Overview

Dataset statistics

Number of variables20
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 KiB
Average record size in memory162.7 B

Variable types

Text1
Numeric1
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 합계 and 17 other fieldsHigh correlation
동부면 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
합계 is highly overall correlated with 일운면 and 17 other fieldsHigh correlation
일운면 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
남부면 is highly overall correlated with 합계 and 16 other fieldsHigh correlation
거제면 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
사등면 is highly overall correlated with 합계 and 16 other fieldsHigh correlation
연초면 is highly overall correlated with 합계 and 16 other fieldsHigh correlation
하청면 is highly overall correlated with 합계 and 14 other fieldsHigh correlation
장목면 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
장승포동 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
능포동 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
아주동 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
옥포1동 is highly overall correlated with 합계 and 15 other fieldsHigh correlation
옥포2동 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
장평동 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
고현동 is highly overall correlated with 합계 and 17 other fieldsHigh correlation
상문동 is highly overall correlated with 합계 and 16 other fieldsHigh correlation
수양동 is highly overall correlated with 합계 and 14 other fieldsHigh correlation
일운면 is highly imbalanced (51.9%)Imbalance
동부면 is highly imbalanced (71.4%)Imbalance
남부면 is highly imbalanced (69.1%)Imbalance
거제면 is highly imbalanced (60.5%)Imbalance
둔덕면 is highly imbalanced (71.6%)Imbalance
연초면 is highly imbalanced (58.1%)Imbalance
하청면 is highly imbalanced (57.3%)Imbalance
장목면 is highly imbalanced (52.8%)Imbalance
장승포동 is highly imbalanced (53.5%)Imbalance
능포동 is highly imbalanced (56.3%)Imbalance
상문동 is highly imbalanced (51.7%)Imbalance
국적 has unique valuesUnique

Reproduction

Analysis started2024-04-16 06:54:58.793616
Analysis finished2024-04-16 06:55:00.969729
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국적
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-04-16T15:55:01.191798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.8625
Min length2

Characters and Unicode

Total characters309
Distinct characters117
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

Unique80 ?
Unique (%)100.0%

Sample

1st row가나
2nd row그리스
3rd row나이지리아
4th row남아프리카공화국
5th row네덜란드
ValueCountFrequency (%)
가나 1
 
1.2%
그리스 1
 
1.2%
짐바브웨 1
 
1.2%
중국 1
 
1.2%
조지아 1
 
1.2%
일본 1
 
1.2%
인도네시아 1
 
1.2%
인도 1
 
1.2%
이탈리아 1
 
1.2%
체코 1
 
1.2%
Other values (70) 70
87.5%
2024-04-16T15:55:01.555993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 25
 
8.1%
스 14
 
4.5%
리 12
 
3.9%
이 11
 
3.6%
르 10
 
3.2%
국 8
 
2.6%
시 7
 
2.3%
니 6
 
1.9%
드 6
 
1.9%
가 5
 
1.6%
Other values (107) 205
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
99.4%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 25
 
8.1%
스 14
 
4.6%
리 12
 
3.9%
이 11
 
3.6%
르 10
 
3.3%
국 8
 
2.6%
시 7
 
2.3%
니 6
 
2.0%
드 6
 
2.0%
가 5
 
1.6%
Other values (105) 203
66.1%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
99.4%
Common 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 25
 
8.1%
스 14
 
4.6%
리 12
 
3.9%
이 11
 
3.6%
르 10
 
3.3%
국 8
 
2.6%
시 7
 
2.3%
니 6
 
2.0%
드 6
 
2.0%
가 5
 
1.6%
Other values (105) 203
66.1%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
99.4%
ASCII 2
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 25
 
8.1%
스 14
 
4.6%
리 12
 
3.9%
이 11
 
3.6%
르 10
 
3.3%
국 8
 
2.6%
시 7
 
2.3%
니 6
 
2.0%
드 6
 
2.0%
가 5
 
1.6%
Other values (105) 203
66.1%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.425
Minimum1
Maximum1038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-16T15:55:01.668607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median12.5
Q362.5
95-th percentile478.9
Maximum1038
Range1037
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation187.97258
Coefficient of variation (CV)1.9907077
Kurtosis9.1723502
Mean94.425
Median Absolute Deviation (MAD)11
Skewness2.8400319
Sum7554
Variance35333.691
MonotonicityNot monotonic
2024-04-16T15:55:01.776304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 11
 
