Overview

Dataset statistics

Number of variables14
Number of observations96
Missing cells129
Missing cells (%)9.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 KiB
Average record size in memory119.4 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_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 6 other fieldsHigh correlation
PROGRM_GENRE_MLSFC_NM is highly overall correlated with PROGRM_BEGIN_TIME and 6 other fieldsHigh correlation
PROGRM_GENRE_LCLAS_NM is highly overall correlated with PROGRM_BEGIN_TIME and 5 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 6 other fieldsHigh correlation
PROGRM_END_TIME is highly overall correlated with PROGRM_BEGIN_TIME and 6 other fieldsHigh correlation
AUDE_CO is highly overall correlated with PROGRM_BEGIN_TIME and 3 other fieldsHigh correlation
PROGRM_GENRE_LCLAS_NM is highly imbalanced (55.1%)Imbalance
PROGRM_DC has 96 (100.0%) missing valuesMissing
BRDCST_TME_NM has 6 (6.2%) missing valuesMissing
TV_SUBTTLS_CN has 27 (28.1%) 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 09:45:25.017165
Analysis finished2023-12-10 09:45:32.377460
Duration7.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BRDCST_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210714
Minimum20210701
Maximum20210731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-10T18:45:32.659137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210701
5-th percentile20210702
Q120210707
median20210714
Q320210721
95-th percentile20210728
Maximum20210731
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.4464191
Coefficient of variation (CV)4.1791789 × 10-7
Kurtosis-1.0048663
Mean20210714
Median Absolute Deviation (MAD)7
Skewness0.15421787
Sum1.9402286 × 109
Variance71.341996
MonotonicityIncreasing
2023-12-10T18:45:32.980530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20210702 5
 
5.2%
20210715 5
 
5.2%
20210705 5
 
5.2%
20210712 5
 
5.2%
20210719 5
 
5.2%
20210722 4
 
4.2%
20210716 4
 
4.2%
20210714 4
 
4.2%
20210721 4
 
4.2%
20210701 4
 
4.2%
Other values (21) 51
53.1%
ValueCountFrequency (%)
20210701 4
4.2%
20210702 5
5.2%
20210703 4
4.2%
20210704 1
 
1.0%
20210705 5
5.2%
20210706 3
3.1%
20210707 4
4.2%
20210708 4
4.2%
20210709 4
4.2%
20210710 2
 
2.1%
ValueCountFrequency (%)
20210731 2
2.1%
20210730 2
2.1%
20210729 1
 
1.0%
20210728 2
2.1%
20210727 1
 
1.0%
20210726 3
3.1%
20210725 1
 
1.0%
20210724 2
2.1%
20210723 4
4.2%
20210722 4
4.2%

BRDCST_END_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210714
Minimum20210701
Maximum20210731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-10T18:45:33.240946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210701
5-th percentile20210702
Q120210707
median20210714
Q320210721
95-th percentile20210729
Maximum20210731
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.4635376
Coefficient of variation (CV)4.1876489 × 10-7
Kurtosis-1.0041062
Mean20210714
Median Absolute Deviation (MAD)7
Skewness0.15223911
Sum1.9402286 × 109
Variance71.631469
MonotonicityNot monotonic
2023-12-10T18:45:33.543946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20210715 6
 
6.2%
20210712 5
 
5.2%
20210722 5
 
5.2%
20210719 5
 
5.2%
20210705 5
 
5.2%
20210702 5
 
5.2%
20210708 5
 
5.2%
20210716 4
 
4.2%
20210701 4
 
4.2%
20210709 4
 
4.2%
Other values (21) 48
50.0%
ValueCountFrequency (%)
20210701 4
4.2%
20210702 5
5.2%
20210703 3
3.1%
20210704 2
 
2.1%
20210705 5
5.2%
20210706 3
3.1%
20210707 3
3.1%
20210708 5
5.2%
20210709 4
4.2%
20210710 1
 
