Overview

Dataset statistics

Number of variables13
Number of observations217
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 KiB
Average record size in memory116.6 B

Variable types

Numeric10
Categorical3

Dataset

Description장애인출산비용지원 산모기준 집계현황
Author경기복지재단(경기도장애인복지종합지원센터)
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=FJ0VZC7CPEPD77QTO7EO26229929&infSeq=1

Alerts

6급장애인수(명) has constant value ""Constant
기타(명) has constant value ""Constant
기준년도 is highly overall correlated with 전체지원인수(명) and 7 other fieldsHigh correlation
전체지원인수(명) is highly overall correlated with 기준년도 and 2 other fieldsHigh correlation
1급장애인수(명) is highly overall correlated with 기준년도 and 3 other fieldsHigh correlation
2급장애인수(명) is highly overall correlated with 기준년도 and 4 other fieldsHigh correlation
3급장애인수(명) is highly overall correlated with 기준년도 and 3 other fieldsHigh correlation
4급장애인수(명) is highly overall correlated with 기준년도 and 4 other fieldsHigh correlation
5급장애인수(명) is highly overall correlated with 기준년도 and 5 other fieldsHigh correlation
중증장애인수(장애의정도가심한장애인)(명) is highly overall correlated with 기준년도 and 2 other fieldsHigh correlation
경증장애인수(장애의정도가심하지않은장애인)(명) is highly overall correlated with 기준년도 and 3 other fieldsHigh correlation
총계(명) has 13 (6.0%) zerosZeros
전체지원인수(명) has 94 (43.3%) zerosZeros
1급장애인수(명) has 160 (73.7%) zerosZeros
2급장애인수(명) has 152 (70.0%) zerosZeros
3급장애인수(명) has 171 (78.8%) zerosZeros
4급장애인수(명) has 155 (71.4%) zerosZeros
5급장애인수(명) has 144 (66.4%) zerosZeros
중증장애인수(장애의정도가심한장애인)(명) has 143 (65.9%) zerosZeros
경증장애인수(장애의정도가심하지않은장애인)(명) has 135 (62.2%) zerosZeros

Reproduction

Analysis started2023-12-10 21:06:53.374201
Analysis finished2023-12-10 21:07:02.397548
Duration9.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:02.451936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0046243
Coefficient of variation (CV)0.00099287978
Kurtosis-1.2511193
Mean2019
Median Absolute Deviation (MAD)2
Skewness0
Sum438123
Variance4.0185185
MonotonicityDecreasing
2023-12-11T06:07:02.613631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2022 31
14.3%
2021 31
14.3%
2020 31
14.3%
2019 31
14.3%
2018 31
14.3%
2017 31
14.3%
2016 31
14.3%
ValueCountFrequency (%)
2016 31
14.3%
2017 31
14.3%
2018 31
14.3%
2019 31
14.3%
2020 31
14.3%
2021 31
14.3%
2022 31
14.3%
ValueCountFrequency (%)
2022 31
14.3%
2021 31
14.3%
2020 31
14.3%
2019 31
14.3%
2018 31
14.3%
2017 31
14.3%
2016 31
14.3%

시군명
Categorical

Distinct31
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
가평군
 
7
고양시
 
7
과천시
 
7
광명시
 
7
광주시
 
7
Other values (26)
182 

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시

Common Values

ValueCountFrequency (%)
가평군 7
 
3.2%
고양시 7
 
3.2%
과천시 7
 
3.2%
광명시 7
 
3.2%
광주시 7
 
3.2%
구리시 7
 
3.2%
군포시 7
 
3.2%
김포시 7
 
3.2%
남양주시 7
 
3.2%
동두천시 7
 
3.2%
Other values (21) 147
67.7%

Length

2023-12-11T06:07:02.721971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가평군 7
 
