Overview

Dataset statistics

Number of variables11
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory99.4 B

Variable types

Text2
Numeric9

Dataset

Description언론인 의식 조사 관련 "허위 조작 정보 등 유형별 심각성 평가"에 관한 자료입니다. 자세한 내용은 재단 홈페이지에 방문하여 확인 바랍니다.
Author한국언론진흥재단
URLhttps://www.data.go.kr/data/15060159/fileData.do

Alerts

언론사의 오보 is highly overall correlated with 허위조작정보 가짜뉴스 and 1 other fieldsHigh correlation
낚시성 기사 is highly overall correlated with 어뷰징 기사 and 3 other fieldsHigh correlation
어뷰징 기사 is highly overall correlated with 낚시성 기사 and 3 other fieldsHigh correlation
편파적 기사 is highly overall correlated with 낚시성 기사 and 2 other fieldsHigh correlation
광고홍보성 기사 is highly overall correlated with 낚시성 기사 and 2 other fieldsHigh correlation
SNS 등에 올라온 내용을 팩트체킹이나 추가취재 없이 그대로 이용하는 기사 is highly overall correlated with 낚시성 기사 and 1 other fieldsHigh correlation
허위조작정보 가짜뉴스 is highly overall correlated with 언론사의 오보 and 1 other fieldsHigh correlation
속칭 찌라시 정보 is highly overall correlated with 언론사의 오보 and 1 other fieldsHigh correlation
구분 has unique valuesUnique

Reproduction

Analysis started2024-03-14 17:50:42.186584
Analysis finished2024-03-14 17:51:03.873231
Duration21.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T02:51:04.768784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.4259259
Min length3

Characters and Unicode

Total characters239
Distinct characters28
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

Unique54 ?
Unique (%)100.0%

Sample

1st row성별1
2nd row성별2
3rd row연령1
4th row연령2
5th row연령3
ValueCountFrequency (%)
성별1 1
 
1.9%
소속부서16 1
 
1.9%
권역1 1
 
1.9%
소속부서6 1
 
1.9%
소속부서7 1
 
1.9%
소속부서8 1
 
1.9%
소속부서9 1
 
1.9%
소속부서10 1
 
1.9%
소속부서11 1
 
1.9%
소속부서12 1
 
1.9%
Other values (44) 44
81.5%
2024-03-15T02:51:06.763501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
12.1%
29
12.1%
1 21
 
8.8%
18
 
7.5%
18
 
7.5%
15
 
6.3%
15
 
6.3%
2 9
 
3.8%
7
 
2.9%
4 7
 
2.9%
Other values (18) 71
29.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 174
72.8%
Decimal Number 65
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
29
16.7%
29
16.7%
18
10.3%
18
10.3%
15
8.6%
15
8.6%
7
 
4.0%
7
 
4.0%
5
 
2.9%
5
 
2.9%
Other values (8) 26
14.9%
Decimal Number
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
4 7
 
10.8%
3 7
 
10.8%
5 6
 
9.2%
6 4
 
6.2%
7 4
 
6.2%
8 3
 
4.6%
9 2
 
3.1%
0 2
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 174
72.8%
Common 65
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
29
16.7%
29
16.7%
18
10.3%
18
10.3%
15
8.6%
15
8.6%
7
 
4.0%
7
 
4.0%
5
 
2.9%
5
 
2.9%
Other values (8) 26
14.9%
Common
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
4 7
 
10.8%
3 7
 
10.8%
5 6
 
9.2%
6 4
 
6.2%
7 4
 
6.2%
8 3
 
4.6%
9 2
 
3.1%
0 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 174
72.8%
ASCII 65
 
27.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
29
16.7%
29
16.7%
18
10.3%
18
10.3%
15
8.6%
15
8.6%
7
 
4.0%
7
 
4.0%
5
 
2.9%
5
 
2.9%
Other values (8) 26
14.9%
ASCII
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
4 7
 
