Overview

Dataset statistics

Number of variables9
Number of observations330
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 KiB
Average record size in memory77.4 B

Variable types

Categorical4
Numeric5

Dataset

Description전라남도에 거주하는 장애인의 유형(지체장애, 시각장애, 언어장애, 청각장애, 지적장애, 뇌병변, 자폐성 등)과 인원수, 정도에 관한 데이터를 조회하실 수 있습니다.
URLhttps://www.data.go.kr/data/15105791/fileData.do

Alerts

장애정도 has constant value ""Constant
장애정도.1 has constant value ""Constant
남성 is highly overall correlated with 여성 and 1 other fieldsHigh correlation
여성 is highly overall correlated with 남성 and 1 other fieldsHigh correlation
남성.1 is highly overall correlated with 여성.1 and 1 other fieldsHigh correlation
여성.1 is highly overall correlated with 남성.1 and 1 other fieldsHigh correlation
합계 is highly overall correlated with 남성 and 3 other fieldsHigh correlation
남성 has 36 (10.9%) zerosZeros
여성 has 50 (15.2%) zerosZeros
남성.1 has 87 (26.4%) zerosZeros
여성.1 has 101 (30.6%) zerosZeros

Reproduction

Analysis started2023-12-12 15:23:03.420543
Analysis finished2023-12-12 15:23:06.834563
Duration3.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

Distinct22
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
목포시
 
15
여수시
 
15
순천시
 
15
나주시
 
15
광양시
 
15
Other values (17)
255 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목포시 15
 
4.5%
여수시 15
 
4.5%
순천시 15
 
4.5%
나주시 15
 
4.5%
광양시 15
 
4.5%
담양군 15
 
4.5%
곡성군 15
 
4.5%
구례군 15
 
4.5%
고흥군 15
 
4.5%
보성군 15
 
4.5%
Other values (12) 180
54.5%

Length

2023-12-13T00:23:06.903974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포시 15
 
4.5%
여수시 15
 
4.5%
진도군 15
 
4.5%
완도군 15
 
4.5%
장성군 15
 
4.5%
영광군 15
 
4.5%
함평군 15
 
4.5%
무안군 15
 
4.5%
영암군 15
 
4.5%
해남군 15
 
4.5%
Other values (12) 180
54.5%

장애유형
Categorical

Distinct15
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
지체
 
22
시각
 
22
청각
 
22
언어
 
22
지적
 
22
Other values (10)
220 

Length

Max length5
Median length2
Mean length2.4
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지체 22
 
6.7%
시각 22
 
6.7%
청각 22
 
6.7%
언어 22
 
6.7%
지적 22
 
6.7%
뇌병변 22
 
6.7%
자폐성 22
 
6.7%
정신 22
 
6.7%
신장 22
 
6.7%
심장 22
 
6.7%
Other values (5) 110
33.3%

Length

2023-12-13T00:23:07.386105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지체 22
 
6.7%
시각 22
 
6.7%
청각 22
 
6.7%
언어 22
 
6.7%
지적 22
 
6.7%
뇌병변 22
 
6.7%
자폐성 22
 
6.7%
정신 22
 
6.7%
신장 22
 
6.7%
심장 22
 
6.7%
Other values (5) 110
33.3%

장애정도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
심한 장애
330 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row심한 장애
2nd row심한 장애
3rd row심한 장애
4th row심한 장애
5th row심한 장애

Common Values

ValueCountFrequency (%)
심한 장애 330
100.0%

Length

2023-12-13T00:23:07.526564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:07.635129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
심한 330
50.0%
장애 330
50.0%

남성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct150
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.9
Minimum0
Maximum933
Zeros36
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T00:23:07.757805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median26
Q3110.25
95-th percentile361.55
Maximum933
Range933
Interquartile range (IQR)107.25

Descriptive statistics

Standard deviation147.08211
Coefficient of variation (CV)1.6732891
Kurtosis11.307855
Mean87.9
Median Absolute Deviation (MAD)26
Skewness3.0290875
Sum29007
Variance21633.148
MonotonicityNot monotonic
2023-12-13T00:23:07.917552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
10.9%
1 29
 
8.8%
2 15
 
4.5%
4 9
 
2.7%
3 9
 
2.7%
13 8
 
2.4%
9 7
 
2.1%
5 6
 
1.8%
10 6
 
1.8%
18 5
 
1.5%
Other values (140) 200
60.6%
ValueCountFrequency (%)
0 36
10.9%
1 29
8.8%
2 15
4.5%
3 9
 
