Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells81
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory126.4 B

Variable types

Numeric1
Categorical9
Boolean1
Text3
DateTime1

Dataset

Description샘플 데이터
Author경기도일자리재단
URLhttps://www.bigdata-region.kr/#/dataset/a8ce5695-56b2-4edb-a213-fe7054757a39

Alerts

기업지원정책명 has constant value ""Constant
정책비교함상담등록번호 is highly overall correlated with 성별코드 and 3 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 정책비교함정책번호 and 8 other fieldsHigh correlation
특화대상정책명 is highly overall correlated with 정책비교함정책번호 and 9 other fieldsHigh correlation
거주지원정책명 is highly overall correlated with 정책비교함정책번호 and 5 other fieldsHigh correlation
성별코드 is highly overall correlated with 정책비교함정책번호 and 6 other fieldsHigh correlation
생애주기정책명 is highly overall correlated with 정책비교함정책번호 and 4 other fieldsHigh correlation
특화대상해당무여부 is highly overall correlated with 정책비교함정책번호 and 5 other fieldsHigh correlation
취업상태정책명 is highly overall correlated with 정책비교함정책번호 and 5 other fieldsHigh correlation
구직지원정책명 is highly overall correlated with 정책비교함정책번호 and 9 other fieldsHigh correlation
생활지원정책명 is highly overall correlated with 정책비교함정책번호 and 9 other fieldsHigh correlation
정책비교함정책번호 is highly overall correlated with 성별코드 and 8 other fieldsHigh correlation
정책비교함상담등록번호 is highly imbalanced (73.5%)Imbalance
재직지원정책명 has 26 (86.7%) missing valuesMissing
기업지원정책명 has 28 (93.3%) missing valuesMissing
창업지원정책명 has 27 (90.0%) missing valuesMissing
정책비교함정책번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:12:08.803854
Analysis finished2023-12-10 14:12:11.892421
Duration3.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책비교함정책번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:12:12.005852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-10T23:12:12.241053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

정책비교함상담등록번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
28 
12127
 
1
12128
 
1

Length

Max length5
Median length1
Mean length1.2666667
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
93.3%
12127 1
 
3.3%
12128 1
 
3.3%

Length

2023-12-10T23:12:12.480244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:12.702553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
93.3%
12127 1
 
3.3%
12128 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
17 
M
10 
F
3 

Length

Max length4
Median length4
Mean length2.7
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 17
56.7%
M 10
33.3%
F 3
 
10.0%

Length

2023-12-10T23:12:12.901551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:13.117789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
56.7%
m 10
33.3%
f 3
 
10.0%

생애주기정책명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
중년(35~49세)
14 
청년(18~34세)
11 
<NA>
5 

Length

Max length10
Median length10
Mean length9
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청년(18~34세)
2nd row청년(18~34세)
3rd row청년(18~34세)
4th row중년(35~49세)
5th row중년(35~49세)

Common Values

ValueCountFrequency (%)
중년(35~49세) 14
46.7%
청년(18~34세) 11
36.7%
<NA> 5
 
16.7%

Length

2023-12-10T23:12:13.386888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:13.693704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중년(35~49세 14
46.7%
청년(18~34세 11
36.7%
na 5
 
16.7%

특화대상정책명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
24 
경력단절여성
6 

Length

Max length6
Median length4
Mean length4.4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 24
80.0%
경력단절여성 6
 
20.0%

Length

2023-12-10T23:12:13.975744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:14.261330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
80.0%
경력단절여성 6
 
20.0%

특화대상해당무여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
True
25 
False
5 
ValueCountFrequency (%)
True 25
83.3%
False 5
 
16.7%
2023-12-10T23:12:14.419183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

취업상태정책명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
해당없음
17 
구직자
5 
<NA>
4 
예비창업자
4 

Length

Max length5
Median length4
Mean length3.9666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해당없음 17
56.7%
구직자 5
 
16.7%
<NA> 4
 
13.3%
예비창업자 4
 
13.3%

Length

2023-12-10T23:12:14.662962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:14.980601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당없음 17
56.7%
구직자 5
 
16.7%
na 4
 
13.3%
예비창업자 4
 
13.3%

구직지원정책명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
16 
취업지원금
14 

Length

Max length5
Median length4
Mean length4.4666667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취업지원금
2nd row취업지원금
3rd row취업지원금
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 16
53.3%
취업지원금 14
46.7%

