Overview

Dataset statistics

Number of variables14
Number of observations25
Missing cells45
Missing cells (%)12.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory123.3 B

Variable types

Numeric5
Categorical6
Unsupported1
Text2

Alerts

PROGRM_BRDCST_AREA_NM has constant value ""Constant
PROGRM_GENRE_SCLAS_NM is highly overall correlated with PROGRM_BEGIN_TIME and 5 other fieldsHigh correlation
PROGRM_GENRE_LCLAS_NM is highly overall correlated with PROGRM_NM and 2 other fieldsHigh correlation
PROGRM_GENRE_MLSFC_NM is highly overall correlated with PROGRM_BEGIN_TIME and 5 other fieldsHigh correlation
CHNNEL_NM is highly overall correlated with PROGRM_BEGIN_TIME and 5 other fieldsHigh correlation
PROGRM_NM is highly overall correlated with PROGRM_BEGIN_TIME and 6 other fieldsHigh correlation
BRDCST_DE is highly overall correlated with BRDCST_END_DEHigh correlation
BRDCST_END_DE is highly overall correlated with BRDCST_DEHigh correlation
PROGRM_BEGIN_TIME is highly overall correlated with PROGRM_END_TIME and 4 other fieldsHigh correlation
PROGRM_END_TIME is highly overall correlated with PROGRM_BEGIN_TIME and 4 other fieldsHigh correlation
AUDE_CO is highly overall correlated with CHNNEL_NM and 1 other fieldsHigh correlation
PROGRM_DC has 25 (100.0%) missing valuesMissing
BRDCST_TME_NM has 12 (48.0%) missing valuesMissing
TV_SUBTTLS_CN has 8 (32.0%) missing valuesMissing
PROGRM_BEGIN_TIME has unique valuesUnique
AUDE_CO has unique valuesUnique
PROGRM_DC is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:55:47.164640
Analysis finished2023-12-10 09:55:54.439187
Duration7.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BRDCST_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210716
Minimum20210701
Maximum20210731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T18:55:54.576402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210701
5-th percentile20210701
Q120210708
median20210715
Q320210722
95-th percentile20210729
Maximum20210731
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.7130496
Coefficient of variation (CV)4.805891 × 10-7
Kurtosis-1.2856588
Mean20210716
Median Absolute Deviation (MAD)7
Skewness0.045631319
Sum5.0526789 × 108
Variance94.343333
MonotonicityIncreasing
2023-12-10T18:55:54.818849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20210701 2
 
8.0%
20210714 2
 
8.0%
20210729 2
 
8.0%
20210728 2
 
8.0%
20210722 2
 
8.0%
20210721 2
 
8.0%
20210703 2
 
8.0%
20210715 2
 
8.0%
20210708 2
 
8.0%
20210711 1
 
4.0%
Other values (6) 6
24.0%
ValueCountFrequency (%)
20210701 2
8.0%
20210703 2
8.0%
20210704 1
4.0%
20210707 1
4.0%
20210708 2
8.0%
20210710 1
4.0%
20210711 1
4.0%
20210714 2
8.0%
20210715 2
8.0%
20210718 1
4.0%
ValueCountFrequency (%)
20210731 1
4.0%
20210729 2
8.0%
20210728 2
8.0%
20210725 1
4.0%
20210722 2
8.0%
20210721 2
8.0%
20210718 1
4.0%
20210715 2
8.0%
20210714 2
8.0%
20210711 1
4.0%

BRDCST_END_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210716
Minimum20210701
Maximum20210731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T18:55:55.113043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210701
5-th percentile20210701
Q120210708
median20210715
Q320210722
95-th percentile20210729
Maximum20210731
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.7130496
Coefficient of variation (CV)4.805891 × 10-7
Kurtosis-1.2856588
Mean20210716
Median Absolute Deviation (MAD)7
Skewness0.045631319
Sum5.0526789 × 108
Variance94.343333
MonotonicityIncreasing
2023-12-10T18:55:55.412845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20210701 2
 
