Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells100
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory119.3 B

Variable types

Categorical9
Numeric4
Unsupported1

Alerts

progrm_dc_dc is highly overall correlated with progrm_begin_tm_tm and 10 other fieldsHigh correlation
progrm_nm_nm is highly overall correlated with progrm_begin_tm_tm and 9 other fieldsHigh correlation
date_de is highly overall correlated with progrm_nm_nm and 1 other fieldsHigh correlation
progrm_brdcst_area_cd is highly overall correlated with progrm_begin_tm_tm and 3 other fieldsHigh correlation
chnnel_cd is highly overall correlated with progrm_begin_tm_tm and 7 other fieldsHigh correlation
progrm_genre_sclas_nm is highly overall correlated with progrm_begin_tm_tm and 6 other fieldsHigh correlation
progrm_genre_lclas_nm is highly overall correlated with chnnel_cd and 4 other fieldsHigh correlation
progrm_genre_mlsfc_nm is highly overall correlated with progrm_lt_tmlt and 5 other fieldsHigh correlation
progrm_begin_tm_tm is highly overall correlated with progrm_end_tm_tm and 5 other fieldsHigh correlation
progrm_end_tm_tm is highly overall correlated with progrm_begin_tm_tm and 4 other fieldsHigh correlation
progrm_lt_tmlt is highly overall correlated with chnnel_cd and 4 other fieldsHigh correlation
aude_co_co is highly overall correlated with progrm_dc_dcHigh correlation
progrm_dc_dc is highly imbalanced (69.6%)Imbalance
progrm_genre_lclas_nm is highly imbalanced (59.8%)Imbalance
seq_sn has 100 (100.0%) missing valuesMissing
seq_sn is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:06:18.878551
Analysis finished2023-12-10 10:06:23.817423
Duration4.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

date_de
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20211001
57 
20211002
16 
20211003
16 
20211004
8 
20211031
 
3

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20211001
2nd row20211031
3rd row20211001
4th row20211001
5th row20211001

Common Values

ValueCountFrequency (%)
20211001 57
57.0%
20211002 16
 
16.0%
20211003 16
 
16.0%
20211004 8
 
8.0%
20211031 3
 
3.0%

Length

2023-12-10T19:06:23.949639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:24.138189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20211001 57
57.0%
20211002 16
 
16.0%
20211003 16
 
16.0%
20211004 8
 
8.0%
20211031 3
 
3.0%

chnnel_cd
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KBS1
26 
MBC
19 
KBS2
16 
KBS Story
15 
SBS
8 
Other values (2)
16 

Length

Max length9
Median length5
Mean length4.48
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKBS1
2nd rowKBS Story
3rd rowKBS1
4th rowKBS1
5th rowKBS1

Common Values

ValueCountFrequency (%)
KBS1 26
26.0%
MBC 19
19.0%
KBS2 16
16.0%
KBS Story 15
15.0%
SBS 8
 
8.0%
MBN 8
 
8.0%
아이넷방송 8
 
8.0%

Length

2023-12-10T19:06:24.398247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:24.728190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kbs1 26
22.6%
mbc 19
16.5%
kbs2 16
13.9%
kbs 15
13.0%
story 15
13.0%
sbs 8
 
7.0%
mbn 8
 
7.0%
아이넷방송 8
 
7.0%

progrm_begin_tm_tm
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91418.89
Minimum33510
Maximum183229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:25.058107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33510
5-th percentile34510
Q151020
median72428.5
Q3114754
95-th percentile180532
Maximum183229
Range149719
Interquartile range (IQR)63734

Descriptive statistics

Standard deviation51556.918
Coefficient of variation (CV)0.56396351
Kurtosis-0.94768902
Mean91418.89
Median Absolute Deviation (MAD)27861.5
Skewness0.72730399
Sum9141889
Variance2.6581158 × 109
MonotonicityNot monotonic
2023-12-10T19:06:25.418998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
35010 8
 
8.0%
61226 4
 
4.0%
114754 4
 
4.0%
60156 4
 
4.0%
112737 4
 
4.0%
105748 4
 
4.0%
55356 4
 
4.0%
70818 4
 
4.0%
33510 4
 
4.0%
93056 4
 
4.0%
Other values (15) 56
56.0%
ValueCountFrequency (%)
33510 4
4.0%
34510 3
 
3.0%
35010 8
8.0%
50025 4
4.0%
50027 3
 
3.0%
51020 4
4.0%
54217 4
4.0%
55356 4
4.0%
55931 4
4.0%
60156 4
4.0%
ValueCountFrequency (%)
183229 4
4.0%
180532 4
4.0%
175839 4
4.0%
173651 4
4.0%
173646 4
4.0%
164813 3
3.0%
114754 4
4.0%
112737 4
4.0%
105748 4
4.0%
93056 4
4.0%

progrm_end_tm_tm
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97675.72
Minimum45350
Maximum193932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:25.684578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45350
5-th percentile45350
Q155801
median78980
Q3121123
95-th percentile185626
Maximum193932
Range148582
Interquartile range (IQR)65322

