Overview

Dataset statistics

Number of variables11
Number of observations23
Missing cells9
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory101.7 B

Variable types

Text3
Numeric5
Categorical3

Dataset

Description전라남도 의용소방대 현황(연합회 회장, 본대, 지역대, 전문대, 전담대 수 및 대원수 현황)에 대해 조회할 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15037334/fileData.do

Alerts

전담대 대원수 is highly overall correlated with 본대 and 6 other fieldsHigh correlation
전담대 is highly overall correlated with 본대 and 6 other fieldsHigh correlation
전문대 is highly overall correlated with 전문대 대원수 and 2 other fieldsHigh correlation
본대 is highly overall correlated with 본대 대원수 and 2 other fieldsHigh correlation
지역대 is highly overall correlated with 지역대 대원수 and 2 other fieldsHigh correlation
본대 대원수 is highly overall correlated with 본대 and 2 other fieldsHigh correlation
지역대 대원수 is highly overall correlated with 지역대 and 2 other fieldsHigh correlation
전문대 대원수 is highly overall correlated with 전문대 and 2 other fieldsHigh correlation
전문대 is highly imbalanced (53.6%)Imbalance
전담대 대원수 is highly imbalanced (61.7%)Imbalance
시군명 has 1 (4.3%) missing valuesMissing
남성연합회장 has 1 (4.3%) missing valuesMissing
여성연합회장 has 1 (4.3%) missing valuesMissing
본대 has 1 (4.3%) missing valuesMissing
지역대 has 2 (8.7%) missing valuesMissing
본대 대원수 has 1 (4.3%) missing valuesMissing
지역대 대원수 has 1 (4.3%) missing valuesMissing
전문대 대원수 has 1 (4.3%) missing valuesMissing
지역대 has 6 (26.1%) zerosZeros
지역대 대원수 has 7 (30.4%) zerosZeros
전문대 대원수 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-12 13:52:44.534469
Analysis finished2023-12-12 13:52:47.666869
Duration3.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Memory size316.0 B
2023-12-12T22:52:47.787483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters35
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 row목포시
2nd row여수시
3rd row순천시
4th row나주시
5th row광양시
ValueCountFrequency (%)
목포시 1
 
4.5%
여수시 1
 
4.5%
구례군 1
 
4.5%
진도군 1
 
4.5%
신안군 1
 
4.5%
완도군 1
 
4.5%
장흥군 1
 
4.5%
장성군 1
 
4.5%
함평군 1
 
4.5%
고흥군 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T22:52:48.058308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
영 2
 
3.0%
광 2
 
3.0%
안 2
 
3.0%
흥 2
 
3.0%
장 2
 
3.0%
진 2
 
3.0%
양 2
 
3.0%
Other values (25) 27
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
영 2
 
3.0%
광 2
 
3.0%
안 2
 
3.0%
흥 2
 
3.0%
장 2
 
3.0%
진 2
 
3.0%
양 2
 
3.0%
Other values (25) 27
40.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
영 2
 
3.0%
광 2
 
3.0%
안 2
 
3.0%
흥 2
 
3.0%
장 2
 
3.0%
진 2
 
3.0%
양 2
 
3.0%
Other values (25) 27
40.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
영 2
 
3.0%
광 2
 
3.0%
안 2
 
3.0%
흥 2
 
3.0%
장 2
 
3.0%
진 2
 
3.0%
양 2
 
3.0%
Other values (25) 27
40.9%

남성연합회장
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Memory size316.0 B
2023-12-12T22:52:48.235109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters40
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 row박정태
2nd row김형준
3rd row최낙삼
4th row조승만
5th row권용일
ValueCountFrequency (%)
박정태 1
 
4.5%
김형준 1
 
4.5%
이태용 1
 
4.5%
이현배 1
 
4.5%
정정두 1
 
4.5%
김기욱 1
 
4.5%
정종호 1
 
4.5%
최상복 1
 
4.5%
박경섭 1
 
4.5%
박춘재 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T22:52:48.523297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 5
 
7.6%
박 4
 
6.1%
정 4
 
6.1%
종 3
 
4.5%
이 3
 
4.5%
재 2
 
3.0%
복 2
 
3.0%
성 2
 
3.0%
영 2
 
3.0%
현 2
 
3.0%
Other values (30) 37
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 5
 
7.6%
박 4
 
6.1%
정 4
 
6.1%
종 3
 
4.5%
이 3
 
4.5%
재 2
 
3.0%
복 2
 
3.0%
성 2
 
3.0%
영 2
 
3.0%
현 2
 
3.0%
Other values (30) 37
56.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 5
 