13.8%
2 7
 
8.8%
4 6
 
7.5%
3 5
 
6.2%
6 3
 
3.8%
15 2
 
2.5%
10 2
 
2.5%
73 2
 
2.5%
13 2
 
2.5%
5 2
 
2.5%
Other values (34) 38
47.5%
ValueCountFrequency (%)
1 11
13.8%
2 7
8.8%
3 5
6.2%
4 6
7.5%
5 2
 
2.5%
6 3
 
3.8%
7 2
 
2.5%
8 1
 
1.2%
10 2
 
2.5%
12 1
 
1.2%
ValueCountFrequency (%)
1038 1
1.2%
754 1
1.2%
575 1
1.2%
515 1
1.2%
477 1
1.2%
412 1
1.2%
395 1
1.2%
361 1
1.2%
328 1
1.2%
320 1
1.2%

일운면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
57 
1
7 
2
 
5
36
 
2
4
 
2
Other values (7)
7 

Length

Max length2
Median length2
Mean length1.7875
Min length1

Unique

Unique7 ?
Unique (%)8.8%

Sample

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

Common Values

ValueCountFrequency (%)
57
71.2%
1 7
 
8.8%
2 5
 
6.2%
36 2
 
2.5%
4 2
 
2.5%
12 1
 
1.2%
43 1
 
1.2%
3 1
 
1.2%
28 1
 
1.2%
8 1
 
1.2%
Other values (2) 2
 
2.5%

Length

2024-04-16T15:55:01.886193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 7
30.4%
2 5
21.7%
36 2
 
8.7%
4 2
 
8.7%
12 1
 
4.3%
43 1
 
4.3%
3 1
 
4.3%
28 1
 
4.3%
8 1
 
4.3%
10 1
 
4.3%

동부면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
70 
1
 
4
32
 
1
15
 
1
25
 
1
Other values (3)
 
3

Length

Max length2
Median length2
Mean length1.925
Min length1

Unique

Unique6 ?
Unique (%)7.5%

Sample

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

Common Values

ValueCountFrequency (%)
70
87.5%
1 4
 
5.0%
32 1
 
1.2%
15 1
 
1.2%
25 1
 
1.2%
3 1
 
1.2%
18 1
 
1.2%
4 1
 
1.2%

Length

2024-04-16T15:55:01.984241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:02.075326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4
40.0%
32 1
 
10.0%
15 1
 
10.0%
25 1
 
10.0%
3 1
 
10.0%
18 1
 
10.0%
4 1
 
10.0%

남부면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
69 
4
 
2
1
 
2
2
 
2
16
 
1
Other values (4)
 
4

Length

Max length2
Median length2
Mean length1.9
Min length1

Unique

Unique5 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
69
86.2%
4 2
 
2.5%
1 2
 
2.5%
2 2
 
2.5%
16 1
 
1.2%
8 1
 
1.2%
35 1
 
1.2%
13 1
 
1.2%
3 1
 
1.2%

Length

2024-04-16T15:55:02.403857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:02.494036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 2
18.2%
1 2
18.2%
2 2
18.2%
16 1
9.1%
8 1
9.1%
35 1
9.1%
13 1
9.1%
3 1
9.1%

거제면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
64 
1
 
4
2
 
3
5
 
2
3
 
2
Other values (5)
 
5

Length

Max length2
Median length2
Mean length1.8375
Min length1

Unique

Unique5 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
64
80.0%
1 4
 
5.0%
2 3
 
3.8%
5 2
 
2.5%
3 2
 
2.5%
22 1
 
1.2%
7 1
 
1.2%
8 1
 
1.2%
12 1
 
1.2%
20 1
 
1.2%

Length

2024-04-16T15:55:02.604338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:02.703222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4
25.0%
2 3
18.8%
5 2
12.5%
3 2
12.5%
22 1
 
6.2%
7 1
 
6.2%
8 1
 
6.2%
12 1
 
6.2%
20 1
 
6.2%

둔덕면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
70 
1
 
3
3
 
1
29
 
1
21
 
1
Other values (4)
 
4

Length

Max length2
Median length2
Mean length1.9375
Min length1

Unique

Unique7 ?
Unique (%)8.8%

Sample

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

Common Values

ValueCountFrequency (%)
70
87.5%
1 3
 
3.8%
3 1
 
1.2%
29 1
 
1.2%
21 1
 
1.2%
66 1
 
1.2%
25 1
 
1.2%
12 1
 
1.2%
6 1
 
1.2%

Length

2024-04-16T15:55:02.820269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T15:55:02.933354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3
30.0%
3 1
 
