Overview

Dataset statistics

Number of variables10
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory86.3 B

Variable types

Categorical6
Text1
Numeric3

Alerts

PROGRM_GENRE_LCLAS_NM is highly overall correlated with CHNNEL_NM and 2 other fieldsHigh correlation
PROGRM_GENRE_SCLAS_NM is highly overall correlated with CHNNEL_NM and 2 other fieldsHigh correlation
PROGRM_GENRE_MLSFC_NM is highly overall correlated with CHNNEL_NM and 2 other fieldsHigh correlation
PROGRM_BEGIN_TIME is highly overall correlated with PROGRM_END_TIME and 2 other fieldsHigh correlation
PROGRM_END_TIME is highly overall correlated with PROGRM_BEGIN_TIME and 2 other fieldsHigh correlation
BRDCST_DE is highly overall correlated with PROGRM_BEGIN_TIME and 2 other fieldsHigh correlation
BRDCST_END_DE is highly overall correlated with PROGRM_BEGIN_TIME and 2 other fieldsHigh correlation
CHNNEL_NM is highly overall correlated with PROGRM_GENRE_LCLAS_NM and 2 other fieldsHigh correlation
BRDCST_DE is highly imbalanced (60.1%)Imbalance
BRDCST_END_DE is highly imbalanced (53.4%)Imbalance
WTCHNG_RT has 14 (13.9%) zerosZeros

Reproduction

Analysis started2023-12-10 09:57:54.564784
Analysis finished2023-12-10 09:58:00.928309
Duration6.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BRDCST_DE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
20210701
93 
20210702
 
8

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210701
2nd row20210701
3rd row20210701
4th row20210701
5th row20210701

Common Values

ValueCountFrequency (%)
20210701 93
92.1%
20210702 8
 
7.9%

Length

2023-12-10T18:58:01.042366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:01.227118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210701 93
92.1%
20210702 8
 
7.9%

BRDCST_END_DE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
20210701
91 
20210702
10 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210701
2nd row20210701
3rd row20210701
4th row20210701
5th row20210701

Common Values

ValueCountFrequency (%)
20210701 91
90.1%
20210702 10
 
9.9%

Length

2023-12-10T18:58:01.441318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:01.655188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210701 91
90.1%
20210702 10
 
9.9%

CHNNEL_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
가톨릭평화방송
42 
연합뉴스TV
22 
YTN
19 
tvN
15 
공영쇼핑
 
3

Length

Max length7
Median length6
Mean length5.3465347
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가톨릭평화방송 42
41.6%
연합뉴스TV 22
21.8%
YTN 19
18.8%
tvN 15
 
14.9%
공영쇼핑 3
 
3.0%

Length

2023-12-10T18:58:01.912218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:02.205840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가톨릭평화방송 42
41.6%
연합뉴스tv 22
21.8%
ytn 19
18.8%
tvn 15
 
14.9%
공영쇼핑 3
 
3.0%

PROGRM_NM
Text

Distinct87
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-10T18:58:02.636364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length7.5742574
Min length2

Characters and Unicode

Total characters765
Distinct characters228
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)73.3%

Sample

1st row어쩌다사장(재)
2nd row어쩌다사장(재)
3rd row삼시세끼오늘뭐먹지(재)
4th row프리한닥터W(재)
5th row유퀴즈온더블럭특별판(재)
ValueCountFrequency (%)
가톨릭영상교리 3
 
3.0%
최양업홀정오음악회 2
 
2.0%
ytn24(2555 2
 
2.0%
성체조배 2
 
2.0%
tv매일미사 2
 
2.0%
묵주기도 2
 
2.0%
2021시그니스아시아tv컨퍼런스 2
 
2.0%
뉴스투나잇 2
 
2.0%
뉴스02 2
 
2.0%
하느님의성사 2
 
2.0%
Other values (77) 80
79.2%
2023-12-10T18:58:03.346863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 45
 
