Overview

Dataset statistics

Number of variables17
Number of observations24
Missing cells65
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory151.5 B

Variable types

Numeric10
Categorical4
Text3

Dataset

Description경남도내 야구장 현황을 제공합니다. 시군구멸 시설명, 소유기관, 관리주체 부지면적, 건축면적, 경기장형태의 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3080074

Alerts

시도 has constant value ""Constant
연번 is highly overall correlated with 경기장 중앙 길이High correlation
부지면적(㎡) is highly overall correlated with 건축면적(㎡) and 4 other fieldsHigh correlation
건축면적(㎡) is highly overall correlated with 부지면적(㎡) and 5 other fieldsHigh correlation
연면적(㎡) is highly overall correlated with 건축면적(㎡)High correlation
경기장 중앙 길이 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
경기장 1,3루 길이 is highly overall correlated with 경기장 중앙 길이 and 4 other fieldsHigh correlation
경기장 면적(㎡) is highly overall correlated with 부지면적(㎡) and 3 other fieldsHigh correlation
관람석 좌석수 is highly overall correlated with 부지면적(㎡) and 5 other fieldsHigh correlation
관람석 수용인원(명) is highly overall correlated with 부지면적(㎡) and 5 other fieldsHigh correlation
준공연도 is highly overall correlated with 건축면적(㎡) and 2 other fieldsHigh correlation
시군구 is highly overall correlated with 경기장 중앙 길이 and 2 other fieldsHigh correlation
경기장 바닥재료(내야) is highly overall correlated with 부지면적(㎡) and 1 other fieldsHigh correlation
경기장 바닥재료(외야) is highly overall correlated with 경기장 바닥재료(내야)High correlation
부지면적(㎡) has 1 (4.2%) missing valuesMissing
건축면적(㎡) has 18 (75.0%) missing valuesMissing
연면적(㎡) has 15 (62.5%) missing valuesMissing
관람석 좌석수 has 15 (62.5%) missing valuesMissing
관람석 수용인원(명) has 16 (66.7%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:42:17.063545
Analysis finished2023-12-11 00:42:27.090140
Duration10.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:27.143073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-11T09:42:27.251318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
경상남도
24 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 경상남도
2nd row 경상남도
3rd row 경상남도
4th row 경상남도
5th row 경상남도

Common Values

ValueCountFrequency (%)
경상남도 24
100.0%

Length

2023-12-11T09:42:27.355897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:27.428771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 24
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
창원시
김해시
합천군
통영시
사천시
Other values (6)

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique8 ?
Unique (%)33.3%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 8
33.3%
김해시 6
25.0%
합천군 2
 
8.3%
통영시 1
 
4.2%
사천시 1
 
4.2%
밀양시 1
 
4.2%
거제시 1
 
4.2%
의령군 1
 
4.2%
함안군 1
 
4.2%
창녕군 1
 
4.2%

Length

2023-12-11T09:42:27.535529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 8
33.3%
김해시 6
25.0%
합천군 2
 
8.3%
통영시 1
 
4.2%
사천시 1
 
4.2%
밀양시 1
 
4.2%
거제시 1
 
4.2%
의령군 1
 
4.2%
함안군 1
 
4.2%
창녕군 1
 
4.2%

시설명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T09:42:27.684348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length10.166667
Min length5

Characters and Unicode

Total characters244
Distinct characters68
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

Unique24 ?
Unique (%)100.0%

Sample

1st row88올림픽야구장
2nd row마산종합운동장야구장
3rd row진해공설운동장야구장
4th row대산면 사회인야구장(1)
5th row대산면 사회인야구장(2)
ValueCountFrequency (%)
대산면 5
 
13.9%
야구장 3
 
8.3%
대동생태체육공원 2
 
5.6%
합천야구장(a 1
 
2.8%
남해스포츠파크인조야구장 1
 
2.8%
남지체육공원 1
 
2.8%
스포츠파크생활야구장(리틀 1
 
2.8%
함안 1
 
2.8%
의령친환경야구장 1
 
2.8%
하청 1
 
2.8%
Other values (19) 19
52.8%
2023-12-11T09:42:27.969369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
10.7%
24
 
