Overview

Dataset statistics

Number of variables12
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory106.4 B

Variable types

Numeric8
Categorical4

Dataset

Description해외방송시장조사 보고서 중 OTT관련 이용행태 및 만족도 등에 대한 데이터 중 국가별(영국, 호주, 러시아 등 5개국) 연령, 소득, 인종, 지역, 학력 성별 OTT 서비스 주로 이용하는 시청기기에 대한 통계데이터
URLhttps://www.data.go.kr/data/15102275/fileData.do

Alerts

조사연도 has constant value ""Constant
분류 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 사례수(명) and 1 other fieldsHigh correlation
연번 is highly overall correlated with 분류High correlation
사례수(명) is highly overall correlated with 분류 and 1 other fieldsHigh correlation
텔레비전(비율) is highly overall correlated with 스마트폰(비율) and 1 other fieldsHigh correlation
스마트폰(비율) is highly overall correlated with 텔레비전(비율)High correlation
노트북(비율) is highly overall correlated with 텔레비전(비율)High correlation
연번 has unique valuesUnique
스마트폰(비율) has 2 (2.1%) zerosZeros
데스크탑컴퓨터(비율) has 8 (8.4%) zerosZeros
노트북(비율) has 2 (2.1%) zerosZeros
태블릿(비율) has 10 (10.5%) zerosZeros
차량용디지털멀티미디어방송(비율) has 44 (46.3%) zerosZeros

Reproduction

Analysis started2023-12-12 03:33:49.300716
Analysis finished2023-12-12 03:33:57.400190
Duration8.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:33:57.507691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.7
Q124.5
median48
Q371.5
95-th percentile90.3
Maximum95
Range94
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.568098
Coefficient of variation (CV)0.57433536
Kurtosis-1.2
Mean48
Median Absolute Deviation (MAD)24
Skewness0
Sum4560
Variance760
MonotonicityStrictly increasing
2023-12-12T12:33:57.653732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
2 1
 
1.1%
71 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
95 1
1.1%
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%

조사연도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
2020
95 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 95
100.0%

Length

2023-12-12T12:33:57.810248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:33:57.914934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 95
100.0%

국가
Categorical

Distinct5
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size892.0 B
영국
19 
호주
19 
러시아
19 
브라질
19 
UAE
19 

Length

Max length3
Median length3
Mean length2.6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영국
2nd row호주
3rd row러시아
4th row브라질
5th rowUAE

Common Values

ValueCountFrequency (%)
영국 19
20.0%
호주 19
20.0%
러시아 19
20.0%
브라질 19
20.0%
UAE 19
20.0%

Length

2023-12-12T12:33:58.014688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:33:58.138287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영국 19
20.0%
호주 19
20.0%
러시아 19
20.0%
브라질 19
20.0%
uae 19
20.0%

분류
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size892.0 B
직업별
35 
연령별
25 
성별
10 
학력별
10 
소득별
10 

Length

Max length3
Median length3
Mean length2.8947368
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
직업별 35
36.8%
연령별 25
26.3%
성별 10
 
10.5%
학력별 10
 
10.5%
소득별 10
 
10.5%
국가별 5
 
5.3%

Length

2023-12-12T12:33:58.249629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:33:58.358229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직업별 35
36.8%
연령별 25
26.3%
성별 10
 
10.5%
학력별 10
 
10.5%
소득별 10
 
10.5%
국가별 5
 
5.3%

구분
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size892.0 B
전체
 
5
남성
 
5
여성
 
5
10대
 
5
20대
 
5
Other values (14)
70 

Length

Max length7
Median length6
Mean length4.0526316
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 5
 
5.3%
남성 5
 
5.3%
여성 5
 
5.3%
10대 5
 
5.3%
20대 5
 
5.3%
30대 5
 
5.3%
40대 5
 
5.3%
50대 이상 5
 
5.3%
고졸 이하 5
 
5.3%
대졸 이상 5
 
5.3%
Other values (9) 45
47.4%

Length

2023-12-12T12:33:58.507387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이상 15
 
