Overview

Dataset statistics

Number of variables9
Number of observations37
Missing cells232
Missing cells (%)69.7%
Duplicate rows1
Duplicate rows (%)2.7%
Total size in memory2.9 KiB
Average record size in memory80.6 B

Variable types

Text4
Numeric4
Unsupported1

Dataset

Description샘플 데이터
AuthorMBN
URLhttps://kdx.kr/data/view/29827

Alerts

Dataset has 1 (2.7%) duplicate rowsDuplicates
bcast_seq_no is highly overall correlated with file_sizeHigh correlation
play_sec is highly overall correlated with play_hour and 1 other fieldsHigh correlation
play_hour is highly overall correlated with play_sec and 1 other fieldsHigh correlation
file_size is highly overall correlated with bcast_seq_no and 2 other fieldsHigh correlation
vod_seq_no has 6 (16.2%) missing valuesMissing
bcast_seq_no has 27 (73.0%) missing valuesMissing
play_sec has 27 (73.0%) missing valuesMissing
play_hour has 27 (73.0%) missing valuesMissing
file_size has 27 (73.0%) missing valuesMissing
vod_path has 27 (73.0%) missing valuesMissing
title has 27 (73.0%) missing valuesMissing
contents has 27 (73.0%) missing valuesMissing
Unnamed: 8 has 37 (100.0%) missing valuesMissing
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-11 03:26:17.872567
Analysis finished2024-03-11 03:26:21.267286
Duration3.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

vod_seq_no
Text

MISSING 

Distinct31
Distinct (%)100.0%
Missing6
Missing (%)16.2%
Memory size428.0 B
2024-03-11T12:26:21.463242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length51
Mean length27.064516
Min length6

Characters and Unicode

Total characters839
Distinct characters243
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row444964
2nd row남쪽에서 부러오는 따뜻한 훈풍으로 여름이 가장 먼저 도착하는 곳.
3rd row 남도의 유월은 꽃들이 남발하고 밤새 고기 잡으러 나간 어부들이 돌아오고 잠들었던 물도 깨어나는 아름다운 남도에 뽀빵이 찾아가는데...
4th row445899
5th row천혜의 생태가 보존되어있는 곳.
ValueCountFrequency (%)
곳 4
 
2.0%
수 3
 
1.5%
있는 3
 
1.5%
국내 2
 
1.0%
뽀빠이 2
 
1.0%
순박한 2
 
1.0%
넉넉한 2
 
1.0%
대표 2
 
1.0%
백가지 2
 
1.0%
한폭의 2
 
1.0%
Other values (176) 179
88.2%
2024-03-11T12:26:21.867228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179
 
21.3%
이 22
 
2.6%
4 19
 
2.3%
는 19
 
2.3%
. 14
 
1.7%
의 14
 
1.7%
한 13
 
1.5%
을 13
 
1.5%
지 12
 
1.4%
은 12
 
1.4%
Other values (233) 522
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 564
67.2%
Space Separator 179
 
21.3%
Decimal Number 62
 
7.4%
Other Punctuation 32
 
3.8%
Final Punctuation 1
 
0.1%
Initial Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 22
 
3.9%
는 19
 
3.4%
의 14
 
2.5%
한 13
 
2.3%
을 13
 
2.3%
지 12
 
2.1%
은 12
 
2.1%
도 10
 
1.8%
아 9
 
1.6%
가 9
 
1.6%
Other values (216) 431
76.4%
Decimal Number
ValueCountFrequency (%)
4 19
30.6%
9 10
16.1%
5 5
 
8.1%
6 5
 
8.1%
3 5
 
8.1%
8 5
 
8.1%
2 4
 
6.5%
0 4
 
6.5%
7 3
 
4.8%
1 2
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 14
43.8%
! 9
28.1%
, 8
25.0%
? 1
 
3.1%
Space Separator
ValueCountFrequency (%)
179
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 564
67.2%
Common 275
32.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 22
 
3.9%
는 19
 
3.4%
의 14
 
2.5%
한 13
 
2.3%
을 13
 
2.3%
지 12
 
2.1%
은 12
 
2.1%
도 10
 
1.8%
아 9
 
1.6%
가 9
 
1.6%
Other values (216) 431
76.4%
Common
ValueCountFrequency (%)
179
65.1%
4 19
 
6.9%
. 14
 
5.1%
9 10
 
3.6%
! 9
 
3.3%
, 8
 
2.9%
5 5
 
1.8%
6 5
 
1.8%
3 5
 
1.8%
8 5
 
1.8%
Other values (7) 16
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 564
67.2%
ASCII 273
32.5%
Punctuation 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179
65.6%
4 19
 
