Overview

Dataset statistics

Number of variables9
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory84.3 B

Variable types

Categorical8
Text1

Dataset

Description대구광역시 수성구에서 접수 및 처리한 무연고 사망자에 관한 통계 데이터로 동별 무연고 사망자 수에 대한 정보를 제공합니다.
Author대구광역시 수성구
URLhttps://www.data.go.kr/data/15107869/fileData.do

Alerts

시군구 has constant value ""Constant
2017년 is highly overall correlated with 2018년 and 1 other fieldsHigh correlation
2018년 is highly overall correlated with 2017년 and 1 other fieldsHigh correlation
2019년 is highly overall correlated with 2017년 and 1 other fieldsHigh correlation
2021년 is highly overall correlated with 2022년High correlation
2022년 is highly overall correlated with 2021년High correlation
관리행정동 has unique valuesUnique

Reproduction

Analysis started2024-03-14 14:39:22.922764
Analysis finished2024-03-14 14:39:24.216492
Duration1.29 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size320.0 B
대구광역시 수성구
24 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 수성구
2nd row대구광역시 수성구
3rd row대구광역시 수성구
4th row대구광역시 수성구
5th row대구광역시 수성구

Common Values

ValueCountFrequency (%)
대구광역시 수성구 24
100.0%

Length

2024-03-14T23:39:24.417293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:24.700858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 24
50.0%
수성구 24
50.0%

관리행정동
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
2024-03-14T23:39:25.386374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.9166667
Min length2

Characters and Unicode

Total characters94
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row범어1동
2nd row범어2동
3rd row범어3동
4th row범어4동
5th row만촌1동
ValueCountFrequency (%)
범어1동 1
 
4.0%
상동 1
 
4.0%
칠곡군 1
 
4.0%
고산3동 1
 
4.0%
고산2동 1
 
4.0%
고산1동 1
 
4.0%
범물2동 1
 
4.0%
범물1동 1
 
4.0%
지산2동 1
 
4.0%
지산1동 1
 
4.0%
Other values (15) 15
60.0%
2024-03-14T23:39:26.604070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 20
21.3%
1 7
 
7.4%
2 7
 
7.4%
범 6
 
6.4%
산 6
 
6.4%
3 4
 
4.3%
어 4
 
4.3%
수 3
 
3.2%
성 3
 
3.2%
가 3
 
3.2%
Other values (20) 31
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
76.6%
Decimal Number 20
 
21.3%
Space Separator 1
 
1.1%
Other Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 20
27.8%
범 6
 
8.3%
산 6
 
8.3%
어 4
 
5.6%
수 3
 
4.2%
성 3
 
4.2%
가 3
 
4.2%
촌 3
 
4.2%
만 3
 
4.2%
고 3
 
4.2%
Other values (14) 18
25.0%
Decimal Number
ValueCountFrequency (%)
1 7
35.0%
2 7
35.0%
3 4
20.0%
4 2
 
10.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
76.6%
Common 22
 
23.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 20
27.8%
범 6
 
8.3%
산 6
 
8.3%
어 4
 
5.6%
수 3
 
4.2%
성 3
 
4.2%
가 3
 
4.2%
촌 3
 
4.2%
만 3
 
4.2%
고 3
 
4.2%
Other values (14) 18
25.0%
Common
ValueCountFrequency (%)
1 7
31.8%
2 7
31.8%
3 4
18.2%
4 2
 
9.1%
1
 
4.5%
. 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
76.6%
ASCII 22
 
23.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 20
27.8%
범 6
 
8.3%
산 6
 
8.3%
어 4
 
5.6%
수 3
 
4.2%
성 3
 
4.2%
가 3
 
4.2%
촌 3
 
4.2%
만 3
 
4.2%
고 3
 
4.2%
Other values (14) 18
25.0%
ASCII
ValueCountFrequency (%)
1 7
31.8%
2 7
31.8%
3 4
18.2%
4 2
 
9.1%
1
 
4.5%
. 1
 
4.5%

2017년
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size320.0 B
<NA>
16 
1
5 
0
2 
2
 
1

Length

Max length4
Median length4
Mean length3
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 16
66.7%
1 5
 
20.8%
0 2
 
8.3%
2 1
 
4.2%

Length

2024-03-14T23:39:27.229539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:27.559020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
66.7%
1 5
 
20.8%
0 2
 
8.3%
2 1
 
4.2%

2018년
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size320.0 B
<NA>
16 
1
4 
2
2 
0
2 

Length

Max length4
Median length4
Mean length3
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 16
66.7%
1 4
 
16.7%
2 2
 
8.3%
0 2
 
8.3%

Length

2024-03-14T23:39:27.937311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:28.245557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
66.7%
1 4
 
16.7%
2 2
 
8.3%
0 2
 
8.3%

2019년
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size320.0 B
<NA>
13 
1
7 
2
2 
0
2 

Length

Max length4
Median length4
Mean length2.625
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row1
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 13
54.2%
1 7
29.2%
2 2
 