3.2%
안양시 7
 
3.2%
하남시 7
 
3.2%
포천시 7
 
3.2%
평택시 7
 
3.2%
파주시 7
 
3.2%
이천시 7
 
3.2%
의정부시 7
 
3.2%
의왕시 7
 
3.2%
용인시 7
 
3.2%
Other values (21) 147
67.7%

총계(명)
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.156682
Minimum0
Maximum52
Zeros13
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:02.828025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q315
95-th percentile24.4
Maximum52
Range52
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.5007279
Coefficient of variation (CV)0.83695914
Kurtosis3.1183142
Mean10.156682
Median Absolute Deviation (MAD)5
Skewness1.4147063
Sum2204
Variance72.262374
MonotonicityNot monotonic
2023-12-11T06:07:02.979229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 23
 
10.6%
4 15
 
6.9%
6 14
 
6.5%
0 13
 
6.0%
7 13
 
6.0%
10 12
 
5.5%
14 11
 
5.1%
3 11
 
5.1%
16 10
 
4.6%
5 10
 
4.6%
Other values (23) 85
39.2%
ValueCountFrequency (%)
0 13
6.0%
1 6
 
2.8%
2 23
10.6%
3 11
5.1%
4 15
6.9%
5 10
4.6%
6 14
6.5%
7 13
6.0%
8 6
 
2.8%
9 5
 
2.3%
ValueCountFrequency (%)
52 1
 
0.5%
42 1
 
0.5%
40 1
 
0.5%
32 2
 
0.9%
30 2
 
0.9%
28 1
 
0.5%
27 1
 
0.5%
26 2
 
0.9%
24 6
2.8%
23 1
 
0.5%

전체지원인수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6682028
Minimum0
Maximum27
Zeros94
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:03.090726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile16
Maximum27
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.6756916
Coefficient of variation (CV)1.5472677
Kurtosis2.8476264
Mean3.6682028
Median Absolute Deviation (MAD)1
Skewness1.8247234
Sum796
Variance32.213475
MonotonicityNot monotonic
2023-12-11T06:07:03.196098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 94
43.3%
1 41
18.9%
4 13
 
6.0%
2 9
 
4.1%
7 7
 
3.2%
12 6
 
2.8%
5 5
 
2.3%
8 5
 
2.3%
16 4
 
1.8%
10 4
 
1.8%
Other values (14) 29
 
13.4%
ValueCountFrequency (%)
0 94
43.3%
1 41
18.9%
2 9
 
4.1%
3 2
 
0.9%
4 13
 
6.0%
5 5
 
2.3%
6 4
 
1.8%
7 7
 
3.2%
8 5
 
2.3%
9 2
 
0.9%
ValueCountFrequency (%)
27 1
 
0.5%
26 1
 
0.5%
22 1
 
0.5%
21 1
 
0.5%
20 1
 
0.5%
19 3
1.4%
18 1
 
0.5%
16 4
1.8%
15 2
0.9%
14 4
1.8%

1급장애인수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55760369
Minimum0
Maximum5
Zeros160
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:03.316928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1087404
Coefficient of variation (CV)1.9884023
Kurtosis4.0219974
Mean0.55760369
Median Absolute Deviation (MAD)0
Skewness2.1365016
Sum121
Variance1.2293053
MonotonicityNot monotonic
2023-12-11T06:07:03.424530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 160
73.7%
1 22
 
10.1%
2 16
 
7.4%
3 12
 
5.5%
4 4
 
1.8%
5 3
 
1.4%
ValueCountFrequency (%)
0 160
73.7%
1 22
 
10.1%
2 16
 
7.4%
3 12
 
5.5%
4 4
 
1.8%
5 3
 
1.4%
ValueCountFrequency (%)
5 3
 
1.4%
4 4
 
1.8%
3 12
 
5.5%
2 16
 
7.4%
1 22
 
10.1%
0 160
73.7%

2급장애인수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75115207
Minimum0
Maximum9
Zeros152
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:03.516797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4696973
Coefficient of variation (CV)1.9565909
Kurtosis8.4428951
Mean0.75115207
Median Absolute Deviation (MAD)0
Skewness2.6195046
Sum163
Variance2.1600102
MonotonicityNot monotonic
2023-12-11T06:07:03.615053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 152
70.0%
1 21
 
