Overview

Dataset statistics

Number of variables15
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory135.3 B

Variable types

Text2
Numeric13

Dataset

Description언론인 의식조사 관련 "언론인 전반 직업 만족도"에 관한 자료입니다. 자세한 내용은 재단 홈페이지에 방문하여 확인 바랍니다.
Author한국언론진흥재단
URLhttps://www.data.go.kr/data/15060163/fileData.do

Alerts

2점 is highly overall correlated with 10점 평균High correlation
3점 is highly overall correlated with 10점 평균High correlation
5점 is highly overall correlated with 7점High correlation
7점 is highly overall correlated with 5점High correlation
10점 is highly overall correlated with 10점 평균High correlation
10점 평균 is highly overall correlated with 2점 and 2 other fieldsHigh correlation
구분 has unique valuesUnique
0점 has 10 (18.2%) zerosZeros
1점 has 8 (14.5%) zerosZeros
2점 has 5 (9.1%) zerosZeros
3점 has 2 (3.6%) zerosZeros
4점 has 5 (9.1%) zerosZeros
6점 has 2 (3.6%) zerosZeros
8점 has 1 (1.8%) zerosZeros
10점 has 3 (5.5%) zerosZeros

Reproduction

Analysis started2024-03-14 09:45:28.007653
Analysis finished2024-03-14 09:46:09.472374
Duration41.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size568.0 B
2024-03-14T18:46:10.271002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3818182
Min length2

Characters and Unicode

Total characters241
Distinct characters29
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

Unique55 ?
Unique (%)100.0%

Sample

1st row전체
2nd row성별1
3rd row성별2
4th row연령1
5th row연령2
ValueCountFrequency (%)
전체 1
 
1.8%
소속부서4 1
 
1.8%
소속부서6 1
 
1.8%
소속부서7 1
 
1.8%
소속부서8 1
 
1.8%
소속부서9 1
 
1.8%
소속부서10 1
 
1.8%
소속부서11 1
 
1.8%
소속부서12 1
 
1.8%
소속부서13 1
 
1.8%
Other values (45) 45
81.8%
2024-03-14T18:46:11.613927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
12.0%
29
12.0%
1 21
 
8.7%
18
 
7.5%
18
 
7.5%
16
 
6.6%
15
 
6.2%
2 9
 
3.7%
3 7
 
2.9%
7
 
2.9%
Other values (19) 72
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 176
73.0%
Decimal Number 65
 
27.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
29
16.5%
29
16.5%
18
10.2%
18
10.2%
16
9.1%
15
8.5%
7
 
4.0%
7
 
4.0%
5
 
2.8%
5
 
2.8%
Other values (9) 27
15.3%
Decimal Number
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
3 7
 
10.8%
4 7
 
10.8%
5 6
 
9.2%
7 4
 
6.2%
6 4
 
6.2%
8 3
 
4.6%
0 2
 
3.1%
9 2
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176
73.0%
Common 65
 
27.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
29
16.5%
29
16.5%
18
10.2%
18
10.2%
16
9.1%
15
8.5%
7
 
4.0%
7
 
4.0%
5
 
2.8%
5
 
2.8%
Other values (9) 27
15.3%
Common
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
3 7
 
10.8%
4 7
 
10.8%
5 6
 
9.2%
7 4
 
6.2%
6 4
 
6.2%
8 3
 
4.6%
0 2
 
3.1%
9 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176
73.0%
ASCII 65
 
27.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
29
16.5%
29
16.5%
18
10.2%
18
10.2%
16
9.1%
15
8.5%
7
 
4.0%
7
 
4.0%
5
 
2.8%
5
 
2.8%
Other values (9) 27
15.3%
ASCII
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
3 7
 
10.8%
4 7
 
10.8%
5 6
 
9.2%
7 4
 
6.2%
6 4
 
6.2%
8 3
 
4.6%
0 2
 
3.1%
9 2
 
3.1%
Distinct53
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size568.0 B
2024-03-14T18:46:12.447124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.5090909
Min length2

Characters and Unicode

Total characters303
Distinct characters105
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

Unique51 ?
Unique (%)92.7%

Sample

1st row전체1
2nd row남자
3rd row여자
4th row20대
5th row30~34세
ValueCountFrequency (%)
인터넷언론사 2
 
