Overview

Dataset statistics

Number of variables9
Number of observations64
Missing cells449
Missing cells (%)78.0%
Duplicate rows1
Duplicate rows (%)1.6%
Total size in memory4.9 KiB
Average record size in memory79.1 B

Variable types

Text4
Numeric4
Unsupported1

Dataset

Description샘플 데이터
AuthorMBN
URLhttps://kdx.kr/data/view/29825

Alerts

Dataset has 1 (1.6%) duplicate rowsDuplicates
bcast_seq_no is highly overall correlated with play_sec and 2 other fieldsHigh correlation
play_sec is highly overall correlated with bcast_seq_no and 2 other fieldsHigh correlation
play_hour is highly overall correlated with bcast_seq_no and 2 other fieldsHigh correlation
file_size is highly overall correlated with bcast_seq_no and 2 other fieldsHigh correlation
vod_seq_no has 7 (10.9%) missing valuesMissing
bcast_seq_no has 54 (84.4%) missing valuesMissing
play_sec has 54 (84.4%) missing valuesMissing
play_hour has 54 (84.4%) missing valuesMissing
file_size has 54 (84.4%) missing valuesMissing
vod_path has 54 (84.4%) missing valuesMissing
title has 54 (84.4%) missing valuesMissing
contents has 54 (84.4%) missing valuesMissing
Unnamed: 8 has 64 (100.0%) missing valuesMissing
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 16:00:37.455841
Analysis finished2024-04-17 16:00:39.247982
Duration1.79 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

vod_seq_no
Text

MISSING 

Distinct57
Distinct (%)100.0%
Missing7
Missing (%)10.9%
Memory size644.0 B
2024-04-18T01:00:39.460728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length323
Median length82
Mean length54.368421
Min length6

Characters and Unicode

Total characters3099
Distinct characters422
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row459144
2nd row하루 6시간 영업! 우동 판매량 1,800그릇 신화 야마고에 우동,
3rd row양심경영으로 소비자로 하여금 신뢰와 믿음을 가지게 한
4th row족발 골목을 통째로 접수한 영동 족발
5th row두 대박집을 찾아가 본다
ValueCountFrequency (%)
한 10
 
1.3%
그 6
 
0.8%
있다 5
 
0.6%
수 5
 
0.6%
저녁 4
 
0.5%
대박집 4
 
0.5%
매일 4
 
0.5%
음식 4
 
0.5%
비밀이 4
 
0.5%
점심 4
 
0.5%
Other values (633) 721
93.5%
2024-04-18T01:00:39.842727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
730
 
23.6%
이 74
 
2.4%
는 61
 
2.0%
다 54
 
1.7%
을 47
 
1.5%
한 43
 
1.4%
. 41
 
1.3%
고 40
 
1.3%
, 39
 
1.3%
의 35
 
1.1%
Other values (412) 1935
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2131
68.8%
Space Separator 730
 
23.6%
Decimal Number 125
 
4.0%
Other Punctuation 98
 
3.2%
Final Punctuation 5
 
0.2%
Initial Punctuation 5
 
0.2%
Math Symbol 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 74
 
3.5%
는 61
 
2.9%
다 54
 
2.5%
을 47
 
2.2%
한 43
 
2.0%
고 40
 
1.9%
의 35
 
1.6%
기 34
 
1.6%
에 33
 
1.5%
대 31
 
1.5%
Other values (392) 1679
78.8%
Decimal Number
ValueCountFrequency (%)
1 19
15.2%
6 19
15.2%
4 18
14.4%
0 16
12.8%
9 12
9.6%
5 10
8.0%
2 10
8.0%
3 9
7.2%
8 7
 
5.6%
7 5
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 41
41.8%
, 39
39.8%
! 16
 
16.3%
? 2
 
2.0%
Math Symbol
ValueCountFrequency (%)
~ 3
60.0%
> 1
 
20.0%
< 1
 
20.0%
Space Separator
ValueCountFrequency (%)
730
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 5
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2131
68.8%
Common 968
31.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 74
 
