Overview

Dataset statistics

Number of variables5
Number of observations21
Missing cells36
Missing cells (%)34.3%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory972.0 B
Average record size in memory46.3 B

Variable types

Text2
Unsupported3

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
장애인연금 수급자수 has 17 (81.0%) missing valuesMissing
Unnamed: 1 has 6 (28.6%) missing valuesMissing
Unnamed: 2 has 4 (19.0%) missing valuesMissing
Unnamed: 3 has 5 (23.8%) missing valuesMissing
Unnamed: 4 has 4 (19.0%) missing valuesMissing
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 01:30:20.753819
Analysis finished2024-03-14 01:30:21.065468
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

장애인연금 수급자수
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing17
Missing (%)81.0%
Memory size300.0 B
2024-03-14T10:30:21.138337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7.5
Mean length4.75
Min length2

Characters and Unicode

Total characters19
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row(2013년 12월)
2nd row시도
3rd row합계
4th row전라북도
ValueCountFrequency (%)
2013년 1
20.0%
12월 1
20.0%
시도 1
20.0%
합계 1
20.0%
전라북도 1
20.0%
2024-03-14T10:30:21.354259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2
 
10.5%
1 2
 
10.5%
도 2
 
10.5%
( 1
 
5.3%
0 1
 
5.3%
3 1
 
5.3%
년 1
 
5.3%
1
 
5.3%
월 1
 
5.3%
) 1
 
5.3%
Other values (6) 6
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
52.6%
Decimal Number 6
31.6%
Open Punctuation 1
 
5.3%
Space Separator 1
 
5.3%
Close Punctuation 1
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 2
20.0%
년 1
10.0%
월 1
10.0%
시 1
10.0%
합 1
10.0%
계 1
10.0%
전 1
10.0%
라 1
10.0%
북 1
10.0%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
0 1
16.7%
3 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
52.6%
Common 9
47.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 2
20.0%
년 1
10.0%
월 1
10.0%
시 1
10.0%
합 1
10.0%
계 1
10.0%
전 1
10.0%
라 1
10.0%
북 1
10.0%
Common
ValueCountFrequency (%)
2 2
22.2%
1 2
22.2%
( 1
11.1%
0 1
11.1%
3 1
11.1%
1
11.1%
) 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
52.6%
ASCII 9
47.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2
22.2%
1 2
22.2%
( 1
11.1%
0 1
11.1%
3 1
11.1%
1
11.1%
) 1
11.1%
Hangul
ValueCountFrequency (%)
도 2
20.0%
년 1
10.0%
월 1
10.0%
시 1
10.0%
합 1
10.0%
계 1
10.0%
전 1
10.0%
라 1
10.0%
북 1
10.0%

Unnamed: 1
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing6
Missing (%)28.6%
Memory size300.0 B
2024-03-14T10:30:21.497819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9333333
Min length3

Characters and Unicode

Total characters59
Distinct characters26
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

Unique15 ?
Unique (%)100.0%

Sample

1st row시군구
2nd row전주시
3rd row군산시
4th row익산시
5th row정읍시
ValueCountFrequency (%)
시군구 1
 
6.7%
전주시 1
 
6.7%
군산시 1
 
6.7%
익산시 1
 
6.7%
정읍시 1
 
6.7%
남원시 1
 
6.7%
김제시 1
 
6.7%
완주군 1
 
6.7%
진안군 1
 
6.7%
무주군 1
 
6.7%
Other values (5) 5
33.3%
2024-03-14T10:30:21.751454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
23.7%
군 10
16.9%
시 7
11.9%
주 3
 
5.1%
창 2
 
3.4%
산 2
 
3.4%
안 2
 
3.4%
진 1
 
1.7%
고 1
 
1.7%
순 1
 
1.7%
Other values (16) 16
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45
76.3%
Space Separator 14
 
23.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 10
22.2%
시 7
15.6%
주 3
 
6.7%
창 2
 
4.4%
산 2
 
4.4%
안 2
 
4.4%
진 1
 
2.2%
고 1
 
2.2%
순 1
 
2.2%
실 1
 
2.2%
Other values (15) 15
33.3%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45
76.3%
Common 14
 
23.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 10
22.2%
시 7
15.6%
주 3
 
6.7%
창 2
 
4.4%
산 2
 
4.4%
안 2
 
4.4%
진 1
 
2.2%
고 1
 
2.2%
순 1
 
2.2%
실 1
 
2.2%
Other values (15) 15
33.3%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45
76.3%
ASCII 14
 
23.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14
100.0%
Hangul
ValueCountFrequency (%)
군 10
22.2%
시 7
15.6%
주 3
 
6.7%
창 2
 
4.4%
산 2
 
4.4%
안 2
 
4.4%
진 1
 
2.2%
고 1
 
2.2%
순 1
 
2.2%
실 1
 
2.2%
Other values (15) 15
33.3%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)19.0%
Memory size300.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5
Missing (%)23.8%
Memory size300.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)19.0%
Memory size300.0 B

Correlations

2024-03-14T10:30:21.823322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애인연금 수급자수Unnamed: 1
장애인연금 수급자수1.0000.000
Unnamed: 10.0001.000

Missing values

2024-03-14T10:30:20.862307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:30:20.936695image/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.
2024-03-14T10:30:21.013630image/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

장애인연금 수급자수Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
0(2013년 12월)<NA>NaNNaNNaN
1<NA><NA>NaNNaNNaN
2<NA><NA>NaNNaNNaN
3<NA><NA>NaNNaN단위 : 명
4시도시군구장애인연금 수급자 수NaNNaN
5<NA><NA>수급권자수급자 수수급률
6합계<NA>262561864671.02
7전라북도전주시6318392262.08
8<NA>군산시3213212466.11
9<NA>익산시4513333473.88
장애인연금 수급자수Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
11<NA>남원시1592120275.5
12<NA>김제시2059160978.14
13<NA>완주군1615118773.5
14<NA>진안군52138373.51
15<NA>무주군44833975.67
16<NA>장수군39331078.88
17<NA>임실군59546578.15
18<NA>순창군62848176.59
19<NA>고창군114088477.54
20<NA>부안군105878574.2

Duplicate rows

Most frequently occurring

장애인연금 수급자수Unnamed: 1# duplicates
0<NA><NA>4