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 조사 관련 직업, 환경, 요인별 만족도 조사데이터입니다. 자세한 내용 첨부 파일 확인하시기 바랍니다. (보수 등)
Author한국언론진흥재단
URLhttps://www.data.go.kr/data/15050489/fileData.do

Alerts

보수 is highly overall correlated with 후생복지 and 1 other fieldsHigh correlation
업무 강도 is highly overall correlated with 후생복지 and 2 other fieldsHigh correlation
승진 가능성 is highly overall correlated with 후생복지 and 1 other fieldsHigh correlation
후생복지 is highly overall correlated with 보수 and 3 other fieldsHigh correlation
노후 준비 is highly overall correlated with 보수 and 4 other fieldsHigh correlation
업무 자율성 is highly overall correlated with 전문성 계발 기회 and 1 other fieldsHigh correlation
전문성 계발 기회 is highly overall correlated with 업무 자율성 and 1 other fieldsHigh correlation
직업의 성장 가능성 is highly overall correlated with 업무 강도 and 3 other fieldsHigh correlation
구분 has unique valuesUnique

Reproduction

Analysis started2024-03-14 15:32:56.863036
Analysis finished2024-03-14 15:33:21.365379
Duration24.5 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
2024-03-15T00:33:22.285530image/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%
2024-03-15T00:33:23.781002image/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
2024-03-15T00:33:25.420258image/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%
2024-03-15T00:33:27.748086image/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
2024-03-15T00:33:28.149135image/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
2024-03-15T00:33:28.607056image/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%

보수
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5990741
Minimum2.07
Maximum3.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:28.960908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.278
Q12.455
median2.565
Q32.7
95-th percentile3.091
Maximum3.29
Range1.22
Interquartile range (IQR)0.245

Descriptive statistics

Standard deviation0.24818031
Coefficient of variation (CV)0.095487971
Kurtosis1.7356273
Mean2.5990741
Median Absolute Deviation (MAD)0.13
Skewness0.87893902
Sum140.35
Variance0.061593466
MonotonicityNot monotonic
2024-03-15T00:33:29.206636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.63 3
 
5.6%
2.7 3
 
5.6%
2.5 3
 
5.6%
2.68 3
 
5.6%
2.51 2
 
3.7%
3.29 2
 
3.7%
2.54 2
 
3.7%
2.35 2
 
3.7%
2.43 2
 
3.7%
2.61 2
 
3.7%
Other values (26) 30
55.6%
ValueCountFrequency (%)
2.07 1
1.9%
2.13 1
1.9%
2.2 1
1.9%
2.32 1
1.9%
2.35 2
3.7%
2.36 1
1.9%
2.37 1
1.9%
2.4 1
1.9%
2.41 1
1.9%
2.43 2
3.7%
ValueCountFrequency (%)
3.29 2
3.7%
3.26 1
 
1.9%
3.0 1
 
1.9%
2.93 2
3.7%
2.85 1
 
1.9%
2.77 2
3.7%
2.74 1
 
1.9%
2.72 1
 
1.9%
2.71 1
 
1.9%
2.7 3
5.6%

업무 강도
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9440741
Minimum2.68
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:29.515934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.68
5-th percentile2.74
Q12.8325
median2.895
Q33.06
95-th percentile3.187
Maximum3.5
Range0.82
Interquartile range (IQR)0.2275

Descriptive statistics

Standard deviation0.16210314
Coefficient of variation (CV)0.055060823
Kurtosis1.376747
Mean2.9440741
Median Absolute Deviation (MAD)0.09
Skewness1.0084927
Sum158.98
Variance0.026277428
MonotonicityNot monotonic
2024-03-15T00:33:29.860495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2.88 4
 
7.4%
2.87 3
 
5.6%
2.83 2
 
3.7%
3.07 2
 
3.7%
3.14 2
 
3.7%
2.74 2
 
3.7%
2.92 2
 
3.7%
3.06 2
 
3.7%
3.12 2
 
3.7%
2.9 2
 
3.7%
Other values (28) 31
57.4%
ValueCountFrequency (%)
2.68 1
1.9%
2.72 1
1.9%
2.74 2
3.7%
2.75 1
1.9%
2.76 1
1.9%
2.78 1
1.9%
2.79 1
1.9%
2.8 1
1.9%
2.81 2
3.7%
2.82 1
1.9%
ValueCountFrequency (%)
3.5 1
1.9%
3.32 1
1.9%
3.2 1
1.9%
3.18 1
1.9%
3.15 1
1.9%
3.14 2
3.7%
3.12 2
3.7%
3.11 1
1.9%
3.1 1
1.9%
3.07 2
3.7%

