Overview

Dataset statistics

Number of variables9
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory80.6 B

Variable types

Text3
Numeric6

Dataset

Description인천광역시 직업 선택 요인(수입, 안정성 등) 자료입니다. * 특성별 수입 (%) 안정성 (%) 보람/자아실현/성취 (%) 발전성/장래성 (%) 적성/흥미 (%) 근로시간 (%) 명예/명성 (%) 기타 (%)
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15066310&srcSe=7661IVAWM27C61E190

Alerts

수입 (퍼센트) is highly overall correlated with 보람_자아실현_성취 (퍼센트) and 2 other fieldsHigh correlation
보람_자아실현_성취 (퍼센트) is highly overall correlated with 수입 (퍼센트) and 1 other fieldsHigh correlation
발전성_장래성 (퍼센트) is highly overall correlated with 수입 (퍼센트)High correlation
적성_흥미 (퍼센트) is highly overall correlated with 수입 (퍼센트) and 1 other fieldsHigh correlation
특성별 has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:39:31.850570
Analysis finished2024-03-18 04:39:35.570708
Duration3.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

특성별
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-03-18T13:39:35.699188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7.5
Mean length4.92
Min length2

Characters and Unicode

Total characters246
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row중구
2nd row동구
3rd row미추홀구
4th row연수구
5th row남동구
ValueCountFrequency (%)
미만 7
 
9.7%
5
 
6.9%
이상 3
 
4.2%
기타 2
 
2.8%
중구 1
 
1.4%
기능노무 1
 
1.4%
4인 1
 
1.4%
학생 1
 
1.4%
주부 1
 
1.4%
무직/기타 1
 
1.4%
Other values (49) 49
68.1%
2024-03-18T13:39:36.093896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
 
13.4%
22
 
8.9%
만 15
 
6.1%
~ 11
 
4.5%
세 9
 
3.7%
원 8
 
3.3%
구 8
 
3.3%
미 8
 
3.3%
3 6
 
2.4%
인 5
 
2.0%
Other values (66) 121
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
57.7%
Decimal Number 69
28.0%
Space Separator 22
 
8.9%
Math Symbol 11
 
4.5%
Other Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
상 4
 
2.8%
기 4
 
2.8%
졸 4
 
2.8%
Other values (54) 72
50.7%
Decimal Number
ValueCountFrequency (%)
0 33
47.8%
3 6
 
8.7%
9 5
 
7.2%
5 5
 
7.2%
4 5
 
7.2%
2 5
 
7.2%
1 5
 
7.2%
6 3
 
4.3%
7 2
 
2.9%
Space Separator
ValueCountFrequency (%)
22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
57.7%
Common 104
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
상 4
 
2.8%
기 4
 
2.8%
졸 4
 
2.8%
Other values (54) 72
50.7%
Common
ValueCountFrequency (%)
0 33
31.7%
22
21.2%
~ 11
 
10.6%
3 6
 
5.8%
9 5
 
4.8%
5 5
 
4.8%
4 5
 
4.8%
2 5
 
4.8%
1 5
 
4.8%
6 3
 
2.9%
Other values (2) 4
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
57.7%
ASCII 104
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
31.7%
22
21.2%
~ 11
 
10.6%
3 6
 
5.8%
9 5
 
4.8%
5 5
 
4.8%
4 5
 
4.8%
2 5
 
4.8%
1 5
 
4.8%
6 3
 
2.9%
Other values (2) 4
 
3.8%
Hangul
ValueCountFrequency (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
상 4
 
2.8%
기 4
 
2.8%
졸 4
 
2.8%
Other values (54) 72
50.7%

수입 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.308
Minimum26.9
Maximum52.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T13:39:36.212434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.9
5-th percentile31.035
Q136.35
median40.2
Q344.15
95-th percentile50.655
Maximum52.6
Range25.7
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation5.9936306
Coefficient of variation (CV)0.14869581
Kurtosis-0.43551289
Mean40.308
Median Absolute Deviation (MAD)4.05
Skewness-0.029649173
Sum2015.4
Variance35.923608
MonotonicityNot monotonic
2024-03-18T13:39:36.324245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
37.7 2
 
