Overview

Dataset statistics

Number of variables14
Number of observations101
Missing cells160
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory119.3 B

Variable types

Numeric5
Categorical6
Unsupported1
Text2

Alerts

PROGRM_BRDCST_AREA_NM has constant value ""Constant
PROGRM_GENRE_LCLAS_NM has constant value ""Constant
PROGRM_GENRE_MLSFC_NM is highly overall correlated with PROGRM_BEGIN_TIME and 4 other fieldsHigh correlation
PROGRM_GENRE_SCLAS_NM is highly overall correlated with PROGRM_BEGIN_TIME and 4 other fieldsHigh correlation
PROGRM_NM is highly overall correlated with PROGRM_BEGIN_TIME and 4 other fieldsHigh correlation
CHNNEL_NM is highly overall correlated with PROGRM_BEGIN_TIME and 4 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 5 other fieldsHigh correlation
PROGRM_END_TIME is highly overall correlated with PROGRM_BEGIN_TIME and 5 other fieldsHigh correlation
AUDE_CO is highly overall correlated with PROGRM_BEGIN_TIME and 1 other fieldsHigh correlation
PROGRM_DC has 101 (100.0%) missing valuesMissing
BRDCST_TME_NM has 45 (44.6%) missing valuesMissing
TV_SUBTTLS_CN has 14 (13.9%) missing valuesMissing
AUDE_CO has unique valuesUnique
PROGRM_DC is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:39:54.339197
Analysis finished2023-12-10 09:39:59.653212
Duration5.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BRDCST_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210712
Minimum20210701
Maximum20210722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:00.095942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210701
5-th percentile20210701
Q120210706
median20210712
Q320210716
95-th percentile20210721
Maximum20210722
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.353591
Coefficient of variation (CV)3.143675 × 10-7
Kurtosis-1.1628269
Mean20210712
Median Absolute Deviation (MAD)6
Skewness-0.0449411
Sum2.0412819 × 109
Variance40.368119
MonotonicityNot monotonic
2023-12-10T18:40:00.292837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20210713 7
 
6.9%
20210721 7
 
6.9%
20210720 7
 
6.9%
20210706 7
 
6.9%
20210714 7
 
6.9%
20210712 6
 
5.9%
20210719 6
 
5.9%
20210715 6
 
5.9%
20210701 6
 
5.9%
20210708 6
 
5.9%
Other values (9) 36
35.6%
ValueCountFrequency (%)
20210701 6
5.9%
20210702 5
5.0%
20210704 2
 
2.0%
20210705 6
5.9%
20210706 7
6.9%
20210707 6
5.9%
20210708 6
5.9%
20210709 5
5.0%
20210711 2
 
2.0%
20210712 6
5.9%
ValueCountFrequency (%)
20210722 3
3.0%
20210721 7
6.9%
20210720 7
6.9%
20210719 6
5.9%
20210718 2
 
2.0%
20210716 5
5.0%
20210715 6
5.9%
20210714 7
6.9%
20210713 7
6.9%
20210712 6
5.9%

BRDCST_END_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210712
Minimum20210701
Maximum20210722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:00.492126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210701
5-th percentile20210701
Q120210706
median20210712
Q320210716
95-th percentile20210721
Maximum20210722
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.353591
Coefficient of variation (CV)3.143675 × 10-7
Kurtosis-1.1628269
Mean20210712
Median Absolute Deviation (MAD)6
Skewness-0.0449411
Sum2.0412819 × 109
Variance40.368119
MonotonicityNot monotonic
2023-12-10T18:40:00.669295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20210713 7
 
6.9%
20210721 7
 
6.9%
20210720 7
 
6.9%
20210706 7
 
6.9%
20210714 7
 
6.9%
20210712 6
 
5.9%
20210719 6
 
5.9%
20210715 6
 
5.9%
20210701 6
 
5.9%
20210708 6
 
5.9%
Other values (9) 36
35.6%
ValueCountFrequency (%)
20210701 6
5.9%
20210702 5
5.0%
20210704 2
 