승진 가능성
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8390741
Minimum2.41
Maximum3.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:30.087742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.41
5-th percentile2.5845
Q12.77
median2.84
Q32.9275
95-th percentile3.0935
Maximum3.16
Range0.75
Interquartile range (IQR)0.1575

Descriptive statistics

Standard deviation0.14818739
Coefficient of variation (CV)0.052195677
Kurtosis1.0540462
Mean2.8390741
Median Absolute Deviation (MAD)0.085
Skewness-0.37987569
Sum153.31
Variance0.021959504
MonotonicityNot monotonic
2024-03-15T00:33:30.315096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.84 5
 
9.3%
2.78 4
 
7.4%
2.91 3
 
5.6%
2.87 3
 
5.6%
2.94 2
 
3.7%
2.71 2
 
3.7%
2.5 2
 
3.7%
2.93 2
 
3.7%
2.69 2
 
3.7%
2.73 2
 
3.7%
Other values (20) 27
50.0%
ValueCountFrequency (%)
2.41 1
1.9%
2.5 2
3.7%
2.63 1
1.9%
2.69 2
3.7%
2.7 1
1.9%
2.71 2
3.7%
2.72 1
1.9%
2.73 2
3.7%
2.75 1
1.9%
2.77 2
3.7%
ValueCountFrequency (%)
3.16 1
1.9%
3.12 1
1.9%
3.1 1
1.9%
3.09 2
3.7%
3.0 2
3.7%
2.96 1
1.9%
2.95 2
3.7%
2.94 2
3.7%
2.93 2
3.7%
2.92 2
3.7%

후생복지
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5581481
Minimum2.19
Maximum2.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:30.644897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.19
5-th percentile2.286
Q12.4225
median2.57
Q32.6675
95-th percentile2.801
Maximum2.98
Range0.79
Interquartile range (IQR)0.245

Descriptive statistics

Standard deviation0.17098024
Coefficient of variation (CV)0.066837504
Kurtosis-0.15701582
Mean2.5581481
Median Absolute Deviation (MAD)0.115
Skewness0.033610158
Sum138.14
Variance0.029234242
MonotonicityNot monotonic
2024-03-15T00:33:31.105022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.64 5
 
9.3%
2.57 3
 
5.6%
2.76 3
 
5.6%
2.67 3
 
5.6%
2.66 2
 
3.7%
2.69 2
 
3.7%
2.46 2
 
3.7%
2.39 2
 
3.7%
2.63 2
 
3.7%
2.54 2
 
3.7%
Other values (26) 28
51.9%
ValueCountFrequency (%)
2.19 1
1.9%
2.23 1
1.9%
2.26 1
1.9%
2.3 1
1.9%
2.32 1
1.9%
2.33 1
1.9%
2.35 1
1.9%
2.39 2
3.7%
2.4 2
3.7%
2.41 2
3.7%
ValueCountFrequency (%)
2.98 1
 
1.9%
2.93 1
 
1.9%
2.84 1
 
1.9%
2.78 1
 
1.9%
2.76 3
5.6%
2.72 1
 
1.9%
2.69 2
3.7%
2.68 1
 
1.9%
2.67 3
5.6%
2.66 2
3.7%

노후 준비
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2074074
Minimum1.93
Maximum2.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:31.371500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.93
5-th percentile1.983
Q12.0825
median2.19
Q32.31
95-th percentile2.475
Maximum2.59
Range0.66
Interquartile range (IQR)0.2275

Descriptive statistics

Standard deviation0.16235513
Coefficient of variation (CV)0.073550144
Kurtosis-0.45191653
Mean2.2074074
Median Absolute Deviation (MAD)0.12
Skewness0.42839802
Sum119.2
Variance0.026359189
MonotonicityNot monotonic
2024-03-15T00:33:31.656889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.31 5
 