3.4%
이상 2
 
3.4%
뉴스통신사 2
 
3.4%
기타 1
 
1.7%
영상부 1
 
1.7%
국장/국장대우 1
 
1.7%
국방/통일/북한 1
 
1.7%
편집(편성)/교열부 1
 
1.7%
여론/독자/심의부 1
 
1.7%
사진 1
 
1.7%
Other values (46) 46
78.0%
2024-03-14T18:46:13.586370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 20
 
6.6%
14
 
4.6%
11
 
3.6%
10
 
3.3%
4 8
 
2.6%
~ 8
 
2.6%
8
 
2.6%
7
 
2.3%
7
 
2.3%
0 7
 
2.3%
Other values (95) 203
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 225
74.3%
Decimal Number 37
 
12.2%
Other Punctuation 20
 
6.6%
Math Symbol 8
 
2.6%
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
 
6.2%
11
 
4.9%
10
 
4.4%
8
 
3.6%
7
 
3.1%
7
 
3.1%
6
 
2.7%
5
 
2.2%
5
 
2.2%
5
 
2.2%
Other values (80) 147
65.3%
Decimal Number
ValueCountFrequency (%)
4 8
21.6%
0 7
18.9%
1 6
16.2%
5 5
13.5%
9 4
10.8%
3 4
10.8%
2 2
 
5.4%
6 1
 
2.7%
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 225
74.3%
Common 73
 
24.1%
Latin 5
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14
 
6.2%
11
 
4.9%
10
 
4.4%
8
 
3.6%
7
 
3.1%
7
 
3.1%
6
 
2.7%
5
 
2.2%
5
 
2.2%
5
 
2.2%
Other values (80) 147
65.3%
Common
ValueCountFrequency (%)
/ 20
27.4%
4 8
 
11.0%
~ 8
 
11.0%
0 7
 
9.6%
1 6
 
8.2%
5 5
 
6.8%
9 4
 
5.5%
4
 
5.5%
3 4
 
5.5%
) 2
 
2.7%
Other values (3) 5
 
6.8%
Latin
ValueCountFrequency (%)
I 4
80.0%
T 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 225
74.3%
ASCII 78
 
25.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 20
25.6%
4 8
 
10.3%
~ 8
 
10.3%
0 7
 
9.0%
1 6
 
7.7%
5 5
 
6.4%
I 4
 
5.1%
9 4
 
5.1%
4
 
5.1%
3 4
 
5.1%
Other values (5) 8
 
10.3%
Hangul
ValueCountFrequency (%)
14
 
6.2%
11
 
4.9%
10
 
4.4%
8
 
3.6%
7
 
3.1%
7
 
3.1%
6
 
2.7%
5
 
2.2%
5
 
2.2%
5
 
2.2%
Other values (80) 147
65.3%

사례수
Real number (ℝ)

Distinct51
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329.07273
Minimum8
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:13.828980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile20.3
Q180.5
median209
Q3406
95-th percentile1158.3
Maximum2011
Range2003
Interquartile range (IQR)325.5

Descriptive statistics

Standard deviation386.16931
Coefficient of variation (CV)1.1735075
Kurtosis7.1894785
Mean329.07273
Median Absolute Deviation (MAD)163
Skewness2.4878756
Sum18099
Variance149126.74
MonotonicityNot monotonic
2024-03-14T18:46:14.176652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457 3
 
5.5%
406 2
 
3.6%
160 2
 
3.6%
71 1
 
1.8%
50 1
 
1.8%
12 1
 
1.8%
87 1
 
1.8%
14 1
 
1.8%
34 1
 
1.8%
52 1
 
1.8%
Other values (41) 41
74.5%
ValueCountFrequency (%)
8 1
1.8%
12 1
1.8%
14 1
1.8%
23 1
1.8%
26 1
1.8%
33 1
1.8%
34 1
1.8%
44 1
1.8%
50 1
1.8%
52 1
1.8%
ValueCountFrequency (%)
2011 1
 
1.8%
1392 1
 
1.8%
1376 1
 
1.8%
1065 1
 
1.8%
1018 1
 
1.8%
635 1
 
1.8%
619 1
 
1.8%
457 3
5.5%
449 1
 
1.8%
432 1
 
1.8%

0점
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9181818
Minimum0
Maximum12.5
Zeros10
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:14.417832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.9
Q32.5
95-th percentile3.83
Maximum12.5
Range12.5
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.866378
Coefficient of variation (CV)0.97299329
Kurtosis18.608448
Mean1.9181818
Median Absolute Deviation (MAD)0.8
Skewness3.381796
Sum105.5
Variance3.483367
MonotonicityNot monotonic
2024-03-14T18:46:14.642136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 10
18.2%
2.1 4
 