12.0%
이하 10
 
8.0%
평균 10
 
8.0%
10대 5
 
4.0%
20대 5
 
4.0%
중 5
 
4.0%
무직/구직 5
 
4.0%
은퇴자 5
 
4.0%
가사노동자 5
 
4.0%
학생 5
 
4.0%
Other values (11) 55
44.0%

사례수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.53684
Minimum10
Maximum373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:33:58.663473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q138.5
median75
Q3165.5
95-th percentile282.1
Maximum373
Range363
Interquartile range (IQR)127

Descriptive statistics

Standard deviation87.158673
Coefficient of variation (CV)0.81810828
Kurtosis1.4461906
Mean106.53684
Median Absolute Deviation (MAD)46
Skewness1.2943579
Sum10121
Variance7596.6343
MonotonicityNot monotonic
2023-12-12T12:33:58.839957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 3
 
3.2%
185 2
 
2.1%
103 2
 
2.1%
59 2
 
2.1%
64 2
 
2.1%
15 2
 
2.1%
48 2
 
2.1%
45 2
 
2.1%
34 2
 
2.1%
52 2
 
2.1%
Other values (70) 74
77.9%
ValueCountFrequency (%)
10 1
1.1%
11 1
1.1%
12 1
1.1%
14 1
1.1%
15 2
2.1%
16 1
1.1%
18 1
1.1%
19 1
1.1%
20 1
1.1%
22 1
1.1%
ValueCountFrequency (%)
373 1
1.1%
368 1
1.1%
367 1
1.1%
355 1
1.1%
301 1
1.1%
274 1
1.1%
260 1
1.1%
220 1
1.1%
210 1
1.1%
200 1
1.1%

텔레비전(비율)
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.601053
Minimum11.8
Maximum77.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:33:59.083992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.8
5-th percentile16.14
Q124.55
median36.4
Q345.8
95-th percentile60.24
Maximum77.8
Range66
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation14.591286
Coefficient of variation (CV)0.39865756
Kurtosis-0.34756677
Mean36.601053
Median Absolute Deviation (MAD)11
Skewness0.43491591
Sum3477.1
Variance212.90564
MonotonicityNot monotonic
2023-12-12T12:33:59.243632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.0 4
 
4.2%
25.0 2
 
2.1%
43.8 2
 
2.1%
45.8 2
 
2.1%
38.7 2
 
2.1%
28.1 2
 
2.1%
41.6 2
 
2.1%
40.4 2
 
2.1%
77.8 1
 
1.1%
13.5 1
 
1.1%
Other values (75) 75
78.9%
ValueCountFrequency (%)
11.8 1
1.1%
13.0 1
1.1%
13.3 1
1.1%
13.5 1
1.1%
16.0 1
1.1%
16.2 1
1.1%
16.7 1
1.1%
18.2 1
1.1%
18.7 1
1.1%
18.8 1
1.1%
ValueCountFrequency (%)
77.8 1
1.1%
72.4 1
1.1%
66.7 1
1.1%
64.3 1
1.1%
63.6 1
1.1%
58.8 1
1.1%
58.7 1
1.1%
58.3 1
1.1%
57.5 1
1.1%
56.3 1
1.1%

스마트폰(비율)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.631579
Minimum0
Maximum57.1
Zeros2
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:33:59.447496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.61
Q125.95
median35.4
Q342.3
95-th percentile52.46
Maximum57.1
Range57.1
Interquartile range (IQR)16.35

Descriptive statistics

Standard deviation12.354672
Coefficient of variation (CV)0.36735332
Kurtosis0.025780359
Mean33.631579
Median Absolute Deviation (MAD)7.3
Skewness-0.50812166
Sum3195
Variance152.63793
MonotonicityNot monotonic
2023-12-12T12:33:59.659354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2
 