2.0%
20210705 6
5.9%
20210706 7
6.9%
20210707 6
5.9%
20210708 6
5.9%
20210709 5
5.0%
20210711 2
 
2.0%
20210712 6
5.9%
ValueCountFrequency (%)
20210722 3
3.0%
20210721 7
6.9%
20210720 7
6.9%
20210719 6
5.9%
20210718 2
 
2.0%
20210716 5
5.0%
20210715 6
5.9%
20210714 7
6.9%
20210713 7
6.9%
20210712 6
5.9%

CHNNEL_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
KBS1
49 
KBS2
22 
MBN
21 
SBS
6 
JTBC
 
3

Length

Max length4
Median length4
Mean length3.7326733
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KBS1 49
48.5%
KBS2 22
21.8%
MBN 21
20.8%
SBS 6
 
5.9%
JTBC 3
 
3.0%

Length

2023-12-10T18:40:00.871549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:01.039526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kbs1 49
48.5%
kbs2 22
21.8%
mbn 21
20.8%
sbs 6
 
5.9%
jtbc 3
 
3.0%

PROGRM_BEGIN_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102251.18
Minimum10103
Maximum190857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:01.277867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10103
5-th percentile20000
Q150027
median93005
Q3175910
95-th percentile183237
Maximum190857
Range180754
Interquartile range (IQR)125883

Descriptive statistics

Standard deviation58863.473
Coefficient of variation (CV)0.57567525
Kurtosis-1.4283135
Mean102251.18
Median Absolute Deviation (MAD)42980
Skewness0.25998124
Sum10327369
Variance3.4649084 × 109
MonotonicityNot monotonic
2023-12-10T18:40:01.664582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50025 9
 
8.9%
93006 4
 
4.0%
93005 4
 
4.0%
50005 3
 
3.0%
93004 2
 
2.0%
20000 2
 
2.0%
93002 2
 
2.0%
50026 2
 
2.0%
113300 1
 
1.0%
183040 1
 
1.0%
Other values (71) 71
70.3%
ValueCountFrequency (%)
10103 1
1.0%
10225 1
1.0%
11014 1
1.0%
14853 1
1.0%
15829 1
1.0%
20000 2
2.0%
20155 1
1.0%
45737 1
1.0%
45805 1
1.0%
45931 1
1.0%
ValueCountFrequency (%)
190857 1
1.0%
183350 1
1.0%
183258 1
1.0%
183247 1
1.0%
183244 1
1.0%
183237 1
1.0%
183235 1
1.0%
183223 1
1.0%
183216 1
1.0%
183214 1
1.0%

PROGRM_END_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108670.97
Minimum14742
Maximum195013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:01.895664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14742
5-th percentile23444
Q154553
median100912
Q3185746
95-th percentile194138
Maximum195013
Range180271
Interquartile range (IQR)131193

Descriptive statistics

Standard deviation61385.342
Coefficient of variation (CV)0.56487342
Kurtosis-1.4205288
Mean108670.97
Median Absolute Deviation (MAD)50005
Skewness0.2664124
Sum10975768
Variance3.7681602 × 109
MonotonicityNot monotonic
2023-12-10T18:40:02.125826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100913 3
 
3.0%
100912 3
 
3.0%
185810 2
 
2.0%
100910 2
 
2.0%
50903 2
 
2.0%
50851 2
 
2.0%
50911 2
 
2.0%
50907 2
 
2.0%
15959 2
 
2.0%
24436 1
 
1.0%
Other values (80) 80
79.2%
ValueCountFrequency (%)
14742 1
1.0%
14915 1
1.0%
15620 1
1.0%
15959 2
2.0%
23444 1
1.0%
24436 1
1.0%
24829 1
1.0%
50821 1
1.0%
50851 2
2.0%
50856 1
1.0%
ValueCountFrequency (%)
195013 1
1.0%
194249 1
1.0%
194157 1
1.0%
194148 1
1.0%
194140 1
1.0%
194138 1
1.0%
194126 1
1.0%
194118 1
1.0%
194115 1
1.0%
194112 1
1.0%