4.0%
40.8 2
 
4.0%
43.7 2
 
4.0%
46.0 2
 
4.0%
38.9 2
 
4.0%
39.6 1
 
2.0%
46.2 1
 
2.0%
45.7 1
 
2.0%
38.0 1
 
2.0%
32.3 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
26.9 1
2.0%
29.9 1
2.0%
30.9 1
2.0%
31.2 1
2.0%
31.7 1
2.0%
32.2 1
2.0%
32.3 1
2.0%
33.6 1
2.0%
34.4 1
2.0%
34.7 1
2.0%
ValueCountFrequency (%)
52.6 1
2.0%
52.1 1
2.0%
50.7 1
2.0%
50.6 1
2.0%
47.0 1
2.0%
46.9 1
2.0%
46.3 1
2.0%
46.2 1
2.0%
46.0 2
4.0%
45.7 1
2.0%

안정성 (퍼센트)
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.398
Minimum15.9
Maximum41.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T13:39:36.427167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.9
5-th percentile19.35
Q122.4
median24.75
Q326.675
95-th percentile34.155
Maximum41.5
Range25.6
Interquartile range (IQR)4.275

Descriptive statistics

Standard deviation4.7670848
Coefficient of variation (CV)0.18769529
Kurtosis1.8279236
Mean25.398
Median Absolute Deviation (MAD)2.25
Skewness0.9545932
Sum1269.9
Variance22.725098
MonotonicityNot monotonic
2024-03-18T13:39:36.544127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
21.4 2
 
4.0%
22.1 2
 
4.0%
23.2 2
 
4.0%
24.8 2
 
4.0%
23.8 2
 
4.0%
25.8 2
 
4.0%
24.7 2
 
4.0%
20.3 1
 
2.0%
25.2 1
 
2.0%
25.5 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
15.9 1
2.0%
16.7 1
2.0%
18.9 1
2.0%
19.9 1
2.0%
20.3 1
2.0%
20.5 1
2.0%
21.4 2
4.0%
21.7 1
2.0%
21.8 1
2.0%
22.1 2
4.0%
ValueCountFrequency (%)
41.5 1
2.0%
34.5 1
2.0%
34.2 1
2.0%
34.1 1
2.0%
33.0 1
2.0%
32.0 1
2.0%
31.2 1
2.0%
30.1 1
2.0%
29.4 1
2.0%
28.9 1
2.0%

보람_자아실현_성취 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.48
Minimum3.9
Maximum21.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T13:39:36.652897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile4.66
Q16.6
median8.4
Q39.675
95-th percentile12.05
Maximum21.9
Range18
Interquartile range (IQR)3.075

Descriptive statistics

Standard deviation2.8560712
Coefficient of variation (CV)0.33680085
Kurtosis8.9083575
Mean8.48
Median Absolute Deviation (MAD)1.6
Skewness2.0192578
Sum424
Variance8.1571429
MonotonicityNot monotonic
2024-03-18T13:39:36.763825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
9.5 4
 
8.0%
8.2 3
 
6.0%
6.6 2
 
4.0%
6.5 2
 
4.0%
6.8 2
 
4.0%
8.4 2
 
4.0%
10.0 2
 
4.0%
8.1 2
 
4.0%
4.3 1
 
2.0%
12.5 1
 
2.0%
Other values (29) 29
58.0%
ValueCountFrequency (%)
3.9 1
2.0%
4.2 1
2.0%
4.3 1
2.0%
5.1 1
2.0%
5.2 1
2.0%
5.4 1
2.0%
5.8 1
2.0%
5.9 1
2.0%
6.3 1
2.0%
6.4 1
2.0%
ValueCountFrequency (%)
21.9 1
2.0%
12.7 1
2.0%
12.5 1
2.0%
11.5 1
2.0%
11.3 1
2.0%
11.2 1
2.0%
10.6 1
2.0%
10.3 1
2.0%
10.0 2
4.0%
9.9 1
2.0%

발전성_장래성 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.96
Minimum2.8
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T13:39:36.866889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile5.015
Q18.25
median9.85
Q311.7
95-th percentile15.9
Maximum17.8
Range15
Interquartile range (IQR)3.45