9.3%
2.19 3
 
5.6%
2.25 2
 
3.7%
2.22 2
 
3.7%
2.05 2
 
3.7%
2.07 2
 
3.7%
2.29 2
 
3.7%
2.12 2
 
3.7%
2.42 2
 
3.7%
2.02 2
 
3.7%
Other values (26) 30
55.6%
ValueCountFrequency (%)
1.93 1
1.9%
1.96 1
1.9%
1.97 1
1.9%
1.99 1
1.9%
2.0 2
3.7%
2.02 2
3.7%
2.05 2
3.7%
2.06 1
1.9%
2.07 2
3.7%
2.08 1
1.9%
ValueCountFrequency (%)
2.59 1
1.9%
2.57 1
1.9%
2.54 1
1.9%
2.44 1
1.9%
2.43 1
1.9%
2.42 2
3.7%
2.41 1
1.9%
2.38 1
1.9%
2.37 1
1.9%
2.35 1
1.9%

업무 자율성
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5816667
Minimum2.88
Maximum3.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:32.073841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.88
5-th percentile3.383
Q13.53
median3.58
Q33.6475
95-th percentile3.8005
Maximum3.9
Range1.02
Interquartile range (IQR)0.1175

Descriptive statistics

Standard deviation0.15289348
Coefficient of variation (CV)0.042687802
Kurtosis7.7521448
Mean3.5816667
Median Absolute Deviation (MAD)0.06
Skewness-1.5132924
Sum193.41
Variance0.023376415
MonotonicityNot monotonic
2024-03-15T00:33:32.573995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3.61 4
 
7.4%
3.53 4
 
7.4%
3.67 3
 
5.6%
3.54 3
 
5.6%
3.59 3
 
5.6%
3.47 3
 
5.6%
3.56 3
 
5.6%
3.64 3
 
5.6%
3.78 2
 
3.7%
3.52 2
 
3.7%
Other values (20) 24
44.4%
ValueCountFrequency (%)
2.88 1
 
1.9%
3.31 1
 
1.9%
3.37 1
 
1.9%
3.39 1
 
1.9%
3.45 1
 
1.9%
3.47 3
5.6%
3.49 1
 
1.9%
3.5 1
 
1.9%
3.51 1
 
1.9%
3.52 2
3.7%
ValueCountFrequency (%)
3.9 2
3.7%
3.82 1
 
1.9%
3.79 1
 
1.9%
3.78 2
3.7%
3.75 1
 
1.9%
3.74 1
 
1.9%
3.67 3
5.6%
3.66 1
 
1.9%
3.65 2
3.7%
3.64 3
5.6%

전문성 계발 기회
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8383333
Minimum2.51
Maximum3.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:32.964134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.51
5-th percentile2.613
Q12.7225
median2.785
Q32.9375
95-th percentile3.171
Maximum3.33
Range0.82
Interquartile range (IQR)0.215

Descriptive statistics

Standard deviation0.18833681
Coefficient of variation (CV)0.066354719
Kurtosis0.14966816
Mean2.8383333
Median Absolute Deviation (MAD)0.095
Skewness0.85254944
Sum153.27
Variance0.035470755
MonotonicityNot monotonic
2024-03-15T00:33:33.352547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2.83 4
 
7.4%
2.76 3
 
5.6%
2.78 3
 
5.6%
3.15 3
 
5.6%
2.74 2
 
3.7%
2.96 2
 
3.7%
2.73 2
 
3.7%
2.86 2
 
3.7%
2.69 2
 
3.7%
2.77 2
 
3.7%
Other values (27) 29
53.7%
ValueCountFrequency (%)
2.51 1
1.9%
2.57 1
1.9%
2.6 1
1.9%
2.62 2
3.7%
2.63 1
1.9%
2.65 1
1.9%
2.66 1
1.9%
2.67 1
1.9%
2.68 1
1.9%
2.69 2
3.7%
ValueCountFrequency (%)
3.33 1
 
1.9%
3.29 1
 
1.9%
3.21 1
 
1.9%
3.15 3
5.6%
3.13 1
 
1.9%
3.09 1
 
1.9%
3.07 1
 
1.9%
3.06 1
 
1.9%
3.02 1
 
1.9%
2.96 2
3.7%

직업의 안정성
Real number (ℝ)

Distinct31
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.042037
Minimum2.73
Maximum3.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:33.724081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.73
5-th percentile2.8625
Q12.98
median3.03
Q33.12
95-th percentile3.2635
Maximum3.38
Range0.65
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.13014331
Coefficient of variation (CV)0.042781633
Kurtosis0.49407161
Mean3.042037
Median Absolute Deviation (MAD)0.065
Skewness0.26642533
Sum164.27
Variance0.016937282
MonotonicityNot monotonic
2024-03-15T00:33:34.134989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.98 6
 