7.6%
박 4
 
6.1%
정 4
 
6.1%
종 3
 
4.5%
이 3
 
4.5%
재 2
 
3.0%
복 2
 
3.0%
성 2
 
3.0%
영 2
 
3.0%
현 2
 
3.0%
Other values (30) 37
56.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 5
 
7.6%
박 4
 
6.1%
정 4
 
6.1%
종 3
 
4.5%
이 3
 
4.5%
재 2
 
3.0%
복 2
 
3.0%
성 2
 
3.0%
영 2
 
3.0%
현 2
 
3.0%
Other values (30) 37
56.1%

여성연합회장
Text

MISSING 

Distinct21
Distinct (%)95.5%
Missing1
Missing (%)4.3%
Memory size316.0 B
2023-12-12T22:52:48.698620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters38
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

Unique20 ?
Unique (%)90.9%

Sample

1st row김경숙
2nd row윤시숙
3rd row조유진
4th row류정자
5th row노은순
ValueCountFrequency (%)
김경숙 2
 
9.1%
이나겸 1
 
4.5%
오목화 1
 
4.5%
최설임 1
 
4.5%
백영선 1
 
4.5%
이정경 1
 
4.5%
이애경 1
 
4.5%
유덕희 1
 
4.5%
오영애 1
 
4.5%
김정애 1
 
4.5%
Other values (11) 11
50.0%
2023-12-12T22:52:49.016853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
숙 5
 
7.6%
김 4
 
6.1%
정 4
 
6.1%
경 4
 
6.1%
애 3
 
4.5%
이 3
 
4.5%
영 3
 
4.5%
순 2
 
3.0%
오 2
 
3.0%
최 2
 
3.0%
Other values (28) 34
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
숙 5
 
7.6%
김 4
 
6.1%
정 4
 
6.1%
경 4
 
6.1%
애 3
 
4.5%
이 3
 
4.5%
영 3
 
4.5%
순 2
 
3.0%
오 2
 
3.0%
최 2
 
3.0%
Other values (28) 34
51.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
숙 5
 
7.6%
김 4
 
6.1%
정 4
 
6.1%
경 4
 
6.1%
애 3
 
4.5%
이 3
 
4.5%
영 3
 
4.5%
순 2
 
3.0%
오 2
 
3.0%
최 2
 
3.0%
Other values (28) 34
51.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
숙 5
 
7.6%
김 4
 
6.1%
정 4
 
6.1%
경 4
 
6.1%
애 3
 
4.5%
이 3
 
4.5%
영 3
 
4.5%
순 2
 
3.0%
오 2
 
3.0%
최 2
 
3.0%
Other values (28) 34
51.5%

본대
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)59.1%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean21.409091
Minimum4
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:52:49.132130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13.15
Q118
median22
Q324
95-th percentile29.9
Maximum32
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0761759
Coefficient of variation (CV)0.28381289
Kurtosis2.121857
Mean21.409091
Median Absolute Deviation (MAD)4
Skewness-0.90411034
Sum471
Variance36.919913
MonotonicityNot monotonic
2023-12-12T22:52:49.295916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
22 5
21.7%
24 3
13.0%
18 3
13.0%
16 2
 
8.7%
4 1
 
4.3%
30 1
 
4.3%
28 1
 
4.3%
26 1
 
4.3%
32 1
 
4.3%
20 1
 
4.3%
Other values (3) 3
13.0%
ValueCountFrequency (%)
4 1
 
4.3%
13 1
 
4.3%
16 2
 
8.7%
18 3
13.0%
20 1
 
4.3%
22 5
21.7%
23 1
 
4.3%
24 3
13.0%
26 1
 
4.3%
27 1
 
4.3%
ValueCountFrequency (%)
32 1
 
4.3%
30 1
 
4.3%
28 1
 
4.3%
27 1
 
4.3%
26 1
 
4.3%
24 3
13.0%
23 1
 
4.3%
22 5
21.7%
20 1
 
4.3%
18 3
13.0%

지역대
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)38.1%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean2
Minimum0
Maximum7
Zeros6
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:52:49.445888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0736441
Coefficient of variation (CV)1.0368221
Kurtosis0.44376761
Mean2
Median Absolute Deviation (MAD)1
Skewness1.0784059
Sum42
Variance4.3
MonotonicityNot monotonic
2023-12-12T22:52:49.919100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 6
26.1%
1 5
21.7%
3 3
13.0%
2 3
13.0%
7 1
 