9.7%
3 20
 
9.2%
2 15
 
6.9%
4 3
 
1.4%
5 2
 
0.9%
7 1
 
0.5%
9 1
 
0.5%
8 1
 
0.5%
6 1
 
0.5%
ValueCountFrequency (%)
0 152
70.0%
1 21
 
9.7%
2 15
 
6.9%
3 20
 
9.2%
4 3
 
1.4%
5 2
 
0.9%
6 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
9 1
 
0.5%
8 1
 
0.5%
7 1
 
0.5%
6 1
 
0.5%
5 2
 
0.9%
4 3
 
1.4%
3 20
 
9.2%
2 15
 
6.9%
1 21
 
9.7%
0 152
70.0%

3급장애인수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51612903
Minimum0
Maximum6
Zeros171
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:03.703858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2251106
Coefficient of variation (CV)2.3736518
Kurtosis7.6955194
Mean0.51612903
Median Absolute Deviation (MAD)0
Skewness2.775484
Sum112
Variance1.5008961
MonotonicityNot monotonic
2023-12-11T06:07:03.797355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 171
78.8%
1 18
 
8.3%
3 11
 
5.1%
2 8
 
3.7%
4 4
 
1.8%
6 4
 
1.8%
5 1
 
0.5%
ValueCountFrequency (%)
0 171
78.8%
1 18
 
8.3%
2 8
 
3.7%
3 11
 
5.1%
4 4
 
1.8%
5 1
 
0.5%
6 4
 
1.8%
ValueCountFrequency (%)
6 4
 
1.8%
5 1
 
0.5%
4 4
 
1.8%
3 11
 
5.1%
2 8
 
3.7%
1 18
 
8.3%
0 171
78.8%

4급장애인수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7281106
Minimum0
Maximum8
Zeros155
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:03.892960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4734668
Coefficient of variation (CV)2.0236854
Kurtosis5.5767093
Mean0.7281106
Median Absolute Deviation (MAD)0
Skewness2.3894442
Sum158
Variance2.1711043
MonotonicityNot monotonic
2023-12-11T06:07:03.987750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 155
71.4%
1 23
 
10.6%
2 15
 
6.9%
3 8
 
3.7%
4 6
 
2.8%
5 5
 
2.3%
6 4
 
1.8%
8 1
 
0.5%
ValueCountFrequency (%)
0 155
71.4%
1 23
 
10.6%
2 15
 
6.9%
3 8
 
3.7%
4 6
 
2.8%
5 5
 
2.3%
6 4
 
1.8%
8 1
 
0.5%
ValueCountFrequency (%)
8 1
 
0.5%
6 4
 
1.8%
5 5
 
2.3%
4 6
 
2.8%
3 8
 
3.7%
2 15
 
6.9%
1 23
 
10.6%
0 155
71.4%

5급장애인수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1889401
Minimum0
Maximum11
Zeros144
Zeros (%)66.4%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:04.104855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2103015
Coefficient of variation (CV)1.859052
Kurtosis3.7800173
Mean1.1889401
Median Absolute Deviation (MAD)0
Skewness2.0690349
Sum258
Variance4.8854327
MonotonicityNot monotonic
2023-12-11T06:07:04.208928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 144
66.4%
1 22
 
10.1%
2 12
 
5.5%
4 10
 
4.6%
6 7
 
3.2%
5 7
 
3.2%
7 6
 
2.8%
3 5
 
2.3%
11 1
 
0.5%
9 1
 
0.5%
Other values (2) 2
 
0.9%
ValueCountFrequency (%)
0 144
66.4%
1 22
 
10.1%
2 12
 
5.5%
3 5
 
2.3%
4 10
 
4.6%
5 7
 
3.2%
6 7
 
3.2%
7 6
 
2.8%
8 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
11 1
 
0.5%
10 1
 
0.5%
9 1
 
0.5%
8 1
 
0.5%
7 6
2.8%
6 7
3.2%
5 7
3.2%
4 10
4.6%
3 5
2.3%
2 12
5.5%

6급장애인수(명)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
217 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 217
100.0%

Length

2023-12-11T06:07:04.320594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:07:04.403847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 217
100.0%