7.3%
2.2 3
 
5.5%
3.1 3
 
5.5%
2.3 3
 
5.5%
2.6 2
 
3.6%
1.0 2
 
3.6%
1.1 2
 
3.6%
1.4 2
 
3.6%
0.6 2
 
3.6%
Other values (18) 22
40.0%
ValueCountFrequency (%)
0.0 10
18.2%
0.4 1
 
1.8%
0.6 2
 
3.6%
0.7 1
 
1.8%
0.8 1
 
1.8%
1.0 2
 
3.6%
1.1 2
 
3.6%
1.4 2
 
3.6%
1.6 2
 
3.6%
1.7 1
 
1.8%
ValueCountFrequency (%)
12.5 1
 
1.8%
4.2 1
 
1.8%
3.9 1
 
1.8%
3.8 1
 
1.8%
3.7 1
 
1.8%
3.5 1
 
1.8%
3.1 3
5.5%
3.0 1
 
1.8%
2.7 1
 
1.8%
2.6 2
3.6%

1점
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9509091
Minimum0
Maximum14.3
Zeros8
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:14.857076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.05
median1.8
Q32.2
95-th percentile3.95
Maximum14.3
Range14.3
Interquartile range (IQR)1.15

Descriptive statistics

Standard deviation2.0083043
Coefficient of variation (CV)1.0294197
Kurtosis26.696573
Mean1.9509091
Median Absolute Deviation (MAD)0.6
Skewness4.4054378
Sum107.3
Variance4.0332862
MonotonicityNot monotonic
2024-03-14T18:46:15.086951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 8
 
14.5%
2.0 5
 
9.1%
2.2 4
 
7.3%
1.8 3
 
5.5%
2.6 3
 
5.5%
1.9 3
 
5.5%
1.3 3
 
5.5%
1.6 2
 
3.6%
3.3 2
 
3.6%
1.5 2
 
3.6%
Other values (16) 20
36.4%
ValueCountFrequency (%)
0.0 8
14.5%
0.6 1
 
1.8%
0.7 2
 
3.6%
0.9 2
 
3.6%
1.0 1
 
1.8%
1.1 1
 
1.8%
1.3 3
 
5.5%
1.4 1
 
1.8%
1.5 2
 
3.6%
1.6 2
 
3.6%
ValueCountFrequency (%)
14.3 1
 
1.8%
4.5 1
 
1.8%
4.3 1
 
1.8%
3.8 1
 
1.8%
3.3 2
3.6%
3.1 1
 
1.8%
2.9 1
 
1.8%
2.7 1
 
1.8%
2.6 3
5.5%
2.4 1
 
1.8%

2점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5581818
Minimum0
Maximum10.8
Zeros5
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:15.409902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.35
median4.7
Q36.1
95-th percentile7.63
Maximum10.8
Range10.8
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.2523807
Coefficient of variation (CV)0.49414016
Kurtosis0.42433079
Mean4.5581818
Median Absolute Deviation (MAD)1.4
Skewness-0.15548712
Sum250.7
Variance5.0732189
MonotonicityNot monotonic
2024-03-14T18:46:15.648909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 5
 
9.1%
4.9 2
 
3.6%
5.3 2
 
3.6%
5.0 2
 
3.6%
4.5 2
 
3.6%
6.1 2
 
3.6%
3.7 2
 
3.6%
3.8 2
 
3.6%
5.5 2
 
3.6%
7.1 2
 
3.6%
Other values (30) 32
58.2%
ValueCountFrequency (%)
0.0 5
9.1%
1.9 1
 
1.8%
2.0 1
 
1.8%
2.3 1
 
1.8%
2.4 1
 
1.8%
2.7 1
 
1.8%
2.8 1
 
1.8%
2.9 1
 
1.8%
3.0 1
 
1.8%
3.3 1
 
1.8%
ValueCountFrequency (%)
10.8 1
1.8%
8.3 1
1.8%
7.7 1
1.8%
7.6 1
1.8%
7.1 2
3.6%
6.9 1
1.8%
6.8 1
1.8%
6.7 1
1.8%
6.6 1
1.8%
6.5 2
3.6%