8.0%
20210714 2
 
8.0%
20210729 2
 
8.0%
20210728 2
 
8.0%
20210722 2
 
8.0%
20210721 2
 
8.0%
20210703 2
 
8.0%
20210715 2
 
8.0%
20210708 2
 
8.0%
20210711 1
 
4.0%
Other values (6) 6
24.0%
ValueCountFrequency (%)
20210701 2
8.0%
20210703 2
8.0%
20210704 1
4.0%
20210707 1
4.0%
20210708 2
8.0%
20210710 1
4.0%
20210711 1
4.0%
20210714 2
8.0%
20210715 2
8.0%
20210718 1
4.0%
ValueCountFrequency (%)
20210731 1
4.0%
20210729 2
8.0%
20210728 2
8.0%
20210725 1
4.0%
20210722 2
8.0%
20210721 2
8.0%
20210718 1
4.0%
20210715 2
8.0%
20210714 2
8.0%
20210711 1
4.0%

CHNNEL_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
JTBC
9 
TV CHOSUN
8 
KBS1
4 
KBS2
4 

Length

Max length9
Median length4
Mean length5.6
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJTBC
2nd rowTV CHOSUN
3rd rowKBS1
4th rowKBS1
5th rowKBS2

Common Values

ValueCountFrequency (%)
JTBC 9
36.0%
TV CHOSUN 8
32.0%
KBS1 4
16.0%
KBS2 4
16.0%

Length

2023-12-10T18:55:55.864290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:56.094845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jtbc 9
27.3%
tv 8
24.2%
chosun 8
24.2%
kbs1 4
12.1%
kbs2 4
12.1%

PROGRM_BEGIN_TIME
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96073
Minimum72837
Maximum191134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T18:55:56.328039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72837
5-th percentile73023.4
Q190425
median90644
Q3103015
95-th percentile103107.6
Maximum191134
Range118297
Interquartile range (IQR)12590

Descriptive statistics

Standard deviation22065.225
Coefficient of variation (CV)0.22967145
Kurtosis15.238594
Mean96073
Median Absolute Deviation (MAD)8685
Skewness3.4487347
Sum2401825
Variance4.8687415 × 108
MonotonicityNot monotonic
2023-12-10T18:55:56.562116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
90425 1
 
4.0%
103018 1
 
4.0%
81959 1
 
4.0%
102950 1
 
4.0%
90717 1
 
4.0%
103102 1
 
4.0%
90602 1
 
4.0%
82943 1
 
4.0%
103002 1
 
4.0%
90451 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
72837 1
4.0%
73019 1
4.0%
73041 1
4.0%
81959 1
4.0%
82943 1
4.0%
90013 1
4.0%
90425 1
4.0%
90451 1
4.0%
90534 1
4.0%
90540 1
4.0%
ValueCountFrequency (%)
191134 1
4.0%
103109 1
4.0%
103102 1
4.0%
103035 1
4.0%
103024 1
4.0%
103018 1
4.0%
103015 1
4.0%
103002 1
4.0%
102950 1
4.0%
94051 1
4.0%

PROGRM_END_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101986.36
Minimum75521
Maximum200313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T18:55:56.893642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75521
5-th percentile75627.4
Q195143
median95347
Q3110450
95-th percentile110599.8
Maximum200313
Range124792
Interquartile range (IQR)15307

Descriptive statistics

Standard deviation23400.466
Coefficient of variation (CV)0.22944702
Kurtosis13.55637
Mean101986.36
Median Absolute Deviation (MAD)9823
Skewness3.1519542
Sum2549659
Variance5.4758181 × 108
MonotonicityNot monotonic
2023-12-10T18:55:57.175368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
110451 2
 
8.0%
95143 1
 
4.0%
90756 1
 
4.0%
95347 1
 
4.0%
110300 1
 
4.0%
95306 1
 
4.0%
85524 1
 
4.0%
110510 1
 
4.0%
95203 1
 
4.0%
110611 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
75521 1
4.0%
75609 1
4.0%
75701 1
4.0%
85524 1
4.0%
90756 1
4.0%
94709 1
4.0%
95143 1
4.0%
95203 1
4.0%
95217 1
4.0%
95250 1
4.0%
ValueCountFrequency (%)
200313 1
4.0%
110611 1
4.0%
110555 1
4.0%
110510 1
4.0%
110451 2
8.0%
110450 1
4.0%
110353 1
4.0%
110300 1
4.0%
102836 1
4.0%
102832 1
4.0%

