Overview

Dataset statistics

Number of variables15
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory137.1 B

Variable types

Text2
Numeric3
Categorical10

Dataset

Description다문화가족 서포터즈 위촉 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=XN56EMEDY2BZSF49OK9B31251668&infSeq=1

Alerts

라오스 is highly overall correlated with 필리핀 and 8 other fieldsHigh correlation
우즈벡 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
러시아 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
기타 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
일본 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
캄보디아 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
필리핀 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
태국 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
몽골 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
인도네시아 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
합계 is highly overall correlated with 중국 and 10 other fieldsHigh correlation
중국 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
베트남 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
인도네시아 is highly imbalanced (79.9%)Imbalance
캄보디아 is highly imbalanced (53.4%)Imbalance
러시아 is highly imbalanced (55.5%)Imbalance
라오스 is highly imbalanced (66.2%)Imbalance
시군명 has unique valuesUnique
관리기관 has unique valuesUnique
합계 has 3 (9.4%) zerosZeros
중국 has 8 (25.0%) zerosZeros
베트남 has 5 (15.6%) zerosZeros

Reproduction

Analysis started2023-12-10 22:27:12.988656
Analysis finished2023-12-10 22:27:14.582287
Duration1.59 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T07:27:14.695605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.09375
Min length3

Characters and Unicode

Total characters99
Distinct characters41
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

Unique32 ?
Unique (%)100.0%

Sample

1st row경기도
2nd row수원시
3rd row용인시
4th row고양시
5th row화성시
ValueCountFrequency (%)
경기도 1
 
3.1%
수원시 1
 
3.1%
가평군 1
 
3.1%
과천시 1
 
3.1%
동두천시 1
 
3.1%
여주시 1
 
3.1%
양평군 1
 
3.1%
포천시 1
 
3.1%
의왕시 1
 
3.1%
안성시 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T07:27:14.961194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
양 5
 
5.1%
주 5
 
5.1%
군 4
 
4.0%
포 3
 
3.0%
평 3
 
3.0%
안 3
 
3.0%
성 3
 
3.0%
남 3
 
3.0%
Other values (31) 35
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
양 5
 
5.1%
주 5
 
5.1%
군 4
 
4.0%
포 3
 
3.0%
평 3
 
3.0%
안 3
 
3.0%
성 3
 
3.0%
남 3
 
3.0%
Other values (31) 35
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
양 5
 
5.1%
주 5
 
5.1%
군 4
 
4.0%
포 3
 
3.0%
평 3
 
3.0%
안 3
 
3.0%
성 3
 
3.0%
남 3
 
3.0%
Other values (31) 35
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
양 5
 
5.1%
주 5
 
5.1%
군 4
 
4.0%
포 3
 
3.0%
평 3
 
3.0%
안 3
 
3.0%
성 3
 
3.0%
남 3
 
3.0%
Other values (31) 35
35.4%

관리기관
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T07:27:15.144287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length10.4375
Min length4

Characters and Unicode

Total characters334
Distinct characters50
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

Unique32 ?
Unique (%)100.0%

Sample

1st row경기도청
2nd row수원시다문화가족지원센터
3rd row용인시건강가정다문화지원센터
4th row고양시다문화가족지원센터
5th row화성시가족센터
ValueCountFrequency (%)
경기도청 1
 
3.1%
수원시다문화가족지원센터 1
 
3.1%
가평군건강가정다문화지원센터 1
 
3.1%
과천시건강가정다문화지원센터 1
 
3.1%
동두천시가족센터 1
 
3.1%
여주시가족센터 1
 
3.1%
양평군건강가정다문화지원센터 1
 
3.1%
포천시건강가정다문화지원센터 1
 
3.1%
의왕시건강가정다문화지원센터 1
 
3.1%
안성시건강가정다문화지원센터 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T07:27:15.438654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
가 32
 
9.6%
센 31
 
9.3%
터 31
 
9.3%
시 29
 
8.7%
원 19
 
5.7%
족 19
 
5.7%
화 18
 
5.4%
다 17
 
5.1%
문 17
 
5.1%
지 17
 
5.1%
Other values (40) 104
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 334
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 32
 