PROGRM_NM
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
생활의발견스페셜<KBS1>
16 
6시내고향
16 
2TV생생정보
16 
생생정보마당
15 
내고향스페셜
9 
Other values (5)
29 

Length

Max length14
Median length11
Mean length8.049505
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활의발견스페셜<KBS1>
2nd row6시내고향
3rd row2TV생생정보
4th row고향이보인다
5th rowTV정보쇼알짜왕(본)

Common Values

ValueCountFrequency (%)
생활의발견스페셜<KBS1> 16
15.8%
6시내고향 16
15.8%
2TV생생정보 16
15.8%
생생정보마당 15
14.9%
내고향스페셜 9
8.9%
생활의발견<KBS1> 8
7.9%
고향이보인다 6
 
5.9%
생생정보스페셜 6
 
5.9%
생생정보마당스페셜 6
 
5.9%
TV정보쇼알짜왕(본) 3
 
3.0%

Length

2023-12-10T18:40:02.452072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:02.757991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활의발견스페셜<kbs1 16
15.8%
6시내고향 16
15.8%
2tv생생정보 16
15.8%
생생정보마당 15
14.9%
내고향스페셜 9
8.9%
생활의발견<kbs1 8
7.9%
고향이보인다 6
 
5.9%
생생정보스페셜 6
 
5.9%
생생정보마당스페셜 6
 
5.9%
tv정보쇼알짜왕(본 3
 
3.0%

PROGRM_DC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101
Missing (%)100.0%
Memory size1.0 KiB

BRDCST_TME_NM
Text

MISSING 

Distinct56
Distinct (%)100.0%
Missing45
Missing (%)44.6%
Memory size940.0 B
2023-12-10T18:40:03.234958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.5714286
Min length4

Characters and Unicode

Total characters256
Distinct characters11
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

Unique56 ?
Unique (%)100.0%

Sample

1st row7316회
2nd row1347회
3rd row227회
4th row914회
5th row7317회
ValueCountFrequency (%)
7316회 1
 
1.8%
1347회 1
 
1.8%
7324회 1
 
1.8%
1355회 1
 
1.8%
922회 1
 
1.8%
7325회 1
 
1.8%
1356회 1
 
1.8%
923회 1
 
1.8%
7326회 1
 
1.8%
1357회 1
 
1.8%
Other values (46) 46
82.1%
2023-12-10T18:40:03.968261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 56
21.9%
3 38
14.8%
2 35
13.7%
1 31
12.1%
7 30
11.7%
9 21
 
8.2%
5 15
 
5.9%
6 9
 
3.5%
4 8
 
3.1%
8 8
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 200
78.1%
Other Letter 56
 
21.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 38
19.0%
2 35
17.5%
1 31
15.5%
7 30
15.0%
9 21
10.5%
5 15
 
7.5%
6 9
 
4.5%
4 8
 
4.0%
8 8
 
4.0%
0 5
 
2.5%
Other Letter
ValueCountFrequency (%)
회 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 200
78.1%
Hangul 56
 
21.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 38
19.0%
2 35
17.5%
1 31
15.5%
7 30
15.0%
9 21
10.5%
5 15
 
7.5%
6 9
 
4.5%
4 8
 
4.0%
8 8
 
4.0%
0 5
 
2.5%
Hangul
ValueCountFrequency (%)
회 56
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200
78.1%
Hangul 56
 
21.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 56
100.0%
ASCII
ValueCountFrequency (%)
3 38
19.0%
2 35
17.5%
1 31
15.5%
7 30
15.0%
9 21
10.5%
5 15
 
7.5%
6 9
 
4.5%
4 8
 
4.0%
8 8
 
4.0%
0 5
 
2.5%

PROGRM_BRDCST_AREA_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
전국
101 

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 101
100.0%

Length

2023-12-10T18:40:04.225736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:04.407374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국 101
100.0%

PROGRM_GENRE_LCLAS_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
정보
101 

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 101
100.0%

Length

2023-12-10T18:40:04.568375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:04.890423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보 101
100.0%