Length

2023-12-10T23:12:15.267992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:15.482547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
53.3%
취업지원금 14
46.7%

생활지원정책명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
17 
자산형성
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자산형성
2nd row자산형성
3rd row자산형성
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 17
56.7%
자산형성 13
43.3%

Length

2023-12-10T23:12:15.670179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:15.838788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
56.7%
자산형성 13
43.3%

거주지원정책명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
18 
주택지원
8 
기숙사; 생활관
4 

Length

Max length8
Median length4
Mean length4.5333333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 18
60.0%
주택지원 8
26.7%
기숙사; 생활관 4
 
13.3%

Length

2023-12-10T23:12:16.060419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:16.244658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
52.9%
주택지원 8
23.5%
기숙사 4
 
11.8%
생활관 4
 
11.8%

재직지원정책명
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing26
Missing (%)86.7%
Memory size372.0 B
2023-12-10T23:12:16.453322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10
Min length9

Characters and Unicode

Total characters40
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중소기업 재직자 지원
2nd row중소기업 재직자 지원
3rd row워킹맘/대디 지원
4th row워킹맘/대디 지원
ValueCountFrequency (%)
지원 4
40.0%
중소기업 2
20.0%
재직자 2
20.0%
워킹맘/대디 2
20.0%
2023-12-10T23:12:16.930605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
15.0%
지 4
 
10.0%
원 4
 
10.0%
중 2
 
5.0%
소 2
 
5.0%
기 2
 
5.0%
업 2
 
5.0%
재 2
 
5.0%
직 2
 
5.0%
자 2
 
5.0%
Other values (6) 12
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32
80.0%
Space Separator 6
 
15.0%
Other Punctuation 2
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 4
12.5%
원 4
12.5%
중 2
 
6.2%
소 2
 
6.2%
기 2
 
6.2%
업 2
 
6.2%
재 2
 
6.2%
직 2
 
6.2%
자 2
 
6.2%
워 2
 
6.2%
Other values (4) 8
25.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32
80.0%
Common 8
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 4
12.5%
원 4
12.5%
중 2
 
6.2%
소 2
 
6.2%
기 2
 
6.2%
업 2
 
6.2%
재 2
 
6.2%
직 2
 
6.2%
자 2
 
6.2%
워 2
 
6.2%
Other values (4) 8
25.0%
Common
ValueCountFrequency (%)
6
75.0%
/ 2
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32
80.0%
ASCII 8
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6
75.0%
/ 2
 
25.0%
Hangul
ValueCountFrequency (%)
지 4
12.5%
원 4
12.5%
중 2
 
6.2%
소 2
 
6.2%
기 2
 
6.2%
업 2
 
6.2%
재 2
 
6.2%
직 2
 
6.2%
자 2
 
6.2%
워 2
 
6.2%
Other values (4) 8
25.0%

기업지원정책명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:12:17.188684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고용장려금
2nd row고용장려금
ValueCountFrequency (%)
고용장려금 2
100.0%
2023-12-10T23:12:17.594878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 2
20.0%
용 2
20.0%
장 2
20.0%
려 2
20.0%
금 2
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 2
20.0%
용 2
20.0%
장 2
20.0%
려 2
20.0%
금 2
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 2
20.0%
용 2
20.0%
장 2
20.0%
려 2
20.0%
금 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 2
20.0%
용 2
20.0%
장 2
20.0%
려 2
20.0%
금 2
20.0%

창업지원정책명
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing27
Missing (%)90.0%
Memory size372.0 B
2023-12-10T23:12:17.804645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length6.6666667
Min length4

Characters and Unicode

Total characters20
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row정책자금
2nd row시설ㆍ공간 제공
3rd row시설ㆍ공간 제공
ValueCountFrequency (%)
시설ㆍ공간 2
40.0%
제공 2
40.0%
정책자금 1
20.0%
2023-12-10T23:12:18.272514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 4
20.0%
시 2
10.0%
설 2
10.0%
ㆍ 2
10.0%
간 2
10.0%
2
10.0%
제 2
10.0%
정 1
 
5.0%
책 1
 
5.0%
자 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
90.0%
Space Separator 2
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 4
22.2%
시 2
11.1%
설 2
11.1%
ㆍ 2
11.1%
간 2
11.1%
제 2
11.1%
정 1
 