3.5%
는 61
 
2.9%
다 54
 
2.5%
을 47
 
2.2%
한 43
 
2.0%
고 40
 
1.9%
의 35
 
1.6%
기 34
 
1.6%
에 33
 
1.5%
대 31
 
1.5%
Other values (392) 1679
78.8%
Common
ValueCountFrequency (%)
730
75.4%
. 41
 
4.2%
, 39
 
4.0%
1 19
 
2.0%
6 19
 
2.0%
4 18
 
1.9%
! 16
 
1.7%
0 16
 
1.7%
9 12
 
1.2%
5 10
 
1.0%
Other values (10) 48
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2131
68.8%
ASCII 958
30.9%
Punctuation 10
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
730
76.2%
. 41
 
4.3%
, 39
 
4.1%
1 19
 
2.0%
6 19
 
2.0%
4 18
 
1.9%
! 16
 
1.7%
0 16
 
1.7%
9 12
 
1.3%
5 10
 
1.0%
Other values (8) 38
 
4.0%
Hangul
ValueCountFrequency (%)
이 74
 
3.5%
는 61
 
2.9%
다 54
 
2.5%
을 47
 
2.2%
한 43
 
2.0%
고 40
 
1.9%
의 35
 
1.6%
기 34
 
1.6%
에 33
 
1.5%
대 31
 
1.5%
Other values (392) 1679
78.8%
Punctuation
ValueCountFrequency (%)
’ 5
50.0%
‘ 5
50.0%

bcast_seq_no
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing54
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean1037146.1
Minimum1034431
Maximum1039485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-18T01:00:39.941638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1034431
5-th percentile1034583.1
Q11035472
median1037591
Q31038617.8
95-th percentile1039337.4
Maximum1039485
Range5054
Interquartile range (IQR)3145.75

Descriptive statistics

Standard deviation1860.2241
Coefficient of variation (CV)0.0017935989
Kurtosis-1.4940772
Mean1037146.1
Median Absolute Deviation (MAD)1406
Skewness-0.32447322
Sum10371461
Variance3460433.7
MonotonicityStrictly increasing
2024-04-18T01:00:40.020455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1034431 1
 
1.6%
1034769 1
 
1.6%
1035181 1
 
1.6%
1036345 1
 
1.6%
1037406 1
 
1.6%
1037776 1
 
1.6%
1038131 1
 
1.6%
1038780 1
 
1.6%
1039157 1
 
1.6%
1039485 1
 
1.6%
(Missing) 54
84.4%
ValueCountFrequency (%)
1034431 1
1.6%
1034769 1
1.6%
1035181 1
1.6%
1036345 1
1.6%
1037406 1
1.6%
1037776 1
1.6%
1038131 1
1.6%
1038780 1
1.6%
1039157 1
1.6%
1039485 1
1.6%
ValueCountFrequency (%)
1039485 1
1.6%
1039157 1
1.6%
1038780 1
1.6%
1038131 1
1.6%
1037776 1
1.6%
1037406 1
1.6%
1036345 1
1.6%
1035181 1
1.6%
1034769 1
1.6%
1034431 1
1.6%

play_sec
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)80.0%
Missing54
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean3056.3
Minimum2991
Maximum3138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-18T01:00:40.097140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2991
5-th percentile2996.4
Q13024
median3033
Q33105.25
95-th percentile3138
Maximum3138
Range147
Interquartile range (IQR)81.25

Descriptive statistics

Standard deviation55.625833
Coefficient of variation (CV)0.018200384
Kurtosis-1.2446128
Mean3056.3
Median Absolute Deviation (MAD)27.5
Skewness0.67518205
Sum30563
Variance3094.2333
MonotonicityNot monotonic
2024-04-18T01:00:40.175996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3138 2
 
3.1%
3024 2
 
3.1%
3058 1
 
1.6%
3121 1
 
1.6%
3029 1
 
1.6%
3037 1
 
1.6%
3003 1
 
1.6%
2991 1
 
1.6%
(Missing) 54
84.4%
ValueCountFrequency (%)
2991 1
1.6%
3003 1
1.6%
3024 2
3.1%
3029 1
1.6%
3037 1
1.6%
3058 1
1.6%
3121 1
1.6%
3138 2
3.1%
ValueCountFrequency (%)
3138 2
3.1%
3121 1
1.6%
3058 1
1.6%
3037 1
1.6%
3029 1
1.6%
3024 2
3.1%
3003 1
1.6%
2991 1
1.6%

play_hour
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)80.0%
Missing54
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean0.84897
Minimum0.8308
Maximum0.8717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-18T01:00:40.260211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8308
5-th percentile0.83233
Q10.84
median0.8425
Q30.862525
95-th percentile0.8717
Maximum0.8717
Range0.0409
Interquartile range (IQR)0.022525

