Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells36
Missing cells (%)16.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory95.0 B

Variable types

Text1
Categorical4
Numeric5

Dataset

Description교원해외파견사업에 관한 데이터로 개발도상국에 수학, 과학, ICT 분야 등의 교원들을 보내 기초교육향상 지원을 통한 양질의 교육기회를 제공함
Author교육부 국립국제교육원
URLhttps://www.data.go.kr/data/15052776/fileData.do

Alerts

2015 is highly overall correlated with 2016 and 4 other fieldsHigh correlation
2014 is highly overall correlated with 2019 and 2 other fieldsHigh correlation
2013 is highly overall correlated with 2016 and 6 other fieldsHigh correlation
2021 is highly overall correlated with 2016 and 5 other fieldsHigh correlation
2016 is highly overall correlated with 2017 and 5 other fieldsHigh correlation
2017 is highly overall correlated with 2016 and 4 other fieldsHigh correlation
2018 is highly overall correlated with 2016 and 3 other fieldsHigh correlation
2019 is highly overall correlated with 2022 and 4 other fieldsHigh correlation
2022 is highly overall correlated with 2016 and 6 other fieldsHigh correlation
2013 is highly imbalanced (54.9%)Imbalance
2021 is highly imbalanced (54.9%)Imbalance
2016 has 7 (31.8%) missing valuesMissing
2017 has 6 (27.3%) missing valuesMissing
2018 has 4 (18.2%) missing valuesMissing
2019 has 6 (27.3%) missing valuesMissing
2022 has 13 (59.1%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:51:44.856764
Analysis finished2023-12-12 02:51:48.012496
Duration3.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T11:51:48.146771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4.5
Mean length3.5909091
Min length2

Characters and Unicode

Total characters79
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row스와질랜드
2nd row에티오피아
3rd row카자흐스탄
4th row키르기스스탄
5th row우간다
ValueCountFrequency (%)
스와질랜드 1
 
4.5%
에티오피아 1
 
4.5%
요르단 1
 
4.5%
세르비아 1
 
4.5%
동티모르 1
 
4.5%
태국 1
 
4.5%
페루 1
 
4.5%
파라과이 1
 
4.5%
탄자니아 1
 
4.5%
네팔 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T11:51:48.501072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 5
 
6.3%
아 5
 
6.3%
탄 4
 
5.1%
르 4
 
5.1%
남 2
 
2.5%
베 2
 
2.5%
와 2
 
2.5%
이 2
 
2.5%
레 2
 
2.5%
우 2
 
2.5%
Other values (43) 49
62.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 5
 
6.3%
아 5
 
6.3%
탄 4
 
5.1%
르 4
 
5.1%
남 2
 
2.5%
베 2
 
2.5%
와 2
 
2.5%
이 2
 
2.5%
레 2
 
2.5%
우 2
 
2.5%
Other values (43) 49
62.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 5
 
6.3%
아 5
 
6.3%
탄 4
 
5.1%
르 4
 
5.1%
남 2
 
2.5%
베 2
 
2.5%
와 2
 
2.5%
이 2
 
2.5%
레 2
 
2.5%
우 2
 
2.5%
Other values (43) 49
62.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 5
 
6.3%
아 5
 
6.3%
탄 4
 
5.1%
르 4
 
5.1%
남 2
 
2.5%
베 2
 
2.5%
와 2
 
2.5%
이 2
 
2.5%
레 2
 
2.5%
우 2
 
2.5%
Other values (43) 49
62.0%

2013
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
<NA>
18 
11
 
1
3
 
1
2
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.5
Min length1

Unique

Unique4 ?
Unique (%)18.2%

Sample

1st row11
2nd row3
3rd row2
4th row5
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 18
81.8%
11 1
 
4.5%
3 1
 
4.5%
2 1
 
4.5%
5 1
 
4.5%

Length

2023-12-12T11:51:48.633184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:48.755783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
81.8%
11 1
 
4.5%
3 1
 
4.5%
2 1
 
4.5%
5 1
 
4.5%

2014
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
<NA>
16 
2
2 
4
2 
5
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.1818182
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row5
2nd row3
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
<NA> 16
72.7%
2 2
 
9.1%
4 2
 
9.1%
5 1
 
4.5%
3 1
 
4.5%

Length

2023-12-12T11:51:48.882462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:49.012264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
72.7%
2 2
 
9.1%
4 2
 
9.1%
5 1
 
4.5%
3 1
 
4.5%

2015
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
<NA>
14 
2
4 
3
2 
5
 
1
1
 
1

Length

Max length4
Median length4
Mean length2.9090909
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row5
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
<NA> 14
63.6%
2 4
 
18.2%
3 2
 
9.1%
5 1
 
4.5%
1 1
 
4.5%

Length

2023-12-12T11:51:49.155360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:49.289102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
63.6%
2 4
 
