Overview

Dataset statistics

Number of variables19
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.9 KiB
Average record size in memory153.7 B

Variable types

Text1
Categorical18

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 일운면 and 14 other fieldsHigh correlation
하청면 is highly overall correlated with 일운면 and 15 other fieldsHigh correlation
동부면 is highly overall correlated with 일운면 and 15 other fieldsHigh correlation
거제면 is highly overall correlated with 일운면 and 15 other fieldsHigh correlation
일운면 is highly overall correlated with 동부면 and 16 other fieldsHigh correlation
남부면 is highly overall correlated with 일운면 and 14 other fieldsHigh correlation
사등면 is highly overall correlated with 일운면 and 15 other fieldsHigh correlation
연초면 is highly overall correlated with 일운면 and 16 other fieldsHigh correlation
장목면 is highly overall correlated with 일운면 and 16 other fieldsHigh correlation
장승포동 is highly overall correlated with 일운면 and 15 other fieldsHigh correlation
능포동 is highly overall correlated with 일운면 and 15 other fieldsHigh correlation
아주동 is highly overall correlated with 일운면 and 16 other fieldsHigh correlation
옥포1동 is highly overall correlated with 일운면 and 14 other fieldsHigh correlation
옥포2동 is highly overall correlated with 일운면 and 16 other fieldsHigh correlation
장평동 is highly overall correlated with 일운면 and 16 other fieldsHigh correlation
고현동 is highly overall correlated with 일운면 and 16 other fieldsHigh correlation
상문동 is highly overall correlated with 일운면 and 16 other fieldsHigh correlation
수양동 is highly overall correlated with 일운면 and 8 other fieldsHigh correlation
동부면 is highly imbalanced (72.9%)Imbalance
남부면 is highly imbalanced (66.9%)Imbalance
거제면 is highly imbalanced (62.3%)Imbalance
둔덕면 is highly imbalanced (69.8%)Imbalance
연초면 is highly imbalanced (58.7%)Imbalance
하청면 is highly imbalanced (57.9%)Imbalance
장목면 is highly imbalanced (55.0%)Imbalance
장승포동 is highly imbalanced (56.2%)Imbalance
능포동 is highly imbalanced (57.1%)Imbalance
상문동 is highly imbalanced (50.2%)Imbalance
국 적 has unique valuesUnique

Reproduction

Analysis started2024-04-16 06:55:06.183973
Analysis finished2024-04-16 06:55:08.144060
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국 적
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2024-04-16T15:55:08.313658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length4.0759494
Min length2

Characters and Unicode

Total characters322
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

Unique79 ?
Unique (%)100.0%

Sample

1st row가나
2nd row그리스
3rd row나이지리아
4th row남아프리카공화국
5th row네덜란드
ValueCountFrequency (%)
가나 1
 
1.3%
코트디부아르 1
 
1.3%
카자흐스탄 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%
이집트 1
 
1.3%
Other values (69) 69
87.3%
2024-04-16T15:55:08.648570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 26
 
8.1%
스 13
 
4.0%
리 13
 
4.0%
르 11
 
3.4%
이 11
 
3.4%
국 9
 
2.8%
니 8
 
2.5%
시 7
 
2.2%
드 7
 
2.2%
바 6
 
1.9%
Other values (108) 211
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 318
98.8%
Space Separator 2
 
0.6%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 26
 
8.2%
스 13
 
4.1%
리 13
 
4.1%
르 11
 
3.5%
이 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 (105) 207
65.1%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 318
98.8%
Common 4
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 26
 
8.2%
스 13
 
4.1%
리 13
 
4.1%
르 11
 
3.5%
이 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 (105) 207
65.1%
Common
ValueCountFrequency (%)
2
50.0%
) 1
25.0%
( 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 318
98.8%
ASCII 4
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 26
 
8.2%
스 13
 
4.1%
리 13
 
4.1%
르 11
 
3.5%
이 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 (105) 207
65.1%
ASCII
ValueCountFrequency (%)
2
50.0%
) 1
25.0%
( 1
25.0%

일운면
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size764.0 B
55 
2
7 
1
7 
41
 
