Overview

Dataset statistics

Number of variables11
Number of observations43
Missing cells226
Missing cells (%)47.8%
Duplicate rows1
Duplicate rows (%)2.3%
Total size in memory3.8 KiB
Average record size in memory91.1 B

Variable types

Text3
Unsupported8

Dataset

Description부산도시공사_임대료및임대보증금_20221206
Author부산도시공사
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15045982

Alerts

Dataset has 1 (2.3%) duplicate rowsDuplicates
영구임대 임대료 및 임대보증금 has 30 (69.8%) missing valuesMissing
Unnamed: 1 has 30 (69.8%) missing valuesMissing
Unnamed: 2 has 41 (95.3%) missing valuesMissing
Unnamed: 3 has 30 (69.8%) missing valuesMissing
Unnamed: 4 has 30 (69.8%) missing valuesMissing
Unnamed: 5 has 28 (65.1%) missing valuesMissing
Unnamed: 6 has 3 (7.0%) missing valuesMissing
Unnamed: 7 has 2 (4.7%) missing valuesMissing
Unnamed: 8 has 28 (65.1%) missing valuesMissing
Unnamed: 9 has 2 (4.7%) missing valuesMissing
Unnamed: 10 has 2 (4.7%) missing valuesMissing
Unnamed: 1 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: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 16:20:30.643702
Analysis finished2023-12-10 16:20:31.511251
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영구임대 임대료 및 임대보증금
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing30
Missing (%)69.8%
Memory size476.0 B
2023-12-11T01:20:31.646052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6923077
Min length5

Characters and Unicode

Total characters100
Distinct characters27
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

Unique13 ?
Unique (%)100.0%

Sample

1st row지 구 명
2nd row부 곡(13평)
3rd row부 곡(14평)
4th row다대3(12평)
5th row학장1(12평)
ValueCountFrequency (%)
부 2
 
11.1%
지 1
 
5.6%
개금2(12평 1
 
5.6%
다대5(12평 1
 
5.6%
다대4(12평 1
 
5.6%
동삼2(12평 1
 
5.6%
송(12평 1
 
5.6%
반 1
 
5.6%
덕천2(12평 1
 
5.6%
동삼1(12평 1
 
5.6%
Other values (7) 7
38.9%
2023-12-11T01:20:32.170421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
14.0%
) 12
12.0%
평 12
12.0%
2 12
12.0%
( 12
12.0%
5
 
5.0%
동 3
 
3.0%
다 3
 
3.0%
대 3
 
3.0%
3 3
 
3.0%
Other values (17) 21
21.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39
39.0%
Decimal Number 32
32.0%
Close Punctuation 12
 
12.0%
Open Punctuation 12
 
12.0%
Space Separator 5
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 12
30.8%
동 3
 
7.7%
다 3
 
7.7%
대 3
 
7.7%
곡 2
 
5.1%
부 2
 
5.1%
삼 2
 
5.1%
금 1
 
2.6%
송 1
 
2.6%
반 1
 
2.6%
Other values (9) 9
23.1%
Decimal Number
ValueCountFrequency (%)
1 14
43.8%
2 12
37.5%
3 3
 
9.4%
4 2
 
6.2%
5 1
 
3.1%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61
61.0%
Hangul 39
39.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 12
30.8%
동 3
 
7.7%
다 3
 
7.7%
대 3
 
7.7%
곡 2
 
5.1%
부 2
 
5.1%
삼 2
 
5.1%
금 1
 
2.6%
송 1
 
2.6%
반 1
 
2.6%
Other values (9) 9
23.1%
Common
ValueCountFrequency (%)
1 14
23.0%
) 12
19.7%
2 12
19.7%
( 12
19.7%
5
 
8.2%
3 3
 
4.9%
4 2
 
3.3%
5 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61
61.0%
Hangul 39
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
23.0%
) 12
19.7%
2 12
19.7%
( 12
19.7%
5
 