9.6%
센 31
 
9.3%
터 31
 
9.3%
시 29
 
8.7%
원 19
 
5.7%
족 19
 
5.7%
화 18
 
5.4%
다 17
 
5.1%
문 17
 
5.1%
지 17
 
5.1%
Other values (40) 104
31.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 334
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 32
 
9.6%
센 31
 
9.3%
터 31
 
9.3%
시 29
 
8.7%
원 19
 
5.7%
족 19
 
5.7%
화 18
 
5.4%
다 17
 
5.1%
문 17
 
5.1%
지 17
 
5.1%
Other values (40) 104
31.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 334
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
가 32
 
9.6%
센 31
 
9.3%
터 31
 
9.3%
시 29
 
8.7%
원 19
 
5.7%
족 19
 
5.7%
화 18
 
5.4%
다 17
 
5.1%
문 17
 
5.1%
지 17
 
5.1%
Other values (40) 104
31.1%

합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.75
Minimum0
Maximum156
Zeros3
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:27:15.535715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4.5
Q36.25
95-th percentile12
Maximum156
Range156
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation26.890459
Coefficient of variation (CV)2.7579958
Kurtosis30.949371
Mean9.75
Median Absolute Deviation (MAD)1.5
Skewness5.5226225
Sum312
Variance723.09677
MonotonicityNot monotonic
2023-12-11T07:27:15.624882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 5
15.6%
4 5
15.6%
6 4
12.5%
5 4
12.5%
0 3
9.4%
2 3
9.4%
12 2
 
6.2%
9 2
 
6.2%
11 2
 
6.2%
156 1
 
3.1%
ValueCountFrequency (%)
0 3
9.4%
2 3
9.4%
3 5
15.6%
4 5
15.6%
5 4
12.5%
6 4
12.5%
7 1
 
3.1%
9 2
 
6.2%
11 2
 
6.2%
12 2
 
6.2%
ValueCountFrequency (%)
156 1
 
3.1%
12 2
 
6.2%
11 2
 
6.2%
9 2
 
6.2%
7 1
 
3.1%
6 4
12.5%
5 4
12.5%
4 5
15.6%
3 5
15.6%
2 3
9.4%

중국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6875
Minimum0
Maximum43
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:27:15.715705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1
Q32
95-th percentile4
Maximum43
Range43
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation7.4549724
Coefficient of variation (CV)2.7739432
Kurtosis30.170626
Mean2.6875
Median Absolute Deviation (MAD)1
Skewness5.4237098
Sum86
Variance55.576613
MonotonicityNot monotonic
2023-12-11T07:27:15.795587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 11
34.4%
0 8
25.0%
2 7
21.9%
4 3
 
9.4%
3 2
 
6.2%
43 1
 
3.1%
ValueCountFrequency (%)
0 8
25.0%
1 11
34.4%
2 7
21.9%
3 2
 
6.2%
4 3
 
9.4%
43 1
 
3.1%
ValueCountFrequency (%)
43 1
 
3.1%
4 3
 
9.4%
3 2
 
6.2%
2 7
21.9%
1 11
34.4%
0 8
25.0%

베트남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6875
Minimum0
Maximum43
Zeros5
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:27:15.880254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3.45
Maximum43
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.4159266
Coefficient of variation (CV)2.7594146
Kurtosis30.879528
Mean2.6875
Median Absolute Deviation (MAD)1
Skewness5.5134694
Sum86
Variance54.995968
MonotonicityNot monotonic
2023-12-11T07:27:15.961900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 13
40.6%
2 10
31.2%
0 5
 
15.6%
3 2
 
6.2%
43 1
 
3.1%
4 1
 
3.1%
ValueCountFrequency (%)
0 5
 
15.6%
1 13
40.6%
2 10
31.2%
3 2
 
6.2%
4 1
 
3.1%
43 1
 
3.1%
ValueCountFrequency (%)
43 1
 
3.1%
4 1
 
3.1%
3 2
 
6.2%
2 10
31.2%
1 13
40.6%
0 5
 
15.6%

필리핀
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
17 
1
12 
2
2 
16
 
1

Length

Max length2
Median length1
Mean length1.03125
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row16
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 17
53.1%
1 12
37.5%
2 2
 
6.2%
16 1
 
3.1%

Length

2023-12-11T07:27:16.054359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:16.136700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
53.1%
1 12
37.5%
2 2
 
