Overview

Dataset statistics

Number of variables12
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory108.4 B

Variable types

Text2
Numeric10

Dataset

Description언론인 의식조사 관련 "기사 오보의 발생 원인"에 관한 자료입니다. (기자의 사실 미확인 또는 불충분한 취재, 정보원 측의 부정확한 정보제공, 특종에 대한 욕심 등)
Author한국언론진흥재단
URLhttps://www.data.go.kr/data/15050487/fileData.do

Alerts

정보원 측의 의도적인 잘못된 정보제공 is highly overall correlated with 마감시간에 따른 압박감 and 1 other fieldsHigh correlation
마감시간에 따른 압박감 is highly overall correlated with 정보원 측의 의도적인 잘못된 정보제공High correlation
오보에 대한 제재 미비 is highly overall correlated with 정보원 측의 의도적인 잘못된 정보제공High correlation
대분류 has unique valuesUnique
정보원 측의 의도적인 잘못된 정보제공 has 1 (1.9%) zerosZeros
오보에 대한 제재 미비 has 1 (1.9%) zerosZeros
기자의 의도적 조작 has 1 (1.9%) zerosZeros
낙종에 대한 우려 has 1 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 23:16:23.605371
Analysis finished2023-12-12 23:16:33.171247
Duration9.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Text

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size564.0 B
2023-12-13T08:16:33.330792image/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%
2023-12-13T08:16:33.694317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 29
12.1%
속 29
12.1%
1 21
 
8.8%
부 18
 
7.5%
서 18
 
7.5%
매 15
 
6.3%
체 15
 
6.3%
2 9
 
3.8%
령 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 (%)
소 29
16.7%
속 29
16.7%
부 18
10.3%
서 18
10.3%
매 15
8.6%
체 15
8.6%
령 7
 
4.0%
연 7
 
4.0%
경 5
 
2.9%
위 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 (%)
소 29
16.7%
속 29
16.7%
부 18
10.3%
서 18
10.3%
매 15
8.6%
체 15
8.6%
령 7
 
4.0%
연 7
 
4.0%
경 5
 
2.9%
위 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 (%)
소 29
16.7%
속 29
16.7%
부 18
10.3%
서 18
10.3%
매 15
8.6%
체 15
8.6%
령 7
 
4.0%
연 7
 
4.0%
경 5
 
2.9%
위 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%

중분류
Text

Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size564.0 B
2023-12-13T08:16:33.916702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.4444444
Min length2

Characters and Unicode

Total characters294
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 (%)
인터넷언론사 2
 
3.3%
이상 2
 
3.3%
기타 2
 
3.3%
뉴스통신사 2
 
3.3%
15~19년 1
 
1.7%
지역/전국 1
 
1.7%
취재(보도)일반 1
 
1.7%
남자 1
 
1.7%
국방/통일/북한 1
 
1.7%
편집(편성)/교열부 1
 
1.7%
Other values (46) 46
76.7%
2023-12-13T08:16:34.383377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 20
 
6.8%
부 14
 
4.8%
사 11
 
3.7%
국 9
 
3.1%
4 8
 
2.7%
장 8
 
2.7%
~ 8
 
2.7%
대 7
 
2.4%
0 7
 
2.4%
일 7
 
2.4%
Other values (94) 195
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 217
73.8%
Decimal Number 37
 
12.6%
Other Punctuation 20
 
6.8%
Math Symbol 8
 
2.7%
Space Separator 6
 
2.0%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Uppercase Letter 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 14
 
6.5%
사 11
 
5.1%
국 9
 
4.1%
장 8
 
3.7%
대 7
 
3.2%
일 7
 
3.2%
지 6
 
2.8%
간 5
 
2.3%
년 5
 
2.3%
스 5
 
2.3%
Other values (79) 140
64.5%
Decimal Number
ValueCountFrequency (%)
4 8
21.6%
0 7
18.9%
5 5
13.5%
3 5
13.5%
1 5
13.5%
9 4
10.8%
2 2
 