2
8
 
2
Other values (5)
6 

Length

Max length2
Median length2
Mean length1.7468354
Min length1

Unique

Unique4 ?
Unique (%)5.1%

Sample

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

Common Values

ValueCountFrequency (%)
55
69.6%
2 7
 
8.9%
1 7
 
8.9%
41 2
 
2.5%
8 2
 
2.5%
5 2
 
2.5%
49 1
 
1.3%
3 1
 
1.3%
24 1
 
1.3%
4 1
 
1.3%

Length

2024-04-16T15:55:08.754131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:08.849024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7
29.2%
1 7
29.2%
41 2
 
8.3%
8 2
 
8.3%
5 2
 
8.3%
49 1
 
4.2%
3 1
 
4.2%
24 1
 
4.2%
4 1
 
4.2%

동부면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size764.0 B
70 
1
 
3
33
 
1
17
 
1
28
 
1
Other values (3)
 
3

Length

Max length2
Median length2
Mean length1.9367089
Min length1

Unique

Unique6 ?
Unique (%)7.6%

Sample

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

Common Values

ValueCountFrequency (%)
70
88.6%
1 3
 
3.8%
33 1
 
1.3%
17 1
 
1.3%
28 1
 
1.3%
3 1
 
1.3%
16 1
 
1.3%
4 1
 
1.3%

Length

2024-04-16T15:55:08.955501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:09.055536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3
33.3%
33 1
 
11.1%
17 1
 
11.1%
28 1
 
11.1%
3 1
 
11.1%
16 1
 
11.1%
4 1
 
11.1%

남부면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size764.0 B
67 
1
 
3
2
 
2
3
 
2
4
 
1
Other values (4)
 
4

Length

Max length2
Median length2
Mean length1.8860759
Min length1

Unique

Unique5 ?
Unique (%)6.3%

Sample

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

Common Values

ValueCountFrequency (%)
67
84.8%
1 3
 
3.8%
2 2
 
2.5%
3 2
 
2.5%
4 1
 
1.3%
22 1
 
1.3%
9 1
 
1.3%
53 1
 
1.3%
12 1
 
1.3%

Length

2024-04-16T15:55:09.166260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:09.263291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3
25.0%
2 2
16.7%
3 2
16.7%
4 1
 
8.3%
22 1
 
8.3%
9 1
 
8.3%
53 1
 
8.3%
12 1
 
8.3%

거제면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size764.0 B
64 
1
 
5
2
 
4
26
 
1
5
 
1
Other values (4)
 
4

Length

Max length2
Median length2
Mean length1.8481013
Min length1

Unique

Unique6 ?
Unique (%)7.6%

Sample

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

Common Values

ValueCountFrequency (%)
64
81.0%
1 5
 
6.3%
2 4
 
5.1%
26 1
 
1.3%
5 1
 
1.3%
8 1
 
1.3%
13 1
 
1.3%
21 1
 
1.3%
6 1
 
1.3%

Length

2024-04-16T15:55:09.363634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:09.687344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5
33.3%
2 4
26.7%
26 1
 
6.7%
5 1
 
6.7%
8 1
 
6.7%
13 1
 
6.7%
21 1
 
6.7%
6 1
 
6.7%

둔덕면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size764.0 B
68 
1
 
3
4
 
1
35
 
1
16
 
1
Other values (5)
 
5

Length

Max length2
Median length2
Mean length1.9240506
Min length1

Unique

Unique8 ?
Unique (%)10.1%

Sample

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

Common Values

ValueCountFrequency (%)
68
86.1%
1 3
 
3.8%
4 1
 
1.3%
35 1
 
1.3%
16 1
 
1.3%
2 1
 
1.3%
75 1
 
1.3%
23 1
 
1.3%
12 1
 
1.3%
5 1
 
1.3%

Length

2024-04-16T15:55:09.785727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:09.879082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3
27.3%
4 1
 