5.9%
뉴 31
 
4.1%
2 27
 
3.5%
( 24
 
3.1%
) 24
 
3.1%
0 21
 
2.7%
기 16
 
2.1%
의 14
 
1.8%
재 14
 
1.8%
이 12
 
1.6%
Other values (218) 537
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 570
74.5%
Decimal Number 88
 
11.5%
Uppercase Letter 58
 
7.6%
Open Punctuation 24
 
3.1%
Close Punctuation 24
 
3.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 45
 
7.9%
뉴 31
 
5.4%
기 16
 
2.8%
의 14
 
2.5%
재 14
 
2.5%
이 12
 
2.1%
사 12
 
2.1%
도 9
 
1.6%
시 9
 
1.6%
리 8
 
1.4%
Other values (190) 400
70.2%
Uppercase Letter
ValueCountFrequency (%)
T 12
20.7%
Y 9
15.5%
N 8
13.8%
C 6
10.3%
V 5
8.6%
P 4
 
6.9%
B 3
 
5.2%
O 2
 
3.4%
L 2
 
3.4%
F 1
 
1.7%
Other values (6) 6
10.3%
Decimal Number
ValueCountFrequency (%)
2 27
30.7%
0 21
23.9%
4 10
 
11.4%
5 10
 
11.4%
1 9
 
10.2%
3 5
 
5.7%
7 2
 
2.3%
9 2
 
2.3%
6 2
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 570
74.5%
Common 137
 
17.9%
Latin 58
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 45
 
7.9%
뉴 31
 
5.4%
기 16
 
2.8%
의 14
 
2.5%
재 14
 
2.5%
이 12
 
2.1%
사 12
 
2.1%
도 9
 
1.6%
시 9
 
1.6%
리 8
 
1.4%
Other values (190) 400
70.2%
Latin
ValueCountFrequency (%)
T 12
20.7%
Y 9
15.5%
N 8
13.8%
C 6
10.3%
V 5
8.6%
P 4
 
6.9%
B 3
 
5.2%
O 2
 
3.4%
L 2
 
3.4%
F 1
 
1.7%
Other values (6) 6
10.3%
Common
ValueCountFrequency (%)
2 27
19.7%
( 24
17.5%
) 24
17.5%
0 21
15.3%
4 10
 
7.3%
5 10
 
7.3%
1 9
 
6.6%
3 5
 
3.6%
7 2
 
1.5%
9 2
 
1.5%
Other values (2) 3
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 570
74.5%
ASCII 195
 
25.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 45
 
7.9%
뉴 31
 
5.4%
기 16
 
2.8%
의 14
 
2.5%
재 14
 
2.5%
이 12
 
2.1%
사 12
 
2.1%
도 9
 
1.6%
시 9
 
1.6%
리 8
 
1.4%
Other values (190) 400
70.2%
ASCII
ValueCountFrequency (%)
2 27
13.8%
( 24
12.3%
) 24
12.3%
0 21
10.8%
T 12
 
6.2%
4 10
 
5.1%
5 10
 
5.1%
1 9
 
4.6%
Y 9
 
4.6%
N 8
 
4.1%
Other values (18) 41
21.0%

PROGRM_BEGIN_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110318.98
Minimum10
Maximum235205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:03.654504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13604
Q140113
median95010
Q3180017
95-th percentile224923
Maximum235205
Range235195
Interquartile range (IQR)139904

Descriptive statistics

Standard deviation74690.803
Coefficient of variation (CV)0.67704399
Kurtosis-1.3910171
Mean110318.98
Median Absolute Deviation (MAD)65000
Skewness0.20854873
Sum11142217
Variance5.578716 × 109
MonotonicityNot monotonic
2023-12-10T18:58:03.970744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 2
 