5.4%
6 1
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
T 1
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 20
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 217
73.8%
Common 75
 
25.5%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 14
 
6.5%
사 11
 
5.1%
국 9
 
4.1%
장 8
 
3.7%
대 7
 
3.2%
일 7
 
3.2%
지 6
 
2.8%
간 5
 
2.3%
년 5
 
2.3%
스 5
 
2.3%
Other values (79) 140
64.5%
Common
ValueCountFrequency (%)
/ 20
26.7%
4 8
 
10.7%
~ 8
 
10.7%
0 7
 
9.3%
6
 
8.0%
5 5
 
6.7%
3 5
 
6.7%
1 5
 
6.7%
9 4
 
5.3%
( 2
 
2.7%
Other values (3) 5
 
6.7%
Latin
ValueCountFrequency (%)
I 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 217
73.8%
ASCII 77
 
26.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 20
26.0%
4 8
 
10.4%
~ 8
 
10.4%
0 7
 
9.1%
6
 
7.8%
5 5
 
6.5%
3 5
 
6.5%
1 5
 
6.5%
9 4
 
5.2%
( 2
 
2.6%
Other values (5) 7
 
9.1%
Hangul
ValueCountFrequency (%)
부 14
 
6.5%
사 11
 
5.1%
국 9
 
4.1%
장 8
 
3.7%
대 7
 
3.2%
일 7
 
3.2%
지 6
 
2.8%
간 5
 
2.3%
년 5
 
2.3%
스 5
 
2.3%
Other values (79) 140
64.5%

사례수
Real number (ℝ)

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.77778
Minimum10
Maximum1424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:34.530801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile26.35
Q184.5
median183
Q3389.5
95-th percentile1032.85
Maximum1424
Range1414
Interquartile range (IQR)305

Descriptive statistics

Standard deviation311.03113
Coefficient of variation (CV)1.0733436
Kurtosis5.4533828
Mean289.77778
Median Absolute Deviation (MAD)139
Skewness2.2236545
Sum15648
Variance96740.365
MonotonicityNot monotonic
2023-12-13T08:16:34.695449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2
 
3.7%
390 2
 
3.7%
144 2
 
3.7%
40 2
 
3.7%
1424 1
 
1.9%
143 1
 
1.9%
125 1
 
1.9%
14 1
 
1.9%
34 1
 
1.9%
48 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
10 2
3.7%
14 1
1.9%
33 1
1.9%
34 1
1.9%
39 1
1.9%
40 2
3.7%
48 1
1.9%
50 1
1.9%
55 1
1.9%
68 1
1.9%
ValueCountFrequency (%)
1424 1
1.9%
1392 1
1.9%
1040 1
1.9%
1029 1
1.9%
564 1
1.9%
532 1
1.9%
491 1
1.9%
464 1
1.9%
428 1
1.9%
420 1
1.9%

기자의 사실 미확인 또는 불충분한 취재
Real number (ℝ)

Distinct46
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.990741
Minimum82.4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:34.880359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82.4
5-th percentile84.98
Q189.5
median91.6
Q395.075
95-th percentile100
Maximum100
Range17.6
Interquartile range (IQR)5.575

Descriptive statistics

Standard deviation4.0740443
Coefficient of variation (CV)0.044287548
Kurtosis-0.010932249
Mean91.990741
Median Absolute Deviation (MAD)2.4
Skewness0.043053224
Sum4967.5
Variance16.597837
MonotonicityNot monotonic
2023-12-13T08:16:35.020409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
100.0 4
 
7.4%
90.4 2
 
3.7%
91.2 2
 
3.7%
91.6 2
 
3.7%
89.5 2
 
3.7%
88.9 2
 
3.7%
95.1 1
 
1.9%
92.0 1
 
1.9%
85.4 1
 
1.9%
97.5 1
 
1.9%
Other values (36) 36
66.7%
ValueCountFrequency (%)
82.4 1
1.9%
83.9 1
1.9%
84.2 1
1.9%
85.4 1
1.9%
85.8 1
1.9%
88.0 1
1.9%
88.2 1
1.9%
88.7 1
1.9%
88.9 2
3.7%
89.1 1
1.9%
ValueCountFrequency (%)
100.0 4
7.4%
97.9 1
 