Descriptive statistics

Standard deviation3.0476488
Coefficient of variation (CV)0.30598884
Kurtosis0.75136159
Mean9.96
Median Absolute Deviation (MAD)1.75
Skewness0.3649014
Sum498
Variance9.2881633
MonotonicityNot monotonic
2024-03-18T13:39:36.969162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
8.7 3
 
6.0%
10.5 3
 
6.0%
7.3 2
 
4.0%
8.5 2
 
4.0%
11.7 2
 
4.0%
8.1 2
 
4.0%
12.5 2
 
4.0%
9.6 2
 
4.0%
9.9 2
 
4.0%
15.9 2
 
4.0%
Other values (27) 28
56.0%
ValueCountFrequency (%)
2.8 1
2.0%
4.4 1
2.0%
4.7 1
2.0%
5.4 1
2.0%
5.9 1
2.0%
7.2 1
2.0%
7.3 2
4.0%
7.4 1
2.0%
7.9 1
2.0%
8.1 2
4.0%
ValueCountFrequency (%)
17.8 1
2.0%
17.0 1
2.0%
15.9 2
4.0%
14.6 1
2.0%
12.5 2
4.0%
12.4 1
2.0%
12.3 1
2.0%
12.2 1
2.0%
12.1 1
2.0%
11.8 1
2.0%

적성_흥미 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.486
Minimum1.4
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T13:39:37.094037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile4.99
Q17
median8.15
Q39.65
95-th percentile12.97
Maximum23
Range21.6
Interquartile range (IQR)2.65

Descriptive statistics

Standard deviation3.0685375
Coefficient of variation (CV)0.36159999
Kurtosis9.7087253
Mean8.486
Median Absolute Deviation (MAD)1.4
Skewness2.0869211
Sum424.3
Variance9.4159224
MonotonicityNot monotonic
2024-03-18T13:39:37.191271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
8.4 3
 
6.0%
8.0 2
 
4.0%
6.7 2
 
4.0%
7.2 2
 
4.0%
9.5 2
 
4.0%
9.7 2
 
4.0%
7.7 2
 
4.0%
7.0 2
 
4.0%
5.1 2
 
4.0%
9.3 2
 
4.0%
Other values (29) 29
58.0%
ValueCountFrequency (%)
1.4 1
2.0%
4.8 1
2.0%
4.9 1
2.0%
5.1 2
4.0%
5.5 1
2.0%
6.0 1
2.0%
6.3 1
2.0%
6.4 1
2.0%
6.6 1
2.0%
6.7 2
4.0%
ValueCountFrequency (%)
23.0 1
2.0%
13.9 1
2.0%
13.6 1
2.0%
12.2 1
2.0%
11.1 1
2.0%
10.9 1
2.0%
10.4 1
2.0%
10.3 1
2.0%
10.1 1
2.0%
10.0 1
2.0%

근로시간 (퍼센트)
Real number (ℝ)

Distinct35
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.906
Minimum0.8
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T13:39:37.293111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.3
Q13.125
median4.05
Q34.575
95-th percentile6.155
Maximum9.9
Range9.1
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation1.5824819
Coefficient of variation (CV)0.4051413
Kurtosis3.2283114
Mean3.906
Median Absolute Deviation (MAD)0.65
Skewness0.82735444
Sum195.3
Variance2.504249
MonotonicityNot monotonic
2024-03-18T13:39:37.405149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4.3 4
 
8.0%
3.5 3
 
6.0%
3.7 3
 
6.0%
4.5 3
 
6.0%
3.6 2
 
4.0%
4.4 2
 
4.0%
1.3 2
 
4.0%
4.7 2
 
4.0%
4.1 2
 
4.0%
4.6 2
 
4.0%
Other values (25) 25
50.0%
ValueCountFrequency (%)
0.8 1
2.0%
1.1 1
2.0%
1.3 2
4.0%
1.8 1
2.0%
1.9 1
2.0%
2.0 1
2.0%
2.3 1
2.0%
2.5 1
2.0%
2.6 1
2.0%
2.7 1
2.0%
ValueCountFrequency (%)
9.9 1
2.0%
6.7 1
2.0%
6.2 1
2.0%
6.1 1
2.0%
5.8 1
2.0%
5.7 1
2.0%
5.2 1
2.0%
4.9 1
2.0%
4.8 1
2.0%
4.7 2
4.0%

