Overview

Dataset statistics

Number of variables14
Number of observations88
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory125.5 B

Variable types

Text1
Categorical11
Numeric2

Dataset

Description국적 및 체류자격별 사증발급 허가 승인 현황을 제공 ( 제공항목은 국적, 성별, 체류자격) 제공연도 2010년~2015년
Author법무부
URLhttps://www.data.go.kr/data/3074981/fileData.do

Alerts

거주(F-2) is highly overall correlated with 결혼이민(F-6) and 1 other fieldsHigh correlation
공무(A-2) is highly overall correlated with 결혼이민(F-6) and 1 other fieldsHigh correlation
단기방문(C-3) is highly overall correlated with 일반연수(D-4) and 2 other fieldsHigh correlation
결혼이민(F-6) is highly overall correlated with 공무(A-2) and 3 other fieldsHigh correlation
유학(D-2) is highly overall correlated with 결혼이민(F-6)High correlation
일반연수(D-4) is highly overall correlated with 단기방문(C-3) and 1 other fieldsHigh correlation
무역경영(D-9) is highly overall correlated with 단기방문(C-3)High correlation
재외동포(F-4) is highly overall correlated with 결혼이민(F-6)High correlation
방문취업(H-2) is highly overall correlated with 단기방문(C-3) and 1 other fieldsHigh correlation
공무(A-2) is highly imbalanced (91.0%)Imbalance
단기취업(C-4) is highly imbalanced (91.0%)Imbalance
유학(D-2) is highly imbalanced (91.0%)Imbalance
일반연수(D-4) is highly imbalanced (64.4%)Imbalance
취재(D-5) is highly imbalanced (91.0%)Imbalance
무역경영(D-9) is highly imbalanced (84.4%)Imbalance
방문동거(F-1) is highly imbalanced (84.5%)Imbalance
거주(F-2) is highly imbalanced (88.7%)Imbalance
재외동포(F-4) is highly imbalanced (91.0%)Imbalance
방문취업(H-2) is highly imbalanced (88.7%)Imbalance
단기방문(C-3) has 54 (61.4%) zerosZeros
결혼이민(F-6) has 63 (71.6%) zerosZeros

Reproduction

Analysis started2023-12-12 10:01:05.282426
Analysis finished2023-12-12 10:01:06.817092
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국적(Nationality)
Text

Distinct44
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2023-12-12T19:01:06.993512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.5227273
Min length2

Characters and Unicode

Total characters310
Distinct characters84
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

Unique0 ?
Unique (%)0.0%

Sample

1st row베트남
2nd row베트남
3rd row우즈베키스탄
4th row우즈베키스탄
5th row아랍에미리트연합
ValueCountFrequency (%)
베트남 2
 
2.3%
우즈베키스탄 2
 
2.3%
세르비아 2
 
2.3%
스리랑카 2
 
2.3%
캄보디아 2
 
2.3%
미얀마 2
 
2.3%
미국 2
 
2.3%
페루 2
 
2.3%
멕시코 2
 
2.3%
쿠바 2
 
2.3%
Other values (34) 68
77.3%
2023-12-12T19:01:07.332027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 18
 
5.8%
아 18
 
5.8%
인 12
 
3.9%
국 12
 
3.9%
리 10
 
3.2%
시 10
 
3.2%
이 10
 
3.2%
키 8
 
2.6%
탄 8
 
2.6%
트 8
 
2.6%
Other values (74) 196
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 306
98.7%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 18
 
5.9%
아 18
 
5.9%
인 12
 
3.9%
국 12
 
3.9%
리 10
 
3.3%
시 10
 
3.3%
이 10
 
3.3%
키 8
 
2.6%
탄 8
 
2.6%
트 8
 
2.6%
Other values (72) 192
62.7%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
98.7%
Common 4
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 18
 
5.9%
아 18
 
5.9%
인 12
 
3.9%
국 12
 
3.9%
리 10
 
3.3%
시 10
 
3.3%
이 10
 
3.3%
키 8
 
2.6%
탄 8
 
2.6%
트 8
 
2.6%
Other values (72) 192
62.7%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 306
98.7%
ASCII 4
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 18
 
5.9%
아 18
 
5.9%
인 12
 
3.9%
국 12
 
3.9%
리 10
 
3.3%
시 10
 
3.3%
이 10
 
3.3%
키 8
 
2.6%
탄 8
 
2.6%
트 8
 
2.6%
Other values (72) 192
62.7%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

성별(Sex)
Categorical

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
남(M)
44 
여(F)
44 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남(M)
2nd row여(F)
3rd row남(M)
4th row여(F)
5th row남(M)

Common Values

ValueCountFrequency (%)
남(M) 44
50.0%
여(F) 44
50.0%

Length

2023-12-12T19:01:07.485838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:07.593120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남(m 44
50.0%
여(f 44
50.0%

공무(A-2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
87 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 87
98.9%
1 1
 
1.1%

Length

2023-12-12T19:01:07.714267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:07.854136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 87
98.9%
1 1
 
