Overview

Dataset statistics

Number of variables15
Number of observations21
Missing cells60
Missing cells (%)19.0%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory2.7 KiB
Average record size in memory130.3 B

Variable types

Text11
Numeric4

Dataset

Description부산광역시남구_인구현황_20220731
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3080514

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
통 is highly overall correlated with 반 High correlation
반 is highly overall correlated with 통 High correlation
면적(제곱킬로미터) is highly overall correlated with 구성비(퍼센트)High correlation
구성비(퍼센트) is highly overall correlated with 면적(제곱킬로미터) High correlation
구분 has 4 (19.0%) missing valuesMissing
세대수 has 4 (19.0%) missing valuesMissing
인구수(계) has 4 (19.0%) missing valuesMissing
인구수(남) has 4 (19.0%) missing valuesMissing
인구수(여) has 4 (19.0%) missing valuesMissing
18세이상인구수(계) has 4 (19.0%) missing valuesMissing
18세이상인구수(남) has 4 (19.0%) missing valuesMissing
18세이상인구수(여) has 4 (19.0%) missing valuesMissing
65세이상인구수(계) has 4 (19.0%) missing valuesMissing
65세이상인구수(남) has 4 (19.0%) missing valuesMissing
65세이상인구수(여) has 4 (19.0%) missing valuesMissing
통 has 4 (19.0%) missing valuesMissing
반 has 4 (19.0%) missing valuesMissing
면적(제곱킬로미터) has 4 (19.0%) missing valuesMissing
구성비(퍼센트) has 4 (19.0%) missing valuesMissing

Reproduction

Analysis started2024-04-19 05:24:55.631682
Analysis finished2024-04-19 05:24:58.268993
Duration2.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:24:58.382565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters68
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)100.0%

Sample

1st row대연1동
2nd row대연3동
3rd row대연4동
4th row대연5동
5th row대연6동
ValueCountFrequency (%)
대연3동 1
 
5.3%
감만2동 1
 
5.3%
당 1
 
5.3%
용 1
 
5.3%
문현4동 1
 
5.3%
문현3동 1
 
5.3%
문현2동 1
 
5.3%
문현1동 1
 
5.3%
우암동 1
 
5.3%
감만1동 1
 
5.3%
Other values (9) 9
47.4%
2024-04-19T14:24:58.691017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 17
25.0%
대 5
 
7.4%
용 5
 
7.4%
연 5
 
7.4%
현 4
 
5.9%
문 4
 
5.9%
호 4
 
5.9%
1 4
 
5.9%
2 3
 
4.4%
4 3
 
4.4%
Other values (9) 14
20.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
75.0%
Decimal Number 15
 
22.1%
Space Separator 2
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
33.3%
대 5
 
9.8%
용 5
 
9.8%
연 5
 
9.8%
현 4
 
7.8%
문 4
 
7.8%
호 4
 
7.8%
감 2
 
3.9%
만 2
 
3.9%
우 1
 
2.0%
Other values (2) 2
 
3.9%
Decimal Number
ValueCountFrequency (%)
1 4
26.7%
2 3
20.0%
4 3
20.0%
3 3
20.0%
6 1
 
6.7%
5 1
 
6.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51
75.0%
Common 17
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
33.3%
대 5
 
9.8%
용 5
 
9.8%
연 5
 
9.8%
현 4
 
7.8%
문 4
 
7.8%
호 4
 
7.8%
감 2
 
3.9%
만 2
 
3.9%
우 1
 
2.0%
Other values (2) 2
 
3.9%
Common
ValueCountFrequency (%)
1 4
23.5%
2 3
17.6%
4 3
17.6%
3 3
17.6%
2
11.8%
6 1
 
5.9%
5 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51
75.0%
ASCII 17
 
25.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 17
33.3%
대 5
 
9.8%
용 5
 
9.8%
연 5
 
9.8%
현 4
 
7.8%
문 4
 
7.8%
호 4
 
7.8%
감 2
 
3.9%
만 2
 
3.9%
우 1
 
2.0%
Other values (2) 2
 
3.9%
ASCII
ValueCountFrequency (%)
1 4
23.5%
2 3
17.6%
4 3
17.6%
3 3
17.6%
2
11.8%
6 1
 