10.0%
29 1
 
10.0%
21 1
 
10.0%
66 1
 
10.0%
25 1
 
10.0%
12 1
 
10.0%
6 1
 
10.0%

사등면
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
55 
1
6 
2
 
4
7
 
2
3
 
2
Other values (10)
11 

Length

Max length2
Median length2
Mean length1.7875
Min length1

Unique

Unique9 ?
Unique (%)11.2%

Sample

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

Common Values

ValueCountFrequency (%)
55
68.8%
1 6
 
7.5%
2 4
 
5.0%
7 2
 
2.5%
3 2
 
2.5%
22 2
 
2.5%
5 1
 
1.2%
83 1
 
1.2%
33 1
 
1.2%
14 1
 
1.2%
Other values (5) 5
 
6.2%

Length

2024-04-16T15:55:03.038303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 6
24.0%
2 4
16.0%
7 2
 
8.0%
3 2
 
8.0%
22 2
 
8.0%
5 1
 
4.0%
83 1
 
4.0%
33 1
 
4.0%
14 1
 
4.0%
26 1
 
4.0%
Other values (4) 4
16.0%

연초면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
61 
1
 
4
2
 
2
10
 
1
7
 
1
Other values (11)
11 

Length

Max length2
Median length2
Mean length1.8875
Min length1

Unique

Unique13 ?
Unique (%)16.2%

Sample

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

Common Values

ValueCountFrequency (%)
61
76.2%
1 4
 
5.0%
2 2
 
2.5%
10 1
 
1.2%
7 1
 
1.2%
9 1
 
1.2%
85 1
 
1.2%
25 1
 
1.2%
36 1
 
1.2%
16 1
 
1.2%
Other values (6) 6
 
7.5%

Length

2024-04-16T15:55:03.138606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 4
21.1%
2 2
 
10.5%
10 1
 
5.3%
7 1
 
5.3%
9 1
 
5.3%
85 1
 
5.3%
25 1
 
5.3%
36 1
 
5.3%
16 1
 
5.3%
26 1
 
5.3%
Other values (5) 5
26.3%

하청면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
61 
3
 
5
1
 
4
2
 
2
4
 
1
Other values (7)
7 

Length

Max length2
Median length2
Mean length1.8375
Min length1

Unique

Unique8 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
61
76.2%
3 5
 
6.2%
1 4
 
5.0%
2 2
 
2.5%
4 1
 
1.2%
52 1
 
1.2%
20 1
 
1.2%
16 1
 
1.2%
28 1
 
1.2%
22 1
 
1.2%
Other values (2) 2
 
2.5%

Length

2024-04-16T15:55:03.257531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 5
26.3%
1 4
21.1%
2 2
 
10.5%
4 1
 
5.3%
52 1
 
5.3%
20 1
 
5.3%
16 1
 
5.3%
28 1
 
5.3%
22 1
 
5.3%
10 1
 
5.3%

장목면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
58 
1
7 
2
 
5
3
 
2
6
 
2
Other values (6)
6 

Length

Max length2
Median length2
Mean length1.775
Min length1

Unique

Unique6 ?
Unique (%)7.5%

Sample

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

Common Values

ValueCountFrequency (%)
58
72.5%
1 7
 
8.8%
2 5
 
6.2%
3 2
 
2.5%
6 2
 
2.5%
53 1
 
1.2%
18 1
 
1.2%
97 1
 
1.2%
11 1
 
1.2%
7 1
 
1.2%

Length

2024-04-16T15:55:03.352058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 7
31.8%
2 5
22.7%
3 2
 
9.1%
6 2
 
9.1%
53 1
 
4.5%
18 1
 
4.5%
97 1
 
4.5%
11 1
 
4.5%
7 1
 
4.5%
5 1
 
4.5%

장승포동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
58 
1
6 
7
 
3
5
 
2
14
 
2
Other values (9)
9 

Length

Max length2
Median length2
Mean length1.825
Min length1

Unique

Unique9 ?
Unique (%)11.2%

Sample

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

Common Values

ValueCountFrequency (%)
58
72.5%
1 6
 
7.5%
7 3
 
3.8%
5 2
 
2.5%
14 2
 
2.5%
81 1
 
1.2%
42 1
 
1.2%
93 1
 
1.2%
12 1
 
1.2%
2 1
 
1.2%
Other values (4) 4
 
5.0%

Length

2024-04-16T15:55:03.473731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 6
27.3%
7 3
13.6%
5 2
 