1.0%
ValueCountFrequency (%)
20210731 2
 
2.1%
20210730 2
 
2.1%
20210729 2
 
2.1%
20210728 1
 
1.0%
20210727 1
 
1.0%
20210726 3
3.1%
20210725 2
 
2.1%
20210724 1
 
1.0%
20210723 4
4.2%
20210722 5
5.2%

CHNNEL_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size900.0 B
MBN
30 
KBS1
27 
KBS2
17 
SBS
7 
TV CHOSUN
5 
Other values (2)
10 

Length

Max length9
Median length4
Mean length3.8229167
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKBS1
2nd rowKBS1
3rd rowKBS2
4th rowMBN
5th rowKBS1

Common Values

ValueCountFrequency (%)
MBN 30
31.2%
KBS1 27
28.1%
KBS2 17
17.7%
SBS 7
 
7.3%
TV CHOSUN 5
 
5.2%
MBC 5
 
5.2%
JTBC 5
 
5.2%

Length

2023-12-10T18:45:33.950235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:34.157481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mbn 30
29.7%
kbs1 27
26.7%
kbs2 17
16.8%
sbs 7
 
6.9%
tv 5
 
5.0%
chosun 5
 
5.0%
mbc 5
 
5.0%
jtbc 5
 
5.0%

PROGRM_BEGIN_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146218.02
Minimum22131
Maximum231249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-10T18:45:34.427572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22131
5-th percentile51873.25
Q193005
median175911
Q3183281
95-th percentile224553.75
Maximum231249
Range209118
Interquartile range (IQR)90276

Descriptive statistics

Standard deviation57064.82
Coefficient of variation (CV)0.39027214
Kurtosis-1.2895932
Mean146218.02
Median Absolute Deviation (MAD)38915.5
Skewness-0.35781031
Sum14036930
Variance3.2563936 × 109
MonotonicityNot monotonic
2023-12-10T18:45:34.838436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93006 5
 
5.2%
93005 5
 
5.2%
93002 4
 
4.2%
93004 2
 
2.1%
93003 2
 
2.1%
175922 1
 
1.0%
85952 1
 
1.0%
175938 1
 
1.0%
45737 1
 
1.0%
205825 1
 
1.0%
Other values (73) 73
76.0%
ValueCountFrequency (%)
22131 1
1.0%
45727 1
1.0%
45737 1
1.0%
45805 1
1.0%
45931 1
1.0%
53854 1
1.0%
53901 1
1.0%
53924 1
1.0%
53956 1
1.0%
80007 1
1.0%
ValueCountFrequency (%)
231249 1
1.0%
231222 1
1.0%
230927 1
1.0%
225015 1
1.0%
225006 1
1.0%
224403 1
1.0%
223837 1
1.0%
223734 1
1.0%
205919 1
1.0%
205905 1
1.0%

PROGRM_END_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135380.59
Minimum833
Maximum222534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-10T18:45:35.194628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum833
5-th percentile3824.5
Q1100910
median150730
Q3194111.25
95-th percentile205405.75
Maximum222534
Range221701
Interquartile range (IQR)93201.25

Descriptive statistics

Standard deviation66023.131
Coefficient of variation (CV)0.48768534
Kurtosis-0.92513526
Mean135380.59
Median Absolute Deviation (MAD)49814.5
Skewness-0.52865824
Sum12996537
Variance4.3590538 × 109
MonotonicityNot monotonic
2023-12-10T18:45:35.460476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100912 3
 
3.1%
100913 3
 
3.1%
100910 3
 
3.1%
205358 2
 
2.1%
205400 2
 
2.1%
185810 2
 
2.1%
185741 1
 
1.0%
185723 1
 
1.0%
194148 1
 
1.0%
185819 1
 
1.0%
Other values (77) 77
80.2%
ValueCountFrequency (%)
833 1
1.0%
834 1
1.0%
1533 1
1.0%
1932 1
1.0%
2050 1
1.0%
4416 1
1.0%
4706 1
1.0%
5318 1
1.0%
24042 1
1.0%
54538 1
1.0%
ValueCountFrequency (%)
222534 1
1.0%
215249 1
1.0%
215231 1
1.0%
215148 1
1.0%
205423 1
1.0%
205400 2
2.1%
205358 2
2.1%
202929 1
1.0%
202847 1
1.0%
202815 1
1.0%