18.2%
3 2
 
9.1%
5 1
 
4.5%
1 1
 
4.5%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)73.3%
Missing7
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean6.4666667
Minimum2
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:51:49.387757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13.5
median6
Q39.5
95-th percentile12.3
Maximum13
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7007078
Coefficient of variation (CV)0.5722744
Kurtosis-1.1077469
Mean6.4666667
Median Absolute Deviation (MAD)3
Skewness0.49931317
Sum97
Variance13.695238
MonotonicityNot monotonic
2023-12-12T11:51:49.503401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 2
 
9.1%
3 2
 
9.1%
6 2
 
9.1%
2 2
 
9.1%
7 1
 
4.5%
9 1
 
4.5%
12 1
 
4.5%
11 1
 
4.5%
5 1
 
4.5%
13 1
 
4.5%
(Missing) 7
31.8%
ValueCountFrequency (%)
2 2
9.1%
3 2
9.1%
4 2
9.1%
5 1
4.5%
6 2
9.1%
7 1
4.5%
9 1
4.5%
10 1
4.5%
11 1
4.5%
12 1
4.5%
ValueCountFrequency (%)
13 1
4.5%
12 1
4.5%
11 1
4.5%
10 1
4.5%
9 1
4.5%
7 1
4.5%
6 2
9.1%
5 1
4.5%
4 2
9.1%
3 2
9.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)68.8%
Missing6
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean6.9375
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:51:49.608312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q13
median4.5
Q311.25
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation5.0526396
Coefficient of variation (CV)0.72830841
Kurtosis-1.3741449
Mean6.9375
Median Absolute Deviation (MAD)2.5
Skewness0.55216212
Sum111
Variance25.529167
MonotonicityNot monotonic
2023-12-12T11:51:49.708694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 3
13.6%
4 2
 
9.1%
2 2
 
9.1%
15 2
 
9.1%
5 1
 
4.5%
11 1
 
4.5%
14 1
 
4.5%
7 1
 
4.5%
12 1
 
4.5%
1 1
 
4.5%
(Missing) 6
27.3%
ValueCountFrequency (%)
1 1
 
4.5%
2 2
9.1%
3 3
13.6%
4 2
9.1%
5 1
 
4.5%
7 1
 
4.5%
10 1
 
4.5%
11 1
 
4.5%
12 1
 
4.5%
14 1
 
4.5%
ValueCountFrequency (%)
15 2
9.1%
14 1
 
4.5%
12 1
 
4.5%
11 1
 
4.5%
10 1
 
4.5%
7 1
 
4.5%
5 1
 
4.5%
4 2
9.1%
3 3
13.6%
2 2
9.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)55.6%
Missing4
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean6.0555556
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:51:50.071843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.85
Q12
median4.5
Q39.5
95-th percentile13.45
Maximum16
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.5175764
Coefficient of variation (CV)0.74602179
Kurtosis-0.45230616
Mean6.0555556
Median Absolute Deviation (MAD)2.5
Skewness0.80761861
Sum109
Variance20.408497
MonotonicityNot monotonic
2023-12-12T11:51:50.191588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 5
22.7%
5 2
 
9.1%
4 2
 
9.1%
11 2
 
9.1%
8 2
 
9.1%
16 1
 
4.5%
13 1
 
4.5%
10 1
 
4.5%
3 1
 
4.5%
1 1
 
4.5%
(Missing) 4
18.2%
ValueCountFrequency (%)
1 1
 
4.5%
2 5
22.7%
3 1
 
4.5%
4 2
 
9.1%
5 2
 
9.1%
8 2
 
9.1%
10 1
 
4.5%
11 2
 
9.1%
13 1
 
4.5%
16 1
 
4.5%
ValueCountFrequency (%)
16 1
 
4.5%
13 1
 
4.5%
11 2
 
9.1%
10 1
 
4.5%
8 2
 
9.1%
5 2
 
9.1%
4 2
 
9.1%
3 1
 
4.5%
2 5
22.7%
1 1
 
4.5%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)43.8%
Missing6
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean4.375
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:51:50.311580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median3.5
Q35
95-th percentile9.75
Maximum12
Range11
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation3.0956959
Coefficient of variation (CV)0.70758764
Kurtosis1.3176164
Mean4.375
Median Absolute Deviation (MAD)1.5
Skewness1.3545555
Sum70
Variance9.5833333
MonotonicityNot monotonic
2023-12-12T11:51:50.422448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 4
18.2%
4 3
13.6%
9 2
 
9.1%
5 2
 
9.1%
1 2
 
9.1%
2 2
 
9.1%
12 1
 
4.5%
(Missing) 6
27.3%
ValueCountFrequency (%)
1 2
9.1%
2 2
9.1%
3 4
18.2%
4 3
13.6%
5 2
9.1%
9 2
9.1%
12 1
 
4.5%
ValueCountFrequency (%)
12 1
 
4.5%
9 2
9.1%
5 2
9.1%
4 3
13.6%
3 4
18.2%
2 2
9.1%
1 2
9.1%

2021
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
<NA>
18 
7
 
1
24
 
1
2
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.5
Min length1

Unique

Unique4 ?
Unique (%)18.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row7
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 18
81.8%
7 1
 
