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-03-11T12:25:03.732483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum231
5-th percentile339
Q1480
median548
Q3747
95-th percentile1180.2
Maximum1272
Range1041
Interquartile range (IQR)267

Descriptive statistics

Standard deviation309.82181
Coefficient of variation (CV)0.47445913
Kurtosis0.67155311
Mean653
Median Absolute Deviation (MAD)85.5
Skewness1.0143015
Sum6530
Variance95989.556
MonotonicityNot monotonic
2024-03-11T12:25:03.817447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1068 1
 
1.2%
573 1
 
1.2%
523 1
 
1.2%
471 1
 
1.2%
492 1
 
1.2%
231 1
 
1.2%
782 1
 
1.2%
1272 1
 
1.2%
476 1
 
1.2%
642 1
 
1.2%
(Missing) 72
87.8%
ValueCountFrequency (%)
231 1
1.2%
471 1
1.2%
476 1
1.2%
492 1
1.2%
523 1
1.2%
573 1
1.2%
642 1
1.2%
782 1
1.2%
1068 1
1.2%
1272 1
1.2%
ValueCountFrequency (%)
1272 1
1.2%
1068 1
1.2%
782 1
1.2%
642 1
1.2%
573 1
1.2%
523 1
1.2%
492 1
1.2%
476 1
1.2%
471 1
1.2%
231 1
1.2%

play_hour
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing72
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean0.18139
Minimum0.0642
Maximum0.3533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-03-11T12:25:03.896482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0642
5-th percentile0.09417
Q10.133325
median0.15225
Q30.207475
95-th percentile0.32783
Maximum0.3533
Range0.2891
Interquartile range (IQR)0.07415

Descriptive statistics

Standard deviation0.086052909
Coefficient of variation (CV)0.47440823
Kurtosis0.67078519
Mean0.18139
Median Absolute Deviation (MAD)0.02375
Skewness1.0144693
Sum1.8139
Variance0.0074051032
MonotonicityNot monotonic
2024-03-11T12:25:03.977170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.2967 1
 
1.2%
0.1592 1
 
1.2%
0.1453 1
 
1.2%
0.1308 1
 
1.2%
0.1367 1
 
1.2%
0.0642 1
 
1.2%
0.2172 1
 
1.2%
0.3533 1
 
1.2%
0.1322 1
 
1.2%
0.1783 1
 
1.2%
(Missing) 72
87.8%
ValueCountFrequency (%)
0.0642 1
1.2%
0.1308 1
1.2%
0.1322 1
1.2%
0.1367 1
1.2%
0.1453 1
1.2%
0.1592 1
1.2%
0.1783 1
1.2%
0.2172 1
1.2%
0.2967 1
1.2%
0.3533 1
1.2%
ValueCountFrequency (%)
0.3533 1
1.2%
0.2967 1
1.2%
0.2172 1
1.2%
0.1783 1
1.2%
0.1592 1
1.2%
0.1453 1
1.2%
0.1367 1
1.2%
0.1322 1
1.2%
0.1308 1
1.2%
0.0642 1
1.2%

file_size
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing72
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean98848692
Minimum35069542
Maximum1.9636833 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-03-11T12:25:04.058152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35069542
5-th percentile51061736
Q172703869
median81872638
Q31.1547306 × 108
95-th percentile1.7972436 × 108
Maximum1.9636833 × 108
Range1.6129879 × 108
Interquartile range (IQR)42769194

Descriptive statistics

Standard deviation47823369
Coefficient of variation (CV)0.48380376
Kurtosis0.77332428
Mean98848692
Median Absolute Deviation (MAD)11910542
Skewness1.0578478
Sum9.8848692 × 108
Variance2.2870747 × 1015
MonotonicityNot monotonic
2024-03-11T12:25:04.143254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
159381718 1
 