10.8%
3 7
 
10.8%
5 6
 
9.2%
6 4
 
6.2%
7 4
 
6.2%
8 3
 
4.6%
9 2
 
3.1%
0 2
 
3.1%
Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T02:51:07.833685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.5555556
Min length2

Characters and Unicode

Total characters300
Distinct characters104
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)92.6%

Sample

1st row남자
2nd row여자
3rd row20대
4th row30~34세
5th row35~39세
ValueCountFrequency (%)
뉴스통신사 2
 
3.4%
이상 2
 
3.4%
인터넷언론사 2
 
3.4%
1
 
1.7%
영상부 1
 
1.7%
남자 1
 
1.7%
기타 1
 
1.7%
국방/통일/북한 1
 
1.7%
편집(편성)/교열부 1
 
1.7%
여론/독자/심의부 1
 
1.7%
Other values (45) 45
77.6%
2024-03-15T02:51:09.191822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 20
 
6.7%
14
 
4.7%
11
 
3.7%
10
 
3.3%
4 8
 
2.7%
~ 8
 
2.7%
8
 
2.7%
7
 
2.3%
7
 
2.3%
0 7
 
2.3%
Other values (94) 200
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 223
74.3%
Decimal Number 36
 
12.0%
Other Punctuation 20
 
6.7%
Math Symbol 8
 
2.7%
Uppercase Letter 5
 
1.7%
Space Separator 4
 
1.3%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14
 
6.3%
11
 
4.9%
10
 
4.5%
8
 
3.6%
7
 
3.1%
7
 
3.1%
6
 
2.7%
5
 
2.2%
5
 
2.2%
5
 
2.2%
Other values (79) 145
65.0%
Decimal Number
ValueCountFrequency (%)
4 8
22.2%
0 7
19.4%
5 5
13.9%
1 5
13.9%
9 4
11.1%
3 4
11.1%
2 2
 
5.6%
6 1
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
I 4
80.0%
T 1
 
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 20
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 223
74.3%
Common 72
 
24.0%
Latin 5
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14
 
6.3%
11
 
4.9%
10
 
4.5%
8
 
3.6%
7
 
3.1%
7
 
3.1%
6
 
2.7%
5
 
2.2%
5
 
2.2%
5
 
2.2%
Other values (79) 145
65.0%
Common
ValueCountFrequency (%)
/ 20
27.8%
4 8
 
11.1%
~ 8
 
11.1%
0 7
 
9.7%
5 5
 
6.9%
1 5
 
6.9%
9 4
 
5.6%
4
 
5.6%
3 4
 
5.6%
) 2
 
2.8%
Other values (3) 5
 
6.9%
Latin
ValueCountFrequency (%)
I 4
80.0%
T 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 223
74.3%
ASCII 77
 
25.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 20
26.0%
4 8
 
10.4%
~ 8
 
10.4%
0 7
 
9.1%
5 5
 
6.5%
1 5
 
6.5%
9 4
 
5.2%
4
 
5.2%
3 4
 
5.2%
I 4
 
5.2%
Other values (5) 8
 
10.4%
Hangul
ValueCountFrequency (%)
14
 
6.3%
11
 
4.9%
10
 
4.5%
8
 
3.6%
7
 
3.1%
7
 
3.1%
6
 
2.7%
5
 
2.2%
5
 
2.2%
5
 
2.2%
Other values (79) 145
65.0%

사례수
Real number (ℝ)

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.92593
Minimum8
Maximum1392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:09.481087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile19.85
Q177.25
median206
Q3404.25
95-th percentile1034.45
Maximum1392
Range1384
Interquartile range (IQR)327

Descriptive statistics

Standard deviation312.37307
Coefficient of variation (CV)1.0484924
Kurtosis4.5902915
Mean297.92593
Median Absolute Deviation (MAD)159
Skewness2.0456416
Sum16088
Variance97576.938
MonotonicityNot monotonic
2024-03-15T02:51:09.852964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457 3
 