4.5%
24 1
 
4.5%
2 1
 
4.5%
1 1
 
4.5%

Length

2023-12-12T11:51:50.580919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:50.709253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
81.8%
7 1
 
4.5%
24 1
 
4.5%
2 1
 
4.5%
1 1
 
4.5%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)66.7%
Missing13
Missing (%)59.1%
Infinite0
Infinite (%)0.0%
Mean4.4444444
Minimum2
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:51:50.801650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q35
95-th percentile9
Maximum11
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8771128
Coefficient of variation (CV)0.64735037
Kurtosis3.1076979
Mean4.4444444
Median Absolute Deviation (MAD)2
Skewness1.6122214
Sum40
Variance8.2777778
MonotonicityNot monotonic
2023-12-12T11:51:50.892515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3
 
13.6%
5 2
 
9.1%
4 1
 
4.5%
6 1
 
4.5%
11 1
 
4.5%
3 1
 
4.5%
(Missing) 13
59.1%
ValueCountFrequency (%)
2 3
13.6%
3 1
 
4.5%
4 1
 
4.5%
5 2
9.1%
6 1
 
4.5%
11 1
 
4.5%
ValueCountFrequency (%)
11 1
 
4.5%
6 1
 
4.5%
5 2
9.1%
4 1
 
4.5%
3 1
 
4.5%
2 3
13.6%

Interactions

2023-12-12T11:51:47.026608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.261805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.673143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.126788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.582164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:47.134011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.333265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.759735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.220268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.675555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:47.242523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.421900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.859755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.317199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.769095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:47.315403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.513689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.956417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.418880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.858185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:47.403730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:45.596625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.039358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.506513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:46.943652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:51:50.980368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분201320142015201620172018201920212022
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20131.0001.0001.0001.0001.0001.0001.0001.000NaN0.000
20141.0001.0001.0000.5980.9130.4160.0001.000NaN0.000
20151.0001.0000.5981.0001.0000.0000.0001.0000.0001.000
20161.0001.0000.9131.0001.0000.8110.7820.3791.0000.647
20171.0001.0000.4160.0000.8111.0000.9130.4091.0000.647
20181.0001.0000.0000.0000.7820.9131.0000.0001.0000.690
20191.0001.0001.0001.0000.3790.4090.0001.0001.0000.897
20211.000NaNNaN0.0001.0001.0001.0001.0001.0001.000
20221.0000.0000.0001.0000.6470.6470.6900.8971.0001.000
2023-12-12T11:51:51.102127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2015201420132021
20151.0000.0001.0001.000
20140.0001.0001.000NaN
20131.0001.0001.000NaN
20211.000NaNNaN1.000
2023-12-12T11:51:51.294539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201620172018201920222013201420152021
20161.0000.8160.8250.3070.7211.0000.0000.6321.000
20170.8161.0000.9510.1880.5291.0000.0000.0001.000
20180.8250.9511.0000.1820.3881.0000.0000.0001.000
20190.3070.1880.1821.0000.6551.0001.0001.0001.000
20220.7210.5290.3880.6551.0001.0001.0001.0001.000
20131.0001.0001.0001.0001.0001.0001.0001.000NaN
20140.0000.0000.0001.0001.0001.0001.0000.000NaN
20150.6320.0000.0001.0001.0001.0000.0001.0001.000
20211.0001.0001.0001.0001.000NaNNaN1.0001.000

Missing values

2023-12-12T11:51:47.561867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:51:47.735547image/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.
2023-12-12T11:51:47.897681image/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

구분201320142015201620172018201920212022
0스와질랜드1155745<NA><NA><NA>
1에티오피아3324344<NA><NA>
2카자흐스탄2233223<NA>4
3키르기스스탄543944976
4우간다<NA>224555<NA><NA>
5케냐<NA>4<NA><NA><NA><NA><NA><NA><NA>
6말레이시아<NA><NA>2121111122411
7칠레<NA><NA>1<NA><NA><NA><NA><NA><NA>
8피지<NA><NA>21114161<NA><NA>
9베트남<NA><NA><NA>5784<NA>2
구분201320142015201620172018201920212022
12남아공<NA><NA><NA>615103<NA><NA>
13네팔<NA><NA><NA>2225<NA>3
14탄자니아<NA><NA><NA>101211<NA><NA><NA>
15파라과이<NA><NA><NA>212222
16페루<NA><NA><NA>3333<NA><NA>
17태국<NA><NA><NA><NA>10821<NA>
18동티모르<NA><NA><NA><NA><NA>14<NA><NA>
19세르비아<NA><NA><NA><NA><NA>23<NA>5
20요르단<NA><NA><NA><NA><NA><NA><NA><NA>2
21우즈베키스탄<NA><NA><NA><NA><NA><NA><NA><NA>5