Overview

Dataset statistics

Number of variables14
Number of observations222
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.0 KiB
Average record size in memory124.6 B

Variable types

Categorical2
Numeric12

Dataset

Description전북특별자치도의 장애인유형별, 등급별 등록에 관한 정보입니다.항목 : 시군구, 장애유형, 급수별(1급~6급) 등장애 1급(남)의 장애유형별 환자수 장애인복지법 시행규칙 제2조의 장애인의 장애등급표에 의한 장애등급 사정기준(1-6급)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15059705/fileData.do

Alerts

1급(남) is highly overall correlated with 1급(여) and 6 other fieldsHigh correlation
1급(여) is highly overall correlated with 1급(남) and 6 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(남) and 6 other fieldsHigh correlation
3급(남) is highly overall correlated with 1급(남) and 6 other fieldsHigh correlation
3급(여) is highly overall correlated with 1급(남) and 6 other fieldsHigh correlation
4급(남) is highly overall correlated with 4급(여) and 4 other fieldsHigh correlation
4급(여) is highly overall correlated with 4급(남) and 4 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 1급(남) and 9 other fieldsHigh correlation
6급(여) is highly overall correlated with 1급(남) and 9 other fieldsHigh correlation
1급(남) has 70 (31.5%) zerosZeros
1급(여) has 97 (43.7%) zerosZeros
2급(남) has 46 (20.7%) zerosZeros
2급(여) has 60 (27.0%) zerosZeros
3급(남) has 39 (17.6%) zerosZeros
3급(여) has 55 (24.8%) zerosZeros
4급(남) has 92 (41.4%) zerosZeros
4급(여) has 103 (46.4%) zerosZeros
5급(남) has 95 (42.8%) zerosZeros
5급(여) has 102 (45.9%) zerosZeros
6급(남) has 162 (73.0%) zerosZeros
6급(여) has 162 (73.0%) zerosZeros

Reproduction

Analysis started2024-03-14 23:21:17.032541
Analysis finished2024-03-14 23:21:54.742977
Duration37.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

Distinct15
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
전주시덕진구
15 
전주시완산구
15 
군산시
15 
익산시
15 
정읍시
15 
Other values (10)
147 

Length

Max length6
Median length3
Mean length3.4054054
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시덕진구 15
 
6.8%
전주시완산구 15
 
6.8%
군산시 15
 
6.8%
익산시 15
 
6.8%
정읍시 15
 
6.8%
남원시 15
 
6.8%
김제시 15
 
6.8%
완주군 15
 
6.8%
임실군 15
 
6.8%
순창군 15
 
6.8%
Other values (5) 72
32.4%

Length

2024-03-15T08:21:54.954345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시덕진구 15
 
6.8%
전주시완산구 15
 
6.8%
군산시 15
 
6.8%
익산시 15
 
6.8%
정읍시 15
 
6.8%
남원시 15
 
6.8%
김제시 15
 
6.8%
완주군 15
 
6.8%
임실군 15
 
6.8%
순창군 15
 
6.8%
Other values (5) 72
32.4%

장애유형
Categorical

Distinct15
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
지체
15 
시각
15 
청각
15 
언어
15 
지적
15 
Other values (10)
147 

Length

Max length5
Median length2
Mean length2.4054054
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지체 15
 
6.8%
시각 15
 
6.8%
청각 15
 
6.8%
언어 15
 
6.8%
지적 15
 
6.8%
뇌병변 15
 
6.8%
자폐성 15
 
6.8%
정신 15
 
6.8%
신장 15
 
6.8%
심장 15
 
6.8%
Other values (5) 72
32.4%

Length

2024-03-15T08:21:55.395766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지체 15
 
