Overview

Dataset statistics

Number of variables8
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory71.6 B

Variable types

Categorical1
Text2
Numeric5

Dataset

Description육아부담 또는 가사부담, 여성을 원하는 회사나 일자리가 적어서, 불평등한 근로조건(채용 임금 등), 다양한 형태의 구인정보 부족, 여성 취업 지원 정책 미비 등의 정보입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15066279&srcSe=7661IVAWM27C61E190

Alerts

육아부담 또는 가사부담(2021년) is highly overall correlated with 여성을 원하는 회사나 일자리가 적어서(2021년)High correlation
여성을 원하는 회사나 일자리가 적어서(2021년) is highly overall correlated with 육아부담 또는 가사부담(2021년)High correlation
특성별(2) has unique valuesUnique

Reproduction

Analysis started2024-04-20 20:25:36.773758
Analysis finished2024-04-20 20:25:43.899901
Duration7.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

특성별(1)
Categorical

Distinct9
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
군구별
10 
직업별
8 
월평균소득별
8 
연령별
6 
가구원수별
5 
Other values (4)
13 

Length

Max length7
Median length3
Mean length4.04
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row군구별
2nd row군구별
3rd row군구별
4th row군구별
5th row군구별

Common Values

ValueCountFrequency (%)
군구별 10
20.0%
직업별 8
16.0%
월평균소득별 8
16.0%
연령별 6
12.0%
가구원수별 5
10.0%
학력별 4
 
8.0%
주거형태별 4
 
8.0%
주거점유형태별 3
 
6.0%
성별 2
 
4.0%

Length

2024-04-21T05:25:44.014385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:25:44.216493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군구별 10
20.0%
직업별 8
16.0%
월평균소득별 8
16.0%
연령별 6
12.0%
가구원수별 5
10.0%
학력별 4
 
8.0%
주거형태별 4
 
8.0%
주거점유형태별 3
 
6.0%
성별 2
 
4.0%

특성별(2)
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2024-04-21T05:25:44.861075image/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%
2024-04-21T05:25:45.710863image/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%

육아부담 또는 가사부담(2021년)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.382
Minimum19.5
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.0 B
2024-04-21T05:25:45.939564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.5
5-th percentile26.78
Q130.15
median31.8
Q334.2
95-th percentile38.655
Maximum49
Range29.5
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation4.8620853
Coefficient of variation (CV)0.15014778
Kurtosis3.7270194
Mean32.382
Median Absolute Deviation (MAD)2.35
Skewness0.82070841
Sum1619.1
Variance23.639873
MonotonicityNot monotonic
2024-04-21T05:25:46.180938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
31.8 2
 
4.0%
34.2 2
 
4.0%
29.1 2
 
4.0%
28.5 2
 
4.0%
32.3 2
 
4.0%
31.1 2
 
4.0%
31.5 1
 
2.0%
32.7 1
 
2.0%
31.6 1
 
2.0%
31.2 1
 
2.0%
Other values (34) 34
68.0%
ValueCountFrequency (%)
19.5 1
2.0%
21.7 1
2.0%
26.6 1
2.0%
27.0 1
2.0%
27.9 1
2.0%
28.5 2
4.0%
29.1 2
4.0%
29.2 1
2.0%
29.4 1
2.0%
30.0 1
2.0%
ValueCountFrequency (%)
49.0 1
2.0%
46.7 1
2.0%
38.7 1
2.0%
38.6 1
2.0%
38.0 1
2.0%
37.0 1
2.0%
36.6 1
2.0%
36.4 1
2.0%
36.1 1
2.0%
35.0 1
2.0%

여성을 원하는 회사나 일자리가 적어서(2021년)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.846
Minimum9.7
Maximum36.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.0 B
2024-04-21T05:25:46.422245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.7
5-th percentile19.235
Q121.35
median22.8
Q324.1
95-th percentile27.325
Maximum36.4
Range26.7
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation3.5859823
Coefficient of variation (CV)0.15696325
Kurtosis6.7837354
Mean22.846
Median Absolute Deviation (MAD)1.4
Skewness-0.045173343
Sum1142.3
Variance12.859269
MonotonicityNot monotonic
2024-04-21T05:25:46.664772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
23.9 3
 