11.1%
3.12 3
 
5.6%
3.04 3
 
5.6%
2.88 3
 
5.6%
3.09 3
 
5.6%
2.99 3
 
5.6%
3.03 2
 
3.7%
3.26 2
 
3.7%
2.92 2
 
3.7%
2.97 2
 
3.7%
Other values (21) 25
46.3%
ValueCountFrequency (%)
2.73 1
 
1.9%
2.77 1
 
1.9%
2.83 1
 
1.9%
2.88 3
5.6%
2.9 1
 
1.9%
2.92 2
 
3.7%
2.95 1
 
1.9%
2.96 1
 
1.9%
2.97 2
 
3.7%
2.98 6
11.1%
ValueCountFrequency (%)
3.38 1
 
1.9%
3.33 1
 
1.9%
3.27 1
 
1.9%
3.26 2
3.7%
3.21 2
3.7%
3.18 1
 
1.9%
3.15 2
3.7%
3.14 1
 
1.9%
3.13 1
 
1.9%
3.12 3
5.6%

직업의 성장 가능성
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5462963
Minimum2.18
Maximum2.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T00:33:34.534816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.18
5-th percentile2.3325
Q12.4325
median2.51
Q32.6
95-th percentile2.868
Maximum2.97
Range0.79
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.17221417
Coefficient of variation (CV)0.067633203
Kurtosis0.26167585
Mean2.5462963
Median Absolute Deviation (MAD)0.09
Skewness0.67968468
Sum137.5
Variance0.029657722
MonotonicityNot monotonic
2024-03-15T00:33:35.175036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2.49 4
 