5.6%
책 1
 
5.6%
자 1
 
5.6%
금 1
 
5.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
90.0%
Common 2
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 4
22.2%
시 2
11.1%
설 2
11.1%
ㆍ 2
11.1%
간 2
11.1%
제 2
11.1%
정 1
 
5.6%
책 1
 
5.6%
자 1
 
5.6%
금 1
 
5.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16
80.0%
Compat Jamo 2
 
10.0%
ASCII 2
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 4
25.0%
시 2
12.5%
설 2
12.5%
간 2
12.5%
제 2
12.5%
정 1
 
6.2%
책 1
 
6.2%
자 1
 
6.2%
금 1
 
6.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%
ASCII
ValueCountFrequency (%)
2
100.0%

등록일시
Date

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2019-04-11 17:29:00
Maximum2019-05-28 13:44:00
2023-12-10T23:12:18.466889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:18.680865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2019-04-15
10 
2019-04-12
7 
2019-05-15
7 
2019-04-11
3 
2019-05-28
3 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-04-11
2nd row2019-04-11
3rd row2019-04-11
4th row2019-04-12
5th row2019-04-12

Common Values

ValueCountFrequency (%)
2019-04-15 10
33.3%
2019-04-12 7
23.3%
2019-05-15 7
23.3%
2019-04-11 3
 
10.0%
2019-05-28 3
 
10.0%

Length

2023-12-10T23:12:18.890872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:19.080624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-04-15 10
33.3%
2019-04-12 7
23.3%
2019-05-15 7
23.3%
2019-04-11 3
 
10.0%
2019-05-28 3
 
10.0%

Interactions

2023-12-10T23:12:10.338032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:12:19.232761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정책비교함정책번호정책비교함상담등록번호성별코드생애주기정책명특화대상해당무여부취업상태정책명거주지원정책명재직지원정책명창업지원정책명등록일시데이터기준일자
정책비교함정책번호1.0000.1931.0000.9520.8990.9651.0000.0000.0000.9660.998
정책비교함상담등록번호0.1931.000NaN0.1100.0000.0000.000NaNNaN0.8750.000
성별코드1.000NaN1.0000.0000.6370.333NaNNaNNaN1.0001.000
생애주기정책명0.9520.1100.0001.0000.0000.2640.000NaNNaN1.0000.596
특화대상해당무여부0.8990.0000.6370.0001.0001.0000.000NaNNaN1.0000.648
취업상태정책명0.9650.0000.3330.2641.0001.0000.000NaNNaN1.0000.847
거주지원정책명1.0000.000NaN0.0000.0000.0001.000NaNNaN1.0001.000
재직지원정책명0.000NaNNaNNaNNaNNaNNaN1.000NaN1.000NaN
창업지원정책명0.000NaNNaNNaNNaNNaNNaNNaN1.0001.000NaN
등록일시0.9660.8751.0001.0001.0001.0001.0001.0001.0001.0001.000
데이터기준일자0.9980.0001.0000.5960.6480.8471.000NaNNaN1.0001.000
2023-12-10T23:12:19.511236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정책비교함상담등록번호데이터기준일자특화대상정책명거주지원정책명성별코드생애주기정책명특화대상해당무여부취업상태정책명구직지원정책명생활지원정책명
정책비교함상담등록번호1.0000.0001.0000.0001.0000.1690.0000.0001.0001.000
데이터기준일자0.0001.0001.0000.8940.9050.6680.7330.8441.0001.000
특화대상정책명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
거주지원정책명0.0000.8941.0001.0001.0000.0000.0000.0001.0001.000
성별코드1.0000.9051.0001.0001.0000.0000.4330.4821.0001.000
생애주기정책명0.1690.6681.0000.0000.0001.0000.0000.4131.0001.000
특화대상해당무여부0.0000.7331.0000.0000.4330.0001.0000.9791.0001.000
취업상태정책명0.0000.8441.0000.0000.4820.4130.9791.0001.0001.000
구직지원정책명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
생활지원정책명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:12:19.737138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정책비교함정책번호정책비교함상담등록번호성별코드생애주기정책명특화대상정책명특화대상해당무여부취업상태정책명구직지원정책명생활지원정책명거주지원정책명데이터기준일자
정책비교함정책번호1.0000.0000.7390.6531.0000.6190.6631.0001.0000.8370.840
정책비교함상담등록번호0.0001.0001.0000.1691.0000.0000.0001.0001.0000.0000.000
성별코드0.7391.0001.0000.0001.0000.4330.4821.0001.0001.0000.905
생애주기정책명0.6530.1690.0001.0001.0000.0000.4131.0001.0000.0000.668
특화대상정책명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
특화대상해당무여부0.6190.0000.4330.0001.0001.0000.9791.0001.0000.0000.733
취업상태정책명0.6630.0000.4820.4131.0000.9791.0001.0001.0000.0000.844
구직지원정책명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
생활지원정책명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
거주지원정책명0.8370.0001.0000.0001.0000.0000.0001.0001.0001.0000.894
데이터기준일자0.8400.0000.9050.6681.0000.7330.8441.0001.0000.8941.000