1.9%
97.5 1
 
1.9%
96.5 1
 
1.9%
96.3 1
 
1.9%
96.0 1
 
1.9%
95.7 1
 
1.9%
95.4 1
 
1.9%
95.3 1
 
1.9%
95.2 1
 
1.9%

정보원 측의 부정확한 정보제공
Real number (ℝ)

Distinct47
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.494444
Minimum35.3
Maximum64.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:35.158218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.3
5-th percentile41.3
Q150.05
median53.1
Q356.35
95-th percentile61.52
Maximum64.6
Range29.3
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation6.1972732
Coefficient of variation (CV)0.11805579
Kurtosis0.9928933
Mean52.494444
Median Absolute Deviation (MAD)3.3
Skewness-0.59446847
Sum2834.7
Variance38.406195
MonotonicityNot monotonic
2023-12-13T08:16:35.324388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
51.2 2
 
3.7%
53.4 2
 
3.7%
51.0 2
 
3.7%
51.9 2
 
3.7%
45.4 2
 
3.7%
64.6 2
 
3.7%
57.5 2
 
3.7%
40.0 1
 
1.9%
35.3 1
 
1.9%
60.4 1
 
1.9%
Other values (37) 37
68.5%
ValueCountFrequency (%)
35.3 1
1.9%
35.7 1
1.9%
40.0 1
1.9%
42.0 1
1.9%
45.1 1
1.9%
45.4 2
3.7%
46.2 1
1.9%
47.1 1
1.9%
47.5 1
1.9%
47.8 1
1.9%
ValueCountFrequency (%)
64.6 2
3.7%
63.6 1
1.9%
60.4 1
1.9%
60.0 1
1.9%
59.8 1
1.9%
58.9 1
1.9%
58.8 1
1.9%
58.3 1
1.9%
58.0 1
1.9%
57.5 2
3.7%

특종에 대한 욕심
Real number (ℝ)

Distinct48
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.675926
Minimum27.3
Maximum64.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:35.462912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.3
5-th percentile29.16
Q137.9
median42.25
Q345.975
95-th percentile52.885
Maximum64.3
Range37
Interquartile range (IQR)8.075

Descriptive statistics

Standard deviation7.2331505
Coefficient of variation (CV)0.17355704
Kurtosis0.96547711
Mean41.675926
Median Absolute Deviation (MAD)3.8
Skewness0.19368106
Sum2250.5
Variance52.318466
MonotonicityNot monotonic
2023-12-13T08:16:35.616041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
47.1 2
 
3.7%
43.6 2
 
3.7%
40.0 2
 
3.7%
27.3 2
 
3.7%
39.7 2
 
3.7%
40.3 2
 
3.7%
41.9 1
 
1.9%
39.4 1
 
1.9%
64.3 1
 
1.9%
52.1 1
 
1.9%
Other values (38) 38
70.4%
ValueCountFrequency (%)
27.3 2
3.7%
27.6 1
1.9%
30.0 1
1.9%
31.0 1
1.9%
31.7 1
1.9%
32.5 1
1.9%
32.9 1
1.9%
33.8 1
1.9%
34.1 1
1.9%
34.8 1
1.9%
ValueCountFrequency (%)
64.3 1
1.9%
54.9 1
1.9%
53.6 1
1.9%
52.5 1
1.9%
52.1 1
1.9%
47.4 1
1.9%
47.3 1
1.9%
47.1 2
3.7%
47.0 1
1.9%
46.7 1
1.9%

기자의 단순 실수
Real number (ℝ)