Descriptive statistics

Standard deviation0.015457256
Coefficient of variation (CV)0.01820707
Kurtosis-1.2391463
Mean0.84897
Median Absolute Deviation (MAD)0.0076
Skewness0.67686452
Sum8.4897
Variance0.00023892678
MonotonicityNot monotonic
2024-04-18T01:00:40.350460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.8717 2
 
3.1%
0.84 2
 
3.1%
0.8494 1
 
1.6%
0.8669 1
 
1.6%
0.8414 1
 
1.6%
0.8436 1
 
1.6%
0.8342 1
 
1.6%
0.8308 1
 
1.6%
(Missing) 54
84.4%
ValueCountFrequency (%)
0.8308 1
1.6%
0.8342 1
1.6%
0.84 2
3.1%
0.8414 1
1.6%
0.8436 1
1.6%
0.8494 1
1.6%
0.8669 1
1.6%
0.8717 2
3.1%
ValueCountFrequency (%)
0.8717 2
3.1%
0.8669 1
1.6%
0.8494 1
1.6%
0.8436 1
1.6%
0.8414 1
1.6%
0.84 2
3.1%
0.8342 1
1.6%
0.8308 1
1.6%

file_size
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing54
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean5.067931 × 108
Minimum4.9929697 × 108
Maximum5.163996 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-18T01:00:40.437715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.9929697 × 108
5-th percentile4.9930211 × 108
Q15.0067851 × 108
median5.0619782 × 108
Q35.1257369 × 108
95-th percentile5.1614975 × 108
Maximum5.163996 × 108
Range17102632
Interquartile range (IQR)11895178

Descriptive statistics

Standard deviation6799418.3
Coefficient of variation (CV)0.013416556
Kurtosis-1.6284106
Mean5.067931 × 108
Median Absolute Deviation (MAD)6422425.5
Skewness0.32953722
Sum5.067931 × 109
Variance4.6232089 × 1013
MonotonicityNot monotonic
2024-04-18T01:00:40.531435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
513918977 1
 
1.6%
504062074 1
 
1.6%
516399601 1
 
1.6%
515844386 1
 
1.6%
499308395 1
 
1.6%
500242390 1
 
1.6%
508537819 1
 
1.6%
501986867 1
 
1.6%
508333562 1
 
1.6%
499296969 1
 
1.6%
(Missing) 54
84.4%
ValueCountFrequency (%)
499296969 1
1.6%
499308395 1
1.6%
500242390 1
1.6%
501986867 1
1.6%
504062074 1
1.6%
508333562 1
1.6%
508537819 1
1.6%
513918977 1
1.6%
515844386 1
1.6%
516399601 1
1.6%
ValueCountFrequency (%)
516399601 1
1.6%
515844386 1
1.6%
513918977 1
1.6%
508537819 1
1.6%
508333562 1
1.6%
504062074 1
1.6%
501986867 1
1.6%
500242390 1
1.6%
499308395 1
1.6%
499296969 1
1.6%

vod_path
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing54
Missing (%)84.4%
Memory size644.0 B
2024-04-18T01:00:40.721500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length61
Mean length60.6
Min length57

Characters and Unicode

Total characters606
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/mbnvod2/591/2012/09/29/20120929020815_20_591_1034431_360.mp4
2nd row/mbnvod2/591/2012/10/06/20121006013715_20_591_1034769_360.mp4
3rd row/mbnvod2/591/2012/10/13/20121013014257_20_591_1035181_360.mp4
4th row/mbnvod2/591/2012/11/03/20121103014507_20_591_1036345_360.mp4
5th row/mbnvod2/591/2012/11/24/20121124013100_20_591_1037406_360.mp4
ValueCountFrequency (%)
mbnvod2/591/2012/09/29/20120929020815_20_591_1034431_360.mp4 1
10.0%
mbnvod2/591/2012/10/06/20121006013715_20_591_1034769_360.mp4 1
10.0%
mbnvod2/591/2012/10/13/20121013014257_20_591_1035181_360.mp4 1
10.0%
mbnvod2/591/2012/11/03/20121103014507_20_591_1036345_360.mp4 1
10.0%
mbnvod2/591/2012/11/24/20121124013100_20_591_1037406_360.mp4 1
10.0%
mbnvod2/591/2012/12/01/20121201010601_20_591_1037776_360.mp4 1
10.0%
mbnvod2/591/2012/12/08/20121208014111_20_591_1038131_360.mp4 1
10.0%
mbnvod2/5/2012/12/22/20121222190641_20_5_1038780_360.mp4 1
10.0%
mbnvod2/591/2012/12/29/20121229223129_20_591_1039157_360.mp4 1
10.0%
mbnvod2/591/2013/01/05/20130105211106_20_591_1039485_360.mp4 1
10.0%
2024-04-18T01:00:40.994104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 99
16.3%
0 85
14.0%
2 82
13.5%
/ 60
9.9%
_ 40
 