PROGRM_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size900.0 B
생생정보마당
22 
6시내고향
17 
2TV생생정보
17 
생생정보마당스페셜
8 
식객허영만의백반기행(본)
5 
Other values (9)
27 

Length

Max length14
Median length13
Mean length7.1875
Min length4

Unique

Unique2 ?
Unique (%)2.1%

Sample

1st row6시내고향
2nd row한국인의밥상
3rd row2TV생생정보
4th row생생정보마당
5th row팔도밥상스페셜

Common Values

ValueCountFrequency (%)
생생정보마당 22
22.9%
6시내고향 17
17.7%
2TV생생정보 17
17.7%
생생정보마당스페셜 8
 
8.3%
식객허영만의백반기행(본) 5
 
5.2%
맛있는이야기미라클푸드(본) 5
 
5.2%
한국인의밥상 4
 
4.2%
전지적참견시점 4
 
4.2%
백종원의골목식당 4
 
4.2%
팔도밥상스페셜 3
 
3.1%
Other values (4) 7
 
7.3%

Length

2023-12-10T18:45:35.845992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
생생정보마당 22
22.9%
6시내고향 17
17.7%
2tv생생정보 17
17.7%
생생정보마당스페셜 8
 
8.3%
식객허영만의백반기행(본 5
 
5.2%
맛있는이야기미라클푸드(본 5
 
5.2%
한국인의밥상 4
 
4.2%
전지적참견시점 4
 
4.2%
백종원의골목식당 4
 
4.2%
팔도밥상스페셜 3
 
3.1%
Other values (4) 7
 
7.3%

PROGRM_DC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing96
Missing (%)100.0%
Memory size996.0 B

BRDCST_TME_NM
Text

MISSING 

Distinct90
Distinct (%)100.0%
Missing6
Missing (%)6.2%
Memory size900.0 B
2023-12-10T18:45:36.479061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.3777778
Min length4

Characters and Unicode

Total characters394
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

Unique90 ?
Unique (%)100.0%

Sample

1st row7316회
2nd row518회
3rd row1347회
4th row914회
5th row7317회
ValueCountFrequency (%)
1351회 1
 
1.1%
179회 1
 
1.1%
119회 1
 
1.1%
927회 1
 
1.1%
1360회 1
 
1.1%
7329회 1
 
1.1%
926회 1
 
1.1%
278회 1
 
1.1%
807회 1
 
1.1%
1359회 1
 
1.1%
Other values (80) 80
88.9%
2023-12-10T18:45:37.436640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 90
22.8%
1 66
16.8%
3 51
12.9%
2 41
10.4%
7 37
9.4%
9 33
 
8.4%
5 21
 
5.3%
8 15
 
3.8%
0 15
 
3.8%
6 15
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 304
77.2%
Other Letter 90
 
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 66
21.7%
3 51
16.8%
2 41
13.5%
7 37
12.2%
9 33
10.9%
5 21
 
6.9%
8 15
 
4.9%
0 15
 
4.9%
6 15
 
4.9%
4 10
 
3.3%
Other Letter
ValueCountFrequency (%)
회 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 304
77.2%
Hangul 90
 
22.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 66
21.7%
3 51
16.8%
2 41
13.5%
7 37
12.2%
9 33
10.9%
5 21
 
6.9%
8 15
 
4.9%
0 15
 
4.9%
6 15
 
4.9%
4 10
 
3.3%
Hangul
ValueCountFrequency (%)
회 90
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304
77.2%
Hangul 90
 
22.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 90
100.0%
ASCII
ValueCountFrequency (%)
1 66
21.7%
3 51
16.8%
2 41
13.5%
7 37
12.2%
9 33
10.9%
5 21
 
6.9%
8 15
 
4.9%
0 15
 
4.9%
6 15
 
4.9%
4 10
 
3.3%

PROGRM_BRDCST_AREA_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
전국
96 

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 96
100.0%

Length

2023-12-10T18:45:37.706404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:37.866306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국 96
100.0%

PROGRM_GENRE_LCLAS_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
정보
87 
오락
9 