6.2%
16 1
 
3.1%

일본
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
17 
1
14 
14
 
1

Length

Max length2
Median length1
Mean length1.03125
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row14
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 17
53.1%
1 14
43.8%
14 1
 
3.1%

Length

2023-12-11T07:27:16.224462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:16.308645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
53.1%
1 14
43.8%
14 1
 
3.1%

몽골
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
25 
1
6 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row6
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 25
78.1%
1 6
 
18.8%
6 1
 
3.1%

Length

2023-12-11T07:27:16.393486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:16.475910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
78.1%
1 6
 
18.8%
6 1
 
3.1%

태국
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
25 
1
6 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row6
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 25
78.1%
1 6
 
18.8%
6 1
 
3.1%

Length

2023-12-11T07:27:16.558193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:16.650516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
78.1%
1 6
 
18.8%
6 1
 
3.1%

우즈벡
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
22 
1
8 
8
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.09375
Min length1

Unique

Unique2 ?
Unique (%)6.2%

Sample

1st row8
2nd row1
3rd row1
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 22
68.8%
1 8
 
25.0%
8 1
 
3.1%
<NA> 1
 
3.1%

Length

2023-12-11T07:27:16.765095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:16.852870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
68.8%
1 8
 
25.0%
8 1
 
3.1%
na 1
 
3.1%

인도네시아
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
31 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.09375
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row0
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 31
96.9%
<NA> 1
 
3.1%

Length

2023-12-11T07:27:17.143589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:17.223801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
96.9%
na 1
 
3.1%

캄보디아
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
27 
1
4 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row4
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 27
84.4%
1 4
 
12.5%
4 1
 
3.1%

Length

2023-12-11T07:27:17.301840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:17.377826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
84.4%
1 4
 
12.5%
4 1
 
3.1%

러시아
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
26 
1
3 
5
 
1
<NA>
 
1
2
 
1

Length

Max length4
Median length1
Mean length1.09375
Min length1

Unique

Unique3 ?
Unique (%)9.4%

Sample

1st row5
2nd row1
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 26
81.2%
1 3
 
9.4%
5 1
 
3.1%
<NA> 1
 
3.1%
2 1
 
3.1%

Length

2023-12-11T07:27:17.464251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:17.552723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26
81.2%
1 3
 
9.4%
5 1
 
3.1%
na 1
 
3.1%
2 1
 
3.1%

라오스
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
29 
1
 
2
<NA>
 
1

Length

Max length4
Median length1
Mean length1.09375
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row1
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 29
90.6%
1 2
 
6.2%
<NA> 1
 
3.1%

Length

2023-12-11T07:27:17.645032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:17.726650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
90.6%
1 2
 
6.2%
na 1
 
3.1%

기타
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
23 
1
6 
2
 
2
10
 
1

Length

Max length2
Median length1
Mean length1.03125
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row10
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 23
71.9%
1 6
 
18.8%
2 2
 
6.2%
10 1
 
3.1%

Length

2023-12-11T07:27:17.818243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:17.903376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
71.9%
1 6
 
