Overview

Dataset statistics

Number of variables14
Number of observations133
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.2 KiB
Average record size in memory125.0 B

Variable types

Categorical2
Numeric12

Dataset

Description전라남도 영광군 장애인 등록현황
Author전라남도 영광군
URLhttps://www.data.go.kr/data/15025251/fileData.do

Alerts

1급(남) is highly overall correlated with 1급(여) and 1 other fieldsHigh correlation
1급(여) is highly overall correlated with 1급(남) and 4 other fieldsHigh correlation
2급(남) is highly overall correlated with 1급(남) and 4 other fieldsHigh correlation
2급(여) is highly overall correlated with 1급(여) and 3 other fieldsHigh correlation
3급(남) is highly overall correlated with 1급(여) and 4 other fieldsHigh correlation
3급(여) is highly overall correlated with 1급(여) and 4 other fieldsHigh correlation
4급(남) is highly overall correlated with 4급(여) and 4 other fieldsHigh correlation
4급(여) is highly overall correlated with 3급(남) and 6 other fieldsHigh correlation
5급(남) is highly overall correlated with 4급(남) and 4 other fieldsHigh correlation
5급(여) is highly overall correlated with 4급(남) and 4 other fieldsHigh correlation
6급(남) is highly overall correlated with 4급(남) and 4 other fieldsHigh correlation
6급(여) is highly overall correlated with 4급(남) and 4 other fieldsHigh correlation
1급(남) has 76 (57.1%) zerosZeros
1급(여) has 86 (64.7%) zerosZeros
2급(남) has 61 (45.9%) zerosZeros
2급(여) has 71 (53.4%) zerosZeros
3급(남) has 53 (39.8%) zerosZeros
3급(여) has 70 (52.6%) zerosZeros
4급(남) has 72 (54.1%) zerosZeros
4급(여) has 86 (64.7%) zerosZeros
5급(남) has 84 (63.2%) zerosZeros
5급(여) has 91 (68.4%) zerosZeros
6급(남) has 90 (67.7%) zerosZeros
6급(여) has 95 (71.4%) zerosZeros

Reproduction

Analysis started2023-12-12 00:20:53.600961
Analysis finished2023-12-12 00:21:09.185803
Duration15.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Categorical

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
영광읍
15 
백수읍
14 
염산면
14 
홍농읍
13 
대마면
12 
Other values (7)
65 

Length

Max length8
Median length3
Mean length3.1503759
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영광읍 15
11.3%
백수읍 14
10.5%
염산면 14
10.5%
홍농읍 13
9.8%
대마면 12
9.0%
군서면 12
9.0%
군남면 12
9.0%
법성면 12
9.0%
불갑면 10
7.5%
묘량면 9
6.8%
Other values (2) 10
7.5%

Length

2023-12-12T09:21:09.269658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영광읍 15
11.3%
백수읍 14
10.5%
염산면 14
10.5%
홍농읍 13
9.8%
대마면 12
9.0%
군서면 12
9.0%
군남면 12
9.0%
법성면 12
9.0%
불갑면 10
7.5%
묘량면 9
6.8%
Other values (2) 10
7.5%

장애유형
Categorical

Distinct15
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
지체
12 
시각
12 
지적
12 
청각
11 
뇌병변
11 
Other values (10)
75 

Length

Max length5
Median length2
Mean length2.3834586
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지체 12
9.0%
시각 12
9.0%
지적 12
9.0%
청각 11
8.3%
뇌병변 11
8.3%
정신 11
8.3%
언어 10
 
7.5%
신장 10
 
7.5%
호흡기 10
 
7.5%
뇌전증 8
 
6.0%
Other values (5) 26
19.5%

Length

2023-12-12T09:21:09.411575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지체 12
9.0%
시각 12
9.0%
지적 12
9.0%
청각 11
8.3%
뇌병변 11
8.3%
정신 11
8.3%
언어 10
 
7.5%
신장 10
 
7.5%
호흡기 10
 
7.5%
뇌전증 8
 
6.0%
Other values (5) 26
19.5%

1급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3834586
Minimum0
Maximum18
Zeros76
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:09.526094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.8490344
Coefficient of variation (CV)2.0593564
Kurtosis14.573745
Mean1.3834586
Median Absolute Deviation (MAD)0
Skewness3.5138263
Sum184
Variance8.116997
MonotonicityNot monotonic
2023-12-12T09:21:09.650057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 76
57.1%
1 26
 
