Overview

Dataset statistics

Number of variables4
Number of observations30
Missing cells30
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory38.4 B

Variable types

Categorical2
Text1
Unsupported1

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://www.bigdata-region.kr/#/dataset/6a390ea2-cbe5-4abb-96a3-e529e6db8ac2

Alerts

시도명 has constant value ""Constant
기준년도 has constant value ""Constant
유동인구지수(현황) has 30 (100.0%) missing valuesMissing
시군구명 has unique valuesUnique
유동인구지수(현황) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 14:09:17.046146
Analysis finished2023-12-10 14:09:17.471860
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T23:09:17.646603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:09:17.836507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:09:18.112278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length4.8666667
Min length3

Characters and Unicode

Total characters146
Distinct characters46
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

Unique30 ?
Unique (%)100.0%

Sample

1st row성남시 중원구
2nd row양주시
3rd row용인시 수지구
4th row연천군
5th row성남시 수정구
ValueCountFrequency (%)
고양시 3
 
7.0%
안산시 2
 
4.7%
수원시 2
 
4.7%
안양시 2
 
4.7%
성남시 2
 
4.7%
용인시 2
 
4.7%
양평군 1
 
2.3%
일산동구 1
 
2.3%
일산서구 1
 
2.3%
덕양구 1
 
2.3%
Other values (26) 26
60.5%
2023-12-10T23:09:18.808425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
19.9%
구 14
 
9.6%
13
 
8.9%
양 9
 
6.2%
안 8
 
5.5%
산 5
 
3.4%
수 4
 
2.7%
천 4
 
2.7%
남 4
 
2.7%
주 4
 
2.7%
Other values (36) 52
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
91.1%
Space Separator 13
 
8.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
21.8%
구 14
 
10.5%
양 9
 
6.8%
안 8
 
6.0%
산 5
 
3.8%
수 4
 
3.0%
천 4
 
3.0%
남 4
 
3.0%
주 4
 
3.0%
원 4
 
3.0%
Other values (35) 48
36.1%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
91.1%
Common 13
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
21.8%
구 14
 
10.5%
양 9
 
6.8%
안 8
 
6.0%
산 5
 
3.8%
수 4
 
3.0%
천 4
 
3.0%
남 4
 
3.0%
주 4
 
3.0%
원 4
 
3.0%
Other values (35) 48
36.1%
Common
ValueCountFrequency (%)
13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
91.1%
ASCII 13
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
21.8%
구 14
 
10.5%
양 9
 
6.8%
안 8
 
6.0%
산 5
 
3.8%
수 4
 
3.0%
천 4
 
3.0%
남 4
 
3.0%
주 4
 
3.0%
원 4
 
3.0%
Other values (35) 48
36.1%
ASCII
ValueCountFrequency (%)
13
100.0%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2017
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 30
100.0%

Length

2023-12-10T23:09:19.237947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:09:19.479761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 30
100.0%

유동인구지수(현황)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

Missing values

2023-12-10T23:09:17.219310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:09:17.402022image/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

시도명시군구명기준년도유동인구지수(현황)
0경기도성남시 중원구2017<NA>
1경기도양주시2017<NA>
2경기도용인시 수지구2017<NA>
3경기도연천군2017<NA>
4경기도성남시 수정구2017<NA>
5경기도구리시2017<NA>
6경기도동두천시2017<NA>
7경기도안성시2017<NA>
8경기도안산시 단원구2017<NA>
9경기도안양시 동안구2017<NA>
시도명시군구명기준년도유동인구지수(현황)
20경기도안산시 상록구2017<NA>
21경기도안양시 만안구2017<NA>
22경기도여주시2017<NA>
23경기도고양시 덕양구2017<NA>
24경기도고양시 일산서구2017<NA>
25경기도고양시 일산동구2017<NA>
26경기도양평군2017<NA>
27경기도김포시2017<NA>
28경기도시흥시2017<NA>
29경기도부천시2017<NA>