7.0%
. 14
 
5.1%
9 10
 
3.7%
! 9
 
3.3%
, 8
 
2.9%
5 5
 
1.8%
6 5
 
1.8%
3 5
 
1.8%
8 5
 
1.8%
Other values (5) 14
 
5.1%
Hangul
ValueCountFrequency (%)
이 22
 
3.9%
는 19
 
3.4%
의 14
 
2.5%
한 13
 
2.3%
을 13
 
2.3%
지 12
 
2.1%
은 12
 
2.1%
도 10
 
1.8%
아 9
 
1.6%
가 9
 
1.6%
Other values (216) 431
76.4%
Punctuation
ValueCountFrequency (%)
” 1
50.0%
“ 1
50.0%

bcast_seq_no
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing27
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean1030019.8
Minimum1028117
Maximum1031935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2024-03-11T12:26:21.971696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1028117
5-th percentile1028306.9
Q11029066.2
median1029985.5
Q31030997.8
95-th percentile1031756.3
Maximum1031935
Range3818
Interquartile range (IQR)1931.5

Descriptive statistics

Standard deviation1292.3805
Coefficient of variation (CV)0.0012547142
Kurtosis-1.2347072
Mean1030019.8
Median Absolute Deviation (MAD)1070.5
Skewness0.031305889
Sum10300198
Variance1670247.3
MonotonicityStrictly increasing
2024-03-11T12:26:22.074827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1028117 1
 
2.7%
1028539 1
 
2.7%
1028969 1
 
2.7%
1029358 1
 
2.7%
1029760 1
 
2.7%
1030211 1
 
2.7%
1030661 1
 
2.7%
1031110 1
 
2.7%
1031538 1
 
2.7%
1031935 1
 
2.7%
(Missing) 27
73.0%
ValueCountFrequency (%)
1028117 1
2.7%
1028539 1
2.7%
1028969 1
2.7%
1029358 1
2.7%
1029760 1
2.7%
1030211 1
2.7%
1030661 1
2.7%
1031110 1
2.7%
1031538 1
2.7%
1031935 1
2.7%
ValueCountFrequency (%)
1031935 1
2.7%
1031538 1
2.7%
1031110 1
2.7%
1030661 1
2.7%
1030211 1
2.7%
1029760 1
2.7%
1029358 1
2.7%
1028969 1
2.7%
1028539 1
2.7%
1028117 1
2.7%

play_sec
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing27
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean2983.4
Minimum2624
Maximum3163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2024-03-11T12:26:22.172235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2624
5-th percentile2736.5
Q12927.5
median3022.5
Q33084
95-th percentile3134.65
Maximum3163
Range539
Interquartile range (IQR)156.5

Descriptive statistics

Standard deviation153.74018
Coefficient of variation (CV)0.051531871
Kurtosis2.790918
Mean2983.4
Median Absolute Deviation (MAD)73.5
Skewness-1.4833675
Sum29834
Variance23636.044
MonotonicityNot monotonic
2024-03-11T12:26:22.262075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2917 1
 
2.7%
2959 1
 
2.7%
3020 1
 
2.7%
3092 1
 
2.7%
3025 1
 
2.7%
2874 1
 
2.7%
2624 1
 
2.7%
3100 1
 
2.7%
3060 1
 
2.7%
3163 1
 
2.7%
(Missing) 27
73.0%
ValueCountFrequency (%)
2624 1
2.7%
2874 1
2.7%
2917 1
2.7%
2959 1
2.7%
3020 1
2.7%
3025 1
2.7%
3060 1
2.7%
3092 1
2.7%
3100 1
2.7%
3163 1
2.7%
ValueCountFrequency (%)
3163 1
2.7%
3100 1
2.7%
3092 1
2.7%
3060 1
2.7%
3025 1
2.7%
3020 1
2.7%
2959 1
2.7%
2917 1
2.7%
2874 1
2.7%
2624 1
2.7%

play_hour
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing27
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean0.82872
Minimum0.7289
Maximum0.8786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2024-03-11T12:26:22.348096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7289
5-th percentile0.76013
Q10.8132
median0.8396
Q30.856675
95-th percentile0.870725
Maximum0.8786
Range0.1497
Interquartile range (IQR)0.043475

