Overview

Dataset statistics

Number of variables18
Number of observations78
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory145.7 B

Variable types

Text1
Categorical17

Dataset

Description2023년 12월 기준 경상남도 거제시 외국인 인구현황 자료로, 남, 여, 5세 단위 구분, 각 면동 별 인구를 국적 별로 분류하였습니다.
Author경상남도 거제시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3079542

Alerts

둔덕면 is highly overall correlated with 동부면 and 14 other fieldsHigh correlation
능포동 is highly overall correlated with 동부면 and 14 other fieldsHigh correlation
사등면 is highly overall correlated with 동부면 and 14 other fieldsHigh correlation
동부면 is highly overall correlated with 남부면 and 14 other fieldsHigh correlation
남부면 is highly overall correlated with 동부면 and 14 other fieldsHigh correlation
거제면 is highly overall correlated with 동부면 and 14 other fieldsHigh correlation
연초면 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
하청면 is highly overall correlated with 동부면 and 14 other fieldsHigh correlation
장목면 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
장승포동 is highly overall correlated with 동부면 and 14 other fieldsHigh correlation
아주동 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
옥포1동 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
옥포2동 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
장평동 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
고현동 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
상문동 is highly overall correlated with 동부면 and 15 other fieldsHigh correlation
수양동 is highly overall correlated with 연초면 and 7 other fieldsHigh correlation
동부면 is highly imbalanced (72.6%)Imbalance
남부면 is highly imbalanced (67.9%)Imbalance
거제면 is highly imbalanced (61.3%)Imbalance
둔덕면 is highly imbalanced (67.9%)Imbalance
연초면 is highly imbalanced (56.5%)Imbalance
하청면 is highly imbalanced (56.7%)Imbalance
장목면 is highly imbalanced (53.6%)Imbalance
장승포동 is highly imbalanced (53.7%)Imbalance
능포동 is highly imbalanced (56.4%)Imbalance
상문동 is highly imbalanced (52.9%)Imbalance
국 적 has unique valuesUnique

Reproduction

Analysis started2024-04-16 07:01:45.584899
Analysis finished2024-04-16 07:01:47.618773
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국 적
Text

UNIQUE 

Distinct78
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size756.0 B
2024-04-16T16:01:47.793731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length4.0512821
Min length2

Characters and Unicode

Total characters316
Distinct characters118
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

Unique78 ?
Unique (%)100.0%

Sample

1st row가나
2nd row그리스
3rd row나이지리아
4th row남아프리카공화국
5th row네덜란드
ValueCountFrequency (%)
가나 1
 
1.3%
이집트 1
 
1.3%
체코 1
 
1.3%
짐바브웨 1
 
1.3%
중국 1
 
1.3%
일본 1
 
1.3%
인도네시아 1
 
1.3%
인도 1
 
1.3%
크로아티아 1
 
1.3%
이란 1
 
1.3%
Other values (68) 68
87.2%
2024-04-16T16:01:48.147169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 25
 
7.9%
스 12
 
3.8%
리 12
 
3.8%
이 11
 
3.5%
르 11
 
3.5%
국 9
 
2.8%
니 8
 
2.5%
시 7
 
2.2%
드 7
 
2.2%
바 6
 
1.9%
Other values (108) 208
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 313
99.1%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 25
 
8.0%
스 12
 
3.8%
리 12
 
3.8%
이 11
 
3.5%
르 11
 
3.5%
국 9
 
2.9%
니 8
 
2.6%
시 7
 
2.2%
드 7
 
2.2%
바 6
 
1.9%
Other values (105) 205
65.5%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 313
99.1%
Common 3
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 25
 
8.0%
스 12
 
3.8%
리 12
 
3.8%
이 11
 
3.5%
르 11
 
3.5%
국 9
 
2.9%
니 8
 
2.6%
시 7
 
2.2%
드 7
 
2.2%
바 6
 
1.9%
Other values (105) 205
65.5%
Common
ValueCountFrequency (%)
( 1
33.3%
) 1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 313
99.1%
ASCII 3
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 25
 