2.1%
42.7 2
 
2.1%
16.9 2
 
2.1%
46.2 2
 
2.1%
25.0 2
 
2.1%
35.4 2
 
2.1%
53.3 2
 
2.1%
42.3 2
 
2.1%
8.3 1
 
1.1%
56.8 1
 
1.1%
Other values (77) 77
81.1%
ValueCountFrequency (%)
0.0 2
2.1%
8.3 1
1.1%
9.1 1
1.1%
10.3 1
1.1%
13.6 1
1.1%
13.7 1
1.1%
14.3 1
1.1%
16.7 1
1.1%
16.9 2
2.1%
18.2 1
1.1%
ValueCountFrequency (%)
57.1 1
1.1%
56.8 1
1.1%
56.0 1
1.1%
53.3 2
2.1%
52.1 1
1.1%
52.0 1
1.1%
50.0 1
1.1%
47.7 1
1.1%
46.9 1
1.1%
46.2 2
2.1%

데스크탑컴퓨터(비율)
Real number (ℝ)

ZEROS 

Distinct65
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3221053
Minimum0
Maximum33.3
Zeros8
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:33:59.837896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.45
median7.1
Q311.8
95-th percentile17.26
Maximum33.3
Range33.3
Interquartile range (IQR)7.35

Descriptive statistics

Standard deviation5.7694017
Coefficient of variation (CV)0.69326228
Kurtosis3.0648509
Mean8.3221053
Median Absolute Deviation (MAD)3.1
Skewness1.2618561
Sum790.6
Variance33.285996
MonotonicityNot monotonic
2023-12-12T12:34:00.024501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.4%
4.1 3
 
3.2%
5.7 3
 
3.2%
7.9 3
 
3.2%
8.3 3
 
3.2%
11.8 3
 
3.2%
6.5 3
 
3.2%
5.3 2
 
2.1%
3.4 2
 
2.1%
15.0 2
 
2.1%
Other values (55) 63
66.3%
ValueCountFrequency (%)
0.0 8
8.4%
1.6 1
 
1.1%
1.7 1
 
1.1%
2.1 1
 
1.1%
3.1 1
 
1.1%
3.3 1
 
1.1%
3.4 2
 
2.1%
3.8 1
 
1.1%
3.9 1
 
1.1%
4.0 1
 
1.1%
ValueCountFrequency (%)
33.3 1
1.1%
24.2 1
1.1%
22.7 1
1.1%
20.0 1
1.1%
17.4 1
1.1%
17.2 1
1.1%
16.8 1
1.1%
15.9 1
1.1%
15.8 1
1.1%
15.0 2
2.1%

노트북(비율)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.348421
Minimum0
Maximum41.7
Zeros2
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:34:00.232181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.31
Q19.95
median15.4
Q320.05
95-th percentile26.42
Maximum41.7
Range41.7
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation7.4162682
Coefficient of variation (CV)0.48319421
Kurtosis0.72869069
Mean15.348421
Median Absolute Deviation (MAD)4.9
Skewness0.45412701
Sum1458.1
Variance55.001035
MonotonicityNot monotonic
2023-12-12T12:34:00.429193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.0 3
 
3.2%
20.2 2
 
2.1%
9.1 2
 
2.1%
15.4 2
 
2.1%
11.4 2
 
2.1%
0.0 2
 
2.1%
7.1 2
 
2.1%
16.2 2
 
2.1%
16.0 2
 
2.1%
10.0 2
 
2.1%
Other values (70) 74
77.9%
ValueCountFrequency (%)
0.0 2
2.1%
2.3 1
1.1%
2.7 1
1.1%
3.4 1
1.1%
4.7 1
1.1%
5.4 1
1.1%
5.6 1
1.1%
5.9 1
1.1%
6.5 1
1.1%
6.7 1
1.1%
ValueCountFrequency (%)
41.7 1
 
1.1%
31.3 1
 
1.1%
30.3 1
 
1.1%
30.0 1
 
1.1%
26.7 1
 
1.1%
26.3 1
 
1.1%
26.1 1
 
1.1%
25.4 1
 
1.1%
25.0 3
3.2%
23.5 1
 
1.1%

태블릿(비율)
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1810526
Minimum0
Maximum20
Zeros10
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:34:00.626251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6
median5.1
Q37.25
95-th percentile10.5
Maximum20
Range20
Interquartile range (IQR)4.65

Descriptive statistics

Standard deviation3.7453317
Coefficient of variation (CV)0.72289011
Kurtosis2.9579507
Mean5.1810526
Median Absolute Deviation (MAD)2.4
Skewness1.2055221
Sum492.2
Variance14.02751
MonotonicityNot monotonic
2023-12-12T12:34:00.852874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.5%
6.7 4
 
