Overview

Dataset statistics

Number of variables5
Number of observations525
Missing cells95
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.7 KiB
Average record size in memory42.3 B

Variable types

Numeric2
Text3

Dataset

Description한국언론진흥재단 미디어 이슈 (20년 5호)에서 코로나19 관련 시민 인식 조사를 정리한 데이터입니다. 자세한 내용은 홈페이지 참고 바랍니다.
Author한국언론진흥재단
URLhttps://www.data.go.kr/data/15086550/fileData.do

Alerts

중분류 has 95 (18.1%) missing valuesMissing
번호 has unique valuesUnique
대분류 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:22:04.860821
Analysis finished2023-12-12 23:22:05.748379
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct525
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263
Minimum1
Maximum525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T08:22:06.179816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27.2
Q1132
median263
Q3394
95-th percentile498.8
Maximum525
Range524
Interquartile range (IQR)262

Descriptive statistics

Standard deviation151.69871
Coefficient of variation (CV)0.5768012
Kurtosis-1.2
Mean263
Median Absolute Deviation (MAD)131
Skewness0
Sum138075
Variance23012.5
MonotonicityStrictly increasing
2023-12-13T08:22:06.372627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
362 1
 
0.2%
360 1
 
0.2%
359 1
 
0.2%
358 1
 
0.2%
357 1
 
0.2%
356 1
 
0.2%
355 1
 
0.2%
354 1
 
0.2%
353 1
 
0.2%
Other values (515) 515
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
525 1
0.2%
524 1
0.2%
523 1
0.2%
522 1
0.2%
521 1
0.2%
520 1
0.2%
519 1
0.2%
518 1
0.2%
517 1
0.2%
516 1
0.2%

대분류
Text

UNIQUE 

Distinct525
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-13T08:22:06.637405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length32
Mean length18.982857
Min length3

Characters and Unicode

Total characters9966
Distinct characters141
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique525 ?
Unique (%)100.0%

Sample

1st row성별1
2nd row성별2
3rd row연령1
4th row연령2
5th row연령3
ValueCountFrequency (%)
코로나 280
 
11.3%
발생으로 216
 
8.7%
이용 126
 
5.1%
코로나바이러스 117
 
4.7%
미디어 96
 
3.9%
활동의 90
 
3.6%
일상적인 90
 
3.6%
동의 80
 
3.2%
재택근무 57
 
2.3%
감염 55
 
2.2%
Other values (408) 1281
51.5%
2023-12-13T08:22:07.060943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1963
 
19.7%
로 613
 
6.2%
코 399
 
4.0%
나 397
 
4.0%
이 258
 
2.6%
발 226
 
2.3%
변 226
 
2.3%
생 226
 
2.3%
화 226
 
2.3%
의 222
 
2.2%
Other values (131) 5210
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7017
70.4%
Space Separator 1963
 
19.7%
Decimal Number 905
 
9.1%
Other Punctuation 29
 
0.3%
Close Punctuation 24
 
0.2%
Open Punctuation 24
 
0.2%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 613
 
8.7%
코 399
 
5.7%
나 397
 
5.7%
이 258
 
3.7%
발 226
 
3.2%
변 226
 
3.2%
생 226
 
3.2%
화 226
 
3.2%
의 222
 
3.2%
으 216
 
3.1%
Other values (114) 4008
57.1%
Decimal Number
ValueCountFrequency (%)
1 194
21.4%
2 147
16.2%
3 117
12.9%
4 99
10.9%
5 77
 
8.5%
9 61
 
6.7%
7 56
 
6.2%
6 56
 
6.2%
8 55
 
6.1%
0 43
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 27
93.1%
/ 2
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
R 2
50.0%
Q 2
50.0%
Space Separator
ValueCountFrequency (%)
1963
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7017
70.4%
Common 2945
29.6%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 613
 