8.0%
스 12
 
3.8%
리 12
 
3.8%
이 11
 
3.5%
르 11
 
3.5%
국 9
 
2.9%
니 8
 
2.6%
시 7
 
2.2%
드 7
 
2.2%
바 6
 
1.9%
Other values (105) 205
65.5%
ASCII
ValueCountFrequency (%)
( 1
33.3%
) 1
33.3%
1
33.3%

동부면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
69 
1
 
3
33
 
1
16
 
1
26
 
1
Other values (3)
 
3

Length

Max length2
Median length2
Mean length1.9358974
Min length1

Unique

Unique6 ?
Unique (%)7.7%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
69
88.5%
1 3
 
3.8%
33 1
 
1.3%
16 1
 
1.3%
26 1
 
1.3%
3 1
 
1.3%
18 1
 
1.3%
4 1
 
1.3%

Length

2024-04-16T16:01:48.277961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:01:48.395091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3
33.3%
33 1
 
11.1%
16 1
 
11.1%
26 1
 
11.1%
3 1
 
11.1%
18 1
 
11.1%
4 1
 
11.1%

남부면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
67 
4
 
3
1
 
2
2
 
2
24
 
1
Other values (3)
 
3

Length

Max length2
Median length2
Mean length1.8974359
Min length1

Unique

Unique4 ?
Unique (%)5.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
67
85.9%
4 3
 
3.8%
1 2
 
2.6%
2 2
 
2.6%
24 1
 
1.3%
8 1
 
1.3%
49 1
 
1.3%
10 1
 
1.3%

Length

2024-04-16T16:01:48.502444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:01:48.597496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 3
27.3%
1 2
18.2%
2 2
18.2%
24 1
 
9.1%
8 1
 
9.1%
49 1
 
9.1%
10 1
 
9.1%

거제면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size756.0 B
63 
1
 
4
2
 
4
7
 
2
23
 
1
Other values (4)
 
4

Length

Max length2
Median length2
Mean length1.8461538
Min length1

Unique

Unique5 ?
Unique (%)6.4%

Sample

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

Common Values

ValueCountFrequency (%)
63
80.8%
1 4
 
5.1%
2 4
 
5.1%
7 2
 
2.6%
23 1
 
1.3%
5 1
 
1.3%
13 1
 
1.3%
3 1
 
1.3%
24 1
 
1.3%

Length

2024-04-16T16:01:48.697716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:01:48.790872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4
26.7%
2 4
26.7%
7 2
13.3%
23 1
 
6.7%
5 1
 
6.7%
13 1
 
6.7%
3 1
 
6.7%
24 1
 
6.7%

둔덕면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
67 
1
 
3
4
 
2
15
 
2
38
 
1
Other values (3)
 
3

Length

Max length2
Median length2
Mean length1.9230769
Min length1

Unique

Unique4 ?
Unique (%)5.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
67
85.9%
1 3
 
3.8%
4 2
 
2.6%
15 2
 
2.6%
38 1
 
1.3%
3 1
 
1.3%
73 1
 
1.3%
26 1
 
1.3%

Length

2024-04-16T16:01:48.892420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:01:48.988327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3
27.3%
4 2
18.2%
15 2
18.2%
38 1
 
9.1%
3 1
 
9.1%
73 1
 
9.1%
26 1
 
9.1%

사등면
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size756.0 B
52 
1
10 
2
 
2
8
 
2
7
 
1
Other values (11)
11 

Length

Max length2
Median length2
Mean length1.7692308
Min length1

Unique

Unique12 ?
Unique (%)15.4%

Sample

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

Common Values

ValueCountFrequency (%)
52
66.7%
1 10
 
12.8%
2 2
 
2.6%
8 2
 
2.6%
7 1
 
1.3%
5 1
 
1.3%
76 1
 
1.3%
27 1
 
1.3%
13 1
 
1.3%
23 1
 
1.3%
Other values (6) 6
 
7.7%

Length

2024-04-16T16:01:49.097512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 10
38.5%
2 2
 