6.0%
22.4 2
 
4.0%
21.5 2
 
4.0%
20.6 2
 
4.0%
24.1 2
 
4.0%
22.2 2
 
4.0%
20.9 2
 
4.0%
25.3 2
 
4.0%
22.7 2
 
4.0%
24.0 2
 
4.0%
Other values (29) 29
58.0%
ValueCountFrequency (%)
9.7 1
2.0%
14.4 1
2.0%
19.1 1
2.0%
19.4 1
2.0%
19.5 1
2.0%
20.6 2
4.0%
20.7 1
2.0%
20.9 2
4.0%
21.1 1
2.0%
21.2 1
2.0%
ValueCountFrequency (%)
36.4 1
2.0%
28.5 1
2.0%
28.0 1
2.0%
26.5 1
2.0%
26.2 1
2.0%
25.5 1
2.0%
25.3 2
4.0%
25.0 1
2.0%
24.8 1
2.0%
24.4 1
2.0%

불평등한 근로조건(채용 임금 등)(2021년)
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.352
Minimum9.7
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.0 B
2024-04-21T05:25:46.899745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.7
5-th percentile13.91
Q116.65
median18.4
Q320
95-th percentile22.585
Maximum27
Range17.3
Interquartile range (IQR)3.35

Descriptive statistics

Standard deviation3.0779161
Coefficient of variation (CV)0.16771557
Kurtosis1.854842
Mean18.352
Median Absolute Deviation (MAD)1.75
Skewness-0.22165782
Sum917.6
Variance9.4735673
MonotonicityNot monotonic
2024-04-21T05:25:47.142591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
19.6 3
 
6.0%
16.5 2
 
4.0%
19.0 2
 
4.0%
20.6 2
 
4.0%
17.9 2
 
4.0%
19.3 2
 
4.0%
15.5 1
 
2.0%
20.8 1
 
2.0%
21.0 1
 
2.0%
16.8 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
9.7 1
2.0%
10.2 1
2.0%
13.1 1
2.0%
14.9 1
2.0%
15.2 1
2.0%
15.4 1
2.0%
15.5 1
2.0%
15.8 1
2.0%
15.9 1
2.0%
16.2 1
2.0%
ValueCountFrequency (%)
27.0 1
2.0%
24.7 1
2.0%
22.9 1
2.0%
22.2 1
2.0%
22.0 1
2.0%
21.4 1
2.0%
21.0 1
2.0%
20.8 1
2.0%
20.6 2
4.0%
20.5 1
2.0%

다양한 형태의 구인정보 부족(2021년)
Real number (ℝ)

Distinct33
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.684
Minimum6.6
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.0 B
2024-04-21T05:25:47.374760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.6
5-th percentile10.325
Q113.3
median14.7
Q315.4
95-th percentile19.37
Maximum28
Range21.4
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation3.2437922
Coefficient of variation (CV)0.22090658
Kurtosis5.5373299
Mean14.684
Median Absolute Deviation (MAD)1.1
Skewness1.2108243
Sum734.2
Variance10.522188
MonotonicityNot monotonic
2024-04-21T05:25:47.577547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15.4 4
 
8.0%
12.7 3
 
6.0%
13.8 3
 
6.0%
13.7 2
 
4.0%
14.1 2
 
4.0%
15.0 2
 
4.0%
15.1 2
 
4.0%
14.0 2
 
4.0%
15.3 2
 
4.0%
15.9 2
 
4.0%
Other values (23) 26
52.0%
ValueCountFrequency (%)
6.6 1
 
2.0%
8.6 1
 
2.0%
10.1 1
 
2.0%
10.6 1
 
2.0%
11.0 2
4.0%
12.5 1
 
2.0%
12.6 1
 
2.0%
12.7 3
6.0%
12.8 1
 
2.0%
13.2 1
 
2.0%
ValueCountFrequency (%)
28.0 1
2.0%
21.5 1
2.0%
20.0 1
2.0%
18.6 2
4.0%
17.9 1
2.0%
17.3 1
2.0%
17.0 1
2.0%
16.0 1
2.0%
15.9 2
4.0%
15.8 1
2.0%