8.7%
코 399
 
5.7%
나 397
 
5.7%
이 258
 
3.7%
발 226
 
3.2%
변 226
 
3.2%
생 226
 
3.2%
화 226
 
3.2%
의 222
 
3.2%
으 216
 
3.1%
Other values (114) 4008
57.1%
Common
ValueCountFrequency (%)
1963
66.7%
1 194
 
6.6%
2 147
 
5.0%
3 117
 
4.0%
4 99
 
3.4%
5 77
 
2.6%
9 61
 
2.1%
7 56
 
1.9%
6 56
 
1.9%
8 55
 
1.9%
Other values (5) 120
 
4.1%
Latin
ValueCountFrequency (%)
R 2
50.0%
Q 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7017
70.4%
ASCII 2949
29.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1963
66.6%
1 194
 
6.6%
2 147
 
5.0%
3 117
 
4.0%
4 99
 
3.4%
5 77
 
2.6%
9 61
 
2.1%
7 56
 
1.9%
6 56
 
1.9%
8 55
 
1.9%
Other values (7) 124
 
4.2%
Hangul
ValueCountFrequency (%)
로 613
 
8.7%
코 399
 
5.7%
나 397
 
5.7%
이 258
 
3.7%
발 226
 
3.2%
변 226
 
3.2%
생 226
 
3.2%
화 226
 
3.2%
의 222
 
3.2%
으 216
 
3.1%
Other values (114) 4008
57.1%

중분류
Text

MISSING 

Distinct430
Distinct (%)100.0%
Missing95
Missing (%)18.1%
Memory size4.2 KiB
2023-12-13T08:22:07.414694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length31
Mean length17.22093
Min length4

Characters and Unicode

Total characters7405
Distinct characters273
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique430 ?
Unique (%)100.0%

Sample

1st row1) 집1
2nd row1) 집2
3rd row1) 집3
4th row1) 집4
5th row1) 집5
ValueCountFrequency (%)
등 101
 
5.2%
2 54
 
2.8%
1 54
 
2.8%
3 48
 
2.5%
6 42
 
2.2%
5 42
 
2.2%
4 42
 
2.2%
7 37
 
1.9%
8 26
 
1.3%
9 22
 
1.1%
Other values (520) 1479
76.0%
2023-12-13T08:22:07.938166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1523
 
20.6%
) 424
 
5.7%
1 202
 
2.7%
이 185
 
2.5%
2 146
 
2.0%
3 134
 
1.8%
4 128
 
1.7%
5 125
 
1.7%
등 107
 
1.4%
스 106
 
1.4%
Other values (263) 4325
58.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4365
58.9%
Space Separator 1523
 
20.6%
Decimal Number 916
 
12.4%
Close Punctuation 424
 
5.7%
Other Punctuation 112
 
1.5%
Uppercase Letter 59
 
0.8%
Open Punctuation 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 185
 
4.2%
등 107
 
2.5%
스 106
 
2.4%
다 100
 
2.3%
나 97
 
2.2%
시 68
 
1.6%
트 61
 
1.4%
가 60
 
1.4%
무 60
 
1.4%
재 55
 
1.3%
Other values (240) 3466
79.4%
Decimal Number
ValueCountFrequency (%)
1 202
22.1%
2 146
15.9%
3 134
14.6%
4 128
14.0%
5 125
13.6%
6 80
 
8.7%
7 37
 
4.0%
8 26
 
2.8%
9 22
 
2.4%
0 16
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
T 24
40.7%
V 12
20.3%
O 6
 
10.2%
P 6
 
10.2%
C 6
 
10.2%
G 5
 
8.5%
Other Punctuation
ValueCountFrequency (%)
, 80
71.4%
. 15
 
13.4%
· 12
 
10.7%
/ 5
 
4.5%
Space Separator
ValueCountFrequency (%)
1523
100.0%
Close Punctuation
ValueCountFrequency (%)
) 424
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4365
58.9%
Common 2981
40.3%
Latin 59
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 185
 