3점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9127273
Minimum0
Maximum25
Zeros2
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:15.880603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.25
Q13.3
median5
Q35.9
95-th percentile8.75
Maximum25
Range25
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation3.4403948
Coefficient of variation (CV)0.70030242
Kurtosis21.381127
Mean4.9127273
Median Absolute Deviation (MAD)1.3
Skewness3.7351974
Sum270.2
Variance11.836316
MonotonicityNot monotonic
2024-03-14T18:46:16.112878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5.4 3
 
5.5%
3.3 3
 
5.5%
6.0 3
 
5.5%
1.9 3
 
5.5%
5.9 2
 
3.6%
0.0 2
 
3.6%
3.4 2
 
3.6%
3.2 2
 
3.6%
5.0 2
 
3.6%
3.8 2
 
3.6%
Other values (29) 31
56.4%
ValueCountFrequency (%)
0.0 2
3.6%
0.9 1
 
1.8%
1.4 1
 
1.8%
1.9 3
5.5%
2.4 1
 
1.8%
2.5 1
 
1.8%
2.8 1
 
1.8%
3.0 1
 
1.8%
3.2 2
3.6%
3.3 3
5.5%
ValueCountFrequency (%)
25.0 1
 
1.8%
10.0 1
 
1.8%
9.8 1
 
1.8%
8.3 1
 
1.8%
7.1 1
 
1.8%
6.6 1
 
1.8%
6.5 1
 
1.8%
6.3 2
3.6%
6.1 1
 
1.8%
6.0 3
5.5%

4점
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5454545
Minimum0
Maximum16.7
Zeros5
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:16.393178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median3.2
Q34.25
95-th percentile6.63
Maximum16.7
Range16.7
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.4361337
Coefficient of variation (CV)0.68711464
Kurtosis15.194073
Mean3.5454545
Median Absolute Deviation (MAD)0.7
Skewness2.8204473
Sum195
Variance5.9347475
MonotonicityNot monotonic
2024-03-14T18:46:16.647960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.2 7
 
12.7%
0.0 5
 
9.1%
3.0 3
 
5.5%
5.6 3
 
5.5%
2.0 2
 
3.6%
3.7 2
 
3.6%
4.7 2
 
3.6%
2.7 2
 
3.6%
3.5 2
 
3.6%
2.8 2
 
3.6%
Other values (22) 25
45.5%
ValueCountFrequency (%)
0.0 5
9.1%
0.5 1
 
1.8%
0.9 1
 
1.8%
1.9 1
 
1.8%
2.0 2
 
3.6%
2.3 1
 
1.8%
2.6 1
 
1.8%
2.7 2
 
3.6%
2.8 2
 
3.6%
2.9 1
 
1.8%
ValueCountFrequency (%)
16.7 1
 
1.8%
6.8 1
 
1.8%
6.7 1
 
1.8%
6.6 1
 
1.8%
5.6 3
5.5%
5.1 1
 
1.8%
4.9 1
 
1.8%
4.7 2
3.6%
4.6 2
3.6%
4.3 1
 
1.8%

5점
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.547273
Minimum8.3
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:16.886184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile15.07
Q119.75
median22.3
Q324.85
95-th percentile31.85
Maximum37.5
Range29.2
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation5.3897658
Coefficient of variation (CV)0.23904292
Kurtosis1.9797333
Mean22.547273
Median Absolute Deviation (MAD)2.6
Skewness0.533754
Sum1240.1
Variance29.049576
MonotonicityNot monotonic
2024-03-14T18:46:17.115727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
26.6 3
 
5.5%
23.1 2
 
3.6%
17.6 2
 
3.6%
24.8 2
 
3.6%
23.0 2
 
3.6%
28.2 2
 
3.6%
20.6 2
 
3.6%
19.7 2
 
3.6%
22.5 2
 
3.6%
26.7 2
 
3.6%
Other values (30) 34
61.8%
ValueCountFrequency (%)
8.3 1
1.8%
11.5 1
1.8%
14.3 1
1.8%
15.4 1
1.8%
16.9 1
1.8%
17.4 1
1.8%
17.6 2
3.6%
17.7 1
1.8%
18.2 2
3.6%
19.4 1
1.8%
ValueCountFrequency (%)
37.5 1
 