6.8%
시각 15
 
6.8%
청각 15
 
6.8%
언어 15
 
6.8%
지적 15
 
6.8%
뇌병변 15
 
6.8%
자폐성 15
 
6.8%
정신 15
 
6.8%
신장 15
 
6.8%
심장 15
 
6.8%
Other values (5) 72
32.4%

1급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.351351
Minimum0
Maximum348
Zeros70
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:21:55.805461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q320
95-th percentile128.9
Maximum348
Range348
Interquartile range (IQR)20

Descriptive statistics

Standard deviation48.004835
Coefficient of variation (CV)2.0557626
Kurtosis12.735376
Mean23.351351
Median Absolute Deviation (MAD)3
Skewness3.2319473
Sum5184
Variance2304.4642
MonotonicityNot monotonic
2024-03-15T08:21:56.239053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
31.5%
1 29
 
13.1%
2 11
 
5.0%
3 8
 
3.6%
4 8
 
3.6%
5 6
 
2.7%
18 5
 
2.3%
7 5
 
2.3%
11 4
 
1.8%
19 3
 
1.4%
Other values (58) 73
32.9%
ValueCountFrequency (%)
0 70
31.5%
1 29
13.1%
2 11
 
5.0%
3 8
 
3.6%
4 8
 
3.6%
5 6
 
2.7%
6 2
 
0.9%
7 5
 
2.3%
8 2
 
0.9%
9 1
 
0.5%
ValueCountFrequency (%)
348 1
0.5%
221 1
0.5%
194 1
0.5%
187 1
0.5%
186 1
0.5%
183 1
0.5%
172 1
0.5%
169 1
0.5%
161 1
0.5%
157 1
0.5%

1급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.045045
Minimum0
Maximum293
Zeros97
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:21:56.657291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315.75
95-th percentile100.85
Maximum293
Range293
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation40.068581
Coefficient of variation (CV)2.2204755
Kurtosis15.638204
Mean18.045045
Median Absolute Deviation (MAD)1
Skewness3.6059618
Sum4006
Variance1605.4912
MonotonicityNot monotonic
2024-03-15T08:21:57.100460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97
43.7%
1 18
 
8.1%
3 11
 
5.0%
2 8
 
3.6%
5 6
 
2.7%
14 4
 
1.8%
9 4
 
1.8%
12 4
 
1.8%
10 4
 
1.8%
16 3
 
1.4%
Other values (46) 63
28.4%
ValueCountFrequency (%)
0 97
43.7%
1 18
 
8.1%
2 8
 
3.6%
3 11
 
5.0%
4 2
 
0.9%
5 6
 
2.7%
6 2
 
0.9%
7 3
 
1.4%
8 1
 
0.5%
9 4
 
1.8%
ValueCountFrequency (%)
293 1
0.5%
210 1
0.5%
202 1
0.5%
185 1
0.5%
147 1
0.5%
145 1
0.5%
139 1
0.5%
127 1
0.5%
126 1
0.5%
115 1
0.5%

2급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.009009
Minimum0
Maximum483
Zeros46
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:21:57.538703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q355
95-th percentile188.9
Maximum483
Range483
Interquartile range (IQR)54

Descriptive statistics

Standard deviation76.134258
Coefficient of variation (CV)1.7299698
Kurtosis8.7917657
Mean44.009009
Median Absolute Deviation (MAD)8
Skewness2.7266094
Sum9770
Variance5796.4253
MonotonicityNot monotonic
2024-03-15T08:21:58.193145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
20.7%
1 24
 
10.8%
3 11
 
5.0%
2 11
 
5.0%
5 6
 
2.7%
14 4
 
1.8%
4 4
 
1.8%
7 4
 
1.8%
19 4
 
1.8%
6 4
 
1.8%
Other values (75) 104
46.8%
ValueCountFrequency (%)
0 46
20.7%
1 24
10.8%
2 11
 
5.0%
3 11
 
5.0%
4 4
 
1.8%
5 6
 
2.7%
6 4
 
1.8%
7 4
 
1.8%
8 2
 
0.9%
9 2
 
0.9%
ValueCountFrequency (%)
483 1
0.5%
376 1
0.5%
344 1
0.5%
336 1
0.5%
334 1
0.5%
315 1
0.5%
226 1
0.5%
224 1
0.5%
218 1
0.5%
211 1
0.5%