2.7%
4 9
 
2.7%
5 6
 
1.8%
6 3
 
0.9%
7 5
 
1.5%
8 3
 
0.9%
9 7
 
2.1%
ValueCountFrequency (%)
933 1
0.3%
883 1
0.3%
845 1
0.3%
802 1
0.3%
797 1
0.3%
737 1
0.3%
541 1
0.3%
507 1
0.3%
458 2
0.6%
454 1
0.3%

여성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct130
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.054545
Minimum0
Maximum619
Zeros50
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T00:23:08.069278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median13
Q387
95-th percentile250.25
Maximum619
Range619
Interquartile range (IQR)86

Descriptive statistics

Standard deviation101.42479
Coefficient of variation (CV)1.6085246
Kurtosis8.2097827
Mean63.054545
Median Absolute Deviation (MAD)13
Skewness2.6261638
Sum20808
Variance10286.988
MonotonicityNot monotonic
2023-12-13T00:23:08.240081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
15.2%
1 35
 
10.6%
3 25
 
7.6%
2 20
 
6.1%
4 9
 
2.7%
5 6
 
1.8%
7 5
 
1.5%
33 4
 
1.2%
6 4
 
1.2%
8 4
 
1.2%
Other values (120) 168
50.9%
ValueCountFrequency (%)
0 50
15.2%
1 35
10.6%
2 20
 
6.1%
3 25
7.6%
4 9
 
2.7%
5 6
 
1.8%
6 4
 
1.2%
7 5
 
1.5%
8 4
 
1.2%
9 3
 
0.9%
ValueCountFrequency (%)
619 1
0.3%
559 1
0.3%
543 1
0.3%
497 1
0.3%
462 1
0.3%
450 1
0.3%
448 1
0.3%
423 1
0.3%
386 1
0.3%
367 1
0.3%

장애정도.1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
심하지 않은
330 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row심하지 않은
2nd row심하지 않은
3rd row심하지 않은
4th row심하지 않은
5th row심하지 않은

Common Values

ValueCountFrequency (%)
심하지 않은 330
100.0%

Length

2023-12-13T00:23:08.389447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:08.484277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
심하지 330
50.0%
않은 330
50.0%

남성.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.72727
Minimum0
Maximum3482
Zeros87
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T00:23:08.629733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q371.5
95-th percentile781.3
Maximum3482
Range3482
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation385.86335
Coefficient of variation (CV)2.8221389
Kurtosis38.585716
Mean136.72727
Median Absolute Deviation (MAD)8
Skewness5.53426
Sum45120
Variance148890.53
MonotonicityNot monotonic
2023-12-13T00:23:08.791460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 87
26.4%
1 29
 
8.8%
2 11
 
3.3%
4 9
 
2.7%
6 9
 
2.7%
13 8
 
2.4%
7 6
 
1.8%
11 6
 
1.8%
3 6
 
1.8%
8 6
 
1.8%
Other values (113) 153
46.4%
ValueCountFrequency (%)
0 87
26.4%
1 29
 
8.8%
2 11
 
3.3%
3 6
 
1.8%
4 9
 
2.7%
5 4
 
1.2%
6 9
 
2.7%
7 6
 
1.8%
8 6
 
1.8%
9 2
 
0.6%
ValueCountFrequency (%)
3482 1
0.3%
3395 1
0.3%
2681 1
0.3%
1755 1
0.3%
1470 1
0.3%
1293 1
0.3%
1227 1
0.3%
1163 1
0.3%
991 1
0.3%
930 1
0.3%

여성.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.03333
Minimum0
Maximum3343
Zeros101
Zeros (%)30.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T00:23:09.056024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q348.5
95-th percentile839.1
Maximum3343
Range3343
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation377.9218
Coefficient of variation (CV)2.8841653
Kurtosis30.205842
Mean131.03333
Median Absolute Deviation (MAD)4
Skewness4.9365741
Sum43241
Variance142824.88
MonotonicityNot monotonic
2023-12-13T00:23:09.227788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 101
30.6%
1 23
 
7.0%
3 20
 
6.1%
2 20
 
6.1%
4 12
 
3.6%
6 9
 
2.7%
11 7
 
2.1%
5 7
 
2.1%
12 5
 
1.5%
7 5
 
1.5%
Other values (102) 121
36.7%
ValueCountFrequency (%)
0 101
30.6%
1 23
 
7.0%
2 20
 
6.1%
3 20
 
6.1%
4 12
 
3.6%
5 7
 
2.1%
6 9
 
2.7%
7 5
 
1.5%
8 5
 
1.5%
9 3
 
0.9%
ValueCountFrequency (%)
3343 1
0.3%
2817 1
0.3%
2584 1
0.3%
1747 1
0.3%
1630 1
0.3%
1458 1
0.3%
1260 1
0.3%
1246 1
0.3%
1191 1
0.3%
1102 1
0.3%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct206
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.71515
Minimum1
Maximum8255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T00:23:09.393429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q116
median70.5
Q3429.75
95-th percentile1880.55
Maximum8255
Range8254
Interquartile range (IQR)413.75