19.5%
2 8
 
6.0%
3 6
 
4.5%
4 6
 
4.5%
5 3
 
2.3%
9 2
 
1.5%
6 2
 
1.5%
15 1
 
0.8%
18 1
 
0.8%
Other values (2) 2
 
1.5%
ValueCountFrequency (%)
0 76
57.1%
1 26
 
19.5%
2 8
 
6.0%
3 6
 
4.5%
4 6
 
4.5%
5 3
 
2.3%
6 2
 
1.5%
8 1
 
0.8%
9 2
 
1.5%
14 1
 
0.8%
ValueCountFrequency (%)
18 1
 
0.8%
15 1
 
0.8%
14 1
 
0.8%
9 2
 
1.5%
8 1
 
0.8%
6 2
 
1.5%
5 3
 
2.3%
4 6
4.5%
3 6
4.5%
2 8
6.0%

1급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0977444
Minimum0
Maximum14
Zeros86
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:09.785721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5.4
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.32856
Coefficient of variation (CV)2.1212225
Kurtosis12.345685
Mean1.0977444
Median Absolute Deviation (MAD)0
Skewness3.2645842
Sum146
Variance5.4221918
MonotonicityNot monotonic
2023-12-12T09:21:09.900103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 86
64.7%
1 19
 
14.3%
2 8
 
6.0%
3 8
 
6.0%
5 3
 
2.3%
6 2
 
1.5%
4 2
 
1.5%
7 1
 
0.8%
14 1
 
0.8%
12 1
 
0.8%
Other values (2) 2
 
1.5%
ValueCountFrequency (%)
0 86
64.7%
1 19
 
14.3%
2 8
 
6.0%
3 8
 
6.0%
4 2
 
1.5%
5 3
 
2.3%
6 2
 
1.5%
7 1
 
0.8%
8 1
 
0.8%
11 1
 
0.8%
ValueCountFrequency (%)
14 1
 
0.8%
12 1
 
0.8%
11 1
 
0.8%
8 1
 
0.8%
7 1
 
0.8%
6 2
 
1.5%
5 3
 
2.3%
4 2
 
1.5%
3 8
6.0%
2 8
6.0%

2급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1578947
Minimum0
Maximum42
Zeros61
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:10.015013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile12.2
Maximum42
Range42
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.0073719
Coefficient of variation (CV)1.9023344
Kurtosis18.07838
Mean3.1578947
Median Absolute Deviation (MAD)1
Skewness3.7386175
Sum420
Variance36.088517
MonotonicityNot monotonic
2023-12-12T09:21:10.123695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 61
45.9%
1 18
 
13.5%
2 11
 
8.3%
4 9
 
6.8%
3 7
 
5.3%
10 4
 
3.0%
9 3
 
2.3%
7 3
 
2.3%
5 3
 
2.3%
8 3
 
2.3%
Other values (9) 11
 
8.3%
ValueCountFrequency (%)
0 61
45.9%
1 18
 
13.5%
2 11
 
8.3%
3 7
 
5.3%
4 9
 
6.8%
5 3
 
2.3%
6 2
 
1.5%
7 3
 
2.3%
8 3
 
2.3%
9 3
 
2.3%
ValueCountFrequency (%)
42 1
 
0.8%
34 1
 
0.8%
23 1
 
0.8%
18 1
 
0.8%
16 1
 
0.8%
15 1
 
0.8%
14 1
 
0.8%
11 2
1.5%
10 4
3.0%
9 3
2.3%

2급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1428571
Minimum0
Maximum27
Zeros71
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:10.253261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile8
Maximum27
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.9603008
Coefficient of variation (CV)1.8481404
Kurtosis14.900569
Mean2.1428571
Median Absolute Deviation (MAD)0
Skewness3.3212569
Sum285
Variance15.683983
MonotonicityNot monotonic
2023-12-12T09:21:10.353396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 71
53.4%
1 19
 