Descriptive statistics

Standard deviation0.042704535
Coefficient of variation (CV)0.051530716
Kurtosis2.7880913
Mean0.82872
Median Absolute Deviation (MAD)0.0204
Skewness-1.4829611
Sum8.2872
Variance0.0018236773
MonotonicityNot monotonic
2024-03-11T12:26:22.480703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.8103 1
 
2.7%
0.8219 1
 
2.7%
0.8389 1
 
2.7%
0.8589 1
 
2.7%
0.8403 1
 
2.7%
0.7983 1
 
2.7%
0.7289 1
 
2.7%
0.8611 1
 
2.7%
0.85 1
 
2.7%
0.8786 1
 
2.7%
(Missing) 27
73.0%
ValueCountFrequency (%)
0.7289 1
2.7%
0.7983 1
2.7%
0.8103 1
2.7%
0.8219 1
2.7%
0.8389 1
2.7%
0.8403 1
2.7%
0.85 1
2.7%
0.8589 1
2.7%
0.8611 1
2.7%
0.8786 1
2.7%
ValueCountFrequency (%)
0.8786 1
2.7%
0.8611 1
2.7%
0.8589 1
2.7%
0.85 1
2.7%
0.8403 1
2.7%
0.8389 1
2.7%
0.8219 1
2.7%
0.8103 1
2.7%
0.7983 1
2.7%
0.7289 1
2.7%

file_size
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing27
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean4.663279 × 108
Minimum3.7536646 × 108
Maximum5.2515542 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2024-03-11T12:26:22.587309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7536646 × 108
5-th percentile3.9021387 × 108
Q14.3693822 × 108
median4.7620035 × 108
Q35.0711671 × 108
95-th percentile5.2163027 × 108
Maximum5.2515542 × 108
Range1.4978896 × 108
Interquartile range (IQR)70178490

Descriptive statistics

Standard deviation50176695
Coefficient of variation (CV)0.1075996
Kurtosis-0.73037345
Mean4.663279 × 108
Median Absolute Deviation (MAD)39114352
Skewness-0.5915852
Sum4.663279 × 109
Variance2.5177007 × 1015
MonotonicityNot monotonic
2024-03-11T12:26:22.691365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
436790449 1
 
2.7%
475517328 1
 
2.7%
375366463 1
 
2.7%
501519511 1
 
2.7%
408360691 1
 
2.7%
476883363 1
 
2.7%
437381537 1
 
2.7%
517321753 1
 
2.7%
508982444 1
 
2.7%
525155418 1
 
2.7%
(Missing) 27
73.0%
ValueCountFrequency (%)
375366463 1
2.7%
408360691 1
2.7%
436790449 1
2.7%
437381537 1
2.7%
475517328 1
2.7%
476883363 1
2.7%
501519511 1
2.7%
508982444 1
2.7%
517321753 1
2.7%
525155418 1
2.7%
ValueCountFrequency (%)
525155418 1
2.7%
517321753 1
2.7%
508982444 1
2.7%
501519511 1
2.7%
476883363 1
2.7%
475517328 1
2.7%
437381537 1
2.7%
436790449 1
2.7%
408360691 1
2.7%
375366463 1
2.7%

vod_path
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing27
Missing (%)73.0%
Memory size428.0 B
2024-03-11T12:26:22.871849image/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/mbnvod2/579/2012/06/16/20120616130853_20_579_1028117_360.mp4
2nd row/mbnvod2/579/2012/06/23/20120623144947_20_579_1028539_360.mp4
3rd row/mbnvod2/579/2012/06/30/20120630130431_20_579_1028969_360.mp4
4th row/mbnvod2/579/2012/07/07/20120707145935_20_579_1029358_360.mp4
5th row/mbnvod2/579/2012/07/14/20120714124100_20_579_1029760_360.mp4
ValueCountFrequency (%)
mbnvod2/579/2012/06/16/20120616130853_20_579_1028117_360.mp4 1
10.0%
mbnvod2/579/2012/06/23/20120623144947_20_579_1028539_360.mp4 1
10.0%
mbnvod2/579/2012/06/30/20120630130431_20_579_1028969_360.mp4 1
10.0%
mbnvod2/579/2012/07/07/20120707145935_20_579_1029358_360.mp4 1
10.0%
mbnvod2/579/2012/07/14/20120714124100_20_579_1029760_360.mp4 1
10.0%
mbnvod2/579/2012/07/21/20120721145153_20_579_1030211_360.mp4 1
10.0%
mbnvod2/579/2012/07/28/20120728142755_20_579_1030661_360.mp4 1
10.0%
mbnvod2/579/2012/08/04/20120804150426_20_579_1031110_360.mp4 1
10.0%
mbnvod2/579/2012/08/11/20120811130947_20_579_1031538_360.mp4 1
10.0%
mbnvod2/579/2012/08/18/20120818143618_20_579_1031935_360.mp4 1
10.0%
2024-03-11T12:26:23.142307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 86
14.1%
2 75
12.3%
1 66
10.8%
/ 60
9.8%
_ 40
 
