Overview

Dataset statistics

Number of variables9
Number of observations25
Missing cells2
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory81.3 B

Variable types

Text4
Numeric4
Categorical1

Dataset

Description전라남도 시군의 수영장(시설명, 소유기관, 관리주체, 면적, 길이, 폭 등)에 관한 데이터를 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15037312/fileData.do

Alerts

실내외 구분 has constant value ""Constant
건축면적 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 건축면적High correlation
부지면적 has 2 (8.0%) missing valuesMissing
시설명 has unique valuesUnique
연면적 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:15:47.003298
Analysis finished2023-12-12 12:15:49.747363
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Text

Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T21:15:49.903206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.08
Min length3

Characters and Unicode

Total characters77
Distinct characters33
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

Unique17 ?
Unique (%)68.0%

Sample

1st row목포시
2nd row여수시
3rd row여수시
4th row순천시
5th row순천시
ValueCountFrequency (%)
순천시 3
 
12.0%
광양시 3
 
12.0%
여수시 2
 
8.0%
해남군 1
 
4.0%
목포시 1
 
4.0%
강진군 1
 
4.0%
완도군 1
 
4.0%
장성군 1
 
4.0%
영광군 1
 
4.0%
무안군 1
 
4.0%
Other values (10) 10
40.0%
2023-12-12T21:15:50.299469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 15
19.5%
시 10
 
13.0%
순 4
 
5.2%
광 4
 
5.2%
양 4
 
5.2%
천 3
 
3.9%
성 3
 
3.9%
여 2
 
2.6%
수 2
 
2.6%
장 2
 
2.6%
Other values (23) 28
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75
97.4%
Space Separator 2
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 15
20.0%
시 10
 
13.3%
순 4
 
5.3%
광 4
 
5.3%
양 4
 
5.3%
천 3
 
4.0%
성 3
 
4.0%
여 2
 
2.7%
수 2
 
2.7%
장 2
 
2.7%
Other values (22) 26
34.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75
97.4%
Common 2
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 15
20.0%
시 10
 
13.3%
순 4
 
5.3%
광 4
 
5.3%
양 4
 
5.3%
천 3
 
4.0%
성 3
 
4.0%
여 2
 
2.7%
수 2
 
2.7%
장 2
 
2.7%
Other values (22) 26
34.7%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75
97.4%
ASCII 2
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 15
20.0%
시 10
 
13.3%
순 4
 
5.3%
광 4
 
5.3%
양 4
 
5.3%
천 3
 
4.0%
성 3
 
4.0%
여 2
 
2.7%
수 2
 
2.7%
장 2
 
2.7%
Other values (22) 26
34.7%
ASCII
ValueCountFrequency (%)
2
100.0%

시설명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T21:15:50.574991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.12
Min length5

Characters and Unicode

Total characters228
Distinct characters61
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

Unique25 ?
Unique (%)100.0%

Sample

1st row실내 수영장
2nd row여수국민체육센터수영장
3rd row진남수영장
4th row팔마 수영장
5th row신대유청소년수영장
ValueCountFrequency (%)
수영장 4
 
13.3%
실내 1
 
3.3%
구례군민체육센터수영장 1
 
3.3%
완도실내수영장 1
 
3.3%
장성실내수영장 1
 
3.3%
영광실내수영장 1
 
3.3%
무안스포츠파크수영장 1
 
3.3%
영암국민체육센터수영장 1
 
3.3%
우슬국민체육센터수영장 1
 
3.3%
강진국민체육센터수영장 1
 
3.3%
Other values (17) 17
56.7%
2023-12-12T21:15:51.020201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 27
 
11.8%
영 27
 
11.8%
장 27
 
11.8%
센 12
 
5.3%
터 12
 
5.3%
민 10
 
4.4%
육 9
 
3.9%
체 9
 
3.9%
국 7
 
3.1%
내 6
 
2.6%
Other values (51) 82
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 222
97.4%
Space Separator 6
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 27
 
12.2%
영 27
 
12.2%
장 27
 
12.2%
센 12
 
5.4%
터 12
 
5.4%
민 10
 
4.5%
육 9
 
4.1%
체 9
 
4.1%
국 7
 
3.2%
내 6
 
2.7%
Other values (50) 76
34.2%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 222
97.4%
Common 6
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 27
 
12.2%
영 27
 
12.2%
장 27
 
12.2%
센 12
 
5.4%
터 12
 
5.4%
민 10
 
4.5%
육 9
 
4.1%
체 9
 
4.1%
국 7
 
3.2%
내 6
 
2.7%
Other values (50) 76
34.2%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 222
97.4%
ASCII 6
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 27
 