PROGRM_GENRE_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
생활정보
58 
정보종합
43 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활정보 58
57.4%
정보종합 43
42.6%

Length

2023-12-10T18:40:05.209905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:05.377264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활정보 58
57.4%
정보종합 43
42.6%

PROGRM_GENRE_SCLAS_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
정보종합
43 
생활정보(종합)
25 
생활정보(가사)
24 
생활정보(지역)
6 
생활정보(생활경제)
 
3

Length

Max length10
Median length8
Mean length6.3564356
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활정보(가사)
2nd row생활정보(종합)
3rd row정보종합
4th row생활정보(지역)
5th row생활정보(생활경제)

Common Values

ValueCountFrequency (%)
정보종합 43
42.6%
생활정보(종합) 25
24.8%
생활정보(가사) 24
23.8%
생활정보(지역) 6
 
5.9%
생활정보(생활경제) 3
 
3.0%

Length

2023-12-10T18:40:05.577526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:05.835126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보종합 43
42.6%
생활정보(종합 25
24.8%
생활정보(가사 24
23.8%
생활정보(지역 6
 
5.9%
생활정보(생활경제 3
 
3.0%

AUDE_CO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean676.30459
Minimum21.426
Maximum2438.406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:06.189210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.426
5-th percentile71.493
Q1131.219
median239.48
Q31433.574
95-th percentile2050.33
Maximum2438.406
Range2416.98
Interquartile range (IQR)1302.355

Descriptive statistics

Standard deviation747.9811
Coefficient of variation (CV)1.1059826
Kurtosis-0.70991253
Mean676.30459
Median Absolute Deviation (MAD)133.411
Skewness0.97873419
Sum68306.764
Variance559475.73
MonotonicityNot monotonic
2023-12-10T18:40:06.433093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.069 1
 
1.0%
116.884 1
 
1.0%
147.127 1
 
1.0%
1316.626 1
 
1.0%
1949.665 1
 
1.0%
203.861 1
 
1.0%
173.763 1
 
1.0%
190.15 1
 
1.0%
105.912 1
 
1.0%
1623.363 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
21.426 1
1.0%
24.97 1
1.0%
26.672 1
1.0%
50.973 1
1.0%
68.669 1
1.0%
71.493 1
1.0%
75.221 1
1.0%
76.023 1
1.0%
77.912 1
1.0%
79.21 1
1.0%
ValueCountFrequency (%)
2438.406 1
1.0%
2240.983 1
1.0%
2182.486 1
1.0%
2100.494 1
1.0%
2064.629 1
1.0%
2050.33 1
1.0%
2045.203 1
1.0%
2038.851 1
1.0%
2034.759 1
1.0%
1990.236 1
1.0%

TV_SUBTTLS_CN
Text

MISSING 

Distinct87
Distinct (%)100.0%
Missing14
Missing (%)13.9%
Memory size940.0 B
2023-12-10T18:40:06.824071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.321839
Min length8

Characters and Unicode

Total characters1159
Distinct characters257
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

Unique87 ?
Unique (%)100.0%

Sample

1st row시장,전통,백신,접종,오늘
2nd row씨,오늘,물,복분자,만두
3rd row읍,체험,보라,꽃,곳
4th row비오틴,주름,몸,피부,콜라겐
5th row사랑,원래,곳,음,
ValueCountFrequency (%)
시장,전통,백신,접종,오늘 1
 
1.1%
더덕,재첩,보물,맛,해설 1
 
1.1%
맛,두부,오믈렛,일,아내 1
 
1.1%
어머니,참치,오늘,꽃,사장님 1
 
1.1%
감자,축제,체리,감물,음성 1
 
1.1%
질,건강,유산균,균,토 1
 
1.1%
속,쓰레기,일,밥,오늘 1
 
1.1%
맛,주인장,슈퍼,우먼,손님 1
 
1.1%
망고,시장,애플,오늘,카누 1
 
1.1%
할머니,맛,추억,자리,시절 1
 
1.1%
Other values (77) 77
88.5%
2023-12-10T18:40:07.511971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 347
29.9%
오 32
 