Descriptive statistics

Standard deviation929.95432
Coefficient of variation (CV)2.2209713
Kurtosis31.011809
Mean418.71515
Median Absolute Deviation (MAD)66.5
Skewness4.8814236
Sum138176
Variance864815.04
MonotonicityNot monotonic
2023-12-13T00:23:09.563745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 14
 
4.2%
11 8
 
2.4%
28 7
 
2.1%
13 7
 
2.1%
1 6
 
1.8%
20 6
 
1.8%
2 6
 
1.8%
17 6
 
1.8%
7 6
 
1.8%
3 6
 
1.8%
Other values (196) 258
78.2%
ValueCountFrequency (%)
1 6
1.8%
2 6
1.8%
3 6
1.8%
4 14
4.2%
5 4
 
1.2%
6 1
 
0.3%
7 6
1.8%
8 4
 
1.2%
9 1
 
0.3%
10 4
 
1.2%
ValueCountFrequency (%)
8255 1
0.3%
7464 1
0.3%
6464 1
0.3%
3870 1
0.3%
3820 1
0.3%
3791 1
0.3%
3018 1
0.3%
2979 1
0.3%
2714 1
0.3%
2568 1
0.3%

Interactions

2023-12-13T00:23:06.129948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.848690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.429428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.079504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.630662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:06.227279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.947631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.550125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.170918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.734632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:06.325445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.071407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.692398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.284040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.834447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:06.427208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.201436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.827299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.400653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.930543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:06.522752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.314624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.960752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.531363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:06.034415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:23:09.665684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구장애유형남성여성남성.1여성.1합계
시군구1.0000.0000.3260.1730.0000.0000.000
장애유형0.0001.0000.5770.6520.5570.6230.608
남성0.3260.5771.0000.8830.7990.9440.818
여성0.1730.6520.8831.0000.7110.7600.754
남성.10.0000.5570.7990.7111.0000.9330.939
여성.10.0000.6230.9440.7600.9331.0000.940
합계0.0000.6080.8180.7540.9390.9401.000
2023-12-13T00:23:09.790689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애유형시군구
장애유형1.0000.000
시군구0.0001.000
2023-12-13T00:23:09.884024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남성여성남성.1여성.1합계시군구장애유형
남성1.0000.9500.2780.3120.8910.1290.275
여성0.9501.0000.3520.3730.9000.0620.305
남성.10.2780.3521.0000.9510.6070.0000.274
여성.10.3120.3730.9511.0000.6260.0000.307
합계0.8910.9000.6070.6261.0000.0000.325
시군구0.1290.0620.0000.0000.0001.0000.000
장애유형0.2750.3050.2740.3070.3250.0001.000

Missing values

2023-12-13T00:23:06.634355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:23:06.779480image/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

시군구장애유형장애정도남성여성장애정도.1남성.1여성.1합계
0목포시지체심한 장애737462심하지 않은268125846464
1목포시시각심한 장애153117심하지 않은6545271451
2목포시청각심한 장애210199심하지 않은5645191492
3목포시언어심한 장애3119심하지 않은4820118
4목포시지적심한 장애845543심하지 않은001388
5목포시뇌병변심한 장애401333심하지 않은3432121289
6목포시자폐성심한 장애11128심하지 않은00139
7목포시정신심한 장애255236심하지 않은13495
8목포시신장심한 장애267168심하지 않은7949563
9목포시심장심한 장애137심하지 않은0222
시군구장애유형장애정도남성여성장애정도.1남성.1여성.1합계
320신안군뇌병변심한 장애8868심하지 않은7647279
321신안군자폐성심한 장애91심하지 않은0010
322신안군정신심한 장애5033심하지 않은0083
323신안군신장심한 장애3920심하지 않은8370
324신안군심장심한 장애31심하지 않은004
325신안군호흡기심한 장애91심하지 않은0010
326신안군간심한 장애00심하지 않은14317
327신안군안면심한 장애22심하지 않은004
328신안군장루.요루심한 장애01심하지 않은17725
329신안군뇌전증심한 장애01심하지 않은427