Missing values

2023-12-10T23:12:10.577538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:12:11.007825image/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.
2023-12-10T23:12:11.640204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정책비교함정책번호정책비교함상담등록번호성별코드생애주기정책명특화대상정책명특화대상해당무여부취업상태정책명구직지원정책명생활지원정책명거주지원정책명재직지원정책명기업지원정책명창업지원정책명등록일시데이터기준일자
010<NA>청년(18~34세)<NA>Y해당없음취업지원금자산형성주택지원<NA><NA><NA>2019-04-11 17:292019-04-11
120<NA>청년(18~34세)<NA>Y해당없음취업지원금자산형성주택지원<NA><NA><NA>2019-04-11 17:292019-04-11
230<NA>청년(18~34세)<NA>Y해당없음취업지원금자산형성주택지원<NA><NA><NA>2019-04-11 17:292019-04-11
340<NA>중년(35~49세)<NA>Y해당없음<NA><NA>주택지원<NA><NA><NA>2019-04-12 09:562019-04-12
450<NA>중년(35~49세)<NA>Y해당없음<NA><NA>주택지원<NA><NA><NA>2019-04-12 09:562019-04-12
560<NA><NA><NA>Y해당없음취업지원금<NA><NA><NA><NA><NA>2019-04-12 11:062019-04-12
670<NA><NA><NA>Y해당없음취업지원금<NA><NA><NA><NA><NA>2019-04-12 11:092019-04-12
780<NA><NA><NA>Y해당없음취업지원금<NA><NA><NA><NA><NA>2019-04-12 11:102019-04-12
890M청년(18~34세)경력단절여성N구직자<NA>자산형성<NA><NA><NA><NA>2019-04-12 15:172019-04-12
9100M청년(18~34세)경력단절여성N구직자<NA>자산형성<NA><NA><NA><NA>2019-04-12 15:172019-04-12
정책비교함정책번호정책비교함상담등록번호성별코드생애주기정책명특화대상정책명특화대상해당무여부취업상태정책명구직지원정책명생활지원정책명거주지원정책명재직지원정책명기업지원정책명창업지원정책명등록일시데이터기준일자
20210M중년(35~49세)<NA>Y예비창업자<NA><NA><NA><NA><NA><NA>2019-05-15 13:342019-05-15
21220M중년(35~49세)<NA>Y예비창업자<NA><NA><NA><NA><NA><NA>2019-05-15 13:342019-05-15
22230M중년(35~49세)<NA>Y예비창업자<NA><NA><NA><NA><NA><NA>2019-05-15 13:342019-05-15
23240M중년(35~49세)<NA>Y예비창업자<NA><NA><NA><NA><NA><NA>2019-05-15 13:342019-05-15
24250M중년(35~49세)<NA>Y<NA><NA><NA><NA><NA><NA><NA>2019-05-15 13:372019-05-15
25260M중년(35~49세)<NA>Y<NA><NA><NA><NA><NA><NA><NA>2019-05-15 13:372019-05-15
26270M중년(35~49세)<NA>Y<NA><NA><NA><NA><NA><NA><NA>2019-05-15 15:372019-05-15
27280F중년(35~49세)경력단절여성N구직자취업지원금자산형성주택지원<NA><NA><NA>2019-05-28 13:422019-05-28
28290F중년(35~49세)경력단절여성N구직자취업지원금자산형성주택지원<NA><NA><NA>2019-05-28 13:442019-05-28
29300F중년(35~49세)경력단절여성N구직자취업지원금자산형성주택지원<NA><NA><NA>2019-05-28 13:442019-05-28