5.6%
160 2
 
3.7%
406 2
 
3.7%
1376 1
 
1.9%
71 1
 
1.9%
12 1
 
1.9%
87 1
 
1.9%
14 1
 
1.9%
34 1
 
1.9%
52 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
8 1
1.9%
12 1
1.9%
14 1
1.9%
23 1
1.9%
26 1
1.9%
33 1
1.9%
34 1
1.9%
44 1
1.9%
50 1
1.9%
52 1
1.9%
ValueCountFrequency (%)
1392 1
 
1.9%
1376 1
 
1.9%
1065 1
 
1.9%
1018 1
 
1.9%
635 1
 
1.9%
619 1
 
1.9%
457 3
5.6%
449 1
 
1.9%
432 1
 
1.9%
426 1
 
1.9%

언론사의 오보
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6781481
Minimum3.48
Maximum4.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:10.250499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.48
5-th percentile3.523
Q13.63
median3.67
Q33.7275
95-th percentile3.817
Maximum4.07
Range0.59
Interquartile range (IQR)0.0975

Descriptive statistics

Standard deviation0.10131353
Coefficient of variation (CV)0.02754471
Kurtosis3.6915497
Mean3.6781481
Median Absolute Deviation (MAD)0.05
Skewness1.1674606
Sum198.62
Variance0.01026443
MonotonicityNot monotonic
2024-03-15T02:51:10.631042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3.63 5
 
9.3%
3.74 4
 
7.4%
3.7 4
 
7.4%
3.64 4
 
7.4%
3.67 3
 
5.6%
3.62 3
 
5.6%
3.68 3
 
5.6%
3.76 3
 
5.6%
3.65 2
 
3.7%
3.57 2
 
3.7%
Other values (18) 21
38.9%
ValueCountFrequency (%)
3.48 1
 
1.9%
3.5 1
 
1.9%
3.51 1
 
1.9%
3.53 1
 
1.9%
3.56 1
 
1.9%
3.57 2
3.7%
3.59 1
 
1.9%
3.6 1
 
1.9%
3.61 1
 
1.9%
3.62 3
5.6%
ValueCountFrequency (%)
4.07 1
 
1.9%
3.94 1
 
1.9%
3.83 1
 
1.9%
3.81 1
 
1.9%
3.8 2
3.7%
3.76 3
5.6%
3.74 4
7.4%
3.73 1
 
1.9%
3.72 2
3.7%
3.71 1
 
1.9%

낚시성 기사
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3111111
Minimum3.87
Maximum4.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:11.011748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.87
5-th percentile4.1165
Q14.2525
median4.31
Q34.3575
95-th percentile4.5
Maximum4.63
Range0.76
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.12068463
Coefficient of variation (CV)0.027993857
Kurtosis3.2764503
Mean4.3111111
Median Absolute Deviation (MAD)0.055
Skewness-0.41425865
Sum232.8
Variance0.01456478
MonotonicityNot monotonic
2024-03-15T02:51:11.419086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4.33 5
 
9.3%
4.24 4
 
7.4%
4.27 4
 
7.4%
4.3 4
 
7.4%
4.31 3
 
5.6%
4.35 3
 
5.6%
4.32 2
 
3.7%
4.29 2
 
3.7%
4.5 2
 
3.7%
4.22 2
 
3.7%
Other values (21) 23
42.6%
ValueCountFrequency (%)
3.87 1
 
1.9%
4.09 1
 
1.9%
4.11 1
 
1.9%
4.12 1
 
1.9%
4.18 1
 
1.9%
4.21 1
 
1.9%
4.22 2
3.7%
4.24 4
7.4%
4.25 2
3.7%
4.26 1
 
1.9%
ValueCountFrequency (%)
4.63 1
1.9%
4.59 1
1.9%
4.5 2
3.7%
4.49 1
1.9%
4.45 1
1.9%
4.43 1
1.9%
4.42 1
1.9%
4.4 2
3.7%
4.39 1
1.9%
4.38 1
1.9%

