Overview

Dataset statistics

Number of variables6
Number of observations44
Missing cells4
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory54.0 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description제주특별자치도 내 인구와 관련된 데이터로 읍면동별로 장애인 인구, 중증 장애인 인구, 경증 장애인 인구 데이터를 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15056372/fileData.do

Alerts

행정시 is highly overall correlated with 데이터기준일자High correlation
데이터기준일자 is highly overall correlated with 장애인 인구 and 3 other fieldsHigh correlation
장애인 인구 is highly overall correlated with 심한장애 and 2 other fieldsHigh correlation
심한장애 is highly overall correlated with 장애인 인구 and 2 other fieldsHigh correlation
심하지 않은 장애 is highly overall correlated with 장애인 인구 and 2 other fieldsHigh correlation
데이터기준일자 is highly imbalanced (84.4%)Imbalance
읍면동 has 1 (2.3%) missing valuesMissing
장애인 인구 has 1 (2.3%) missing valuesMissing
심한장애 has 1 (2.3%) missing valuesMissing
심하지 않은 장애 has 1 (2.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:57:28.296121
Analysis finished2023-12-12 22:57:29.485555
Duration1.19 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정시
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
제주시
26 
서귀포시
17 
<NA>
 
1

Length

Max length4
Median length3
Mean length3.4090909
Min length3

Unique

Unique1 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 26
59.1%
서귀포시 17
38.6%
<NA> 1
 
2.3%

Length

2023-12-13T07:57:29.548656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:57:29.650368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 26
59.1%
서귀포시 17
38.6%
na 1
 
2.3%

읍면동
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing1
Missing (%)2.3%
Memory size484.0 B
2023-12-13T07:57:29.855840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1627907
Min length2

Characters and Unicode

Total characters136
Distinct characters63
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

Unique43 ?
Unique (%)100.0%

Sample

1st row한림읍
2nd row애월읍
3rd row구좌읍
4th row조천읍
5th row한경면
ValueCountFrequency (%)
한림읍 1
 
2.3%
송산동 1
 
2.3%
정방동 1
 
2.3%
이호동 1
 
2.3%
도두동 1
 
2.3%
대정읍 1
 
2.3%
남원읍 1
 
2.3%
성산읍 1
 
2.3%
안덕면 1
 
2.3%
표선면 1
 
2.3%
Other values (33) 33
76.7%
2023-12-13T07:57:30.200208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 32
23.5%
도 9
 
6.6%
읍 7
 
5.1%
면 5
 
3.7%
1 4
 
2.9%
2 4
 
2.9%
천 4
 
2.9%
대 3
 
2.2%
이 3
 
2.2%
삼 3
 
2.2%
Other values (53) 62
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
94.1%
Decimal Number 8
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 32
25.0%
도 9
 
7.0%
읍 7
 
5.5%
면 5
 
3.9%
천 4
 
3.1%
대 3
 
2.3%
이 3
 
2.3%
삼 3
 
2.3%
일 2
 
1.6%
산 2
 
1.6%
Other values (51) 58
45.3%
Decimal Number
ValueCountFrequency (%)
1 4
50.0%
2 4
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
94.1%
Common 8
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 32
25.0%
도 9
 
7.0%
읍 7
 
5.5%
면 5
 
3.9%
천 4
 
3.1%
대 3
 
2.3%
이 3
 
2.3%
삼 3
 
2.3%
일 2
 
1.6%
산 2
 
1.6%
Other values (51) 58
45.3%
Common
ValueCountFrequency (%)
1 4
50.0%
2 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128
94.1%
ASCII 8
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 32
25.0%
도 9
 
7.0%
읍 7
 
5.5%
면 5
 
3.9%
천 4
 
3.1%
대 3
 
2.3%
이 3
 
2.3%
삼 3
 
2.3%
일 2
 
1.6%
산 2
 
1.6%
Other values (51) 58
45.3%
ASCII
ValueCountFrequency (%)
1 4
50.0%
2 4
50.0%

장애인 인구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)100.0%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean864.55814
Minimum170
Maximum2279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T07:57:30.332492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum170
5-th percentile182.9
Q1361.5
median706
Q31340
95-th percentile1979.2
Maximum2279
Range2109
Interquartile range (IQR)978.5

Descriptive statistics

Standard deviation600.02374
Coefficient of variation (CV)0.69402359
Kurtosis-0.56734897
Mean864.55814
Median Absolute Deviation (MAD)454
Skewness0.68961224
Sum37176
Variance360028.49
MonotonicityNot monotonic
2023-12-13T07:57:30.448929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1562 1
 
2.3%
2279 1
 
2.3%
261 1
 
2.3%
192 1
 
2.3%
1402 1
 
2.3%
1463 1
 
2.3%
1170 1
 
2.3%
828 1
 
2.3%
876 1
 
2.3%
252 1
 
2.3%
Other values (33) 33
75.0%
ValueCountFrequency (%)
170 1
2.3%
174 1
2.3%
182 1
2.3%
191 1
2.3%
192 1
2.3%
218 1
2.3%
221 1
2.3%
252 1
2.3%
261 1
2.3%
277 1
2.3%
ValueCountFrequency (%)
2279 1
2.3%
2114 1
2.3%
1992 1
2.3%
1864 1
2.3%
1621 1
2.3%
1576 1
2.3%
1562 1
2.3%
1541 1
2.3%
1463 1
2.3%
1424 1
2.3%