Distinct43
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.248148
Minimum16.7
Maximum40.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:35.769456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.7
5-th percentile20.715
Q127.475
median30.4
Q333.75
95-th percentile37.5
Maximum40.9
Range24.2
Interquartile range (IQR)6.275

Descriptive statistics

Standard deviation5.1725232
Coefficient of variation (CV)0.17100297
Kurtosis0.022714668
Mean30.248148
Median Absolute Deviation (MAD)3.3
Skewness-0.48393517
Sum1633.4
Variance26.754997
MonotonicityNot monotonic
2023-12-13T08:16:35.901974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
29.5 3
 
5.6%
32.0 2
 
3.7%
33.6 2
 
3.7%
20.0 2
 
3.7%
30.3 2
 
3.7%
32.2 2
 
3.7%
30.0 2
 
3.7%
37.5 2
 
3.7%
34.1 2
 
3.7%
25.7 2
 
3.7%
Other values (33) 33
61.1%
ValueCountFrequency (%)
16.7 1
1.9%
20.0 2
3.7%
21.1 1
1.9%
21.4 1
1.9%
23.3 1
1.9%
24.0 1
1.9%
24.6 1
1.9%
24.9 1
1.9%
25.0 1
1.9%
25.6 1
1.9%
ValueCountFrequency (%)
40.9 1
1.9%
39.0 1
1.9%
37.5 2
3.7%
36.8 1
1.9%
36.4 1
1.9%
35.9 1
1.9%
35.4 1
1.9%
35.1 1
1.9%
34.9 1
1.9%
34.4 1
1.9%

정보원 측의 의도적인 잘못된 정보제공
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.040741
Minimum0
Maximum50
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:36.038113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.33
Q121.9
median25.05
Q331.525
95-th percentile38.57
Maximum50
Range50
Interquartile range (IQR)9.625

Descriptive statistics

Standard deviation8.0203401
Coefficient of variation (CV)0.30799201
Kurtosis2.0452438
Mean26.040741
Median Absolute Deviation (MAD)4.55
Skewness0.07588394
Sum1406.2
Variance64.325856
MonotonicityNot monotonic
2023-12-13T08:16:36.214167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
22.9 3
 
5.6%
31.7 2
 
3.7%
31.0 2
 
3.7%
32.4 2
 
3.7%
17.4 2
 
3.7%
25.6 2
 
3.7%
27.7 2
 
3.7%
22.8 1
 
1.9%
24.7 1
 
1.9%
15.2 1
 
1.9%
Other values (36) 36
66.7%
ValueCountFrequency (%)
0.0 1
1.9%
15.0 1
1.9%
15.2 1
1.9%
15.4 1
1.9%
17.4 2
3.7%
17.6 1
1.9%
17.9 1
1.9%
19.1 1
1.9%
19.6 1
1.9%
19.8 1
1.9%
ValueCountFrequency (%)
50.0 1
1.9%
41.8 1
1.9%
40.0 1
1.9%
37.8 1
1.9%
36.7 1
1.9%
36.2 1
1.9%
34.6 1
1.9%
34.1 1
1.9%
33.7 1
1.9%
32.5 1
1.9%

마감시간에 따른 압박감
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.327778
Minimum9.1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:36.361575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.1
5-th percentile14.765
Q119
median23.1
Q326.475
95-th percentile35.055
Maximum40
Range30.9
Interquartile range (IQR)7.475

Descriptive statistics

Standard deviation6.3431263
Coefficient of variation (CV)0.27191301
Kurtosis0.74670245
Mean23.327778
Median Absolute Deviation (MAD)3.45
Skewness0.57184805
Sum1259.7
Variance40.235252
MonotonicityNot monotonic
2023-12-13T08:16:36.543034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
20.0 2
 