어뷰징 기사
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2881481
Minimum3.81
Maximum4.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:11.836468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.81
5-th percentile4.0625
Q14.22
median4.285
Q34.34
95-th percentile4.5205
Maximum4.88
Range1.07
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.15290982
Coefficient of variation (CV)0.035658706
Kurtosis4.8701926
Mean4.2881481
Median Absolute Deviation (MAD)0.06
Skewness0.65082945
Sum231.56
Variance0.023381412
MonotonicityNot monotonic
2024-03-15T02:51:12.251605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
4.22 6
 
11.1%
4.34 5
 
9.3%
4.29 4
 
7.4%
4.32 2
 
3.7%
4.38 2
 
3.7%
4.25 2
 
3.7%
4.27 2
 
3.7%
4.03 2
 
3.7%
4.31 2
 
3.7%
4.28 2
 
3.7%
Other values (22) 25
46.3%
ValueCountFrequency (%)
3.81 1
 
1.9%
4.03 2
 
3.7%
4.08 1
 
1.9%
4.1 1
 
1.9%
4.16 1
 
1.9%
4.17 1
 
1.9%
4.19 1
 
1.9%
4.2 1
 
1.9%
4.21 1
 
1.9%
4.22 6
11.1%
ValueCountFrequency (%)
4.88 1
1.9%
4.61 1
1.9%
4.54 1
1.9%
4.51 1
1.9%
4.5 1
1.9%
4.45 1
1.9%
4.42 1
1.9%
4.38 2
3.7%
4.36 1
1.9%
4.35 1
1.9%

편파적 기사
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0357407
Minimum3.61
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:12.643858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.61
5-th percentile3.8695
Q13.95
median4.035
Q34.12
95-th percentile4.2135
Maximum4.5
Range0.89
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.13699748
Coefficient of variation (CV)0.033946055
Kurtosis2.5137393
Mean4.0357407
Median Absolute Deviation (MAD)0.085
Skewness0.23287502
Sum217.93
Variance0.018768309
MonotonicityNot monotonic
2024-03-15T02:51:13.153282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3.95 4
 
7.4%
4.05 4
 
7.4%
4.14 3
 
5.6%
3.93 2
 
3.7%
3.94 2
 
3.7%
3.98 2
 
3.7%
4.16 2
 
3.7%
3.97 2
 
3.7%
4.21 2
 
3.7%
4.03 2
 
3.7%
Other values (24) 29
53.7%
ValueCountFrequency (%)
3.61 1
1.9%
3.8 1
1.9%
3.85 1
1.9%
3.88 1
1.9%
3.89 1
1.9%
3.9 1
1.9%
3.91 1
1.9%
3.92 2
3.7%
3.93 2
3.7%
3.94 2
3.7%
ValueCountFrequency (%)
4.5 1
 
1.9%
4.27 1
 
1.9%
4.22 1
 
1.9%
4.21 2
3.7%
4.2 1
 
1.9%
4.19 1
 
1.9%
4.17 1
 
1.9%
4.16 2
3.7%
4.14 3
5.6%
4.12 2
3.7%

광고홍보성 기사
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8290741
Minimum3.52
Maximum4.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:13.526284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.52
5-th percentile3.7065
Q13.7825
median3.82
Q33.875
95-th percentile3.9635
Maximum4.25
Range0.73
Interquartile range (IQR)0.0925

Descriptive statistics

Standard deviation0.10466962
Coefficient of variation (CV)0.027335492
Kurtosis4.990968
Mean3.8290741
Median Absolute Deviation (MAD)0.045
Skewness0.87130734
Sum206.77
Variance0.01095573
MonotonicityNot monotonic
2024-03-15T02:51:14.108679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3.83 6
 
