Overview

Dataset statistics

Number of variables11
Number of observations382
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.3 KiB
Average record size in memory97.3 B

Variable types

Categorical2
Numeric9

Dataset

Description경상남도 진주시 30개 읍면동 지역의 장애인 유형별 및 장애 정도별 등록 남여 전체 인원 현황을 제공합니다.(2023.7.31.)
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15119924

Alerts

합계_전체 is highly overall correlated with 합계_남성 and 7 other fieldsHigh correlation
합계_남성 is highly overall correlated with 합계_전체 and 7 other fieldsHigh correlation
합계_여성 is highly overall correlated with 합계_전체 and 7 other fieldsHigh correlation
심한 장애_소계 is highly overall correlated with 합계_전체 and 4 other fieldsHigh correlation
삼한 장애_남성 is highly overall correlated with 합계_전체 and 4 other fieldsHigh correlation
심한 장애_여성 is highly overall correlated with 합계_전체 and 4 other fieldsHigh correlation
심하지 않은 장애_소계 is highly overall correlated with 합계_전체 and 4 other fieldsHigh correlation
심하지 않은 장애_남성 is highly overall correlated with 합계_전체 and 4 other fieldsHigh correlation
심하지 않은 장애_여성 is highly overall correlated with 합계_전체 and 4 other fieldsHigh correlation
합계_남성 has 20 (5.2%) zerosZeros
합계_여성 has 71 (18.6%) zerosZeros
심한 장애_소계 has 63 (16.5%) zerosZeros
삼한 장애_남성 has 83 (21.7%) zerosZeros
심한 장애_여성 has 119 (31.2%) zerosZeros
심하지 않은 장애_소계 has 121 (31.7%) zerosZeros
심하지 않은 장애_남성 has 141 (36.9%) zerosZeros
심하지 않은 장애_여성 has 175 (45.8%) zerosZeros

Reproduction

Analysis started2023-12-10 22:53:36.464739
Analysis finished2023-12-10 22:53:45.562724
Duration9.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Categorical

Distinct31
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
문산읍
 
15
가호동
 
15
평거동
 
15
상대동
 
15
상봉동
 
15
Other values (26)
307 

Length

Max length4
Median length3
Mean length3.0942408
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
문산읍 15
 
3.9%
가호동 15
 
3.9%
평거동 15
 
3.9%
상대동 15
 
3.9%
상봉동 15
 
3.9%
정촌면 15
 
3.9%
천전동 14
 
3.7%
상평동 14
 
3.7%
하대동 14
 
3.7%
중앙동 14
 
3.7%
Other values (21) 236
61.8%

Length

2023-12-11T07:53:45.628283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
문산읍 15
 
3.9%
평거동 15
 
3.9%
상대동 15
 
3.9%
상봉동 15
 
3.9%
정촌면 15
 
3.9%
가호동 15
 
3.9%
천전동 14
 
3.7%
상평동 14
 
3.7%
하대동 14
 
3.7%
중앙동 14
 
3.7%
Other values (21) 236
61.8%

장애유형
Categorical

Distinct15
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
지체
31 
시각
30 
청각
30 
지적
30 
뇌병변
30 
Other values (10)
231 

Length

Max length5
Median length2
Mean length2.3848168
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지체
2nd row시각
3rd row청각
4th row언어
5th row지적

Common Values

ValueCountFrequency (%)
지체 31
 
8.1%
시각 30
 
7.9%
청각 30
 
7.9%
지적 30
 
7.9%
뇌병변 30
 
7.9%
정신 30
 
7.9%
신장 30
 
7.9%
언어 29
 
7.6%
자폐성 28
 
7.3%
26
 
6.8%
Other values (5) 88
23.0%

Length

2023-12-11T07:53:45.745355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지체 31
 
8.1%
시각 30
 
7.9%
청각 30
 
7.9%
지적 30
 
7.9%
뇌병변 30
 
7.9%
정신 30
 
7.9%
신장 30
 
7.9%
언어 29
 
7.6%
자폐성 28
 
7.3%
26
 
6.8%
Other values (5) 88
23.0%

합계_전체
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.507853
Minimum1
Maximum672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:45.889064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q356
95-th percentile169.3
Maximum672
Range671
Interquartile range (IQR)53

