Overview

Dataset statistics

Number of variables9
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory83.9 B

Variable types

Categorical1
Text1
Numeric7

Dataset

Description2015년부산광역시강서구사회조사결과(취업경쟁력요인)
Author부산광역시 강서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3045858

Alerts

학력 is highly overall correlated with 유사업종실무경력 and 1 other fieldsHigh correlation
인간관계 is highly overall correlated with 정부,부산시의 취업지원 정책사업 and 1 other fieldsHigh correlation
유사업종실무경력 is highly overall correlated with 학력 and 2 other fieldsHigh correlation
정부,부산시의 취업지원 정책사업 is highly overall correlated with 인간관계High correlation
특정 자격증 취득 is highly overall correlated with 학력 and 2 other fieldsHigh correlation
현장실습 참여 is highly overall correlated with 인간관계 and 1 other fieldsHigh correlation
기타 is highly overall correlated with 특정 자격증 취득High correlation
항목 has unique valuesUnique
학력 has 1 (3.7%) zerosZeros
유사업종실무경력 has 1 (3.7%) zerosZeros
정부,부산시의 취업지원 정책사업 has 2 (7.4%) zerosZeros
특정 자격증 취득 has 1 (3.7%) zerosZeros
현장실습 참여 has 1 (3.7%) zerosZeros
기타 has 2 (7.4%) zerosZeros

Reproduction

Analysis started2023-12-10 16:54:45.188721
Analysis finished2023-12-10 16:54:51.148307
Duration5.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size348.0 B
월가구소득
8 
연령별
6 
직업별
5 
교육수준
4 
성별
2 

Length

Max length5
Median length4
Mean length3.5925926
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성별
2nd row성별
3rd row연령별
4th row연령별
5th row연령별

Common Values

ValueCountFrequency (%)
월가구소득 8
29.6%
연령별 6
22.2%
직업별 5
18.5%
교육수준 4
14.8%
성별 2
 
7.4%
구역 2
 
7.4%

Length

2023-12-11T01:54:51.278832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:54:51.439082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
월가구소득 8
29.6%
연령별 6
22.2%
직업별 5
18.5%
교육수준 4
14.8%
성별 2
 
7.4%
구역 2
 
7.4%

항목
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T01:54:51.695985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.3703704
Min length1

Characters and Unicode

Total characters145
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row남
2nd row여
3rd row15-19세
4th row20-29세
5th row30-39세
ValueCountFrequency (%)
남 1
 
3.4%
농어업 1
 
3.4%
일반구역 1
 
3.4%
이상 1
 
3.4%
700만원 1
 
3.4%
600-700만원 1
 
3.4%
500-600만원 1
 
3.4%
400-500만원 1
 
3.4%
300-400만원 1
 
3.4%
200-300만원 1
 
3.4%
Other values (19) 19
65.5%
2023-12-11T01:54:52.151102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
22.8%
- 11
 
7.6%
만 9
 
6.2%
원 8
 
5.5%
세 6
 
4.1%
5 5
 
3.4%
9 5
 
3.4%
3 4
 
2.8%
4 4
 
2.8%
졸 4
 
2.8%
Other values (38) 56
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
46.9%
Decimal Number 64
44.1%
Dash Punctuation 11
 
7.6%
Space Separator 2
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
만 9
 
13.2%
원 8
 
11.8%
세 6
 
8.8%
졸 4
 
5.9%
이 4
 
5.9%
상 3
 
4.4%
역 2
 
2.9%
구 2
 
2.9%
무 2
 
2.9%
업 1
 
1.5%
Other values (27) 27
39.7%
Decimal Number
ValueCountFrequency (%)
0 33
51.6%
5 5
 
7.8%
9 5
 
7.8%
3 4
 
6.2%
4 4
 
6.2%
2 4
 
6.2%
1 4
 
6.2%
6 3
 
4.7%
7 2
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
53.1%
Hangul 68
46.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
만 9
 
13.2%
원 8
 
11.8%
세 6
 
8.8%
졸 4
 
5.9%
이 4
 
5.9%
상 3
 
4.4%
역 2
 
2.9%
구 2
 
2.9%
무 2
 
2.9%
업 1
 
1.5%
Other values (27) 27
39.7%
Common
ValueCountFrequency (%)
0 33
42.9%
- 11
 
14.3%
5 5
 
6.5%
9 5
 
6.5%
3 4
 
5.2%
4 4
 
5.2%
2 4
 
5.2%
1 4
 
5.2%
6 3
 
3.9%
7 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
53.1%
Hangul 68
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
42.9%
- 11
 