여성 취업 지원 정책 미비(2021년)
Real number (ℝ)

Distinct34
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.09
Minimum4.8
Maximum13.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.0 B
2024-04-21T05:25:47.783085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.8
5-th percentile7.78
Q19.325
median10.2
Q311
95-th percentile12.565
Maximum13.5
Range8.7
Interquartile range (IQR)1.675

Descriptive statistics

Standard deviation1.6427019
Coefficient of variation (CV)0.16280494
Kurtosis2.3713302
Mean10.09
Median Absolute Deviation (MAD)0.8
Skewness-0.8526084
Sum504.5
Variance2.6984694
MonotonicityNot monotonic
2024-04-21T05:25:47.994898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10.6 3
 
6.0%
11.0 3
 
6.0%
9.3 3
 
6.0%
10.5 2
 
4.0%
10.1 2
 
4.0%
10.0 2
 
4.0%
9.4 2
 
4.0%
9.6 2
 
4.0%
11.6 2
 
4.0%
9.8 2
 
4.0%
Other values (24) 27
54.0%
ValueCountFrequency (%)
4.8 1
 
2.0%
5.3 1
 
2.0%
7.6 1
 
2.0%
8.0 2
4.0%
8.5 1
 
2.0%
8.6 1
 
2.0%
8.7 1
 
2.0%
9.1 1
 
2.0%
9.2 1
 
2.0%
9.3 3
6.0%
ValueCountFrequency (%)
13.5 1
 
2.0%
13.3 1
 
2.0%
12.7 1
 
2.0%
12.4 1
 
2.0%
11.9 1
 
2.0%
11.6 2
4.0%
11.5 1
 
2.0%
11.4 1
 
2.0%
11.3 2
4.0%
11.0 3
6.0%

기타(2021년)
Text

Distinct28
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2024-04-21T05:25:48.536356image/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

Unique16 ?
Unique (%)32.0%

Sample

1st row1.4
2nd row4.2
3rd row0.5
4th row1.1
5th row1.8
ValueCountFrequency (%)
1.2 5
 
10.0%
1.3 4
 
8.0%
1.7 4
 
8.0%
1.8 3
 
6.0%
1.9 3
 
6.0%
0.8 3
 
6.0%
1.5 2
 
4.0%
2 2
 
4.0%
1.6 2
 
4.0%
2.4 2
 
4.0%
Other values (18) 20
40.0%
2024-04-21T05:25:49.315829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 45
32.1%
1 30
21.4%
2 19
13.6%
3 7
 
5.0%
8 7
 
5.0%
0 7
 
5.0%
7 6
 
4.3%
4 6
 
4.3%
9 4
 
2.9%
6 4
 
2.9%
Other values (2) 5
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94
67.1%
Other Punctuation 45
32.1%
Dash Punctuation 1
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30
31.9%
2 19
20.2%
3 7
 
7.4%
8 7
 
7.4%
0 7
 
7.4%
7 6
 
6.4%
4 6
 
6.4%
9 4
 
4.3%
6 4
 
4.3%
5 4
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 45
32.1%
1 30
21.4%
2 19
13.6%
3 7
 
5.0%
8 7
 
5.0%
0 7
 
5.0%
7 6
 
4.3%
4 6
 
4.3%
9 4
 
2.9%
6 4
 
2.9%
Other values (2) 5
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 45
32.1%
1 30
21.4%
2 19
13.6%
3 7
 
