Overview

Dataset statistics

Number of variables3
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory660.0 B
Average record size in memory30.0 B

Variable types

Text3

Dataset

Description인천광역시 부평구 동별 남성 인구 현황 데이터입니다.(동별,인구수 남자,19세이상 남자)ex) 부평1동,17988,15538
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15089246&srcSe=7661IVAWM27C61E190

Alerts

동별 has unique valuesUnique
인구수 남자 has unique valuesUnique
19세이상 남자 has unique valuesUnique

Reproduction

Analysis started2024-01-28 17:40:49.411453
Analysis finished2024-01-28 17:40:49.640661
Duration0.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동별
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-01-29T02:40:49.776305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9545455
Min length3

Characters and Unicode

Total characters87
Distinct characters20
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

Unique22 ?
Unique (%)100.0%

Sample

1st row부평1동
2nd row부평2동
3rd row부평3동
4th row부평4동
5th row부평5동
ValueCountFrequency (%)
부평1동 1
 
4.5%
부평2동 1
 
4.5%
십정1동 1
 
4.5%
일신동 1
 
4.5%
부개3동 1
 
4.5%
부개2동 1
 
4.5%
부개1동 1
 
4.5%
삼산2동 1
 
4.5%
삼산1동 1
 
4.5%
갈산2동 1
 
4.5%
Other values (12) 12
54.5%
2024-01-29T02:40:50.073682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
25.3%
부 9
10.3%
산 8
 
9.2%
1 7
 
8.0%
2 7
 
8.0%
평 6
 
6.9%
곡 4
 
4.6%
3 3
 
3.4%
개 3
 
3.4%
4 2
 
2.3%
Other values (10) 16
18.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
75.9%
Decimal Number 21
 
24.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
75.9%
Common 21
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
Common
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
75.9%
ASCII 21
 
24.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
ASCII
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

인구수 남자
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-01-29T02:40:50.248065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length4.5454545
Min length4

Characters and Unicode

Total characters100
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row16862
2nd row7434
3rd row6959
4th row17496
5th row16267
ValueCountFrequency (%)
16862 1
 
4.5%
7434 1
 
4.5%
12331 1
 
4.5%
6058 1
 
4.5%
13942 1
 
4.5%
9453 1
 
4.5%
8113 1
 
4.5%
13443 1
 
4.5%
16234 1
 
4.5%
8644 1
 
4.5%
Other values (12) 12
54.5%
2024-01-29T02:40:50.544627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
17.0%
3 17
17.0%
4 13
13.0%
6 10
10.0%
8 9
9.0%
7 8
8.0%
0 8
8.0%
9 7
7.0%
2 6
 
6.0%
5 4
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99
99.0%
Other Punctuation 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
17.2%
3 17
17.2%
4 13
13.1%
6 10
10.1%
8 9
9.1%
7 8
8.1%
0 8
8.1%
9 7
7.1%
2 6
 
6.1%
5 4
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
17.0%
3 17
17.0%
4 13
13.0%
6 10
10.0%
8 9
9.0%
7 8
8.0%
0 8
8.0%
9 7
7.0%
2 6
 
6.0%
5 4
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
17.0%
3 17
17.0%
4 13
13.0%
6 10
10.0%
8 9
9.0%
7 8
8.0%
0 8
8.0%
9 7
7.0%
2 6
 
6.0%
5 4
 
4.0%

19세이상 남자
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-01-29T02:40:50.737811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.5
Min length4

Characters and Unicode

Total characters99
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row14868
2nd row6567
3rd row6200
4th row15466
5th row13921
ValueCountFrequency (%)
14868 1
 
4.5%
6567 1
 
4.5%
10646 1
 
4.5%
5214 1
 
4.5%
12054 1
 
4.5%
8106 1
 
4.5%
7287 1
 
4.5%
10822 1
 
4.5%
13772 1
 
4.5%
7586 1
 
4.5%
Other values (12) 12
54.5%
2024-01-29T02:40:51.040625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
17.2%
6 16
16.2%
2 13
13.1%
4 10
10.1%
8 10
10.1%
7 10
10.1%
0 9
9.1%
5 7
7.1%
3 4
 
4.0%
9 2
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98
99.0%
Other Punctuation 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
17.3%
6 16
16.3%
2 13
13.3%
4 10
10.2%
8 10
10.2%
7 10
10.2%
0 9
9.2%
5 7
7.1%
3 4
 
4.1%
9 2
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
17.2%
6 16
16.2%
2 13
13.1%
4 10
10.1%
8 10
10.1%
7 10
10.1%
0 9
9.1%
5 7
7.1%
3 4
 
4.0%
9 2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
17.2%
6 16
16.2%
2 13
13.1%
4 10
10.1%
8 10
10.1%
7 10
10.1%
0 9
9.1%
5 7
7.1%
3 4
 
4.0%
9 2
 
2.0%

Correlations

2024-01-29T02:40:51.131578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별인구수 남자19세이상 남자
동별1.0001.0001.000
인구수 남자1.0001.0001.000
19세이상 남자1.0001.0001.000

Missing values

2024-01-29T02:40:49.545651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T02:40:49.614071image/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

동별인구수 남자19세이상 남자
0부평1동1686214868
1부평2동74346567
2부평3동69596200
3부평4동1749615466
4부평5동1626713921
5부평6동73806475
6산곡1동73736221
7산곡2동1474012024
8산곡3동101328840
9산곡4동80886866
동별인구수 남자19세이상 남자
12갈산1동80497191
13갈산2동86447586
14삼산1동1623413772
15삼산2동1344310822
16부개1동81137287
17부개2동94538106
18부개3동1394212054
19일신동60585214
20십정1동1233110646
21십정2동11,31310,243