7.7%
8 2
 
7.7%
7 1
 
3.8%
5 1
 
3.8%
76 1
 
3.8%
27 1
 
3.8%
13 1
 
3.8%
23 1
 
3.8%
17 1
 
3.8%
Other values (5) 5
19.2%

연초면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size756.0 B
59 
1
 
4
15
 
2
21
 
2
2
 
2
Other values (9)
9 

Length

Max length2
Median length2
Mean length1.8974359
Min length1

Unique

Unique9 ?
Unique (%)11.5%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
59
75.6%
1 4
 
5.1%
15 2
 
2.6%
21 2
 
2.6%
2 2
 
2.6%
7 1
 
1.3%
3 1
 
1.3%
14 1
 
1.3%
91 1
 
1.3%
30 1
 
1.3%
Other values (4) 4
 
5.1%

Length

2024-04-16T16:01:49.198037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 4
21.1%
15 2
10.5%
21 2
10.5%
2 2
10.5%
7 1
 
5.3%
3 1
 
5.3%
14 1
 
5.3%
91 1
 
5.3%
30 1
 
5.3%
38 1
 
5.3%
Other values (3) 3
15.8%

하청면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size756.0 B
59 
2
7 
1
 
3
3
 
2
44
 
1
Other values (6)
6 

Length

Max length2
Median length2
Mean length1.8076923
Min length1

Unique

Unique7 ?
Unique (%)9.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
59
75.6%
2 7
 
9.0%
1 3
 
3.8%
3 2
 
2.6%
44 1
 
1.3%
15 1
 
1.3%
9 1
 
1.3%
33 1
 
1.3%
19 1
 
1.3%
8 1
 
1.3%

Length

2024-04-16T16:01:49.300711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 7
36.8%
1 3
15.8%
3 2
 
10.5%
44 1
 
5.3%
15 1
 
5.3%
9 1
 
5.3%
33 1
 
5.3%
19 1
 
5.3%
8 1
 
5.3%
5 1
 
5.3%

장목면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size756.0 B
56 
1
11 
3
 
3
4
 
2
38
 
1
Other values (5)
 
5

Length

Max length2
Median length2
Mean length1.7564103
Min length1

Unique

Unique6 ?
Unique (%)7.7%

Sample

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

Common Values

ValueCountFrequency (%)
56
71.8%
1 11
 
14.1%
3 3
 
3.8%
4 2
 
2.6%
38 1
 
1.3%
14 1
 
1.3%
6 1
 
1.3%
5 1
 
1.3%
89 1
 
1.3%
7 1
 
1.3%

Length

2024-04-16T16:01:49.398458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:01:49.505766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11
50.0%
3 3
 
13.6%
4 2
 
9.1%
38 1
 
4.5%
14 1
 
4.5%
6 1
 
4.5%
5 1
 
4.5%
89 1
 
4.5%
7 1
 
4.5%

장승포동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size756.0 B
57 
1
 
5
4
 
3
2
 
2
20
 
2
Other values (9)
9 

Length

Max length2
Median length2
Mean length1.8205128
Min length1

Unique

Unique9 ?
Unique (%)11.5%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
57
73.1%
1 5
 
6.4%
4 3
 
3.8%
2 2
 
2.6%
20 2
 
2.6%
65 1
 
1.3%
34 1
 
1.3%
91 1
 
1.3%
10 1
 
1.3%
82 1
 
1.3%
Other values (4) 4
 
5.1%

Length

2024-04-16T16:01:49.612353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 5
23.8%
4 3
14.3%
2 2
 