14.3%
5 5
 
6.5%
9 5
 
6.5%
3 4
 
5.2%
4 4
 
5.2%
2 4
 
5.2%
1 4
 
5.2%
6 3
 
3.9%
7 2
 
2.6%
Hangul
ValueCountFrequency (%)
만 9
 
13.2%
원 8
 
11.8%
세 6
 
8.8%
졸 4
 
5.9%
이 4
 
5.9%
상 3
 
4.4%
역 2
 
2.9%
구 2
 
2.9%
무 2
 
2.9%
업 1
 
1.5%
Other values (27) 27
39.7%

학력
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2555556
Minimum0
Maximum33.1
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:52.306567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29
Q12.55
median6.7
Q311.15
95-th percentile18.67
Maximum33.1
Range33.1
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation7.4425664
Coefficient of variation (CV)0.90152218
Kurtosis3.4122157
Mean8.2555556
Median Absolute Deviation (MAD)4.3
Skewness1.5558935
Sum222.9
Variance55.391795
MonotonicityNot monotonic
2023-12-11T01:54:52.444389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.4 2
 
7.4%
4.3 2
 
7.4%
10.3 1
 
3.7%
6.7 1
 
3.7%
11.0 1
 
3.7%
13.6 1
 
3.7%
9.2 1
 
3.7%
16.2 1
 
3.7%
10.9 1
 
3.7%
8.7 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
0.0 1
3.7%
0.2 1
3.7%
0.5 1
3.7%
1.7 1
3.7%
1.9 1
3.7%
2.4 2
7.4%
2.7 1
3.7%
3.6 1
3.7%
4.3 2
7.4%
4.7 1
3.7%
ValueCountFrequency (%)
33.1 1
3.7%
19.6 1
3.7%
16.5 1
3.7%
16.2 1
3.7%
15.2 1
3.7%
13.6 1
3.7%
11.3 1
3.7%
11.0 1
3.7%
10.9 1
3.7%
10.3 1
3.7%

인간관계
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.481481
Minimum1.3
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:52.602766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile4.49
Q19.6
median13.6
Q316.2
95-th percentile26.92
Maximum32
Range30.7
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation6.8435279
Coefficient of variation (CV)0.50762433
Kurtosis1.593012
Mean13.481481
Median Absolute Deviation (MAD)3.7
Skewness0.90011327
Sum364
Variance46.833875
MonotonicityNot monotonic
2023-12-11T01:54:52.733403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
14.7 2
 
7.4%
9.6 2
 
7.4%
14.2 2
 
7.4%
18.1 1
 
3.7%
9.9 1
 
3.7%
15.9 1
 
3.7%
4.7 1
 
3.7%
4.4 1
 
3.7%
9.5 1
 
3.7%
19.1 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
1.3 1
3.7%
4.4 1
3.7%
4.7 1
3.7%
6.4 1
3.7%
6.7 1
3.7%
9.5 1
3.7%
9.6 2
7.4%
9.9 1
3.7%
10.1 1
3.7%
11.3 1
3.7%
ValueCountFrequency (%)
32.0 1
3.7%
29.2 1
3.7%
21.6 1
3.7%
19.1 1
3.7%
18.1 1
3.7%
16.6 1
3.7%
16.5 1
3.7%
15.9 1
3.7%
14.7 2
7.4%
14.2 2
7.4%

유사업종실무경력
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.666667
Minimum0
Maximum54.7
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:52.879248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.6
Q117.15
median25.9
Q329.45
95-th percentile43.63
Maximum54.7
Range54.7
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation11.491134
Coefficient of variation (CV)0.46585677
Kurtosis1.2926848
Mean24.666667
Median Absolute Deviation (MAD)4.9
Skewness0.32469506
Sum666
Variance132.04615
MonotonicityNot monotonic
2023-12-11T01:54:53.010430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
22.3 2
 
7.4%
30.8 1
 
3.7%
54.7 1
 
3.7%
21.9 1
 
3.7%
23.5 1
 
3.7%
28.6 1
 
3.7%
26.6 1
 
3.7%
24.6 1
 
3.7%
27.2 1
 
3.7%
26.4 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0.0 1
3.7%
4.8 1
3.7%
10.8 1
3.7%
13.6 1
3.7%
15.2 1
3.7%
15.3 1
3.7%
17.1 1
3.7%
17.2 1
3.7%
21.9 1
3.7%
22.3 2
7.4%
ValueCountFrequency (%)
54.7 1
3.7%
46.6 1
3.7%
36.7 1
3.7%
34.6 1
3.7%
31.5 1
3.7%
30.8 1
3.7%
29.6 1
3.7%
29.3 1
3.7%
28.9 1
3.7%
28.6 1
3.7%

정부,부산시의 취업지원 정책사업
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8074074
Minimum0
Maximum4.9
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:53.206409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q11.25
median1.7
Q32.2
95-th percentile3.68
Maximum4.9
Range4.9
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation1.101721
Coefficient of variation (CV)0.60955875
Kurtosis1.3608566
Mean1.8074074
Median Absolute Deviation (MAD)0.5
Skewness0.83821018
Sum48.8
Variance1.2137892
MonotonicityNot monotonic
2023-12-11T01:54:53.369048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 2
 