1.2%
87852077 1
 
1.2%
75893199 1
 
1.2%
72208907 1
 
1.2%
74188756 1
 
1.2%
35069542 1
 
1.2%
122487806 1
 
1.2%
196368333 1
 
1.2%
70607752 1
 
1.2%
94428835 1
 
1.2%
(Missing) 72
87.8%
ValueCountFrequency (%)
35069542 1
1.2%
70607752 1
1.2%
72208907 1
1.2%
74188756 1
1.2%
75893199 1
1.2%
87852077 1
1.2%
94428835 1
1.2%
122487806 1
1.2%
159381718 1
1.2%
196368333 1
1.2%
ValueCountFrequency (%)
196368333 1
1.2%
159381718 1
1.2%
122487806 1
1.2%
94428835 1
1.2%
87852077 1
1.2%
75893199 1
1.2%
74188756 1
1.2%
72208907 1
1.2%
70607752 1
1.2%
35069542 1
1.2%

vod_path
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing72
Missing (%)87.8%
Memory size788.0 B
2024-03-11T12:25:04.316714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length61
Mean length61
Min length61

Characters and Unicode

Total characters610
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row/mbnvod2/812/2019/09/23/20190923182349_20_812_1223503_360.mp4
2nd row/mbnvod2/812/2019/09/23/20190923182626_20_812_1223504_360.mp4
3rd row/mbnvod2/812/2019/09/23/20190923182856_20_812_1223505_360.mp4
4th row/mbnvod2/812/2019/09/23/20190923183406_20_812_1223506_360.mp4
5th row/mbnvod2/812/2019/09/23/20190923183653_20_812_1223507_360.mp4
ValueCountFrequency (%)
mbnvod2/812/2019/09/23/20190923182349_20_812_1223503_360.mp4 1
10.0%
mbnvod2/812/2019/09/23/20190923182626_20_812_1223504_360.mp4 1
10.0%
mbnvod2/812/2019/09/23/20190923182856_20_812_1223505_360.mp4 1
10.0%
mbnvod2/812/2019/09/23/20190923183406_20_812_1223506_360.mp4 1
10.0%
mbnvod2/812/2019/09/23/20190923183653_20_812_1223507_360.mp4 1
10.0%
mbnvod2/812/2019/09/23/20190923183828_20_812_1223508_360.mp4 1
10.0%
mbnvod2/812/2019/09/24/20190924172850_20_812_1223599_360.mp4 1
10.0%
mbnvod2/812/2019/09/24/20190924173143_20_812_1223600_360.mp4 1
10.0%
mbnvod2/812/2019/09/24/20190924173814_20_812_1223601_360.mp4 1
10.0%
mbnvod2/812/2019/09/24/20190924173958_20_812_1223602_360.mp4 1
10.0%
2024-03-11T12:25:04.598517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 107
17.5%
0 72
11.8%
1 63
10.3%
/ 60
9.8%
9 44
7.2%
3 42
 
6.9%
_ 40
 
6.6%
8 33
 
5.4%
4 23
 
3.8%
m 20
 
3.3%
Other values (10) 106
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 420
68.9%
Lowercase Letter 80
 
13.1%
Other Punctuation 70
 
11.5%
Connector Punctuation 40
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 107
25.5%
0 72
17.1%
1 63
15.0%
9 44
10.5%
3 42
 
10.0%
8 33
 
7.9%
4 23
 
5.5%
6 19
 
4.5%
5 12
 
2.9%
7 5
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
m 20
25.0%
v 10
12.5%
o 10
12.5%
d 10
12.5%
n 10
12.5%
b 10
12.5%
p 10
12.5%
Other Punctuation
ValueCountFrequency (%)
/ 60
85.7%
. 10
 
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 530
86.9%
Latin 80
 
13.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 107
20.2%
0 72
13.6%
1 63
11.9%
/ 60
11.3%
9 44
8.3%
3 42
 
7.9%
_ 40
 
7.5%
8 33
 
6.2%
4 23
 
4.3%
6 19
 
3.6%
Other values (3) 27
 
5.1%
Latin
ValueCountFrequency (%)
m 20
25.0%
v 10
12.5%
o 10
12.5%
d 10
12.5%
n 10
12.5%
b 10
12.5%
p 10
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 107
17.5%
0 72
11.8%
1 63
10.3%
/ 60
9.8%
9 44
7.2%
3 42
 
6.9%
_ 40
 
6.6%
8 33
 
5.4%
4 23
 
3.8%
m 20
 
3.3%
Other values (10) 106
17.4%

title
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing72
Missing (%)87.8%
Memory size788.0 B
2024-03-11T12:25:04.763953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31.5
Mean length30.2
Min length26