4.2%
7.4 3
 
3.2%
1.9 3
 
3.2%
5.9 3
 
3.2%
6.5 3
 
3.2%
2.7 3
 
3.2%
3.0 2
 
2.1%
2.9 2
 
2.1%
10.5 2
 
2.1%
Other values (48) 60
63.2%
ValueCountFrequency (%)
0.0 10
10.5%
0.8 1
 
1.1%
1.3 1
 
1.1%
1.5 2
 
2.1%
1.6 1
 
1.1%
1.7 1
 
1.1%
1.9 3
 
3.2%
2.0 1
 
1.1%
2.1 1
 
1.1%
2.3 1
 
1.1%
ValueCountFrequency (%)
20.0 1
1.1%
17.7 1
1.1%
16.7 1
1.1%
10.7 1
1.1%
10.5 2
2.1%
10.3 1
1.1%
9.8 1
1.1%
9.2 1
1.1%
9.1 1
1.1%
8.9 1
1.1%

차량용디지털멀티미디어방송(비율)
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91052632
Minimum0
Maximum7.1
Zeros44
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:34:01.015510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.6
Q31.45
95-th percentile3.13
Maximum7.1
Range7.1
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation1.2683943
Coefficient of variation (CV)1.3930342
Kurtosis6.4940554
Mean0.91052632
Median Absolute Deviation (MAD)0.6
Skewness2.1957478
Sum86.5
Variance1.6088242
MonotonicityNot monotonic
2023-12-12T12:34:01.147845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 44
46.3%
1.1 4
 
4.2%
1.0 4
 
4.2%
0.6 4
 
4.2%
1.5 4
 
4.2%
0.8 3
 
3.2%
0.7 3
 
3.2%
1.6 3
 
3.2%
1.7 2
 
2.1%
0.5 2
 
2.1%
Other values (19) 22
23.2%
ValueCountFrequency (%)
0.0 44
46.3%
0.3 1
 
1.1%
0.5 2
 
2.1%
0.6 4
 
4.2%
0.7 3
 
3.2%
0.8 3
 
3.2%
0.9 1
 
1.1%
1.0 4
 
4.2%
1.1 4
 
4.2%
1.2 2
 
2.1%
ValueCountFrequency (%)
7.1 1
1.1%
5.3 1
1.1%
4.3 1
1.1%
3.9 1
1.1%
3.2 1
1.1%
3.1 1
1.1%
3.0 1
1.1%
2.7 1
1.1%
2.6 1
1.1%
2.5 1
1.1%

