Overview

Dataset statistics

Number of variables3
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory678.0 B
Average record size in memory32.3 B

Variable types

Text1
Numeric2

Dataset

Description인천광역시 미추홀구 동별 장애인으로 등록된 자의 현황에 대한 데이터로 행정동별 심한 장애, 심하지 않은 장애로 구분한 데이터를 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3044789&srcSe=7661IVAWM27C61E190

Alerts

심한 장애 is highly overall correlated with 심하지 않은 장애High correlation
심하지 않은 장애 is highly overall correlated with 심한 장애High correlation
읍면동 has unique valuesUnique
심하지 않은 장애 has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:01:04.723589
Analysis finished2024-03-18 04:01:05.457456
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-18T13:01:05.563340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1904762
Min length3

Characters and Unicode

Total characters88
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row숭의1·3동
2nd row숭의2동
3rd row숭의4동
4th row용현1·4동
5th row용현2동
ValueCountFrequency (%)
숭의1·3동 1
 
4.8%
주안1동 1
 
4.8%
관교동 1
 
4.8%
주안8동 1
 
4.8%
주안7동 1
 
4.8%
주안6동 1
 
4.8%
주안5동 1
 
4.8%
주안4동 1
 
4.8%
주안3동 1
 
4.8%
주안2동 1
 
4.8%
Other values (11) 11
52.4%
2024-03-18T13:01:06.038085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
23.9%
안 8
 
9.1%
주 8
 
9.1%
1 5
 
5.7%
2 5
 
5.7%
3 4
 
4.5%
용 4
 
4.5%
현 4
 
4.5%
숭 3
 
3.4%
의 3
 
3.4%
Other values (13) 23
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
71.6%
Decimal Number 22
 
25.0%
Other Punctuation 3
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Decimal Number
ValueCountFrequency (%)
1 5
22.7%
2 5
22.7%
3 4
18.2%
4 3
13.6%
5 2
 
9.1%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
71.6%
Common 25
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Common
ValueCountFrequency (%)
1 5
20.0%
2 5
20.0%
3 4
16.0%
4 3
12.0%
· 3
12.0%
5 2
 
8.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
71.6%
ASCII 22
 
25.0%
None 3
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
ASCII
ValueCountFrequency (%)
1 5
22.7%
2 5
22.7%
3 4
18.2%
4 3
13.6%
5 2
 
9.1%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
None
ValueCountFrequency (%)
· 3
100.0%

심한 장애
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.80952
Minimum184
Maximum701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T13:01:06.164898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184
5-th percentile245
Q1302
median372
Q3428
95-th percentile603
Maximum701
Range517
Interquartile range (IQR)126

Descriptive statistics

Standard deviation119.75751
Coefficient of variation (CV)0.3144814
Kurtosis1.6362299
Mean380.80952
Median Absolute Deviation (MAD)63
Skewness1.0358996
Sum7997
Variance14341.862
MonotonicityNot monotonic
2024-03-18T13:01:06.261833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
372 2
 
9.5%
326 1
 
4.8%
362 1
 
4.8%
268 1
 
4.8%
257 1
 
4.8%
403 1
 
4.8%
302 1
 
4.8%
373 1
 
4.8%
435 1
 
4.8%
245 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
184 1
4.8%
245 1
4.8%
257 1
4.8%
268 1
4.8%
299 1
4.8%
302 1
4.8%
326 1
4.8%
327 1
4.8%
362 1
4.8%
372 2
9.5%
ValueCountFrequency (%)
701 1
4.8%
603 1
4.8%
515 1
4.8%
462 1
4.8%
435 1
4.8%
428 1
4.8%
403 1
4.8%
388 1
4.8%
375 1
4.8%
373 1
4.8%

심하지 않은 장애
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean754.09524
Minimum431
Maximum1418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T13:01:06.372530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum431
5-th percentile449
Q1639
median751
Q3812
95-th percentile1182
Maximum1418
Range987
Interquartile range (IQR)173

Descriptive statistics

Standard deviation231.80787
Coefficient of variation (CV)0.30739867
Kurtosis2.5335953
Mean754.09524
Median Absolute Deviation (MAD)111
Skewness1.2640987
Sum15836
Variance53734.89
MonotonicityNot monotonic
2024-03-18T13:01:06.499275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
640 1
 
4.8%
647 1
 
4.8%
479 1
 
4.8%
547 1
 
4.8%
812 1
 
4.8%
639 1
 
4.8%
755 1
 
4.8%
822 1
 
4.8%
769 1
 
4.8%
449 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
431 1
4.8%
449 1
4.8%
479 1
4.8%
547 1
4.8%
611 1
4.8%
639 1
4.8%
640 1
4.8%
647 1
4.8%
723 1
4.8%
741 1
4.8%
ValueCountFrequency (%)
1418 1
4.8%
1182 1
4.8%
968 1
4.8%
890 1
4.8%
822 1
4.8%
812 1
4.8%
809 1
4.8%
769 1
4.8%
755 1
4.8%
753 1
4.8%

Interactions

2024-03-18T13:01:05.220568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:01:05.095299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:01:05.287153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:01:05.157007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:01:06.564391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동심한 장애심하지 않은 장애
읍면동1.0001.0001.000
심한 장애1.0001.0000.983
심하지 않은 장애1.0000.9831.000
2024-03-18T13:01:06.655245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심한 장애심하지 않은 장애
심한 장애1.0000.941
심하지 않은 장애0.9411.000

Missing values

2024-03-18T13:01:05.369898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:01:05.429648image/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

읍면동심한 장애심하지 않은 장애
0숭의1·3동326640
1숭의2동299647
2숭의4동375723
3용현1·4동388809
4용현2동327741
5용현3동184431
6용현5동7011418
7학익1동462968
8학익2동372753
9도화1동515890
읍면동심한 장애심하지 않은 장애
11주안1동362611
12주안2동428751
13주안3동245449
14주안4동372769
15주안5동435822
16주안6동373755
17주안7동302639
18주안8동403812
19관교동257547
20문학동268479