5.9%
5 1
 
5.9%

세대수
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:24:58.861853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1176471
Min length5

Characters and Unicode

Total characters87
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

Unique17 ?
Unique (%)100.0%

Sample

1st row9,223
2nd row15,924
3rd row5,474
4th row8,347
5th row5,873
ValueCountFrequency (%)
15,924 1
 
5.9%
5,755 1
 
5.9%
3,893 1
 
5.9%
7,064 1
 
5.9%
5,099 1
 
5.9%
5,824 1
 
5.9%
5,777 1
 
5.9%
3,312 1
 
5.9%
9,223 1
 
5.9%
5,474 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:24:59.158199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 17
19.5%
5 12
13.8%
7 11
12.6%
3 10
11.5%
2 7
8.0%
4 7
8.0%
8 7
8.0%
9 6
 
6.9%
1 4
 
4.6%
6 3
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
80.5%
Other Punctuation 17
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12
17.1%
7 11
15.7%
3 10
14.3%
2 7
10.0%
4 7
10.0%
8 7
10.0%
9 6
8.6%
1 4
 
5.7%
6 3
 
4.3%
0 3
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 17
19.5%
5 12
13.8%
7 11
12.6%
3 10
11.5%
2 7
8.0%
4 7
8.0%
8 7
8.0%
9 6
 
6.9%
1 4
 
4.6%
6 3
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 17
19.5%
5 12
13.8%
7 11
12.6%
3 10
11.5%
2 7
8.0%
4 7
8.0%
8 7
8.0%
9 6
 
6.9%
1 4
 
4.6%
6 3
 
3.4%

인구수(계)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:24:59.323287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.7058824
Min length5

Characters and Unicode

Total characters97
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

Unique17 ?
Unique (%)100.0%

Sample

1st row15,811
2nd row32,711
3rd row12,142
4th row16,943
5th row15,050
ValueCountFrequency (%)
32,711 1
 
5.9%
12,011 1
 
5.9%
7,983 1
 
5.9%
16,591 1
 
5.9%
8,428 1
 
5.9%
13,255 1
 
5.9%
12,713 1
 
5.9%
6,700 1
 
5.9%
15,811 1
 
5.9%
12,142 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:24:59.614904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20
20.6%
, 17
17.5%
2 10
10.3%
5 8
 
8.2%
0 7
 
7.2%
8 7
 
7.2%
3 6
 
6.2%
7 6
 
6.2%
4 6
 
6.2%
6 5
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
82.5%
Other Punctuation 17
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20
25.0%
2 10
12.5%
5 8
 
10.0%
0 7
 
8.8%
8 7
 
8.8%
3 6
 
7.5%
7 6
 
7.5%
4 6
 
7.5%
6 5
 
6.2%
9 5
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20
20.6%
, 17
17.5%
2 10
10.3%
5 8
 
8.2%
0 7
 
7.2%
8 7
 
7.2%
3 6
 
6.2%
7 6
 
6.2%
4 6
 
6.2%
6 5
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20
20.6%
, 17
17.5%
2 10
10.3%
5 8
 
8.2%
0 7
 
7.2%
8 7
 
7.2%
3 6
 
6.2%
7 6
 
6.2%
4 6
 
6.2%
6 5
 
5.2%

인구수(남)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:24:59.775334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1176471
Min length5

Characters and Unicode

Total characters87
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

Unique17 ?
Unique (%)100.0%

Sample

1st row7,992
2nd row16,052
3rd row5,931
4th row8,119
5th row7,247
ValueCountFrequency (%)
16,052 1
 
5.9%
6,131 1
 
5.9%
3,865 1
 
5.9%
8,073 1
 
5.9%
4,169 1
 
5.9%
6,477 1
 
5.9%
6,263 1
 
5.9%
3,284 1
 
5.9%
7,992 1
 
5.9%
5,931 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:00.064650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 17
19.5%
1 12
13.8%
6 8
9.2%
0 7
8.0%
3 7
8.0%
7 7
8.0%
2 6
 