1.8%
37.4 1
 
1.8%
36.4 1
 
1.8%
29.9 1
 
1.8%
28.2 2
3.6%
26.7 2
3.6%
26.6 3
5.5%
26.1 1
 
1.8%
25.7 1
 
1.8%
24.9 1
 
1.8%

6점
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.925455
Minimum0
Maximum25
Zeros2
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:17.615577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.6
Q19.1
median10.6
Q312.55
95-th percentile15.49
Maximum25
Range25
Interquartile range (IQR)3.45

Descriptive statistics

Standard deviation3.782622
Coefficient of variation (CV)0.34622102
Kurtosis4.2507061
Mean10.925455
Median Absolute Deviation (MAD)1.9
Skewness0.084947409
Sum600.9
Variance14.308229
MonotonicityNot monotonic
2024-03-14T18:46:17.891271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
10.3 3
 
5.5%
12.5 3
 
5.5%
10.6 2
 
3.6%
0.0 2
 
3.6%
9.1 2
 
3.6%
10.2 2
 
3.6%
11.3 2
 
3.6%
8.1 2
 
3.6%
12.4 2
 
3.6%
11.2 1
 
1.8%
Other values (34) 34
61.8%
ValueCountFrequency (%)
0.0 2
3.6%
4.9 1
1.8%
5.9 1
1.8%
6.1 1
1.8%
7.4 1
1.8%
8.1 2
3.6%
8.5 1
1.8%
8.6 1
1.8%
8.7 1
1.8%
8.9 1
1.8%
ValueCountFrequency (%)
25.0 1
1.8%
16.0 1
1.8%
15.7 1
1.8%
15.4 1
1.8%
15.0 1
1.8%
14.9 1
1.8%
14.6 1
1.8%
14.5 1
1.8%
14.3 1
1.8%
13.8 1
1.8%

7점
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.341818
Minimum8.3
Maximum30.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:18.131301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile10.9
Q117.65
median19.4
Q320.8
95-th percentile26.59
Maximum30.8
Range22.5
Interquartile range (IQR)3.15

Descriptive statistics

Standard deviation4.4341558
Coefficient of variation (CV)0.22925227
Kurtosis1.4790004
Mean19.341818
Median Absolute Deviation (MAD)1.7
Skewness0.0038727833
Sum1063.8
Variance19.661737
MonotonicityNot monotonic
2024-03-14T18:46:18.370222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
19.5 3
 
5.5%
20.6 3
 
5.5%
16.9 3
 
5.5%
20.5 2
 
3.6%
30.8 2
 
3.6%
18.3 2
 
3.6%
19.4 2
 
3.6%
17.0 2
 
3.6%
21.8 2
 
3.6%
18.0 2
 
3.6%
Other values (31) 32
58.2%
ValueCountFrequency (%)
8.3 1
 
1.8%
9.1 1
 
1.8%
9.5 1
 
1.8%
11.5 1
 
1.8%
12.5 1
 
1.8%
14.0 1
 
1.8%
15.2 1
 
1.8%
16.2 1
 
1.8%
16.9 3
5.5%
17.0 2
3.6%
ValueCountFrequency (%)
30.8 2
3.6%
26.8 1
1.8%
26.5 1
1.8%
26.1 1
1.8%
25.0 1
1.8%
24.3 1
1.8%
23.0 1
1.8%
22.3 1
1.8%
21.8 2
3.6%
21.4 1
1.8%

8점
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.970909
Minimum0
Maximum32.4
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:18.601024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.47
Q116.45
median17.6
Q319.15
95-th percentile25.48
Maximum32.4
Range32.4
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation4.5174834
Coefficient of variation (CV)0.25137757
Kurtosis6.5338688
Mean17.970909
Median Absolute Deviation (MAD)1.5
Skewness-0.17357417
Sum988.4
Variance20.407657
MonotonicityNot monotonic
2024-03-14T18:46:18.995316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
17.3 3
 
5.5%
18.0 3
 
5.5%
20.6 2
 
3.6%
16.9 2
 
3.6%
17.1 2
 
3.6%
18.2 2
 
3.6%
21.7 2
 
3.6%
18.7 2
 
3.6%
17.6 2
 
3.6%
17.8 2
 
3.6%
Other values (33) 33
60.0%
ValueCountFrequency (%)
0.0 1
1.8%
7.7 1
1.8%
14.4 1
1.8%
14.5 1
1.8%
14.7 1
1.8%
14.8 1
1.8%
15.0 1
1.8%
15.2 1
1.8%
15.5 1
1.8%
15.6 1
1.8%
ValueCountFrequency (%)
32.4 1
1.8%
30.8 1
1.8%
26.6 1
1.8%
25.0 1
1.8%
21.7 2
3.6%
21.4 1
1.8%
20.6 2
3.6%
20.4 1
1.8%
20.2 1
1.8%
19.9 1
1.8%