9.1%
35 1
 
9.1%
16 1
 
9.1%
2 1
 
9.1%
75 1
 
9.1%
23 1
 
9.1%
12 1
 
9.1%
5 1
 
9.1%

사등면
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size764.0 B
53 
1
7 
3
 
3
2
 
3
8
 
2
Other values (11)
11 

Length

Max length2
Median length2
Mean length1.7721519
Min length1

Unique

Unique11 ?
Unique (%)13.9%

Sample

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

Common Values

ValueCountFrequency (%)
53
67.1%
1 7
 
8.9%
3 3
 
3.8%
2 3
 
3.8%
8 2
 
2.5%
4 1
 
1.3%
5 1
 
1.3%
78 1
 
1.3%
33 1
 
1.3%
16 1
 
1.3%
Other values (6) 6
 
7.6%

Length

2024-04-16T15:55:09.979254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 7
26.9%
3 3
11.5%
2 3
11.5%
8 2
 
7.7%
4 1
 
3.8%
5 1
 
3.8%
78 1
 
3.8%
33 1
 
3.8%
16 1
 
3.8%
23 1
 
3.8%
Other values (5) 5
19.2%

연초면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
61 
1
 
3
14
 
2
2
 
2
8
 
1
Other values (10)
10 

Length

Max length2
Median length2
Mean length1.9113924
Min length1

Unique

Unique11 ?
Unique (%)13.9%

Sample

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

Common Values

ValueCountFrequency (%)
61
77.2%
1 3
 
3.8%
14 2
 
2.5%
2 2
 
2.5%
8 1
 
1.3%
82 1
 
1.3%
21 1
 
1.3%
33 1
 
1.3%
3 1
 
1.3%
15 1
 
1.3%
Other values (5) 5
 
6.3%

Length

2024-04-16T15:55:10.075001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 3
16.7%
14 2
11.1%
2 2
11.1%
8 1
 
5.6%
82 1
 
5.6%
21 1
 
5.6%
33 1
 
5.6%
3 1
 
5.6%
15 1
 
5.6%
22 1
 
5.6%
Other values (4) 4
22.2%

하청면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size764.0 B
60 
2
7 
1
 
3
3
 
1
41
 
1
Other values (7)
7 

Length

Max length2
Median length2
Mean length1.8227848
Min length1

Unique

Unique9 ?
Unique (%)11.4%

Sample

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

Common Values

ValueCountFrequency (%)
60
75.9%
2 7
 
8.9%
1 3
 
3.8%
3 1
 
1.3%
41 1
 
1.3%
17 1
 
1.3%
11 1
 
1.3%
32 1
 
1.3%
21 1
 
1.3%
8 1
 
1.3%
Other values (2) 2
 
2.5%

Length

2024-04-16T15:55:10.177983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 7
36.8%
1 3
15.8%
3 1
 
5.3%
41 1
 
5.3%
17 1
 
5.3%
11 1
 
5.3%
32 1
 
5.3%
21 1
 
5.3%
8 1
 
5.3%
5 1
 
5.3%

장목면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size764.0 B
58 
1
9 
3
 
3
6
 
2
41
 
1
Other values (6)
6 

Length

Max length2
Median length2
Mean length1.7721519
Min length1

Unique

Unique7 ?
Unique (%)8.9%

Sample

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

Common Values

ValueCountFrequency (%)
58
73.4%
1 9
 
11.4%
3 3
 
3.8%
6 2
 
2.5%
41 1
 
1.3%
17 1
 
1.3%
93 1
 
1.3%
2 1
 
1.3%
9 1
 
1.3%
4 1
 
1.3%

Length

2024-04-16T15:55:10.276585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 9
42.9%
3 3
 
14.3%
6 2
 
9.5%
41 1
 
4.8%
17 1
 
4.8%
93 1
 
4.8%
2 1
 
4.8%
9 1
 
4.8%
4 1
 
4.8%
5 1
 
4.8%

장승포동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
59 
1
 
5
2
 
2
4
 
2
72
 
1
Other values (10)
10 

Length

Max length2
Median length2
Mean length1.835443
Min length1

Unique

Unique11 ?
Unique (%)13.9%

Sample

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

Common Values

ValueCountFrequency (%)
59
74.7%
1 5
 
6.3%
2 2
 
2.5%
4 2
 
2.5%
72 1
 
1.3%
37 1
 
1.3%
98 1
 
1.3%
11 1
 
1.3%
64 1
 
1.3%
6 1
 
1.3%
Other values (5) 5
 
6.3%

Length

2024-04-16T15:55:10.370936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 5
25.0%
2 2
 