9.8%
24
 
9.8%
) 12
 
4.9%
( 12
 
4.9%
12
 
4.9%
7
 
2.9%
6
 
2.5%
6
 
2.5%
6
 
2.5%
Other values (58) 109
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195
79.9%
Close Punctuation 12
 
4.9%
Open Punctuation 12
 
4.9%
Space Separator 12
 
4.9%
Decimal Number 11
 
4.5%
Uppercase Letter 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
 
13.3%
24
 
12.3%
24
 
12.3%
7
 
3.6%
6
 
3.1%
6
 
3.1%
6
 
3.1%
6
 
3.1%
5
 
2.6%
5
 
2.6%
Other values (47) 80
41.0%
Decimal Number
ValueCountFrequency (%)
1 3
27.3%
2 3
27.3%
8 2
18.2%
3 1
 
9.1%
4 1
 
9.1%
5 1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 195
79.9%
Common 47
 
19.3%
Latin 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
 
13.3%
24
 
12.3%
24
 
12.3%
7
 
3.6%
6
 
3.1%
6
 
3.1%
6
 
3.1%
6
 
3.1%
5
 
2.6%
5
 
2.6%
Other values (47) 80
41.0%
Common
ValueCountFrequency (%)
) 12
25.5%
( 12
25.5%
12
25.5%
1 3
 
6.4%
2 3
 
6.4%
8 2
 
4.3%
3 1
 
2.1%
4 1
 
2.1%
5 1
 
2.1%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 195
79.9%
ASCII 49
 
20.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
26
 
13.3%
24
 
12.3%
24
 
12.3%
7
 
3.6%
6
 
3.1%
6
 
3.1%
6
 
3.1%
6
 
3.1%
5
 
2.6%
5
 
2.6%
Other values (47) 80
41.0%
ASCII
ValueCountFrequency (%)
) 12
24.5%
( 12
24.5%
12
24.5%
1 3
 
6.1%
2 3
 
6.1%
8 2
 
4.1%
A 1
 
2.0%
3 1
 
2.0%
4 1
 
2.0%
5 1
 
2.0%
Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T09:42:28.096337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2083333
Min length3

Characters and Unicode

Total characters77
Distinct characters22
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

Unique9 ?
Unique (%)37.5%

Sample

1st row창원시
2nd row창원시
3rd row창원시
4th row창원시
5th row창원시
ValueCountFrequency (%)
창원시 8
33.3%
김해시 6
25.0%
합천군 2
 
8.3%
통영시 1
 
4.2%
사천시 1
 
4.2%
밀양시 1
 
4.2%
거제시 1
 
4.2%
의령군 1
 
4.2%
함안군 1
 
4.2%
창녕군 1
 
4.2%
2023-12-11T09:42:28.347784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
23.4%
9
11.7%
8
10.4%
7
 
9.1%
6
 
7.8%
6
 
7.8%
5
 
6.5%
3
 
3.9%
2
 
2.6%
1
 
1.3%
Other values (12) 12
15.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
93.5%
Space Separator 5
 
6.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18
25.0%
9
12.5%
8
11.1%
7
 
9.7%
6
 
8.3%
6
 
8.3%
3
 
4.2%
2
 
2.8%
1
 
1.4%
1
 
1.4%
Other values (11) 11
15.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
93.5%
Common 5
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18
25.0%
9
12.5%
8
11.1%
7
 
9.7%
6
 
8.3%
6
 
8.3%
3
 
4.2%
2
 
2.8%
1
 
1.4%
1
 
1.4%
Other values (11) 11
15.3%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
93.5%
ASCII 5
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
18
25.0%
9
12.5%
8
11.1%
7
 
9.7%
6
 
8.3%
6
 
8.3%
3
 
4.2%
2
 
2.8%
1
 
1.4%
1
 
1.4%
Other values (11) 11
15.3%
ASCII
ValueCountFrequency (%)
5
100.0%
Distinct14
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T09:42:28.504597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.625
Min length3

Characters and Unicode

Total characters135
Distinct characters45
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

Unique8 ?
Unique (%)33.3%

Sample

1st row창원시
2nd row시설관리공단
3rd row시설관리공단
4th row창원시
5th row창원시
ValueCountFrequency (%)
창원시 6
22.2%
시설관리공단 2
 