9점
Real number (ℝ)

Distinct39
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2818182
Minimum2
Maximum12.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:19.409764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.65
Q15.9
median7.1
Q38.55
95-th percentile11.01
Maximum12.5
Range10.5
Interquartile range (IQR)2.65

Descriptive statistics

Standard deviation2.1704318
Coefficient of variation (CV)0.2980618
Kurtosis0.35087326
Mean7.2818182
Median Absolute Deviation (MAD)1.3
Skewness0.043259235
Sum400.5
Variance4.7107744
MonotonicityNot monotonic
2024-03-14T18:46:19.843405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5.8 4
 
7.3%
7.1 3
 
5.5%
7.0 2
 
3.6%
6.7 2
 
3.6%
7.2 2
 
3.6%
11.5 2
 
3.6%
10.8 2
 
3.6%
6.9 2
 
3.6%
8.6 2
 
3.6%
6.5 2
 
3.6%
Other values (29) 32
58.2%
ValueCountFrequency (%)
2.0 1
 
1.8%
2.6 1
 
1.8%
3.3 1
 
1.8%
3.8 1
 
1.8%
4.2 1
 
1.8%
4.6 1
 
1.8%
5.1 1
 
1.8%
5.3 1
 
1.8%
5.7 2
3.6%
5.8 4
7.3%
ValueCountFrequency (%)
12.5 1
1.8%
11.5 2
3.6%
10.8 2
3.6%
10.5 1
1.8%
9.8 1
1.8%
9.6 1
1.8%
9.2 1
1.8%
9.1 1
1.8%
8.8 1
1.8%
8.7 1
1.8%

10점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0490909
Minimum0
Maximum14
Zeros3
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:20.229728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.33
Q13.75
median4.9
Q36.3
95-th percentile8.89
Maximum14
Range14
Interquartile range (IQR)2.55

Descriptive statistics

Standard deviation2.5453569
Coefficient of variation (CV)0.50412182
Kurtosis2.8762687
Mean5.0490909
Median Absolute Deviation (MAD)1.4
Skewness0.91832961
Sum277.7
Variance6.4788418
MonotonicityNot monotonic
2024-03-14T18:46:20.640004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
6.3 4
 
7.3%
3.5 3
 
5.5%
5.5 3
 
5.5%
4.9 3
 
5.5%
5.7 3
 
5.5%
0.0 3
 
5.5%
5.3 2
 
3.6%
4.3 2
 
3.6%
6.8 2
 
3.6%
3.8 2
 
3.6%
Other values (28) 28
50.9%
ValueCountFrequency (%)
0.0 3
5.5%
1.9 1
 
1.8%
2.0 1
 
1.8%
2.1 1
 
1.8%
2.3 1
 
1.8%
2.9 1
 
1.8%
3.3 1
 
1.8%
3.4 1
 
1.8%
3.5 3
5.5%
3.7 1
 
1.8%
ValueCountFrequency (%)
14.0 1
1.8%
12.3 1
1.8%
9.1 1
1.8%
8.8 1
1.8%
8.7 1
1.8%
7.4 1
1.8%
7.3 1
1.8%
7.0 1
1.8%
6.8 2
3.6%
6.6 1
1.8%

10점 평균
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1345455
Minimum4.63
Maximum7.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-14T18:46:21.046728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.63
5-th percentile5.775
Q15.955
median6.09
Q36.235
95-th percentile6.745
Maximum7.12
Range2.49
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.37170686
Coefficient of variation (CV)0.060592405
Kurtosis4.6902897
Mean6.1345455
Median Absolute Deviation (MAD)0.14
Skewness-0.46968332
Sum337.4
Variance0.13816599
MonotonicityNot monotonic
2024-03-14T18:46:21.447250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
6.09 3
 