12.2%
영 27
 
12.2%
장 27
 
12.2%
센 12
 
5.4%
터 12
 
5.4%
민 10
 
4.5%
육 9
 
4.1%
체 9
 
4.1%
국 7
 
3.2%
내 6
 
2.7%
Other values (50) 76
34.2%
ASCII
ValueCountFrequency (%)
6
100.0%

소유기관
Text

Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T21:15:51.273470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.16
Min length3

Characters and Unicode

Total characters79
Distinct characters36
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

Unique17 ?
Unique (%)68.0%

Sample

1st row목포시
2nd row여수시
3rd row여수시
4th row순천시
5th row순천시
ValueCountFrequency (%)
순천시 3
 
12.0%
광양시 3
 
12.0%
여수시 2
 
8.0%
해남군 1
 
4.0%
목포시 1
 
4.0%
강진군 1
 
4.0%
완도군 1
 
4.0%
장성군 1
 
4.0%
영광군 1
 
4.0%
무안군 1
 
4.0%
Other values (10) 10
40.0%
2023-12-12T21:15:51.772543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 14
17.7%
시 10
 
12.7%
순 4
 
5.1%
광 4
 
5.1%
양 4
 
5.1%
성 3
 
3.8%
천 3
 
3.8%
흥 2
 
2.5%
영 2
 
2.5%
2
 
2.5%
Other values (26) 31
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
97.5%
Space Separator 2
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 14
18.2%
시 10
 
13.0%
순 4
 
5.2%
광 4
 
5.2%
양 4
 
5.2%
성 3
 
3.9%
천 3
 
3.9%
흥 2
 
2.6%
영 2
 
2.6%
도 2
 
2.6%
Other values (25) 29
37.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
97.5%
Common 2
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 14
18.2%
시 10
 
13.0%
순 4
 
5.2%
광 4
 
5.2%
양 4
 
5.2%
성 3
 
3.9%
천 3
 
3.9%
흥 2
 
2.6%
영 2
 
2.6%
도 2
 
2.6%
Other values (25) 29
37.7%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
97.5%
ASCII 2
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 14
18.2%
시 10
 
13.0%
순 4
 
5.2%
광 4
 
5.2%
양 4
 
5.2%
성 3
 
3.9%
천 3
 
3.9%
흥 2
 
2.6%
영 2
 
2.6%
도 2
 
2.6%
Other values (25) 29
37.7%
ASCII
ValueCountFrequency (%)
2
100.0%

관리주체
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T21:15:52.031230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length5.04
Min length3

Characters and Unicode

Total characters126
Distinct characters53
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)80.0%

Sample

1st row체육시설관리과
2nd row여수시
3rd row여수시
4th row체육시설관리소
5th row체육시설관리소
ValueCountFrequency (%)
체육시설관리소 3
 
12.0%
여수시 2
 
8.0%
체육시설관리과 1
 
4.0%
화순군 1
 
4.0%
완도군(체육진흥과 1
 
4.0%
장성군(문화시설사업소 1
 
4.0%
영광군 1
 
4.0%
무안군 1
 
4.0%
영암군 1
 
4.0%
해남군 1
 
4.0%
Other values (12) 12
48.0%
2023-12-12T21:15:52.440347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 14
 
11.1%
시 10
 
7.9%
설 6
 
4.8%
육 6
 
4.8%
체 5
 
4.0%
관 5
 
4.0%
리 5
 
4.0%
( 4
 
3.2%
) 4
 
3.2%
소 4
 
3.2%
Other values (43) 63
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110
87.3%
Open Punctuation 4
 
3.2%
Close Punctuation 4
 
3.2%
Uppercase Letter 4
 
3.2%
Space Separator 3
 
2.4%
Connector Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 14
 
12.7%
시 10
 
9.1%
설 6
 
5.5%
육 6
 
5.5%
체 5
 
4.5%
관 5
 
4.5%
리 5
 
4.5%
소 4
 
3.6%
흥 3
 
2.7%
과 3
 
2.7%
Other values (35) 49
44.5%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
M 1
25.0%
Y 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 110
87.3%
Common 12
 
9.5%
Latin 4
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 14
 
12.7%
시 10
 
9.1%
설 6
 
5.5%
육 6
 
5.5%
체 5
 
4.5%
관 5
 
4.5%
리 5
 
4.5%
소 4
 
3.6%
흥 3
 
2.7%
과 3
 
2.7%
Other values (35) 49
44.5%
Common
ValueCountFrequency (%)
( 4
33.3%
) 4
33.3%
3
25.0%
_ 1
 
8.3%
Latin
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
M 1
25.0%
Y 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 110
87.3%
ASCII 16
 
