Overview

Dataset statistics

Number of variables9
Number of observations39
Missing cells42
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory79.4 B

Variable types

Numeric4
Categorical2
Text3

Dataset

Description전라북도교육청 폐교재산 현황에 대한 데이터로 지역, 폐교명, 위치, 토지면적, 건물면적, 관리계획 등의 항목을 제공합니다.
Author전라북도교육청
URLhttps://www.data.go.kr/data/15021709/fileData.do

Alerts

순 is highly overall correlated with 지역 and 1 other fieldsHigh correlation
폐교년도 is highly overall correlated with 토지면적 and 1 other fieldsHigh correlation
토지면적 is highly overall correlated with 폐교년도 and 1 other fieldsHigh correlation
건물면적 is highly overall correlated with 폐교년도 and 1 other fieldsHigh correlation
지역 is highly overall correlated with 순 and 1 other fieldsHigh correlation
관리계획 is highly overall correlated with 순 and 1 other fieldsHigh correlation
건물면적 has 7 (17.9%) missing valuesMissing
비고 has 35 (89.7%) missing valuesMissing
순 has unique valuesUnique
폐교명 has unique valuesUnique
위치 has unique valuesUnique
토지면적 has unique valuesUnique

Reproduction

Analysis started2023-12-16 15:35:07.815314
Analysis finished2023-12-16 15:35:19.549946
Duration11.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-16T15:35:20.049102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q110.5
median20
Q329.5
95-th percentile37.1
Maximum39
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.57008771
Kurtosis-1.2
Mean20
Median Absolute Deviation (MAD)10
Skewness0
Sum780
Variance130
MonotonicityStrictly increasing
2023-12-16T15:35:20.836391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 1
 
2.6%
2 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
30 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
39 1
2.6%
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%

지역
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
부안
8 
군산
6 
익산
5 
정읍
4 
임실
3 
Other values (9)
13 

Length

Max length3
Median length2
Mean length2.1025641
Min length2

Unique

Unique5 ?
Unique (%)12.8%

Sample

1st row전주
2nd row군산
3rd row군산
4th row군산
5th row군산

Common Values

ValueCountFrequency (%)
부안 8
20.5%
군산 6
15.4%
익산 5
12.8%
정읍 4
10.3%
임실 3
 
7.7%
남원 2
 
5.1%
무주 2
 
5.1%
순창 2
 
5.1%
부안　 2
 
5.1%
전주 1
 
2.6%
Other values (4) 4
10.3%

Length

2023-12-16T15:35:21.571961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부안 10
25.6%
군산 6
15.4%
익산 5
12.8%
정읍 4
 
10.3%
임실 3
 
7.7%
무주 3
 
7.7%
남원 2
 
5.1%
순창 2
 
5.1%
전주 1
 
2.6%
완주 1
 
2.6%
Other values (2) 2
 
5.1%

폐교명
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-16T15:35:22.213319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length4.6153846
Min length3

Characters and Unicode

Total characters180
Distinct characters67
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

Unique39 ?
Unique (%)100.0%

Sample

1st row도강초
2nd row해성초 내초분교
3rd row대야초광산분교
4th row동산중
5th row선유도초 명도분교
ValueCountFrequency (%)
위도초 3
 
6.1%
주산초 2
 
4.1%
선유도초 2
 
4.1%
도강초 1
 
2.0%
대성고 1
 
2.0%
석계분교 1
 
2.0%
원촌초 1
 
2.0%
관촌동초 1
 
2.0%
오궁초 1
 
2.0%
임실서초 1
 
2.0%
Other values (35) 35
71.4%
2023-12-16T15:35:23.931869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
초 36
20.0%
도 11
 
6.1%
분 11
 
6.1%
교 11
 
6.1%
10
 
5.6%
성 6
 
3.3%
산 6
 
3.3%
여 3
 
1.7%
주 3
 
1.7%
촌 3
 
1.7%
Other values (57) 80
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
94.4%
Space Separator 10
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
초 36
21.2%
도 11
 
6.5%
분 11
 
6.5%
교 11
 
6.5%
성 6
 
3.5%
산 6
 
3.5%
여 3
 
1.8%
주 3
 
1.8%
촌 3
 
1.8%
청 3
 
1.8%
Other values (56) 77
45.3%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
94.4%
Common 10
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
초 36
21.2%
도 11
 