6.9%
9 6
 
6.9%
8 6
 
6.9%
4 6
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
80.5%
Other Punctuation 17
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
17.1%
6 8
11.4%
0 7
10.0%
3 7
10.0%
7 7
10.0%
2 6
8.6%
9 6
8.6%
8 6
8.6%
4 6
8.6%
5 5
7.1%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 17
19.5%
1 12
13.8%
6 8
9.2%
0 7
8.0%
3 7
8.0%
7 7
8.0%
2 6
 
6.9%
9 6
 
6.9%
8 6
 
6.9%
4 6
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 17
19.5%
1 12
13.8%
6 8
9.2%
0 7
8.0%
3 7
8.0%
7 7
8.0%
2 6
 
6.9%
9 6
 
6.9%
8 6
 
6.9%
4 6
 
6.9%

인구수(여)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:25:00.233387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1176471
Min length5

Characters and Unicode

Total characters87
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

Unique17 ?
Unique (%)100.0%

Sample

1st row7,819
2nd row16,659
3rd row6,211
4th row8,824
5th row7,803
ValueCountFrequency (%)
16,659 1
 
5.9%
5,880 1
 
5.9%
4,118 1
 
5.9%
8,518 1
 
5.9%
4,259 1
 
5.9%
6,778 1
 
5.9%
6,450 1
 
5.9%
3,416 1
 
5.9%
7,819 1
 
5.9%
6,211 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:00.518375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 17
19.5%
8 11
12.6%
6 10
11.5%
1 8
9.2%
5 8
9.2%
4 8
9.2%
2 7
8.0%
7 6
 
6.9%
3 5
 
5.7%
0 4
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
80.5%
Other Punctuation 17
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 11
15.7%
6 10
14.3%
1 8
11.4%
5 8
11.4%
4 8
11.4%
2 7
10.0%
7 6
8.6%
3 5
7.1%
0 4
 
5.7%
9 3
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 17
19.5%
8 11
12.6%
6 10
11.5%
1 8
9.2%
5 8
9.2%
4 8
9.2%
2 7
8.0%
7 6
 
6.9%
3 5
 
5.7%
0 4
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 17
19.5%
8 11
12.6%
6 10
11.5%
1 8
9.2%
5 8
9.2%
4 8
9.2%
2 7
8.0%
7 6
 
6.9%
3 5
 
5.7%
0 4
 
4.6%

18세이상인구수(계)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:25:00.679205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.7058824
Min length5

Characters and Unicode

Total characters97
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

Unique17 ?
Unique (%)100.0%

Sample

1st row14,765
2nd row27,736
3rd row10,473
4th row14,972
5th row12,127
ValueCountFrequency (%)
27,736 1
 
5.9%
10,807 1
 
5.9%
7,375 1
 
5.9%
14,315 1
 
5.9%
7,831 1
 
5.9%
11,745 1
 
5.9%
11,517 1
 
5.9%
6,091 1
 
5.9%
14,765 1
 
5.9%
10,473 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:00.967690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18
18.6%
, 17
17.5%
7 16
16.5%
3 8
8.2%
4 8
8.2%
0 6
 
6.2%
5 6
 
6.2%
2 5
 
5.2%
6 5
 
5.2%
9 4
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
82.5%
Other Punctuation 17
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
22.5%
7 16
20.0%
3 8
10.0%
4 8
10.0%
0 6
 
7.5%
5 6
 
7.5%
2 5
 
6.2%
6 5
 
6.2%
9 4
 
5.0%
8 4
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18
18.6%
, 17
17.5%
7 16
16.5%
3 8
8.2%
4 8
8.2%
0 6
 
6.2%
5 6
 
6.2%
2 5
 
5.2%
6 5
 
5.2%
9 4
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18
18.6%
, 17
17.5%
7 16
16.5%
3 8
8.2%
4 8
8.2%
0 6
 
6.2%
5 6
 
6.2%
2 5
 
5.2%
6 5
 
5.2%
9 4
 
4.1%

18세이상인구수(남)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:25:01.134566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1176471
Min length5