심한장애
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)95.3%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean336.88372
Minimum52
Maximum975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T07:57:30.566118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile64.9
Q1158.5
median255
Q3514
95-th percentile883.9
Maximum975
Range923
Interquartile range (IQR)355.5

Descriptive statistics

Standard deviation253.0832
Coefficient of variation (CV)0.75124793
Kurtosis0.11678415
Mean336.88372
Median Absolute Deviation (MAD)175
Skewness0.95959868
Sum14486
Variance64051.105
MonotonicityNot monotonic
2023-12-13T07:57:30.694494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
78 2
 
4.5%
248 2
 
4.5%
598 1
 
2.3%
79 1
 
2.3%
89 1
 
2.3%
503 1
 
2.3%
525 1
 
2.3%
373 1
 
2.3%
261 1
 
2.3%
276 1
 
2.3%
Other values (31) 31
70.5%
ValueCountFrequency (%)
52 1
2.3%
54 1
2.3%
64 1
2.3%
73 1
2.3%
77 1
2.3%
78 2
4.5%
79 1
2.3%
80 1
2.3%
89 1
2.3%
154 1
2.3%
ValueCountFrequency (%)
975 1
2.3%
901 1
2.3%
892 1
2.3%
811 1
2.3%
638 1
2.3%
624 1
2.3%
617 1
2.3%
598 1
2.3%
565 1
2.3%
535 1
2.3%

심하지 않은 장애
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)95.3%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean527.67442
Minimum106
Maximum1387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T07:57:30.820361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile114.8
Q1206
median421
Q3823
95-th percentile1164
Maximum1387
Range1281
Interquartile range (IQR)617

Descriptive statistics

Standard deviation355.50874
Coefficient of variation (CV)0.67372745
Kurtosis-0.69302195
Mean527.67442
Median Absolute Deviation (MAD)249
Skewness0.61117426
Sum22690
Variance126386.46
MonotonicityNot monotonic
2023-12-13T07:57:30.951321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
114 2
 
4.5%
271 2
 
4.5%
964 1
 
2.3%
140 1
 
2.3%
172 1
 
2.3%
899 1
 
2.3%
938 1
 
2.3%
797 1
 
2.3%
567 1
 
2.3%
600 1
 
2.3%
Other values (31) 31
70.5%
ValueCountFrequency (%)
106 1
2.3%
114 2
4.5%
122 1
2.3%
128 1
2.3%
140 1
2.3%
148 1
2.3%
172 1
2.3%
173 1
2.3%
191 1
2.3%
197 1
2.3%
ValueCountFrequency (%)
1387 1
2.3%
1213 1
2.3%
1181 1
2.3%
1011 1
2.3%
1004 1
2.3%
964 1
2.3%
938 1
2.3%
917 1
2.3%
899 1
2.3%
889 1
2.3%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
2022-12-31
43 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.8636364
Min length4

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 43
97.7%
<NA> 1
 
2.3%

Length

2023-12-13T07:57:31.080331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:57:31.210370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 43
97.7%
na 1
 
2.3%

Interactions

2023-12-13T07:57:28.933612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:28.515247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:28.726762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:29.009628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:28.588136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:28.788492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:29.089051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:28.655252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:57:28.862904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:57:31.283856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정시읍면동장애인 인구심한장애심하지 않은 장애
행정시1.0001.0000.0000.0000.000
읍면동1.0001.0001.0001.0001.000
장애인 인구0.0001.0001.0000.9580.922
심한장애0.0001.0000.9581.0000.805
심하지 않은 장애0.0001.0000.9220.8051.000
2023-12-13T07:57:31.376419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정시데이터기준일자
행정시1.0001.000
데이터기준일자1.0001.000
2023-12-13T07:57:31.458815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애인 인구심한장애심하지 않은 장애행정시데이터기준일자
장애인 인구1.0000.9880.9920.0001.000
심한장애0.9881.0000.9690.0001.000
심하지 않은 장애0.9920.9691.0000.0001.000
행정시0.0000.0000.0001.0001.000
데이터기준일자1.0001.0001.0001.0001.000

Missing values

2023-12-13T07:57:29.191563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:57:29.294579image/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.
2023-12-13T07:57:29.409181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

행정시읍면동장애인 인구심한장애심하지 않은 장애데이터기준일자
0제주시한림읍15625989642022-12-31
1제주시애월읍227989213872022-12-31
2제주시구좌읍12784298492022-12-31
3제주시조천읍157656510112022-12-31
4제주시한경면8062815252022-12-31
5제주시추자면182541282022-12-31
6제주시우도면174521222022-12-31
7제주시일도1동191771142022-12-31
8제주시일도2동162161710042022-12-31
9제주시이도1동4451742712022-12-31
행정시읍면동장애인 인구심한장애심하지 않은 장애데이터기준일자
34서귀포시천지동221731482022-12-31
35서귀포시효돈동3691542152022-12-31
36서귀포시영천동4722012712022-12-31
37서귀포시동홍동12535357182022-12-31
38서귀포시서홍동5362323042022-12-31
39서귀포시대륜동7232554682022-12-31
40서귀포시대천동6332214122022-12-31
41서귀포시중문동6062483582022-12-31
42서귀포시예래동277801972022-12-31
43<NA><NA><NA><NA><NA><NA>