6.6%
3 32
 
5.3%
5 30
 
5.0%
9 29
 
4.8%
4 22
 
3.6%
m 20
 
3.3%
Other values (10) 107
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 416
68.6%
Lowercase Letter 80
 
13.2%
Other Punctuation 70
 
11.6%
Connector Punctuation 40
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 99
23.8%
0 85
20.4%
2 82
19.7%
3 32
 
7.7%
5 30
 
7.2%
9 29
 
7.0%
4 22
 
5.3%
6 19
 
4.6%
7 10
 
2.4%
8 8
 
1.9%
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 526
86.8%
Latin 80
 
13.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 99
18.8%
0 85
16.2%
2 82
15.6%
/ 60
11.4%
_ 40
7.6%
3 32
 
6.1%
5 30
 
5.7%
9 29
 
5.5%
4 22
 
4.2%
6 19
 
3.6%
Other values (3) 28
 
5.3%
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 606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 99
16.3%
0 85
14.0%
2 82
13.5%
/ 60
9.9%
_ 40
 
6.6%
3 32
 
5.3%
5 30
 
5.0%
9 29
 
4.8%
4 22
 
3.6%
m 20
 
3.3%
Other values (10) 107
17.7%

title
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing54
Missing (%)84.4%
Memory size644.0 B
2024-04-18T01:00:41.197019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length46.5
Mean length41.7
Min length31

Characters and Unicode

Total characters417
Distinct characters131
Distinct categories6 ?
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[대박의 비밀 1회] 사누끼의 전설 달걀우동, 골목의 영광 족발
2nd row[대박의 비밀 2회] 홍콩 딤섬집 '린흥' 100년의 비밀, 불타는 구두를 신어라
3rd row[대박의 비밀 3회] 55년 전통의 대박 추어탕, 10평 대박 가게 일본 츠케면
4th row[대박의 비밀 4회] 춤 추는 불고기, 동네 빵집의 대박 신화
5th row[대박의 비밀 5회] 통닭 골목의 지존, 비빔밥을 맛보다
ValueCountFrequency (%)
비밀 11
 
10.0%
대박의 10
 
9.1%
대박 5
 
4.5%
골목의 2
 
1.8%
60년 2
 
1.8%
전통 2
 
1.8%
매출 2
 
1.8%
연 1
 
0.9%
30억 1
 
0.9%
장어 1
 
0.9%
Other values (73) 73
66.4%
2024-04-18T01:00:41.722011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
24.0%
의 19
 
4.6%
박 17
 
4.1%
대 15
 
3.6%
비 14
 
3.4%
0 12
 
2.9%
회 11
 
2.6%
밀 11
 
2.6%
] 10
 
2.4%
[ 10
 
2.4%
Other values (121) 198
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 251
60.2%
Space Separator 100
 
24.0%
Decimal Number 34
 
8.2%
Other Punctuation 12
 
2.9%
Close Punctuation 10
 
2.4%
Open Punctuation 10
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 19
 
7.6%
박 17
 
6.8%
대 15
 
6.0%
비 14
 
5.6%
회 11
 
4.4%
밀 11
 
4.4%
전 6
 
2.4%
불 5
 
2.0%
년 5
 
2.0%
집 5
 
2.0%
Other values (106) 143
57.0%
Decimal Number
ValueCountFrequency (%)
0 12
35.3%
1 7
20.6%
5 4
 
11.8%
3 3
 
8.8%
6 3
 
8.8%
8 1
 
2.9%
7 1
 
2.9%
9 1
 
2.9%
2 1
 
2.9%
4 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 10
83.3%
' 2
 
16.7%
Space Separator
ValueCountFrequency (%)
100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 10
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
60.2%
Common 166
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 19
 