10.0%
4 2
 
10.0%
72 1
 
5.0%
37 1
 
5.0%
98 1
 
5.0%
11 1
 
5.0%
64 1
 
5.0%
6 1
 
5.0%
7 1
 
5.0%
Other values (4) 4
20.0%

능포동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size764.0 B
61 
1
 
6
2
 
2
9
 
2
4
 
2
Other values (5)
 
6

Length

Max length2
Median length2
Mean length1.7974684
Min length1

Unique

Unique4 ?
Unique (%)5.1%

Sample

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

Common Values

ValueCountFrequency (%)
61
77.2%
1 6
 
7.6%
2 2
 
2.5%
9 2
 
2.5%
4 2
 
2.5%
5 2
 
2.5%
20 1
 
1.3%
25 1
 
1.3%
3 1
 
1.3%
6 1
 
1.3%

Length

2024-04-16T15:55:10.467867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:10.563528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6
33.3%
2 2
 
11.1%
9 2
 
11.1%
4 2
 
11.1%
5 2
 
11.1%
20 1
 
5.6%
25 1
 
5.6%
3 1
 
5.6%
6 1
 
5.6%

아주동
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size764.0 B
45 
1
6 
2
5 
5
 
3
6
 
3
Other values (14)
17 

Length

Max length3
Median length2
Mean length1.7848101
Min length1

Unique

Unique12 ?
Unique (%)15.2%

Sample

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

Common Values

ValueCountFrequency (%)
45
57.0%
1 6
 
7.6%
2 5
 
6.3%
5 3
 
3.8%
6 3
 
3.8%
3 3
 
3.8%
40 2
 
2.5%
50 1
 
1.3%
337 1
 
1.3%
4 1
 
1.3%
Other values (9) 9
 
11.4%

Length

2024-04-16T15:55:10.691870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 6
17.6%
2 5
14.7%
5 3
 
8.8%
6 3
 
8.8%
3 3
 
8.8%
40 2
 
5.9%
13 1
 
2.9%
286 1
 
2.9%
42 1
 
2.9%
10 1
 
2.9%
Other values (8) 8
23.5%

옥포1동
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size764.0 B
32 
1
6 
4
5 
2
4 
5
4 
Other values (22)
28 

Length

Max length2
Median length2
Mean length1.6835443
Min length1

Unique

Unique16 ?
Unique (%)20.3%

Sample

1st row
2nd row31
3rd row1
4th row2
5th row5

Common Values

ValueCountFrequency (%)
32
40.5%
1 6
 
7.6%
4 5
 
6.3%
2 4
 
5.1%
5 4
 
5.1%
31 2
 
2.5%
9 2
 
2.5%
11 2
 
2.5%
23 2
 
2.5%
3 2
 
2.5%
Other values (17) 18
22.8%

Length

2024-04-16T15:55:10.807726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 6
 
12.8%
4 5
 
10.6%
2 4
 
8.5%
5 4
 
8.5%
23 2
 
4.3%
89 2
 
4.3%
3 2
 
4.3%
11 2
 
4.3%
9 2
 
4.3%
31 2
 
4.3%
Other values (16) 16
34.0%

옥포2동
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size764.0 B
29 
2
8 
1
8 
3
7 
4
7 
Other values (14)
20 

Length

Max length2
Median length2
Mean length1.556962
Min length1

Unique

Unique11 ?
Unique (%)13.9%

Sample

1st row
2nd row4
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
29
36.7%
2 8
 
10.1%
1 8
 
10.1%
3 7
 
8.9%
4 7
 
8.9%
13 4
 
5.1%
6 3
 
3.8%
21 2
 
2.5%
5 1
 
1.3%
25 1
 
1.3%
Other values (9) 9
 
11.4%

Length

2024-04-16T15:55:10.922847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 8
16.0%
1 8
16.0%
3 7
14.0%
4 7
14.0%
13 4
8.0%
6 3
 
6.0%
21 2
 
4.0%
22 1
 
2.0%
44 1
 
2.0%
32 1
 
2.0%
Other values (8) 8
16.0%

장평동
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size764.0 B
28 
1
13 
2
6 
3
4 
4
3 
Other values (21)
25 