2.0%
20715 1
 
1.0%
120010 1
 
1.0%
103010 1
 
1.0%
100010 1
 
1.0%
95010 1
 
1.0%
94010 1
 
1.0%
90010 1
 
1.0%
80010 1
 
1.0%
73010 1
 
1.0%
Other values (90) 90
89.1%
ValueCountFrequency (%)
10 1
1.0%
1806 1
1.0%
5502 1
1.0%
5640 1
1.0%
10010 1
1.0%
13604 1
1.0%
15221 1
1.0%
15504 1
1.0%
20000 2
2.0%
20003 1
1.0%
ValueCountFrequency (%)
235205 1
1.0%
235014 1
1.0%
231028 1
1.0%
230010 1
1.0%
225204 1
1.0%
224923 1
1.0%
223010 1
1.0%
222010 1
1.0%
221010 1
1.0%
220010 1
1.0%

PROGRM_END_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113083.11
Minimum4047
Maximum235850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:04.254422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4047
5-th percentile14542
Q141253
median102850
Q3185850
95-th percentile225850
Maximum235850
Range231803
Interquartile range (IQR)144597

Descriptive statistics

Standard deviation74850.616
Coefficient of variation (CV)0.66190801
Kurtosis-1.4026886
Mean113083.11
Median Absolute Deviation (MAD)67891
Skewness0.19053095
Sum11421394
Variance5.6026147 × 109
MonotonicityNot monotonic
2023-12-10T18:58:04.515901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15959 2
 
2.0%
35111 1
 
1.0%
60350 1
 
1.0%
102850 1
 
1.0%
95850 1
 
1.0%
94850 1
 
1.0%
93850 1
 
1.0%
85850 1
 
1.0%
75850 1
 
1.0%
72850 1
 
1.0%
Other values (90) 90
89.1%
ValueCountFrequency (%)
4047 1
1.0%
4928 1
1.0%
5850 1
1.0%
13211 1
1.0%
14514 1
1.0%
14542 1
1.0%
15730 1
1.0%
15949 1
1.0%
15959 2
2.0%
22252 1
1.0%
ValueCountFrequency (%)
235850 1
1.0%
235635 1
1.0%
234345 1
1.0%
233618 1
1.0%
230246 1
1.0%
225850 1
1.0%
223922 1
1.0%
223805 1
1.0%
222850 1
1.0%
221850 1
1.0%

PROGRM_GENRE_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
정보
48 
보도
39 
오락
9 
드라마&영화
5 

Length

Max length6
Median length2
Mean length2.1980198
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정보 48
47.5%
보도 39
38.6%
오락 9
 
8.9%
드라마&영화 5
 
5.0%

Length

2023-12-10T18:58:04.826445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:05.087485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보 48
47.5%
보도 39
38.6%
오락 9
 
8.9%
드라마&영화 5
 
5.0%

PROGRM_GENRE_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
생활정보
47 
뉴스
38 
오락기타
8 
드라마
5 
버라이어티쇼
 
1
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.2277228
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활정보 47
46.5%
뉴스 38
37.6%
오락기타 8
 
7.9%
드라마 5
 
5.0%
버라이어티쇼 1
 
1.0%
보도기타 1
 
1.0%
다큐멘터리 1
 
1.0%

Length

2023-12-10T18:58:05.337551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:05.575823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활정보 47
46.5%
뉴스 38
37.6%
오락기타 8
 
7.9%
드라마 5
 
5.0%
버라이어티쇼 1
 
1.0%
보도기타 1
 
1.0%
다큐멘터리 1
 
1.0%

PROGRM_GENRE_SCLAS_NM
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
생활정보(종교)
42 
뉴스(종합)
25 
뉴스(스트레이트)
12 
오락기타
8 
미니시리즈
5 
Other values (6)
9 

Length

Max length10
Median length9
Mean length7.1188119
Min length4

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row오락기타
2nd row오락기타
3rd row오락기타
4th row생활정보(기타)
5th row오락기타