4.2%
등 107
 
2.5%
스 106
 
2.4%
다 100
 
2.3%
나 97
 
2.2%
시 68
 
1.6%
트 61
 
1.4%
가 60
 
1.4%
무 60
 
1.4%
재 55
 
1.3%
Other values (240) 3466
79.4%
Common
ValueCountFrequency (%)
1523
51.1%
) 424
 
14.2%
1 202
 
6.8%
2 146
 
4.9%
3 134
 
4.5%
4 128
 
4.3%
5 125
 
4.2%
6 80
 
2.7%
, 80
 
2.7%
7 37
 
1.2%
Other values (7) 102
 
3.4%
Latin
ValueCountFrequency (%)
T 24
40.7%
V 12
20.3%
O 6
 
10.2%
P 6
 
10.2%
C 6
 
10.2%
G 5
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4365
58.9%
ASCII 3028
40.9%
None 12
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1523
50.3%
) 424
 
14.0%
1 202
 
6.7%
2 146
 
4.8%
3 134
 
4.4%
4 128
 
4.2%
5 125
 
4.1%
6 80
 
2.6%
, 80
 
2.6%
7 37
 
1.2%
Other values (12) 149
 
4.9%
Hangul
ValueCountFrequency (%)
이 185
 
4.2%
등 107
 
2.5%
스 106
 
2.4%
다 100
 
2.3%
나 97
 
2.2%
시 68
 
1.6%
트 61
 
1.4%
가 60
 
1.4%
무 60
 
1.4%
재 55
 
1.3%
Other values (240) 3466
79.4%
None
ValueCountFrequency (%)
· 12
100.0%

소분류
Text

Distinct123
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-13T08:22:08.253142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length54
Mean length13.131429
Min length5

Characters and Unicode

Total characters6894
Distinct characters201
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)15.4%

Sample

1st row1) 남성
2nd row2) 여성
3rd row 1) 만20-29세
4th row 2) 만30-39세
5th row 3) 만40-49세
ValueCountFrequency (%)
약간 115
 
6.2%
2 96
 
5.2%
1 96
 
5.2%
4 91
 
4.9%
3 91
 
4.9%
5 83
 
4.5%
많이 82
 
4.4%
줄었다 76
 
4.1%
늘었다 72
 
3.9%
없다 52
 
2.8%
Other values (199) 990
53.7%
2023-12-13T08:22:08.722815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2349
34.1%
) 533
 
7.7%
다 405
 
5.9%
이 180
 
2.6%
었 148
 
2.1%
1 119
 
1.7%
약 116
 
1.7%
간 115
 
1.7%
지 113
 
1.6%
2 103
 
1.5%
Other values (191) 2713
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3319
48.1%
Space Separator 2349
34.1%
Decimal Number 627
 
9.1%
Close Punctuation 533
 
7.7%
Other Punctuation 42
 
0.6%
Dash Punctuation 14
 
0.2%
Open Punctuation 8
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 405
 
12.2%
이 180
 
5.4%
었 148
 
4.5%
약 116
 
3.5%
간 115
 
3.5%
지 113
 
3.4%
없 100
 
3.0%
많 86
 
2.6%
줄 76
 
2.3%
늘 72
 
2.2%
Other values (172) 1908
57.5%
Decimal Number
ValueCountFrequency (%)
1 119
19.0%
2 103
16.4%
4 98
15.6%
3 97
15.5%
5 90
14.4%
6 47
 
7.5%
0 32
 
5.1%
9 29
 
4.6%
7 7
 
1.1%
8 5
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 30
71.4%
/ 11
 
26.2%
· 1
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
2349
100.0%
Close Punctuation
ValueCountFrequency (%)
) 533
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3573
51.8%
Hangul 3319
48.1%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 405
 
12.2%
이 180
 
5.4%
었 148
 
4.5%
약 116
 
3.5%
간 115
 
3.5%
지 113
 
3.4%
없 100
 
3.0%
많 86
 
2.6%
줄 76
 
2.3%
늘 72
 
2.2%
Other values (172) 1908
57.5%
Common
ValueCountFrequency (%)
2349
65.7%
) 533
 