11.1%
3.79 5
 
9.3%
3.82 4
 
7.4%
3.88 4
 
7.4%
3.81 3
 
5.6%
3.75 3
 
5.6%
3.86 3
 
5.6%
3.8 2
 
3.7%
3.84 2
 
3.7%
3.76 2
 
3.7%
Other values (18) 20
37.0%
ValueCountFrequency (%)
3.52 1
 
1.9%
3.64 1
 
1.9%
3.7 1
 
1.9%
3.71 2
3.7%
3.73 1
 
1.9%
3.74 1
 
1.9%
3.75 3
5.6%
3.76 2
3.7%
3.77 1
 
1.9%
3.78 1
 
1.9%
ValueCountFrequency (%)
4.25 1
 
1.9%
4.04 1
 
1.9%
3.97 1
 
1.9%
3.96 2
3.7%
3.95 1
 
1.9%
3.94 1
 
1.9%
3.93 1
 
1.9%
3.92 1
 
1.9%
3.91 1
 
1.9%
3.88 4
7.4%
Distinct33
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1935185
Minimum3.73
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:14.423884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.73
5-th percentile3.962
Q14.1225
median4.21
Q34.2575
95-th percentile4.447
Maximum4.5
Range0.77
Interquartile range (IQR)0.135

Descriptive statistics

Standard deviation0.15096116
Coefficient of variation (CV)0.035998688
Kurtosis1.6893754
Mean4.1935185
Median Absolute Deviation (MAD)0.075
Skewness-0.64937395
Sum226.45
Variance0.022789273
MonotonicityNot monotonic
2024-03-15T02:51:14.780089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4.23 4
 
7.4%
4.18 4
 
7.4%
4.21 4
 
7.4%
4.25 3
 
5.6%
4.07 2
 
3.7%
4.12 2
 
3.7%
4.29 2
 
3.7%
4.22 2
 
3.7%
4.16 2
 
3.7%
4.04 2
 
3.7%
Other values (23) 27
50.0%
ValueCountFrequency (%)
3.73 1
1.9%
3.77 1
1.9%
3.91 1
1.9%
3.99 1
1.9%
4.0 1
1.9%
4.04 2
3.7%
4.06 1
1.9%
4.07 2
3.7%
4.09 1
1.9%
4.1 1
1.9%
ValueCountFrequency (%)
4.5 1
1.9%
4.49 1
1.9%
4.46 1
1.9%
4.44 1
1.9%
4.37 2
3.7%
4.36 1
1.9%
4.34 2
3.7%
4.33 1
1.9%
4.29 2
3.7%
4.27 1
1.9%

허위조작정보 가짜뉴스
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9174074
Minimum3.69
Maximum4.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:15.108075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.69
5-th percentile3.74
Q13.86
median3.92
Q33.98
95-th percentile4.0405
Maximum4.2
Range0.51
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.10129697
Coefficient of variation (CV)0.025858166
Kurtosis0.89225587
Mean3.9174074
Median Absolute Deviation (MAD)0.06
Skewness0.17415663
Sum211.54
Variance0.010261076
MonotonicityNot monotonic
2024-03-15T02:51:15.524535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3.96 4
 
7.4%
4.01 4
 
7.4%
3.92 4
 
7.4%
3.91 3
 
5.6%
3.86 3
 
5.6%
3.87 3
 
5.6%
3.93 3
 
5.6%
4.0 3
 
5.6%
3.74 2
 
3.7%
3.94 2
 
3.7%
Other values (18) 23
42.6%
ValueCountFrequency (%)
3.69 1
 
1.9%
3.72 1
 
1.9%
3.74 2
3.7%
3.75 1
 
1.9%
3.77 1
 
1.9%
3.81 1
 
1.9%
3.82 2
3.7%
3.84 1
 
1.9%
3.85 2
3.7%
3.86 3
5.6%
ValueCountFrequency (%)
4.2 1
 
1.9%
4.18 1
 
1.9%
4.06 1
 
1.9%
4.03 1
 
1.9%
4.02 2
3.7%
4.01 4
7.4%
4.0 3
5.6%
3.98 2
3.7%
3.96 4
7.4%
3.95 1
 
1.9%

속칭 찌라시 정보
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7818519
Minimum3.47
Maximum4.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T02:51:15.899350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.47
5-th percentile3.58
Q13.7225
median3.79
Q33.865
95-th percentile3.9135
Maximum4.06
Range0.59
Interquartile range (IQR)0.1425