2급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.720721
Minimum0
Maximum399
Zeros60
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:21:58.514980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q342.25
95-th percentile159
Maximum399
Range399
Interquartile range (IQR)42.25

Descriptive statistics

Standard deviation58.992395
Coefficient of variation (CV)1.7494405
Kurtosis9.7137286
Mean33.720721
Median Absolute Deviation (MAD)5
Skewness2.7497847
Sum7486
Variance3480.1026
MonotonicityNot monotonic
2024-03-15T08:21:58.928393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
27.0%
1 28
 
12.6%
2 8
 
3.6%
3 8
 
3.6%
4 6
 
2.7%
7 5
 
2.3%
23 5
 
2.3%
12 5
 
2.3%
5 3
 
1.4%
6 3
 
1.4%
Other values (65) 91
41.0%
ValueCountFrequency (%)
0 60
27.0%
1 28
12.6%
2 8
 
3.6%
3 8
 
3.6%
4 6
 
2.7%
5 3
 
1.4%
6 3
 
1.4%
7 5
 
2.3%
8 3
 
1.4%
9 2
 
0.9%
ValueCountFrequency (%)
399 1
 
0.5%
303 1
 
0.5%
283 1
 
0.5%
235 1
 
0.5%
214 1
 
0.5%
178 1
 
0.5%
172 1
 
0.5%
167 1
 
0.5%
163 3
1.4%
159 2
0.9%

3급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.95045
Minimum0
Maximum843
Zeros39
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:21:59.419660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9.5
Q355.75
95-th percentile303.45
Maximum843
Range843
Interquartile range (IQR)54.75

Descriptive statistics

Standard deviation126.6956
Coefficient of variation (CV)2.0786656
Kurtosis17.190401
Mean60.95045
Median Absolute Deviation (MAD)9.5
Skewness3.7605808
Sum13531
Variance16051.776
MonotonicityNot monotonic
2024-03-15T08:21:59.689830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
 
17.6%
1 19
 
8.6%
2 17
 
7.7%
3 11
 
5.0%
4 7
 
3.2%
8 5
 
2.3%
14 5
 
2.3%
9 4
 
1.8%
7 4
 
1.8%
6 4
 
1.8%
Other values (78) 107
48.2%
ValueCountFrequency (%)
0 39
17.6%
1 19
8.6%
2 17
7.7%
3 11
 
5.0%
4 7
 
3.2%
5 1
 
0.5%
6 4
 
1.8%
7 4
 
1.8%
8 5
 
2.3%
9 4
 
1.8%
ValueCountFrequency (%)
843 1
0.5%
825 1
0.5%
782 1
0.5%
527 1
0.5%
417 1
0.5%
413 1
0.5%
409 1
0.5%
387 1
0.5%
358 1
0.5%
323 1
0.5%

3급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.788288
Minimum0
Maximum498
Zeros55
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:22:00.084539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q348
95-th percentile214.85
Maximum498
Range498
Interquartile range (IQR)47

Descriptive statistics

Standard deviation80.777866
Coefficient of variation (CV)1.8878499
Kurtosis9.5509054
Mean42.788288
Median Absolute Deviation (MAD)4
Skewness2.8573238
Sum9499
Variance6525.0636
MonotonicityNot monotonic
2024-03-15T08:22:00.450065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
24.8%
2 19
 
