Overview

Dataset statistics

Number of variables17
Number of observations437
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.3 KiB
Average record size in memory148.3 B

Variable types

Categorical5
Numeric12

Dataset

Description경기도 용인시 장애인 유형별, 등급별 등록현황입니다. 읍면동, 장애유형, 장애등급별 등록수 등의 데이터를 제공합니다. ※ 데이터기준일자 : 2014-02-28
URLhttps://www.data.go.kr/data/15050968/fileData.do

Alerts

시도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
시군구 is highly overall correlated with 읍면동High correlation
읍면동 is highly overall correlated with 시군구High correlation
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 8 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 2급 여 and 5 other fieldsHigh correlation
5급 여 is highly overall correlated with 2급 여 and 5 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 202 (46.2%) zerosZeros
1급 여 has 238 (54.5%) zerosZeros
2급 남 has 151 (34.6%) zerosZeros
2급 여 has 192 (43.9%) zerosZeros
3급 남 has 123 (28.1%) zerosZeros
3급 여 has 179 (41.0%) zerosZeros
4급 남 has 231 (52.9%) zerosZeros
4급 여 has 265 (60.6%) zerosZeros
5급 남 has 219 (50.1%) zerosZeros
5급 여 has 239 (54.7%) zerosZeros
6급 남 has 314 (71.9%) zerosZeros
6급 여 has 315 (72.1%) zerosZeros

Reproduction

Analysis started2023-12-12 08:53:41.095685
Analysis finished2023-12-12 08:53:59.112269
Duration18.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
경기도
437 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 437
100.0%

Length

2023-12-12T17:53:59.189294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:59.288184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 437
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
용인시 기흥구
156 
용인시 처인구
153 
용인시 수지구
128 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용인시 처인구
2nd row용인시 처인구
3rd row용인시 처인구
4th row용인시 처인구
5th row용인시 처인구

Common Values

ValueCountFrequency (%)
용인시 기흥구 156
35.7%
용인시 처인구 153
35.0%
용인시 수지구 128
29.3%

Length

2023-12-12T17:53:59.383330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:59.510053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용인시 437
50.0%
기흥구 156
 
17.8%
처인구 153
 
17.5%
수지구 128
 
14.6%

읍면동
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
포곡읍
 
15
양지면
 
15
중앙동
 
15
구성동
 
15
이동면
 
15
Other values (26)
362 

Length

Max length5
Median length3
Mean length3.2585812
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
포곡읍 15
 
3.4%
양지면 15
 
3.4%
중앙동 15
 
3.4%
구성동 15
 
3.4%
이동면 15
 
3.4%
동천동 15
 
3.4%
상현1동 15
 
3.4%
유림동 15
 
3.4%
죽전1동 15
 
3.4%
영덕동 15
 
3.4%
Other values (21) 287
65.7%

Length

2023-12-12T17:53:59.634653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포곡읍 15
 
3.4%
유림동 15
 
3.4%
양지면 15
 
3.4%
동백동 15
 
3.4%
상갈동 15
 
3.4%
죽전1동 15
 
3.4%
영덕동 15
 
3.4%
상현1동 15
 
3.4%
동천동 15
 
3.4%
이동면 15
 
3.4%
Other values (21) 287
65.7%

장애유형
Categorical

Distinct15
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
지체
31 
시각
31 
청각
31 
지적
31 
뇌병변
31 
Other values (10)
282 

Length

Max length5
Median length2
Mean length2.3524027
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지체 31
 
7.1%
시각 31
 
7.1%
청각 31
 
7.1%
지적 31
 
7.1%
뇌병변 31
 
7.1%
자폐성 31
 
7.1%
정신 31
 
7.1%
신장 31
 
7.1%
장루.요루 31
 
7.1%
언어 30
 
6.9%
Other values (5) 128
29.3%

Length

2023-12-12T17:53:59.790051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지체 31
 