Characters and Unicode

Total characters87
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

Unique17 ?
Unique (%)100.0%

Sample

1st row7,464
2nd row13,452
3rd row5,075
4th row7,088
5th row5,775
ValueCountFrequency (%)
13,452 1
 
5.9%
5,498 1
 
5.9%
3,553 1
 
5.9%
6,929 1
 
5.9%
3,854 1
 
5.9%
5,673 1
 
5.9%
5,616 1
 
5.9%
2,960 1
 
5.9%
7,464 1
 
5.9%
5,075 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:01.418080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 17
19.5%
5 15
17.2%
3 9
10.3%
6 9
10.3%
7 8
9.2%
8 7
8.0%
2 6
 
6.9%
4 5
 
5.7%
9 5
 
5.7%
1 3
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
80.5%
Other Punctuation 17
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 15
21.4%
3 9
12.9%
6 9
12.9%
7 8
11.4%
8 7
10.0%
2 6
 
8.6%
4 5
 
7.1%
9 5
 
7.1%
1 3
 
4.3%
0 3
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 17
19.5%
5 15
17.2%
3 9
10.3%
6 9
10.3%
7 8
9.2%
8 7
8.0%
2 6
 
6.9%
4 5
 
5.7%
9 5
 
5.7%
1 3
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 17
19.5%
5 15
17.2%
3 9
10.3%
6 9
10.3%
7 8
9.2%
8 7
8.0%
2 6
 
6.9%
4 5
 
5.7%
9 5
 
5.7%
1 3
 
3.4%

18세이상인구수(여)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:25:01.590695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1176471
Min length5

Characters and Unicode

Total characters87
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

Unique17 ?
Unique (%)100.0%

Sample

1st row7,301
2nd row14,284
3rd row5,398
4th row7,884
5th row6,352
ValueCountFrequency (%)
14,284 1
 
5.9%
5,309 1
 
5.9%
3,822 1
 
5.9%
7,386 1
 
5.9%
3,977 1
 
5.9%
6,072 1
 
5.9%
5,901 1
 
5.9%
3,131 1
 
5.9%
7,301 1
 
5.9%
5,398 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:01.878489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 17
19.5%
3 12
13.8%
7 9
10.3%
1 8
9.2%
2 7
8.0%
8 7
8.0%
9 7
8.0%
5 6
 
6.9%
6 6
 
6.9%
0 5
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
80.5%
Other Punctuation 17
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12
17.1%
7 9
12.9%
1 8
11.4%
2 7
10.0%
8 7
10.0%
9 7
10.0%
5 6
8.6%
6 6
8.6%
0 5
7.1%
4 3
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 17
19.5%
3 12
13.8%
7 9
10.3%
1 8
9.2%
2 7
8.0%
8 7
8.0%
9 7
8.0%
5 6
 
6.9%
6 6
 
6.9%
0 5
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 17
19.5%
3 12
13.8%
7 9
10.3%
1 8
9.2%
2 7
8.0%
8 7
8.0%
9 7
8.0%
5 6
 
6.9%
6 6
 
6.9%
0 5
 
5.7%

65세이상인구수(계)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:25:02.042348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters85
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

Unique17 ?
Unique (%)100.0%

Sample

1st row3,650
2nd row4,349
3rd row2,943
4th row3,540
5th row2,397
ValueCountFrequency (%)
4,349 1
 
5.9%
3,290 1
 
5.9%
2,315 1
 
5.9%
3,278 1
 
5.9%
2,130 1
 
5.9%
3,167 1
 
5.9%
3,832 1
 
5.9%
2,051 1
 
5.9%
3,650 1
 
5.9%
2,943 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:02.336524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 17
20.0%
3 14
16.5%
2 9
10.6%
5 9
10.6%
4 6
 
7.1%
9 6
 
7.1%
0 6
 
7.1%
8 6
 
7.1%
1 6
 
7.1%
7 4
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
80.0%
Other Punctuation 17
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14
20.6%
2 9
13.2%
5 9
13.2%
4 6
8.8%
9 6
8.8%
0 6
8.8%
8 6
8.8%
1 6
8.8%
7 4
 