18.8%
2 2
 
6.2%
10 1
 
3.1%

Interactions

2023-12-11T07:27:14.120947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:13.714675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:13.923230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:14.191922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:13.789372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:13.997516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:14.270294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:13.855066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:14.061492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:27:17.965877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명관리기관합계중국베트남필리핀일본몽골태국우즈벡캄보디아러시아라오스기타
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기관1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0001.0000.6590.6591.0001.0001.0001.0001.0001.0001.0000.4171.000
중국1.0001.0000.6591.0000.6591.0001.0001.0001.0001.0001.0001.0000.4171.000
베트남1.0001.0000.6590.6591.0001.0001.0001.0001.0001.0001.0001.0000.4171.000
필리핀1.0001.0001.0001.0001.0001.0000.6970.6540.6940.6560.6510.9070.8750.878
일본1.0001.0001.0001.0001.0000.6971.0000.9400.9400.9410.9330.6610.4370.675
몽골1.0001.0001.0001.0001.0000.6540.9401.0000.9380.9330.9470.6550.4310.673
태국1.0001.0001.0001.0001.0000.6940.9400.9381.0000.9500.9330.6540.4310.660
우즈벡1.0001.0001.0001.0001.0000.6560.9410.9330.9501.0000.9390.6500.4320.805
캄보디아1.0001.0001.0001.0001.0000.6510.9330.9470.9330.9391.0000.6500.4300.650
러시아1.0001.0001.0001.0001.0000.9070.6610.6550.6540.6500.6501.0001.0000.866
라오스1.0001.0000.4170.4170.4170.8750.4370.4310.4310.4320.4301.0001.0000.859
기타1.0001.0001.0001.0001.0000.8780.6750.6730.6600.8050.6500.8660.8591.000
2023-12-11T07:27:18.084487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
라오스우즈벡러시아기타일본캄보디아필리핀태국몽골인도네시아
라오스1.0000.6620.9650.6340.6670.6580.6550.6590.6591.000
우즈벡0.6621.0000.6610.8520.7020.6970.6690.7250.6831.000
러시아0.9650.6611.0000.5260.6740.6620.5980.6660.6671.000
기타0.6340.8520.5261.0000.6920.6610.5460.6730.6891.000
일본0.6670.7020.6740.6921.0000.6830.7190.7000.7001.000
캄보디아0.6580.6970.6620.6610.6831.0000.6630.6830.7161.000
필리핀0.6550.6690.5980.5460.7190.6631.0000.7150.6661.000
태국0.6590.7250.6660.6730.7000.6830.7151.0000.6941.000
몽골0.6590.6830.6670.6890.7000.7160.6660.6941.0001.000
인도네시아1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-11T07:27:18.188549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계중국베트남필리핀일본몽골태국우즈벡인도네시아캄보디아러시아라오스기타
합계1.0000.9200.6770.9660.9830.9830.9830.9831.0000.9830.9650.2720.966
중국0.9201.0000.5980.9660.9830.9830.9830.9831.0000.9830.9650.2720.966
베트남0.6770.5981.0000.9660.9830.9830.9830.9831.0000.9830.9650.2720.966
필리핀0.9660.9660.9661.0000.7190.6660.7150.6691.0000.6630.5980.6550.546
일본0.9830.9830.9830.7191.0000.7000.7000.7021.0000.6830.6740.6670.692
몽골0.9830.9830.9830.6660.7001.0000.6940.6831.0000.7160.6670.6590.689
태국0.9830.9830.9830.7150.7000.6941.0000.7251.0000.6830.6660.6590.673
우즈벡0.9830.9830.9830.6690.7020.6830.7251.0001.0000.6970.6610.6620.852
인도네시아1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
캄보디아0.9830.9830.9830.6630.6830.7160.6830.6971.0001.0000.6620.6580.661
러시아0.9650.9650.9650.5980.6740.6670.6660.6611.0000.6621.0000.9650.526
라오스0.2720.2720.2720.6550.6670.6590.6590.6621.0000.6580.9651.0000.634
기타0.9660.9660.9660.5460.6920.6890.6730.8521.0000.6610.5260.6341.000

Missing values

2023-12-11T07:27:14.377360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:27:14.527042image/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

시군명관리기관합계중국베트남필리핀일본몽골태국우즈벡인도네시아캄보디아러시아라오스기타
0경기도경기도청15643431614668045110
1수원시수원시다문화가족지원센터12431101100100
2용인시용인시건강가정다문화지원센터9420100100001
3고양시고양시다문화가족지원센터9211111<NA><NA>1<NA><NA>1
4화성시화성시가족센터0000000000000
5성남시성남시다문화가족지원센터3020100000000
6부천시부천시다문화가족지원센터12422101100001
7남양주시남양주시가족센터6221100000000
8안산시안산시다문화가족지원센터11341000000210
9평택시평택시건강가정다문화지원센터7211111000000
시군명관리기관합계중국베트남필리핀일본몽골태국우즈벡인도네시아캄보디아러시아라오스기타
22구리시구리시건강가정다문화지원센터0000000000000
23안성시안성시건강가정다문화지원센터5120000100001
24의왕시의왕시건강가정다문화지원센터2110000000000
25포천시포천시건강가정다문화지원센터4010010000002
26양평군양평군건강가정다문화지원센터2010000001000
27여주시여주시가족센터3111000000000
28동두천시동두천시가족센터4110000100001
29과천시과천시건강가정다문화지원센터0000000000000
30가평군가평군건강가정다문화지원센터3020100000000
31연천군연천군가족원센터2001001000000