7.4%
1.3 2
 
7.4%
0.9 2
 
7.4%
1.8 2
 
7.4%
2.9 2
 
7.4%
2.0 2
 
7.4%
1.6 2
 
7.4%
2.2 2
 
7.4%
1.7 1
 
3.7%
4.9 1
 
3.7%
Other values (9) 9
33.3%
ValueCountFrequency (%)
0.0 2
7.4%
0.5 1
3.7%
0.7 1
3.7%
0.9 2
7.4%
1.2 1
3.7%
1.3 2
7.4%
1.4 1
3.7%
1.5 1
3.7%
1.6 2
7.4%
1.7 1
3.7%
ValueCountFrequency (%)
4.9 1
3.7%
3.8 1
3.7%
3.4 1
3.7%
2.9 2
7.4%
2.4 1
3.7%
2.2 2
7.4%
2.0 2
7.4%
1.9 1
3.7%
1.8 2
7.4%
1.7 1
3.7%

특정 자격증 취득
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9703704
Minimum0
Maximum22.8
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:53.528973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.55
Q16.9
median9.1
Q310.75
95-th percentile16.68
Maximum22.8
Range22.8
Interquartile range (IQR)3.85

Descriptive statistics

Standard deviation4.7459144
Coefficient of variation (CV)0.5290656
Kurtosis1.986237
Mean8.9703704
Median Absolute Deviation (MAD)1.8
Skewness0.7133345
Sum242.2
Variance22.523704
MonotonicityNot monotonic
2023-12-11T01:54:53.670372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
10.7 2
 
7.4%
7.9 2
 
7.4%
10.2 1
 
3.7%
12.1 1
 
3.7%
9.1 1
 
3.7%
6.5 1
 
3.7%
15.7 1
 
3.7%
10.0 1
 
3.7%
9.9 1
 
3.7%
7.3 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
0.0 1
3.7%
1.1 1
3.7%
2.6 1
3.7%
4.3 1
3.7%
4.5 1
3.7%
5.7 1
3.7%
6.5 1
3.7%
7.3 1
3.7%
7.4 1
3.7%
7.7 1
3.7%
ValueCountFrequency (%)
22.8 1
3.7%
17.1 1
3.7%
15.7 1
3.7%
12.1 1
3.7%
11.3 1
3.7%
10.9 1
3.7%
10.8 1
3.7%
10.7 2
7.4%
10.2 1
3.7%
10.0 1
3.7%

현장실습 참여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9296296
Minimum0
Maximum14.7
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:54.111907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.75
Q12.7
median3.5
Q34.55
95-th percentile5.6
Maximum14.7
Range14.7
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation2.5001254
Coefficient of variation (CV)0.63622417
Kurtosis13.708596
Mean3.9296296
Median Absolute Deviation (MAD)0.9
Skewness3.1031632
Sum106.1
Variance6.2506268
MonotonicityNot monotonic
2023-12-11T01:54:54.375824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3.4 3
 
11.1%
2.7 3
 
11.1%
4.7 2
 
7.4%
5.6 2
 
7.4%
4.4 2
 
7.4%
3.9 1
 
3.7%
4.9 1
 
3.7%
4.0 1
 
3.7%
2.1 1
 
3.7%
3.6 1
 
3.7%
Other values (10) 10
37.0%
ValueCountFrequency (%)
0.0 1
 
3.7%
1.6 1
 
3.7%
2.1 1
 
3.7%
2.2 1
 
3.7%
2.5 1
 
3.7%
2.7 3
11.1%
2.9 1
 
3.7%
3.1 1
 
3.7%
3.4 3
11.1%
3.5 1
 
3.7%
ValueCountFrequency (%)
14.7 1
3.7%
5.6 2
7.4%
5.2 1
3.7%
4.9 1
3.7%
4.7 2
7.4%
4.4 2
7.4%
4.2 1
3.7%
4.0 1
3.7%
3.9 1
3.7%
3.6 1
3.7%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1814815
Minimum0
Maximum3.1
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:54.502043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.7
median0.9
Q31.6
95-th percentile2.45
Maximum3.1
Range3.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.74886429
Coefficient of variation (CV)0.63383498
Kurtosis0.41470087
Mean1.1814815
Median Absolute Deviation (MAD)0.5
Skewness0.64580568
Sum31.9
Variance0.56079772
MonotonicityNot monotonic
2023-12-11T01:54:54.628721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.4 4
14.8%
0.8 3
11.1%
0.9 3
11.1%
0.0 2
 