7.1%
시각 31
 
7.1%
청각 31
 
7.1%
지적 31
 
7.1%
뇌병변 31
 
7.1%
자폐성 31
 
7.1%
정신 31
 
7.1%
신장 31
 
7.1%
장루.요루 31
 
7.1%
언어 30
 
6.9%
Other values (5) 128
29.3%

1급 남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.215103
Minimum0
Maximum37
Zeros202
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:53:59.973054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile14.2
Maximum37
Range37
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.4055231
Coefficient of variation (CV)1.6812908
Kurtosis8.6928113
Mean3.215103
Median Absolute Deviation (MAD)1
Skewness2.6133705
Sum1405
Variance29.21968
MonotonicityNot monotonic
2023-12-12T17:54:00.122780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 202
46.2%
1 63
 
14.4%
2 34
 
7.8%
7 18
 
4.1%
4 16
 
3.7%
3 14
 
3.2%
5 14
 
3.2%
9 13
 
3.0%
6 9
 
2.1%
8 8
 
1.8%
Other values (17) 46
 
10.5%
ValueCountFrequency (%)
0 202
46.2%
1 63
 
14.4%
2 34
 
7.8%
3 14
 
3.2%
4 16
 
3.7%
5 14
 
3.2%
6 9
 
2.1%
7 18
 
4.1%
8 8
 
1.8%
9 13
 
3.0%
ValueCountFrequency (%)
37 1
 
0.2%
32 1
 
0.2%
31 1
 
0.2%
29 1
 
0.2%
27 1
 
0.2%
21 1
 
0.2%
20 1
 
0.2%
19 2
 
0.5%
18 5
1.1%
17 2
 
0.5%

1급 여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5286041
Minimum0
Maximum43
Zeros238
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:00.283512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile12
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.9299898
Coefficient of variation (CV)1.9496883
Kurtosis16.697147
Mean2.5286041
Median Absolute Deviation (MAD)0
Skewness3.3978454
Sum1105
Variance24.304799
MonotonicityNot monotonic
2023-12-12T17:54:00.437075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 238
54.5%
1 62
 
14.2%
2 23
 
5.3%
4 15
 
3.4%
3 15
 
3.4%
6 14
 
3.2%
7 12
 
2.7%
5 8
 
1.8%
11 8
 
1.8%
8 7
 
1.6%
Other values (13) 35
 
8.0%
ValueCountFrequency (%)
0 238
54.5%
1 62
 
14.2%
2 23
 
5.3%
3 15
 
3.4%
4 15
 
3.4%
5 8
 
1.8%
6 14
 
3.2%
7 12
 
2.7%
8 7
 
1.6%
9 5
 
1.1%
ValueCountFrequency (%)
43 1
 
0.2%
35 1
 
0.2%
23 2
0.5%
22 1
 
0.2%
19 4
0.9%
18 1
 
0.2%
17 1
 
0.2%
16 3
0.7%
15 3
0.7%
13 1
 
0.2%

2급 남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5286041
Minimum0
Maximum75
Zeros151
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:00.590236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile19.2
Maximum75
Range75
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.6501623
Coefficient of variation (CV)1.3837421
Kurtosis15.648648
Mean5.5286041
Median Absolute Deviation (MAD)2
Skewness2.7149708
Sum2416
Variance58.524983
MonotonicityNot monotonic
2023-12-12T17:54:00.789952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 151
34.6%
1 61
14.0%
2 23
 
5.3%
7 18
 
4.1%
9 15
 
3.4%
3 14
 
3.2%
5 14
 
3.2%
8 14
 
3.2%
6 14
 
3.2%
4 12
 
2.7%
Other values (22) 101
23.1%
ValueCountFrequency (%)
0 151
34.6%
1 61
14.0%
2 23
 