14.9%
1 119
 
3.3%
2 103
 
2.9%
4 98
 
2.7%
3 97
 
2.7%
5 90
 
2.5%
6 47
 
1.3%
0 32
 
0.9%
, 30
 
0.8%
Other values (7) 75
 
2.1%
Latin
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3574
51.8%
Hangul 3319
48.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2349
65.7%
) 533
 
14.9%
1 119
 
3.3%
2 103
 
2.9%
4 98
 
2.7%
3 97
 
2.7%
5 90
 
2.5%
6 47
 
1.3%
0 32
 
0.9%
, 30
 
0.8%
Other values (8) 76
 
2.1%
Hangul
ValueCountFrequency (%)
다 405
 
12.2%
이 180
 
5.4%
었 148
 
4.5%
약 116
 
3.5%
간 115
 
3.5%
지 113
 
3.4%
없 100
 
3.0%
많 86
 
2.6%
줄 76
 
2.3%
늘 72
 
2.2%
Other values (172) 1908
57.5%
None
ValueCountFrequency (%)
· 1
100.0%

사례수
Real number (ℝ)

Distinct283
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.51048
Minimum1
Maximum878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T08:22:08.855042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median84
Q3260
95-th percentile522.2
Maximum878
Range877
Interquartile range (IQR)237

Descriptive statistics

Standard deviation181.62353
Coefficient of variation (CV)1.1040241
Kurtosis1.210359
Mean164.51048
Median Absolute Deviation (MAD)75
Skewness1.3327391
Sum86368
Variance32987.105
MonotonicityNot monotonic
2023-12-13T08:22:08.985135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 11
 
2.1%
6 10
 
1.9%
12 9
 
1.7%
11 9
 
1.7%
8 9
 
1.7%
7 8
 
1.5%
2 7
 
1.3%
4 7
 
1.3%
15 7
 
1.3%
1 6
 
1.1%
Other values (273) 442
84.2%
ValueCountFrequency (%)
1 6
1.1%
2 7
1.3%
3 5
1.0%
4 7
1.3%
5 5
1.0%
6 10
1.9%
7 8
1.5%
8 9
1.7%
9 11
2.1%
10 4
 
0.8%
ValueCountFrequency (%)
878 1
0.2%
875 1
0.2%
811 1
0.2%
791 1
0.2%
786 1
0.2%
711 1
0.2%
676 1
0.2%
671 1
0.2%
669 1
0.2%
663 1
0.2%

Interactions

2023-12-13T08:22:05.425710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:05.244044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:05.514634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:05.325434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:22:09.076324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사례수
번호1.0000.374
사례수0.3741.000
2023-12-13T08:22:09.152956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사례수
번호1.000-0.074
사례수-0.0741.000

Missing values

2023-12-13T08:22:05.633860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:22:05.714779image/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

번호대분류중분류소분류사례수
01성별1<NA>1) 남성511
12성별2<NA>2) 여성489
23연령1<NA>1) 만20-29세183
34연령2<NA>2) 만30-39세186
45연령3<NA>3) 만40-49세221
56연령4<NA>4) 만50-59세234
67연령5<NA>5) 만60-69세176
78거주 지역1<NA>1) 서울194
89거주 지역2<NA>2) 부산67
910거주 지역3<NA>3) 대구48
번호대분류중분류소분류사례수
515516정치적 성향4<NA>4) 약간 보수적166
516517정치적 성향5<NA>5) 매우 보수적23
517518종교1<NA>1) 불교150
518519종교2<NA>2) 개신교204
519520종교3<NA>3) 천주교109
520521종교4<NA>4) 원불교3
521522종교5<NA>5) 유교3
522523종교6<NA>7) 대순진리회2
523524종교7<NA>9) 기타15
524525종교8<NA>10) 종교 없음514