1.1%

단기방문(C-3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75
Minimum0
Maximum43
Zeros54
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T19:01:07.954772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.25
95-th percentile16.3
Maximum43
Range43
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation6.5745067
Coefficient of variation (CV)2.3907297
Kurtosis17.650095
Mean2.75
Median Absolute Deviation (MAD)0
Skewness3.7928776
Sum242
Variance43.224138
MonotonicityNot monotonic
2023-12-12T19:01:08.092181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 54
61.4%
1 12
 
13.6%
2 4
 
4.5%
4 2
 
2.3%
8 2
 
2.3%
10 2
 
2.3%
5 2
 
2.3%
17 2
 
2.3%
15 1
 
1.1%
27 1
 
1.1%
Other values (6) 6
 
6.8%
ValueCountFrequency (%)
0 54
61.4%
1 12
 
13.6%
2 4
 
4.5%
3 1
 
1.1%
4 2
 
2.3%
5 2
 
2.3%
7 1
 
1.1%
8 2
 
2.3%
9 1
 
1.1%
10 2
 
2.3%
ValueCountFrequency (%)
43 1
1.1%
27 1
1.1%
18 1
1.1%
17 2
2.3%
15 1
1.1%
12 1
1.1%
10 2
2.3%
9 1
1.1%
8 2
2.3%
7 1
1.1%

단기취업(C-4)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
87 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 87
98.9%
1 1
 
1.1%

Length

2023-12-12T19:01:08.235513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:08.364058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 87
98.9%
1 1
 
1.1%

유학(D-2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
87 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 87
98.9%
1 1
 
1.1%

Length

2023-12-12T19:01:08.470857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:08.596243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 87
98.9%
1 1
 
1.1%

일반연수(D-4)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
78 
1
9 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 78
88.6%
1 9
 
10.2%
3 1
 
1.1%

Length

2023-12-12T19:01:08.741745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:08.852437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 78
88.6%
1 9
 
10.2%
3 1
 
1.1%

취재(D-5)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
87 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 87
98.9%
2 1
 
1.1%

Length

2023-12-12T19:01:08.965929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:09.097454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 87
98.9%
2 1
 
1.1%

무역경영(D-9)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
86 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86
97.7%
1 2
 
2.3%

Length

2023-12-12T19:01:09.229435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:09.352515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 86
97.7%
1 2
 
2.3%

방문동거(F-1)
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
85 
1
 
2
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 85
96.6%
1 2
 
2.3%
6 1
 
1.1%

Length

2023-12-12T19:01:09.458822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:09.570198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 85
96.6%
1 2
 
2.3%
6 1
 
1.1%

거주(F-2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
86 
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86
97.7%
2 1
 
1.1%
1 1
 
1.1%

Length

2023-12-12T19:01:09.681622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:09.796521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 86
97.7%
2 1
 
1.1%
1 1
 
1.1%

재외동포(F-4)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
87 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 87
98.9%
2 1
 
1.1%

Length

2023-12-12T19:01:09.920120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:10.068288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 87
98.9%
2 1
 
1.1%

결혼이민(F-6)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8181818
Minimum0
Maximum37
Zeros63
Zeros (%)71.6%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T19:01:10.192065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9.95
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.3959232
Coefficient of variation (CV)2.9677578
Kurtosis25.619966
Mean1.8181818
Median Absolute Deviation (MAD)0
Skewness4.7386992
Sum160
Variance29.115987
MonotonicityNot monotonic
2023-12-12T19:01:10.666409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 63
71.6%
1 7
 
8.0%
2 4
 
4.5%
3 3
 
3.4%
5 2
 
2.3%
4 1
 
1.1%
37 1
 
1.1%
6 1
 
1.1%
11 1
 
1.1%
7 1
 
1.1%
Other values (4) 4
 
4.5%
ValueCountFrequency (%)
0 63
71.6%
1 7
 
8.0%
2 4
 
4.5%
3 3
 
3.4%
4 1
 
1.1%
5 2
 
2.3%
6 1
 
1.1%
7 1
 
1.1%
8 1
 
1.1%
11 1
 
1.1%
ValueCountFrequency (%)
37 1
1.1%
27 1
1.1%
14 1
1.1%
12 1
1.1%
11 1
1.1%
8 1
1.1%
7 1
1.1%
6 1
1.1%
5 2
2.3%
4 1
1.1%

방문취업(H-2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size836.0 B
0
86 
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86
97.7%
2 1
 
1.1%
1 1
 
1.1%

Length

2023-12-12T19:01:10.783537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:01:10.901026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 86
97.7%
2 1
 