5.5%
6.13 3
 
5.5%
5.79 3
 
5.5%
6.22 3
 
5.5%
6.39 2
 
3.6%
5.97 2
 
3.6%
6.14 2
 
3.6%
6.07 2
 
3.6%
6.23 2
 
3.6%
6.1 2
 
3.6%
Other values (28) 31
56.4%
ValueCountFrequency (%)
4.63 1
 
1.8%
5.72 1
 
1.8%
5.74 1
 
1.8%
5.79 3
5.5%
5.8 1
 
1.8%
5.81 1
 
1.8%
5.85 1
 
1.8%
5.87 1
 
1.8%
5.89 2
3.6%
5.92 1
 
1.8%
ValueCountFrequency (%)
7.12 1
1.8%
6.96 1
1.8%
6.78 1
1.8%
6.73 1
1.8%
6.72 1
1.8%
6.64 1
1.8%
6.56 1
1.8%
6.54 1
1.8%
6.53 1
1.8%
6.39 2
3.6%

Interactions

2024-03-14T18:46:06.072385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:28.722328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:32.253909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:35.495137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:38.731464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:42.048721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:45.206309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:48.483299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:50.977135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:53.931536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:57.053008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:00.252867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:02.894933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:06.328765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:29.188123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:32.519501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:35.759221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:38.988040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:42.305339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:45.471259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:48.738634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:51.132746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:54.188222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:57.315208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:00.410992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:03.060608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:06.568973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:29.448711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:32.768303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:36.011216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:39.228047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:42.548586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:45.728541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:48.980904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:51.277794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:54.430447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:57.565499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:00.563707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:03.211653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:06.820336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:29.714001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:33.027670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:36.267523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:39.476296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:42.799389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:45.991678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:49.229909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:51.426145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:54.676601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:57.829783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:00.747102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:03.433821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:07.058916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:29.964924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:33.267635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:36.512078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:39.709296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:43.035400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:46.237474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:49.465830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:51.582774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:54.912100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:58.069845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:00.881654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:03.675832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:07.293947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:30.216022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:33.513082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:36.753731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:39.948536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:43.268153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:46.485152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:49.607468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:51.823464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:55.145631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:58.311344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:01.100218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:03.917381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:07.545648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:30.483402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:33.771829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:37.012064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:40.198049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:43.524159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:46.742625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:49.755235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:52.076391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:55.397871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:58.567451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:01.353799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:04.175036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:07.747751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:30.738012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:34.018892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:37.254644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:40.435325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:43.761070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:46.994951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:49.891059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:52.313961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:55.630708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:58.814994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:01.594787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:04.417731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:07.883673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:30.987889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:34.260019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:37.500461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:40.669668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:43.998520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:47.241614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:50.024772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:52.549252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:55.865276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:59.055666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:01.831883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:04.665877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:08.058639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:31.231771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:34.497423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:37.743525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:40.900971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:44.229013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:47.482751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:50.156177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:52.786142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:56.092166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:59.289474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:02.066097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:04.904134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:08.235917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:31.490508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:34.744480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:37.993881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:41.153441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:44.476267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:47.734730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:50.297752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:53.030483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:56.336931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:59.545851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:02.313976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:05.155799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:08.370106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:31.744705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:34.995177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:38.235011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:41.390254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:44.721750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:47.981729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:50.494734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:53.446313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:56.573502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:59.793583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:02.552337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:05.396301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:08.518982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:32.008341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:35.258365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:38.490463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:41.816608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:44.970500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:48.242868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:50.743614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:53.695155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:45:56.822774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:00.079706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:02.762570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:46:05.653986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:46:21.716685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분중분류사례수0점1점2점3점4점5점6점7점8점9점10점10점 평균
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사례수1.0001.0001.0000.0770.0000.0000.2290.0000.0000.0000.0000.0000.3700.0000.000
0점1.0001.0000.0771.0000.3480.3870.6180.3010.3590.3870.5980.6900.5710.5040.691
1점1.0001.0000.0000.3481.0000.5600.0910.4420.4870.4880.4290.0700.0000.4610.441
2점1.0001.0000.0000.3870.5601.0000.2130.6660.6420.3550.5820.4290.0000.7920.652
3점1.0001.0000.2290.6180.0910.2131.0000.7680.5190.7220.5540.7550.6190.6010.677
4점1.0001.0000.0000.3010.4420.6660.7681.0000.7720.7060.7140.6630.0000.5980.398
5점1.0001.0000.0000.3590.4870.6420.5190.7721.0000.5720.6370.7010.1760.6930.461
6점1.0001.0000.0000.3870.4880.3550.7220.7060.5721.0000.5830.7320.1920.5640.483
7점1.0001.0000.0000.5980.4290.5820.5540.7140.6370.5831.0000.6750.0000.6160.594
8점1.0001.0000.0000.6900.0700.4290.7550.6630.7010.7320.6751.0000.5120.4920.778
9점1.0001.0000.3700.5710.0000.0000.6190.0000.1760.1920.0000.5121.0000.0000.526
10점1.0001.0000.0000.5040.4610.7920.6010.5980.6930.5640.6160.4920.0001.0000.683
10점 평균1.0001.0000.0000.6910.4410.6520.6770.3980.4610.4830.5940.7780.5260.6831.000
2024-03-14T18:46:22.077867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수0점1점2점3점4점5점6점7점8점9점10점10점 평균
사례수1.0000.4440.0940.1700.2480.2020.1230.028-0.011-0.290-0.1980.131-0.155
0점0.4441.0000.2280.1530.2740.292-0.0010.082-0.116-0.219-0.234-0.215-0.498
1점0.0940.2281.0000.2870.1870.252-0.3200.2020.071-0.054-0.039-0.219-0.469
2점0.1700.1530.2871.0000.2810.389-0.180-0.1060.152-0.205-0.145-0.328-0.514
3점0.2480.2740.1870.2811.0000.118-0.059-0.1170.064-0.228-0.282-0.327-0.719
4점0.2020.2920.2520.3890.1181.000-0.3040.164-0.067-0.076-0.153-0.030-0.270
5점0.123-0.001-0.320-0.180-0.059-0.3041.000-0.322-0.596-0.277-0.0480.143-0.008
6점0.0280.0820.202-0.106-0.1170.164-0.3221.000-0.026-0.031-0.317-0.058-0.037
7점-0.011-0.1160.0710.1520.064-0.067-0.596-0.0261.000-0.023-0.091-0.4090.022
8점-0.290-0.219-0.054-0.205-0.228-0.076-0.277-0.031-0.0231.0000.187-0.0000.336
9점-0.198-0.234-0.039-0.145-0.282-0.153-0.048-0.317-0.0910.1871.0000.1740.411
10점0.131-0.215-0.219-0.328-0.327-0.0300.143-0.058-0.409-0.0000.1741.0000.585
10점 평균-0.155-0.498-0.469-0.514-0.719-0.270-0.008-0.0370.0220.3360.4110.5851.000

