Overview

Dataset statistics

Number of variables4
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory38.4 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description계룡시 관내 장애인 유형별, 장애 정도별 등록 현황에 관한 데이터로서, 장애우 복지향상을 위한 정책수립 기초자료에 대한 공공데이터를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=310&beforeMenuCd=DOM_000000201001001000&publicdatapk=15093853

Alerts

남성 is highly overall correlated with 여성High correlation
여성 is highly overall correlated with 남성High correlation
남성 has 9 (30.0%) zerosZeros
여성 has 9 (30.0%) zerosZeros

Reproduction

Analysis started2024-01-09 20:51:20.735265
Analysis finished2024-01-09 20:51:21.277596
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

장애 정도
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
심한 장애
15 
심하지 않은 장애
15 

Length

Max length9
Median length7
Mean length7
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 15
50.0%
심하지 않은 장애 15
50.0%

Length

2024-01-10T05:51:21.338647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:51:21.429928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장애 30
40.0%
심한 15
20.0%
심하지 15
20.0%
않은 15
20.0%

장애유형
Text

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-10T05:51:21.564507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.4
Min length1

Characters and Unicode

Total characters72
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지체
2nd row시각
3rd row청각
4th row언어
5th row지적
ValueCountFrequency (%)
지체 2
 
6.7%
시각 2
 
6.7%
청각 2
 
6.7%
언어 2
 
6.7%
지적 2
 
6.7%
뇌병변 2
 
6.7%
자폐성 2
 
6.7%
정신 2
 
6.7%
신장 2
 
6.7%
심장 2
 
6.7%
Other values (5) 10
33.3%
2024-01-10T05:51:21.846720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 6
 
8.3%
지 4
 
5.6%
각 4
 
5.6%
루 4
 
5.6%
뇌 4
 
5.6%
신 4
 
5.6%
전 2
 
2.8%
요 2
 
2.8%
. 2
 
2.8%
면 2
 
2.8%
Other values (19) 38
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
97.2%
Other Punctuation 2
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 6
 
8.6%
지 4
 
5.7%
각 4
 
5.7%
루 4
 
5.7%
뇌 4
 
5.7%
신 4
 
5.7%
전 2
 
2.9%
요 2
 
2.9%
면 2
 
2.9%
안 2
 
2.9%
Other values (18) 36
51.4%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
97.2%
Common 2
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 6
 
8.6%
지 4
 
5.7%
각 4
 
5.7%
루 4
 
5.7%
뇌 4
 
5.7%
신 4
 
5.7%
전 2
 
2.9%
요 2
 
2.9%
면 2
 
2.9%
안 2
 
2.9%
Other values (18) 36
51.4%
Common
ValueCountFrequency (%)
. 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
97.2%
ASCII 2
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 6
 
8.6%
지 4
 
5.7%
각 4
 
5.7%
루 4
 
5.7%
뇌 4
 
5.7%
신 4
 
5.7%
전 2
 
2.9%
요 2
 
2.9%
면 2
 
2.9%
안 2
 
2.9%
Other values (18) 36
51.4%
ASCII
ValueCountFrequency (%)
. 2
100.0%

남성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.966667
Minimum0
Maximum363
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-10T05:51:21.948132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q329.25
95-th percentile98.4
Maximum363
Range363
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation69.385223
Coefficient of variation (CV)2.1705492
Kurtosis18.758264
Mean31.966667
Median Absolute Deviation (MAD)6
Skewness4.0461085
Sum959
Variance4814.3092
MonotonicityNot monotonic
2024-01-10T05:51:22.042089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9
30.0%
1 3
 
10.0%
4 2
 
6.7%
6 2
 
6.7%
7 1
 
3.3%
15 1
 
3.3%
36 1
 
3.3%
102 1
 
3.3%
85 1
 
3.3%
363 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
0 9
30.0%
1 3
 
10.0%
4 2
 
6.7%
6 2
 
6.7%
7 1
 
3.3%
11 1
 
3.3%
15 1
 
3.3%
20 1
 
3.3%
25 1
 
3.3%
27 1
 
3.3%
ValueCountFrequency (%)
363 1
3.3%
102 1
3.3%
94 1
3.3%
85 1
3.3%
72 1
3.3%
49 1
3.3%
36 1
3.3%
30 1
3.3%
27 1
3.3%
25 1
3.3%

여성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.933333
Minimum0
Maximum303
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-10T05:51:22.137767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q321.25
95-th percentile82.2
Maximum303
Range303
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation57.821922
Coefficient of variation (CV)2.3190611
Kurtosis19.516329
Mean24.933333
Median Absolute Deviation (MAD)2.5
Skewness4.1661758
Sum748
Variance3343.3747
MonotonicityNot monotonic
2024-01-10T05:51:22.225707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9
30.0%
1 4
13.3%
2 2
 
6.7%
7 2
 
6.7%
10 1
 
3.3%
22 1
 
3.3%
102 1
 
3.3%
50 1
 
3.3%
303 1
 
3.3%
46 1
 
3.3%
Other values (7) 7
23.3%
ValueCountFrequency (%)
0 9
30.0%
1 4
13.3%
2 2
 
6.7%
3 1
 
3.3%
7 2
 
6.7%
10 1
 
3.3%
13 1
 
3.3%
14 1
 
3.3%
19 1
 
3.3%
22 1
 
3.3%
ValueCountFrequency (%)
303 1
3.3%
102 1
3.3%
58 1
3.3%
55 1
3.3%
50 1
3.3%
46 1
3.3%
31 1
3.3%
22 1
3.3%
19 1
3.3%
14 1
3.3%

Interactions

2024-01-10T05:51:21.026078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:20.848835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:21.102612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:20.942017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:51:22.294907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애 정도장애유형남성여성
장애 정도1.0000.0000.1680.266
장애유형0.0001.0000.1790.000
남성0.1680.1791.0000.975
여성0.2660.0000.9751.000
2024-01-10T05:51:22.371876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남성여성장애 정도
남성1.0000.9530.089
여성0.9531.0000.160
장애 정도0.0890.1601.000

Missing values

2024-01-10T05:51:21.188198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:51:21.251452image/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

장애 정도장애유형남성여성
0심한 장애지체7246
1심한 장애시각1113
2심한 장애청각2019
3심한 장애언어63
4심한 장애지적9458
5심한 장애뇌병변4955
6심한 장애자폐성257
7심한 장애정신3031
8심한 장애신장2714
9심한 장애심장00
장애 정도장애유형남성여성
20심하지 않은 장애뇌병변3622
21심하지 않은 장애자폐성00
22심하지 않은 장애정신00
23심하지 않은 장애신장1510
24심하지 않은 장애심장01
25심하지 않은 장애호흡기00
26심하지 않은 장애간42
27심하지 않은 장애안면00
28심하지 않은 장애장루.요루77
29심하지 않은 장애뇌전증10