Descriptive statistics

Standard deviation89.26691
Coefficient of variation (CV)1.8789927
Kurtosis18.811501
Mean47.507853
Median Absolute Deviation (MAD)12
Skewness3.950211
Sum18148
Variance7968.5813
MonotonicityNot monotonic
2023-12-11T07:53:46.030377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 44
 
11.5%
2 36
 
9.4%
3 22
 
5.8%
4 19
 
5.0%
5 12
 
3.1%
14 9
 
2.4%
8 9
 
2.4%
10 8
 
2.1%
6 8
 
2.1%
9 8
 
2.1%
Other values (112) 207
54.2%
ValueCountFrequency (%)
1 44
11.5%
2 36
9.4%
3 22
5.8%
4 19
5.0%
5 12
 
3.1%
6 8
 
2.1%
7 7
 
1.8%
8 9
 
2.4%
9 8
 
2.1%
10 8
 
2.1%
ValueCountFrequency (%)
672 1
0.3%
610 1
0.3%
584 1
0.3%
541 1
0.3%
473 1
0.3%
429 1
0.3%
409 1
0.3%
403 1
0.3%
359 1
0.3%
335 1
0.3%

합계_남성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.620419
Minimum0
Maximum381
Zeros20
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:46.172062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q12
median8.5
Q331
95-th percentile97.5
Maximum381
Range381
Interquartile range (IQR)29

Descriptive statistics

Standard deviation52.973572
Coefficient of variation (CV)1.9179134
Kurtosis19.87267
Mean27.620419
Median Absolute Deviation (MAD)7.5
Skewness4.0996857
Sum10551
Variance2806.1994
MonotonicityNot monotonic
2023-12-11T07:53:46.305138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 51
 
13.4%
2 32
 
8.4%
4 24
 
6.3%
5 20
 
5.2%
0 20
 
5.2%
3 17
 
4.5%
7 11
 
2.9%
14 9
 
2.4%
6 8
 
2.1%
8 8
 
2.1%
Other values (82) 182
47.6%
ValueCountFrequency (%)
0 20
 
5.2%
1 51
13.4%
2 32
8.4%
3 17
 
4.5%
4 24
6.3%
5 20
 
5.2%
6 8
 
2.1%
7 11
 
2.9%
8 8
 
2.1%
9 8
 
2.1%
ValueCountFrequency (%)
381 1
0.3%
378 1
0.3%
360 1
0.3%
333 1
0.3%
279 1
0.3%
259 1
0.3%
245 1
0.3%
237 1
0.3%
209 1
0.3%
202 1
0.3%

합계_여성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.887435
Minimum0
Maximum294
Zeros71
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:46.446138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile72
Maximum294
Range294
Interquartile range (IQR)23

Descriptive statistics

Standard deviation36.739994
Coefficient of variation (CV)1.8473974
Kurtosis17.387891
Mean19.887435
Median Absolute Deviation (MAD)5
Skewness3.71222
Sum7597
Variance1349.8272
MonotonicityNot monotonic
2023-12-11T07:53:46.575366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
18.6%
1 54
 
14.1%
2 28
 
7.3%
3 25
 
6.5%
8 9
 
2.4%
7 9
 
2.4%
5 9
 
2.4%
10 8
 
2.1%
6 8
 
2.1%
4 8
 
2.1%
Other values (67) 153
40.1%
ValueCountFrequency (%)
0 71
18.6%
1 54
14.1%
2 28
 
7.3%
3 25
 
6.5%
4 8
 
2.1%
5 9
 
2.4%
6 8
 
2.1%
7 9
 
2.4%
8 9
 
2.4%
9 4
 
1.0%
ValueCountFrequency (%)
294 1
0.3%
229 1
0.3%
224 1
0.3%
208 1
0.3%
194 1
0.3%
170 1
0.3%
166 1
0.3%
164 1
0.3%
157 1
0.3%
126 1
0.3%

심한 장애_소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.594241
Minimum0
Maximum178
Zeros63
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:46.707045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q325
95-th percentile74.95
Maximum178
Range178
Interquartile range (IQR)24

