Overview

Dataset statistics

Number of variables3
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory982.0 B
Average record size in memory28.9 B

Variable types

Text1
Categorical1
Numeric1

Dataset

Description부산광역시남구외국인현황_20210131
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15009663

Reproduction

Analysis started2023-12-10 17:48:52.968748
Analysis finished2023-12-10 17:48:53.703044
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동명
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T02:48:53.930857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8823529
Min length3

Characters and Unicode

Total characters132
Distinct characters18
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 row대연1동
2nd row대연1동
3rd row대연3동
4th row대연3동
5th row대연4동
ValueCountFrequency (%)
대연1동 2
 
5.9%
용당동 2
 
5.9%
문현3동 2
 
5.9%
문현2동 2
 
5.9%
문현1동 2
 
5.9%
우암동 2
 
5.9%
감만2동 2
 
5.9%
감만1동 2
 
5.9%
용호4동 2
 
5.9%
대연3동 2
 
5.9%
Other values (7) 14
41.2%
2023-12-11T02:48:54.544695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 34
25.8%
대 10
 
7.6%
용 10
 
7.6%
연 10
 
7.6%
문 8
 
6.1%
호 8
 
6.1%
1 8
 
6.1%
현 8
 
6.1%
4 6
 
4.5%
3 6
 
4.5%
Other values (8) 24
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
77.3%
Decimal Number 30
 
22.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 34
33.3%
대 10
 
9.8%
용 10
 
9.8%
연 10
 
9.8%
문 8
 
7.8%
호 8
 
7.8%
현 8
 
7.8%
감 4
 
3.9%
만 4
 
3.9%
당 2
 
2.0%
Other values (2) 4
 
3.9%
Decimal Number
ValueCountFrequency (%)
1 8
26.7%
4 6
20.0%
3 6
20.0%
2 6
20.0%
5 2
 
6.7%
6 2
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
77.3%
Common 30
 
22.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 34
33.3%
대 10
 
9.8%
용 10
 
9.8%
연 10
 
9.8%
문 8
 
7.8%
호 8
 
7.8%
현 8
 
7.8%
감 4
 
3.9%
만 4
 
3.9%
당 2
 
2.0%
Other values (2) 4
 
3.9%
Common
ValueCountFrequency (%)
1 8
26.7%
4 6
20.0%
3 6
20.0%
2 6
20.0%
5 2
 
6.7%
6 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102
77.3%
ASCII 30
 
22.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 34
33.3%
대 10
 
9.8%
용 10
 
9.8%
연 10
 
9.8%
문 8
 
7.8%
호 8
 
7.8%
현 8
 
7.8%
감 4
 
3.9%
만 4
 
3.9%
당 2
 
2.0%
Other values (2) 4
 
3.9%
ASCII
ValueCountFrequency (%)
1 8
26.7%
4 6
20.0%
3 6
20.0%
2 6
20.0%
5 2
 
6.7%
6 2
 
6.7%

남성_여성
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
남
17 
여
17 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row여
3rd row남
4th row여
5th row남

Common Values

ValueCountFrequency (%)
남 17
50.0%
여 17
50.0%

Length

2023-12-11T02:48:54.855195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:48:55.092072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 17
50.0%
여 17
50.0%

외국인인구수
Real number (ℝ)

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.82353
Minimum4
Maximum1158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T02:48:55.330465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11.95
Q125.25
median45
Q384.5
95-th percentile822.95
Maximum1158
Range1154
Interquartile range (IQR)59.25

Descriptive statistics

Standard deviation280.89149
Coefficient of variation (CV)1.9262426
Kurtosis7.6626788
Mean145.82353
Median Absolute Deviation (MAD)21
Skewness2.8772852
Sum4958
Variance78900.029
MonotonicityNot monotonic
2023-12-11T02:48:55.670592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 2
 
5.9%
85 2
 
5.9%
55 2
 
5.9%
37 2
 
5.9%
691 1
 
2.9%
83 1
 
2.9%
23 1
 
2.9%
47 1
 
2.9%
27 1
 
2.9%
29 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
4 1
2.9%
10 1
2.9%
13 1
2.9%
14 1
2.9%
21 1
2.9%
22 1
2.9%
23 1
2.9%
25 2
5.9%
26 1
2.9%
27 1
2.9%
ValueCountFrequency (%)
1158 1
2.9%
1068 1
2.9%
691 1
2.9%
468 1
2.9%
209 1
2.9%
202 1
2.9%
86 1
2.9%
85 2
5.9%
83 1
2.9%
65 1
2.9%

Interactions

2023-12-11T02:48:53.161692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:48:55.903714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명남성_여성외국인인구수
동명1.0000.0000.893
남성_여성0.0001.0000.000
외국인인구수0.8930.0001.000
2023-12-11T02:48:56.082455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인인구수남성_여성
외국인인구수1.0000.000
남성_여성0.0001.000

Missing values

2023-12-11T02:48:53.423643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:48:53.632352image/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

동명남성_여성외국인인구수
0대연1동남691
1대연1동여468
2대연3동남1158
3대연3동여1068
4대연4동남13
5대연4동여33
6대연5동남85
7대연5동여86
8대연6동남55
9대연6동여50
동명남성_여성외국인인구수
24우암동남14
25우암동여37
26문현1동남10
27문현1동여29
28문현2동남25
29문현2동여37
30문현3동남27
31문현3동여47
32문현4동남25
33문현4동여23