3.7%
24.4 2
 
3.7%
21.4 2
 
3.7%
25.0 2
 
3.7%
22.5 2
 
3.7%
26.5 2
 
3.7%
24.3 2
 
3.7%
25.4 2
 
3.7%
30.0 2
 
3.7%
28.3 2
 
3.7%
Other values (33) 34
63.0%
ValueCountFrequency (%)
9.1 1
1.9%
14.0 1
1.9%
14.7 1
1.9%
14.8 1
1.9%
15.2 1
1.9%
15.7 1
1.9%
16.0 1
1.9%
16.1 1
1.9%
16.7 1
1.9%
17.1 1
1.9%
ValueCountFrequency (%)
40.0 1
1.9%
39.4 1
1.9%
38.5 1
1.9%
33.2 1
1.9%
32.5 1
1.9%
30.0 2
3.7%
29.1 1
1.9%
28.8 1
1.9%
28.3 2
3.7%
26.9 1
1.9%

오보에 대한 제재 미비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.42963
Minimum0
Maximum40
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:36.714108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q112.325
median14.1
Q316.4
95-th percentile27.07
Maximum40
Range40
Interquartile range (IQR)4.075

Descriptive statistics

Standard deviation6.2363399
Coefficient of variation (CV)0.4041795
Kurtosis6.1563351
Mean15.42963
Median Absolute Deviation (MAD)2.15
Skewness1.9010918
Sum833.2
Variance38.891936
MonotonicityNot monotonic
2023-12-13T08:16:36.879826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
16.4 4
 
7.4%
13.8 3
 
5.6%
12.3 2
 
3.7%
14.8 2
 
3.7%
10.0 2
 
3.7%
12.0 2
 
3.7%
13.9 2
 
3.7%
15.9 2
 
3.7%
10.4 2
 
3.7%
12.7 2
 
3.7%
Other values (29) 31
57.4%
ValueCountFrequency (%)
0.0 1
1.9%
9.1 1
1.9%
10.0 2
3.7%
10.4 2
3.7%
10.8 1
1.9%
10.9 1
1.9%
11.0 1
1.9%
11.9 1
1.9%
12.0 2
3.7%
12.3 2
3.7%
ValueCountFrequency (%)
40.0 1
1.9%
35.0 1
1.9%
32.4 1
1.9%
24.2 1
1.9%
22.4 1
1.9%
21.5 1
1.9%
20.7 1
1.9%
17.9 1
1.9%
17.7 1
1.9%
17.6 1
1.9%

기자의 의도적 조작
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.546296
Minimum0
Maximum18.3
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:37.053810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.755
Q17.65
median10.7
Q313.075
95-th percentile16.46
Maximum18.3
Range18.3
Interquartile range (IQR)5.425

Descriptive statistics

Standard deviation3.8061873
Coefficient of variation (CV)0.36090274
Kurtosis0.012108081
Mean10.546296
Median Absolute Deviation (MAD)2.55
Skewness-0.14713465
Sum569.5
Variance14.487061
MonotonicityNot monotonic
2023-12-13T08:16:37.220043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6.8 3
 
5.6%
11.7 2
 
3.7%
10.0 2
 
3.7%
15.4 2
 
3.7%
4.3 2
 
3.7%
9.1 2
 
3.7%
15.9 2
 
3.7%
7.6 2
 
3.7%
5.9 1
 
1.9%
8.3 1
 
1.9%
Other values (35) 35
64.8%
ValueCountFrequency (%)
0.0 1
 
1.9%
4.3 2
3.7%
5.0 1
 
1.9%
5.9 1
 
1.9%
6.0 1
 
1.9%
6.1 1
 
1.9%
6.6 1
 
1.9%
6.8 3
5.6%
7.1 1
 
1.9%
7.6 2
3.7%
ValueCountFrequency (%)
18.3 1
1.9%
18.0 1
1.9%
17.5 1
1.9%
15.9 2
3.7%
15.4 2
3.7%
14.6 1
1.9%
14.5 1
1.9%
14.0 1
1.9%
13.6 1
1.9%
13.3 1
1.9%

낙종에 대한 우려
Real number (ℝ)

