Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells81
Missing cells (%)27.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory50.7 B

Variable types

Text6

Dataset

Description법무부 출입국 외국인정책본부의 소속기관 전국 출입국 외국인청 관할 구역 및 주소현황입니다.
Author법무부
URLhttps://www.data.go.kr/data/15040715/fileData.do

Alerts

전화번호 구분 has 38 (77.6%) missing valuesMissing
비고 has 43 (87.8%) missing valuesMissing
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:46:46.756421
Analysis finished2023-12-12 15:46:47.533312
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전국 출입국외국인청/사무소
Text

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T00:46:47.703538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length16.489796
Min length8

Characters and Unicode

Total characters808
Distinct characters64
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)87.8%

Sample

1st row인천공항출입국 외국인청
2nd row인천공항출입국 외국인청
3rd row서울출입국 외국인청
4th row부산출입국 외국인청
5th row인천출입국 외국인청
ValueCountFrequency (%)
외국인사무소 30
25.0%
외국인청 15
 
12.5%
인천공항출입국 4
 
3.3%
창원출입국 4
 
3.3%
춘천출입국 4
 
3.3%
대전출입국 4
 
3.3%
부산출입국 3
 
2.5%
수원출입국 3
 
2.5%
광주출입국 3
 
2.5%
여수출입국 3
 
2.5%
Other values (39) 47
39.2%
2023-12-13T00:46:48.112345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
165
20.4%
국 96
11.9%
출 71
 
8.8%
소 56
 
6.9%
인 55
 
6.8%
외 49
 
6.1%
입 47
 
5.8%
사 31
 
3.8%
무 31
 
3.8%
장 24
 
3.0%
Other values (54) 183
22.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 643
79.6%
Space Separator 165
 
20.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 96
14.9%
출 71
11.0%
소 56
 
8.7%
인 55
 
8.6%
외 49
 
7.6%
입 47
 
7.3%
사 31
 
4.8%
무 31
 
4.8%
장 24
 
3.7%
청 17
 
2.6%
Other values (53) 166
25.8%
Space Separator
ValueCountFrequency (%)
165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 643
79.6%
Common 165
 
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 96
14.9%
출 71
11.0%
소 56
 
8.7%
인 55
 
8.6%
외 49
 
7.6%
입 47
 
7.3%
사 31
 
4.8%
무 31
 
4.8%
장 24
 
3.7%
청 17
 
2.6%
Other values (53) 166
25.8%
Common
ValueCountFrequency (%)
165
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 643
79.6%
ASCII 165
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165
100.0%
Hangul
ValueCountFrequency (%)
국 96
14.9%
출 71
11.0%
소 56
 
8.7%
인 55
 
8.6%
외 49
 
7.6%
입 47
 
7.3%
사 31
 
4.8%
무 31
 
4.8%
장 24
 
3.7%
청 17
 
2.6%
Other values (53) 166
25.8%

관할구역
Text

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T00:46:48.367189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length113
Median length37
Mean length26.306122
Min length2

Characters and Unicode

Total characters1289
Distinct characters124
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)83.7%

Sample

1st row인천국제공항
2nd row인천국제공항
3rd row서울특별시 관악구 광진구 강남구(한국도심공항터미널 제외) 강동구 동작구 송파구 성동구 서초구 용산구(서울역도심공항터미널 제외), 경기도 안양시 과천시 성남시 하남시
4th row부산광역시(김해국제공항 제외) 양산시
5th row인천광역시(인천국제공항 제외), 경기도 부천시 김포시
ValueCountFrequency (%)
제외 13
 
5.7%
경기도 9
 
3.9%
전국 4
 
1.7%
전라남도 4
 
1.7%
충청남도 4
 
1.7%
경상남도 4
 
1.7%
경상북도 3
 
1.3%
서울특별시 3
 
1.3%
강원도 3
 
1.3%
여수시 3
 
1.3%
Other values (136) 179
78.2%
2023-12-13T00:46:48.731733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
423
32.8%
시 94
 
7.3%
군 49
 
3.8%
도 46
 
3.6%
구 30
 
2.3%
항 27
 
2.1%
제 24
 
1.9%
천 24
 
1.9%
남 23
 
1.8%
경 22
 
1.7%
Other values (114) 527
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 826
64.1%
Space Separator 423
32.8%
Close Punctuation 14
 
1.1%
Open Punctuation 14
 
1.1%
Other Punctuation 12
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 94
 
