Overview

Dataset statistics

Number of variables3
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory757.0 B
Average record size in memory30.3 B

Variable types

Text1
Numeric1
Categorical1

Dataset

Description경상북도 구미시 기초생활수급자 현황 데이터로 행정동별, 인원수(명), 데이터기준일 항목으로 구성되어 있습니다.
URLhttps://www.data.go.kr/data/15113613/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
행정동명 has unique valuesUnique
인원 수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:32:45.327023
Analysis finished2023-12-12 06:32:45.645804
Duration0.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T15:32:45.789490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.32
Min length3

Characters and Unicode

Total characters83
Distinct characters43
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

Unique25 ?
Unique (%)100.0%

Sample

1st row선산읍
2nd row고아읍
3rd row산동읍
4th row무을면
5th row옥성면
ValueCountFrequency (%)
선산읍 1
 
4.0%
형곡1동 1
 
4.0%
진미동 1
 
4.0%
인동동 1
 
4.0%
임오동 1
 
4.0%
상모사곡동 1
 
4.0%
광평동 1
 
4.0%
공단동 1
 
4.0%
비산동 1
 
4.0%
신평2동 1
 
4.0%
Other values (15) 15
60.0%
2023-12-12T15:32:46.236969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 19
22.9%
면 5
 
6.0%
평 5
 
6.0%
산 4
 
4.8%
곡 3
 
3.6%
읍 3
 
3.6%
1 2
 
2.4%
형 2
 
2.4%
2 2
 
2.4%
신 2
 
2.4%
Other values (33) 36
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
95.2%
Decimal Number 4
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 19
24.1%
면 5
 
6.3%
평 5
 
6.3%
산 4
 
5.1%
곡 3
 
3.8%
읍 3
 
3.8%
형 2
 
2.5%
신 2
 
2.5%
원 2
 
2.5%
선 2
 
2.5%
Other values (31) 32
40.5%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
95.2%
Common 4
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 19
24.1%
면 5
 
6.3%
평 5
 
6.3%
산 4
 
5.1%
곡 3
 
3.8%
읍 3
 
3.8%
형 2
 
2.5%
신 2
 
2.5%
원 2
 
2.5%
선 2
 
2.5%
Other values (31) 32
40.5%
Common
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
95.2%
ASCII 4
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 19
24.1%
면 5
 
6.3%
평 5
 
6.3%
산 4
 
5.1%
곡 3
 
3.8%
읍 3
 
3.8%
형 2
 
2.5%
신 2
 
2.5%
원 2
 
2.5%
선 2
 
2.5%
Other values (31) 32
40.5%
ASCII
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

인원 수(명)
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.56
Minimum39
Maximum2267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T15:32:46.420773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile85.6
Q1137
median390
Q3858
95-th percentile1334.4
Maximum2267
Range2228
Interquartile range (IQR)721

Descriptive statistics

Standard deviation541.07779
Coefficient of variation (CV)0.98996961
Kurtosis2.7855327
Mean546.56
Median Absolute Deviation (MAD)292
Skewness1.5141011
Sum13664
Variance292765.17
MonotonicityNot monotonic
2023-12-12T15:32:46.866665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
556 1
 
4.0%
809 1
 
4.0%
763 1
 
4.0%
970 1
 
4.0%
2267 1
 
4.0%
409 1
 
4.0%
1063 1
 
4.0%
161 1
 
4.0%
39 1
 
4.0%
191 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
39 1
4.0%
85 1
4.0%
88 1
4.0%
98 1
4.0%
109 1
4.0%
119 1
4.0%
137 1
4.0%
147 1
4.0%
158 1
4.0%
161 1
4.0%
ValueCountFrequency (%)
2267 1
4.0%
1369 1
4.0%
1196 1
4.0%
1063 1
4.0%
970 1
4.0%
865 1
4.0%
858 1
4.0%
809 1
4.0%
763 1
4.0%
650 1
4.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-03-31
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-31
2nd row2023-03-31
3rd row2023-03-31
4th row2023-03-31
5th row2023-03-31

Common Values

ValueCountFrequency (%)
2023-03-31 25
100.0%

Length

2023-12-12T15:32:47.010979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:32:47.115220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-31 25
100.0%

Interactions

2023-12-12T15:32:45.417180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:32:47.171922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명인원 수(명)
행정동명1.0001.000
인원 수(명)1.0001.000

Missing values

2023-12-12T15:32:45.526891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:32:45.614821image/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

행정동명인원 수(명)데이터기준일자
0선산읍5562023-03-31
1고아읍8092023-03-31
2산동읍1192023-03-31
3무을면852023-03-31
4옥성면882023-03-31
5도개면982023-03-31
6해평면1672023-03-31
7장천면1092023-03-31
8송정동6502023-03-31
9원평동8652023-03-31
행정동명인원 수(명)데이터기준일자
15신평1동1472023-03-31
16신평2동1582023-03-31
17비산동1912023-03-31
18공단동392023-03-31
19광평동1612023-03-31
20상모사곡동10632023-03-31
21임오동4092023-03-31
22인동동22672023-03-31
23진미동9702023-03-31
24양포동7632023-03-31