9.1%
14 2
 
9.1%
81 1
 
4.5%
42 1
 
4.5%
93 1
 
4.5%
12 1
 
4.5%
2 1
 
4.5%
60 1
 
4.5%
Other values (3) 3
13.6%

능포동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
61 
1
 
5
2
 
3
3
 
3
10
 
2
Other values (6)
 
6

Length

Max length2
Median length2
Mean length1.825
Min length1

Unique

Unique6 ?
Unique (%)7.5%

Sample

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

Common Values

ValueCountFrequency (%)
61
76.2%
1 5
 
6.2%
2 3
 
3.8%
3 3
 
3.8%
10 2
 
2.5%
28 1
 
1.2%
24 1
 
1.2%
8 1
 
1.2%
6 1
 
1.2%
5 1
 
1.2%

Length

2024-04-16T15:55:03.565349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 5
26.3%
2 3
15.8%
3 3
15.8%
10 2
 
10.5%
28 1
 
5.3%
24 1
 
5.3%
8 1
 
5.3%
6 1
 
5.3%
5 1
 
5.3%
11 1
 
5.3%

아주동
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
44 
1
9 
6
 
4
2
 
2
10
 
2
Other values (19)
19 

Length

Max length3
Median length2
Mean length1.8
Min length1

Unique

Unique19 ?
Unique (%)23.8%

Sample

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

Common Values

ValueCountFrequency (%)
44
55.0%
1 9
 
11.2%
6 4
 
5.0%
2 2
 
2.5%
10 2
 
2.5%
13 1
 
1.2%
305 1
 
1.2%
4 1
 
1.2%
12 1
 
1.2%
19 1
 
1.2%
Other values (14) 14
 
17.5%

Length

2024-04-16T15:55:03.668708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 9
25.0%
6 4
 
11.1%
2 2
 
5.6%
10 2
 
5.6%
8 1
 
2.8%
9 1
 
2.8%
40 1
 
2.8%
58 1
 
2.8%
5 1
 
2.8%
24 1
 
2.8%
Other values (13) 13
36.1%

옥포1동
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
24 
1
11 
3
5 
4
4 
2
4 
Other values (24)
32 

Length

Max length3
Median length2
Mean length1.5875
Min length1

Unique

Unique18 ?
Unique (%)22.5%

Sample

1st row
2nd row27
3rd row1
4th row3
5th row6

Common Values

ValueCountFrequency (%)
24
30.0%
1 11
13.8%
3 5
 
6.2%
4 4
 
5.0%
2 4
 
5.0%
5 3
 
3.8%
6 3
 
3.8%
7 2
 
2.5%
24 2
 
2.5%
9 2
 
2.5%
Other values (19) 20
25.0%

Length

2024-04-16T15:55:03.782894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 11
19.6%
3 5
 
8.9%
4 4
 
7.1%
2 4
 
7.1%
5 3
 
5.4%
6 3
 
5.4%
9 2
 
3.6%
15 2
 
3.6%
24 2
 
3.6%
7 2
 
3.6%
Other values (18) 18
32.1%

옥포2동
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size772.0 B
29 
1
9 
4
7 
2
7 
3
4 
Other values (18)
24 

Length

Max length2
Median length2
Mean length1.55
Min length1

Unique

Unique13 ?
Unique (%)16.2%

Sample

1st row
2nd row3
3rd row1
4th row5
5th row4

Common Values

ValueCountFrequency (%)
29
36.2%
1 9
 
11.2%
4 7
 
8.8%
2 7
 
8.8%
3 4
 
5.0%
5 3
 
3.8%
11 2
 
2.5%
24 2
 
2.5%
6 2
 
2.5%
7 2
 
2.5%
Other values (13) 13
16.2%

Length

2024-04-16T15:55:03.887711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 9
17.6%
2 7
13.7%
4 7
13.7%
3 4
 