ZEROS 

Distinct38
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2518519
Minimum0
Maximum20
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T08:16:37.398551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.33
Q17.05
median8.3
Q39.075
95-th percentile11.97
Maximum20
Range20
Interquartile range (IQR)2.025

Descriptive statistics

Standard deviation2.9394994
Coefficient of variation (CV)0.356223
Kurtosis5.761046
Mean8.2518519
Median Absolute Deviation (MAD)0.9
Skewness1.0428071
Sum445.6
Variance8.6406569
MonotonicityNot monotonic
2023-12-13T08:16:37.581992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
9.0 5
 
9.3%
8.3 3
 
5.6%
8.5 3
 
5.6%
7.7 3
 
5.6%
9.4 2
 
3.7%
8.8 2
 
3.7%
6.0 2
 
3.7%
8.1 2
 
3.7%
10.0 2
 
3.7%
9.2 2
 
3.7%
Other values (28) 28
51.9%
ValueCountFrequency (%)
0.0 1
1.9%
2.5 1
1.9%
2.9 1
1.9%
5.1 1
1.9%
5.5 1
1.9%
5.7 1
1.9%
5.8 1
1.9%
6.0 2
3.7%
6.1 1
1.9%
6.2 1
1.9%
ValueCountFrequency (%)
20.0 1
1.9%
16.7 1
1.9%
12.1 1
1.9%
11.9 1
1.9%
11.2 1
1.9%
10.9 1
1.9%
10.0 2
3.7%
9.4 2
3.7%
9.3 1
1.9%
9.2 2
3.7%

Interactions

2023-12-13T08:16:31.906994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:23.923429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.764485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.675170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.572619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.806102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.687696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.529064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.330871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.177618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.984715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.006485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.847692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.761808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.678269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.902076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.768832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.607500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.412353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.252351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.299545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.089662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.931489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.848529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.776507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.982583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.864913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.684007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.512281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.331234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.373706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.162992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.019224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.926955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.869692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.061391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.947354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.758707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.592348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.400385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.462092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.249949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.123631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.034506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.951762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.139782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.027551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.844999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.677282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.473516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.530114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.328258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.204856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.120433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.319434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.211281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.094773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.919831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.746051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.536729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.606394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.402050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.292354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.208097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.404063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.286745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.167217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.993200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.818943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.602978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.678703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.482790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.380788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.290569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.490261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.385028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.259043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.077681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.896173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.678440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.755967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.572002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.471791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.390131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.590189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.500988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.347386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.158168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.012389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.762175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:32.829306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:24.666954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:25.567441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:26.476538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:27.694188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:28.601117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:29.441447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:30.248399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.095577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:16:31.830890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:16:38.029781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류사례수기자의 사실 미확인 또는 불충분한 취재정보원 측의 부정확한 정보제공특종에 대한 욕심기자의 단순 실수정보원 측의 의도적인 잘못된 정보제공마감시간에 따른 압박감오보에 대한 제재 미비기자의 의도적 조작낙종에 대한 우려
대분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사례수1.0001.0001.0000.0000.0000.0000.0000.0000.5040.0000.2880.000
기자의 사실 미확인 또는 불충분한 취재1.0001.0000.0001.0000.5980.5990.5840.4930.6500.4160.5210.665
정보원 측의 부정확한 정보제공1.0001.0000.0000.5981.0000.4140.5940.7140.3390.7950.5080.556
특종에 대한 욕심1.0001.0000.0000.5990.4141.0000.4860.6110.2980.5600.3560.842
기자의 단순 실수1.0001.0000.0000.5840.5940.4861.0000.3930.5040.0000.0000.631
정보원 측의 의도적인 잘못된 정보제공1.0001.0000.0000.4930.7140.6110.3931.0000.6730.9140.3900.752
마감시간에 따른 압박감1.0001.0000.5040.6500.3390.2980.5040.6731.0000.2710.0000.524
오보에 대한 제재 미비1.0001.0000.0000.4160.7950.5600.0000.9140.2711.0000.3290.792
기자의 의도적 조작1.0001.0000.2880.5210.5080.3560.0000.3900.0000.3291.0000.801
낙종에 대한 우려1.0001.0000.0000.6650.5560.8420.6310.7520.5240.7920.8011.000
2023-12-13T08:16:38.185500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수기자의 사실 미확인 또는 불충분한 취재정보원 측의 부정확한 정보제공특종에 대한 욕심기자의 단순 실수정보원 측의 의도적인 잘못된 정보제공마감시간에 따른 압박감오보에 대한 제재 미비기자의 의도적 조작낙종에 대한 우려
사례수1.000-0.2760.019-0.0390.2420.140-0.011-0.0190.0790.012
기자의 사실 미확인 또는 불충분한 취재-0.2761.000-0.009-0.033-0.3710.248-0.489-0.1280.207-0.404
정보원 측의 부정확한 정보제공0.019-0.0091.000-0.4500.1540.195-0.441-0.360-0.072-0.065
특종에 대한 욕심-0.039-0.033-0.4501.000-0.414-0.216-0.0170.0040.088-0.061
기자의 단순 실수0.242-0.3710.154-0.4141.000-0.2350.1930.089-0.3490.163
정보원 측의 의도적인 잘못된 정보제공0.1400.2480.195-0.216-0.2351.000-0.521-0.5970.349-0.462
마감시간에 따른 압박감-0.011-0.489-0.441-0.0170.193-0.5211.0000.410-0.3780.304
오보에 대한 제재 미비-0.019-0.128-0.3600.0040.089-0.5970.4101.000-0.1760.255
기자의 의도적 조작0.0790.207-0.0720.088-0.3490.349-0.378-0.1761.000-0.375
낙종에 대한 우려0.012-0.404-0.065-0.0610.163-0.4620.3040.255-0.3751.000