5.9%
6 2
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 17
20.0%
3 14
16.5%
2 9
10.6%
5 9
10.6%
4 6
 
7.1%
9 6
 
7.1%
0 6
 
7.1%
8 6
 
7.1%
1 6
 
7.1%
7 4
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 17
20.0%
3 14
16.5%
2 9
10.6%
5 9
10.6%
4 6
 
7.1%
9 6
 
7.1%
0 6
 
7.1%
8 6
 
7.1%
1 6
 
7.1%
7 4
 
4.7%

65세이상인구수(남)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:25:02.508420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.4117647
Min length3

Characters and Unicode

Total characters75
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

Unique17 ?
Unique (%)100.0%

Sample

1st row1,573
2nd row1,895
3rd row1,247
4th row1,544
5th row1,062
ValueCountFrequency (%)
1,895 1
 
5.9%
1,471 1
 
5.9%
966 1
 
5.9%
1,385 1
 
5.9%
894 1
 
5.9%
1,331 1
 
5.9%
1,661 1
 
5.9%
848 1
 
5.9%
1,573 1
 
5.9%
1,247 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:02.833988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16
21.3%
, 12
16.0%
8 8
10.7%
6 7
9.3%
3 7
9.3%
5 6
 
8.0%
4 6
 
8.0%
9 4
 
5.3%
7 4
 
5.3%
2 3
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63
84.0%
Other Punctuation 12
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
25.4%
8 8
12.7%
6 7
11.1%
3 7
11.1%
5 6
 
9.5%
4 6
 
9.5%
9 4
 
6.3%
7 4
 
6.3%
2 3
 
4.8%
0 2
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
21.3%
, 12
16.0%
8 8
10.7%
6 7
9.3%
3 7
9.3%
5 6
 
8.0%
4 6
 
8.0%
9 4
 
5.3%
7 4
 
5.3%
2 3
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16
21.3%
, 12
16.0%
8 8
10.7%
6 7
9.3%
3 7
9.3%
5 6
 
8.0%
4 6
 
8.0%
9 4
 
5.3%
7 4
 
5.3%
2 3
 
4.0%

65세이상인구수(여)
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-19T14:25:02.992236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8823529
Min length3

Characters and Unicode

Total characters83
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

Unique17 ?
Unique (%)100.0%

Sample

1st row2,077
2nd row2,454
3rd row1,696
4th row1,996
5th row1,335
ValueCountFrequency (%)
2,454 1
 
5.9%
1,819 1
 
5.9%
1,349 1
 
5.9%
1,893 1
 
5.9%
1,236 1
 
5.9%
1,836 1
 
5.9%
2,171 1
 
5.9%
1,203 1
 
5.9%
2,077 1
 
5.9%
1,696 1
 
5.9%
Other values (7) 7
41.2%
2024-04-19T14:25:03.291226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 16
19.3%
1 16
19.3%
2 8
9.6%
9 8
9.6%
3 8
9.6%
7 6
 
7.2%
4 5
 
6.0%
6 5
 
6.0%
0 5
 
6.0%
8 4
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
80.7%
Other Punctuation 16
 
19.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
23.9%
2 8
11.9%
9 8
11.9%
3 8
11.9%
7 6
 
9.0%
4 5
 
7.5%
6 5
 
7.5%
0 5
 
7.5%
8 4
 
6.0%
5 2
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 83
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 16
19.3%
1 16
19.3%
2 8
9.6%
9 8
9.6%
3 8
9.6%
7 6
 
7.2%
4 5
 
6.0%
6 5
 
6.0%
0 5
 
6.0%
8 4
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 16
19.3%
1 16
19.3%
2 8
9.6%
9 8
9.6%
3 8
9.6%
7 6
 
7.2%
4 5
 
6.0%
6 5
 
6.0%
0 5
 
6.0%
8 4
 
4.8%

통
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)82.4%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean21.882353
Minimum13
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:25:03.397785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile13.8
Q116
median20
Q325
95-th percentile34.8
Maximum42
Range29
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9912636
Coefficient of variation (CV)0.36519215
Kurtosis0.97763696
Mean21.882353
Median Absolute Deviation (MAD)5
Skewness1.1332583
Sum372
Variance63.860294
MonotonicityNot monotonic
2024-04-19T14:25:03.495008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
16 2
9.5%
23 2
9.5%
14 2
9.5%
31 1
 