7.8%
5 3
 
5.9%
11 2
 
3.9%
24 2
 
3.9%
6 2
 
3.9%
7 2
 
3.9%
20 1
 
2.0%
Other values (12) 12
23.5%

장평동
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
32 
1
10 
2
5 
4
4 
3
 
3
Other values (22)
26 

Length

Max length3
Median length2
Mean length1.675
Min length1

Unique

Unique19 ?
Unique (%)23.8%

Sample

1st row
2nd row6
3rd row1
4th row5
5th row3

Common Values

ValueCountFrequency (%)
32
40.0%
1 10
 
12.5%
2 5
 
6.2%
4 4
 
5.0%
3 3
 
3.8%
6 3
 
3.8%
27 2
 
2.5%
5 2
 
2.5%
68 1
 
1.2%
34 1
 
1.2%
Other values (17) 17
21.2%

Length

2024-04-16T15:55:03.991582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 10
20.8%
2 5
 
10.4%
4 4
 
8.3%
3 3
 
6.2%
6 3
 
6.2%
27 2
 
4.2%
5 2
 
4.2%
10 1
 
2.1%
83 1
 
2.1%
55 1
 
2.1%
Other values (16) 16
33.3%

고현동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
48 
3
7 
1
6 
4
5 
6
 
3
Other values (9)
11 

Length

Max length3
Median length2
Mean length1.725
Min length1

Unique

Unique7 ?
Unique (%)8.8%

Sample

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

Common Values

ValueCountFrequency (%)
48
60.0%
3 7
 
8.8%
1 6
 
7.5%
4 5
 
6.2%
6 3
 
3.8%
15 2
 
2.5%
2 2
 
2.5%
94 1
 
1.2%
40 1
 
1.2%
8 1
 
1.2%
Other values (4) 4
 
5.0%

Length

2024-04-16T15:55:04.101515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 7
21.9%
1 6
18.8%
4 5
15.6%
6 3
9.4%
15 2
 
6.2%
2 2
 
6.2%
94 1
 
3.1%
40 1
 
3.1%
8 1
 
3.1%
115 1
 
3.1%
Other values (3) 3
9.4%

상문동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
56 
1
9 
2
 
5
7
 
2
8
 
1
Other values (7)
7 

Length

Max length2
Median length2
Mean length1.7375
Min length1

Unique

Unique8 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
56
70.0%
1 9
 
11.2%
2 5
 
6.2%
7 2
 
2.5%
8 1
 
1.2%
3 1
 
1.2%
5 1
 
1.2%
4 1
 
1.2%
38 1
 
1.2%
23 1
 
1.2%
Other values (2) 2
 
2.5%

Length

2024-04-16T15:55:04.207908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 9
37.5%
2 5
20.8%
7 2
 
8.3%
8 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%
4 1
 
4.2%
38 1
 
4.2%
23 1
 
4.2%
9 1
 
4.2%

수양동
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
53 
3
6 
1
 
5
2
 
2
4
 
2
Other values (11)
12 

Length

Max length3
Median length2
Mean length1.775
Min length1

Unique

Unique10 ?
Unique (%)12.5%

Sample

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

Common Values

ValueCountFrequency (%)
53
66.2%
3 6
 
7.5%
1 5
 
6.2%
2 2
 
2.5%
4 2
 
2.5%
7 2
 
2.5%
11 1
 
1.2%
16 1
 
1.2%
25 1
 
1.2%
22 1
 
1.2%
Other values (6) 6
 
7.5%

Length

2024-04-16T15:55:04.338453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 6
22.2%
1 5
18.5%
2 2
 