Descriptive statistics

Standard deviation51000.879
Coefficient of variation (CV)0.5221449
Kurtosis-0.92462799
Mean97675.72
Median Absolute Deviation (MAD)30850
Skewness0.75484251
Sum9767572
Variance2.6010897 × 109
MonotonicityNot monotonic
2023-12-10T19:06:25.907507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
45350 15
 
15.0%
100949 4
 
4.0%
62857 4
 
4.0%
64757 4
 
4.0%
120105 4
 
4.0%
114424 4
 
4.0%
60210 4
 
4.0%
75305 4
 
4.0%
65450 4
 
4.0%
121123 4
 
4.0%
Other values (13) 49
49.0%
ValueCountFrequency (%)
45350 15
15.0%
50910 3
 
3.0%
50913 4
 
4.0%
55801 4
 
4.0%
60210 4
 
4.0%
60817 4
 
4.0%
62857 4
 
4.0%
64757 4
 
4.0%
65450 4
 
4.0%
75305 4
 
4.0%
ValueCountFrequency (%)
193932 4
4.0%
185626 4
4.0%
185437 4
4.0%
175322 4
4.0%
175318 4
4.0%
165652 3
3.0%
121123 4
4.0%
120105 4
4.0%
114424 4
4.0%
100949 4
4.0%

progrm_nm_nm
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2TV생생정보
19 
제32회금산인삼축제기념프라임콘서트
8 
생활의발견스페셜(재)
8 
생활의발견스페셜<KBS1>
7 
고향이보인다
 
4
Other values (14)
54 

Length

Max length18
Median length14
Mean length9.65
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활의발견스페셜<KBS1>
2nd row2TV생생정보
3rd row생활의발견스페셜<KBS1>
4th row생활의발견스페셜<KBS1>
5th row생활의발견스페셜(대전)(재)

Common Values

ValueCountFrequency (%)
2TV생생정보 19
19.0%
제32회금산인삼축제기념프라임콘서트 8
 
8.0%
생활의발견스페셜(재) 8
 
8.0%
생활의발견스페셜<KBS1> 7
 
7.0%
고향이보인다 4
 
4.0%
내고향스페셜 4
 
4.0%
생생투데이사람과세상 4
 
4.0%
생생투데이사람과세상(울산) 4
 
4.0%
6시내고향 4
 
4.0%
생방송부라보 4
 
4.0%
Other values (9) 34
34.0%

Length

2023-12-10T19:06:26.164225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2tv생생정보 19
19.0%
생활의발견스페셜(재 8
 
8.0%
제32회금산인삼축제기념프라임콘서트 8
 
8.0%
생활의발견스페셜<kbs1 7
 
7.0%
모닝와이드3부 4
 
4.0%
생생정보마당(본 4
 
4.0%
생생정보마당스페셜 4
 
4.0%
전국시대(광주 4
 
4.0%
생생정보스페셜 4
 
4.0%
전국시대스페셜(대구 4
 
4.0%
Other values (9) 34
34.0%

progrm_dc_dc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
92 
<1부>
 
4
<2부>
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 92
92.0%
<1부> 4
 
4.0%
<2부> 4
 
4.0%

Length

2023-12-10T19:06:26.414335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:26.599428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 92
92.0%
1부 4
 
4.0%
2부 4
 
4.0%

seq_sn
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

progrm_brdcst_area_cd
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전국
66 
부산 경남
 
4
울산
 
4
부산
 
4
서울/경기/대전/대구/광주/울산/강원/충북/충남/전북/전남/경북/제주
 
4
Other values (5)
18 

Length

Max length38
Median length2
Mean length4.34
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전국 66
66.0%
부산 경남 4
 
4.0%
울산 4
 
4.0%
부산 4
 
4.0%
서울/경기/대전/대구/광주/울산/강원/충북/충남/전북/전남/경북/제주 4
 
4.0%
전북 4
 
4.0%
대구 경북 4
 
4.0%
광주 전남 4
 
4.0%
대전 충남 3
 
3.0%
서울/경기/광주/전북/전남/제주 3
 
3.0%

Length

2023-12-10T19:06:26.789769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:27.031358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국 66
57.4%
부산 8
 