Descriptive statistics

Standard deviation26.669559
Coefficient of variation (CV)1.4342914
Kurtosis6.2977495
Mean18.594241
Median Absolute Deviation (MAD)7
Skewness2.3065214
Sum7103
Variance711.26537
MonotonicityNot monotonic
2023-12-11T07:53:46.848279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
16.5%
1 40
 
10.5%
2 26
 
6.8%
3 19
 
5.0%
4 12
 
3.1%
9 12
 
3.1%
6 12
 
3.1%
14 10
 
2.6%
12 9
 
2.4%
5 9
 
2.4%
Other values (73) 170
44.5%
ValueCountFrequency (%)
0 63
16.5%
1 40
10.5%
2 26
6.8%
3 19
 
5.0%
4 12
 
3.1%
5 9
 
2.4%
6 12
 
3.1%
7 6
 
1.6%
8 7
 
1.8%
9 12
 
3.1%
ValueCountFrequency (%)
178 1
0.3%
135 1
0.3%
132 1
0.3%
121 1
0.3%
108 1
0.3%
103 1
0.3%
102 2
0.5%
101 2
0.5%
99 1
0.3%
97 1
0.3%

삼한 장애_남성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.078534
Minimum0
Maximum124
Zeros83
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:46.982262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4.5
Q314
95-th percentile44
Maximum124
Range124
Interquartile range (IQR)13

Descriptive statistics

Standard deviation16.39727
Coefficient of variation (CV)1.4800938
Kurtosis8.9390722
Mean11.078534
Median Absolute Deviation (MAD)4.5
Skewness2.5960759
Sum4232
Variance268.87046
MonotonicityNot monotonic
2023-12-11T07:53:47.124812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83
21.7%
1 41
 
10.7%
2 30
 
7.9%
4 22
 
5.8%
5 21
 
5.5%
3 15
 
3.9%
9 15
 
3.9%
6 11
 
2.9%
8 10
 
2.6%
7 9
 
2.4%
Other values (49) 125
32.7%
ValueCountFrequency (%)
0 83
21.7%
1 41
10.7%
2 30
 
7.9%
3 15
 
3.9%
4 22
 
5.8%
5 21
 
5.5%
6 11
 
2.9%
7 9
 
2.4%
8 10
 
2.6%
9 15
 
3.9%
ValueCountFrequency (%)
124 1
0.3%
83 1
0.3%
80 1
0.3%
72 1
0.3%
69 1
0.3%
68 1
0.3%
67 1
0.3%
65 1
0.3%
61 1
0.3%
60 2
0.5%

심한 장애_여성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5157068
Minimum0
Maximum54
Zeros119
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:47.248281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q310.75
95-th percentile33
Maximum54
Range54
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation10.89552
Coefficient of variation (CV)1.4496999
Kurtosis4.1056182
Mean7.5157068
Median Absolute Deviation (MAD)3
Skewness2.0370564
Sum2871
Variance118.71235
MonotonicityNot monotonic
2023-12-11T07:53:47.375621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 119
31.2%
1 43
 
11.3%
3 26
 
6.8%
2 20
 
5.2%
6 16
 
4.2%
5 15
 
3.9%
4 14
 
3.7%
8 13
 
3.4%
13 11
 
2.9%
7 9
 
2.4%
Other values (32) 96
25.1%
ValueCountFrequency (%)
0 119
31.2%
1 43
 
11.3%
2 20
 
5.2%
3 26
 
6.8%
4 14
 
3.7%
5 15
 
3.9%
6 16
 
4.2%
7 9
 
2.4%
8 13
 
3.4%
9 6
 
1.6%
ValueCountFrequency (%)
54 1
 
0.3%
52 4
1.0%
43 2
0.5%
42 1
 
0.3%
40 1
 
0.3%
39 3
0.8%
37 2
0.5%
36 1
 
0.3%
35 2
0.5%
34 1
 
0.3%

심하지 않은 장애_소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.913613
Minimum0
Maximum575
Zeros121
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:47.778338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q319.75
95-th percentile130.75
Maximum575
Range575
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation72.326145
Coefficient of variation (CV)2.5014565
Kurtosis21.053141
Mean28.913613
Median Absolute Deviation (MAD)2
Skewness4.2526526
Sum11045
Variance5231.0713
MonotonicityNot monotonic
2023-12-11T07:53:47.916849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 121
31.7%
1 49
12.8%
2 30
 