14.3%
4 9
 
6.8%
3 7
 
5.3%
2 6
 
4.5%
8 5
 
3.8%
7 5
 
3.8%
6 4
 
3.0%
9 2
 
1.5%
21 1
 
0.8%
Other values (4) 4
 
3.0%
ValueCountFrequency (%)
0 71
53.4%
1 19
 
14.3%
2 6
 
4.5%
3 7
 
5.3%
4 9
 
6.8%
5 1
 
0.8%
6 4
 
3.0%
7 5
 
3.8%
8 5
 
3.8%
9 2
 
1.5%
ValueCountFrequency (%)
27 1
 
0.8%
21 1
 
0.8%
14 1
 
0.8%
13 1
 
0.8%
9 2
 
1.5%
8 5
3.8%
7 5
3.8%
6 4
3.0%
5 1
 
0.8%
4 9
6.8%

3급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9398496
Minimum0
Maximum67
Zeros53
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:10.447080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile17.4
Maximum67
Range67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.4187376
Coefficient of variation (CV)2.136817
Kurtosis26.682158
Mean3.9398496
Median Absolute Deviation (MAD)1
Skewness4.5347334
Sum524
Variance70.875142
MonotonicityNot monotonic
2023-12-12T09:21:10.580373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 53
39.8%
1 24
18.0%
2 16
 
12.0%
3 5
 
3.8%
6 4
 
3.0%
8 4
 
3.0%
7 4
 
3.0%
5 4
 
3.0%
10 3
 
2.3%
4 3
 
2.3%
Other values (11) 13
 
9.8%
ValueCountFrequency (%)
0 53
39.8%
1 24
18.0%
2 16
 
12.0%
3 5
 
3.8%
4 3
 
2.3%
5 4
 
3.0%
6 4
 
3.0%
7 4
 
3.0%
8 4
 
3.0%
9 1
 
0.8%
ValueCountFrequency (%)
67 1
0.8%
38 1
0.8%
37 1
0.8%
25 1
0.8%
22 1
0.8%
20 1
0.8%
18 1
0.8%
17 1
0.8%
15 2
1.5%
12 2
1.5%

3급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9774436
Minimum0
Maximum55
Zeros70
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:10.704422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile12.2
Maximum55
Range55
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.5936909
Coefficient of variation (CV)2.2145477
Kurtosis31.197069
Mean2.9774436
Median Absolute Deviation (MAD)0
Skewness4.8278262
Sum396
Variance43.47676
MonotonicityNot monotonic
2023-12-12T09:21:10.840034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 70
52.6%
1 15
 
11.3%
3 7
 
5.3%
2 7
 
5.3%
4 7
 
5.3%
6 6
 
4.5%
5 5
 
3.8%
8 3
 
2.3%
11 2
 
1.5%
10 2
 
1.5%
Other values (9) 9
 
6.8%
ValueCountFrequency (%)
0 70
52.6%
1 15
 
11.3%
2 7
 
5.3%
3 7
 
5.3%
4 7
 
5.3%
5 5
 
3.8%
6 6
 
4.5%
7 1
 
0.8%
8 3
 
2.3%
9 1
 
0.8%
ValueCountFrequency (%)
55 1
0.8%
28 1
0.8%
25 1
0.8%
22 1
0.8%
16 1
0.8%
15 1
0.8%
14 1
0.8%
11 2
1.5%
10 2
1.5%
9 1
0.8%

4급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0977444
Minimum0
Maximum76
Zeros72
Zeros (%)54.1%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:10.948225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile17.4
Maximum76
Range76
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.7385116
Coefficient of variation (CV)2.8209273
Kurtosis38.94126
Mean3.0977444
Median Absolute Deviation (MAD)0
Skewness5.5408041
Sum412
Variance76.361586
MonotonicityNot monotonic
2023-12-12T09:21:11.066173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 72
54.1%
1 25
 
18.8%
2 8
 
6.0%
3 8
 
6.0%
19 2
 
1.5%
11 2
 
1.5%
7 2
 
1.5%
6 2
 
1.5%
76 1
 
0.8%
5 1
 
0.8%
Other values (10) 10
 
7.5%
ValueCountFrequency (%)
0 72
54.1%
1 25
 
18.8%
2 8
 
6.0%
3 8
 
6.0%
4 1
 
0.8%
5 1
 
0.8%
6 2
 
1.5%
7 2
 
1.5%
8 1
 
0.8%
10 1
 
0.8%
ValueCountFrequency (%)
76 1
0.8%
37 1
0.8%
33 1
0.8%
26 1
0.8%
19 2
1.5%
18 1
0.8%
17 1
0.8%
14 1
0.8%
13 1
0.8%
11 2
1.5%