5.3%
3 14
 
3.2%
4 12
 
2.7%
5 14
 
3.2%
6 14
 
3.2%
7 18
 
4.1%
8 14
 
3.2%
9 15
 
3.4%
ValueCountFrequency (%)
75 1
 
0.2%
31 1
 
0.2%
29 2
0.5%
28 2
0.5%
27 3
0.7%
26 1
 
0.2%
25 1
 
0.2%
24 3
0.7%
23 2
0.5%
22 2
0.5%

2급 여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2265446
Minimum0
Maximum74
Zeros192
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:00.963621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile15
Maximum74
Range74
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.671618
Coefficient of variation (CV)1.5785041
Kurtosis29.607277
Mean4.2265446
Median Absolute Deviation (MAD)1
Skewness3.8290033
Sum1847
Variance44.510486
MonotonicityNot monotonic
2023-12-12T17:54:01.142734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 192
43.9%
1 48
 
11.0%
2 23
 
5.3%
4 19
 
4.3%
7 14
 
3.2%
3 13
 
3.0%
6 13
 
3.0%
8 13
 
3.0%
9 12
 
2.7%
12 12
 
2.7%
Other values (17) 78
17.8%
ValueCountFrequency (%)
0 192
43.9%
1 48
 
11.0%
2 23
 
5.3%
3 13
 
3.0%
4 19
 
4.3%
5 10
 
2.3%
6 13
 
3.0%
7 14
 
3.2%
8 13
 
3.0%
9 12
 
2.7%
ValueCountFrequency (%)
74 1
 
0.2%
44 1
 
0.2%
27 1
 
0.2%
25 1
 
0.2%
23 1
 
0.2%
22 1
 
0.2%
21 2
0.5%
20 1
 
0.2%
18 4
0.9%
17 3
0.7%

3급 남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0800915
Minimum0
Maximum77
Zeros123
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:01.327355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile32.2
Maximum77
Range77
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.106676
Coefficient of variation (CV)1.5687193
Kurtosis8.4561633
Mean7.0800915
Median Absolute Deviation (MAD)2
Skewness2.6185873
Sum3094
Variance123.35825
MonotonicityNot monotonic
2023-12-12T17:54:01.511298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 123
28.1%
1 73
16.7%
2 33
 
7.6%
3 28
 
6.4%
10 14
 
3.2%
5 14
 
3.2%
4 13
 
3.0%
9 12
 
2.7%
11 12
 
2.7%
7 11
 
2.5%
Other values (35) 104
23.8%
ValueCountFrequency (%)
0 123
28.1%
1 73
16.7%
2 33
 
7.6%
3 28
 
6.4%
4 13
 
3.0%
5 14
 
3.2%
6 7
 
1.6%
7 11
 
2.5%
8 10
 
2.3%
9 12
 
2.7%
ValueCountFrequency (%)
77 1
 
0.2%
68 1
 
0.2%
54 1
 
0.2%
53 1
 
0.2%
52 2
0.5%
49 1
 
0.2%
45 1
 
0.2%
41 1
 
0.2%
40 3
0.7%
38 1
 
0.2%

3급 여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.215103
Minimum0
Maximum218
Zeros179
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:01.669429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile20.4
Maximum218
Range218
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.612592
Coefficient of variation (CV)2.4184742
Kurtosis186.55308
Mean5.215103
Median Absolute Deviation (MAD)1
Skewness11.506702
Sum2279
Variance159.07748
MonotonicityNot monotonic
2023-12-12T17:54:01.844768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 179
41.0%
1 61
 
14.0%
2 27
 
6.2%
4 17
 
3.9%
6 15
 
3.4%
5 14
 
3.2%
9 12
 
2.7%
7 12
 
2.7%
3 11
 
2.5%
12 10
 
2.3%
Other values (23) 79
18.1%
ValueCountFrequency (%)
0 179
41.0%
1 61
 
14.0%
2 27
 
6.2%
3 11
 
2.5%
4 17
 
3.9%
5 14
 
3.2%
6 15
 
3.4%
7 12
 
2.7%
8 6
 
1.4%
9 12
 
2.7%
ValueCountFrequency (%)
218 1
 
0.2%
55 1
 
0.2%
39 1
 
0.2%
36 1
 
0.2%
34 2
0.5%
31 1
 
0.2%
27 3
0.7%
26 1
 
0.2%
25 3
0.7%
24 2
0.5%

4급 남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9359268
Minimum0
Maximum75
Zeros231
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:01.975908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile30
Maximum75
Range75
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.590708
Coefficient of variation (CV)2.3482334
Kurtosis14.445632
Mean4.9359268
Median Absolute Deviation (MAD)0
Skewness3.6375751
Sum2157
Variance134.34451
MonotonicityNot monotonic
2023-12-12T17:54:02.151565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 231
52.9%
1 46
 