5.0%
8 7
 
5.0%
0 7
 
5.0%
7 6
 
4.3%
4 6
 
4.3%
9 4
 
2.9%
6 4
 
2.9%
Other values (2) 5
 
3.6%

Interactions

2024-04-21T05:25:42.207086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:37.269111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:38.521166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:39.747661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:41.015739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:42.454028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:37.520832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:38.772646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:40.008289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:41.265693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:42.889334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:37.779778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:39.019825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:40.265340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:41.505478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:43.155689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:38.048106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:39.281996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:40.537137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:41.756089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:43.327071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:38.285818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:39.516091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:40.775007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T05:25:41.976604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T05:25:49.574751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특성별(1)특성별(2)육아부담 또는 가사부담(2021년)여성을 원하는 회사나 일자리가 적어서(2021년)불평등한 근로조건(채용 임금 등)(2021년)다양한 형태의 구인정보 부족(2021년)여성 취업 지원 정책 미비(2021년)기타(2021년)
특성별(1)1.0001.0000.2830.0000.0000.0000.0000.000
특성별(2)1.0001.0001.0001.0001.0001.0001.0001.000
육아부담 또는 가사부담(2021년)0.2831.0001.0000.6600.4970.5650.6080.549
여성을 원하는 회사나 일자리가 적어서(2021년)0.0001.0000.6601.0000.6590.5620.7370.866
불평등한 근로조건(채용 임금 등)(2021년)0.0001.0000.4970.6591.0000.6400.2490.789
다양한 형태의 구인정보 부족(2021년)0.0001.0000.5650.5620.6401.0000.6490.912
여성 취업 지원 정책 미비(2021년)0.0001.0000.6080.7370.2490.6491.0000.779
기타(2021년)0.0001.0000.5490.8660.7890.9120.7791.000
2024-04-21T05:25:49.887882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
육아부담 또는 가사부담(2021년)여성을 원하는 회사나 일자리가 적어서(2021년)불평등한 근로조건(채용 임금 등)(2021년)다양한 형태의 구인정보 부족(2021년)여성 취업 지원 정책 미비(2021년)특성별(1)
육아부담 또는 가사부담(2021년)1.000-0.661-0.439-0.358-0.1210.139
여성을 원하는 회사나 일자리가 적어서(2021년)-0.6611.0000.1170.114-0.1890.000
불평등한 근로조건(채용 임금 등)(2021년)-0.4390.1171.000-0.190-0.1870.000
다양한 형태의 구인정보 부족(2021년)-0.3580.114-0.1901.0000.0170.000
여성 취업 지원 정책 미비(2021년)-0.121-0.189-0.1870.0171.0000.000
특성별(1)0.1390.0000.0000.0000.0001.000

Missing values

2024-04-21T05:25:43.558854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T05:25:43.798719image/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

특성별(1)특성별(2)육아부담 또는 가사부담(2021년)여성을 원하는 회사나 일자리가 적어서(2021년)불평등한 근로조건(채용 임금 등)(2021년)다양한 형태의 구인정보 부족(2021년)여성 취업 지원 정책 미비(2021년)기타(2021년)
0군구별중구46.79.715.213.713.31.4
1군구별동구34.819.427.06.68.04.2
2군구별미추홀구19.528.019.220.012.70.5
3군구별연수구31.328.518.111.010.01.1
4군구별남동구34.320.713.118.611.51.8
5군구별부평구36.622.720.68.69.41.9
6군구별계양구29.136.415.812.85.30.6
7군구별서구32.814.424.715.49.53.2
8군구별강화군21.726.29.728.013.50.8
9군구별옹진군49.019.110.210.111.30.4
특성별(1)특성별(2)육아부담 또는 가사부담(2021년)여성을 원하는 회사나 일자리가 적어서(2021년)불평등한 근로조건(채용 임금 등)(2021년)다양한 형태의 구인정보 부족(2021년)여성 취업 지원 정책 미비(2021년)기타(2021년)
40주거형태별연립/다세대주택27.024.122.015.99.71.3
41주거형태별기타34.123.018.511.011.02.4
42주거점유형태별자가32.323.117.714.710.51.7
43주거점유형태별전세30.524.419.315.98.61.3
44주거점유형태별월세 및 기타30.921.222.212.710.72.2
45가구원수별1인30.021.322.912.510.62.8
46가구원수별2인28.525.019.514.810.41.7
47가구원수별3인29.424.319.615.010.11.6
48가구원수별4인36.122.215.414.710.11.5
49가구원수별5인 이상37.019.517.215.49.81.2