4급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.481203
Minimum0
Maximum101
Zeros86
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:11.189707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile27.4
Maximum101
Range101
Interquartile range (IQR)2

Descriptive statistics

Standard deviation14.374642
Coefficient of variation (CV)3.207764
Kurtosis22.001353
Mean4.481203
Median Absolute Deviation (MAD)0
Skewness4.5020376
Sum596
Variance206.63033
MonotonicityNot monotonic
2023-12-12T09:21:11.325936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 86
64.7%
2 11
 
8.3%
1 10
 
7.5%
3 8
 
6.0%
5 3
 
2.3%
21 2
 
1.5%
101 1
 
0.8%
9 1
 
0.8%
69 1
 
0.8%
63 1
 
0.8%
Other values (9) 9
 
6.8%
ValueCountFrequency (%)
0 86
64.7%
1 10
 
7.5%
2 11
 
8.3%
3 8
 
6.0%
4 1
 
0.8%
5 3
 
2.3%
8 1
 
0.8%
9 1
 
0.8%
10 1
 
0.8%
14 1
 
0.8%
ValueCountFrequency (%)
101 1
0.8%
69 1
0.8%
64 1
0.8%
63 1
0.8%
52 1
0.8%
33 1
0.8%
31 1
0.8%
25 1
0.8%
21 2
1.5%
14 1
0.8%

5급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6390977
Minimum0
Maximum112
Zeros84
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:11.460310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile21
Maximum112
Range112
Interquartile range (IQR)2

Descriptive statistics

Standard deviation12.015355
Coefficient of variation (CV)3.3017402
Kurtosis51.859023
Mean3.6390977
Median Absolute Deviation (MAD)0
Skewness6.4574114
Sum484
Variance144.36876
MonotonicityNot monotonic
2023-12-12T09:21:11.605039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 84
63.2%
1 13
 
9.8%
2 9
 
6.8%
3 6
 
4.5%
8 3
 
2.3%
13 2
 
1.5%
5 2
 
1.5%
6 2
 
1.5%
21 2
 
1.5%
34 1
 
0.8%
Other values (9) 9
 
6.8%
ValueCountFrequency (%)
0 84
63.2%
1 13
 
9.8%
2 9
 
6.8%
3 6
 
4.5%
4 1
 
0.8%
5 2
 
1.5%
6 2
 
1.5%
7 1
 
0.8%
8 3
 
2.3%
9 1
 
0.8%
ValueCountFrequency (%)
112 1
0.8%
46 1
0.8%
40 1
0.8%
34 1
0.8%
27 1
0.8%
23 1
0.8%
21 2
1.5%
19 1
0.8%
13 2
1.5%
9 1
0.8%

5급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0902256
Minimum0
Maximum139
Zeros91
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:11.753085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile33
Maximum139
Range139
Interquartile range (IQR)1

Descriptive statistics

Standard deviation16.525673
Coefficient of variation (CV)3.2465502
Kurtosis34.847871
Mean5.0902256
Median Absolute Deviation (MAD)0
Skewness5.2835728
Sum677
Variance273.09786
MonotonicityNot monotonic
2023-12-12T09:21:11.932456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 91
68.4%
1 9
 
6.8%
2 7
 
5.3%
8 4
 
3.0%
3 4
 
3.0%
46 2
 
1.5%
25 2
 
1.5%
5 2
 
1.5%
36 1
 
0.8%
4 1
 
0.8%
Other values (10) 10
 
7.5%
ValueCountFrequency (%)
0 91
68.4%
1 9
 
6.8%
2 7
 
5.3%
3 4
 
3.0%
4 1
 
0.8%
5 2
 
1.5%
6 1
 
0.8%
8 4
 
3.0%
9 1
 
0.8%
10 1
 
0.8%
ValueCountFrequency (%)
139 1
0.8%
62 1
0.8%
61 1
0.8%
57 1
0.8%
46 2
1.5%
36 1
0.8%
31 1
0.8%
28 1
0.8%
25 2
1.5%
15 1
0.8%

6급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8646617
Minimum0
Maximum127
Zeros90
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:12.090175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile26.4
Maximum127
Range127
Interquartile range (IQR)2

Descriptive statistics

Standard deviation14.554902
Coefficient of variation (CV)2.991966
Kurtosis39.840249
Mean4.8646617
Median Absolute Deviation (MAD)0
Skewness5.5998437
Sum647
Variance211.84518
MonotonicityNot monotonic
2023-12-12T09:21:12.216269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 90
67.7%
2 9
 