11.4%
군 49
 
5.9%
도 46
 
5.6%
구 30
 
3.6%
항 27
 
3.3%
제 24
 
2.9%
천 24
 
2.9%
남 23
 
2.8%
경 22
 
2.7%
산 17
 
2.1%
Other values (110) 470
56.9%
Space Separator
ValueCountFrequency (%)
423
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 826
64.1%
Common 463
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 94
 
11.4%
군 49
 
5.9%
도 46
 
5.6%
구 30
 
3.6%
항 27
 
3.3%
제 24
 
2.9%
천 24
 
2.9%
남 23
 
2.8%
경 22
 
2.7%
산 17
 
2.1%
Other values (110) 470
56.9%
Common
ValueCountFrequency (%)
423
91.4%
) 14
 
3.0%
( 14
 
3.0%
, 12
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 826
64.1%
ASCII 463
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
423
91.4%
) 14
 
3.0%
( 14
 
3.0%
, 12
 
2.6%
Hangul
ValueCountFrequency (%)
시 94
 
11.4%
군 49
 
5.9%
도 46
 
5.6%
구 30
 
3.6%
항 27
 
3.3%
제 24
 
2.9%
천 24
 
2.9%
남 23
 
2.8%
경 22
 
2.7%
산 17
 
2.1%
Other values (110) 470
56.9%

주소
Text

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T00:46:48.947819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length28
Mean length20.938776
Min length12

Characters and Unicode

Total characters1026
Distinct characters145
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)83.7%

Sample

1st row인천광역시 중구 공항로 272
2nd row인천광역시 중구 공항로 272
3rd row서울특별시 양천구 목동동로 151
4th row부산광역시 중구 충장대로 20
5th row인천광역시 중구 서해대로393
ValueCountFrequency (%)
중구 7
 
2.9%
경기도 7
 
2.9%
서울특별시 6
 
2.5%
인천광역시 5
 
2.1%
경남 5
 
2.1%
전남 5
 
2.1%
강원도 4
 
1.7%
부산광역시 4
 
1.7%
2층 3
 
1.2%
충청남도 3
 
1.2%
Other values (169) 192
79.7%
2023-12-13T00:46:49.263034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
 
18.7%
시 49
 
4.8%
로 43
 
4.2%
2 41
 
4.0%
구 32
 
3.1%
3 23
 
2.2%
1 22
 
2.1%
5 21
 
2.0%
길 18
 
1.8%
남 18
 
1.8%
Other values (135) 567
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 646
63.0%
Space Separator 192
 
18.7%
Decimal Number 178
 
17.3%
Dash Punctuation 3
 
0.3%
Other Punctuation 3
 
0.3%
Uppercase Letter 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 49
 
7.6%
로 43
 
6.7%
구 32
 
5.0%
길 18
 
2.8%
남 18
 
2.8%
서 18
 
2.8%
도 17
 
2.6%
광 17
 
2.6%
동 15
 
2.3%
경 15
 
2.3%
Other values (118) 404
62.5%
Decimal Number
ValueCountFrequency (%)
2 41
23.0%
3 23
12.9%
1 22
12.4%
5 21
11.8%
7 15
 
8.4%
0 14
 
7.9%
4 13
 
7.3%
9 10
 
5.6%
8 10
 
5.6%
6 9
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
192
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 646
63.0%
Common 378
36.8%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 49
 
7.6%
로 43
 
6.7%
구 32
 
5.0%
길 18
 
2.8%
남 18
 
2.8%
서 18
 
2.8%
도 17
 
2.6%
광 17
 
2.6%
동 15
 
2.3%
경 15
 
2.3%
Other values (118) 404
62.5%
Common
ValueCountFrequency (%)
192
50.8%
2 41
 
10.8%
3 23
 
6.1%
1 22
 
5.8%
5 21
 
5.6%
7 15
 
4.0%
0 14
 
3.7%
4 13
 
3.4%
9 10
 
2.6%
8 10
 
2.6%
Other values (5) 17
 
4.5%
Latin
ValueCountFrequency (%)
C 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 646
63.0%
ASCII 380
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
50.5%
2 41
 
10.8%
3 23
 
6.1%
1 22
 
5.8%
5 21
 
5.5%
7 15
 
3.9%
0 14
 
3.7%
4 13
 
3.4%
9 10
 
2.6%
8 10
 
2.6%
Other values (7) 19
 
5.0%
Hangul
ValueCountFrequency (%)
시 49
 
7.6%
로 43
 
6.7%
구 32
 
5.0%
길 18
 
2.8%
남 18
 
2.8%
서 18
 
2.8%
도 17
 
2.6%
광 17
 
2.6%
동 15
 
2.3%
경 15
 
2.3%
Other values (118) 404
62.5%

전화번호
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T00:46:49.749781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.979592
Min length11