4.3%
5 1
 
4.3%
4 1
 
4.3%
6 1
 
4.3%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
0 6
26.1%
1 5
21.7%
2 3
13.0%
3 3
13.0%
4 1
 
4.3%
5 1
 
4.3%
6 1
 
4.3%
7 1
 
4.3%
ValueCountFrequency (%)
7 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
4 1
 
4.3%
3 3
13.0%
2 3
13.0%
1 5
21.7%
0 6
26.1%

전문대
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
1
19 
2
2 
0
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.1304348
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 19
82.6%
2 2
 
8.7%
0 1
 
4.3%
<NA> 1
 
4.3%

Length

2023-12-12T22:52:50.078947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:52:50.231930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 19
82.6%
2 2
 
8.7%
0 1
 
4.3%
na 1
 
4.3%

전담대
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
1
3 

Length

Max length4
Median length4
Mean length3.6086957
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
87.0%
1 3
 
13.0%

Length

2023-12-12T22:52:50.365126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:52:50.483006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
1 3
 
13.0%

본대 대원수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean448.5
Minimum156
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:52:50.592189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum156
5-th percentile292.85
Q1394
median449
Q3503.25
95-th percentile611.45
Maximum630
Range474
Interquartile range (IQR)109.25

Descriptive statistics

Standard deviation113.65141
Coefficient of variation (CV)0.25340337
Kurtosis0.76300893
Mean448.5
Median Absolute Deviation (MAD)58
Skewness-0.5643393
Sum9867
Variance12916.643
MonotonicityNot monotonic
2023-12-12T22:52:50.755952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
156 1
 
4.3%
391 1
 
4.3%
403 1
 
4.3%
292 1
 
4.3%
309 1
 
4.3%
563 1
 
4.3%
567 1
 
4.3%
440 1
 
4.3%
428 1
 
4.3%
356 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
156 1
4.3%
292 1
4.3%
309 1
4.3%
356 1
4.3%
364 1
4.3%
391 1
4.3%
403 1
4.3%
423 1
4.3%
427 1
4.3%
428 1
4.3%
ValueCountFrequency (%)
630 1
4.3%
612 1
4.3%
601 1
4.3%
567 1
4.3%
563 1
4.3%
507 1
4.3%
492 1
4.3%
489 1
4.3%
482 1
4.3%
477 1
4.3%

지역대 대원수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)63.6%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean24.090909
Minimum0
Maximum82
Zeros7
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:52:50.887140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q340.75
95-th percentile75.25
Maximum82
Range82
Interquartile range (IQR)40.75

Descriptive statistics

Standard deviation25.623481
Coefficient of variation (CV)1.0636162
Kurtosis-0.026357247
Mean24.090909
Median Absolute Deviation (MAD)15
Skewness0.94641298
Sum530
Variance656.56277
MonotonicityNot monotonic
2023-12-12T22:52:51.016248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 7
30.4%
15 2
 
8.7%
28 2
 
8.7%
9 1
 
4.3%
82 1
 
4.3%
61 1
 
4.3%
45 1
 
4.3%
37 1
 
4.3%
76 1
 
4.3%
12 1
 
4.3%
Other values (4) 4
17.4%
ValueCountFrequency (%)
0 7
30.4%
7 1
 
4.3%
9 1
 
4.3%
12 1
 
4.3%
15 2
 
8.7%
26 1
 
4.3%
28 2
 
8.7%
37 1
 
4.3%
42 1
 
4.3%
45 1
 
4.3%
ValueCountFrequency (%)
82 1
4.3%
76 1
4.3%
61 1
4.3%
47 1
4.3%
45 1
4.3%
42 1
4.3%
37 1
4.3%
28 2
8.7%
26 1
4.3%
15 2
8.7%

전문대 대원수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)45.5%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean20.181818
Minimum0
Maximum39
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:52:51.178526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q118
median19
Q320
95-th percentile35.55
Maximum39
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.300596
Coefficient of variation (CV)0.36174124
Kurtosis4.4769155
Mean20.181818
Median Absolute Deviation (MAD)1
Skewness0.34560852
Sum444
Variance53.298701
MonotonicityNot monotonic
2023-12-12T22:52:51.324130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
19 5
21.7%
20 5
21.7%
17 4
17.4%
18 2
 