6.8%
1 4
 
3.0%
12 3
 
2.3%
4 3
 
2.3%
3 3
 
2.3%
21 2
 
1.5%
10 2
 
1.5%
6 1
 
0.8%
14 1
 
0.8%
Other values (15) 15
 
11.3%
ValueCountFrequency (%)
0 90
67.7%
1 4
 
3.0%
2 9
 
6.8%
3 3
 
2.3%
4 3
 
2.3%
5 1
 
0.8%
6 1
 
0.8%
8 1
 
0.8%
9 1
 
0.8%
10 2
 
1.5%
ValueCountFrequency (%)
127 1
0.8%
62 1
0.8%
55 1
0.8%
41 1
0.8%
33 1
0.8%
29 1
0.8%
27 1
0.8%
26 1
0.8%
21 2
1.5%
20 1
0.8%

6급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8421053
Minimum0
Maximum108
Zeros95
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:21:12.331758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile25
Maximum108
Range108
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.081066
Coefficient of variation (CV)3.144387
Kurtosis43.208338
Mean3.8421053
Median Absolute Deviation (MAD)0
Skewness5.8140082
Sum511
Variance145.95215
MonotonicityNot monotonic
2023-12-12T09:21:12.721103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 95
71.4%
1 5
 
3.8%
2 4
 
3.0%
5 4
 
3.0%
3 4
 
3.0%
10 2
 
1.5%
6 2
 
1.5%
8 2
 
1.5%
38 2
 
1.5%
12 2
 
1.5%
Other values (10) 11
 
8.3%
ValueCountFrequency (%)
0 95
71.4%
1 5
 
3.8%
2 4
 
3.0%
3 4
 
3.0%
5 4
 
3.0%
6 2
 
1.5%
7 2
 
1.5%
8 2
 
1.5%
9 1
 
0.8%
10 2
 
1.5%
ValueCountFrequency (%)
108 1
0.8%
39 1
0.8%
38 2
1.5%
36 1
0.8%
32 1
0.8%
31 1
0.8%
21 1
0.8%
17 1
0.8%
12 2
1.5%
11 1
0.8%