6.5%
분 11
 
6.5%
교 11
 
6.5%
성 6
 
3.5%
산 6
 
3.5%
여 3
 
1.8%
주 3
 
1.8%
촌 3
 
1.8%
청 3
 
1.8%
Other values (56) 77
45.3%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
94.4%
ASCII 10
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
초 36
21.2%
도 11
 
6.5%
분 11
 
6.5%
교 11
 
6.5%
성 6
 
3.5%
산 6
 
3.5%
여 3
 
1.8%
주 3
 
1.8%
촌 3
 
1.8%
청 3
 
1.8%
Other values (56) 77
45.3%
ASCII
ValueCountFrequency (%)
10
100.0%

폐교년도
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.4615
Minimum1991
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-16T15:35:24.631555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1991.9
Q11995
median2003
Q32010
95-th percentile2020.3
Maximum2023
Range32
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.8243686
Coefficient of variation (CV)0.0049036971
Kurtosis-0.92307129
Mean2003.4615
Median Absolute Deviation (MAD)7
Skewness0.43071095
Sum78135
Variance96.518219
MonotonicityNot monotonic
2023-12-16T15:35:25.351803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1992 6
15.4%
2010 4
 
10.3%
1999 3
 
7.7%
1991 2
 
5.1%
2023 2
 
5.1%
2004 2
 
5.1%
2009 2
 
5.1%
1995 2
 
5.1%
1997 2
 
5.1%
2006 1
 
2.6%
Other values (13) 13
33.3%
ValueCountFrequency (%)
1991 2
 
5.1%
1992 6
15.4%
1993 1
 
2.6%
1995 2
 
5.1%
1996 1
 
2.6%
1997 2
 
5.1%
1998 1
 
2.6%
1999 3
7.7%
2000 1
 
2.6%
2003 1
 
2.6%
ValueCountFrequency (%)
2023 2
5.1%
2020 1
 
2.6%
2019 1
 
2.6%
2018 1
 
2.6%
2017 1
 
2.6%
2012 1
 
2.6%
2011 1
 
2.6%
2010 4
10.3%
2009 2
5.1%
2008 1
 
2.6%

위치
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-16T15:35:26.717872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length12.076923
Min length10

Characters and Unicode

Total characters471
Distinct characters104
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row덕진구 강흥동 124-2
2nd row군산시 내초동 66
3rd row군산시 대야면 접산리 391-15
4th row군산시 금동 26-83
5th row옥도면 말도 산115-34
ValueCountFrequency (%)
군산시 3
 
2.5%
위도면 3
 
2.5%
옥도면 3
 
2.5%
주산면 2
 
1.7%
여산면 2
 
1.7%
고부면 2
 
1.7%
관청리 2
 
1.7%
덕진구 1
 
0.8%
325 1
 
0.8%
대산면 1
 
0.8%
Other values (98) 98
83.1%
2023-12-16T15:35:29.403957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
 
16.8%
리 34
 
7.2%
면 33
 
7.0%
1 28
 
5.9%
- 18
 
3.8%
5 18
 
3.8%
산 17
 
3.6%
2 16
 
3.4%
4 15
 
3.2%
7 14
 
3.0%
Other values (94) 199
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 243
51.6%
Decimal Number 131
27.8%
Space Separator 79
 
16.8%
Dash Punctuation 18
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 34
 
14.0%
면 33
 
13.6%
산 17
 
7.0%
도 10
 
4.1%
동 8
 
3.3%
주 4
 
1.6%
관 3
 
1.2%
여 3
 
1.2%
포 3
 
1.2%
안 3
 
1.2%
Other values (82) 125
51.4%
Decimal Number
ValueCountFrequency (%)
1 28
21.4%
5 18
13.7%
2 16
12.2%
4 15
11.5%
7 14
10.7%
3 14
10.7%
0 8
 
6.1%
6 8
 
6.1%
8 6
 
4.6%
9 4
 
3.1%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 243
51.6%
Common 228
48.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 34
 
14.0%
면 33
 
13.6%
산 17
 
7.0%
도 10
 
4.1%
동 8
 
3.3%
주 4
 
1.6%
관 3
 
1.2%
여 3
 
1.2%
포 3
 
1.2%
안 3
 
1.2%
Other values (82) 125
51.4%
Common
ValueCountFrequency (%)
79
34.6%
1 28
 
12.3%
- 18
 
7.9%
5 18
 
7.9%
2 16
 
7.0%
4 15
 
6.6%
7 14
 
6.1%
3 14
 
6.1%
0 8
 
3.5%
6 8
 
3.5%
Other values (2) 10
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 243
51.6%
ASCII 228
48.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
34.6%
1 28
 