중증장애인수(장애의정도가심한장애인)(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2857143
Minimum0
Maximum13
Zeros143
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:04.492752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.371011
Coefficient of variation (CV)1.8441197
Kurtosis4.7255692
Mean1.2857143
Median Absolute Deviation (MAD)0
Skewness2.1728361
Sum279
Variance5.6216931
MonotonicityNot monotonic
2023-12-11T06:07:04.603733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 143
65.9%
1 17
 
7.8%
2 15
 
6.9%
4 12
 
5.5%
3 7
 
3.2%
6 7
 
3.2%
7 5
 
2.3%
5 4
 
1.8%
8 3
 
1.4%
9 2
 
0.9%
Other values (2) 2
 
0.9%
ValueCountFrequency (%)
0 143
65.9%
1 17
 
7.8%
2 15
 
6.9%
3 7
 
3.2%
4 12
 
5.5%
5 4
 
1.8%
6 7
 
3.2%
7 5
 
2.3%
8 3
 
1.4%
9 2
 
0.9%
ValueCountFrequency (%)
13 1
 
0.5%
11 1
 
0.5%
9 2
 
0.9%
8 3
 
1.4%
7 5
 
2.3%
6 7
3.2%
5 4
 
1.8%
4 12
5.5%
3 7
3.2%
2 15
6.9%
Distinct17
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1751152
Minimum0
Maximum16
Zeros135
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T06:07:04.716487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.7879839
Coefficient of variation (CV)1.7415096
Kurtosis2.7609929
Mean2.1751152
Median Absolute Deviation (MAD)0
Skewness1.8898282
Sum472
Variance14.348822
MonotonicityNot monotonic
2023-12-11T06:07:04.820994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 135
62.2%
1 15
 
6.9%
5 10
 
4.6%
3 10
 
4.6%
4 8
 
3.7%
6 7
 
3.2%
2 5
 
2.3%
12 5
 
2.3%
8 4
 
1.8%
10 3
 
1.4%
Other values (7) 15
 
6.9%
ValueCountFrequency (%)
0 135
62.2%
1 15
 
6.9%
2 5
 
2.3%
3 10
 
4.6%
4 8
 
3.7%
5 10
 
4.6%
6 7
 
3.2%
7 3
 
1.4%
8 4
 
1.8%
9 3
 
1.4%
ValueCountFrequency (%)
16 1
 
0.5%
15 2
 
0.9%
14 3
1.4%
13 1
 
0.5%
12 5
2.3%
11 2
 
0.9%
10 3
1.4%
9 3
1.4%
8 4
1.8%
7 3
1.4%

기타(명)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
217 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 217
100.0%

Length

2023-12-11T06:07:04.938429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:07:05.037434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 217
100.0%