8.2%
3 3
 
4.9%
4 2
 
3.3%
5 1
 
1.6%
Hangul
ValueCountFrequency (%)
평 12
30.8%
동 3
 
7.7%
다 3
 
7.7%
대 3
 
7.7%
곡 2
 
5.1%
부 2
 
5.1%
삼 2
 
5.1%
금 1
 
2.6%
송 1
 
2.6%
반 1
 
2.6%
Other values (9) 9
23.1%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)69.8%
Memory size476.0 B

Unnamed: 2
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing41
Missing (%)95.3%
Memory size476.0 B
2023-12-11T01:20:32.423306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10.5
Mean length10.5
Min length10

Characters and Unicode

Total characters21
Distinct characters10
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row255 (28.2)
2nd row298 (31.32)
ValueCountFrequency (%)
255 1
25.0%
28.2 1
25.0%
298 1
25.0%
31.32 1
25.0%
2023-12-11T01:20:32.821336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5
23.8%
5 2
 
9.5%
2
 
9.5%
( 2
 
9.5%
8 2
 
9.5%
. 2
 
9.5%
) 2
 
9.5%
3 2
 
9.5%
9 1
 
4.8%
1 1
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
61.9%
Control 2
 
9.5%
Open Punctuation 2
 
9.5%
Other Punctuation 2
 
9.5%
Close Punctuation 2
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5
38.5%
5 2
 
15.4%
8 2
 
15.4%
3 2
 
15.4%
9 1
 
7.7%
1 1
 
7.7%
Control
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5
23.8%
5 2
 
9.5%
2
 
9.5%
( 2
 
9.5%
8 2
 
9.5%
. 2
 
9.5%
) 2
 
9.5%
3 2
 
9.5%
9 1
 
4.8%
1 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5
23.8%
5 2
 
9.5%
2
 
9.5%
( 2
 
9.5%
8 2
 
9.5%
. 2
 
9.5%
) 2
 
9.5%
3 2
 
9.5%
9 1
 
4.8%
1 1
 
4.8%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)69.8%
Memory size476.0 B

Unnamed: 4
Text

MISSING 

Distinct12
Distinct (%)92.3%
Missing30
Missing (%)69.8%
Memory size476.0 B
2023-12-11T01:20:33.078244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length14.846154
Min length2

Characters and Unicode

Total characters193
Distinct characters17
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)84.6%

Sample

1st row임대
2nd row기간
3rd row21.7.12~ 23.7.11
4th row22.05.01~ 24.4.30
5th row22.7.24~ 24.7.23
ValueCountFrequency (%)
5
 
17.2%
22.12.01 2
 
6.9%
24.11.30 2
 
6.9%
21.7.06 1
 
3.4%
23.5.26 1
 
3.4%
23.11.12 1
 
3.4%
21.11.13 1
 
3.4%
23.8.17 1
 
3.4%
21.8.18 1
 
3.4%
23.07.20 1
 
3.4%
Other values (13) 13
44.8%
2023-12-11T01:20:33.604806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 44
22.8%
2 37
19.2%
1 27
14.0%
0 12
 
6.2%
3 12
 
6.2%
~ 11
 
5.7%
11
 
5.7%
7 10
 
5.2%
4 7
 
3.6%
6
 
3.1%
Other values (7) 16
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117
60.6%
Other Punctuation 44
 
22.8%
Math Symbol 11
 
5.7%
Space Separator 11
 
5.7%
Control 6
 
3.1%
Other Letter 4
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 37
31.6%
1 27
23.1%
0 12
 
10.3%
3 12
 
10.3%
7 10
 
8.5%
4 7
 
6.0%
5 6
 
5.1%
6 3
 
2.6%
8 3
 
2.6%
Other Letter
ValueCountFrequency (%)
간 1
25.0%
기 1
25.0%
대 1
25.0%
임 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 44
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Control
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189
97.9%
Hangul 4
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 44
23.3%
2 37
19.6%
1 27
14.3%
0 12
 