12.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 14
 
12.7%
시 10
 
9.1%
설 6
 
5.5%
육 6
 
5.5%
체 5
 
4.5%
관 5
 
4.5%
리 5
 
4.5%
소 4
 
3.6%
흥 3
 
2.7%
과 3
 
2.7%
Other values (35) 49
44.5%
ASCII
ValueCountFrequency (%)
( 4
25.0%
) 4
25.0%
3
18.8%
_ 1
 
6.2%
A 1
 
6.2%
C 1
 
6.2%
M 1
 
6.2%
Y 1
 
6.2%

부지면적
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)100.0%
Missing2
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean19891.13
Minimum1576
Maximum156000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T21:15:52.584229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1576
5-th percentile1891.4
Q16621.5
median10403
Q314632.5
95-th percentile83798.8
Maximum156000
Range154424
Interquartile range (IQR)8011

Descriptive statistics

Standard deviation34542.098
Coefficient of variation (CV)1.7365578
Kurtosis12.016201
Mean19891.13
Median Absolute Deviation (MAD)4597
Skewness3.42504
Sum457496
Variance1.1931565 × 109
MonotonicityNot monotonic
2023-12-12T21:15:52.795339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
156000 1
 
4.0%
9491 1
 
4.0%
3200 1
 
4.0%
8021 1
 
4.0%
1874 1
 
4.0%
4245 1
 
4.0%
12491 1
 
4.0%
10403 1
 
4.0%
1576 1
 
4.0%
2048 1
 
4.0%
Other values (13) 13
52.0%
(Missing) 2
 
8.0%
ValueCountFrequency (%)
1576 1
4.0%
1874 1
4.0%
2048 1
4.0%
3200 1
4.0%
4245 1
4.0%
5222 1
4.0%
8021 1
4.0%
8691 1
4.0%
9491 1
4.0%
9830 1
4.0%
ValueCountFrequency (%)
156000 1
4.0%
90556 1
4.0%
22984 1
4.0%
21203 1
4.0%
15619 1
4.0%
15000 1
4.0%
14265 1
4.0%
12939 1
4.0%
12491 1
4.0%
11300 1
4.0%

건축면적
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2179.28
Minimum591
Maximum4635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T21:15:52.956990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum591
5-th percentile1019
Q11567
median1832
Q32289
95-th percentile4512.2
Maximum4635
Range4044
Interquartile range (IQR)722

Descriptive statistics

Standard deviation1168.7142
Coefficient of variation (CV)0.53628455
Kurtosis0.1894363
Mean2179.28
Median Absolute Deviation (MAD)296
Skewness1.1846085
Sum54482
Variance1365892.9
MonotonicityNot monotonic
2023-12-12T21:15:53.102947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1612 2
 
8.0%
4579 1
 
4.0%
591 1
 
4.0%
1834 1
 
4.0%
1536 1
 
4.0%
1874 1
 
4.0%
4245 1
 
4.0%
2289 1
 
4.0%
1973 1
 
4.0%
1576 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
591 1
4.0%
1009 1
4.0%
1059 1
4.0%
1357 1
4.0%
1391 1
4.0%
1536 1
4.0%
1567 1
4.0%
1576 1
4.0%
1612 2
8.0%
1690 1
4.0%
ValueCountFrequency (%)
4635 1
4.0%
4579 1
4.0%
4245 1
4.0%
4198 1
4.0%
3947 1
4.0%
2656 1
4.0%
2289 1
4.0%
1973 1
4.0%
1874 1
4.0%
1854 1
4.0%

연면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4229.6
Minimum1576
Maximum12895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T21:15:53.266871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1576
5-th percentile1709.8
Q12752
median3323
Q34847
95-th percentile7722.8
Maximum12895
Range11319
Interquartile range (IQR)2095

Descriptive statistics

Standard deviation2528.8137
Coefficient of variation (CV)0.59788484
Kurtosis4.6295705
Mean4229.6
Median Absolute Deviation (MAD)1166
Skewness1.8757216
Sum105740
Variance6394898.8
MonotonicityNot monotonic
2023-12-12T21:15:53.425660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6486 1
 
4.0%
3299 1
 
4.0%
3323 1
 
4.0%
2752 1
 
4.0%
2450 1
 
4.0%
2998 1
 
4.0%
4245 1
 
4.0%
4847 1
 
4.0%
2997 1
 
4.0%
1576 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1576 1
4.0%
1681 1
4.0%
1825 1
4.0%
2068 1
4.0%
2157 1
4.0%
2450 1
4.0%
2752 1
4.0%
2997 1
4.0%
2998 1
4.0%
3082 1
4.0%
ValueCountFrequency (%)
12895 1
4.0%
7840 1
4.0%
7254 1
4.0%
6496 1
4.0%
6486 1
4.0%
6095 1
4.0%
4847 1
4.0%
4788 1
4.0%
4367 1
4.0%
4245 1
4.0%