Interactions

2023-12-11T06:07:01.152941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:53.702421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.439736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.163321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.930744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.839052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.617565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.458220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.288400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.116087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.278306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:53.786466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.518153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.233040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.001618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.911722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.706466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.539644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.375555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.199590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.376633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:53.866868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.599851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.298038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.065013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.976809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.792549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.622097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.454301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.275350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.481886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:53.936155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.679200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.360369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.348751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.053157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.892041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.708108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.537741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.359164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.576869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.003893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.747328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.442670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.408838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.122028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.988139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.786739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.637980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.438230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.678034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.068393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.812105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.529642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.473919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.186627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.059164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.861182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.712883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.518238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.783201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.142651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.879241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.615496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.540008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.267097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.134743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.944638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.791601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.599896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.873227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.217469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.953061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.691742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.613312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.357724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.222440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.031839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.877034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.680276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:01.956783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.289055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.017551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.765407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.682583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.436648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.294846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.113752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.947031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.761014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:02.044313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:54.359595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.086558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:55.841541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:56.756963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:57.525400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:58.371242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:06:59.195177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.026578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:07:00.841215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:07:05.110893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도시군명총계(명)전체지원인수(명)1급장애인수(명)2급장애인수(명)3급장애인수(명)4급장애인수(명)5급장애인수(명)중증장애인수(장애의정도가심한장애인)(명)경증장애인수(장애의정도가심하지않은장애인)(명)
기준년도1.0000.0000.0000.5660.4660.5160.4620.4880.4660.5340.531
시군명0.0001.0000.7370.4620.0000.1640.2900.3740.0000.2530.571
총계(명)0.0000.7371.0000.8140.2400.5450.4100.3960.5710.7070.815
전체지원인수(명)0.5660.4620.8141.0000.0000.0000.0000.0000.0000.8090.921
1급장애인수(명)0.4660.0000.2400.0001.0000.5950.4010.5780.6120.0000.000
2급장애인수(명)0.5160.1640.5450.0000.5951.0000.6240.7820.9410.0000.000
3급장애인수(명)0.4620.2900.4100.0000.4010.6241.0000.7710.6520.0000.000
4급장애인수(명)0.4880.3740.3960.0000.5780.7820.7711.0000.7900.0000.000
5급장애인수(명)0.4660.0000.5710.0000.6120.9410.6520.7901.0000.0000.000
중증장애인수(장애의정도가심한장애인)(명)0.5340.2530.7070.8090.0000.0000.0000.0000.0001.0000.702
경증장애인수(장애의정도가심하지않은장애인)(명)0.5310.5710.8150.9210.0000.0000.0000.0000.0000.7021.000
2023-12-11T06:07:05.268397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도총계(명)전체지원인수(명)1급장애인수(명)2급장애인수(명)3급장애인수(명)4급장애인수(명)5급장애인수(명)중증장애인수(장애의정도가심한장애인)(명)경증장애인수(장애의정도가심하지않은장애인)(명)시군명
기준년도1.0000.0470.612-0.600-0.646-0.512-0.637-0.6910.6940.7480.000
총계(명)0.0471.0000.4470.1940.2120.2800.2510.2560.3390.3540.349
전체지원인수(명)0.6120.4471.000-0.279-0.275-0.176-0.253-0.3260.8540.8850.170
1급장애인수(명)-0.6000.194-0.2791.0000.6950.4730.5820.748-0.412-0.4420.000
2급장애인수(명)-0.6460.212-0.2750.6951.0000.5590.6470.747-0.449-0.4820.049
3급장애인수(명)-0.5120.280-0.1760.4730.5591.0000.7150.709-0.360-0.3860.115
4급장애인수(명)-0.6370.251-0.2530.5820.6470.7151.0000.801-0.435-0.4670.144
5급장애인수(명)-0.6910.256-0.3260.7480.7470.7090.8011.000-0.485-0.5210.000
중증장애인수(장애의정도가심한장애인)(명)0.6940.3390.854-0.412-0.449-0.360-0.435-0.4851.0000.8670.088
경증장애인수(장애의정도가심하지않은장애인)(명)0.7480.3540.885-0.442-0.482-0.386-0.467-0.5210.8671.0000.215
시군명0.0000.3490.1700.0000.0490.1150.1440.0000.0880.2151.000

Missing values

2023-12-11T06:07:02.177948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:07:02.332353image/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

기준년도시군명총계(명)전체지원인수(명)1급장애인수(명)2급장애인수(명)3급장애인수(명)4급장애인수(명)5급장애인수(명)6급장애인수(명)중증장애인수(장애의정도가심한장애인)(명)경증장애인수(장애의정도가심하지않은장애인)(명)기타(명)
02022가평군22000000110
12022고양시15150000003120
22022과천시00000000000
32022광명시44000000220
42022광주시77000000250
52022구리시22000000110
62022군포시44000000130
72022김포시1111000000470
82022남양주시12120000002100
92022동두천시22000000110
기준년도시군명총계(명)전체지원인수(명)1급장애인수(명)2급장애인수(명)3급장애인수(명)4급장애인수(명)5급장애인수(명)6급장애인수(명)중증장애인수(장애의정도가심한장애인)(명)경증장애인수(장애의정도가심하지않은장애인)(명)기타(명)
2072016오산시60410010000
2082016용인시161441060000
2092016의왕시20101000000
2102016의정부시130133420000
2112016이천시90330030000
2122016파주시121310250000
2132016평택시91110240000
2142016포천시60120120000
2152016하남시60101310000
2162016화성시2204323100000