4.8%
33 1
 
4.8%
20 1
 
4.8%
18 1
 
4.8%
42 1
 
4.8%
21 1
 
4.8%
13 1
 
4.8%
Other values (4) 4
19.0%
(Missing) 4
19.0%
ValueCountFrequency (%)
13 1
4.8%
14 2
9.5%
15 1
4.8%
16 2
9.5%
18 1
4.8%
19 1
4.8%
20 1
4.8%
21 1
4.8%
23 2
9.5%
25 1
4.8%
ValueCountFrequency (%)
42 1
4.8%
33 1
4.8%
31 1
4.8%
29 1
4.8%
25 1
4.8%
23 2
9.5%
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%

반
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean156.58824
Minimum84
Maximum357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:25:03.591008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile84.8
Q1108
median135
Q3174
95-th percentile308.2
Maximum357
Range273
Interquartile range (IQR)66

Descriptive statistics

Standard deviation74.881622
Coefficient of variation (CV)0.4782072
Kurtosis2.4395864
Mean156.58824
Median Absolute Deviation (MAD)38
Skewness1.5990094
Sum2662
Variance5607.2574
MonotonicityNot monotonic
2024-04-19T14:25:03.713116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
296 1
 
4.8%
120 1
 
4.8%
155 1
 
4.8%
97 1
 
4.8%
129 1
 
4.8%
219 1
 
4.8%
85 1
 
4.8%
175 1
 
4.8%
174 1
 
4.8%
90 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
84 1
4.8%
85 1
4.8%
90 1
4.8%
97 1
4.8%
108 1
4.8%
112 1
4.8%
120 1
4.8%
129 1
4.8%
135 1
4.8%
153 1
4.8%
ValueCountFrequency (%)
357 1
4.8%
296 1
4.8%
219 1
4.8%
175 1
4.8%
174 1
4.8%
173 1
4.8%
155 1
4.8%
153 1
4.8%
135 1
4.8%
129 1
4.8%

면적(제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean1.5776471
Minimum0.62
Maximum3.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:25:03.833981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.62
5-th percentile0.676
Q10.89
median1.18
Q31.96
95-th percentile3.672
Maximum3.84
Range3.22
Interquartile range (IQR)1.07

Descriptive statistics

Standard deviation0.97653424
Coefficient of variation (CV)0.61898143
Kurtosis1.153909
Mean1.5776471
Median Absolute Deviation (MAD)0.35
Skewness1.3971956
Sum26.82
Variance0.95361912
MonotonicityNot monotonic
2024-04-19T14:25:03.943892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3.84 1
 
4.8%
0.69 1
 
4.8%
0.84 1
 
4.8%
0.62 1
 
4.8%
0.83 1
 
4.8%
1.44 1
 
4.8%
0.89 1
 
4.8%
2.67 1
 
4.8%
0.97 1
 
4.8%
1.52 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
0.62 1
4.8%
0.69 1
4.8%
0.83 1
4.8%
0.84 1
4.8%
0.89 1
4.8%
0.97 1
4.8%
1.0 1
4.8%
1.11 1
4.8%
1.18 1
4.8%
1.44 1
4.8%
ValueCountFrequency (%)
3.84 1
4.8%
3.63 1
4.8%
2.67 1
4.8%
2.03 1
4.8%
1.96 1
4.8%
1.6 1
4.8%
1.52 1
4.8%
1.44 1
4.8%
1.18 1
4.8%
1.11 1
4.8%

구성비(퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean5.8823529
Minimum2.31
Maximum14.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:25:04.069775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.31
5-th percentile2.518
Q13.32
median4.4
Q37.31
95-th percentile13.696
Maximum14.32
Range12.01
Interquartile range (IQR)3.99