8.3%
0 2
 
8.3%

Length

2024-03-14T23:39:28.447825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:28.722071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 13
54.2%
1 7
29.2%
2 2
 
8.3%
0 2
 
8.3%

2020년
Categorical

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size320.0 B
0
15 
1
7 
3
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row3

Common Values

ValueCountFrequency (%)
0 15
62.5%
1 7
29.2%
3 2
 
8.3%

Length

2024-03-14T23:39:29.080754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:29.384737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15
62.5%
1 7
29.2%
3 2
 
8.3%

2021년
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size320.0 B
0
19 
3
2 
2
2 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row3
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
79.2%
3 2
 
8.3%
2 2
 
8.3%
1 1
 
4.2%

Length

2024-03-14T23:39:29.717691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:30.037946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
79.2%
3 2
 
8.3%
2 2
 
8.3%
1 1
 
4.2%

2022년
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size320.0 B
0
12 
1
6 
2
3 
3
2 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row3
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 12
50.0%
1 6
25.0%
2 3
 
12.5%
3 2
 
8.3%
6 1
 
4.2%

Length

2024-03-14T23:39:30.381414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:30.700649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12
50.0%
1 6
25.0%
2 3
 
12.5%
3 2
 
8.3%
6 1
 
4.2%

2023년
Categorical

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size320.0 B
0
9 
1
7 
2
4 
3
3 
4
1 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 9
37.5%
1 7
29.2%
2 4
16.7%
3 3
 
12.5%
4 1
 
4.2%

Length

2024-03-14T23:39:31.061448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:39:31.388483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9
37.5%
1 7
29.2%
2 4
16.7%
3 3
 
12.5%
4 1
 
4.2%

Correlations

2024-03-14T23:39:31.611180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리행정동2017년2018년2019년2020년2021년2022년2023년
관리행정동1.0001.0001.0001.0001.0001.0001.0001.000
2017년1.0001.0001.0001.0000.5820.0000.5820.392
2018년1.0001.0001.0000.9430.0000.0000.2700.270
2019년1.0001.0000.9431.0000.5550.7150.5120.000
2020년1.0000.5820.0000.5551.0000.1480.4350.067
2021년1.0000.0000.0000.7150.1481.0000.7400.000
2022년1.0000.5820.2700.5120.4350.7401.0000.622
2023년1.0000.3920.2700.0000.0670.0000.6221.000
2024-03-14T23:39:31.840809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년2021년2018년2023년2019년2022년2020년
2017년1.0000.0001.0000.2310.8940.1410.141
2021년0.0001.0000.0000.0000.3440.6620.113
2018년1.0000.0001.0000.0000.6670.0000.000
2023년0.2310.0000.0001.0000.0000.2600.000
2019년0.8940.3440.6670.0001.0000.4530.189
2022년0.1410.6620.0000.2600.4531.0000.338
2020년0.1410.1130.0000.0000.1890.3381.000
2024-03-14T23:39:32.031498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년2018년2019년2020년2021년2022년2023년
2017년1.0001.0000.8940.1410.0000.1410.231
2018년1.0001.0000.6670.0000.0000.0000.000
2019년0.8940.6671.0000.1890.3440.4530.000
2020년0.1410.0000.1891.0000.1130.3380.000
2021년0.0000.0000.3440.1131.0000.6620.000
2022년0.1410.0000.4530.3380.6621.0000.260
2023년0.2310.0000.0000.0000.0000.2601.000

Missing values

2024-03-14T23:39:23.605751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:39:24.045614image/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

시군구관리행정동2017년2018년2019년2020년2021년2022년2023년
0대구광역시 수성구범어1동<NA>1<NA>0331
1대구광역시 수성구범어2동<NA><NA><NA>0000
2대구광역시 수성구범어3동<NA><NA>10000
3대구광역시 수성구범어4동<NA><NA><NA>1011
4대구광역시 수성구만촌1동<NA><NA><NA>3001
5대구광역시 수성구만촌2동1<NA>11011
6대구광역시 수성구만촌3동<NA><NA><NA>0011
7대구광역시 수성구수성1가<NA>1<NA>0012
8대구광역시 수성구수성2.3가<NA><NA><NA>0000
9대구광역시 수성구수성4가<NA><NA><NA>1001
시군구관리행정동2017년2018년2019년2020년2021년2022년2023년
14대구광역시 수성구파동<NA>221020
15대구광역시 수성구두산동1<NA>11013
16대구광역시 수성구지산1동<NA><NA>21262
17대구광역시 수성구지산2동<NA><NA><NA>0001
18대구광역시 수성구범물1동1111223
19대구광역시 수성구범물2동<NA><NA><NA>0000
20대구광역시 수성구고산1동<NA><NA><NA>0000
21대구광역시 수성구고산2동2210002
22대구광역시 수성구고산3동0000000
23대구광역시 수성구칠곡군 왜관읍0000100