Common Values

ValueCountFrequency (%)
생활정보(종교) 42
41.6%
뉴스(종합) 25
24.8%
뉴스(스트레이트) 12
 
11.9%
오락기타 8
 
7.9%
미니시리즈 5
 
5.0%
생활정보(기타) 4
 
4.0%
버라이어티쇼 1
 
1.0%
뉴스(스포츠) 1
 
1.0%
생활정보(과학기술) 1
 
1.0%
보도기타 1
 
1.0%

Length

2023-12-10T18:58:05.834839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
생활정보(종교 42
41.6%
뉴스(종합 25
24.8%
뉴스(스트레이트 12
 
11.9%
오락기타 8
 
7.9%
미니시리즈 5
 
5.0%
생활정보(기타 4
 
4.0%
버라이어티쇼 1
 
1.0%
뉴스(스포츠 1
 
1.0%
생활정보(과학기술 1
 
1.0%
보도기타 1
 
1.0%

WTCHNG_RT
Real number (ℝ)

ZEROS 

Distinct85
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58190277
Minimum0
Maximum11.03266
Zeros14
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:06.108213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02483
median0.31403
Q30.88587
95-th percentile1.39971
Maximum11.03266
Range11.03266
Interquartile range (IQR)0.86104

Descriptive statistics

Standard deviation1.1813273
Coefficient of variation (CV)2.0301111
Kurtosis62.30038
Mean0.58190277
Median Absolute Deviation (MAD)0.31287
Skewness7.1778076
Sum58.77218
Variance1.3955342
MonotonicityNot monotonic
2023-12-10T18:58:06.417858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
13.9%
0.12287 3
 
3.0%
1.01001 2
 
2.0%
0.31403 1
 
1.0%
0.03911 1
 
1.0%
0.15495 1
 
1.0%
0.17814 1
 
1.0%
0.14165 1
 
1.0%
0.10544 1
 
1.0%
0.04114 1
 
1.0%
Other values (75) 75
74.3%
ValueCountFrequency (%)
0.0 14
13.9%
0.00116 1
 
1.0%
0.00126 1
 
1.0%
0.00274 1
 
1.0%
0.00737 1
 
1.0%
0.00746 1
 
1.0%
0.00991 1
 
1.0%
0.01245 1
 
1.0%
0.01256 1
 
1.0%
0.01992 1
 
1.0%
ValueCountFrequency (%)
11.03266 1
1.0%
3.3222 1
1.0%
1.63445 1
1.0%
1.53565 1
1.0%
1.40399 1
1.0%
1.39971 1
1.0%
1.36511 1
1.0%
1.18839 1
1.0%
1.15498 1
1.0%
1.14188 1
1.0%