Descriptive statistics

Standard deviation0.11353786
Coefficient of variation (CV)0.030021763
Kurtosis0.89036605
Mean3.7818519
Median Absolute Deviation (MAD)0.07
Skewness-0.47639959
Sum204.22
Variance0.012890846
MonotonicityNot monotonic
2024-03-15T02:51:16.144829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3.79 6
 
11.1%
3.74 3
 
5.6%
3.7 3
 
5.6%
3.72 3
 
5.6%
3.89 3
 
5.6%
3.75 2
 
3.7%
3.9 2
 
3.7%
3.58 2
 
3.7%
3.87 2
 
3.7%
3.85 2
 
3.7%
Other values (20) 26
48.1%
ValueCountFrequency (%)
3.47 1
 
1.9%
3.5 1
 
1.9%
3.58 2
3.7%
3.59 1
 
1.9%
3.62 1
 
1.9%
3.65 1
 
1.9%
3.7 3
5.6%
3.71 1
 
1.9%
3.72 3
5.6%
3.73 1
 
1.9%
ValueCountFrequency (%)
4.06 1
 
1.9%
4.0 1
 
1.9%
3.92 1
 
1.9%
3.91 2
3.7%
3.9 2
3.7%
3.89 3
5.6%
3.88 2
3.7%
3.87 2
3.7%
3.85 2
3.7%
3.84 2
3.7%

Interactions

2024-03-15T02:51:00.687849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:42.778187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:45.039502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:47.461601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:49.989628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:52.177932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:54.030050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:56.094471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:58.430448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:00.937662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:43.036595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:45.255554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:47.755372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:50.280214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:52.457792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:54.367628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:56.358640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:58.684258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:01.185091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:43.299867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:45.506239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:48.080193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:50.570853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:52.694384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:54.518080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:56.527471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:58.931367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:01.446051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:43.463718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:45.767893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:48.330472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:50.872638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:52.929265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:54.778461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:56.710816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:59.185152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:01.688281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:43.707427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:46.010470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:48.590826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:51.020407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:53.067542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:55.026416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:56.910229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:59.433699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:01.928925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:43.950293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:46.250201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:48.843959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:51.153224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:53.200397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:55.272646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:57.354548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:59.667619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:02.184968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:44.283096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:46.513969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:49.127338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:51.410636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:53.393111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:55.473041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:57.632659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:59.950968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:02.460010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:44.729255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:46.952501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:49.440219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:51.684865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:53.661339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:55.656176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:57.911381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:00.200435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:02.721975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:44.883745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:47.207141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:49.736823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:51.936386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:53.810195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:55.845368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:50:58.168855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:51:00.445717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:51:16.354513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분중분류사례수언론사의 오보낚시성 기사어뷰징 기사편파적 기사광고홍보성 기사SNS 등에 올라온 내용을 팩트체킹이나 추가취재 없이 그대로 이용하는 기사허위조작정보 가짜뉴스속칭 찌라시 정보
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사례수1.0001.0001.0000.0000.0000.0000.0000.0000.2780.0000.000
언론사의 오보1.0001.0000.0001.0000.6130.8270.6620.4630.2250.6340.639
낚시성 기사1.0001.0000.0000.6131.0000.8970.6010.8830.8760.2350.217
어뷰징 기사1.0001.0000.0000.8270.8971.0000.8540.8480.6300.4410.578
편파적 기사1.0001.0000.0000.6620.6010.8541.0000.7410.1880.1700.697
광고홍보성 기사1.0001.0000.0000.4630.8830.8480.7411.0000.8490.6490.367
SNS 등에 올라온 내용을 팩트체킹이나 추가취재 없이 그대로 이용하는 기사1.0001.0000.2780.2250.8760.6300.1880.8491.0000.4520.000
허위조작정보 가짜뉴스1.0001.0000.0000.6340.2350.4410.1700.6490.4521.0000.771
속칭 찌라시 정보1.0001.0000.0000.6390.2170.5780.6970.3670.0000.7711.000
2024-03-15T02:51:16.699046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수언론사의 오보낚시성 기사어뷰징 기사편파적 기사광고홍보성 기사SNS 등에 올라온 내용을 팩트체킹이나 추가취재 없이 그대로 이용하는 기사허위조작정보 가짜뉴스속칭 찌라시 정보
사례수1.000-0.232-0.077-0.287-0.232-0.1830.113-0.118-0.186
언론사의 오보-0.2321.0000.2320.2880.3290.249-0.1910.5650.601
낚시성 기사-0.0770.2321.0000.8360.5590.5890.6820.3110.050
어뷰징 기사-0.2870.2880.8361.0000.6580.6520.6270.3970.218
편파적 기사-0.2320.3290.5590.6581.0000.5280.2160.3010.167
광고홍보성 기사-0.1830.2490.5890.6520.5281.0000.4000.4380.322
SNS 등에 올라온 내용을 팩트체킹이나 추가취재 없이 그대로 이용하는 기사0.113-0.1910.6820.6270.2160.4001.0000.1620.013
허위조작정보 가짜뉴스-0.1180.5650.3110.3970.3010.4380.1621.0000.799
속칭 찌라시 정보-0.1860.6010.0500.2180.1670.3220.0130.7991.000