7.9%
3 15
 
3.9%
4 13
 
3.4%
8 11
 
2.9%
5 8
 
2.1%
7 7
 
1.8%
10 6
 
1.6%
6 5
 
1.3%
Other values (79) 117
30.6%
ValueCountFrequency (%)
0 121
31.7%
1 49
12.8%
2 30
 
7.9%
3 15
 
3.9%
4 13
 
3.4%
5 8
 
2.1%
6 5
 
1.3%
7 7
 
1.8%
8 11
 
2.9%
9 3
 
0.8%
ValueCountFrequency (%)
575 1
0.3%
483 1
0.3%
447 1
0.3%
432 1
0.3%
372 1
0.3%
339 1
0.3%
327 1
0.3%
322 1
0.3%
296 1
0.3%
281 1
0.3%

심하지 않은 장애_남성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.541885
Minimum0
Maximum318
Zeros141
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:48.065411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310.75
95-th percentile71.9
Maximum318
Range318
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation41.772626
Coefficient of variation (CV)2.525264
Kurtosis21.285034
Mean16.541885
Median Absolute Deviation (MAD)1
Skewness4.3148414
Sum6319
Variance1744.9523
MonotonicityNot monotonic
2023-12-11T07:53:48.215698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141
36.9%
1 53
 
13.9%
2 28
 
7.3%
3 15
 
3.9%
5 11
 
2.9%
6 9
 
2.4%
4 8
 
2.1%
8 7
 
1.8%
10 6
 
1.6%
11 5
 
1.3%
Other values (60) 99
25.9%
ValueCountFrequency (%)
0 141
36.9%
1 53
 
13.9%
2 28
 
7.3%
3 15
 
3.9%
4 8
 
2.1%
5 11
 
2.9%
6 9
 
2.4%
7 4
 
1.0%
8 7
 
1.8%
9 4
 
1.0%
ValueCountFrequency (%)
318 1
0.3%
292 1
0.3%
261 1
0.3%
257 1
0.3%
214 1
0.3%
200 1
0.3%
193 1
0.3%
190 1
0.3%
174 1
0.3%
158 1
0.3%

심하지 않은 장애_여성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.371728
Minimum0
Maximum257
Zeros175
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T07:53:48.344536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile62.8
Maximum257
Range257
Interquartile range (IQR)8

Descriptive statistics

Standard deviation30.842552
Coefficient of variation (CV)2.4929867
Kurtosis20.705714
Mean12.371728
Median Absolute Deviation (MAD)1
Skewness4.1484527
Sum4726
Variance951.26303
MonotonicityNot monotonic
2023-12-11T07:53:48.470352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
45.8%
1 50
 
13.1%
2 27
 
7.1%
3 10
 
2.6%
6 8
 
2.1%
18 7
 
1.8%
17 7
 
1.8%
5 6
 
1.6%
11 5
 
1.3%
4 5
 
1.3%
Other values (55) 82
21.5%
ValueCountFrequency (%)
0 175
45.8%
1 50
 
13.1%
2 27
 
7.1%
3 10
 
2.6%
4 5
 
1.3%
5 6
 
1.6%
6 8
 
2.1%
7 3
 
0.8%
8 3
 
0.8%
9 4
 
1.0%
ValueCountFrequency (%)
257 1
0.3%
191 1
0.3%
186 1
0.3%
175 1
0.3%
158 1
0.3%
146 1
0.3%
138 1
0.3%
132 1
0.3%
127 1
0.3%
107 1
0.3%