7.6%
박 17
 
6.8%
대 15
 
6.0%
비 14
 
5.6%
회 11
 
4.4%
밀 11
 
4.4%
전 6
 
2.4%
불 5
 
2.0%
년 5
 
2.0%
집 5
 
2.0%
Other values (106) 143
57.0%
Common
ValueCountFrequency (%)
100
60.2%
0 12
 
7.2%
] 10
 
6.0%
[ 10
 
6.0%
, 10
 
6.0%
1 7
 
4.2%
5 4
 
2.4%
3 3
 
1.8%
6 3
 
1.8%
' 2
 
1.2%
Other values (5) 5
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 251
60.2%
ASCII 166
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100
60.2%
0 12
 
7.2%
] 10
 
6.0%
[ 10
 
6.0%
, 10
 
6.0%
1 7
 
4.2%
5 4
 
2.4%
3 3
 
1.8%
6 3
 
1.8%
' 2
 
1.2%
Other values (5) 5
 
3.0%
Hangul
ValueCountFrequency (%)
의 19
 
7.6%
박 17
 
6.8%
대 15
 
6.0%
비 14
 
5.6%
회 11
 
4.4%
밀 11
 
4.4%
전 6
 
2.4%
불 5
 
2.0%
년 5
 
2.0%
집 5
 
2.0%
Other values (106) 143
57.0%

contents
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing54
Missing (%)84.4%
Memory size644.0 B
2024-04-18T01:00:41.901334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length34.5
Mean length30.4
Min length21

Characters and Unicode

Total characters304
Distinct characters121
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<사누끼의 전설 달걀우동, 골목의 영광 족발>
2nd row<100년 딤섬, 불타는 구두를 신어라>
3rd row<55년 전통의 대박 추어탕, 10평 대박 가게 일본 츠케면>
4th row<춤 추는 불고기, 동네 빵집의 대박 신화>
5th row<통닭 골목의 지존, 비빔밥을 맛보다>
ValueCountFrequency (%)
대박 5
 
6.4%
골목의 2
 
2.6%
60년 2
 
2.6%
전통 2
 
2.6%
매출 2
 
2.6%
사누끼의 1
 
1.3%
제주 1
 
1.3%
향연 1
 
1.3%
멋의 1
 
1.3%
맛과 1
 
1.3%
Other values (60) 60
76.9%
2024-04-18T01:00:42.178254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
68
22.4%
0 11
 
3.6%
< 10
 
3.3%
, 10
 
3.3%
> 10
 
3.3%
의 8
 
2.6%
박 7
 
2.3%
전 6
 
2.0%
통 5
 
1.6%
불 5
 
1.6%
Other values (111) 164
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
60.2%
Space Separator 68
 
22.4%
Decimal Number 23
 
7.6%
Math Symbol 20
 
6.6%
Other Punctuation 10
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 8
 
4.4%
박 7
 
3.8%
전 6
 
3.3%
통 5
 
2.7%
불 5
 
2.7%
년 5
 
2.7%
대 5
 
2.7%
연 4
 
2.2%
집 4
 
2.2%
골 3
 
1.6%
Other values (102) 131
71.6%
Decimal Number
ValueCountFrequency (%)
0 11
47.8%
1 5
21.7%
5 3
 
13.0%
3 2
 
8.7%
6 2
 
8.7%
Math Symbol
ValueCountFrequency (%)
< 10
50.0%
> 10
50.0%
Space Separator
ValueCountFrequency (%)
68
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
60.2%
Common 121
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 8
 
4.4%
박 7
 
3.8%
전 6
 
3.3%
통 5
 
2.7%
불 5
 
2.7%
년 5
 
2.7%
대 5
 
2.7%
연 4
 
2.2%
집 4
 
2.2%
골 3
 
1.6%
Other values (102) 131
71.6%
Common
ValueCountFrequency (%)
68
56.2%
0 11
 
9.1%
< 10
 
8.3%
, 10
 
8.3%
> 10
 
8.3%
1 5
 
4.1%
5 3
 
2.5%
3 2
 
1.7%
6 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
60.2%
ASCII 121
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68
56.2%
0 11
 