12.3%
- 18
 
7.9%
5 18
 
7.9%
2 16
 
7.0%
4 15
 
6.6%
7 14
 
6.1%
3 14
 
6.1%
0 8
 
3.5%
6 8
 
3.5%
Other values (2) 10
 
4.4%
Hangul
ValueCountFrequency (%)
리 34
 
14.0%
면 33
 
13.6%
산 17
 
7.0%
도 10
 
4.1%
동 8
 
3.3%
주 4
 
1.6%
관 3
 
1.2%
여 3
 
1.2%
포 3
 
1.2%
안 3
 
1.2%
Other values (82) 125
51.4%

토지면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12260.608
Minimum198
Maximum35385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-16T15:35:30.672171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile1108.1
Q19390.5
median12119
Q314721
95-th percentile20306.9
Maximum35385
Range35187
Interquartile range (IQR)5330.5

Descriptive statistics

Standard deviation6760.6392
Coefficient of variation (CV)0.55141143
Kurtosis2.9203591
Mean12260.608
Median Absolute Deviation (MAD)2778
Skewness0.83892803
Sum478163.7
Variance45706243
MonotonicityNot monotonic
2023-12-16T15:35:31.789221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7922.0 1
 
2.6%
4398.8 1
 
2.6%
10973.0 1
 
2.6%
7795.0 1
 
2.6%
10323.0 1
 
2.6%
12739.0 1
 
2.6%
11456.0 1
 
2.6%
16477.0 1
 
2.6%
19004.0 1
 
2.6%
12005.0 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
198.0 1
2.6%
992.0 1
2.6%
1121.0 1
2.6%
1689.0 1
2.6%
2678.0 1
2.6%
4398.8 1
2.6%
7469.0 1
2.6%
7795.0 1
2.6%
7922.0 1
2.6%
8806.0 1
2.6%
ValueCountFrequency (%)
35385.0 1
2.6%
26858.0 1
2.6%
19579.0 1
2.6%
19004.0 1
2.6%
18686.0 1
2.6%
17449.0 1
2.6%
16477.0 1
2.6%
15797.0 1
2.6%
15773.0 1
2.6%
14897.0 1
2.6%

건물면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing7
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean1503.53
Minimum508.3
Maximum7577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-16T15:35:33.318543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum508.3
5-th percentile540.6155
Q1910.13
median1229.9
Q31697.555
95-th percentile2524.7495
Maximum7577
Range7068.7
Interquartile range (IQR)787.425

Descriptive statistics

Standard deviation1232.8543
Coefficient of variation (CV)0.8199732
Kurtosis19.865906
Mean1503.53
Median Absolute Deviation (MAD)392.195
Skewness4.062052
Sum48112.96
Variance1519929.7
MonotonicityNot monotonic
2023-12-16T15:35:34.039490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1121.97 1
 
2.6%
1257.34 1
 
2.6%
2025.8 1
 
2.6%
1732.91 1
 
2.6%
833.65 1
 
2.6%
509.04 1
 
2.6%
566.45 1
 
2.6%
930.84 1
 
2.6%
2505.62 1
 
2.6%
2548.13 1
 
2.6%
Other values (22) 22
56.4%
(Missing) 7
 
17.9%
ValueCountFrequency (%)
508.3 1
2.6%
509.04 1
2.6%
566.45 1
2.6%
670.69 1
2.6%
752.5 1
2.6%
755.84 1
2.6%
833.65 1
2.6%
848.0 1
2.6%
930.84 1
2.6%
988.72 1
2.6%
ValueCountFrequency (%)
7577.0 1
2.6%
2548.13 1
2.6%
2505.62 1
2.6%
2064.35 1
2.6%
2025.8 1
2.6%
2019.18 1
2.6%
1782.0 1
2.6%
1732.91 1
2.6%
1685.77 1
2.6%
1618.04 1
2.6%

관리계획
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
자체활용
23 
대부
9 
보존
6 
매각예정
 
1

Length

Max length4
Median length4
Mean length3.2307692
Min length2

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row자체활용
2nd row자체활용
3rd row자체활용
4th row자체활용
5th row보존

Common Values

ValueCountFrequency (%)
자체활용 23
59.0%
대부 9
 
23.1%
보존 6
 
15.4%
매각예정 1
 
2.6%

Length

2023-12-16T15:35:34.665407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:35:35.319190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자체활용 23
59.0%
대부 9
 