Missing values

2024-03-15T02:51:03.135239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:51:03.656257image/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

구분중분류사례수언론사의 오보낚시성 기사어뷰징 기사편파적 기사광고홍보성 기사SNS 등에 올라온 내용을 팩트체킹이나 추가취재 없이 그대로 이용하는 기사허위조작정보 가짜뉴스속칭 찌라시 정보
0성별1남자13763.674.314.284.033.84.163.913.75
1성별2여자6353.644.294.253.983.844.293.93.81
2연령120대2173.574.274.23.923.794.213.823.74
3연령230~34세3913.624.344.323.853.794.343.873.73
4연령335~39세3823.594.334.323.993.844.343.883.7
5연령440~44세3173.684.334.314.093.824.243.963.87
6연령545~49세2253.724.384.334.123.934.173.963.7
7연령650대3723.84.314.244.193.834.043.953.83
8연령760대 이상1073.643.873.813.83.523.773.853.82
9매체유형1신문사10653.624.254.213.993.754.153.853.72
구분중분류사례수언론사의 오보낚시성 기사어뷰징 기사편파적 기사광고홍보성 기사SNS 등에 올라온 내용을 팩트체킹이나 추가취재 없이 그대로 이용하는 기사허위조작정보 가짜뉴스속칭 찌라시 정보
44직위3부장/부장대우2093.834.354.294.23.944.124.033.89
45직위4차장/차장대우4573.634.354.314.063.824.253.923.8
46직위5평기자10183.634.324.293.943.814.293.873.72
47경력11~4년4263.614.274.223.953.794.213.873.78
48경력25~9년4573.644.284.273.893.734.263.863.7
49경력310~14년3993.654.374.334.053.854.253.963.78
50경력415~19년2723.634.274.284.063.824.223.963.84
51경력520년 이상4573.764.324.254.143.884.093.93.77
52권역1서울13923.664.334.294.033.834.233.863.71
53권역2그 외 지역6193.674.244.223.983.784.144.013.89