2.8%
어 32
 
2.8%
맛 29
 
2.5%
늘 26
 
2.2%
자 19
 
1.6%
니 17
 
1.5%
머 17
 
1.5%
장 16
 
1.4%
지 14
 
1.2%
Other values (247) 610
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 812
70.1%
Other Punctuation 347
29.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
오 32
 
3.9%
어 32
 
3.9%
맛 29
 
3.6%
늘 26
 
3.2%
자 19
 
2.3%
니 17
 
2.1%
머 17
 
2.1%
장 16
 
2.0%
지 14
 
1.7%
아 13
 
1.6%
Other values (246) 597
73.5%
Other Punctuation
ValueCountFrequency (%)
, 347
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 812
70.1%
Common 347
29.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
오 32
 
3.9%
어 32
 
3.9%
맛 29
 
3.6%
늘 26
 
3.2%
자 19
 
2.3%
니 17
 
2.1%
머 17
 
2.1%
장 16
 
2.0%
지 14
 
1.7%
아 13
 
1.6%
Other values (246) 597
73.5%
Common
ValueCountFrequency (%)
, 347
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 812
70.1%
ASCII 347
29.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 347
100.0%
Hangul
ValueCountFrequency (%)
오 32
 
3.9%
어 32
 
3.9%
맛 29
 
3.6%
늘 26
 
3.2%
자 19
 
2.3%
니 17
 
2.1%
머 17
 
2.1%
장 16
 
2.0%
지 14
 
1.7%
아 13
 
1.6%
Other values (246) 597
73.5%

Interactions

2023-12-10T18:39:58.288218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:55.384723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:56.126807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:56.828710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:57.541754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:58.413840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:55.530134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:56.258778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:56.971753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:57.700002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:58.541444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:55.662140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:56.395312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:57.110996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:57.832388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:58.682600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:55.826175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:56.555251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:57.251637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:57.979559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:58.829703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:55.975395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:56.711419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:57.409112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:58.143103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:40:07.685229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEPROGRM_NMBRDCST_TME_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMAUDE_COTV_SUBTTLS_CN
BRDCST_DE1.0001.0000.0000.0000.0000.0001.0000.0000.0000.0001.000
BRDCST_END_DE1.0001.0000.0000.0000.0000.0001.0000.0000.0000.0001.000
CHNNEL_NM0.0000.0001.0000.8230.8251.0001.0001.0000.9910.7171.000
PROGRM_BEGIN_TIME0.0000.0000.8231.0000.9970.9581.0000.5380.7880.6131.000
PROGRM_END_TIME0.0000.0000.8250.9971.0000.9621.0000.5380.7880.6521.000
PROGRM_NM0.0000.0001.0000.9580.9621.0001.0001.0001.0000.8461.000
BRDCST_TME_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
PROGRM_GENRE_MLSFC_NM0.0000.0001.0000.5380.5381.0001.0001.0001.0000.6051.000
PROGRM_GENRE_SCLAS_NM0.0000.0000.9910.7880.7881.0001.0001.0001.0000.6851.000
AUDE_CO0.0000.0000.7170.6130.6520.8461.0000.6050.6851.0001.000
TV_SUBTTLS_CN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:40:07.913540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NMPROGRM_NMCHNNEL_NM
PROGRM_GENRE_MLSFC_NM1.0000.9850.9590.985
PROGRM_GENRE_SCLAS_NM0.9851.0000.9740.860
PROGRM_NM0.9590.9741.0000.974
CHNNEL_NM0.9850.8600.9741.000
2023-12-10T18:40:08.131787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRDCST_DEBRDCST_END_DEPROGRM_BEGIN_TIMEPROGRM_END_TIMEAUDE_COCHNNEL_NMPROGRM_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_SCLAS_NM
BRDCST_DE1.0001.000-0.043-0.0440.0810.0000.0000.0000.000
BRDCST_END_DE1.0001.000-0.043-0.0440.0810.0000.0000.0000.000
PROGRM_BEGIN_TIME-0.043-0.0431.0000.9860.5870.7090.8800.5670.660
PROGRM_END_TIME-0.044-0.0440.9861.0000.5670.7130.8890.5670.660
AUDE_CO0.0810.0810.5870.5671.0000.3670.4150.4480.342
CHNNEL_NM0.0000.0000.7090.7130.3671.0000.9740.9850.860
PROGRM_NM0.0000.0000.8800.8890.4150.9741.0000.9590.974
PROGRM_GENRE_MLSFC_NM0.0000.0000.5670.5670.4480.9850.9591.0000.985
PROGRM_GENRE_SCLAS_NM0.0000.0000.6600.6600.3420.8600.9740.9851.000