PROGRM_NM
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
세계테마기행
8 
TV정보쇼알짜왕(본)
5 
영상앨범산<KBS2>
4 
뉴체인지(본)
4 
걸어서세계속으로
3 

Length

Max length11
Median length9
Mean length8.32
Min length6

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st rowTV정보쇼알짜왕(본)
2nd row세계테마기행
3rd row걸어서세계속으로
4th row김영철의동네한바퀴
5th row영상앨범산<KBS2>

Common Values

ValueCountFrequency (%)
세계테마기행 8
32.0%
TV정보쇼알짜왕(본) 5
20.0%
영상앨범산<KBS2> 4
16.0%
뉴체인지(본) 4
16.0%
걸어서세계속으로 3
 
12.0%
김영철의동네한바퀴 1
 
4.0%

Length

2023-12-10T18:55:57.417124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:58.024866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세계테마기행 8
32.0%
tv정보쇼알짜왕(본 5
20.0%
영상앨범산<kbs2 4
16.0%
뉴체인지(본 4
16.0%
걸어서세계속으로 3
 
12.0%
김영철의동네한바퀴 1
 
4.0%

PROGRM_DC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing25
Missing (%)100.0%
Memory size357.0 B

BRDCST_TME_NM
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing12
Missing (%)48.0%
Memory size332.0 B
2023-12-10T18:55:58.370729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.3846154
Min length2

Characters and Unicode

Total characters44
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row227회
2nd row129회
3rd row6회
4th row228회
5th row789회
ValueCountFrequency (%)
227회 1
 
7.7%
129회 1
 
7.7%
6회 1
 
7.7%
228회 1
 
7.7%
789회 1
 
7.7%
7회 1
 
7.7%
229회 1
 
7.7%
790회 1
 
7.7%
8회 1
 
7.7%
230회 1
 
7.7%
Other values (3) 3
23.1%
2023-12-10T18:55:58.956414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 13
29.5%
2 9
20.5%
9 6
13.6%
7 5
 
11.4%
1 3
 
6.8%
8 3
 
6.8%
0 2
 
4.5%
3 2
 
4.5%
6 1
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
70.5%
Other Letter 13
29.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9
29.0%
9 6
19.4%
7 5
16.1%
1 3
 
9.7%
8 3
 
9.7%
0 2
 
6.5%
3 2
 
6.5%
6 1
 
3.2%
Other Letter
ValueCountFrequency (%)
회 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
70.5%
Hangul 13
29.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9
29.0%
9 6
19.4%
7 5
16.1%
1 3
 
9.7%
8 3
 
9.7%
0 2
 
6.5%
3 2
 
6.5%
6 1
 
3.2%
Hangul
ValueCountFrequency (%)
회 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
70.5%
Hangul 13
29.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 13
100.0%
ASCII
ValueCountFrequency (%)
2 9
29.0%
9 6
19.4%
7 5
16.1%
1 3
 
9.7%
8 3
 
9.7%
0 2
 
6.5%
3 2
 
6.5%
6 1
 
3.2%

PROGRM_BRDCST_AREA_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
전국
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전국 25
100.0%

Length

2023-12-10T18:55:59.222144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:59.436777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국 25
100.0%

PROGRM_GENRE_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
정보
21 
오락
4 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정보 21
84.0%
오락 4
 
16.0%

Length

2023-12-10T18:55:59.742072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:59.940370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보 21
84.0%
오락 4
 
16.0%

PROGRM_GENRE_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
다큐멘터리
16 
생활정보
5 
토크쇼
4 

Length

Max length5
Median length5
Mean length4.48
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활정보
2nd row다큐멘터리
3rd row다큐멘터리
4th row다큐멘터리
5th row다큐멘터리