8.6%
1 17
 
7.7%
4 12
 
5.4%
3 11
 
5.0%
5 5
 
2.3%
6 4
 
1.8%
8 4
 
1.8%
22 4
 
1.8%
7 3
 
1.4%
Other values (75) 88
39.6%
ValueCountFrequency (%)
0 55
24.8%
1 17
 
7.7%
2 19
 
8.6%
3 11
 
5.0%
4 12
 
5.4%
5 5
 
2.3%
6 4
 
1.8%
7 3
 
1.4%
8 4
 
1.8%
9 2
 
0.9%
ValueCountFrequency (%)
498 1
0.5%
437 1
0.5%
430 1
0.5%
322 1
0.5%
289 1
0.5%
286 1
0.5%
247 1
0.5%
235 1
0.5%
227 1
0.5%
224 1
0.5%

4급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.599099
Minimum0
Maximum921
Zeros92
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:22:00.705632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q321
95-th percentile233.95
Maximum921
Range921
Interquartile range (IQR)21

Descriptive statistics

Standard deviation122.16189
Coefficient of variation (CV)2.801936
Kurtosis25.212391
Mean43.599099
Median Absolute Deviation (MAD)1.5
Skewness4.6968703
Sum9679
Variance14923.526
MonotonicityNot monotonic
2024-03-15T08:22:00.956282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92
41.4%
1 19
 
8.6%
4 9
 
4.1%
3 6
 
2.7%
2 5
 
2.3%
13 4
 
1.8%
20 4
 
1.8%
5 3
 
1.4%
11 3
 
1.4%
10 3
 
1.4%
Other values (64) 74
33.3%
ValueCountFrequency (%)
0 92
41.4%
1 19
 
8.6%
2 5
 
2.3%
3 6
 
2.7%
4 9
 
4.1%
5 3
 
1.4%
6 2
 
0.9%
7 1
 
0.5%
8 1
 
0.5%
9 2
 
0.9%
ValueCountFrequency (%)
921 1
0.5%
818 1
0.5%
717 1
0.5%
632 1
0.5%
432 1
0.5%
420 1
0.5%
363 1
0.5%
336 1
0.5%
314 1
0.5%
301 1
0.5%

4급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.297297
Minimum0
Maximum1117
Zeros103
Zeros (%)46.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:22:01.290412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q316
95-th percentile245.9
Maximum1117
Range1117
Interquartile range (IQR)16

Descriptive statistics

Standard deviation153.50865
Coefficient of variation (CV)3.1139365
Kurtosis23.843873
Mean49.297297
Median Absolute Deviation (MAD)1
Skewness4.6839947
Sum10944
Variance23564.907
MonotonicityNot monotonic
2024-03-15T08:22:01.664109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
46.4%
1 11
 
5.0%
5 7
 
3.2%
3 6
 
2.7%
6 6
 
2.7%
4 6
 
2.7%
2 5
 
2.3%
10 5
 
2.3%
13 4
 
1.8%
11 4
 
1.8%
Other values (53) 65
29.3%
ValueCountFrequency (%)
0 103
46.4%
1 11
 
5.0%
2 5
 
2.3%
3 6
 
2.7%
4 6
 
2.7%
5 7
 
3.2%
6 6
 
2.7%
7 2
 
0.9%
8 2
 
0.9%
9 2
 
0.9%
ValueCountFrequency (%)
1117 1
0.5%
989 1
0.5%
895 1
0.5%
717 1
0.5%
701 1
0.5%
662 1
0.5%
466 1
0.5%
455 1
0.5%
447 1
0.5%
372 1
0.5%

5급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.95045
Minimum0
Maximum1351
Zeros95
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:22:02.088884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q322.5
95-th percentile324.5
Maximum1351
Range1351
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation193.35928
Coefficient of variation (CV)3.0716108
Kurtosis27.31073
Mean62.95045
Median Absolute Deviation (MAD)2
Skewness4.9738082
Sum13975
Variance37387.812
MonotonicityNot monotonic
2024-03-15T08:22:02.594087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
42.8%
1 14
 