Missing values

2023-12-10T18:39:59.026002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:39:59.339143image/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.
2023-12-10T18:39:59.559128image/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
02021070120210701KBS15000550900생활의발견스페셜<KBS1><NA><NA>전국정보생활정보생활정보(가사)106.069<NA>
12021070120210701KBS11759221857416시내고향<NA>7316회전국정보생활정보생활정보(종합)1883.066시장,전통,백신,접종,오늘
22021070120210701KBS21832231941382TV생생정보<NA>1347회전국정보정보종합정보종합1295.686씨,오늘,물,복분자,만두
32021070120210701SBS112809115219고향이보인다<NA><NA>전국정보생활정보생활정보(지역)109.263읍,체험,보라,꽃,곳
42021070120210701JTBC9042595143TV정보쇼알짜왕(본)<NA>227회전국정보생활정보생활정보(생활경제)71.493비오틴,주름,몸,피부,콜라겐
52021070120210701MBN93006100912생생정보마당<NA>914회전국정보정보종합정보종합165.845사랑,원래,곳,음,
62021070220210702KBS15000550856생활의발견스페셜<KBS1><NA><NA>전국정보생활정보생활정보(가사)132.955음식,세종,커피,등속,대왕
72021070220210702KBS11758281857476시내고향<NA>7317회전국정보생활정보생활정보(종합)1802.339수박,전,윤,할아버지,총장
82021070220210702KBS21832371941152TV생생정보<NA>1348회전국정보정보종합정보종합1080.799맛,고구마,병,순,냉면
92021070220210702SBS112923115316고향이보인다<NA><NA>전국정보생활정보생활정보(지역)77.912선수,씨름,씨름단,장사,민속
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
912021072020210720MBN93006100913생생정보마당<NA>927회전국정보정보종합정보종합233.672<NA>
922021072120210721KBS12000023444생활의발견<KBS1><NA><NA>전국정보생활정보생활정보(가사)79.21어르신,대상,포진,요가,바다
932021072120210721KBS15002550911생활의발견스페셜<KBS1><NA><NA>전국정보생활정보생활정보(가사)252.525곰,박자,꽃,때,산
942021072120210721KBS15101055807내고향스페셜<NA><NA>전국정보생활정보생활정보(종합)307.971콩,맛,물,콩국수,오늘
952021072120210721KBS11759061858196시내고향<NA>7330회전국정보생활정보생활정보(종합)2064.629삼치,어머니,오늘,때,감자
962021072120210721KBS21832581941482TV생생정보<NA>1361회전국정보정보종합정보종합1549.082맛,딸,어머니,짬뽕,우유
972021072120210721MBN93004100924생생정보마당<NA>928회전국정보정보종합정보종합224.54마담,인물,공개,색,
982021072220210722KBS15003050851생활의발견스페셜<KBS1><NA><NA>전국정보생활정보생활정보(가사)68.669아귀,뼈,때,더위,키
992021072220210722KBS11758501858106시내고향<NA>7331회전국정보생활정보생활정보(종합)1623.957시장,홍,포도,여주,오늘
1002021072220210722KBS21908571950132TV생생정보<NA>1362회전국정보정보종합정보종합2034.759김치,떡볶이,맛,섬,치킨