6.3%
3 12
 
6.3%
~ 11
 
5.8%
11
 
5.8%
7 10
 
5.3%
4 7
 
3.7%
6
 
3.2%
Other values (3) 12
 
6.3%
Hangul
ValueCountFrequency (%)
간 1
25.0%
기 1
25.0%
대 1
25.0%
임 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189
97.9%
Hangul 4
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 44
23.3%
2 37
19.6%
1 27
14.3%
0 12
 
6.3%
3 12
 
6.3%
~ 11
 
5.8%
11
 
5.8%
7 10
 
5.3%
4 7
 
3.7%
6
 
3.2%
Other values (3) 12
 
6.3%
Hangul
ValueCountFrequency (%)
간 1
25.0%
기 1
25.0%
대 1
25.0%
임 1
25.0%

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28
Missing (%)65.1%
Memory size476.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)7.0%
Memory size476.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)4.7%
Memory size476.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28
Missing (%)65.1%
Memory size476.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)4.7%
Memory size476.0 B

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)4.7%
Memory size476.0 B

Correlations

2023-12-11T01:20:33.757838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영구임대 임대료 및 임대보증금Unnamed: 2Unnamed: 4
영구임대 임대료 및 임대보증금1.0000.0001.000
Unnamed: 20.0001.000NaN
Unnamed: 41.000NaN1.000

Missing values

2023-12-11T01:20:30.849970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:20:31.058132image/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.
2023-12-11T01:20:31.299043image/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

영구임대 임대료 및 임대보증금Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
0<NA>NaN<NA>NaN<NA>NaNNaNNaNNaN23.6.1.기준NaN
1지 구 명세대수<NA>최초임대표 준 임 대 보 증 금NaNNaN표 준 임 대 료NaNNaN
2<NA>(전용㎡)<NA>입주기간기 초1차탈락일 반기 초1차탈락일 반
3<NA>NaN<NA>NaN<NA>수급자NaN(장애인)수급자NaN(장애인)
4<NA>NaN<NA>NaN<NA>NaN2차탈락할증1(20%)NaN2차탈락할증1(20%)
5<NA>NaN<NA>NaN<NA>NaN3차탈락할증2(10%)NaN3차탈락할증2(10%)
6부 곡(13평)553255 (28.2)91.0821.7.12~ 23.7.11220032706650470005950088800
7<NA>NaN<NA>NaN<NA>NaN43506650NaN7200088800
8<NA>NaN<NA>NaN<NA>NaN57906650NaN8880088800
9부 곡(14평)NaN298 (31.32)NaN<NA>2530376076705400068400102100
영구임대 임대료 및 임대보증금Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
33<NA>NaN<NA>NaN<NA>NaN1000011460NaN134800136000
34다대4(12평)1920\n(27.41)<NA>95.0621.11.13 ~ 23.11.1221504390114604550073600135600
35<NA>NaN<NA>NaN<NA>NaN663010010NaN99500135600
36<NA>NaN<NA>NaN<NA>NaN963010010NaN134100135600
37다대5(12평)2107\n(27.01)<NA>96.0722.12.01~ 24.11.3021604670110504580071200136800
38<NA>NaN<NA>NaN<NA>NaN719011050NaN96600136800
39<NA>NaN<NA>NaN<NA>NaN1055011050NaN130600136800
40동백(13평)125\n(29.88)<NA>07.0523.5.26 ~ 25.5.2524004770123605230067600123900
41<NA>NaN<NA>NaN<NA>NaN714012360NaN82800123900
42<NA>NaN<NA>NaN<NA>NaN1030012360NaN103200123900

Duplicate rows

Most frequently occurring

영구임대 임대료 및 임대보증금Unnamed: 2Unnamed: 4# duplicates
0<NA><NA><NA>29