8.7%
22 1
 
4.3%
39 1
 
4.3%
36 1
 
4.3%
21 1
 
4.3%
0 1
 
4.3%
27 1
 
4.3%
(Missing) 1
 
4.3%
ValueCountFrequency (%)
0 1
 
4.3%
17 4
17.4%
18 2
 
8.7%
19 5
21.7%
20 5
21.7%
21 1
 
4.3%
22 1
 
4.3%
27 1
 
4.3%
36 1
 
4.3%
39 1
 
4.3%
ValueCountFrequency (%)
39 1
 
4.3%
36 1
 
4.3%
27 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 5
21.7%
19 5
21.7%
18 2
 
8.7%
17 4
17.4%
0 1
 
4.3%

전담대 대원수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
17
 
1
27
 
1
26
 
1

Length

Max length4
Median length4
Mean length3.7391304
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
87.0%
17 1
 
4.3%
27 1
 
4.3%
26 1
 
4.3%

Length

2023-12-12T22:52:51.502106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:52:51.645151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
17 1
 
4.3%
27 1
 
4.3%
26 1
 
4.3%

Interactions

2023-12-12T22:52:46.735291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:44.947850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.345316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.821328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.242865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.819803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.025751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.441474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.908319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.338272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.917290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.104388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.529969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.997179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.453740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:47.000253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.184313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.629363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.065483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.577732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:47.099965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.258423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:45.722903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.139502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:52:46.652995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:52:51.735331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명남성연합회장여성연합회장본대지역대전문대본대 대원수지역대 대원수전문대 대원수전담대 대원수
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
남성연합회장1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
여성연합회장1.0001.0001.0000.5870.9521.0001.0000.9441.0001.000
본대1.0001.0000.5871.0000.7900.4580.7620.3400.6861.000
지역대1.0001.0000.9520.7901.0000.0000.7460.9350.0001.000
전문대1.0001.0001.0000.4580.0001.0000.3280.0001.0001.000
본대 대원수1.0001.0001.0000.7620.7460.3281.0000.5380.7461.000
지역대 대원수1.0001.0000.9440.3400.9350.0000.5381.0000.0001.000
전문대 대원수1.0001.0001.0000.6860.0001.0000.7460.0001.0001.000
전담대 대원수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T22:52:51.901317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전담대 대원수전담대전문대
전담대 대원수1.0001.0001.000
전담대1.0001.0001.000
전문대1.0001.0001.000
2023-12-12T22:52:52.011014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본대지역대본대 대원수지역대 대원수전문대 대원수전문대전담대전담대 대원수
본대1.0000.4330.8810.3770.0550.2531.0001.000
지역대0.4331.0000.2610.989-0.0810.0001.0001.000
본대 대원수0.8810.2611.0000.2410.0140.0001.0001.000
지역대 대원수0.3770.9890.2411.0000.0080.0001.0001.000
전문대 대원수0.055-0.0810.0140.0081.0000.9461.0001.000
전문대0.2530.0000.0000.0000.9461.0001.0001.000
전담대1.0001.0001.0001.0001.0001.0001.0001.000
전담대 대원수1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T22:52:47.240858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:52:47.404641image/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.
2023-12-12T22:52:47.555197image/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

시군명남성연합회장여성연합회장본대지역대전문대전담대본대 대원수지역대 대원수전문대 대원수전담대 대원수
0목포시박정태김경숙411<NA>156917<NA>
1여수시김형준윤시숙1671<NA>3648222<NA>
2순천시최낙삼조유진2451<NA>4236119<NA>
3나주시조승만류정자3002<NA>612039<NA>
4광양시권용일노은순1801<NA>458017<NA>
5담양군윤중천천양례2412<NA>4771536<NA>
6보성군고영백고미정2441<NA>4924517<NA>
7해남군박성주백미실2831<NA>6303719<NA>
8영암군김영준김순심2201<NA>489018<NA>
9영광군이종두박영화2211<NA>4821519<NA>
시군명남성연합회장여성연합회장본대지역대전문대전담대본대 대원수지역대 대원수전문대 대원수전담대 대원수
13고흥군박춘재오영애3261<NA>6017620<NA>
14함평군박경섭유덕희1801<NA>356020<NA>
15장성군최상복이애경2221<NA>4282820<NA>
16장흥군정종호이정경2011<NA>4401219<NA>
17완도군김기욱백영선23311567422117
18신안군정정두최설임2730156347027
19진도군이현배오목화1301130902726
20구례군이태용박금숙1611<NA>292717<NA>
21곡성군허화중김경숙2221<NA>4032619<NA>
22<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>