23.1%
보존 6
 
15.4%
매각예정 1
 
2.6%

비고
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing35
Missing (%)89.7%
Memory size444.0 B
2023-12-16T15:35:35.963330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters12
Distinct characters4
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

Unique0 ?
Unique (%)0.0%

Sample

1st row이전
2nd row통합이전
3rd row이전
4th row통합이전
ValueCountFrequency (%)
이전 2
50.0%
통합이전 2
50.0%
2023-12-16T15:35:37.572336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 4
33.3%
전 4
33.3%
통 2
16.7%
합 2
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 4
33.3%
전 4
33.3%
통 2
16.7%
합 2
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 4
33.3%
전 4
33.3%
통 2
16.7%
합 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 4
33.3%
전 4
33.3%
통 2
16.7%
합 2
16.7%

Interactions

2023-12-16T15:35:16.235980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:09.273177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:11.285837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:13.548902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:16.621134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:10.018121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:11.741022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:14.310027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:17.019119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:10.613745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:12.093628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:14.845564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:17.412757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:10.923752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:12.836031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:35:15.584748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:35:38.377692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순지역폐교명폐교년도위치토지면적건물면적관리계획비고
순1.0000.9341.0000.5491.0000.5090.6230.8101.000
지역0.9341.0001.0000.3901.0000.3500.8330.9291.000
폐교명1.0001.0001.0001.0001.0001.0001.0001.0001.000
폐교년도0.5490.3901.0001.0001.0000.6860.8500.7341.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.000
토지면적0.5090.3501.0000.6861.0001.0000.8000.8020.000
건물면적0.6230.8331.0000.8501.0000.8001.0000.0000.000
관리계획0.8100.9291.0000.7341.0000.8020.0001.0000.000
비고1.0001.0001.0001.0001.0000.0000.0000.0001.000
2023-12-16T15:35:39.280392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역관리계획
지역1.0000.689
관리계획0.6891.000
2023-12-16T15:35:39.662406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순폐교년도토지면적건물면적지역관리계획
순1.000-0.380-0.137-0.0810.6900.549
폐교년도-0.3801.0000.5660.5170.0000.484
토지면적-0.1370.5661.0000.6760.0000.452
건물면적-0.0810.5170.6761.0000.3280.000
지역0.6900.0000.0000.3281.0000.689
관리계획0.5490.4840.4520.0000.6891.000

Missing values

2023-12-16T15:35:18.029961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:35:18.827636image/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.
2023-12-16T15:35:19.334780image/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

순지역폐교명폐교년도위치토지면적건물면적관리계획비고
01전주도강초1999덕진구 강흥동 124-27922.0848.0자체활용<NA>
12군산해성초 내초분교2017군산시 내초동 664398.8508.3자체활용<NA>
23군산대야초광산분교2023군산시 대야면 접산리 391-1517449.0<NA>자체활용<NA>
34군산동산중2023군산시 금동 26-8326858.07577.0자체활용이전
45군산선유도초 명도분교1992옥도면 말도 산115-341689.0<NA>보존<NA>
56군산선유도초 방축도분교1992옥도면 방축도 산130-12678.0<NA>보존<NA>
67군산어청도초 연도분교2003옥도면 연도리 377469.0<NA>보존<NA>
78익산웅북초2004웅포면 제성리 557-113955.91384.2자체활용<NA>
89익산여산서초2009여산면 두여리 1184-212119.01543.5자체활용<NA>
910익산여산남초2009여산면 원수리 465-414545.01685.77자체활용<NA>
순지역폐교명폐교년도위치토지면적건물면적관리계획비고
2930부안난신초1997줄포면 신리 30012005.0930.84대부<NA>
3031부안주산초 석계분교1998주산면 백석리 579-712438.0566.45대부<NA>
3132부안주산초 덕림분교1999주산면 덕림리 487-3012334.0509.04대부<NA>
3233부안마포초1999변산면 마포리 1729975.0833.65대부<NA>
3334부안의복초2000계화면 의복리 46013099.01732.91대부<NA>
3435부안고성초2006행안면 삼간리 405-119579.02025.8대부<NA>
3536부안보안초2010보안면 하입석리 573-211640.01257.34대부<NA>
3637부안위도초 상왕분교1992위도면 상왕등리 산143198.0<NA>보존<NA>
3738부안위도초 하왕분교1992위도면 하왕등리 산711121.0<NA>보존<NA>
3839부안위도초 거륜분교1992위도면 거륜리 산2-1992.0<NA>보존<NA>