6.3%
2 8
 
3.6%
3 6
 
2.7%
9 5
 
2.3%
12 4
 
1.8%
7 4
 
1.8%
4 4
 
1.8%
6 4
 
1.8%
8 4
 
1.8%
Other values (62) 74
33.3%
ValueCountFrequency (%)
0 95
42.8%
1 14
 
6.3%
2 8
 
3.6%
3 6
 
2.7%
4 4
 
1.8%
6 4
 
1.8%
7 4
 
1.8%
8 4
 
1.8%
9 5
 
2.3%
10 3
 
1.4%
ValueCountFrequency (%)
1351 1
0.5%
1349 1
0.5%
1278 1
0.5%
1073 1
0.5%
662 1
0.5%
639 1
0.5%
598 1
0.5%
538 1
0.5%
409 1
0.5%
374 1
0.5%

5급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.63964
Minimum0
Maximum1574
Zeros102
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:22:02.945117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315
95-th percentile317.6
Maximum1574
Range1574
Interquartile range (IQR)15

Descriptive statistics

Standard deviation222.27631
Coefficient of variation (CV)3.2861841
Kurtosis24.657288
Mean67.63964
Median Absolute Deviation (MAD)1
Skewness4.7894593
Sum15016
Variance49406.756
MonotonicityNot monotonic
2024-03-15T08:22:03.432364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
45.9%
2 13
 
5.9%
1 11
 
5.0%
3 7
 
3.2%
5 5
 
2.3%
4 5
 
2.3%
13 4
 
1.8%
9 4
 
1.8%
26 3
 
1.4%
6 3
 
1.4%
Other values (54) 65
29.3%
ValueCountFrequency (%)
0 102
45.9%
1 11
 
5.0%
2 13
 
5.9%
3 7
 
3.2%
4 5
 
2.3%
5 5
 
2.3%
6 3
 
1.4%
7 3
 
1.4%
8 1
 
0.5%
9 4
 
1.8%
ValueCountFrequency (%)
1574 1
0.5%
1455 1
0.5%
1314 1
0.5%
1244 1
0.5%
901 1
0.5%
888 1
0.5%
735 1
0.5%
643 1
0.5%
572 1
0.5%
466 1
0.5%

6급(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.936937
Minimum0
Maximum1895
Zeros162
Zeros (%)73.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:22:03.864559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317.75
95-th percentile579.55
Maximum1895
Range1895
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation271.14736
Coefficient of variation (CV)3.15519
Kurtosis25.372603
Mean85.936937
Median Absolute Deviation (MAD)0
Skewness4.7709044
Sum19078
Variance73520.892
MonotonicityNot monotonic
2024-03-15T08:22:04.383987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162
73.0%
55 3
 
1.4%
15 2
 
0.9%
114 2
 
0.9%
1490 1
 
0.5%
119 1
 
0.5%
30 1
 
0.5%
235 1
 
0.5%
97 1
 
0.5%
22 1
 
0.5%
Other values (47) 47
 
21.2%
ValueCountFrequency (%)
0 162
73.0%
12 1
 
0.5%
15 2
 
0.9%
17 1
 
0.5%
18 1
 
0.5%
22 1
 
0.5%
23 1
 
0.5%
26 1
 
0.5%
29 1
 
0.5%
30 1
 
0.5%
ValueCountFrequency (%)
1895 1
0.5%
1804 1
0.5%
1779 1
0.5%
1490 1
0.5%
890 1
0.5%
798 1
0.5%
772 1
0.5%
722 1
0.5%
700 1
0.5%
651 1
0.5%

6급(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.981982
Minimum0
Maximum1203
Zeros162
Zeros (%)73.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:22:04.848080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.75
95-th percentile378.15
Maximum1203
Range1203
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation172.27774
Coefficient of variation (CV)3.0233722
Kurtosis21.46982
Mean56.981982
Median Absolute Deviation (MAD)0
Skewness4.3998984
Sum12650
Variance29679.62
MonotonicityNot monotonic
2024-03-15T08:22:05.567493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162
73.0%
30 2
 