9.1%
< 10
 
8.3%
, 10
 
8.3%
> 10
 
8.3%
1 5
 
4.1%
5 3
 
2.5%
3 2
 
1.7%
6 2
 
1.7%
Hangul
ValueCountFrequency (%)
의 8
 
4.4%
박 7
 
3.8%
전 6
 
3.3%
통 5
 
2.7%
불 5
 
2.7%
년 5
 
2.7%
대 5
 
2.7%
연 4
 
2.2%
집 4
 
2.2%
골 3
 
1.6%
Other values (102) 131
71.6%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing64
Missing (%)100.0%
Memory size708.0 B

Interactions

2024-04-18T01:00:38.681481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:37.853651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.104517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.375093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.746922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:37.910940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.166848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.443188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.816711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:37.974335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.232820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.517103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.890025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.042190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.310093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:00:38.599346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:00:42.280626image/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_no1.0001.0001.0000.9081.0001.0001.0001.000
play_sec1.0001.0001.0001.0000.9241.0001.0001.000
play_hour1.0000.9081.0001.0000.9241.0001.0001.000
file_size1.0001.0000.9240.9241.0001.0001.0001.000
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
2024-04-18T01:00:42.393273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
bcast_seq_noplay_secplay_hourfile_size
bcast_seq_no1.000-0.854-0.854-0.552
play_sec-0.8541.0001.0000.829
play_hour-0.8541.0001.0000.829
file_size-0.5520.8290.8291.000

Missing values

2024-04-18T01:00:38.976679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:00:39.077737image/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.
2024-04-18T01:00:39.175483image/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>
1459144103443131380.8717513918977/mbnvod2/591/2012/09/29/20120929020815_20_591_1034431_360.mp4[대박의 비밀 1회] 사누끼의 전설 달걀우동, 골목의 영광 족발<사누끼의 전설 달걀우동, 골목의 영광 족발><NA>
2하루 6시간 영업! 우동 판매량 1,800그릇 신화 야마고에 우동,<NA><NA><NA><NA><NA><NA><NA><NA>
3양심경영으로 소비자로 하여금 신뢰와 믿음을 가지게 한<NA><NA><NA><NA><NA><NA><NA><NA>
4족발 골목을 통째로 접수한 영동 족발<NA><NA><NA><NA><NA><NA><NA><NA>
5두 대박집을 찾아가 본다<NA><NA><NA><NA><NA><NA><NA><NA>
6459883103476930580.8494504062074/mbnvod2/591/2012/10/06/20121006013715_20_591_1034769_360.mp4[대박의 비밀 2회] 홍콩 딤섬집 '린흥' 100년의 비밀, 불타는 구두를 신어라<100년 딤섬, 불타는 구두를 신어라><NA>
7100년이 다 되어가는 전통 속에서 맛을 지켜가는 딤섬집 린흥<NA><NA><NA><NA><NA><NA><NA><NA>
8대박집 린흥을 버티게 하는 힘의 비밀을 찾아보자.<NA><NA><NA><NA><NA><NA><NA><NA>
9대부분 수작업으로 이뤄지는 구두 제작 과정<NA><NA><NA><NA><NA><NA><NA><NA>
vod_seq_nobcast_seq_noplay_secplay_hourfile_sizevod_pathtitlecontentsUnnamed: 8
54아삭한 겨울배추와 토란을 삶아 1년치를 8개의 냉동고에서 저장<NA><NA><NA><NA><NA><NA><NA><NA>
55470399103948529910.8308499296969/mbnvod2/591/2013/01/05/20130105211106_20_591_1039485_360.mp4[대박의 비밀 10회]곱창에 인생을 걸다, 황태명인의 집<곱창에 인생을 걸다, 황태명인의 집><NA>
56<NA><NA><NA><NA><NA><NA><NA><NA><NA>
57매일 소 3, 4마리 분량의 곱창을 파는 대박집<NA><NA><NA><NA><NA><NA><NA><NA>
58스물일곱에 돌쟁이 갓난쟁이를 업고 시작해 15년을 곱창만 판 여사장<NA><NA><NA><NA><NA><NA><NA><NA>
59곱창에 인생을 건 억척 전라도 아줌마의 성공기를 들여다본다.<NA><NA><NA><NA><NA><NA><NA><NA>
60하늘이 말리는 황태<NA><NA><NA><NA><NA><NA><NA><NA>
61눈이 소복이 쌓인 황태 덕장, 절경을 구경할 수 있는 곳<NA><NA><NA><NA><NA><NA><NA><NA>
62남편은 말리고, 아내는 요리하고 인제 황태명인의 집<NA><NA><NA><NA><NA><NA><NA><NA>
63<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>7