9.5%
20 2
 
9.5%
65 1
 
4.8%
34 1
 
4.8%
91 1
 
4.8%
10 1
 
4.8%
82 1
 
4.8%
6 1
 
4.8%
Other values (3) 3
14.3%

능포동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size756.0 B
59 
1
7 
2
 
2
4
 
2
5
 
2
Other values (6)
6 

Length

Max length2
Median length2
Mean length1.8076923
Min length1

Unique

Unique6 ?
Unique (%)7.7%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
59
75.6%
1 7
 
9.0%
2 2
 
2.6%
4 2
 
2.6%
5 2
 
2.6%
22 1
 
1.3%
12 1
 
1.3%
25 1
 
1.3%
8 1
 
1.3%
10 1
 
1.3%

Length

2024-04-16T16:01:49.712968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 7
36.8%
2 2
 
10.5%
4 2
 
10.5%
5 2
 
10.5%
22 1
 
5.3%
12 1
 
5.3%
25 1
 
5.3%
8 1
 
5.3%
10 1
 
5.3%
3 1
 
5.3%

아주동
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size756.0 B
44 
1
7 
2
5 
3
 
3
4
 
2
Other values (17)
17 

Length

Max length3
Median length2
Mean length1.7948718
Min length1

Unique

Unique17 ?
Unique (%)21.8%

Sample

1st row
2nd row1
3rd row
4th row6
5th row

Common Values

ValueCountFrequency (%)
44
56.4%
1 7
 
9.0%
2 5
 
6.4%
3 3
 
3.8%
4 2
 
2.6%
11 1
 
1.3%
6 1
 
1.3%
372 1
 
1.3%
26 1
 
1.3%
257 1
 
1.3%
Other values (12) 12
 
15.4%

Length

2024-04-16T16:01:49.811258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 7
20.6%
2 5
14.7%
3 3
 
8.8%
4 2
 
5.9%
17 1
 
2.9%
5 1
 
2.9%
45 1
 
2.9%
40 1
 
2.9%
15 1
 
2.9%
10 1
 
2.9%
Other values (11) 11
32.4%

옥포1동
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size756.0 B
30 
1
10 
3
5 
2
4 
5
 
3
Other values (22)
26 

Length

Max length3
Median length2
Mean length1.6538462
Min length1

Unique

Unique18 ?
Unique (%)23.1%

Sample

1st row
2nd row29
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
30
38.5%
1 10
 
12.8%
3 5
 
6.4%
2 4
 
5.1%
5 3
 
3.8%
18 2
 
2.6%
8 2
 
2.6%
4 2
 
2.6%
30 2
 
2.6%
7 1
 
1.3%
Other values (17) 17
21.8%

Length

2024-04-16T16:01:49.916916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 10
20.8%
3 5
 
10.4%
2 4
 
8.3%
5 3
 
6.2%
18 2
 
4.2%
8 2
 
4.2%
4 2
 
4.2%
30 2
 
4.2%
24 1
 
2.1%
23 1
 
2.1%
Other values (16) 16
33.3%

옥포2동
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size756.0 B
28 
1
9 
2
8 
3
7 
5
4 
Other values (16)
22 

Length

Max length2
Median length2
Mean length1.5384615
Min length1

Unique

Unique10 ?
Unique (%)12.8%

Sample

1st row
2nd row5
3rd row1
4th row6
5th row7

Common Values

ValueCountFrequency (%)
28
35.9%
1 9
 
11.5%
2 8
 
10.3%
3 7
 
9.0%
5 4
 
5.1%
31 2
 
2.6%
4 2
 
2.6%
19 2
 
2.6%
9 2
 
2.6%
7 2
 
2.6%
Other values (11) 12
15.4%

Length

2024-04-16T16:01:50.022219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 9
18.0%
2 8
16.0%
3 7
14.0%
5 4
 
8.0%
31 2
 
4.0%
4 2
 
4.0%
19 2
 
4.0%
9 2
 
4.0%
7 2
 
4.0%
6 2
 
4.0%
Other values (10) 10
20.0%

장평동
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size756.0 B
25 
1
14 
2
9 
3
3 
8
 