10.5%
2 27
 
6.2%
4 16
 
3.7%
3 16
 
3.7%
7 12
 
2.7%
6 12
 
2.7%
5 8
 
1.8%
12 8
 
1.8%
8 6
 
1.4%
Other values (35) 55
 
12.6%
ValueCountFrequency (%)
0 231
52.9%
1 46
 
10.5%
2 27
 
6.2%
3 16
 
3.7%
4 16
 
3.7%
5 8
 
1.8%
6 12
 
2.7%
7 12
 
2.7%
8 6
 
1.4%
9 4
 
0.9%
ValueCountFrequency (%)
75 1
0.2%
72 1
0.2%
68 2
0.5%
66 1
0.2%
64 1
0.2%
51 1
0.2%
49 1
0.2%
48 1
0.2%
47 2
0.5%
46 1
0.2%

4급 여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5423341
Minimum0
Maximum82
Zeros265
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:02.313532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile36.2
Maximum82
Range82
Interquartile range (IQR)2

Descriptive statistics

Standard deviation12.387333
Coefficient of variation (CV)2.7270855
Kurtosis14.01273
Mean4.5423341
Median Absolute Deviation (MAD)0
Skewness3.6970602
Sum1985
Variance153.44602
MonotonicityNot monotonic
2023-12-12T17:54:02.466311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 265
60.6%
1 46
 
10.5%
2 26
 
5.9%
3 17
 
3.9%
8 9
 
2.1%
4 8
 
1.8%
6 7
 
1.6%
5 6
 
1.4%
9 5
 
1.1%
11 4
 
0.9%
Other values (28) 44
 
10.1%
ValueCountFrequency (%)
0 265
60.6%
1 46
 
10.5%
2 26
 
5.9%
3 17
 
3.9%
4 8
 
1.8%
5 6
 
1.4%
6 7
 
1.6%
7 3
 
0.7%
8 9
 
2.1%
9 5
 
1.1%
ValueCountFrequency (%)
82 1
 
0.2%
74 1
 
0.2%
72 1
 
0.2%
65 1
 
0.2%
62 1
 
0.2%
59 3
0.7%
51 2
0.5%
48 1
 
0.2%
47 1
 
0.2%
46 1
 
0.2%

5급 남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.382151
Minimum0
Maximum152
Zeros219
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:02.633713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile63
Maximum152
Range152
Interquartile range (IQR)6

Descriptive statistics

Standard deviation22.285737
Coefficient of variation (CV)2.6587134
Kurtosis15.445167
Mean8.382151
Median Absolute Deviation (MAD)0
Skewness3.8820663
Sum3663
Variance496.65408
MonotonicityNot monotonic
2023-12-12T17:54:02.804923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219
50.1%
1 29
 
6.6%
2 26
 
5.9%
3 21
 
4.8%
4 16
 
3.7%
5 14
 
3.2%
7 14
 
3.2%
6 12
 
2.7%
9 11
 
2.5%
8 10
 
2.3%
Other values (40) 65
 
14.9%
ValueCountFrequency (%)
0 219
50.1%
1 29
 
6.6%
2 26
 
5.9%
3 21
 
4.8%
4 16
 
3.7%
5 14
 
3.2%
6 12
 
2.7%
7 14
 
3.2%
8 10
 
2.3%
9 11
 
2.5%
ValueCountFrequency (%)
152 1
0.2%
127 1
0.2%
123 1
0.2%
120 2
0.5%
116 1
0.2%
115 1
0.2%
111 1
0.2%
105 1
0.2%
102 1
0.2%
94 1
0.2%

5급 여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0297483
Minimum0
Maximum143
Zeros239
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:03.289937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile63.4
Maximum143
Range143
Interquartile range (IQR)3