7.0%
경남 4
 
3.5%
울산 4
 
3.5%
서울/경기/대전/대구/광주/울산/강원/충북/충남/전북/전남/경북/제주 4
 
3.5%
전북 4
 
3.5%
대구 4
 
3.5%
경북 4
 
3.5%
광주 4
 
3.5%
전남 4
 
3.5%
Other values (3) 9
 
7.8%

progrm_lt_tmlt
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466.99
Minimum81
Maximum1184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:27.242757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile83
Q1163
median452
Q3490
95-th percentile1084
Maximum1184
Range1103
Interquartile range (IQR)327

Descriptive statistics

Standard deviation335.31323
Coefficient of variation (CV)0.71803085
Kurtosis-0.26978863
Mean466.99
Median Absolute Deviation (MAD)169
Skewness0.87148767
Sum46699
Variance112434.96
MonotonicityNot monotonic
2023-12-10T19:06:27.433950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
84 11
 
11.0%
163 8
 
8.0%
1034 8
 
8.0%
474 7
 
7.0%
444 4
 
4.0%
1184 4
 
4.0%
464 4
 
4.0%
283 4
 
4.0%
460 4
 
4.0%
332 4
 
4.0%
Other values (11) 42
42.0%
ValueCountFrequency (%)
81 4
 
4.0%
83 3
 
3.0%
84 11
11.0%
163 8
8.0%
232 4
 
4.0%
283 4
 
4.0%
332 4
 
4.0%
385 4
 
4.0%
422 4
 
4.0%
444 4
 
4.0%
ValueCountFrequency (%)
1184 4
4.0%
1084 3
 
3.0%
1070 4
4.0%
1034 8
8.0%
574 4
4.0%
490 4
4.0%
474 7
7.0%
464 4
4.0%
463 4
4.0%
461 4
4.0%

progrm_genre_lclas_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정보
92 
오락
 
8

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 92
92.0%
오락 8
 
8.0%

Length

2023-12-10T19:06:27.663670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:27.833078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보 92
92.0%
오락 8
 
8.0%

progrm_genre_mlsfc_nm
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
생활정보
57 
정보종합
35 
음악쇼
8 

Length

Max length4
Median length4
Mean length3.92
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활정보 57
57.0%
정보종합 35
35.0%
음악쇼 8
 
8.0%

Length

2023-12-10T19:06:28.034688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:28.323837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활정보 57
57.0%
정보종합 35
35.0%
음악쇼 8
 
8.0%

progrm_genre_sclas_nm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
생활정보(종합)
43 
정보종합
35 
생활정보(가사)
10 
대중가요쇼
8 
생활정보(지역)
 
4

Length

Max length8
Median length8
Mean length6.36
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활정보(종합) 43
43.0%
정보종합 35
35.0%
생활정보(가사) 10
 
10.0%
대중가요쇼 8
 
8.0%
생활정보(지역) 4
 
4.0%

Length

2023-12-10T19:06:28.638033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:29.103181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활정보(종합 43
43.0%
정보종합 35
35.0%
생활정보(가사 10
 
10.0%
대중가요쇼 8
 
8.0%
생활정보(지역 4
 
4.0%

aude_mesureunit_cd
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
유료매체가입가구
26 
가구
25 
개인
25 
유료매체가입개인
24 

Length

Max length8
Median length5
Mean length5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가구
2nd row개인
3rd row유료매체가입가구
4th row유료매체가입개인
5th row가구

Common Values

ValueCountFrequency (%)
유료매체가입가구 26
26.0%
가구 25
25.0%
개인 25
25.0%
유료매체가입개인 24
24.0%

Length

2023-12-10T19:06:29.432494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:29.685378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유료매체가입가구 26
26.0%
가구 25
25.0%
개인 25
25.0%
유료매체가입개인 24
24.0%

aude_co_co
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.76
Minimum1
Maximum2041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:29.935924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median65
Q3193.25
95-th percentile1397.8
Maximum2041
Range2040
Interquartile range (IQR)186.25

Descriptive statistics

Standard deviation432.55156
Coefficient of variation (CV)1.8583586
Kurtosis7.1242688
Mean232.76
Median Absolute Deviation (MAD)62
Skewness2.7348212
Sum23276
Variance187100.85
MonotonicityNot monotonic
2023-12-10T19:06:30.367126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
7.0%
3 6
 