명예_명성 (퍼센트)
Text

Distinct25
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-03-18T13:39:37.581875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.72
Min length1

Characters and Unicode

Total characters136
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)22.0%

Sample

1st row3.4
2nd row0.9
3rd row1.7
4th row1.9
5th row0.5
ValueCountFrequency (%)
2 5
 
10.0%
1.9 4
 
8.0%
2.2 4
 
8.0%
1.7 3
 
6.0%
1.5 3
 
6.0%
2.1 3
 
6.0%
2.5 3
 
6.0%
2.7 2
 
4.0%
2.8 2
 
4.0%
0.9 2
 
4.0%
Other values (15) 19
38.0%
2024-03-18T13:39:37.843186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 43
31.6%
2 28
20.6%
1 23
16.9%
5 8
 
5.9%
9 7
 
5.1%
3 6
 
4.4%
7 5
 
3.7%
0 5
 
3.7%
8 4
 
2.9%
4 3
 
2.2%
Other values (2) 4
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91
66.9%
Other Punctuation 43
31.6%
Dash Punctuation 2
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 28
30.8%
1 23
25.3%
5 8
 
8.8%
9 7
 
7.7%
3 6
 
6.6%
7 5
 
5.5%
0 5
 
5.5%
8 4
 
4.4%
4 3
 
3.3%
6 2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43
31.6%
2 28
20.6%
1 23
16.9%
5 8
 
5.9%
9 7
 
5.1%
3 6
 
4.4%
7 5
 
3.7%
0 5
 
3.7%
8 4
 
2.9%
4 3
 
2.2%
Other values (2) 4
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43
31.6%
2 28
20.6%
1 23
16.9%
5 8
 
5.9%
9 7
 
5.1%
3 6
 
4.4%
7 5
 
3.7%
0 5
 
3.7%
8 4
 
2.9%
4 3
 
2.2%
Other values (2) 4
 
2.9%

기타 (퍼센트)
Text

Distinct27
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-03-18T13:39:38.061745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8
Min length1

Characters and Unicode

Total characters140
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)24.0%

Sample

1st row1.2
2nd row4.2
3rd row0.8
4th row0.9
5th row2.8
ValueCountFrequency (%)
0.4 5
 
10.0%
2.2 4
 
8.0%
1.1 3
 
6.0%
0.8 3
 
6.0%
0.9 3
 
6.0%
0.1 2
 
4.0%
0.2 2
 
4.0%
0.3 2
 
4.0%
0.6 2
 
4.0%
0.7 2
 
4.0%
Other values (17) 22
44.0%
2024-03-18T13:39:38.348604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 45
32.1%
0 21
15.0%
1 20
14.3%
2 16
 
11.4%
4 9
 
6.4%
8 6
 
4.3%
9 6
 
4.3%
6 5
 
3.6%
3 4
 
2.9%
7 4
 
2.9%
Other values (2) 4
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93
66.4%
Other Punctuation 45
32.1%
Dash Punctuation 2
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21
22.6%
1 20
21.5%
2 16
17.2%
4 9
9.7%
8 6
 
6.5%
9 6
 
6.5%
6 5
 
5.4%
3 4
 
4.3%
7 4
 
4.3%
5 2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 45
32.1%
0 21
15.0%
1 20
14.3%
2 16
 
11.4%
4 9
 
6.4%
8 6
 
4.3%
9 6
 
4.3%
6 5
 
3.6%
3 4
 
2.9%
7 4
 
2.9%
Other values (2) 4
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 45
32.1%
0 21
15.0%
1 20
14.3%
2 16
 