Descriptive statistics

Standard deviation20.570566
Coefficient of variation (CV)2.9262166
Kurtosis15.432807
Mean7.0297483
Median Absolute Deviation (MAD)0
Skewness3.9048099
Sum3072
Variance423.1482
MonotonicityNot monotonic
2023-12-12T17:54:03.460575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 239
54.7%
1 36
 
8.2%
2 35
 
8.0%
3 22
 
5.0%
4 17
 
3.9%
5 15
 
3.4%
6 10
 
2.3%
10 6
 
1.4%
7 5
 
1.1%
9 5
 
1.1%
Other values (33) 47
 
10.8%
ValueCountFrequency (%)
0 239
54.7%
1 36
 
8.2%
2 35
 
8.0%
3 22
 
5.0%
4 17
 
3.9%
5 15
 
3.4%
6 10
 
2.3%
7 5
 
1.1%
8 4
 
0.9%
9 5
 
1.1%
ValueCountFrequency (%)
143 1
 
0.2%
127 1
 
0.2%
113 1
 
0.2%
110 1
 
0.2%
104 1
 
0.2%
98 1
 
0.2%
95 1
 
0.2%
90 3
0.7%
87 1
 
0.2%
78 1
 
0.2%

6급 남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.592677
Minimum0
Maximum209
Zeros314
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:03.625987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile79
Maximum209
Range209
Interquartile range (IQR)5

Descriptive statistics

Standard deviation30.486706
Coefficient of variation (CV)2.6298244
Kurtosis12.616289
Mean11.592677
Median Absolute Deviation (MAD)0
Skewness3.4236204
Sum5066
Variance929.43921
MonotonicityNot monotonic
2023-12-12T17:54:03.820577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 314
71.9%
9 8
 
1.8%
8 8
 
1.8%
5 7
 
1.6%
7 5
 
1.1%
6 5
 
1.1%
3 4
 
0.9%
4 4
 
0.9%
10 4
 
0.9%
14 3
 
0.7%
Other values (55) 75
 
17.2%
ValueCountFrequency (%)
0 314
71.9%
1 2
 
0.5%
2 3
 
0.7%
3 4
 
0.9%
4 4
 
0.9%
5 7
 
1.6%
6 5
 
1.1%
7 5
 
1.1%
8 8
 
1.8%
9 8
 
1.8%
ValueCountFrequency (%)
209 1
0.2%
180 1
0.2%
170 1
0.2%
159 1
0.2%
144 1
0.2%
143 1
0.2%
141 1
0.2%
128 2
0.5%
124 1
0.2%
123 1
0.2%

6급 여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1006865
Minimum0
Maximum91
Zeros315
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T17:54:04.008622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile45
Maximum91
Range91
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.457071
Coefficient of variation (CV)2.5336609
Kurtosis9.6233044
Mean6.1006865
Median Absolute Deviation (MAD)0
Skewness3.1077163
Sum2666
Variance238.92103
MonotonicityNot monotonic
2023-12-12T17:54:04.208017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 315
72.1%
4 12
 
2.7%
2 9
 
2.1%
3 8
 
1.8%
5 7
 
1.6%
6 6
 
1.4%
7 6
 
1.4%
1 5
 
1.1%
8 4
 
0.9%
28 3
 
0.7%
Other values (39) 62
 
14.2%
ValueCountFrequency (%)
0 315
72.1%
1 5
 
1.1%
2 9
 
2.1%
3 8
 
1.8%
4 12
 
2.7%
5 7
 
1.6%
6 6
 
1.4%
7 6
 
1.4%
8 4
 
0.9%
9 3
 
0.7%
ValueCountFrequency (%)
91 1
 
0.2%
83 1
 
0.2%
78 1
 
0.2%
76 1
 
0.2%
74 3
0.7%
69 1
 
0.2%
65 1
 
0.2%
60 1
 
0.2%
57 2
0.5%
56 3
0.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2014-02-28
437 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014-02-28
2nd row2014-02-28
3rd row2014-02-28
4th row2014-02-28
5th row2014-02-28