7.4%
2.5 3
 
5.6%
2.42 3
 
5.6%
2.51 3
 
5.6%
2.47 3
 
5.6%
2.84 3
 
5.6%
2.54 2
 
3.7%
2.41 2
 
3.7%
2.4 2
 
3.7%
2.53 2
 
3.7%
Other values (23) 27
50.0%
ValueCountFrequency (%)
2.18 1
 
1.9%
2.25 1
 
1.9%
2.3 1
 
1.9%
2.35 2
3.7%
2.36 1
 
1.9%
2.4 2
3.7%
2.41 2
3.7%
2.42 3
5.6%
2.43 1
 
1.9%
2.44 1
 
1.9%
ValueCountFrequency (%)
2.97 1
 
1.9%
2.92 2
3.7%
2.84 3
5.6%
2.83 1
 
1.9%
2.79 1
 
1.9%
2.75 1
 
1.9%
2.71 1
 
1.9%
2.69 1
 
1.9%
2.67 1
 
1.9%
2.61 1
 
1.9%

Interactions

2024-03-15T00:33:18.126384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:57.649824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:00.178429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:02.156193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:03.911639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:06.269365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:08.938598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:11.337863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:13.585068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:15.730141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:18.294768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:57.976427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:00.338919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:02.312384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:04.172261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:06.538540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:09.202111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:11.579690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:13.750942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:15.992447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:18.466690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:58.251345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:00.481850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:02.450228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:04.419934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:06.796453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:09.454894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:11.820216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:13.994315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:16.241287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:18.700265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:58.580050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:00.679893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:02.572460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:04.662036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:07.195390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:09.695003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:12.096141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:14.217692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:16.544162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:18.874564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:58.846758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:01.005106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:02.751769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:04.923731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:07.364480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:09.961305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:12.264643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:14.468932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:16.808176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:19.252119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:59.123180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:01.165411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:02.906123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:05.195141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:07.618605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:10.232355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:12.423204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:14.729001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:16.982872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:19.417207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:59.390372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:01.334094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:03.090503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:05.462018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:07.899310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:10.500147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:12.628162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:14.993119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:17.275805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:19.613147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:59.647528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:01.472968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:03.274401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:05.703156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:08.148194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:10.750748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:12.865862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:15.192338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:17.526518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:19.860790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:59.807354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:01.605396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:03.441326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:05.840275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:08.400143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:10.994559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:13.094698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:15.313021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:17.745769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:20.128527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:32:59.974904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:01.808091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:03.657909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:06.006196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:08.665932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:11.168025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:13.350246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:15.474521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:33:17.925858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:33:35.475995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분중분류사례수보수업무 강도승진 가능성후생복지노후 준비업무 자율성전문성 계발 기회직업의 안정성직업의 성장 가능성
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사례수1.0001.0001.0000.0000.0000.0000.2890.0000.0000.4950.0000.000
보수1.0001.0000.0001.0000.8240.7880.8330.8650.0530.6490.6540.535
업무 강도1.0001.0000.0000.8241.0000.6680.6310.6700.0000.7050.4650.564
승진 가능성1.0001.0000.0000.7880.6681.0000.5190.7150.4320.2970.0000.330
후생복지1.0001.0000.2890.8330.6310.5191.0000.8000.3320.7430.7370.474
노후 준비1.0001.0000.0000.8650.6700.7150.8001.0000.5160.4110.5380.574
업무 자율성1.0001.0000.0000.0530.0000.4320.3320.5161.0000.6580.3860.618
전문성 계발 기회1.0001.0000.4950.6490.7050.2970.7430.4110.6581.0000.6720.793
직업의 안정성1.0001.0000.0000.6540.4650.0000.7370.5380.3860.6721.0000.721
직업의 성장 가능성1.0001.0000.0000.5350.5640.3300.4740.5740.6180.7930.7211.000
2024-03-15T00:33:35.817536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수보수업무 강도승진 가능성후생복지노후 준비업무 자율성전문성 계발 기회직업의 안정성직업의 성장 가능성
사례수1.000-0.341-0.1860.012-0.310-0.2540.029-0.019-0.3110.108
보수-0.3411.0000.5000.4270.7870.709-0.177-0.0810.4240.143
업무 강도-0.1860.5001.0000.4980.7090.6930.1760.4080.1440.528
승진 가능성0.0120.4270.4981.0000.5680.5510.1410.281-0.1530.483
후생복지-0.3100.7870.7090.5681.0000.8450.1050.2320.3770.405
노후 준비-0.2540.7090.6930.5510.8451.0000.1260.1430.4740.528
업무 자율성0.029-0.1770.1760.1410.1050.1261.0000.799-0.1630.526
전문성 계발 기회-0.019-0.0810.4080.2810.2320.1430.7991.000-0.3010.603
직업의 안정성-0.3110.4240.144-0.1530.3770.474-0.163-0.3011.000-0.109
직업의 성장 가능성0.1080.1430.5280.4830.4050.5280.5260.603-0.1091.000

Missing values

2024-03-15T00:33:20.515984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:33:21.156217image/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

구분중분류사례수보수업무 강도승진 가능성후생복지노후 준비업무 자율성전문성 계발 기회직업의 안정성직업의 성장 가능성
0성별1남자13762.62.972.912.572.23.612.863.032.56
1성별2여자6352.42.792.692.392.093.592.742.992.53
2연령120대2172.542.852.772.452.223.823.023.122.92
3연령230~34세3912.352.862.72.412.053.662.793.02.51
4연령335~39세3822.432.812.82.42.073.582.772.992.35
5연령440~44세3172.632.92.862.532.153.542.742.972.46
6연령545~49세2252.612.892.922.642.143.532.753.042.43
7연령650대3722.73.112.922.642.313.532.833.022.6
8연령760대 이상1072.573.073.092.722.423.63.073.032.92
9매체유형1신문사10652.322.832.782.352.023.532.792.982.47
구분중분류사례수보수업무 강도승진 가능성후생복지노후 준비업무 자율성전문성 계발 기회직업의 안정성직업의 성장 가능성
44직위3부장/부장대우2092.73.022.932.632.293.522.782.952.5
45직위4차장/차장대우4572.562.822.842.442.073.512.692.982.41
46직위5평기자10182.452.872.722.462.123.652.833.052.54
47경력11~4년4262.472.962.842.492.193.742.962.982.83
48경력25~9년4572.362.862.692.42.093.642.763.012.41
49경력310~14년3992.492.752.772.412.053.552.782.962.42
50경력415~19년2722.772.923.02.662.223.522.693.042.49
51경력520년 이상4572.683.062.942.662.283.532.863.092.56
52권역1서울13922.612.932.822.572.193.632.843.072.54
53권역2그 외 지역6192.372.882.872.392.113.542.782.92.57