Characters and Unicode

Total characters302
Distinct characters86
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row[MBN 프레스룸] 프레스 콕 - 법원이 압수수색 허가, 왜?
2nd row[MBN 프레스룸] 프레스 더 - '취재'와 '침해' 사이
3rd row[MBN 프레스룸] 프레스 더 - 윤석열 향한 가짜 뉴스
4th row[MBN 프레스룸] 프레스 콕 - 고심 깊은 무소의 뿔
5th row[MBN 프레스룸] 프레스 人 - 나경원 저격수 된 홍반장?
ValueCountFrequency (%)
프레스 10
 
12.8%
10
 
12.8%
프레스룸 10
 
12.8%
mbn 6
 
7.7%
콕 6
 
7.7%
더 3
 
3.8%
압수수색 2
 
2.6%
윤석열 2
 
2.6%
文-트럼프 1
 
1.3%
된 1
 
1.3%
Other values (27) 27
34.6%
2024-03-11T12:25:05.036083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
68
22.5%
스 22
 
7.3%
프 21
 
7.0%
레 20
 
6.6%
[ 10
 
3.3%
] 10
 
3.3%
룸 10
 
3.3%
' 10
 
3.3%
- 7
 
2.3%
N 6
 
2.0%
Other values (76) 118
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 167
55.3%
Space Separator 68
22.5%
Other Punctuation 20
 
6.6%
Uppercase Letter 18
 
6.0%
Open Punctuation 10
 
3.3%
Close Punctuation 10
 
3.3%
Dash Punctuation 7
 
2.3%
Decimal Number 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 22
 
13.2%
프 21
 
12.6%
레 20
 
12.0%
룸 10
 
6.0%
콕 6
 
3.6%
수 5
 
3.0%
의 4
 
2.4%
더 3
 
1.8%
장 2
 
1.2%
사 2
 
1.2%
Other values (63) 72
43.1%
Other Punctuation
ValueCountFrequency (%)
' 10
50.0%
/ 4
 
20.0%
? 3
 
15.0%
" 2
 
10.0%
, 1
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
N 6
33.3%
B 6
33.3%
M 6
33.3%
Space Separator
ValueCountFrequency (%)
68
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 10
100.0%
Close Punctuation
ValueCountFrequency (%)
] 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 165
54.6%
Common 117
38.7%
Latin 18
 
6.0%
Han 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 22
 
13.3%
프 21
 
12.7%
레 20
 
12.1%
룸 10
 
6.1%
콕 6
 
3.6%
수 5
 
3.0%
의 4
 
2.4%
더 3
 
1.8%
장 2
 
1.2%
사 2
 
1.2%
Other values (61) 70
42.4%
Common
ValueCountFrequency (%)
68
58.1%
[ 10
 
8.5%
] 10
 
8.5%
' 10
 
8.5%
- 7
 
6.0%
/ 4
 
3.4%
? 3
 
2.6%
" 2
 
1.7%
1 2
 
1.7%
, 1
 
0.9%
Latin
ValueCountFrequency (%)
N 6
33.3%
B 6
33.3%
M 6
33.3%
Han
ValueCountFrequency (%)
文 1
50.0%
人 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 165
54.6%
ASCII 135
44.7%
CJK 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68
50.4%
[ 10
 
7.4%
] 10
 
7.4%
' 10
 
7.4%
- 7
 
5.2%
N 6
 
4.4%
B 6
 
4.4%
M 6
 
4.4%
/ 4
 
3.0%
? 3
 
2.2%
Other values (3) 5
 
3.7%
Hangul
ValueCountFrequency (%)
스 22
 
13.3%
프 21
 
12.7%
레 20
 
12.1%
룸 10
 
6.1%
콕 6
 
3.6%
수 5
 
3.0%
의 4
 
2.4%
더 3
 
1.8%
장 2
 
1.2%
사 2
 
1.2%
Other values (61) 70
42.4%
CJK
ValueCountFrequency (%)
文 1
50.0%
人 1
50.0%

contents
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
<NA>
72 
<진행>
10 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 72
87.8%
<진행> 10
 
12.2%

Length

2024-03-11T12:25:05.150658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-11T12:25:05.223876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 72
87.8%
진행 10
 
12.2%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82
Missing (%)100.0%
Memory size870.0 B