Length

Max length3
Median length2
Mean length1.6075949
Min length1

Unique

Unique17 ?
Unique (%)21.5%

Sample

1st row
2nd row8
3rd row1
4th row4
5th row1

Common Values

ValueCountFrequency (%)
28
35.4%
1 13
16.5%
2 6
 
7.6%
3 4
 
5.1%
4 3
 
3.8%
7 2
 
2.5%
45 2
 
2.5%
8 2
 
2.5%
5 2
 
2.5%
13 1
 
1.3%
Other values (16) 16
20.3%

Length

2024-04-16T15:55:11.037766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 13
25.5%
2 6
 
11.8%
3 4
 
7.8%
4 3
 
5.9%
7 2
 
3.9%
45 2
 
3.9%
8 2
 
3.9%
5 2
 
3.9%
11 1
 
2.0%
123 1
 
2.0%
Other values (15) 15
29.4%

고현동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
48 
2
6 
3
6 
1
6 
20
 
2
Other values (10)
11 

Length

Max length3
Median length2
Mean length1.7468354
Min length1

Unique

Unique9 ?
Unique (%)11.4%

Sample

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

Common Values

ValueCountFrequency (%)
48
60.8%
2 6
 
7.6%
3 6
 
7.6%
1 6
 
7.6%
20 2
 
2.5%
6 2
 
2.5%
24 1
 
1.3%
25 1
 
1.3%
88 1
 
1.3%
41 1
 
1.3%
Other values (5) 5
 
6.3%

Length

2024-04-16T15:55:11.140511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 6
19.4%
3 6
19.4%
1 6
19.4%
20 2
 
6.5%
6 2
 
6.5%
24 1
 
3.2%
25 1
 
3.2%
88 1
 
3.2%
41 1
 
3.2%
9 1
 
3.2%
Other values (4) 4
12.9%

상문동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size764.0 B
56 
1
8 
2
 
3
9
 
2
3
 
2
Other values (6)
8 

Length

Max length2
Median length2
Mean length1.7468354
Min length1

Unique

Unique4 ?
Unique (%)5.1%

Sample

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

Common Values

ValueCountFrequency (%)
56
70.9%
1 8
 
10.1%
2 3
 
3.8%
9 2
 
2.5%
3 2
 
2.5%
6 2
 
2.5%
8 2
 
2.5%
45 1
 
1.3%
4 1
 
1.3%
29 1
 
1.3%

Length

2024-04-16T15:55:11.241962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 8
34.8%
2 3
 
13.0%
9 2
 
8.7%
3 2
 
8.7%
6 2
 
8.7%
8 2
 
8.7%
45 1
 
4.3%
4 1
 
4.3%
29 1
 
4.3%
14 1
 
4.3%

수양동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
53 
3
 
4
1
 
4
4
 
3
2
 
2
Other values (10)
13 

Length

Max length3
Median length2
Mean length1.7848101
Min length1

Unique

Unique7 ?
Unique (%)8.9%

Sample

1st row
2nd row2
3rd row
4th row3
5th row

Common Values

ValueCountFrequency (%)
53
67.1%
3 4
 
5.1%
1 4
 
5.1%
4 3
 
3.8%
2 2
 
2.5%
6 2
 
2.5%
18 2
 
2.5%
5 2
 
2.5%
8 1
 
1.3%
12 1
 
1.3%
Other values (5) 5
 
6.3%

Length

2024-04-16T15:55:11.345644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 4
15.4%
1 4
15.4%
4 3
11.5%
2 2
7.7%
6 2
7.7%
18 2
7.7%
5 2
7.7%
8 1
 