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 87
90.6%
오락 9
 
9.4%

Length

2023-12-10T18:45:38.032725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:38.193822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보 87
90.6%
오락 9
 
9.4%

PROGRM_GENRE_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
정보종합
47 
생활정보
22 
다큐멘터리
18 
오락기타
9 

Length

Max length5
Median length4
Mean length4.1875
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정보종합 47
49.0%
생활정보 22
22.9%
다큐멘터리 18
 
18.8%
오락기타 9
 
9.4%

Length

2023-12-10T18:45:38.373869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:38.556915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보종합 47
49.0%
생활정보 22
22.9%
다큐멘터리 18
 
18.8%
오락기타 9
 
9.4%

PROGRM_GENRE_SCLAS_NM
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
정보종합
47 
생활정보(종합)
22 
다큐멘터리(역사기행문예)
10 
오락기타
9 
다큐멘터리(기타)
5 

Length

Max length13
Median length4
Mean length6.2708333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정보종합 47
49.0%
생활정보(종합) 22
22.9%
다큐멘터리(역사기행문예) 10
 
10.4%
오락기타 9
 
9.4%
다큐멘터리(기타) 5
 
5.2%
다큐멘터리(휴먼) 3
 
3.1%

Length

2023-12-10T18:45:38.754761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:38.951771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보종합 47
49.0%
생활정보(종합 22
22.9%
다큐멘터리(역사기행문예 10
 
10.4%
오락기타 9
 
9.4%
다큐멘터리(기타 5
 
5.2%
다큐멘터리(휴먼 3
 
3.1%

AUDE_CO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean989.02292
Minimum21.426
Maximum2438.406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-10T18:45:39.380127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.426
5-th percentile75.8225
Q1150.16
median1110.668
Q31633.2035
95-th percentile2069.762
Maximum2438.406
Range2416.98
Interquartile range (IQR)1483.0435

Descriptive statistics

Standard deviation781.16881
Coefficient of variation (CV)0.78983894
Kurtosis-1.5845367
Mean989.02292
Median Absolute Deviation (MAD)877.1135
Skewness0.090032874
Sum94946.2
Variance610224.71
MonotonicityNot monotonic
2023-12-10T18:45:40.302976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1883.066 1
 
1.0%
305.037 1
 
1.0%
1549.082 1
 
1.0%
2064.629 1
 
1.0%
233.672 1
 
1.0%
1492.838 1
 
1.0%
1876.559 1
 
1.0%
81.567 1
 
1.0%
24.97 1
 
1.0%
1662.525 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
21.426 1
1.0%
24.97 1
1.0%
26.672 1
1.0%
50.973 1
1.0%
75.221 1
1.0%
76.023 1
1.0%
81.567 1
1.0%
83.503 1
1.0%
84.813 1
1.0%
84.891 1
1.0%
ValueCountFrequency (%)
2438.406 1
1.0%
2240.983 1
1.0%
2182.486 1
1.0%
2100.494 1
1.0%
2085.161 1
1.0%
2064.629 1
1.0%
2052.276 1
1.0%
2050.33 1
1.0%
2045.203 1
1.0%
2038.851 1
1.0%

TV_SUBTTLS_CN
Text

MISSING 

Distinct69
Distinct (%)100.0%
Missing27
Missing (%)28.1%
Memory size900.0 B
2023-12-10T18:45:40.788859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length12.623188
Min length3

Characters and Unicode

Total characters871
Distinct characters226
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

Unique69 ?
Unique (%)100.0%

Sample

1st row시장,전통,백신,접종,오늘
2nd row국수,밀가루,밀,맛,때
3rd row씨,오늘,물,복분자,만두
4th row사랑,원래,곳,음,
5th row멸치,맛,생,봄,시장
ValueCountFrequency (%)
어머니,떡,방앗간,부부,지금 1
 
1.4%
감자,호박,때,맛,여름 1
 
1.4%
진자,검사,여름,음식,병원 1
 
1.4%
수박,맛,오늘,추어탕,전복 1
 
1.4%
집,얼,구,튀김,사랑 1
 
1.4%
달인,로열,젤리,맛,벌 1
 
1.4%
낙지,맛,얼음,물,치킨 1
 
1.4%
오늘,지금,어머니,문어,할아버지 1
 
1.4%
통증,만성,새싹,보리,때 1
 
1.4%
나지,음,안녕 1
 
1.4%
Other values (59) 59
85.5%
2023-12-10T18:45:41.594440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 267
30.7%
맛 27
 