Common Values

ValueCountFrequency (%)
다큐멘터리 16
64.0%
생활정보 5
 
20.0%
토크쇼 4
 
16.0%

Length

2023-12-10T18:56:00.200130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:56:00.446586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다큐멘터리 16
64.0%
생활정보 5
 
20.0%
토크쇼 4
 
16.0%

PROGRM_GENRE_SCLAS_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
다큐멘터리(역사기행문예)
16 
생활정보(생활경제)
5 
토크쇼
4 

Length

Max length13
Median length13
Mean length10.8
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활정보(생활경제)
2nd row다큐멘터리(역사기행문예)
3rd row다큐멘터리(역사기행문예)
4th row다큐멘터리(역사기행문예)
5th row다큐멘터리(역사기행문예)

Common Values

ValueCountFrequency (%)
다큐멘터리(역사기행문예) 16
64.0%
생활정보(생활경제) 5
 
20.0%
토크쇼 4
 
16.0%

Length

2023-12-10T18:56:00.698248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:56:00.908703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다큐멘터리(역사기행문예 16
64.0%
생활정보(생활경제 5
 
20.0%
토크쇼 4
 
16.0%

AUDE_CO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.94816
Minimum71.493
Maximum2052.276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-10T18:56:01.117390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71.493
5-th percentile80.2628
Q1138.214
median277.628
Q3568.628
95-th percentile1693.7716
Maximum2052.276
Range1980.783
Interquartile range (IQR)430.414

Descriptive statistics

Standard deviation573.09739
Coefficient of variation (CV)1.1394761
Kurtosis1.8462718
Mean502.94816
Median Absolute Deviation (MAD)179.019
Skewness1.7243585
Sum12573.704
Variance328440.62
MonotonicityNot monotonic
2023-12-10T18:56:01.377288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
71.493 1
 
4.0%
275.747 1
 
4.0%
1700.656 1
 
4.0%
277.628 1
 
4.0%
87.294 1
 
4.0%
78.505 1
 
4.0%
208.876 1
 
4.0%
757.137 1
 
4.0%
301.418 1
 
4.0%
98.609 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
71.493 1
4.0%
78.505 1
4.0%
87.294 1
4.0%
88.876 1
4.0%
98.609 1
4.0%
135.415 1
4.0%
138.214 1
4.0%
144.609 1
4.0%
190.15 1
4.0%
208.876 1
4.0%
ValueCountFrequency (%)
2052.276 1
4.0%
1700.656 1
4.0%
1666.234 1
4.0%
1420.929 1
4.0%
757.137 1
4.0%
595.232 1
4.0%
568.628 1
4.0%
529.543 1
4.0%
333.838 1
4.0%
321.606 1
4.0%

TV_SUBTTLS_CN
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing8
Missing (%)32.0%
Memory size332.0 B
2023-12-10T18:56:01.818289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length13.176471
Min length11

Characters and Unicode

Total characters224
Distinct characters95
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)100.0%

Sample

1st row비오틴,주름,몸,피부,콜라겐
2nd row폭포,수,구아,브라질,곳
3rd row어머니,떡,방앗간,부부,지금
4th row길,때,덕유산,삿갓,남
5th row운동,몸,독소,건강,지방
ValueCountFrequency (%)
비오틴,주름,몸,피부,콜라겐 1
 
5.9%
질,건강,유산균,균,토 1
 
5.9%
균,둘레,프리,건강,바이오 1
 
5.9%
당뇨,여주,아버지,때,수 1
 
5.9%
길,대청호,물,산,선수 1
 
5.9%
콜라겐,피부,엄마,건강,저분자 1
 
5.9%
단백질,아빠,운동,때,오빠 1
 
5.9%
숲,나무,산,때,마음 1
 
5.9%
콜라겐,때,물,제가,비타민 1
 
5.9%
폭포,수,구아,브라질,곳 1
 
5.9%
Other values (7) 7
41.2%
2023-12-10T18:56:02.744719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 68
30.4%
산 7
 
3.1%
때 6
 
2.7%
건 4
 
1.8%
강 4
 
1.8%
질 4
 
1.8%
부 4
 
1.8%
라 4
 
1.8%
오 3
 
1.3%
유 3
 
1.3%
Other values (85) 117
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 156
69.6%
Other Punctuation 68
30.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 7
 