Characters and Unicode

Total characters587
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

Unique49 ?
Unique (%)100.0%

Sample

1st row032-740-7391
2nd row032-740-7361
3rd row02-2650-6214
4th row051-461-3091
5th row032-890-6300
ValueCountFrequency (%)
032-740-7391 1
 
2.0%
02-362-8432 1
 
2.0%
02-731-1799 1
 
2.0%
055-344-7830 1
 
2.0%
051-254-3917 1
 
2.0%
031-364-5700 1
 
2.0%
031-683-6938 1
 
2.0%
031-8024-9612 1
 
2.0%
054-459-3505 1
 
2.0%
054-247-5363 1
 
2.0%
Other values (39) 39
79.6%
2023-12-13T00:46:50.107892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 98
16.7%
0 96
16.4%
3 70
11.9%
1 53
9.0%
5 53
9.0%
2 47
8.0%
6 45
7.7%
4 40
6.8%
8 31
 
5.3%
9 29
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 489
83.3%
Dash Punctuation 98
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96
19.6%
3 70
14.3%
1 53
10.8%
5 53
10.8%
2 47
9.6%
6 45
9.2%
4 40
8.2%
8 31
 
6.3%
9 29
 
5.9%
7 25
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 587
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 98
16.7%
0 96
16.4%
3 70
11.9%
1 53
9.0%
5 53
9.0%
2 47
8.0%
6 45
7.7%
4 40
6.8%
8 31
 
5.3%
9 29
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 98
16.7%
0 96
16.4%
3 70
11.9%
1 53
9.0%
5 53
9.0%
2 47
8.0%
6 45
7.7%
4 40
6.8%
8 31
 
5.3%
9 29
 
4.9%

전화번호 구분
Text

MISSING 

Distinct10
Distinct (%)90.9%
Missing38
Missing (%)77.6%
Memory size524.0 B
2023-12-13T00:46:50.297110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length12.181818
Min length4

Characters and Unicode

Total characters134
Distinct characters25
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)81.8%

Sample

1st row032-740-7391~2(제1터미널)
2nd row032-740-7361~2(제2터미널)
3rd row051-461-3091~5
4th row064-741-5411~6
5th row042-220-2001~2,4
ValueCountFrequency (%)
주간 2
18.2%
032-740-7391~2(제1터미널 1
9.1%
032-740-7361~2(제2터미널 1
9.1%
051-461-3091~5 1
9.1%
064-741-5411~6 1
9.1%
042-220-2001~2,4 1
9.1%
야간 1
9.1%
063-249-8693~4 1
9.1%
야간,공휴일 1
9.1%
055-344-7830~5 1
9.1%
2023-12-13T00:46:50.618031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14
 
10.4%
- 14
 
10.4%
4 12
 
9.0%
2 11
 
8.2%
1 10
 
7.5%
3 9
 
6.7%
~ 7
 
5.2%
5 6
 
4.5%
6 6
 
4.5%
( 6
 
4.5%
Other values (15) 39
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
59.7%
Other Letter 19
 
14.2%
Dash Punctuation 14
 
10.4%
Math Symbol 7
 
5.2%
Open Punctuation 6
 
4.5%
Close Punctuation 6
 
4.5%
Other Punctuation 2
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
17.5%
4 12
15.0%
2 11
13.8%
1 10
12.5%
3 9
11.2%
5 6
7.5%
6 6
7.5%
7 6
7.5%
9 4
 
5.0%
8 2
 
2.5%
Other Letter
ValueCountFrequency (%)
간 4
21.1%
주 2
10.5%
제 2
10.5%
터 2
10.5%
미 2
10.5%
널 2
10.5%
야 2
10.5%
공 1
 
5.3%
휴 1
 
5.3%
일 1
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115
85.8%
Hangul 19
 
14.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14
12.2%
- 14
12.2%
4 12
10.4%
2 11
9.6%
1 10
8.7%
3 9
7.8%
~ 7
 
6.1%
5 6
 
5.2%
6 6
 
5.2%
( 6
 
5.2%
Other values (5) 20
17.4%
Hangul
ValueCountFrequency (%)
간 4
21.1%
주 2
10.5%
제 2
10.5%
터 2
10.5%
미 2
10.5%
널 2
10.5%
야 2
10.5%
공 1
 