Interactions

2024-03-11T12:25:02.747392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:01.870153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.179409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.449513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.830025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:01.973645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.243205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.518285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.905690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.037740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.304284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.586647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.976987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.108465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.379196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:25:02.662842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-11T12:25:05.277154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitle
vod_seq_no1.0001.0001.0001.0001.0001.0001.000
bcast_seq_no1.0001.0000.0000.0000.0001.0001.000
play_sec1.0000.0001.0001.0001.0001.0001.000
play_hour1.0000.0001.0001.0001.0001.0001.000
file_size1.0000.0001.0001.0001.0001.0001.000
vod_path1.0001.0001.0001.0001.0001.0001.000
title1.0001.0001.0001.0001.0001.0001.000
2024-03-11T12:25:05.356530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
vod_seq_nocontents
vod_seq_no1.0001.000
contents1.0001.000
2024-03-11T12:25:05.671453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
bcast_seq_noplay_secplay_hourfile_sizevod_seq_nocontents
bcast_seq_no1.0000.0060.006-0.0551.0001.000
play_sec0.0061.0001.0000.9881.0001.000
play_hour0.0061.0001.0000.9881.0001.000
file_size-0.0550.9880.9881.0001.0001.000
vod_seq_no1.0001.0001.0001.0001.0001.000
contents1.0001.0001.0001.0001.0001.000

Missing values

2024-03-11T12:25:03.075243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-11T12:25:03.182318image/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.
2024-03-11T12:25:03.282144image/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

vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontentsUnnamed: 8
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1908493122350310680.2967159381718/mbnvod2/812/2019/09/23/20190923182349_20_812_1223503_360.mp4[MBN 프레스룸] 프레스 콕 - 법원이 압수수색 허가, 왜?<진행><NA>
2김형오 기자<NA><NA><NA><NA><NA><NA><NA><NA>
3<NA><NA><NA><NA><NA><NA><NA><NA><NA>
4<출연><NA><NA><NA><NA><NA><NA><NA><NA>
5윤경호 매일경제 논설위원<NA><NA><NA><NA><NA><NA><NA><NA>
6김태일 기자<NA><NA><NA><NA><NA><NA><NA><NA>
7김근희 기자<NA><NA><NA><NA><NA><NA><NA><NA>
8유호정 기자<NA><NA><NA><NA><NA><NA><NA><NA>
990849512235045730.159287852077/mbnvod2/812/2019/09/23/20190923182626_20_812_1223504_360.mp4[MBN 프레스룸] 프레스 더 - '취재'와 '침해' 사이<진행><NA>
vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontentsUnnamed: 8
72유호정 기자<NA><NA><NA><NA><NA><NA><NA><NA>
7390874112236026420.178394428835/mbnvod2/812/2019/09/24/20190924173958_20_812_1223602_360.mp4[프레스룸] 프레스 콕 / 윤석열 '사생결단'?<진행><NA>
74김형오 기자<NA><NA><NA><NA><NA><NA><NA><NA>
75<NA><NA><NA><NA><NA><NA><NA><NA><NA>
76<출연><NA><NA><NA><NA><NA><NA><NA><NA>
77윤영걸 전 매경닷컴 대표<NA><NA><NA><NA><NA><NA><NA><NA>
78김태일 기자<NA><NA><NA><NA><NA><NA><NA><NA>
79김근희 기자<NA><NA><NA><NA><NA><NA><NA><NA>
80유호정 기자<NA><NA><NA><NA><NA><NA><NA><NA>
81<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontents# duplicates
9<NA><NA><NA><NA><NA><NA><NA><NA>12
0<출연><NA><NA><NA><NA><NA><NA><NA>10
5김형오 기자<NA><NA><NA><NA><NA><NA><NA>10
6유호정 기자<NA><NA><NA><NA><NA><NA><NA>10
2김근희 기자<NA><NA><NA><NA><NA><NA><NA>6
4김태일 기자<NA><NA><NA><NA><NA><NA><NA>6
7윤경호 매일경제 논설위원<NA><NA><NA><NA><NA><NA><NA>6
1김근희 기자<NA><NA><NA><NA><NA><NA><NA>4
3김태일 기자<NA><NA><NA><NA><NA><NA><NA>4
8윤영걸 전 매경닷컴 대표<NA><NA><NA><NA><NA><NA><NA>4