3.1%
어 21
 
2.4%
오 18
 
2.1%
늘 17
 
2.0%
장 15
 
1.7%
니 14
 
1.6%
머 13
 
1.5%
자 12
 
1.4%
지 11
 
1.3%
Other values (216) 456
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 604
69.3%
Other Punctuation 267
30.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
맛 27
 
4.5%
어 21
 
3.5%
오 18
 
3.0%
늘 17
 
2.8%
장 15
 
2.5%
니 14
 
2.3%
머 13
 
2.2%
자 12
 
2.0%
지 11
 
1.8%
때 10
 
1.7%
Other values (215) 446
73.8%
Other Punctuation
ValueCountFrequency (%)
, 267
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 604
69.3%
Common 267
30.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
맛 27
 
4.5%
어 21
 
3.5%
오 18
 
3.0%
늘 17
 
2.8%
장 15
 
2.5%
니 14
 
2.3%
머 13
 
2.2%
자 12
 
2.0%
지 11
 
1.8%
때 10
 
1.7%
Other values (215) 446
73.8%
Common
ValueCountFrequency (%)
, 267
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 604
69.3%
ASCII 267
30.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 267
100.0%
Hangul
ValueCountFrequency (%)
맛 27
 
4.5%
어 21
 
3.5%
오 18
 
3.0%
늘 17
 
2.8%
장 15
 
2.5%
니 14
 
2.3%
머 13
 
2.2%
자 12
 
2.0%
지 11
 
1.8%
때 10
 
1.7%
Other values (215) 446
73.8%

Interactions

2023-12-10T18:45:30.378901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:26.583020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:27.786863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:28.722650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:29.604210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:30.549056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:26.768399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:28.040382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:28.969221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:29.779927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:30.704312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:26.940208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:28.238292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:29.146457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:29.947938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:30.838445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:27.463314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:28.377391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:29.289604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:30.080660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:31.005519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:27.618050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:28.517830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:29.429127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:45:30.227176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:45:41.794176image/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_DE1.0000.9990.0000.0000.0000.0001.0000.0000.0000.0000.0001.000
BRDCST_END_DE0.9991.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.000
CHNNEL_NM0.0000.0001.0000.9140.7891.0001.0000.8120.8780.9030.7691.000
PROGRM_BEGIN_TIME0.0000.0000.9141.0001.0000.9981.0000.8670.8610.8010.6791.000
PROGRM_END_TIME0.0000.0000.7891.0001.0000.9621.0000.9940.9740.8210.6581.000
PROGRM_NM0.0000.0001.0000.9980.9621.0001.0001.0001.0001.0000.8061.000
BRDCST_TME_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
PROGRM_GENRE_LCLAS_NM0.0000.0000.8120.8670.9941.0001.0001.0001.0001.0000.5441.000
PROGRM_GENRE_MLSFC_NM0.0000.0000.8780.8610.9741.0001.0001.0001.0001.0000.7461.000
PROGRM_GENRE_SCLAS_NM0.0000.0000.9030.8010.8211.0001.0001.0001.0001.0000.7111.000
AUDE_CO0.0000.0000.7690.6790.6580.8061.0000.5440.7460.7111.0001.000
TV_SUBTTLS_CN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:45:42.107392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PROGRM_GENRE_SCLAS_NMPROGRM_NMCHNNEL_NMPROGRM_GENRE_MLSFC_NMPROGRM_GENRE_LCLAS_NM
PROGRM_GENRE_SCLAS_NM1.0000.9550.7970.9890.978
PROGRM_NM0.9551.0000.9600.9440.934
CHNNEL_NM0.7970.9601.0000.8060.857
PROGRM_GENRE_MLSFC_NM0.9890.9440.8061.0000.989
PROGRM_GENRE_LCLAS_NM0.9780.9340.8570.9891.000
2023-12-10T18:45:42.349537image/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_DE1.0001.000-0.079-0.102-0.0730.0000.0000.0000.0000.000
BRDCST_END_DE1.0001.000-0.066-0.112-0.0710.0000.0000.0000.0000.000
PROGRM_BEGIN_TIME-0.079-0.0661.0000.5300.6450.5590.8230.9060.7730.631
PROGRM_END_TIME-0.102-0.1120.5301.0000.5030.5740.8320.9070.7690.636
AUDE_CO-0.073-0.0710.6450.5031.0000.5220.4800.4000.5350.464
CHNNEL_NM0.0000.0000.5590.5740.5221.0000.9600.8570.8060.797
PROGRM_NM0.0000.0000.8230.8320.4800.9601.0000.9340.9440.955
PROGRM_GENRE_LCLAS_NM0.0000.0000.9060.9070.4000.8570.9341.0000.9890.978
PROGRM_GENRE_MLSFC_NM0.0000.0000.7730.7690.5350.8060.9440.9891.0000.989
PROGRM_GENRE_SCLAS_NM0.0000.0000.6310.6360.4640.7970.9550.9780.9891.000

