Overview

Dataset statistics

Number of variables6
Number of observations358
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)0.6%
Total size in memory18.3 KiB
Average record size in memory52.4 B

Variable types

Categorical2
Numeric4

Dataset

Description경상북도 시군의 장애인 유형별, 등급별 남성과 여성수 현황입니다(시군구, 장애유형, 심한장애, 심하지 않은 장애, 남성 수, 여성수)
Author경상북도
URLhttps://www.data.go.kr/data/15063014/fileData.do

Alerts

Dataset has 2 (0.6%) duplicate rowsDuplicates
심한장애 남성 is highly overall correlated with 심한장애 여성High correlation
심한장애 여성 is highly overall correlated with 심한장애 남성High correlation
심하지 않은 장애 남성 is highly overall correlated with 심하지않은 장애 여성High correlation
심하지않은 장애 여성 is highly overall correlated with 심하지 않은 장애 남성High correlation
심한장애 남성 has 34 (9.5%) zerosZeros
심한장애 여성 has 51 (14.2%) zerosZeros
심하지 않은 장애 남성 has 82 (22.9%) zerosZeros
심하지않은 장애 여성 has 93 (26.0%) zerosZeros

Reproduction

Analysis started2024-03-15 02:28:41.505581
Analysis finished2024-03-15 02:28:45.704107
Duration4.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

Distinct24
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
울릉군
 
16
경산시
 
15
의성군
 
15
경주시
 
15
김천시
 
15
Other values (19)
282 

Length

Max length6
Median length3
Mean length3.2513966
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포항시 남구
2nd row포항시 남구
3rd row포항시 남구
4th row포항시 남구
5th row포항시 남구

Common Values

ValueCountFrequency (%)
울릉군 16
 
4.5%
경산시 15
 
4.2%
의성군 15
 
4.2%
경주시 15
 
4.2%
김천시 15
 
4.2%
안동시 15
 
4.2%
구미시 15
 
4.2%
영주시 15
 
4.2%
영천시 15
 
4.2%
상주시 15
 
4.2%
Other values (14) 207
57.8%

Length

2024-03-15T11:28:45.953731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포항시 30
 
7.7%
울릉군 16
 
4.1%
경산시 15
 
3.9%
울진군 15
 
3.9%
예천군 15
 
3.9%
칠곡군 15
 
3.9%
성주군 15
 
3.9%
고령군 15
 
3.9%
청도군 15
 
3.9%
영덕군 15
 
3.9%
Other values (15) 222
57.2%

유형
Categorical

Distinct15
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
장루.요루
25 
뇌전증
25 
지체
24 
시각
24 
청각
24 
Other values (10)
236 

Length

Max length5
Median length2
Mean length2.4106145
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장루.요루 25
 
7.0%
뇌전증 25
 
7.0%
지체 24
 
6.7%
시각 24
 
6.7%
청각 24
 
6.7%
언어 24
 
6.7%
지적 24
 
6.7%
뇌병변 24
 
6.7%
자폐성 24
 
6.7%
정신 24
 
6.7%
Other values (5) 116
32.4%

Length

2024-03-15T11:28:46.439857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
장루.요루 25
 
7.0%
뇌전증 25
 
7.0%
지체 24
 
6.7%
시각 24
 
6.7%
청각 24
 
6.7%
언어 24
 
6.7%
지적 24
 
6.7%
뇌병변 24
 
6.7%
자폐성 24
 
6.7%
정신 24
 
6.7%
Other values (5) 116
32.4%

심한장애 남성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct165
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.41061
Minimum0
Maximum1236
Zeros34
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-03-15T11:28:46.865653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median24
Q3118.75
95-th percentile474.4
Maximum1236
Range1236
Interquartile range (IQR)115.75

Descriptive statistics

Standard deviation187.64012
Coefficient of variation (CV)1.746942
Kurtosis9.8528196
Mean107.41061
Median Absolute Deviation (MAD)24
Skewness2.9105888
Sum38453
Variance35208.814
MonotonicityNot monotonic
2024-03-15T11:28:47.333336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
 