6.0%
9 4
 
4.0%
7 4
 
4.0%
5 4
 
4.0%
23 3
 
3.0%
86 2
 
2.0%
49 2
 
2.0%
4 2
 
2.0%
256 2
 
2.0%
Other values (55) 64
64.0%
ValueCountFrequency (%)
1 7
7.0%
2 2
 
2.0%
3 6
6.0%
4 2
 
2.0%
5 4
4.0%
6 2
 
2.0%
7 4
4.0%
8 2
 
2.0%
9 4
4.0%
21 1
 
1.0%
ValueCountFrequency (%)
2041 1
1.0%
1965 1
1.0%
1700 1
1.0%
1607 1
1.0%
1451 1
1.0%
1395 1
1.0%
1044 1
1.0%
995 1
1.0%
939 1
1.0%
904 1
1.0%

Interactions

2023-12-10T19:06:22.476452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:20.333681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:21.030276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:21.853863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:22.654464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:20.523006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:21.209367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:22.015083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:22.846009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:20.711989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:21.392384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:22.188046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:23.012709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:20.858545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:21.658162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:22.329550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:30.989458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
date_dechnnel_cdprogrm_begin_tm_tmprogrm_end_tm_tmprogrm_nm_nmprogrm_dc_dcprogrm_brdcst_area_cdprogrm_lt_tmltprogrm_genre_lclas_nmprogrm_genre_mlsfc_nmprogrm_genre_sclas_nmaude_mesureunit_cdaude_co_co
date_de1.0000.5500.6520.9400.885NaN0.6920.5610.1340.4010.6350.0000.489
chnnel_cd0.5501.0000.7990.7880.988NaN0.7260.8981.0000.9140.8570.0000.406
progrm_begin_tm_tm0.6520.7991.0000.9680.9860.8960.9030.7810.4470.5970.7100.0000.214
progrm_end_tm_tm0.9400.7880.9681.0000.9930.8960.8870.7970.4900.5470.6830.0000.489
progrm_nm_nm0.8850.9880.9860.9931.000NaN1.0000.9721.0001.0001.0000.0000.688
progrm_dc_dcNaNNaN0.8960.896NaN1.000NaNNaNNaNNaNNaN0.000NaN
progrm_brdcst_area_cd0.6920.7260.9030.8871.000NaN1.0000.6820.0000.4750.6820.0000.384
progrm_lt_tmlt0.5610.8980.7810.7970.972NaN0.6821.0000.3070.6760.7050.0000.603
progrm_genre_lclas_nm0.1341.0000.4470.4901.000NaN0.0000.3071.0001.0001.0000.0000.000
progrm_genre_mlsfc_nm0.4010.9140.5970.5471.000NaN0.4750.6761.0001.0001.0000.0000.156
progrm_genre_sclas_nm0.6350.8570.7100.6831.000NaN0.6820.7051.0001.0001.0000.0000.400
aude_mesureunit_cd0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
aude_co_co0.4890.4060.2140.4890.688NaN0.3840.6030.0000.1560.4000.0001.000
2023-12-10T19:06:31.334545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
progrm_dc_dcprogrm_nm_nmdate_deaude_mesureunit_cdprogrm_brdcst_area_cdchnnel_cdprogrm_genre_sclas_nmprogrm_genre_lclas_nmprogrm_genre_mlsfc_nm
progrm_dc_dc1.0001.0001.0000.0001.0001.0001.0001.0001.000
progrm_nm_nm1.0001.0000.6360.0000.9490.8940.9230.9090.914
date_de1.0000.6361.0000.0000.3470.3890.2830.1600.328
aude_mesureunit_cd0.0000.0000.0001.0000.0000.0000.0000.0000.000
progrm_brdcst_area_cd1.0000.9490.3470.0001.0000.4710.3390.0000.311
chnnel_cd1.0000.8940.3890.0000.4711.0000.7630.9740.904
progrm_genre_sclas_nm1.0000.9230.2830.0000.3390.7631.0000.9850.990
progrm_genre_lclas_nm1.0000.9090.1600.0000.0000.9740.9851.0000.995
progrm_genre_mlsfc_nm1.0000.9140.3280.0000.3110.9040.9900.9951.000
2023-12-10T19:06:31.557420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
progrm_begin_tm_tmprogrm_end_tm_tmprogrm_lt_tmltaude_co_codate_dechnnel_cdprogrm_nm_nmprogrm_dc_dcprogrm_brdcst_area_cdprogrm_genre_lclas_nmprogrm_genre_mlsfc_nmprogrm_genre_sclas_nmaude_mesureunit_cd
progrm_begin_tm_tm1.0000.995-0.1620.2480.4340.5890.8870.7000.7260.3330.4820.5450.000
progrm_end_tm_tm0.9951.000-0.1350.2550.4340.5860.8820.7000.7010.3880.3910.4560.000
progrm_lt_tmlt-0.162-0.1351.000-0.1520.3990.5350.8331.0000.4250.3190.5710.5480.000
aude_co_co0.2480.255-0.1521.0000.2160.2140.3181.0000.1220.0000.0850.1700.000
date_de0.4340.4340.3990.2161.0000.3890.6361.0000.3470.1600.3280.2830.000
chnnel_cd0.5890.5860.5350.2140.3891.0000.8941.0000.4710.9740.9040.7630.000
progrm_nm_nm0.8870.8820.8330.3180.6360.8941.0001.0000.9490.9090.9140.9230.000
progrm_dc_dc0.7000.7001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
progrm_brdcst_area_cd0.7260.7010.4250.1220.3470.4710.9491.0001.0000.0000.3110.3390.000
progrm_genre_lclas_nm0.3330.3880.3190.0000.1600.9740.9091.0000.0001.0000.9950.9850.000
progrm_genre_mlsfc_nm0.4820.3910.5710.0850.3280.9040.9141.0000.3110.9951.0000.9900.000
progrm_genre_sclas_nm0.5450.4560.5480.1700.2830.7630.9231.0000.3390.9850.9901.0000.000
aude_mesureunit_cd0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T19:06:23.355285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:06:23.695308image/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