Common Values

ValueCountFrequency (%)
2014-02-28 437
100.0%

Length

2023-12-12T17:54:04.378735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:04.495792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014-02-28 437
100.0%

Interactions

2023-12-12T17:53:57.433291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:42.123414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.611651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.945579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.498673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.965656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.199168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.469970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.934772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.219834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.694351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.048684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.547936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:42.250160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.712680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:45.077702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.608104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.099568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.279271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.565126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.061808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.357768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.805839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.167662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.634949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:42.344289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.793665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:45.197546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.707350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.229050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.594532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.663129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.175297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.487260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.943570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.252125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.734112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:42.776705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.896353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:45.316815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.836500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.360430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.670326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.769867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.288400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.597119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.096368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.350076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.827391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:42.875261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.000173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:45.445549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.966090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.483863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.762937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.913578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.400662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.721632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.206829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.453470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.963088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:42.971715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.119015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:45.609346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.103424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.584934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.859004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.054775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.518111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.856302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.336080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.549255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:58.065832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.059083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.234738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:45.764575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.228611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.685162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.942070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.162157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.607942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.976662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.430554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.887551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:58.168920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.154672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.354751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:45.903012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.361279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.781200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.030602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.290764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.727668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.101057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.527556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:56.968939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:58.274187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.229176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.459487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.014972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.467041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.864166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.111642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.418833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.817237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.233958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.607243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.052058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:58.399776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.335890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.589188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.136143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.597683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:48.949430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.199711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.563666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:52.918930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.363143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.729433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.142271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:58.512950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.429611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.725519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.277708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.711735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.033949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.296448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.714197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.033833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.493997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.831686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.252599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:58.623238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:43.523622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:44.834488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:46.400827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:47.836994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:49.111520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:50.385770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:51.812521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:53.133049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:54.576842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:55.934606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:57.341465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:54:04.611247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구읍면동장애유형1급 남1급 여2급 남2급 여3급 남3급 여4급 남4급 여5급 남5급 여6급 남6급 여
시군구1.0001.0000.0000.0000.0000.0720.1240.0000.0000.0000.0340.0000.0920.1230.000
읍면동1.0001.0000.0000.0000.0000.2710.1760.0000.0000.0000.0000.0000.0000.0000.000
장애유형0.0000.0001.0000.6280.6170.6490.6000.7190.4950.7310.7040.6780.6540.7500.743
1급 남0.0000.0000.6281.0000.7860.5360.6170.6640.6090.6850.5250.5560.5710.6100.512
1급 여0.0000.0000.6170.7861.0000.4180.6100.5170.3710.4060.4080.2360.2070.3280.323
2급 남0.0720.2710.6490.5360.4181.0000.9340.8300.5020.5860.6640.6050.5880.7650.635
2급 여0.1240.1760.6000.6170.6100.9341.0000.7320.8400.5110.6410.5580.5270.6020.598
3급 남0.0000.0000.7190.6640.5170.8300.7321.0000.8470.8110.9130.9420.9260.9470.899
3급 여0.0000.0000.4950.6090.3710.5020.8400.8471.0000.7120.7580.8470.8420.8560.757
4급 남0.0000.0000.7310.6850.4060.5860.5110.8110.7121.0000.8140.8930.8340.8230.762
4급 여0.0340.0000.7040.5250.4080.6640.6410.9130.7580.8141.0000.9590.9350.9360.936
5급 남0.0000.0000.6780.5560.2360.6050.5580.9420.8470.8930.9591.0000.9540.9390.930
5급 여0.0920.0000.6540.5710.2070.5880.5270.9260.8420.8340.9350.9541.0000.9450.922
6급 남0.1230.0000.7500.6100.3280.7650.6020.9470.8560.8230.9360.9390.9451.0000.939
6급 여0.0000.0000.7430.5120.3230.6350.5980.8990.7570.7620.9360.9300.9220.9391.000
2023-12-12T17:54:04.786384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구장애유형읍면동
시군구1.0000.0000.967
장애유형0.0001.0000.000
읍면동0.9670.0001.000
2023-12-12T17:54:04.900708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1급 남1급 여2급 남2급 여3급 남3급 여4급 남4급 여5급 남5급 여6급 남6급 여시군구읍면동장애유형
1급 남1.0000.7770.6510.6000.6040.5590.2880.3630.3670.3780.5720.5670.0000.0000.312
1급 여0.7771.0000.5980.6140.6230.6200.3460.4060.3700.3560.6140.6150.0000.0000.318
2급 남0.6510.5981.0000.8500.6260.6040.2900.3320.4270.4580.4430.4420.0160.1010.362
2급 여0.6000.6140.8501.0000.6170.6650.3490.4070.5080.5430.5120.5110.0510.0750.327
3급 남0.6040.6230.6260.6171.0000.8370.3750.4410.2550.2660.5320.5250.0000.0000.361
3급 여0.5590.6200.6040.6650.8371.0000.4010.4620.3240.3340.5980.5980.0000.0000.299
4급 남0.2880.3460.2900.3490.3750.4011.0000.8410.6680.6560.7720.7690.0000.0000.403
4급 여0.3630.4060.3320.4070.4410.4620.8411.0000.6790.6820.8140.8130.0190.0000.348
5급 남0.3670.3700.4270.5080.2550.3240.6680.6791.0000.8940.7670.7630.0000.0000.325
5급 여0.3780.3560.4580.5430.2660.3340.6560.6820.8941.0000.7490.7500.0530.0000.307
6급 남0.5720.6140.4430.5120.5320.5980.7720.8140.7670.7491.0000.9870.0720.0000.391
6급 여0.5670.6150.4420.5110.5250.5980.7690.8130.7630.7500.9871.0000.0000.0000.384
시군구0.0000.0000.0160.0510.0000.0000.0000.0190.0000.0530.0720.0001.0000.9670.000
읍면동0.0000.0000.1010.0750.0000.0000.0000.0000.0000.0000.0000.0000.9671.0000.000
장애유형0.3120.3180.3620.3270.3610.2990.4030.3480.3250.3070.3910.3840.0000.0001.000