7.4%
4 2
 
7.4%
7 2
 
7.4%
11 1
 
3.7%
16 1
 
3.7%
25 1
 
3.7%
22 1
 
3.7%
202 1
 
3.7%
Other values (5) 5
18.5%

Interactions

2024-04-16T15:55:00.603423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T15:55:04.446245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국적합계일운면동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
국적1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0000.8730.9390.8170.8430.8590.9410.9930.9180.8640.9500.9340.9990.9730.9680.9930.9530.9220.975
일운면1.0000.8731.0000.9510.9170.9040.9280.9450.9540.9460.9180.8750.8920.9910.9680.9740.9800.9160.9480.917
동부면1.0000.9390.9511.0000.9620.9660.9740.9450.9950.9770.9670.8980.9190.9830.8950.9600.9720.9130.8880.931
남부면1.0000.8170.9170.9621.0000.9470.9890.9430.9610.9460.9540.8970.9090.9470.8500.9300.9770.8950.8800.826
거제면1.0000.8430.9040.9660.9471.0000.9580.9640.9770.9550.9520.9040.9020.9660.9070.9570.9770.8970.8360.861
둔덕면1.0000.8590.9280.9740.9890.9581.0000.9610.9750.9510.9470.8660.9190.9670.9040.9590.9920.9020.8440.880
사등면1.0000.9410.9450.9450.9430.9640.9611.0000.9780.9590.9390.9540.9450.9820.9520.9690.9820.9490.8760.862
연초면1.0000.9930.9540.9950.9610.9770.9750.9781.0000.9740.9640.9570.9770.9890.9420.9610.9800.9510.8940.927
하청면1.0000.9180.9460.9770.9460.9550.9510.9590.9741.0000.9360.9250.8990.9810.8970.9310.9650.9160.9090.787
장목면1.0000.8640.9180.9670.9540.9520.9470.9390.9640.9361.0000.9220.9770.9680.9120.9490.9740.8980.8660.878
장승포동1.0000.9500.8750.8980.8970.9040.8660.9540.9570.9250.9221.0000.9310.9870.9550.9540.9850.9730.8370.884
능포동1.0000.9340.8920.9190.9090.9020.9190.9450.9770.8990.9770.9311.0000.9850.9420.9510.9750.9290.8970.899
아주동1.0000.9990.9910.9830.9470.9660.9670.9820.9890.9810.9680.9870.9851.0000.9820.9800.9860.9800.9850.972
옥포1동1.0000.9730.9680.8950.8500.9070.9040.9520.9420.8970.9120.9550.9420.9821.0000.9720.9730.9700.9680.982
옥포2동1.0000.9680.9740.9600.9300.9570.9590.9690.9610.9310.9490.9540.9510.9800.9721.0000.9710.9660.9560.960
장평동1.0000.9930.9800.9720.9770.9770.9920.9820.9800.9650.9740.9850.9750.9860.9730.9711.0000.9800.9670.979
고현동1.0000.9530.9160.9130.8950.8970.9020.9490.9510.9160.8980.9730.9290.9800.9700.9660.9801.0000.9200.922
상문동1.0000.9220.9480.8880.8800.8360.8440.8760.8940.9090.8660.8370.8970.9850.9680.9560.9670.9201.0000.946
수양동1.0000.9750.9170.9310.8260.8610.8800.8620.9270.7870.8780.8840.8990.9720.9820.9600.9790.9220.9461.000
2024-04-16T15:55:04.584192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일운면남부면둔덕면하청면아주동연초면장목면능포동옥포2동옥포1동장승포동장평동사등면동부면고현동수양동거제면상문동
일운면1.0000.6960.7220.5850.7440.7340.6890.6290.7570.6860.5680.7550.7220.7590.6590.6220.6720.592
남부면0.6961.0000.8210.7730.6680.7980.8380.7180.6360.4440.6450.6690.7350.8760.6400.5010.8110.616
둔덕면0.7220.8211.0000.7880.7330.8520.8180.7430.7200.5330.5840.7400.7890.9160.6570.5920.8450.553
하청면0.5850.7730.7881.0000.6840.8120.7380.6440.6050.4900.6840.6850.7730.8350.6600.4050.8120.491
아주동0.7440.6680.7330.6841.0000.7970.7330.8120.7710.7350.8220.7900.7870.7090.7810.6960.7310.706
연초면0.7340.7980.8520.8120.7971.0000.7970.8480.6900.5680.7530.7510.8400.8300.7300.4890.8510.571
장목면0.6890.8380.8180.7380.7330.7971.0000.7260.6670.5290.6870.7340.7220.8860.6290.5620.8050.578
능포동0.6290.7180.7430.6440.8120.8480.7261.0000.6720.6050.7130.7350.7430.7530.7080.6050.6740.640
옥포2동0.7570.6360.7200.6050.7710.6900.6670.6721.0000.6690.6730.6830.7290.7200.7220.6860.7090.682