Interactions

2023-12-12T09:21:07.576428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.157035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.343226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.493577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.540202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.790011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.137960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.223238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.272611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.445033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.624813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.291061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.690129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.258101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.437606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.573868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.623409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.899599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.217915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.326756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.357475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.537351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.734107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.417120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.801782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.388089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.521186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.666004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.715020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.003946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.299507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.417919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.443223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.627919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.847694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.539146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.891509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.472229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.593352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.750165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.791461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.090265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.382635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.502500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.525457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.703622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.954039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.647605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.982294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.569043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.675385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.826139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.890690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.452496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.473811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.586879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.635051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.804190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:05.059528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.743896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:08.081465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.662948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.771944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.907938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.003463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.532448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.582028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.676654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.759171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.904650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:05.513255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.858672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:08.174835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.746444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.866022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.979305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.106100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.610097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.701598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.759309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.846991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.020648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:05.646149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.951552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:08.321076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.854716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.955323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.065043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.242861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.692207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.794980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.851903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.949017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.123777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:05.766699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.058842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:08.432547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:54.982971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.061947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.150399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.329112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.796666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.880900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.923491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.036963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.204506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:05.872877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.178287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:08.533893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.063402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.205924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.245030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.416366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.886963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.964556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.995785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.145046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.296481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:05.985868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.273984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:08.656431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.153915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.306835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.339306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.515320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:59.973628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.055857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.077094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.252868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.425284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.104535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.368413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:08.738477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:55.241830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:56.405317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:57.455927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:20:58.652049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:00.059906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:01.136891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:02.176480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:03.361196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:04.534068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:06.202804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:21:07.471474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:21:12.801514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동장애유형1급(남)1급(여)2급(남)2급(여)3급(남)3급(여)4급(남)4급(여)5급(남)5급(여)6급(남)6급(여)
읍면동1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
장애유형0.0001.0000.0000.2000.0000.3780.3490.3170.3990.4540.4530.5270.3590.516
1급(남)0.0000.0001.0000.9610.8740.8810.8920.8500.8440.7350.7970.7850.7860.798
1급(여)0.0000.2000.9611.0000.7970.8380.7590.7310.5410.3930.4430.4390.6360.523
2급(남)0.0000.0000.8740.7971.0000.9780.8490.8090.7520.8050.7680.7510.6720.703
2급(여)0.0000.3780.8810.8380.9781.0000.8530.8350.7540.8030.7390.7490.6690.692
3급(남)0.0000.3490.8920.7590.8490.8531.0000.9670.9160.8210.9360.9340.9150.757
3급(여)0.0000.3170.8500.7310.8090.8350.9671.0000.9270.7620.9140.8940.8710.693
4급(남)0.0000.3990.8440.5410.7520.7540.9160.9271.0000.9050.9830.9740.9500.805
4급(여)0.0000.4540.7350.3930.8050.8030.8210.7620.9051.0000.9250.9340.8450.846
5급(남)0.0000.4530.7970.4430.7680.7390.9360.9140.9830.9251.0000.9930.9580.884
5급(여)0.0000.5270.7850.4390.7510.7490.9340.8940.9740.9340.9931.0000.9580.827
6급(남)0.0000.3590.7860.6360.6720.6690.9150.8710.9500.8450.9580.9581.0000.878
6급(여)0.0000.5160.7980.5230.7030.6920.7570.6930.8050.8460.8840.8270.8781.000
2023-12-12T09:21:12.915287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애유형읍면동
장애유형1.0000.000
읍면동0.0001.000
2023-12-12T09:21:12.993484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1급(남)1급(여)2급(남)2급(여)3급(남)3급(여)4급(남)4급(여)5급(남)5급(여)6급(남)6급(여)읍면동장애유형
1급(남)1.0000.6200.5170.4760.4860.4890.2610.3910.3370.4290.4860.4890.0000.091
1급(여)0.6201.0000.5120.5630.5430.5000.2470.3270.2810.3470.4950.4890.0000.075
2급(남)0.5170.5121.0000.8150.6410.6710.2160.3220.3310.4090.3280.3310.0000.000
2급(여)0.4760.5630.8151.0000.6790.6570.2760.3520.3990.4310.3950.4150.0000.165
3급(남)0.4860.5430.6410.6791.0000.8470.4510.5010.3510.4670.4750.4830.0000.162
3급(여)0.4890.5000.6710.6570.8471.0000.4040.5010.3890.4810.4820.4920.0000.106
4급(남)0.2610.2470.2160.2760.4510.4041.0000.8430.6340.7090.6990.6700.0000.189
4급(여)0.3910.3270.3220.3520.5010.5010.8431.0000.6900.7420.7620.7300.0000.216
5급(남)0.3370.2810.3310.3990.3510.3890.6340.6901.0000.7760.7300.7540.0000.220
5급(여)0.4290.3470.4090.4310.4670.4810.7090.7420.7761.0000.8160.7680.0000.266
6급(남)0.4860.4950.3280.3950.4750.4820.6990.7620.7300.8161.0000.9500.0000.167
6급(여)0.4890.4890.3310.4150.4830.4920.6700.7300.7540.7680.9501.0000.0000.235
읍면동0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
장애유형0.0910.0750.0000.1650.1620.1060.1890.2160.2200.2660.1670.2350.0001.000

Missing values

2023-12-12T09:21:08.870505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:21:09.080741image/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

읍면동장애유형1급(남)1급(여)2급(남)2급(여)3급(남)3급(여)4급(남)4급(여)5급(남)5급(여)6급(남)6급(여)
0영광읍지체1563421675576101112139127108
1영광읍시각37013434666238
2영광읍청각0423141511191419251010
3영광읍언어000011320000
4영광읍지적181442273722000000
5영광읍뇌병변912981715118810126
6영광읍자폐성500000000000
7영광읍정신01743828000000
8영광읍신장1110900008800
9영광읍심장000011000000
읍면동장애유형1급(남)1급(여)2급(남)2급(여)3급(남)3급(여)4급(남)4급(여)5급(남)5급(여)6급(남)6급(여)
123낙월면지체101112232530
124낙월면시각000100000022
125낙월면청각000010100000
126낙월면지적100010000000
127낙월면뇌병변100000000000
128낙월면신장002000000000
129낙월면안마출장소지체000010103441
130낙월면안마출장소시각000000000010
131낙월면안마출장소지적001000000000
132낙월면안마출장소정신000010000000