6.6%
7 35
 
5.7%
5 32
 
5.2%
3 31
 
5.1%
9 29
 
4.8%
4 25
 
4.1%
Other values (10) 131
21.5%

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 (%)
0 86
20.5%
2 75
17.9%
1 66
15.7%
7 35
8.3%
5 32
 
7.6%
3 31
 
7.4%
9 29
 
6.9%
4 25
 
6.0%
6 24
 
5.7%
8 17
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
m 20
25.0%
d 10
12.5%
o 10
12.5%
v 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 (%)
0 86
16.2%
2 75
14.2%
1 66
12.5%
/ 60
11.3%
_ 40
7.5%
7 35
6.6%
5 32
 
6.0%
3 31
 
5.8%
9 29
 
5.5%
4 25
 
4.7%
Other values (3) 51
9.6%
Latin
ValueCountFrequency (%)
m 20
25.0%
d 10
12.5%
o 10
12.5%
v 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 (%)
0 86
14.1%
2 75
12.3%
1 66
10.8%
/ 60
9.8%
_ 40
 
6.6%
7 35
 
5.7%
5 32
 
5.2%
3 31
 
5.1%
9 29
 
4.8%
4 25
 
4.1%
Other values (10) 131
21.5%

title
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing27
Missing (%)73.0%
Memory size428.0 B
2024-03-11T12:26:23.304212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length21.6
Min length18

Characters and Unicode

Total characters216
Distinct characters58
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
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[뽀빠이 팔도유람기 1회] 거제시 다대마을
2nd row[뽀빠이 팔도유람기 2회] 전라남도 신안군
3rd row[뽀빠이 팔도유람기 3회] 충남 당진
4th row[뽀빠이 팔도유람기 4회] 전라북도 김제시
5th row[뽀빠이 팔도유람기 5회] 무의도
ValueCountFrequency (%)
뽀빠이 10
20.4%
팔도유람기 10
20.4%
6회 1
 
2.0%
충청남도 1
 
2.0%
10회 1
 
2.0%
영월 1
 
2.0%
강원도 1
 
2.0%
9회 1
 
2.0%
백미리 1
 
2.0%
화성 1
 
2.0%
Other values (21) 21
42.9%
2024-03-11T12:26:23.599504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
18.1%
도 17
 
7.9%
기 11
 
5.1%
[ 10
 
4.6%
빠 10
 
4.6%
이 10
 
4.6%
팔 10
 
4.6%
유 10
 
4.6%
람 10
 
4.6%
뽀 10
 
4.6%
Other values (48) 79
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 146
67.6%
Space Separator 39
 
18.1%
Decimal Number 11
 
5.1%
Open Punctuation 10
 
4.6%
Close Punctuation 10
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 17
 
11.6%
기 11
 
7.5%
빠 10
 
6.8%
이 10
 
6.8%
팔 10
 
6.8%
유 10
 
6.8%
람 10
 
6.8%
뽀 10
 
6.8%
회 10
 
6.8%
시 3
 
2.1%
Other values (35) 45
30.8%
Decimal Number
ValueCountFrequency (%)
1 2
18.2%
7 1
9.1%
8 1
9.1%
0 1
9.1%
9 1
9.1%
6 1
9.1%
2 1
9.1%
3 1
9.1%
4 1
9.1%
5 1
9.1%
Space Separator
ValueCountFrequency (%)
39
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 10
100.0%
Close Punctuation
ValueCountFrequency (%)
] 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 146
67.6%
Common 70
32.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 17
 