5.3%
휴 1
 
5.3%
일 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115
85.8%
Hangul 19
 
14.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14
12.2%
- 14
12.2%
4 12
10.4%
2 11
9.6%
1 10
8.7%
3 9
7.8%
~ 7
 
6.1%
5 6
 
5.2%
6 6
 
5.2%
( 6
 
5.2%
Other values (5) 20
17.4%
Hangul
ValueCountFrequency (%)
간 4
21.1%
주 2
10.5%
제 2
10.5%
터 2
10.5%
미 2
10.5%
널 2
10.5%
야 2
10.5%
공 1
 
5.3%
휴 1
 
5.3%
일 1
 
5.3%

비고
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing43
Missing (%)87.8%
Memory size524.0 B
2023-12-13T00:46:50.865001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length41.5
Mean length29.666667
Min length16

Characters and Unicode

Total characters178
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row광명역(02-899-9168)
2nd row국적과, 난민과 별관(02-2650-6399)
3rd row김포다문화이주민플러스센터(031-981-0042)
4th row익산다문화이주민플러스센터(063-850-8311)
5th row안산다문화이주민플러스센터(031-364-5750), 시흥다문화이주민플러스센터(031-364-5762)
ValueCountFrequency (%)
광명역(02-899-9168 1
11.1%
국적과 1
11.1%
난민과 1
11.1%
별관(02-2650-6399 1
11.1%
김포다문화이주민플러스센터(031-981-0042 1
11.1%
익산다문화이주민플러스센터(063-850-8311 1
11.1%
안산다문화이주민플러스센터(031-364-5750 1
11.1%
시흥다문화이주민플러스센터(031-364-5762 1
11.1%
아산다문화이주민플러스센터(041-549-7441 1
11.1%
2023-12-13T00:46:51.268421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 14
 
7.9%
0 12
 
6.7%
1 9
 
5.1%
3 8
 
4.5%
4 7
 
3.9%
( 7
 
3.9%
) 7
 
3.9%
6 7
 
3.9%
9 7
 
3.9%
5 6
 
3.4%
Other values (33) 94
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
42.7%
Decimal Number 69
38.8%
Dash Punctuation 14
 
7.9%
Open Punctuation 7
 
3.9%
Close Punctuation 7
 
3.9%
Space Separator 3
 
1.7%
Other Punctuation 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
민 6
 
7.9%
이 5
 
6.6%
주 5
 
6.6%
화 5
 
6.6%
문 5
 
6.6%
다 5
 
6.6%
센 5
 
6.6%
터 5
 
6.6%
플 5
 
6.6%
러 5
 
6.6%
Other values (18) 25
32.9%
Decimal Number
ValueCountFrequency (%)
0 12
17.4%
1 9
13.0%
3 8
11.6%
4 7
10.1%
6 7
10.1%
9 7
10.1%
5 6
8.7%
8 5
7.2%
2 5
7.2%
7 3
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102
57.3%
Hangul 76
42.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
민 6
 
7.9%
이 5
 
6.6%
주 5
 
6.6%
화 5
 
6.6%
문 5
 
6.6%
다 5
 
6.6%
센 5
 
6.6%
터 5
 
6.6%
플 5
 
6.6%
러 5
 
6.6%
Other values (18) 25
32.9%
Common
ValueCountFrequency (%)
- 14
13.7%
0 12
11.8%
1 9
8.8%
3 8
7.8%
4 7
 
6.9%
( 7
 
6.9%
) 7
 
6.9%
6 7
 
6.9%
9 7
 
6.9%
5 6
 
5.9%
Other values (5) 18
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102
57.3%
Hangul 76
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 14
13.7%
0 12
11.8%
1 9
8.8%
3 8
7.8%
4 7
 
6.9%
( 7
 
6.9%
) 7
 
6.9%
6 7
 
6.9%
9 7
 
6.9%
5 6
 
5.9%
Other values (5) 18
17.6%
Hangul
ValueCountFrequency (%)
민 6
 
7.9%
이 5
 
6.6%
주 5
 
6.6%
화 5
 
6.6%
문 5
 
6.6%
다 5
 
6.6%
센 5
 
6.6%
터 5
 
6.6%
플 5
 
6.6%
러 5
 
6.6%
Other values (18) 25
32.9%

Correlations

2023-12-13T00:46:51.424751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전국 출입국외국인청/사무소관할구역주소전화번호전화번호 구분비고
전국 출입국외국인청/사무소1.0001.0001.0001.0000.9241.000
관할구역1.0001.0000.9971.0000.9241.000
주소1.0000.9971.0001.0000.9241.000
전화번호1.0001.0001.0001.0001.0001.000
전화번호 구분0.9240.9240.9241.0001.0000.000
비고1.0001.0001.0001.0000.0001.000