Interactions

2023-12-12T12:33:55.949738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:49.808615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.793828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.557637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.547726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.391253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.268375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.061895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:56.054829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:49.904373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.882305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.646618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.667280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.489771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.363481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.147455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:56.157394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:49.988357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.963428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.760799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.785522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.590268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.452691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.241887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:56.272352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.084993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.050633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.954570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.896941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.706622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.575898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.339784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:56.390600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.166474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.166742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.045231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.995481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.815005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.678080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.449607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:56.487425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.505898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.276059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.147120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.100112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.921274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.763820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.557817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:56.569017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.612594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.352309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.278297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.186481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.010101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.853016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.658321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:56.664942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:50.712079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:51.463645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:52.417916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:53.291792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.140178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:54.955260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:33:55.828359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:34:01.251414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번국가분류구분사례수(명)텔레비전(비율)스마트폰(비율)데스크탑컴퓨터(비율)노트북(비율)태블릿(비율)차량용디지털멀티미디어방송(비율)
연번1.0000.7910.9290.8460.8090.0000.4070.0000.0000.2870.345
국가0.7911.0000.0000.0000.0000.6680.6570.5730.3830.5340.000
분류0.9290.0001.0001.0000.8640.0000.0000.0000.0000.0000.304
구분0.8460.0001.0001.0000.8640.0000.3460.3260.1530.0000.401
사례수(명)0.8090.0000.8640.8641.0000.0000.0000.0000.0000.0000.000
텔레비전(비율)0.0000.6680.0000.0000.0001.0000.7450.3160.4520.4110.000
스마트폰(비율)0.4070.6570.0000.3460.0000.7451.0000.4560.0420.5710.016
데스크탑컴퓨터(비율)0.0000.5730.0000.3260.0000.3160.4561.0000.6650.4770.526
노트북(비율)0.0000.3830.0000.1530.0000.4520.0420.6651.0000.2020.611
태블릿(비율)0.2870.5340.0000.0000.0000.4110.5710.4770.2021.0000.192
차량용디지털멀티미디어방송(비율)0.3450.0000.3040.4010.0000.0000.0160.5260.6110.1921.000
2023-12-12T12:34:01.395615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류구분국가
분류1.0000.9240.000
구분0.9241.0000.000
국가0.0000.0001.000
2023-12-12T12:34:01.496671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사례수(명)텔레비전(비율)스마트폰(비율)데스크탑컴퓨터(비율)노트북(비율)태블릿(비율)차량용디지털멀티미디어방송(비율)국가분류구분
연번1.000-0.403-0.0010.043-0.086-0.056-0.163-0.1110.4330.8010.492
사례수(명)-0.4031.0000.0150.0160.0480.0500.0530.3200.0000.6720.521
텔레비전(비율)-0.0010.0151.000-0.638-0.325-0.602-0.046-0.0900.3180.0000.000
스마트폰(비율)0.0430.016-0.6381.000-0.2530.062-0.4560.1130.3200.0000.121
데스크탑컴퓨터(비율)-0.0860.048-0.325-0.2531.0000.1720.3320.0140.3680.0000.117
노트북(비율)-0.0560.050-0.6020.0620.1721.0000.162-0.1440.2250.0000.032
태블릿(비율)-0.1630.053-0.046-0.4560.3320.1621.0000.0740.3480.0000.000
차량용디지털멀티미디어방송(비율)-0.1110.320-0.0900.1130.014-0.1440.0741.0000.0000.1500.152
국가0.4330.0000.3180.3200.3680.2250.3480.0001.0000.0000.000
분류0.8010.6720.0000.0000.0000.0000.0000.1500.0001.0000.924
구분0.4920.5210.0000.1210.1170.0320.0000.1520.0000.9241.000

Missing values

2023-12-12T12:33:57.100058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:33:57.310289image/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

연번조사연도국가분류구분사례수(명)텔레비전(비율)스마트폰(비율)데스크탑컴퓨터(비율)노트북(비율)태블릿(비율)차량용디지털멀티미디어방송(비율)
012020영국국가별전체37344.229.26.712.66.40.8
122020호주국가별전체30141.222.98.320.37.00.3
232020러시아국가별전체36723.437.914.216.17.41.1
342020브라질국가별전체36845.138.04.99.21.90.8
452020UAE국가별전체35526.542.06.220.03.91.4
562020영국성별남성21043.831.08.610.05.71.0
672020영국성별여성16344.827.04.316.07.40.6
782020호주성별남성14637.719.912.321.97.50.7
892020호주성별여성15544.525.84.518.76.50.0
9102020러시아성별남성18528.133.514.616.26.51.1
연번조사연도국가분류구분사례수(명)텔레비전(비율)스마트폰(비율)데스크탑컴퓨터(비율)노트북(비율)태블릿(비율)차량용디지털멀티미디어방송(비율)
85862020영국소득별평균 이하16141.634.86.29.96.80.6
86872020영국소득별평균 이상11450.021.97.911.47.01.8
87882020호주소득별평균 이하11940.321.010.920.27.60.0
88892020호주소득별평균 이상8354.216.97.218.13.60.0
89902020러시아소득별평균 이하19122.036.616.819.44.70.5
90912020러시아소득별평균 이상12231.134.49.811.510.72.5
91922020브라질소득별평균 이하17840.442.73.411.21.70.6
92932020브라질소득별평균 이상8757.529.95.72.32.32.3
93942020UAE소득별평균 이하16523.642.47.918.84.23.0
94952020UAE소득별평균 이상12235.240.24.119.70.80.0