11.6%
기 11
 
7.5%
빠 10
 
6.8%
이 10
 
6.8%
팔 10
 
6.8%
유 10
 
6.8%
람 10
 
6.8%
뽀 10
 
6.8%
회 10
 
6.8%
시 3
 
2.1%
Other values (35) 45
30.8%
Common
ValueCountFrequency (%)
39
55.7%
[ 10
 
14.3%
] 10
 
14.3%
1 2
 
2.9%
7 1
 
1.4%
8 1
 
1.4%
0 1
 
1.4%
9 1
 
1.4%
6 1
 
1.4%
2 1
 
1.4%
Other values (3) 3
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 146
67.6%
ASCII 70
32.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39
55.7%
[ 10
 
14.3%
] 10
 
14.3%
1 2
 
2.9%
7 1
 
1.4%
8 1
 
1.4%
0 1
 
1.4%
9 1
 
1.4%
6 1
 
1.4%
2 1
 
1.4%
Other values (3) 3
 
4.3%
Hangul
ValueCountFrequency (%)
도 17
 
11.6%
기 11
 
7.5%
빠 10
 
6.8%
이 10
 
6.8%
팔 10
 
6.8%
유 10
 
6.8%
람 10
 
6.8%
뽀 10
 
6.8%
회 10
 
6.8%
시 3
 
2.1%
Other values (35) 45
30.8%

contents
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing27
Missing (%)73.0%
Memory size428.0 B
2024-03-11T12:26:23.815069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length23.2
Min length10

Characters and Unicode

Total characters232
Distinct characters115
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row남도의 작은 어촌.
2nd row말이 필요없는 드넓게 펼쳐진 은빛 백사장과 수려한 자연경관.
3rd row국내 유일 바다로 뜨고 지는 해를 볼 수 있는 곳.
4th row호남평야의 중심. 한반도 벼농사의 발원지.
5th row서울에서 가깝고 언제든지 찾아 갈 수 있는 섬
ValueCountFrequency (%)
서울에서 2
 
3.1%
있는 2
 
3.1%
수 2
 
3.1%
남도의 1
 
1.5%
천 1
 
1.5%
숨결 1
 
1.5%
경주 1
 
1.5%
청정 1
 
1.5%
자연 1
 
1.5%
빼어난 1
 
1.5%
Other values (52) 52
80.0%
2024-03-11T12:26:24.152865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56
24.1%
. 7
 
3.0%
는 7
 
3.0%
의 5
 
2.2%
서 4
 
1.7%
한 4
 
1.7%
수 4
 
1.7%
남 3
 
1.3%
경 3
 
1.3%
유 3
 
1.3%
Other values (105) 136
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
71.6%
Space Separator 56
 
24.1%
Other Punctuation 9
 
3.9%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
는 7
 
4.2%
의 5
 
3.0%
서 4
 
2.4%
한 4
 
2.4%
수 4
 
2.4%
남 3
 
1.8%
경 3
 
1.8%
유 3
 
1.8%
지 3
 
1.8%
자 3
 
1.8%
Other values (101) 127
76.5%
Other Punctuation
ValueCountFrequency (%)
. 7
77.8%
, 2
 
22.2%
Space Separator
ValueCountFrequency (%)
56
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
71.6%
Common 66
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
는 7
 
4.2%
의 5
 
3.0%
서 4
 
2.4%
한 4
 
2.4%
수 4
 
2.4%
남 3
 
1.8%
경 3
 
1.8%
유 3
 
1.8%
지 3
 
1.8%
자 3
 
1.8%
Other values (101) 127
76.5%
Common
ValueCountFrequency (%)
56
84.8%
. 7
 
10.6%
, 2
 
3.0%
~ 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
71.6%
ASCII 66
 
28.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56
84.8%
. 7
 
10.6%
, 2
 
3.0%
~ 1
 
1.5%
Hangul
ValueCountFrequency (%)
는 7
 
4.2%
의 5
 
3.0%
서 4
 
2.4%
한 4
 
2.4%
수 4
 
2.4%
남 3
 
1.8%
경 3
 
1.8%
유 3
 
1.8%
지 3
 
1.8%
자 3
 
1.8%
Other values (101) 127
76.5%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