Missing values

2023-12-12T17:53:58.760721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:53:59.027041image/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

시도시군구읍면동장애유형1급 남1급 여2급 남2급 여3급 남3급 여4급 남4급 여5급 남5급 여6급 남6급 여데이터기준일자
0경기도용인시 처인구포곡읍지체9525186827754812390180742014-02-28
1경기도용인시 처인구포곡읍시각990222439259212014-02-28
2경기도용인시 처인구포곡읍청각211014201012122314792014-02-28
3경기도용인시 처인구포곡읍언어0011302100002014-02-28
4경기도용인시 처인구포곡읍지적178191333170000002014-02-28
5경기도용인시 처인구포곡읍뇌병변314320141641912134752014-02-28
6경기도용인시 처인구포곡읍자폐성2110100000002014-02-28
7경기도용인시 처인구포곡읍정신10748140000002014-02-28
8경기도용인시 처인구포곡읍신장111414001054002014-02-28
9경기도용인시 처인구포곡읍심장0000310000002014-02-28
시도시군구읍면동장애유형1급 남1급 여2급 남2급 여3급 남3급 여4급 남4급 여5급 남5급 여6급 남6급 여데이터기준일자
427경기도용인시 수지구성복동지적10711139120000002014-02-28
428경기도용인시 수지구성복동뇌병변152310132015133122852014-02-28
429경기도용인시 수지구성복동자폐성5010100000002014-02-28
430경기도용인시 수지구성복동정신0051680000002014-02-28
431경기도용인시 수지구성복동신장101660000127002014-02-28
432경기도용인시 수지구성복동심장0031320000002014-02-28
433경기도용인시 수지구성복동호흡기2020110000002014-02-28
434경기도용인시 수지구성복동10002000143002014-02-28
435경기도용인시 수지구성복동안면0000000100002014-02-28
436경기도용인시 수지구성복동장루.요루0010003100002014-02-28