9.5%
1 26
 
7.3%
2 22
 
6.1%
3 18
 
5.0%
5 11
 
3.1%
4 9
 
2.5%
7 7
 
2.0%
6 6
 
1.7%
24 6
 
1.7%
19 5
 
1.4%
Other values (155) 214
59.8%
ValueCountFrequency (%)
0 34
9.5%
1 26
7.3%
2 22
6.1%
3 18
5.0%
4 9
 
2.5%
5 11
 
3.1%
6 6
 
1.7%
7 7
 
2.0%
8 3
 
0.8%
9 3
 
0.8%
ValueCountFrequency (%)
1236 1
0.3%
1098 1
0.3%
946 1
0.3%
899 1
0.3%
874 1
0.3%
843 1
0.3%
835 1
0.3%
793 1
0.3%
779 1
0.3%
762 1
0.3%

심한장애 여성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct150
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.005587
Minimum0
Maximum758
Zeros51
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-03-15T11:28:47.762093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q397.5
95-th percentile333.65
Maximum758
Range758
Interquartile range (IQR)95.5

Descriptive statistics

Standard deviation121.90151
Coefficient of variation (CV)1.6471933
Kurtosis6.7113585
Mean74.005587
Median Absolute Deviation (MAD)12
Skewness2.4280385
Sum26494
Variance14859.978
MonotonicityNot monotonic
2024-03-15T11:28:48.233250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51
 
14.2%
1 38
 
10.6%
2 25
 
7.0%
3 18
 
5.0%
5 10
 
2.8%
4 9
 
2.5%
10 7
 
2.0%
15 6
 
1.7%
8 5
 
1.4%
7 5
 
1.4%
Other values (140) 184
51.4%
ValueCountFrequency (%)
0 51
14.2%
1 38
10.6%
2 25
7.0%
3 18
 
5.0%
4 9
 
2.5%
5 10
 
2.8%
6 4
 
1.1%
7 5
 
1.4%
8 5
 
1.4%
9 2
 
0.6%
ValueCountFrequency (%)
758 1
0.3%
653 1
0.3%
610 1
0.3%
584 1
0.3%
518 1
0.3%
504 1
0.3%
497 1
0.3%
458 1
0.3%
435 1
0.3%
426 1
0.3%

심하지 않은 장애 남성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct131
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.79609
Minimum0
Maximum3406
Zeros82
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-03-15T11:28:48.669319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q380.25
95-th percentile922.45
Maximum3406
Range3406
Interquartile range (IQR)79.25

Descriptive statistics

Standard deviation462.03642
Coefficient of variation (CV)2.628252
Kurtosis22.248954
Mean175.79609
Median Absolute Deviation (MAD)9
Skewness4.3586855
Sum62935
Variance213477.65
MonotonicityNot monotonic
2024-03-15T11:28:49.379011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82
22.9%
1 24
 
6.7%
2 20
 
5.6%
3 14
 
3.9%
4 13
 
3.6%
7 7
 
2.0%
6 7
 
2.0%
10 6
 
1.7%
5 6
 
1.7%
13 5
 
1.4%
Other values (121) 174
48.6%
ValueCountFrequency (%)
0 82
22.9%
1 24
 
6.7%
2 20
 
5.6%
3 14
 
3.9%
4 13
 
3.6%
5 6
 
1.7%
6 7
 
2.0%
7 7
 
2.0%
8 2
 
0.6%
9 5
 
1.4%
ValueCountFrequency (%)
3406 1
0.3%
3394 1
0.3%
2846 1
0.3%
2808 1
0.3%
2620 1
0.3%
2401 1
0.3%
1927 1
0.3%
1550 1
0.3%
1527 1
0.3%
1447 1
0.3%