0.9%
5 2
 
0.9%
52 2
 
0.9%
3 1
 
0.5%
187 1
 
0.5%
63 1
 
0.5%
22 1
 
0.5%
170 1
 
0.5%
48 1
 
0.5%
Other values (48) 48
 
21.6%
ValueCountFrequency (%)
0 162
73.0%
3 1
 
0.5%
5 2
 
0.9%
6 1
 
0.5%
11 1
 
0.5%
16 1
 
0.5%
20 1
 
0.5%
21 1
 
0.5%
22 1
 
0.5%
23 1
 
0.5%
ValueCountFrequency (%)
1203 1
0.5%
1061 1
0.5%
1018 1
0.5%
962 1
0.5%
642 1
0.5%
609 1
0.5%
518 1
0.5%
505 1
0.5%
432 1
0.5%
424 1
0.5%

Interactions

2024-03-15T08:21:50.600557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:18.065330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:21.202220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:24.475370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:27.487443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:30.727780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:33.672832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:35.842703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:38.601850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:41.564423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:45.044970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:48.285824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:50.862652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:18.326375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:21.457518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:24.720580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:27.750761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:30.995872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:33.833361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:36.071677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:38.898617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:41.831504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:45.345456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:48.454681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:51.121989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:18.582050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:21.714054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:24.973412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:27.927702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:31.265373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:33.991207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:36.316309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:39.166442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:42.099214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:45.695226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:48.613444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:51.377801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:18.898510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:21.956827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:25.220964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:28.082278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:31.519540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:34.135593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:36.555225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:39.410314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:42.351463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:45.965351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:48.764471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:51.649815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:19.167498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:22.223776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:25.483631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:28.381654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:31.792833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:34.319057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:36.811498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:39.672628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:42.685681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:46.265475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:48.940930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:51.887980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:19.403131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:22.459858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:25.723164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:28.662451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:32.049649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:34.455614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:37.043317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:39.861483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:42.954644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:46.529343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:49.216150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:52.136499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:19.657508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:22.718880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:25.968301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:29.007281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:32.314487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:34.595236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:37.223179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:40.032740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:43.278237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:46.785880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:49.436595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:52.381393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:19.898825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:22.956117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:26.210521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:29.266325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:32.551596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:34.743418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:37.458012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:40.267460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:43.553452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:47.036344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:49.