3.8%
12 1
 
3.8%
16 1
 
3.8%
Other values (4) 4
15.4%

Correlations

2024-04-16T15:55:11.430455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국 적일운면동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
국 적1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
일운면1.0001.0000.9240.8710.9020.9450.9490.9540.8920.9370.9390.9260.9370.9270.9450.9750.9450.8830.863
동부면1.0000.9241.0000.9450.9810.9760.9980.9810.9970.9710.9700.9340.9170.9000.9140.9700.9170.8570.794
남부면1.0000.8710.9451.0000.9840.9470.9500.9600.9400.9490.9710.9210.9410.8920.8560.9800.8930.8170.700
거제면1.0000.9020.9810.9841.0000.9630.9700.9720.9770.9560.9690.9200.9230.9360.9190.9810.9290.8450.787
둔덕면1.0000.9450.9760.9470.9631.0000.9760.9910.9750.9450.9910.9860.9480.8820.9060.9800.9280.8400.644
사등면1.0000.9490.9980.9500.9700.9761.0000.9830.9850.9700.9670.9500.9670.9320.9510.9810.9520.9140.847
연초면1.0000.9540.9810.9600.9720.9910.9831.0000.9790.9740.9940.9710.9700.9400.9420.9860.9780.8990.935
하청면1.0000.8920.9970.9400.9770.9750.9850.9791.0000.9430.9630.9220.9350.9160.9180.9740.9190.8790.712
장목면1.0000.9370.9710.9490.9560.9450.9700.9740.9431.0000.9590.9190.9550.9330.9440.9860.9490.9530.860
장승포동1.0000.9390.9700.9710.9690.9910.9670.9940.9630.9591.0000.9700.9730.9520.9290.9860.9710.9010.872
능포동1.0000.9260.9340.9210.9200.9860.9500.9710.9220.9190.9701.0000.9480.8930.9070.9620.9130.8250.731
아주동1.0000.9370.9170.9410.9230.9480.9670.9700.9350.9550.9730.9481.0000.9500.9800.9800.9580.9290.917
옥포1동1.0000.9270.9000.8920.9360.8820.9320.9400.9160.9330.9520.8930.9501.0000.9660.9620.9560.9510.939
옥포2동1.0000.9450.9140.8560.9190.9060.9510.9420.9180.9440.9290.9070.9800.9661.0000.9750.9680.9620.936
장평동1.0000.9750.9700.9800.9810.9800.9810.9860.9740.9860.9860.9620.9800.9620.9751.0000.9850.9620.954
고현동1.0000.9450.9170.8930.9290.9280.9520.9780.9190.9490.9710.9130.9580.9560.9680.9851.0000.9260.960
상문동1.0000.8830.8570.8170.8450.8400.9140.8990.8790.9530.9010.8250.9290.9510.9620.9620.9261.0000.903
수양동1.0000.8630.7940.7000.7870.6440.8470.9350.7120.8600.8720.7310.9170.9390.9360.9540.9600.9031.000
2024-04-16T15:55:11.569300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일운면남부면둔덕면하청면아주동연초면장목면능포동옥포2동옥포1동장승포동장평동사등면동부면고현동수양동거제면상문동
일운면1.0000.6370.6050.6470.6720.7290.7620.5550.6960.5880.6850.7450.7470.7690.7010.5210.6980.635
남부면0.6371.0000.8110.7550.6920.7870.8230.7430.5120.4630.8250.7610.7610.8290.6120.3560.7840.546
둔덕면0.6050.8111.0000.8840.7060.8710.7850.7920.5940.4940.8710.7660.8460.9110.6540.2860.8620.558
하청면0.6470.7550.8841.0000.6600.8570.7580.7170.6130.5370.7890.7350.8660.9220.6470.3420.8700.604
아주동0.6720.6920.7060.6601.0000.7760.7320.7040.6610.5960.7920.7530.7560.6530.7210.5900.6450.651
연초면0.7290.7870.8710.8570.7761.0000.8510.7870.6630.5790.8290.7990.8670.8720.6860.5190.8290.618
장목면0.7620.8230.7850.7580.7320.8511.0000.7140.6950.5920.7890.7990.8180.8970.7540.5400.8440.620
능포동0.5550.7430.7920.7170.7040.7870.7141.0000.5960.5140.7830.6970.7500.7920.6170.3580.7410.535
옥포2동0.6960.5120.5940.6130.6610.6630.6950.5961.0000.6630.6210.7200.6900.6460.7650.6420.6340.756
옥포1동0.5880.4630.4940.5370.5960.5790.5920.5140.6631.0000.6180.6310.5530.5510.6310.5760.5480.646