Interactions

2023-12-10T18:57:59.699941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:58.506960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:59.126581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:59.896976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:58.761499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:59.304332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:00.192940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:58.946403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:59.476656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:58:06.618322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMWTCHNG_RT
BRDCST_DE1.0000.9610.0000.0000.8660.8330.1510.0000.2640.000
BRDCST_END_DE0.9611.0000.0350.0000.7770.8840.2700.0710.3490.000
CHNNEL_NM0.0000.0351.0001.0000.0000.0000.8080.7930.9310.275
PROGRM_NM0.0000.0001.0001.0000.8630.8931.0001.0001.0000.992
PROGRM_BEGIN_TIME0.8660.7770.0000.8631.0000.9740.0000.0000.0000.000
PROGRM_END_TIME0.8330.8840.0000.8930.9741.0000.0000.0000.0000.000
PROGRM_GENRE_LCLAS_NM0.1510.2700.8081.0000.0000.0001.0001.0001.0000.766
PROGRM_GENRE_MLSFC_NM0.0000.0710.7931.0000.0000.0001.0001.0001.0000.505
PROGRM_GENRE_SCLAS_NM0.2640.3490.9311.0000.0000.0001.0001.0001.0000.555
WTCHNG_RT0.0000.0000.2750.9920.0000.0000.7660.5050.5551.000
2023-12-10T18:58:06.871432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PROGRM_GENRE_LCLAS_NMBRDCST_DECHNNEL_NMPROGRM_GENRE_SCLAS_NMBRDCST_END_DEPROGRM_GENRE_MLSFC_NM
PROGRM_GENRE_LCLAS_NM1.0000.0980.7670.9630.1770.984
BRDCST_DE0.0981.0000.0000.2390.8210.000
CHNNEL_NM0.7670.0001.0000.8080.0370.664
PROGRM_GENRE_SCLAS_NM0.9630.2390.8081.0000.3170.978
BRDCST_END_DE0.1770.8210.0370.3171.0000.070
PROGRM_GENRE_MLSFC_NM0.9840.0000.6640.9780.0701.000
2023-12-10T18:58:07.480578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PROGRM_BEGIN_TIMEPROGRM_END_TIMEWTCHNG_RTBRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NM
PROGRM_BEGIN_TIME1.0000.8810.0870.6700.5870.0000.0000.0000.000
PROGRM_END_TIME0.8811.0000.0880.6090.7060.0000.0000.0000.000
WTCHNG_RT0.0870.0881.0000.0000.0000.2260.4030.3650.355
BRDCST_DE0.6700.6090.0001.0000.8210.0000.0980.0000.239
BRDCST_END_DE0.5870.7060.0000.8211.0000.0370.1770.0700.317
CHNNEL_NM0.0000.0000.2260.0000.0371.0000.7670.6640.808
PROGRM_GENRE_LCLAS_NM0.0000.0000.4030.0980.1770.7671.0000.9840.963
PROGRM_GENRE_MLSFC_NM0.0000.0000.3650.0000.0700.6640.9841.0000.978
PROGRM_GENRE_SCLAS_NM0.0000.0000.3550.2390.3170.8080.9630.9781.000

Missing values

2023-12-10T18:58:00.430912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:58:00.738083image/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

BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMWTCHNG_RT
02021070120210701tvN어쩌다사장(재)2071535111오락오락기타오락기타0.31403
12021070120210701tvN어쩌다사장(재)4011354654오락오락기타오락기타0.16031
22021070120210701tvN삼시세끼오늘뭐먹지(재)5554964907오락오락기타오락기타0.18549
32021070120210701tvN프리한닥터W(재)6594275235정보생활정보생활정보(기타)0.2407
42021070120210701tvN유퀴즈온더블럭특별판(재)8151090502오락오락기타오락기타0.48732
52021070120210701tvN신박한정리(재)92232111151오락오락기타오락기타0.7739
62021070120210701tvN식스센스2(재)113144130824오락버라이어티쇼버라이어티쇼0.48678
72021070120210701tvN곽씨네LP바(재)132632141941오락오락기타오락기타0.30437
82021070120210701tvN유퀴즈온더블럭(재)143905162531오락오락기타오락기타0.96786
92021070120210701tvN프리한19(재)164335174325오락오락기타오락기타0.88587
BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEPROGRM_GENRE_LCLAS_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMWTCHNG_RT
912021070120210701가톨릭평화방송하루를마치는저녁기도220010220850정보생활정보생활정보(종교)0.02483
922021070120210701가톨릭평화방송가톨릭영상교리221010221850정보생활정보생활정보(종교)0.0
932021070120210701가톨릭평화방송하느님의성사222010222850정보생활정보생활정보(종교)0.0
942021070120210701가톨릭평화방송CPBC뉴스(2230)223010225850정보생활정보생활정보(종교)0.0226
952021070120210701가톨릭평화방송최양업홀정오음악회230010235850정보생활정보생활정보(종교)0.00991
962021070220210702가톨릭평화방송장재봉신부의성경에세이105850정보생활정보생활정보(종교)0.0
972021070220210702가톨릭평화방송2021시그니스아시아TV컨퍼런스1001015949정보생활정보생활정보(종교)0.01256
982021070120210701공영쇼핑레포츠기기2000325959정보생활정보생활정보(기타)0.02958
992021070120210701공영쇼핑신선수산3000334959정보생활정보생활정보(기타)0.02113
1002021070120210701공영쇼핑의류3500335959정보생활정보생활정보(기타)0.04593