Missing values

2023-12-13T00:46:47.242543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:46:47.353430image/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.
2023-12-13T00:46:47.466831image/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

전국 출입국외국인청/사무소관할구역주소전화번호전화번호 구분비고
0인천공항출입국 외국인청인천국제공항인천광역시 중구 공항로 272032-740-7391032-740-7391~2(제1터미널)광명역(02-899-9168)
1인천공항출입국 외국인청인천국제공항인천광역시 중구 공항로 272032-740-7361032-740-7361~2(제2터미널)<NA>
2서울출입국 외국인청서울특별시 관악구 광진구 강남구(한국도심공항터미널 제외) 강동구 동작구 송파구 성동구 서초구 용산구(서울역도심공항터미널 제외), 경기도 안양시 과천시 성남시 하남시서울특별시 양천구 목동동로 15102-2650-6214<NA>국적과, 난민과 별관(02-2650-6399)
3부산출입국 외국인청부산광역시(김해국제공항 제외) 양산시부산광역시 중구 충장대로 20051-461-3091051-461-3091~5<NA>
4인천출입국 외국인청인천광역시(인천국제공항 제외), 경기도 부천시 김포시인천광역시 중구 서해대로393032-890-6300<NA>김포다문화이주민플러스센터(031-981-0042)
5수원출입국 외국인청경기도 군포시 의왕시 수원시 용인시 이천시 화성시 광주시 양평군 여주시경기도 수원시 영통구 반달로 39031-695-3817<NA><NA>
6제주출입국 외국인청제주특별자치도제주특별자치도 제주시 용담로 3064-741-5411064-741-5411~6<NA>
7서울남부출입국 외국인사무소서울특별시 강서구(김포공항 제외) 구로구 금천구 마포구 서대문구 영등포구 양천구, 경기도 광명시서울특별시 양천구 목동동로 15102-2650-4611<NA><NA>
8김해공항출입국 외국인사무소김해국제공항부산광역시 강서구 공항진입로 108051-979-1300<NA><NA>
9대구출입국 외국인사무소대구광역시, 경상북도(포항시 울릉군 영덕군 울진군 구미시 김천시 상주시 칠곡군 문경시 경주시 제외)대구광역시 동구 동촌로 71053-980-3512<NA><NA>
전국 출입국외국인청/사무소관할구역주소전화번호전화번호 구분비고
39양주출입국 외국인사무소 고양출장소경기도 고양시 파주시경기도 고양시 덕양구 화정동 화중로 104번길 50031-936-5031<NA><NA>
40광주출입국 외국인사무소 목포출장소전라남도 목포시 신안군 무안군(무안국제공항 제외) 진도군 해남군 완도군 영암군전남 목포시 백년대로 412번길 26061-283-7294<NA><NA>
41광주출입국 외국인사무소 무안공항출장소무안국제공항전남 무안군 망운면 공항로 970-260061-453-8846<NA><NA>
42창원출입국 외국인사무소 통영출장소경상남도 통영시경남 통영시 남망길 5055-645-3405<NA><NA>
43창원출입국 외국인사무소 사천출장소경상남도 사천시 남해군 하동군경남 사천시 삼천포대교로 450055-835-3988<NA><NA>
44창원출입국 외국인사무소 거제출장소경상남도 거제시경남 거제시 연초면 연사1길 24055-681-8133<NA><NA>
45춘천출입국 외국인사무소 동해출장소강원도 동해시 강릉시 삼척시 태백시 정선군강원도 동해시 해안로 225033-535-5723<NA><NA>
46춘천출입국 외국인사무소 속초출장소강원도 속초시 고성군 양양군강원도 속초시 동명항길 26 속초항만지원센터033-636-8613<NA><NA>
47춘천출입국 외국인사무소 고성출장소고성터미널강원도 고성군 현내면 동해대로 9097033-680-5100<NA><NA>
48전주출입국 외국인사무소 군산출장소전라북도 군산시, 충청남도 장항항전북 군산시 해망로 254063-445-3874<NA><NA>