2
Other values (23)
25 

Length

Max length3
Median length2
Mean length1.6025641
Min length1

Unique

Unique21 ?
Unique (%)26.9%

Sample

1st row
2nd row9
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
25
32.1%
1 14
17.9%
2 9
 
11.5%
3 3
 
3.8%
8 2
 
2.6%
76 2
 
2.6%
6 2
 
2.6%
41 1
 
1.3%
9 1
 
1.3%
7 1
 
1.3%
Other values (18) 18
23.1%

Length

2024-04-16T16:01:50.124998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 14
26.4%
2 9
17.0%
3 3
 
5.7%
8 2
 
3.8%
76 2
 
3.8%
6 2
 
3.8%
52 1
 
1.9%
27 1
 
1.9%
11 1
 
1.9%
21 1
 
1.9%
Other values (17) 17
32.1%

고현동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size756.0 B
47 
1
7 
2
6 
3
5 
42
 
1
Other values (12)
12 

Length

Max length3
Median length2
Mean length1.7307692
Min length1

Unique

Unique13 ?
Unique (%)16.7%

Sample

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

Common Values

ValueCountFrequency (%)
47
60.3%
1 7
 
9.0%
2 6
 
7.7%
3 5
 
6.4%
42 1
 
1.3%
24 1
 
1.3%
30 1
 
1.3%
86 1
 
1.3%
18 1
 
1.3%
9 1
 
1.3%
Other values (7) 7
 
9.0%

Length

2024-04-16T16:01:50.241184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 7
22.6%
2 6
19.4%
3 5
16.1%
42 1
 
3.2%
24 1
 
3.2%
30 1
 
3.2%
86 1
 
3.2%
18 1
 
3.2%
9 1
 
3.2%
7 1
 
3.2%
Other values (6) 6
19.4%

상문동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size756.0 B
56 
1
9 
8
 
3
2
 
3
6
 
1
Other values (6)
6 

Length

Max length2
Median length2
Mean length1.7692308
Min length1

Unique

Unique7 ?
Unique (%)9.0%

Sample

1st row
2nd row
3rd row
4th row8
5th row

Common Values

ValueCountFrequency (%)
56
71.8%
1 9
 
11.5%
8 3
 
3.8%
2 3
 
3.8%
6 1
 
1.3%
50 1
 
1.3%
3 1
 
1.3%
4 1
 
1.3%
25 1
 
1.3%
13 1
 
1.3%

Length

2024-04-16T16:01:50.354362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 9
40.9%
8 3
 
13.6%
2 3
 
13.6%
6 1
 
4.5%
50 1
 
4.5%
3 1
 
4.5%
4 1
 
4.5%
25 1
 
4.5%
13 1
 
4.5%
15 1
 
4.5%

수양동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size756.0 B
53 
1
 
5
2
 
4
5
 
2
6
 
2
Other values (9)
12 

Length

Max length3
Median length2
Mean length1.7948718
Min length1

Unique

Unique6 ?
Unique (%)7.7%

Sample

1st row
2nd row2
3rd row
4th row1
5th row

Common Values

ValueCountFrequency (%)
53
67.9%
1 5
 
6.4%
2 4
 
5.1%
5 2
 
2.6%
6 2
 
2.6%
20 2
 
2.6%
4 2
 
2.6%
3 2
 
2.6%
12 1
 
1.3%
16 1
 
1.3%
Other values (4) 4
 
5.1%

Length

2024-04-16T16:01:50.457816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 5
20.0%
2 4
16.0%
5 2
 