Missing values

2023-12-10T18:45:31.280576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:45:31.901838image/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.
2023-12-10T18:45:32.159648image/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
02021070120210701KBS11759221857416시내고향<NA>7316회전국정보생활정보생활정보(종합)1883.066시장,전통,백신,접종,오늘
12021070120210701KBS1194010202753한국인의밥상<NA>518회전국정보다큐멘터리다큐멘터리(역사기행문예)1305.48국수,밀가루,밀,맛,때
22021070120210701KBS21832231941382TV생생정보<NA>1347회전국정보정보종합정보종합1295.686씨,오늘,물,복분자,만두
32021070120210701MBN93006100912생생정보마당<NA>914회전국정보정보종합정보종합165.845사랑,원래,곳,음,
42021070220210702KBS12213124042팔도밥상스페셜<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)84.891멸치,맛,생,봄,시장
52021070220210702KBS11758281857476시내고향<NA>7317회전국정보생활정보생활정보(종합)1802.339수박,전,윤,할아버지,총장
62021070220210702KBS21832371941152TV생생정보<NA>1348회전국정보정보종합정보종합1080.799맛,고구마,병,순,냉면
72021070220210702MBN93005100911생생정보마당<NA>915회전국정보정보종합정보종합155.862<NA>
82021070220210702TV CHOSUN200518205358식객허영만의백반기행(본)<NA>110회전국정보다큐멘터리다큐멘터리(기타)317.87<NA>
92021070320210703KBS1112109115748팔도밥상<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)810.004호박,감자,음식,애호박,때
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
862021072620210726MBN4572754614생생정보마당스페셜<NA>280회전국정보정보종합정보종합83.503<NA>
872021072620210726MBN93033101041생생정보마당<NA>931회전국정보정보종합정보종합119.493디자이너,전성시대,
882021072720210727MBN93005100922생생정보마당<NA>932회전국정보정보종합정보종합166.148사랑,먹,방,
892021072820210729SBS223734833백종원의골목식당<NA>180회전국오락오락기타오락기타1325.038<NA>
902021072820210728MBN93002100910생생정보마당<NA>933회전국정보정보종합정보종합162.167음,관심,고구마,자체,정원
912021072920210729MBN93002100921생생정보마당<NA>934회전국정보정보종합정보종합163.78일은,
922021073020210730MBN93007100915생생정보마당<NA>935회전국정보정보종합정보종합85.908<NA>
932021073020210730TV CHOSUN200535205358식객허영만의백반기행(본)<NA>114회전국정보다큐멘터리다큐멘터리(기타)654.556<NA>
942021073120210731KBS18000781850팔도밥상스페셜<NA><NA>전국정보다큐멘터리다큐멘터리(역사기행문예)1437.09감자,호박,때,맛,여름
952021073120210731JTBC8585694506맛있는이야기미라클푸드(본)<NA>121회전국정보생활정보생활정보(종합)152.025프로,폴리스,병,항문,미