심하지않은 장애 여성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct121
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.10335
Minimum0
Maximum2485
Zeros93
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-03-15T11:28:49.823208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q348
95-th percentile941.9
Maximum2485
Range2485
Interquartile range (IQR)48

Descriptive statistics

Standard deviation377.60011
Coefficient of variation (CV)2.5495717
Kurtosis13.928922
Mean148.10335
Median Absolute Deviation (MAD)5
Skewness3.5753273
Sum53021
Variance142581.85
MonotonicityNot monotonic
2024-03-15T11:28:50.273436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93
26.0%
1 39
 
10.9%
2 19
 
5.3%
3 16
 
4.5%
5 16
 
4.5%
4 8
 
2.2%
10 7
 
2.0%
8 7
 
2.0%
7 5
 
1.4%
6 5
 
1.4%
Other values (111) 143
39.9%
ValueCountFrequency (%)
0 93
26.0%
1 39
10.9%
2 19
 
5.3%
3 16
 
4.5%
4 8
 
2.2%
5 16
 
4.5%
6 5
 
1.4%
7 5
 
1.4%
8 7
 
2.0%
9 3
 
0.8%
ValueCountFrequency (%)
2485 1
0.3%
2206 1
0.3%
2132 1
0.3%
2104 1
0.3%
1929 1
0.3%
1865 1
0.3%
1718 1
0.3%
1393 1
0.3%
1383 1
0.3%
1379 1
0.3%

Interactions

2024-03-15T11:28:44.523401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:41.905716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:42.957634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:43.822169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:44.745753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:42.163709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:43.171510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:44.018720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:45.039771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:42.428356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:43.338446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:44.189034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:45.212396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:42.712020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:43.571715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:44.356988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:28:50.539180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구유형심한장애 남성심한장애 여성심하지 않은 장애 남성심하지않은 장애 여성
시군구1.0000.0000.2120.2840.0000.000
유형0.0001.0000.5160.5820.5750.649
심한장애 남성0.2120.5161.0000.9670.7480.849
심한장애 여성0.2840.5820.9671.0000.6870.822
심하지 않은 장애 남성0.0000.5750.7480.6871.0000.907
심하지않은 장애 여성0.0000.6490.8490.8220.9071.000
2024-03-15T11:28:50.824162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형시군구
유형1.0000.000
시군구0.0001.000
2024-03-15T11:28:51.074489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심한장애 남성심한장애 여성심하지 않은 장애 남성심하지않은 장애 여성시군구유형
심한장애 남성1.0000.9560.2940.2920.0760.218
심한장애 여성0.9561.0000.3700.3630.1050.257
심하지 않은 장애 남성0.2940.3701.0000.9620.0000.273
심하지않은 장애 여성0.2920.3630.9621.0000.0000.303
시군구0.0760.1050.0000.0001.0000.000
유형0.2180.2570.2730.3030.0001.000

Missing values

2024-03-15T11:28:45.406558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:28:45.598567image/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

시군구유형심한장애 남성심한장애 여성심하지 않은 장애 남성심하지않은 장애 여성
0포항시 남구지체76238326201865
1포항시 남구시각112121582376
2포항시 남구청각237235910751
3포항시 남구언어33154918
4포항시 남구지적66442600
5포항시 남구뇌병변341259298184
6포항시 남구자폐성1562300
7포항시 남구정신22118833
8포항시 남구신장2481554140
9포항시 남구심장291221
시군구유형심한장애 남성심한장애 여성심하지 않은 장애 남성심하지않은 장애 여성
348울릉군자폐성2000
349울릉군정신6700
350울릉군신장5231
351울릉군심장1000
352울릉군간0032
353울릉군안면2000
354울릉군장루.요루0001
355울릉군뇌전증0001
356울릉군장루.요루0001
357울릉군뇌전증0001

Duplicate rows

Most frequently occurring

시군구유형심한장애 남성심한장애 여성심하지 않은 장애 남성심하지않은 장애 여성# duplicates
0울릉군뇌전증00012
1울릉군장루.요루00012