1.1%
1 1
 
1.1%

Interactions

2023-12-12T19:01:06.338984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:01:06.176101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:01:06.421360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:01:06.254120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:01:11.001786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국적(Nationality)성별(Sex)공무(A-2)단기방문(C-3)단기취업(C-4)유학(D-2)일반연수(D-4)취재(D-5)무역경영(D-9)방문동거(F-1)거주(F-2)재외동포(F-4)결혼이민(F-6)방문취업(H-2)
국적(Nationality)1.0000.0000.1320.5780.1320.1320.6860.1321.0000.1440.2910.1320.2230.209
성별(Sex)0.0001.0000.0000.1760.0000.0000.0000.0000.0000.0000.0000.0000.0000.014
공무(A-2)0.1320.0001.0000.4020.0000.0000.0000.0000.0000.0001.0000.0000.8710.000
단기방문(C-3)0.5780.1760.4021.0000.0000.3390.7560.0000.6930.0000.5110.4020.3590.832
단기취업(C-4)0.1320.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
유학(D-2)0.1320.0000.0000.3390.0001.0000.0000.0000.0000.0000.0000.0000.8710.000
일반연수(D-4)0.6860.0000.0000.7560.0000.0001.0000.0000.0000.0000.0000.0000.3260.940
취재(D-5)0.1320.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
무역경영(D-9)1.0000.0000.0000.6930.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
방문동거(F-1)0.1440.0000.0000.0000.0000.0000.0000.0000.0001.0000.8110.0000.7720.000
거주(F-2)0.2910.0001.0000.5110.0000.0000.0000.0000.0000.8111.0000.0000.9860.000
재외동포(F-4)0.1320.0000.0000.4020.0000.0000.0000.0000.0000.0000.0001.0000.8710.000
결혼이민(F-6)0.2230.0000.8710.3590.0000.8710.3260.0000.0000.7720.9860.8711.0000.442
방문취업(H-2)0.2090.0140.0000.8320.0000.0000.9400.0000.0000.0000.0000.0000.4421.000
2023-12-12T19:01:11.159731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단기취업(C-4)거주(F-2)공무(A-2)취재(D-5)성별(Sex)일반연수(D-4)무역경영(D-9)방문동거(F-1)재외동포(F-4)유학(D-2)방문취업(H-2)
단기취업(C-4)1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
거주(F-2)0.0001.0000.9940.0000.0000.0000.0000.4790.0000.0000.000
공무(A-2)0.0000.9941.0000.0000.0000.0000.0000.0000.0000.0000.000
취재(D-5)0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
성별(Sex)0.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.016
일반연수(D-4)0.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.699
무역경영(D-9)0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
방문동거(F-1)0.0000.4790.0000.0000.0000.0000.0001.0000.0000.0000.000
재외동포(F-4)0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
유학(D-2)0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
방문취업(H-2)0.0000.0000.0000.0000.0160.6990.0000.0000.0000.0001.000
2023-12-12T19:01:11.298225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단기방문(C-3)결혼이민(F-6)성별(Sex)공무(A-2)단기취업(C-4)유학(D-2)일반연수(D-4)취재(D-5)무역경영(D-9)방문동거(F-1)거주(F-2)재외동포(F-4)방문취업(H-2)
단기방문(C-3)1.0000.1320.1810.4180.0000.3510.6730.0000.7260.0000.3880.4180.780
결혼이민(F-6)0.1321.0000.0000.6650.0000.6650.1380.0000.0000.4420.8400.6650.199
성별(Sex)0.1810.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.016
공무(A-2)0.4180.6650.0001.0000.0000.0000.0000.0000.0000.0000.9940.0000.000
단기취업(C-4)0.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
유학(D-2)0.3510.6650.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
일반연수(D-4)0.6730.1380.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.699
취재(D-5)0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
무역경영(D-9)0.7260.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
방문동거(F-1)0.0000.4420.0000.0000.0000.0000.0000.0000.0001.0000.4790.0000.000
거주(F-2)0.3880.8400.0000.9940.0000.0000.0000.0000.0000.4791.0000.0000.000
재외동포(F-4)0.4180.6650.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
방문취업(H-2)0.7800.1990.0160.0000.0000.0000.6990.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T19:01:06.560739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:01:06.739621image/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

국적(Nationality)성별(Sex)공무(A-2)단기방문(C-3)단기취업(C-4)유학(D-2)일반연수(D-4)취재(D-5)무역경영(D-9)방문동거(F-1)거주(F-2)재외동포(F-4)결혼이민(F-6)방문취업(H-2)
0베트남남(M)000000000040
1베트남여(F)0000100000370
2우즈베키스탄남(M)000000000010
3우즈베키스탄여(F)010000000060
4아랍에미리트연합남(M)020000000000
5아랍에미리트연합여(F)000000000000
6터키남(M)000000000020
7터키여(F)000000000000
8타이남(M)000000000000
9타이여(F)0000100000110
국적(Nationality)성별(Sex)공무(A-2)단기방문(C-3)단기취업(C-4)유학(D-2)일반연수(D-4)취재(D-5)무역경영(D-9)방문동거(F-1)거주(F-2)재외동포(F-4)결혼이민(F-6)방문취업(H-2)
78모로코남(M)000000000010
79모로코여(F)000000000000
80가나남(M)000000000010
81가나여(F)000000000000
82에티오피아남(M)000010000000
83에티오피아여(F)000000000000
84이집트남(M)030000000010
85이집트여(F)000000000000
86무국적남(M)0430030000001
87무국적여(F)0180000000000