Descriptive statistics

Standard deviation3.6390478
Coefficient of variation (CV)0.61863813
Kurtosis1.1672007
Mean5.8823529
Median Absolute Deviation (MAD)1.3
Skewness1.3994953
Sum100
Variance13.242669
MonotonicityNot monotonic
2024-04-19T14:25:04.181763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
14.32 1
 
4.8%
2.57 1
 
4.8%
3.14 1
 
4.8%
2.31 1
 
4.8%
3.1 1
 
4.8%
5.37 1
 
4.8%
3.32 1
 
4.8%
9.92 1
 
4.8%
3.62 1
 
4.8%
5.67 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
2.31 1
4.8%
2.57 1
4.8%
3.1 1
4.8%
3.14 1
4.8%
3.32 1
4.8%
3.62 1
4.8%
3.73 1
4.8%
4.14 1
4.8%
4.4 1
4.8%
5.37 1
4.8%
ValueCountFrequency (%)
14.32 1
4.8%
13.54 1
4.8%
9.92 1
4.8%
7.57 1
4.8%
7.31 1
4.8%
5.97 1
4.8%
5.67 1
4.8%
5.37 1
4.8%
4.4 1
4.8%
4.14 1
4.8%

Interactions

2024-04-19T14:24:57.300197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.038572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.354197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.666420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:57.407582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.109709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.432379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.748730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:57.501124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.182025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.504729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.833545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:57.584859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.275209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.592296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:24:56.919080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:25:04.292887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)통반면적(제곱킬로미터)구성비(퍼센트)
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
세대수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인구수(계)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인구수(남)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인구수(여)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
18세이상인구수(계)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
18세이상인구수(남)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
18세이상인구수(여)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
65세이상인구수(계)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
65세이상인구수(남)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
65세이상인구수(여)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8590.7350.735
반1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8591.0000.7560.756
면적(제곱킬로미터)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7350.7561.0001.000
구성비(퍼센트)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7350.7561.0001.000
2024-04-19T14:25:04.452160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반면적(제곱킬로미터)구성비(퍼센트)
통1.0000.9850.3060.306
반0.9851.0000.3190.319
면적(제곱킬로미터)0.3060.3191.0001.000
구성비(퍼센트)0.3060.3191.0001.000

Missing values

2024-04-19T14:24:57.712300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:24:57.892848image/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.
2024-04-19T14:24:58.096075image/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

구분세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)통반면적(제곱킬로미터)구성비(퍼센트)
0대연1동9,22315,8117,9927,81914,7657,4647,3013,6501,5732,077311740.973.62
1대연3동15,92432,71116,05216,65927,73613,45214,2844,3491,8952,454332963.8414.32
2대연4동5,47412,1425,9316,21110,4735,0755,3982,9431,2471,696201531.03.73
3대연5동8,34716,9438,1198,82414,9727,0887,8843,5401,5441,996181121.114.14
4대연6동5,87315,0507,2477,80312,1275,7756,3522,3971,0621,335161081.184.4
5용호1동17,15843,65920,90322,75636,67917,39219,2878,5483,7184,830423571.65.97
6용호2동6,59316,5088,0518,45713,8206,6837,1373,5891,6181,971231732.037.57
7용호3동5,70212,0275,8016,22610,9535,2575,6963,5591,5322,027211351.967.31
8용호4동3,8628,4244,0714,3537,4843,5823,9022,1459351,21014901.525.67
9용 당 동3,4788,1794,1164,0637,1473,5863,5611,78080697413843.6313.54
구분세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)통반면적(제곱킬로미터)구성비(퍼센트)
11감만2동3,3126,7003,2843,4166,0912,9603,1312,0518481,20314850.893.32
12우암동5,77712,7136,2636,45011,5175,6165,9013,8321,6612,171292191.445.37
13문현1동5,82413,2556,4776,77811,7455,6736,0723,1671,3311,836191290.833.1
14문현2동5,0998,4284,1694,2597,8313,8543,9772,1308941,23615970.622.31
15문현3동7,06416,5918,0738,51814,3156,9297,3863,2781,3851,893231550.843.14
16문현4동3,8937,9833,8654,1187,3753,5533,8222,3159661,349161200.692.57
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20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)통반면적(제곱킬로미터)구성비(퍼센트)# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>4