Overview

Dataset statistics

Number of variables2
Number of observations1468
Missing cells2934
Missing cells (%)99.9%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory87.4 KiB
Average record size in memory61.0 B

Variable types

Text2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15262/F/1/datasetView.do

Alerts

饉胥 has constant value ""Constant
 has constant value ""Constant
Dataset has 1 (0.1%) duplicate rowsDuplicates
饉胥 has 1467 (99.9%) missing valuesMissing
 has 1467 (99.9%) missing valuesMissing

Reproduction

Analysis started2023-12-11 07:07:50.860125
Analysis finished2023-12-11 07:07:51.302794
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

饉胥
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1467
Missing (%)99.9%
Memory size75.9 KiB
2023-12-11T16:07:51.351633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters16
Distinct characters10
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row##0.00_);_(* \(#
ValueCountFrequency (%)
0.00 1
50.0%
1
50.0%
2023-12-11T16:07:51.689715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
# 3
18.8%
0 3
18.8%
_ 2
12.5%
( 2
12.5%
. 1
 
6.2%
) 1
 
6.2%
; 1
 
6.2%
* 1
 
6.2%
1
 
6.2%
\ 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 7
43.8%
Decimal Number 3
18.8%
Connector Punctuation 2
 
12.5%
Open Punctuation 2
 
12.5%
Close Punctuation 1
 
6.2%
Space Separator 1
 
6.2%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
# 3
42.9%
. 1
 
14.3%
; 1
 
14.3%
* 1
 
14.3%
\ 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
0 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
# 3
18.8%
0 3
18.8%
_ 2
12.5%
( 2
12.5%
. 1
 
6.2%
) 1
 
6.2%
; 1
 
6.2%
* 1
 
6.2%
1
 
6.2%
\ 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 3
18.8%
0 3
18.8%
_ 2
12.5%
( 2
12.5%
. 1
 
6.2%
) 1
 
6.2%
; 1
 
6.2%
* 1
 
6.2%
1
 
6.2%
\ 1
 
6.2%


Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1467
Missing (%)99.9%
Memory size75.9 KiB
2023-12-11T16:07:51.811801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters26
Distinct characters14
Distinct categories7 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row##0.00\);_(* "-"??_);_(@_)
ValueCountFrequency (%)
0.00 1
50.0%
1
50.0%
2023-12-11T16:07:52.104252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 4
15.4%
0 3
11.5%
) 3
11.5%
# 2
7.7%
; 2
7.7%
( 2
7.7%
" 2
7.7%
? 2
7.7%
. 1
 
3.8%
\ 1
 
3.8%
Other values (4) 4
15.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 12
46.2%
Connector Punctuation 4
 
15.4%
Decimal Number 3
 
11.5%
Close Punctuation 3
 
11.5%
Open Punctuation 2
 
7.7%
Space Separator 1
 
3.8%
Dash Punctuation 1
 
3.8%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
# 2
16.7%
; 2
16.7%
" 2
16.7%
? 2
16.7%
. 1
8.3%
\ 1
8.3%
* 1
8.3%
@ 1
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Decimal Number
ValueCountFrequency (%)
0 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 4
15.4%
0 3
11.5%
) 3
11.5%
# 2
7.7%
; 2
7.7%
( 2
7.7%
" 2
7.7%
? 2
7.7%
. 1
 
3.8%
\ 1
 
3.8%
Other values (4) 4
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 4
15.4%
0 3
11.5%
) 3
11.5%
# 2
7.7%
; 2
7.7%
( 2
7.7%
" 2
7.7%
? 2
7.7%
. 1
 
3.8%
\ 1
 
3.8%
Other values (4) 4
15.4%

Missing values

2023-12-11T16:07:51.016403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:07:51.121820image/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.
2023-12-11T16:07:51.237044image/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

饉胥
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN##0_-;\-* ###0_-;_-* "-"_-;_-@_-uNaNNaNNaN##0.00_-;\-* ###0.00_-;_-* "-"??_-;_-@_-##0_);\(\$###0\)##0_);[Red]\(\$###0\)##0.00_);\(\$###0.00\)%##0.00_);[Red]\(\$###0.00\)##0_);\("$"###0\)!##0_);[Red]\("$"###0\)"##0.00_);\("$"###0.00\)'##0.00_);[Red]\("$"###0.00\).##0_);_(* \(###0\);_(* "-"_);_(@_)7##0_);_("$"* \(###0\);_("$"* "-"_);_(@_)?##0.00_);_("$"* \(###0.00\);_("$"* "-"??_);_(@_)6##0.00_);_(* \(###0.00\);_(* "-"??_);_(@_)
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
饉胥
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>
陂0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>
NaN<NA><NA>
NaN<NA><NA>

Duplicate rows

Most frequently occurring

饉胥# duplicates
0<NA><NA>1467