4.5%
때 6
 
3.8%
건 4
 
2.6%
강 4
 
2.6%
질 4
 
2.6%
부 4
 
2.6%
라 4
 
2.6%
오 3
 
1.9%
유 3
 
1.9%
아 3
 
1.9%
Other values (84) 114
73.1%
Other Punctuation
ValueCountFrequency (%)
, 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 156
69.6%
Common 68
30.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 7
 
4.5%
때 6
 
3.8%
건 4
 
2.6%
강 4
 
2.6%
질 4
 
2.6%
부 4
 
2.6%
라 4
 
2.6%
오 3
 
1.9%
유 3
 
1.9%
아 3
 
1.9%
Other values (84) 114
73.1%
Common
ValueCountFrequency (%)
, 68
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 156
69.6%
ASCII 68
30.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 68
100.0%
Hangul
ValueCountFrequency (%)
산 7
 
4.5%
때 6
 
3.8%
건 4
 
2.6%
강 4
 
2.6%
질 4
 
2.6%
부 4
 
2.6%
라 4
 
2.6%
오 3
 
1.9%
유 3
 
1.9%
아 3
 
1.9%
Other values (84) 114
73.1%

Interactions

2023-12-10T18:55:52.338320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.263388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.312335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.323017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.298881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:52.533840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.471848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.540785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.509165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.548351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:52.726777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.690519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.754265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.705324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.769546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:52.962054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.911811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.926492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.877090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.965832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:53.188422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.088132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.124806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.057209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:52.149158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:56:02.954356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEPROGRM_NMBRDCST_TME_NMPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMAUDE_COTV_SUBTTLS_CN
BRDCST_DE1.0001.0000.4280.4230.6530.0001.0000.0980.0000.0000.6161.000
BRDCST_END_DE1.0001.0000.4280.4230.6530.0001.0000.0980.0000.0000.6161.000
CHNNEL_NM0.4280.4281.0000.9740.9791.0001.0000.7020.6360.6360.9301.000
PROGRM_BEGIN_TIME0.4230.4230.9741.0000.9910.9951.0000.5240.5110.5110.9101.000
PROGRM_END_TIME0.6530.6530.9790.9911.0000.9981.0000.6080.5710.5710.9241.000
PROGRM_NM0.0000.0001.0000.9950.9981.0001.0001.0001.0001.0000.9001.000
BRDCST_TME_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
PROGRM_GENRE_LCLAS_NM0.0980.0980.7020.5240.6081.0001.0001.0001.0001.0000.0531.000
PROGRM_GENRE_MLSFC_NM0.0000.0000.6360.5110.5711.0001.0001.0001.0001.0000.5071.000
PROGRM_GENRE_SCLAS_NM0.0000.0000.6360.5110.5711.0001.0001.0001.0001.0000.5071.000
AUDE_CO0.6160.6160.9300.9100.9240.9001.0000.0530.5070.5071.0001.000
TV_SUBTTLS_CN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:56:03.263657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PROGRM_GENRE_SCLAS_NMPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMCHNNEL_NMPROGRM_NM
PROGRM_GENRE_SCLAS_NM1.0000.9781.0000.6400.929
PROGRM_GENRE_LCLAS_NM0.9781.0000.9780.4720.909
PROGRM_GENRE_MLSFC_NM1.0000.9781.0000.6400.929
CHNNEL_NM0.6400.4720.6401.0000.951
PROGRM_NM0.9290.9090.9290.9511.000
2023-12-10T18:56:03.504331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRDCST_DEBRDCST_END_DEPROGRM_BEGIN_TIMEPROGRM_END_TIMEAUDE_COCHNNEL_NMPROGRM_NMPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NM
BRDCST_DE1.0001.000-0.054-0.021-0.1260.0000.0000.1760.0000.000
BRDCST_END_DE1.0001.000-0.054-0.021-0.1260.0000.0000.1760.0000.000
PROGRM_BEGIN_TIME-0.054-0.0541.0000.953-0.0120.7890.9120.3500.5040.504
PROGRM_END_TIME-0.021-0.0210.9531.0000.0510.8050.9240.4100.5690.569
AUDE_CO-0.126-0.126-0.0120.0511.0000.8430.7730.0000.3430.343
CHNNEL_NM0.0000.0000.7890.8050.8431.0000.9510.4720.6400.640
PROGRM_NM0.0000.0000.9120.9240.7730.9511.0000.9090.9290.929
PROGRM_GENRE_LCLAS_NM0.1760.1760.3500.4100.0000.4720.9091.0000.9780.978
PROGRM_GENRE_MLSFC_NM0.0000.0000.5040.5690.3430.6400.9290.9781.0001.000
PROGRM_GENRE_SCLAS_NM0.0000.0000.5040.5690.3430.6400.9290.9781.0001.000