8.0%
6 2
 
8.0%
20 2
 
8.0%
4 2
 
8.0%
3 2
 
8.0%
12 1
 
4.0%
16 1
 
4.0%
19 1
 
4.0%
Other values (3) 3
12.0%

Correlations

2024-04-16T16:01:50.540446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국 적동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
국 적1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
동부면1.0001.0000.9870.9520.9831.0000.9260.9670.9530.9490.9320.9550.9300.9590.9560.9480.8840.770
남부면1.0000.9871.0000.9090.9700.9920.8910.9310.9360.9420.9170.9480.9390.9470.9470.9330.8340.790
거제면1.0000.9520.9091.0000.8980.9810.9290.9570.9050.9370.8890.9610.9400.9510.9600.9590.8250.752
둔덕면1.0000.9830.9700.8981.0000.9990.9530.9550.9450.9730.9780.9610.9280.9110.9770.9400.8170.677
사등면1.0001.0000.9920.9810.9991.0000.9810.9850.9720.9520.9630.9740.9450.9520.9680.9660.9210.848
연초면1.0000.9260.8910.9290.9530.9811.0000.9320.9270.9820.9470.9860.9450.9470.9910.9620.8840.935
하청면1.0000.9670.9310.9570.9550.9850.9321.0000.9430.9430.9730.9830.9320.9390.9690.9490.9330.718
장목면1.0000.9530.9360.9050.9450.9720.9270.9431.0000.9610.9460.9720.9750.9630.9780.9500.8960.832
장승포동1.0000.9490.9420.9370.9730.9520.9820.9430.9611.0000.9660.9790.9610.9470.9950.9490.8690.909
능포동1.0000.9320.9170.8890.9780.9630.9470.9730.9460.9661.0000.9900.9420.9040.9710.9240.9170.729
아주동1.0000.9550.9480.9610.9610.9740.9860.9830.9720.9790.9901.0000.9650.9500.9800.9800.9840.938
옥포1동1.0000.9300.9390.9400.9280.9450.9450.9320.9750.9610.9420.9651.0000.9700.9730.9720.9780.968
옥포2동1.0000.9590.9470.9510.9110.9520.9470.9390.9630.9470.9040.9500.9701.0000.9680.9620.9670.927
장평동1.0000.9560.9470.9600.9770.9680.9910.9690.9780.9950.9710.9800.9730.9681.0000.9830.9720.982
고현동1.0000.9480.9330.9590.9400.9660.9620.9490.9500.9490.9240.9800.9720.9620.9831.0000.9490.919
상문동1.0000.8840.8340.8250.8170.9210.8840.9330.8960.8690.9170.9840.9780.9670.9720.9491.0000.902
수양동1.0000.7700.7900.7520.6770.8480.9350.7180.8320.9090.7290.9380.9680.9270.9820.9190.9021.000
2024-04-16T16:01:50.690187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남부면둔덕면하청면아주동연초면장목면능포동옥포2동옥포1동장승포동장평동사등면동부면고현동수양동거제면상문동
남부면1.0000.7190.7810.6970.6420.7980.7470.7090.6350.7630.6280.8030.8100.6980.4790.7400.584
둔덕면0.7191.0000.8470.7390.7940.8200.9180.6210.6090.8610.7210.8920.7880.7160.3600.7180.557
하청면0.7810.8471.0000.7060.7160.7780.7010.6510.5870.7480.6990.8860.8850.7280.3690.8480.558
아주동0.6970.7390.7061.0000.8350.7700.7550.6350.6500.7930.7300.7590.7190.7970.6240.7330.710
연초면0.6420.7940.7160.8351.0000.7060.7600.6270.5970.7260.7210.8660.7200.7630.5300.7220.599
장목면0.7980.8200.7780.7700.7061.0000.7860.7340.7360.8110.7390.8290.8430.7430.5130.7040.661
능포동0.7470.9180.7010.7550.7600.7861.0000.5630.6160.8280.7080.7910.7840.6540.3810.6730.521
옥포2동0.7090.6210.6510.6350.6270.7340.5631.0000.6780.6290.6450.6720.7440.7090.5690.7020.747
옥포1동0.6350.6090.5870.6500.5970.7360.6160.6781.0000.6560.6820.5920.6120.6970.6820.5560.748