7.4%
0.6 2
 
7.4%
1.6 2
 
7.4%
0.7 2
 
7.4%
1.9 1
 
3.7%
2.6 1
 
3.7%
2.1 1
 
3.7%
Other values (6) 6
22.2%
ValueCountFrequency (%)
0.0 2
7.4%
0.2 1
 
3.7%
0.5 1
 
3.7%
0.6 2
7.4%
0.7 2
7.4%
0.8 3
11.1%
0.9 3
11.1%
1.3 1
 
3.7%
1.4 4
14.8%
1.6 2
7.4%
ValueCountFrequency (%)
3.1 1
 
3.7%
2.6 1
 
3.7%
2.1 1
 
3.7%
2.0 1
 
3.7%
1.9 1
 
3.7%
1.7 1
 
3.7%
1.6 2
7.4%
1.4 4
14.8%
1.3 1
 
3.7%
0.9 3
11.1%

Interactions

2023-12-11T01:54:49.929680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:45.467807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:46.169357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.190153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.893358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.473790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.144249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:50.045194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:45.542034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:46.271637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.318213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.979198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.559121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.258418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:50.183513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:45.623677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:46.663289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.415435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.058301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.650821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.377819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:50.330771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:45.719469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:46.742103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.508228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.144403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.739240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.470658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:50.441100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:45.808998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:46.840290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.587545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.223801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.866513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.557848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:50.550200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:45.885631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:46.955573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.676829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.304394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.941761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.646449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:50.675603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:46.041108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.094646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:47.777885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:48.388761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.036789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:49.803336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:54:54.771313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분항목학력인간관계유사업종실무경력정부,부산시의 취업지원 정책사업특정 자격증 취득현장실습 참여기타
구분1.0001.0000.0000.4680.4410.0000.0000.0000.298
항목1.0001.0001.0001.0001.0001.0001.0001.0001.000
학력0.0001.0001.0000.0000.5870.0000.6470.0000.405
인간관계0.4681.0000.0001.0000.6910.5160.5690.7390.623
유사업종실무경력0.4411.0000.5870.6911.0000.7100.8920.4510.684
정부,부산시의 취업지원 정책사업0.0001.0000.0000.5160.7101.0000.7240.7180.568
특정 자격증 취득0.0001.0000.6470.5690.8920.7241.0000.0000.516
현장실습 참여0.0001.0000.0000.7390.4510.7180.0001.0000.000
기타0.2981.0000.4050.6230.6840.5680.5160.0001.000
2023-12-11T01:54:54.967934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학력인간관계유사업종실무경력정부,부산시의 취업지원 정책사업특정 자격증 취득현장실습 참여기타구분
학력1.000-0.3080.609-0.3870.7370.0070.3470.000
인간관계-0.3081.0000.3250.667-0.0850.7180.1820.247
유사업종실무경력0.6090.3251.0000.1210.7320.5080.4560.186
정부,부산시의 취업지원 정책사업-0.3870.6670.1211.000-0.1720.4470.1160.000
특정 자격증 취득0.737-0.0850.732-0.1721.0000.1380.6470.000
현장실습 참여0.0070.7180.5080.4470.1381.0000.2530.000
기타0.3470.1820.4560.1160.6470.2531.0000.119
구분0.0000.2470.1860.0000.0000.0000.1191.000

Missing values

2023-12-11T01:54:50.860866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:54:51.070680image/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

구분항목학력인간관계유사업종실무경력정부,부산시의 취업지원 정책사업특정 자격증 취득현장실습 참여기타
0성별남10.313.530.81.710.24.71.3
1성별여4.311.313.61.65.72.50.7
2연령별15-19세0.01.30.00.00.00.00.0
3연령별20-29세4.76.415.30.710.71.63.1
4연령별30-39세15.29.625.91.38.53.40.9
5연령별40-49세11.312.631.50.910.95.61.6
6연령별50-59세4.321.629.61.89.53.50.2
7연령별60세이상1.913.615.23.44.34.40.5
8교육수준초졸이하0.514.010.82.92.62.20.8
9교육수준중졸1.716.617.12.04.52.90.9
구분항목학력인간관계유사업종실무경력정부,부산시의 취업지원 정책사업특정 자격증 취득현장실습 참여기타
17월가구소득100만원 미만0.210.14.81.91.13.40.0
18월가구소득100-200만원2.719.117.22.97.75.22.6
19월가구소득200-300만원4.814.726.42.27.33.60.7
20월가구소득300-400만원8.714.227.21.27.94.40.9
21월가구소득400-500만원10.99.524.60.99.92.71.4
22월가구소득500-600만원16.24.426.60.010.02.10.6
23월가구소득600-700만원9.24.728.62.415.72.71.6
24월가구소득700만원 이상13.614.222.31.510.74.00.8
25구역일반구역2.415.923.51.86.54.90.6
26구역개발구역11.09.921.91.69.12.71.4