Missing values

2023-12-10T18:55:53.509036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:55:53.965040image/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.
2023-12-10T18:55:54.295207image/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

BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEPROGRM_NMPROGRM_DCBRDCST_TME_NMPROGRM_BRDCST_AREA_NMPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMAUDE_COTV_SUBTTLS_CN
02021070120210701JTBC9042595143TV정보쇼알짜왕(본)<NA>227회전국정보생활정보생활정보(생활경제)71.493비오틴,주름,몸,피부,콜라겐
12021070120210701TV CHOSUN103018110450세계테마기행<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)275.747<NA>
22021070320210703KBS194027102836걸어서세계속으로<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)1420.929폭포,수,구아,브라질,곳
32021070320210703KBS1191134200313김영철의동네한바퀴<NA>129회전국정보다큐멘터리다큐멘터리(역사기행문예)2052.276어머니,떡,방앗간,부부,지금
42021070420210704KBS27283775521영상앨범산<KBS2><NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)595.232길,때,덕유산,삿갓,남
52021070720210707JTBC9053495250뉴체인지(본)<NA>6회전국오락토크쇼토크쇼135.415운동,몸,독소,건강,지방
62021070820210708JTBC9054095217TV정보쇼알짜왕(본)<NA>228회전국정보생활정보생활정보(생활경제)144.609단백질,배,산양유,근육,연정
72021070820210708TV CHOSUN103024110353세계테마기행<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)333.838<NA>
82021071020210710KBS194051102832걸어서세계속으로<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)1666.234빙하,곰,카,배,바다
92021071120210711KBS27301975609영상앨범산<KBS2><NA>789회전국정보다큐멘터리다큐멘터리(역사기행문예)568.628공룡,능선,산,때,봉
BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEPROGRM_NMPROGRM_DCBRDCST_TME_NMPROGRM_BRDCST_AREA_NMPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMAUDE_COTV_SUBTTLS_CN
152021072120210721JTBC9063395305뉴체인지(본)<NA>8회전국오락토크쇼토크쇼138.214단백질,아빠,운동,때,오빠
162021072120210721TV CHOSUN103015110611세계테마기행<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)321.606<NA>
172021072220210722JTBC9045195203TV정보쇼알짜왕(본)<NA>230회전국정보생활정보생활정보(생활경제)98.609콜라겐,피부,엄마,건강,저분자
182021072220210722TV CHOSUN103002110510세계테마기행<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)301.418<NA>
192021072520210725KBS28294385524영상앨범산<KBS2><NA>791회전국정보다큐멘터리다큐멘터리(역사기행문예)757.137길,대청호,물,산,선수
202021072820210728JTBC9060295306뉴체인지(본)<NA>9회전국오락토크쇼토크쇼208.876당뇨,여주,아버지,때,수
212021072820210728TV CHOSUN103102110300세계테마기행<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)78.505<NA>
222021072920210729JTBC9071795347TV정보쇼알짜왕(본)<NA>231회전국정보생활정보생활정보(생활경제)87.294균,둘레,프리,건강,바이오
232021072920210729TV CHOSUN102950110451세계테마기행<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)277.628<NA>
242021073120210731KBS18195990756걸어서세계속으로<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)1700.656섬,산토리,전,기자,마을