장승포동0.6850.8250.8710.7890.7920.8290.7890.7830.6210.6181.0000.7960.7860.8310.6490.4000.8160.624
장평동0.7450.7610.7660.7350.7530.7990.7990.6970.7200.6310.7961.0000.7600.7340.7920.6360.7680.694
사등면0.7470.7610.8460.8660.7560.8670.8180.7500.6900.5530.7860.7601.0000.8680.7240.4730.8330.641
동부면0.7690.8290.9110.9220.6530.8720.8970.7920.6460.5510.8310.7340.8681.0000.6790.4700.9370.624
고현동0.7010.6120.6540.6470.7210.6860.7540.6170.7650.6310.6490.7920.7240.6791.0000.5990.6940.686
수양동0.5210.3560.2860.3420.5900.5190.5400.3580.6420.5760.4000.6360.4730.4700.5991.0000.4460.627
거제면0.6980.7840.8620.8700.6450.8290.8440.7410.6340.5480.8160.7680.8330.9370.6940.4461.0000.590
상문동0.6350.5460.5580.6040.6510.6180.6200.5350.7560.6460.6240.6940.6410.6240.6860.6270.5901.000
2024-04-16T15:55:11.715936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일운면동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
일운면1.0000.7690.6370.6980.6050.7470.7290.6470.7620.6850.5550.6720.5880.6960.7450.7010.6350.521
동부면0.7691.0000.8290.9370.9110.8680.8720.9220.8970.8310.7920.6530.5510.6460.7340.6790.6240.470
남부면0.6370.8291.0000.7840.8110.7610.7870.7550.8230.8250.7430.6920.4630.5120.7610.6120.5460.356
거제면0.6980.9370.7841.0000.8620.8330.8290.8700.8440.8160.7410.6450.5480.6340.7680.6940.5900.446
둔덕면0.6050.9110.8110.8621.0000.8460.8710.8840.7850.8710.7920.7060.4940.5940.7660.6540.5580.286
사등면0.7470.8680.7610.8330.8461.0000.8670.8660.8180.7860.7500.7560.5530.6900.7600.7240.6410.473
연초면0.7290.8720.7870.8290.8710.8671.0000.8570.8510.8290.7870.7760.5790.6630.7990.6860.6180.519
하청면0.6470.9220.7550.8700.8840.8660.8571.0000.7580.7890.7170.6600.5370.6130.7350.6470.6040.342
장목면0.7620.8970.8230.8440.7850.8180.8510.7581.0000.7890.7140.7320.5920.6950.7990.7540.6200.540
장승포동0.6850.8310.8250.8160.8710.7860.8290.7890.7891.0000.7830.7920.6180.6210.7960.6490.6240.400
능포동0.5550.7920.7430.7410.7920.7500.7870.7170.7140.7831.0000.7040.5140.5960.6970.6170.5350.358
아주동0.6720.6530.6920.6450.7060.7560.7760.6600.7320.7920.7041.0000.5960.6610.7530.7210.6510.590
옥포1동0.5880.5510.4630.5480.4940.5530.5790.5370.5920.6180.5140.5961.0000.6630.6310.6310.6460.576
옥포2동0.6960.6460.5120.6340.5940.6900.6630.6130.6950.6210.5960.6610.6631.0000.7200.7650.7560.642
장평동0.7450.7340.7610.7680.7660.7600.7990.7350.7990.7960.6970.7530.6310.7201.0000.7920.6940.636
고현동0.7010.6790.6120.6940.6540.7240.6860.6470.7540.6490.6170.7210.6310.7650.7921.0000.6860.599
상문동0.6350.6240.5460.5900.5580.6410.6180.6040.6200.6240.5350.6510.6460.7560.6940.6861.0000.627
수양동0.5210.4700.3560.4460.2860.4730.5190.3420.5400.4000.3580.5900.5760.6420.6360.5990.6271.000

Missing values

2024-04-16T15:55:07.908628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T15:55:08.081326image/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 적일운면동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
0가나1
1그리스1131482
2나이지리아111
3남아프리카공화국216234293
4네덜란드1541
5네팔413143722033793192
6노르웨이172134534
7뉴질랜드1124236
8덴마크1821
9도미니카공화국11
국 적일운면동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
69트리니다드토바고12
70티모르민주공화국1163211231563
71파키스탄151
72포르투갈11
73폴란드24
74프랑스21315652123418
75핀란드62
76필리핀8112131624454589447537925
77한국계러시아인1
78한국계중국인4436518374516640163596176145