593381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:52.628349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:20.147351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:23.420832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:26.453933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:29.542724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:32.703658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:34.993147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:37.700644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:40.509083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:43.801882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:47.293954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:49.753092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:52.896530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:20.409205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:23.688125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:26.710263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:29.866540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:32.876030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:35.377308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:37.942012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:40.769818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:44.099286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:47.692927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:49.917422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:53.162561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:20.678973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:23.955938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:26.980098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:30.177058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:33.134343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:35.538223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:38.167350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:41.040803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:44.395463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:47.864131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:50.108876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:53.435118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:20.943681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:24.222824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:27.237955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:30.462442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:33.393755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:35.694035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:38.353490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:41.311395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:44.749793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:48.073028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:21:50.323792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:22:05.882187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구장애유형1급(남)1급(여)2급(남)2급(여)3급(남)3급(여)4급(남)4급(여)5급(남)5급(여)6급(남)6급(여)
시군구1.0000.0000.2540.2190.2040.2540.0000.0000.0000.0000.0000.0000.0000.000
장애유형0.0001.0000.5330.5360.5660.5130.5780.5740.5150.5420.6170.5610.5970.540
1급(남)0.2540.5331.0000.9170.9100.9410.6720.6730.6570.5500.6340.6390.6450.649
1급(여)0.2190.5360.9171.0000.7780.8530.6460.7840.7250.6170.4700.5480.4740.739
2급(남)0.2040.5660.9100.7781.0000.9550.7660.8080.7700.7500.6740.6950.6490.654
2급(여)0.2540.5130.9410.8530.9551.0000.7050.7740.6590.6520.5410.5510.4390.514
3급(남)0.0000.5780.6720.6460.7660.7051.0000.9240.8580.8200.9540.8710.9470.833
3급(여)0.0000.5740.6730.7840.8080.7740.9241.0000.9670.9520.8610.9450.8150.926
4급(남)0.0000.5150.6570.7250.7700.6590.8580.9671.0000.9820.9320.9890.8990.976
4급(여)0.0000.5420.5500.6170.7500.6520.8200.9520.9821.0000.9210.9770.8750.956
5급(남)0.0000.6170.6340.4700.6740.5410.9540.8610.9320.9211.0000.9420.9680.899
5급(여)0.0000.5610.6390.5480.6950.5510.8710.9450.9890.9770.9421.0000.9190.972
6급(남)0.0000.5970.6450.4740.6490.4390.9470.8150.8990.8750.9680.9191.0000.944
6급(여)0.0000.5400.6490.7390.6540.5140.8330.9260.9760.9560.8990.9720.9441.000
2024-03-15T08:22:06.235974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애유형시군구
장애유형1.0000.000
시군구0.0001.000
2024-03-15T08:22:06.497420image/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)시군구장애유형
1급(남)1.0000.9110.8330.8210.6650.6300.2470.2610.3680.4020.6090.6060.1080.256
1급(여)0.9111.0000.8010.8350.6800.6780.3160.3410.4000.4400.6670.6630.0880.247
2급(남)0.8330.8011.0000.9370.7090.6780.2050.1990.2860.3100.4590.4610.0860.278
2급(여)0.8210.8350.9371.0000.7490.7440.3080.3130.3560.3830.5240.5240.1080.244
3급(남)0.6650.6800.7090.7491.0000.9360.3620.4050.1810.2450.5500.5500.0000.300
3급(여)0.6300.6780.6780.7440.9361.0000.4000.4560.2390.2880.5760.5770.0000.271
4급(남)0.2470.3160.2050.3080.3620.4001.0000.9500.7150.7590.7400.7420.0000.234
4급(여)0.2610.3410.1990.3130.4050.4560.9501.0000.6670.7180.7740.7750.0000.251
5급(남)0.3680.4000.2860.3560.1810.2390.7150.6671.0000.9540.7610.7620.0000.329
5급(여)0.4020.4400.3100.3830.2450.2880.7590.7180.9541.0000.7890.7910.0000.263
6급(남)0.6090.6670.4590.5240.5500.5760.7400.7740.7610.7891.0000.9990.0000.314
6급(여)0.6060.6630.4610.5240.5500.5770.7420.7750.7620.7910.9991.0000.0000.249
시군구0.1080.0880.0860.1080.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
장애유형0.2560.2470.2780.2440.3000.2710.2340.2510.3290.2630.3140.2490.0001.000

Missing values

2024-03-15T08:21:53.811444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:21:54.516280image/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

시군구장애유형1급(남)1급(여)2급(남)2급(여)3급(남)3급(여)4급(남)4급(여)5급(남)5급(여)6급(남)6급(여)
0전주시덕진구지체10962218127527322632717107312441490962
1전주시덕진구시각75611223344228275249585329
2전주시덕진구청각139831161249617014824820313586
3전주시덕진구언어00134452550160000
4전주시덕진구지적114103226163305173000000
5전주시덕진구뇌병변161139142138171149100781117610858
6전주시덕진구자폐성2674310212000000
7전주시덕진구정신556983180212000000
8전주시덕진구신장2051621130042713600
9전주시덕진구심장000086003200
시군구장애유형1급(남)1급(여)2급(남)2급(여)3급(남)3급(여)4급(남)4급(여)5급(남)5급(여)6급(남)6급(여)
212부안군뇌병변344446476462443331262923
213부안군자폐성309120000000
214부안군정신7548255658000000
215부안군신장403815000016400
216부안군심장001096000000
217부안군호흡기112192000000
218부안군000001009000
219부안군안면000002000000
220부안군장루.요루0000201361400
221부안군뇌전증000022551000