11.4%
4 9
 
6.4%
8 6
 
4.3%
9 6
 
4.3%
6 5
 
3.6%
3 4
 
2.9%
7 4
 
2.9%
Other values (2) 4
 
2.9%

Interactions

2024-03-18T13:39:34.857811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.119404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.500824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.941188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.468250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.124642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.913530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.179951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.566113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.055509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.586245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.204631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.972239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.239965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.637840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.154302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.709097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.283971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:35.038350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.306918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.715074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.250435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.847372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.384924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:35.109362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.379344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.798931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.321864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.950238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.476093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:35.227552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.437764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:32.860216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:33.384809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.038509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:39:34.543449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:39:38.427096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특성별수입 (퍼센트)안정성 (퍼센트)보람_자아실현_성취 (퍼센트)발전성_장래성 (퍼센트)적성_흥미 (퍼센트)근로시간 (퍼센트)명예_명성 (퍼센트)기타 (퍼센트)
특성별1.0001.0001.0001.0001.0001.0001.0001.0001.000
수입 (퍼센트)1.0001.0000.6970.7170.5740.7290.6590.8010.183
안정성 (퍼센트)1.0000.6971.0000.5420.6950.4920.4050.4270.662
보람_자아실현_성취 (퍼센트)1.0000.7170.5421.0000.4290.6360.3970.6350.635
발전성_장래성 (퍼센트)1.0000.5740.6950.4291.0000.6030.2860.0000.683
적성_흥미 (퍼센트)1.0000.7290.4920.6360.6031.0000.7040.7970.000
근로시간 (퍼센트)1.0000.6590.4050.3970.2860.7041.0000.8760.603
명예_명성 (퍼센트)1.0000.8010.4270.6350.0000.7970.8761.0000.398
기타 (퍼센트)1.0000.1830.6620.6350.6830.0000.6030.3981.000
2024-03-18T13:39:38.540580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수입 (퍼센트)안정성 (퍼센트)보람_자아실현_성취 (퍼센트)발전성_장래성 (퍼센트)적성_흥미 (퍼센트)근로시간 (퍼센트)
수입 (퍼센트)1.000-0.479-0.634-0.591-0.6590.180
안정성 (퍼센트)-0.4791.0000.0800.161-0.057-0.202
보람_자아실현_성취 (퍼센트)-0.6340.0801.0000.3650.705-0.305
발전성_장래성 (퍼센트)-0.5910.1610.3651.0000.446-0.345
적성_흥미 (퍼센트)-0.659-0.0570.7050.4461.000-0.158
근로시간 (퍼센트)0.180-0.202-0.305-0.345-0.1581.000

Missing values

2024-03-18T13:39:35.393444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:39:35.521163image/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

특성별수입 (퍼센트)안정성 (퍼센트)보람_자아실현_성취 (퍼센트)발전성_장래성 (퍼센트)적성_흥미 (퍼센트)근로시간 (퍼센트)명예_명성 (퍼센트)기타 (퍼센트)
0중구39.633.04.37.37.73.53.41.2
1동구37.141.53.92.84.94.70.94.2
2미추홀구40.829.46.614.64.81.31.70.8
3연수구34.730.19.59.79.33.81.90.9
4남동구46.916.76.517.85.13.60.52.8
5부평구43.725.05.97.47.06.12.72.2
6계양구26.918.912.79.023.06.72.9-
7서구44.324.011.34.78.04.32.51
8강화군35.732.09.78.28.21.92.22.2
9옹진군44.234.511.55.41.40.8-2.2
특성별수입 (퍼센트)안정성 (퍼센트)보람_자아실현_성취 (퍼센트)발전성_장래성 (퍼센트)적성_흥미 (퍼센트)근로시간 (퍼센트)명예_명성 (퍼센트)기타 (퍼센트)
40연립/다세대주택46.320.58.49.67.24.121.9
41기타42.625.86.311.78.04.30.41.1
42자가38.726.38.210.58.44.22.11.6
43전세40.923.29.59.99.24.32.10.9
44월세 및 기타50.719.96.68.57.93.71.11.6
451인50.621.75.18.16.73.31.62.9
462인43.324.86.88.47.34.623
473인39.525.79.510.38.43.52.11
484인37.225.38.711.59.54.62.50.7
495인 이상35.626.19.912.110.44.50.90.3