date_dechnnel_cdprogrm_begin_tm_tmprogrm_end_tm_tmprogrm_nm_nmprogrm_dc_dcseq_snprogrm_brdcst_area_cdprogrm_lt_tmltprogrm_genre_lclas_nmprogrm_genre_mlsfc_nmprogrm_genre_sclas_nmaude_mesureunit_cdaude_co_co
020211001KBS15002750910생활의발견스페셜<KBS1><NA><NA>전국84정보생활정보생활정보(가사)가구161
120211031KBS Story34510453502TV생생정보<NA><NA>전국1084정보정보종합정보종합개인1
220211001KBS15002750910생활의발견스페셜<KBS1><NA><NA>전국84정보생활정보생활정보(가사)유료매체가입가구150
320211001KBS15002750910생활의발견스페셜<KBS1><NA><NA>전국84정보생활정보생활정보(가사)유료매체가입개인188
420211001KBS1164813165652생활의발견스페셜(대전)(재)<NA><NA>대전 충남83정보생활정보생활정보(가사)가구47
520211001KBS1164813165652생활의발견스페셜(대전)(재)<NA><NA>대전 충남83정보생활정보생활정보(가사)개인55
620211001KBS1164813165652생활의발견스페셜(대전)(재)<NA><NA>대전 충남83정보생활정보생활정보(가사)유료매체가입가구47
720211031KBS Story34510453502TV생생정보<NA><NA>전국1084정보정보종합정보종합유료매체가입가구1
820211001KBS1173646175318생생투데이사람과세상<NA><NA>부산 경남163정보생활정보생활정보(종합)가구93
920211001KBS1173646175318생생투데이사람과세상<NA><NA>부산 경남163정보생활정보생활정보(종합)개인109
date_dechnnel_cdprogrm_begin_tm_tmprogrm_end_tm_tmprogrm_nm_nmprogrm_dc_dcseq_snprogrm_brdcst_area_cdprogrm_lt_tmltprogrm_genre_lclas_nmprogrm_genre_mlsfc_nmprogrm_genre_sclas_nmaude_mesureunit_cdaude_co_co
9020211003KBS Story33510453502TV생생정보<NA><NA>전국1184정보정보종합정보종합유료매체가입가구3
9120211003KBS Story33510453502TV생생정보<NA><NA>전국1184정보정보종합정보종합유료매체가입개인5
9220211004KBS15002550913생활의발견스페셜<KBS1><NA><NA>전국84정보생활정보생활정보(가사)가구359
9320211004KBS15002550913생활의발견스페셜<KBS1><NA><NA>전국84정보생활정보생활정보(가사)개인482
9420211004KBS15002550913생활의발견스페셜<KBS1><NA><NA>전국84정보생활정보생활정보(가사)유료매체가입가구357
9520211004KBS15002550913생활의발견스페셜<KBS1><NA><NA>전국84정보생활정보생활정보(가사)유료매체가입개인480
9620211004KBS15102055801내고향스페셜<NA><NA>전국474정보생활정보생활정보(종합)가구182
9720211004KBS15102055801내고향스페셜<NA><NA>전국474정보생활정보생활정보(종합)개인256
9820211004KBS15102055801내고향스페셜<NA><NA>전국474정보생활정보생활정보(종합)유료매체가입가구181
9920211004KBS15102055801내고향스페셜<NA><NA>전국474정보생활정보생활정보(종합)유료매체가입개인256