실내외 구분
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
실내
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
실내 25
100.0%

Length

2023-12-12T21:15:53.601232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:15:53.711993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실내 25
100.0%

준공연도
Real number (ℝ)

Distinct18
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.32
Minimum1987
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T21:15:53.841160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile1992.8
Q12006
median2010
Q32015
95-th percentile2018
Maximum2021
Range34
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.0917654
Coefficient of variation (CV)0.0040271163
Kurtosis1.7689491
Mean2009.32
Median Absolute Deviation (MAD)5
Skewness-1.2064318
Sum50233
Variance65.476667
MonotonicityNot monotonic
2023-12-12T21:15:53.992707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2018 3
 
12.0%
2011 3
 
12.0%
2010 3
 
12.0%
2009 2
 
8.0%
2015 1
 
4.0%
2012 1
 
4.0%
2016 1
 
4.0%
2008 1
 
4.0%
2002 1
 
4.0%
2000 1
 
4.0%
Other values (8) 8
32.0%
ValueCountFrequency (%)
1987 1
 
4.0%
1991 1
 
4.0%
2000 1
 
4.0%
2002 1
 
4.0%
2004 1
 
4.0%
2005 1
 
4.0%
2006 1
 
4.0%
2008 1
 
4.0%
2009 2
8.0%
2010 3
12.0%
ValueCountFrequency (%)
2021 1
 
4.0%
2018 3
12.0%
2017 1
 
4.0%
2016 1
 
4.0%
2015 1
 
4.0%
2014 1
 
4.0%
2012 1
 
4.0%
2011 3
12.0%
2010 3
12.0%
2009 2
8.0%

Interactions

2023-12-12T21:15:48.915962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:47.408886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:47.960266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:48.467646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:49.053562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:47.543760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:48.101675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:48.579406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:49.196615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:47.683034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:48.227912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:48.689874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:49.308579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:47.810872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:48.345521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:48.802608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:15:54.130903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구시설명소유기관관리주체부지면적건축면적연면적준공연도
시군구1.0001.0001.0001.0000.0000.7050.0000.000
시설명1.0001.0001.0001.0001.0001.0001.0001.000
소유기관1.0001.0001.0001.0000.0000.7050.0000.000
관리주체1.0001.0001.0001.0000.0000.9460.8700.000
부지면적0.0001.0000.0000.0001.0000.6480.7770.739
건축면적0.7051.0000.7050.9460.6481.0000.8200.408
연면적0.0001.0000.0000.8700.7770.8201.0000.605
준공연도0.0001.0000.0000.0000.7390.4080.6051.000
2023-12-12T21:15:54.271997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부지면적건축면적연면적준공연도
부지면적1.0000.3430.4740.000
건축면적0.3431.0000.6510.172
연면적0.4740.6511.0000.018
준공연도0.0000.1720.0181.000

Missing values

2023-12-12T21:15:49.508755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:15:49.669907image/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

시군구시설명소유기관관리주체부지면적건축면적연면적실내외 구분준공연도
0목포시실내 수영장목포시체육시설관리과1500045796486실내1987
1여수시여수국민체육센터수영장여수시여수시15600016123299실내2010
2여수시진남수영장여수시여수시2298441986496실내2017
3순천시팔마 수영장순천시체육시설관리소1426515677254실내1991
4순천시신대유청소년수영장순천시체육시설관리소1561918543186실내2021
5순천시문화건강센터 수영장순천시체육시설관리소1067816903548실내2014
6나주시나주실내수영장나주시나주시983018533485실내2011
7광양시광양 청소년수련관수영장광양시위탁(YMCA)869110593082실내2006
8광양시커뮤니티센터 수영장광양시위탁(개인)90556463512895실내2004
9광양시광양수영장광양시광양시2120339476095실내2011
시군구시설명소유기관관리주체부지면적건축면적연면적실내외 구분준공연도
15화순군군민종합문화센터수영장화순군화순군522226567840실내2011
16장흥군장흥국민체육센터수영장장흥군장흥군204813574367실내2010
17강진군강진국민체육센터수영장강진군강진군157615761576실내2002
18해남군우슬국민체육센터수영장해남군해남군1040319732997실내2008
19영암군영암국민체육센터수영장영암군영암군1249122894847실내2016
20무안군무안스포츠파크수영장무안군무안군424542454245실내2009
21영광군영광실내수영장영광군영광군187418742998실내2012
22장성군장성실내수영장장성군장성군(문화시설사업소)802115362450실내2015
23완도군완도실내수영장완도군완도군(체육진흥과)320016122752실내2018
24진도군진도국민체육센터수영장진도군진도군949118343323실내2010