Missing values

2023-12-13T08:16:32.935892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:16:33.098425image/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

대분류중분류사례수기자의 사실 미확인 또는 불충분한 취재정보원 측의 부정확한 정보제공특종에 대한 욕심기자의 단순 실수정보원 측의 의도적인 잘못된 정보제공마감시간에 따른 압박감오보에 대한 제재 미비기자의 의도적 조작낙종에 대한 우려
0성별1남자142491.952.541.932.027.720.013.412.58.0
1성별2여자53289.153.441.029.522.232.517.76.08.6
2연령120대27983.950.234.840.917.939.416.86.89.3
3연령230~34세42089.353.341.230.525.528.314.39.38.3
4연령335~39세36291.447.545.932.625.121.515.710.89.4
5연령440~44세27291.254.047.432.019.118.816.911.88.8
6연령545~49세21895.450.943.625.736.216.113.810.67.8
7연령650대35095.758.340.025.731.717.110.914.66.0
8연령760대 이상5594.560.027.336.441.89.112.712.75.5
9매체유형1신문사102991.654.140.430.227.824.414.89.17.5
대분류중분류사례수기자의 사실 미확인 또는 불충분한 취재정보원 측의 부정확한 정보제공특종에 대한 욕심기자의 단순 실수정보원 측의 의도적인 잘못된 정보제공마감시간에 따른 압박감오보에 대한 제재 미비기자의 의도적 조작낙종에 대한 우려
44직위3부장/부장대우26495.153.839.428.033.716.714.811.76.8
45직위4차장/차장대우38091.852.646.129.523.219.716.812.47.9
46직위5평기자104088.751.241.734.122.928.814.78.89.0
47경력11~4년46485.851.934.139.022.833.216.47.89.1
48경력25~9년49188.251.743.828.126.326.915.910.09.2
49경력310~14년31795.348.646.732.224.618.013.912.68.2
50경력415~19년28392.958.042.832.524.718.013.811.75.7
51경력520년 이상40196.554.443.124.932.416.012.013.07.7
52권역1서울139290.451.046.030.724.323.814.511.38.1
53권역2그 외 지역56493.156.931.033.031.022.514.79.48.3