Missing values

2024-03-14T18:46:08.725365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:46:09.241175image/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

구분중분류사례수0점1점2점3점4점5점6점7점8점9점10점10점 평균
0전체전체120112.11.84.95.03.622.511.219.517.37.05.16.09
1성별1남자13762.31.83.94.43.023.011.520.017.17.65.56.19
2성별2여자6351.91.96.96.34.921.410.618.417.85.74.35.89
3연령120대2171.80.96.05.53.719.813.820.317.55.15.56.09
4연령230~34세3913.82.64.65.65.118.211.824.316.43.34.35.85
5연령335~39세3823.12.16.56.04.720.912.619.114.47.13.45.79
6연령440~44세3172.52.24.75.03.224.39.118.018.38.54.16.06
7연령545~49세2250.42.26.75.32.724.910.216.919.17.14.46.08
8연령650대3720.81.32.73.82.723.110.218.819.910.56.26.53
9연령760대 이상1070.00.01.90.90.037.410.314.015.06.514.06.72
구분중분류사례수0점1점2점3점4점5점6점7점8점9점10점10점 평균
45직위3부장/부장대우2091.01.43.33.33.328.29.118.714.811.55.36.33
46직위4차장/차장대우4572.62.26.15.72.822.510.318.218.87.23.55.95
47직위5평기자10182.72.25.55.84.620.412.420.315.85.84.55.92
48경력11~4년4262.60.94.25.93.123.011.320.715.56.66.36.13
49경력25~9년4573.73.35.33.74.621.413.320.416.64.23.55.79
50경력310~14년3992.32.06.86.34.324.89.018.316.85.83.85.81
51경력415~19년2721.11.54.86.63.321.78.520.618.09.24.86.22
52경력520년 이상4570.71.33.53.32.621.712.517.919.79.87.06.54
53권역1서울13922.22.05.04.93.720.712.219.517.47.54.96.1
54권역2그 외 지역6192.11.54.55.23.226.78.919.417.15.85.76.07