장승포동0.7630.8610.7480.7930.7260.8110.8280.6290.6561.0000.7610.7340.7840.7150.4710.7440.569
장평동0.6280.7210.6990.7300.7210.7390.7080.6450.6820.7611.0000.6680.6550.7530.6550.6710.709
사등면0.8030.8920.8860.7590.8660.8290.7910.6720.5920.7340.6681.0000.9260.7650.4840.8750.658
동부면0.8100.7880.8850.7190.7200.8430.7840.7440.6120.7840.6550.9261.0000.7380.4550.8490.674
고현동0.6980.7160.7280.7970.7630.7430.6540.7090.6970.7150.7530.7650.7381.0000.6210.7650.731
수양동0.4790.3600.3690.6240.5300.5130.3810.5690.6820.4710.6550.4840.4550.6211.0000.4230.637
거제면0.7400.7180.8480.7330.7220.7040.6730.7020.5560.7440.6710.8750.8490.7650.4231.0000.557
상문동0.5840.5570.5580.7100.5990.6610.5210.7470.7480.5690.7090.6580.6740.7310.6370.5571.000
2024-04-16T16:01:50.827062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
동부면1.0000.8100.8490.7880.9260.7200.8850.8430.7840.7840.7190.6120.7440.6550.7380.6740.455
남부면0.8101.0000.7400.7190.8030.6420.7810.7980.7630.7470.6970.6350.7090.6280.6980.5840.479
거제면0.8490.7401.0000.7180.8750.7220.8480.7040.7440.6730.7330.5560.7020.6710.7650.5570.423
둔덕면0.7880.7190.7181.0000.8920.7940.8470.8200.8610.9180.7390.6090.6210.7210.7160.5570.360
사등면0.9260.8030.8750.8921.0000.8660.8860.8290.7340.7910.7590.5920.6720.6680.7650.6580.484
연초면0.7200.6420.7220.7940.8661.0000.7160.7060.7260.7600.8350.5970.6270.7210.7630.5990.530
하청면0.8850.7810.8480.8470.8860.7161.0000.7780.7480.7010.7060.5870.6510.6990.7280.5580.369
장목면0.8430.7980.7040.8200.8290.7060.7781.0000.8110.7860.7700.7360.7340.7390.7430.6610.513
장승포동0.7840.7630.7440.8610.7340.7260.7480.8111.0000.8280.7930.6560.6290.7610.7150.5690.471
능포동0.7840.7470.6730.9180.7910.7600.7010.7860.8281.0000.7550.6160.5630.7080.6540.5210.381
아주동0.7190.6970.7330.7390.7590.8350.7060.7700.7930.7551.0000.6500.6350.7300.7970.7100.624
옥포1동0.6120.6350.5560.6090.5920.5970.5870.7360.6560.6160.6501.0000.6780.6820.6970.7480.682
옥포2동0.7440.7090.7020.6210.6720.6270.6510.7340.6290.5630.6350.6781.0000.6450.7090.7470.569
장평동0.6550.6280.6710.7210.6680.7210.6990.7390.7610.7080.7300.6820.6451.0000.7530.7090.655
고현동0.7380.6980.7650.7160.7650.7630.7280.7430.7150.6540.7970.6970.7090.7531.0000.7310.621
상문동0.6740.5840.5570.5570.6580.5990.5580.6610.5690.5210.7100.7480.7470.7090.7311.0000.637
수양동0.4550.4790.4230.3600.4840.5300.3690.5130.4710.3810.6240.6820.5690.6550.6210.6371.000

Missing values

2024-04-16T16:01:47.372317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:01:47.549306image/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

국 적동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
0가나1
1그리스11129592
2나이지리아111
3남아프리카공화국6362381
4네덜란드371
5네팔411153652237282312
6노르웨이11891711
7뉴질랜드1133125
8덴마크133
9도미니카공화국11
국 적동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
68트리니다드토바고12
69티모르민주공화국184241531534
70파키스탄111211
71포르투갈11
72폴란드1214
73프랑스1353731927417
74핀란드52
75필리핀1121120244545824376331322
76한국계러시아인21
77한국계중국인4474163334205381831104159156