옥포1동0.6860.4440.5330.4900.7350.5680.5290.6050.6691.0000.6180.6740.6000.5290.6780.7420.5230.686
장승포동0.5680.6450.5840.6840.8220.7530.6870.7130.6730.6181.0000.7740.7450.6580.6710.5490.6470.502
장평동0.7550.6690.7400.6850.7900.7510.7340.7350.6830.6740.7741.0000.7600.7360.7460.7410.7500.693
사등면0.7220.7350.7890.7730.7870.8400.7220.7430.7290.6000.7450.7601.0000.7520.7260.4980.7630.553
동부면0.7590.8760.9160.8350.7090.8300.8860.7530.7200.5290.6580.7360.7521.0000.6910.5850.8790.615
고현동0.6590.6400.6570.6600.7810.7300.6290.7080.7220.6780.6710.7460.7260.6911.0000.6380.6330.669
수양동0.6220.5010.5920.4050.6960.4890.5620.6050.6860.7420.5490.7410.4980.5850.6381.0000.5400.705
거제면0.6720.8110.8450.8120.7310.8510.8050.6740.7090.5230.6470.7500.7630.8790.6330.5401.0000.543
상문동0.5920.6160.5530.4910.7060.5710.5780.6400.6820.6860.5020.6930.5530.6150.6690.7050.5431.000
2024-04-16T15:55:04.748521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계일운면동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
합계1.0000.5870.6140.5780.6070.6440.7400.8140.6760.6350.7860.7890.8420.7220.7460.8230.7950.6860.710
일운면0.5871.0000.7590.6960.6720.7220.7220.7340.5850.6890.5680.6290.7440.6860.7570.7550.6590.5920.622
동부면0.6140.7591.0000.8760.8790.9160.7520.8300.8350.8860.6580.7530.7090.5290.7200.7360.6910.6150.585
남부면0.5780.6960.8761.0000.8110.8210.7350.7980.7730.8380.6450.7180.6680.4440.6360.6690.6400.6160.501
거제면0.6070.6720.8790.8111.0000.8450.7630.8510.8120.8050.6470.6740.7310.5230.7090.7500.6330.5430.540
둔덕면0.6440.7220.9160.8210.8451.0000.7890.8520.7880.8180.5840.7430.7330.5330.7200.7400.6570.5530.592
사등면0.7400.7220.7520.7350.7630.7891.0000.8400.7730.7220.7450.7430.7870.6000.7290.7600.7260.5530.498
연초면0.8140.7340.8300.7980.8510.8520.8401.0000.8120.7970.7530.8480.7970.5680.6900.7510.7300.5710.489
하청면0.6760.5850.8350.7730.8120.7880.7730.8121.0000.7380.6840.6440.6840.4900.6050.6850.6600.4910.405
장목면0.6350.6890.8860.8380.8050.8180.7220.7970.7381.0000.6870.7260.7330.5290.6670.7340.6290.5780.562
장승포동0.7860.5680.6580.6450.6470.5840.7450.7530.6840.6871.0000.7130.8220.6180.6730.7740.6710.5020.549
능포동0.7890.6290.7530.7180.6740.7430.7430.8480.6440.7260.7131.0000.8120.6050.6720.7350.7080.6400.605
아주동0.8420.7440.7090.6680.7310.7330.7870.7970.6840.7330.8220.8121.0000.7350.7710.7900.7810.7060.696
옥포1동0.7220.6860.5290.4440.5230.5330.6000.5680.4900.5290.6180.6050.7351.0000.6690.6740.6780.6860.742
옥포2동0.7460.7570.7200.6360.7090.7200.7290.6900.6050.6670.6730.6720.7710.6691.0000.6830.7220.6820.686
장평동0.8230.7550.7360.6690.7500.7400.7600.7510.6850.7340.7740.7350.7900.6740.6831.0000.7460.6930.741
고현동0.7950.6590.6910.6400.6330.6570.7260.7300.6600.6290.6710.7080.7810.6780.7220.7461.0000.6690.638
상문동0.6860.5920.6150.6160.5430.5530.5530.5710.4910.5780.5020.6400.7060.6860.6820.6930.6691.0000.705
수양동0.7100.6220.5850.5010.5400.5920.4980.4890.4050.5620.5490.6050.6960.7420.6860.7410.6380.7051.000

Missing values

2024-04-16T15:55:00.701864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T15:55:00.887139image/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

국적합계일운면동부면남부면거제면둔덕면사등면연초면하청면장목면장승포동능포동아주동옥포1동옥포2동장평동고현동상문동수양동
0가나11
1그리스42111273612
2나이지리아3111
3남아프리카공화국36216355383
4네덜란드141643
5네팔477417103812830553273
6노르웨이294112170248664
7뉴질랜드181113237
8덴마크171934
9도미니카공화국211
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