Interactions

2023-12-11T07:53:44.469533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:36.952968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.076055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.891729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.666364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.523724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.402494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.349324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.511944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.569007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.072047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.188131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.974018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.766087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.613218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.503416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.444116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.637579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.705096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.173325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.288139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.060213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.879241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.717948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.622939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.542906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.744516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.814085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.263069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.371435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.152682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.979769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.798759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.718392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.638386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.828948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.912616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.675427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.460495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.240890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.068398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.883383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.837700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.747254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.934039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.999291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.752650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.548125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.317662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.163174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.991261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.947782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.120621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.046893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:45.077167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.830849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.631750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.394957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.250222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.139663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.044697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.208960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.163192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:45.173520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.909801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.724836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.477112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.352610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.239272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.135749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.306687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.276194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:45.254894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:37.993731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:38.808497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:39.576238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:40.446016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:41.321632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:42.235035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:43.414064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:53:44.386277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:53:48.556721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동장애유형합계_전체합계_남성합계_여성심한 장애_소계삼한 장애_남성심한 장애_여성심하지 않은 장애_소계심하지 않은 장애_남성심하지 않은 장애_여성
읍면동1.0000.0000.0000.0000.0000.0000.0000.2640.0000.0000.000
장애유형0.0001.0000.5740.5500.5590.5320.5230.5610.5810.5710.584
합계_전체0.0000.5741.0000.9860.9600.7250.7790.6150.9890.9160.942
합계_남성0.0000.5500.9861.0000.9210.7200.7560.6270.9810.9290.897
합계_여성0.0000.5590.9600.9211.0000.8210.7300.7500.9500.9580.987
심한 장애_소계0.0000.5320.7250.7200.8211.0000.9320.9390.6590.8140.820
삼한 장애_남성0.0000.5230.7790.7560.7300.9321.0000.7800.7230.6680.724
심한 장애_여성0.2640.5610.6150.6270.7500.9390.7801.0000.5520.6900.692
심하지 않은 장애_소계0.0000.5810.9890.9810.9500.6590.7230.5521.0000.9480.953
심하지 않은 장애_남성0.0000.5710.9160.9290.9580.8140.6680.6900.9481.0000.963
심하지 않은 장애_여성0.0000.5840.9420.8970.9870.8200.7240.6920.9530.9631.000
2023-12-11T07:53:48.674451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애유형읍면동
장애유형1.0000.000
읍면동0.0001.000
2023-12-11T07:53:48.755222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계_전체합계_남성합계_여성심한 장애_소계삼한 장애_남성심한 장애_여성심하지 않은 장애_소계심하지 않은 장애_남성심하지 않은 장애_여성읍면동장애유형
합계_전체1.0000.9830.9510.8850.8520.8740.5940.6080.6410.0000.252
합계_남성0.9831.0000.8910.8790.8720.8300.5690.6070.5900.0000.237
합계_여성0.9510.8911.0000.8360.7670.9020.6020.5810.6940.0000.263
심한 장애_소계0.8850.8790.8361.0000.9780.9430.2420.2640.3270.0000.246
삼한 장애_남성0.8520.8720.7670.9781.0000.8670.2040.2320.2840.0000.253
심한 장애_여성0.8740.8300.9020.9430.8671.0000.3150.3220.3870.1050.255
심하지 않은 장애_소계0.5940.5690.6020.2420.2040.3151.0000.9760.9330.0000.256
심하지 않은 장애_남성0.6080.6070.5810.2640.2320.3220.9761.0000.8640.0000.271
심하지 않은 장애_여성0.6410.5900.6940.3270.2840.3870.9330.8641.0000.0000.280
읍면동0.0000.0000.0000.0000.0000.1050.0000.0000.0001.0000.000
장애유형0.2520.2370.2630.2460.2530.2550.2560.2710.2800.0001.000

Missing values

2023-12-11T07:53:45.364609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:53:45.508769image/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

읍면동장애유형합계_전체합계_남성합계_여성심한 장애_소계삼한 장애_남성심한 장애_여성심하지 않은 장애_소계심하지 않은 장애_남성심하지 않은 장애_여성
0문산읍지체26315410959382120411688
1문산읍시각5235171376392811
2문산읍청각79314822913572235
3문산읍언어541220321
4문산읍지적10361421036142000
5문산읍뇌병변75423351272424159
6문산읍자폐성440440000
7문산읍정신915239915239000
8문산읍신장2014619145101
9문산읍심장220220000
읍면동장애유형합계_전체합계_남성합계_여성심한 장애_소계삼한 장애_남성심한 장애_여성심하지 않은 장애_소계심하지 않은 장애_남성심하지 않은 장애_여성
372충무공동언어1284431853
373충무공동지적744034744034000
374충무공동뇌병변774730392514382216
375충무공동자폐성2519625196000
376충무공동정신2718927189000
377충무공동신장3622141911817116
378충무공동호흡기101000101
379충무공동752000752
380충무공동안면110110000
381충무공동장루.요루422000422