Interactions

2024-03-11T12:26:20.648164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:19.605184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:19.958725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.325084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.728632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:19.729600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.038385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.413063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.796281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:19.799577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.115841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.483716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.870010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:19.875961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.218680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-11T12:26:20.558972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-11T12:26:24.236882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontents
vod_seq_no1.0001.0001.0001.0001.0001.0001.0001.000
bcast_seq_no1.0001.0001.0001.0001.0001.0001.0001.000
play_sec1.0001.0001.0001.0000.8401.0001.0001.000
play_hour1.0001.0001.0001.0000.8171.0001.0001.000
file_size1.0001.0000.8400.8171.0001.0001.0001.000
vod_path1.0001.0001.0001.0001.0001.0001.0001.000
title1.0001.0001.0001.0001.0001.0001.0001.000
contents1.0001.0001.0001.0001.0001.0001.0001.000
2024-03-11T12:26:24.539383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
bcast_seq_noplay_secplay_hourfile_size
bcast_seq_no1.0000.4790.4790.721
play_sec0.4791.0001.0000.636
play_hour0.4791.0001.0000.636
file_size0.7210.6360.6361.000

Missing values

2024-03-11T12:26:20.965721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-11T12:26:21.090447image/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.
2024-03-11T12:26:21.195490image/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>
1444964102811729170.8103436790449/mbnvod2/579/2012/06/16/20120616130853_20_579_1028117_360.mp4[뽀빠이 팔도유람기 1회] 거제시 다대마을남도의 작은 어촌.<NA>
2남쪽에서 부러오는 따뜻한 훈풍으로 여름이 가장 먼저 도착하는 곳.<NA><NA><NA><NA><NA><NA><NA><NA>
3남도의 유월은 꽃들이 남발하고 밤새 고기 잡으러 나간 어부들이 돌아오고 잠들었던 물도 깨어나는 아름다운 남도에 뽀빵이 찾아가는데...<NA><NA><NA><NA><NA><NA><NA><NA>
4445899102853929590.8219475517328/mbnvod2/579/2012/06/23/20120623144947_20_579_1028539_360.mp4[뽀빠이 팔도유람기 2회] 전라남도 신안군말이 필요없는 드넓게 펼쳐진 은빛 백사장과 수려한 자연경관.<NA>
5천혜의 생태가 보존되어있는 곳.<NA><NA><NA><NA><NA><NA><NA><NA>
6볼거리, 먹을거리, 인심까지 넉넉한 모두 느낄 수 있는 전라남도 신안군에 흠뻑 빠진 뽀빠이.<NA><NA><NA><NA><NA><NA><NA><NA>
7446960102896930200.8389375366463/mbnvod2/579/2012/06/30/20120630130431_20_579_1028969_360.mp4[뽀빠이 팔도유람기 3회] 충남 당진국내 유일 바다로 뜨고 지는 해를 볼 수 있는 곳.<NA>
8지형의 3분의 2가 바다와 접하고 교통이 편리한 당진은 농업, 어업, 공업까지 발달하였고<NA><NA><NA><NA><NA><NA><NA><NA>
9아시아 최초 군함을 최초한 함상공원은 대표 관광지로 유명하다.<NA><NA><NA><NA><NA><NA><NA><NA>
vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontentsUnnamed: 8
27작은 마을에서 사람들의 발길이 끊임이 없다는데...<NA><NA><NA><NA><NA><NA><NA><NA>
28과연 백미리의 백가지 맛, 백가지 매력이 무엇인지 찾아보자!!!<NA><NA><NA><NA><NA><NA><NA><NA>
29452832103153830600.85508982444/mbnvod2/579/2012/08/11/20120811130947_20_579_1031538_360.mp4[뽀빠이 팔도유람기 9회] 강원도 영월유유히 흐르는 동강 변, 골 깊은 뼝창마을이 이번 주인공~<NA>
30우리에게 낯선 이름 뼝창은 절벽을 이르는 강원도 사투리로,<NA><NA><NA><NA><NA><NA><NA><NA>
31그 명칭처럼 마을 앞은 온통 깎아 지르는 듯한 절벽이 절경을 이룬다.<NA><NA><NA><NA><NA><NA><NA><NA>
32이 물 깊고 산 깊은 뼝창마을은 뽀빠이에게 과연 어떤 이야기를 풀어놓을까?<NA><NA><NA><NA><NA><NA><NA><NA>
33453667103193531630.8786525155418/mbnvod2/579/2012/08/18/20120818143618_20_579_1031935_360.mp4[뽀빠이 팔도유람기 10회] 충청남도 청양충남의 알프스라 불리는 칠갑산<NA>
34칠갑산을 유명한 순박한 웃음이 행복을 부르는 청양<NA><NA><NA><NA><NA><NA><NA><NA>
35시골어르신의 순박한 웃음과 한폭의 그림같은 시골여행 시작합니다!!!<NA><NA><NA><NA><NA><NA><NA><NA>
36<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontents# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>6