7.4%
대동면체육회 2
 
7.4%
상동면 2
 
7.4%
체육시설사업소 2
 
7.4%
합천군 2
 
7.4%
위탁 2
 
7.4%
통영관광개발공사 1
 
3.7%
사천시 1
 
3.7%
김해시도시개발공사 1
 
3.7%
Other values (6) 6
22.2%
2023-12-11T09:42:28.783131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
11.9%
9
 
6.7%
7
 
5.2%
6
 
4.4%
6
 
4.4%
5
 
3.7%
5
 
3.7%
5
 
3.7%
4
 
3.0%
4
 
3.0%
Other values (35) 68
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122
90.4%
Space Separator 9
 
6.7%
Close Punctuation 2
 
1.5%
Open Punctuation 2
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
16
 
13.1%
7
 
5.7%
6
 
4.9%
6
 
4.9%
5
 
4.1%
5
 
4.1%
5
 
4.1%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (32) 60
49.2%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122
90.4%
Common 13
 
9.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
16
 
13.1%
7
 
5.7%
6
 
4.9%
6
 
4.9%
5
 
4.1%
5
 
4.1%
5
 
4.1%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (32) 60
49.2%
Common
ValueCountFrequency (%)
9
69.2%
) 2
 
15.4%
( 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122
90.4%
ASCII 13
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
16
 
13.1%
7
 
5.7%
6
 
4.9%
6
 
4.9%
5
 
4.1%
5
 
4.1%
5
 
4.1%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (32) 60
49.2%
ASCII
ValueCountFrequency (%)
9
69.2%
) 2
 
15.4%
( 2
 
15.4%

부지면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)73.9%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean23034.043
Minimum2047
Maximum126000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:28.887329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2047
5-th percentile4437.8
Q110431.5
median12800
Q322690
95-th percentile67507
Maximum126000
Range123953
Interquartile range (IQR)12258.5

Descriptive statistics

Standard deviation27997.477
Coefficient of variation (CV)1.2154825
Kurtosis8.293921
Mean23034.043
Median Absolute Deviation (MAD)5322
Skewness2.7858121
Sum529783
Variance7.8385872 × 108
MonotonicityNot monotonic
2023-12-11T09:42:29.017013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7478 3
12.5%
67507 2
 
8.3%
15600 2
 
8.3%
11483 2
 
8.3%
12500 2
 
8.3%
22580 1
 
4.2%
15680 1
 
4.2%
2047 1
 
4.2%
4100 1
 
4.2%
126000 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
2047 1
 
4.2%
4100 1
 
4.2%
7478 3
12.5%
9380 1
 
4.2%
11483 2
8.3%
11593 1
 
4.2%
12500 2
8.3%
12800 1
 
4.2%
15000 1
 
4.2%
15600 2
8.3%
ValueCountFrequency (%)
126000 1
4.2%
67507 2
8.3%
27705 1
4.2%
23484 1
4.2%
22800 1
4.2%
22580 1
4.2%
15680 1
4.2%
15600 2
8.3%
15000 1
4.2%
12800 1
4.2%

건축면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing18
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean2320.3833
Minimum95.3
Maximum10327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:29.142073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.3
5-th percentile125.725
Q1235
median391.5
Q31998.5
95-th percentile8370.25
Maximum10327
Range10231.7
Interquartile range (IQR)1763.5

Descriptive statistics

Standard deviation4024.3157
Coefficient of variation (CV)1.7343323
Kurtosis4.9107696
Mean2320.3833
Median Absolute Deviation (MAD)235.35
Skewness2.2024657
Sum13922.3
Variance16195117
MonotonicityNot monotonic
2023-12-11T09:42:29.252537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10327.0 1
 
4.2%
2500.0 1
 
4.2%
95.3 1
 
4.2%
289.0 1
 
4.2%
217.0 1
 
4.2%
494.0 1
 
4.2%
(Missing) 18
75.0%
ValueCountFrequency (%)
95.3 1
4.2%
217.0 1
4.2%
289.0 1
4.2%
494.0 1
4.2%
2500.0 1
4.2%
10327.0 1
4.2%
ValueCountFrequency (%)
10327.0 1
4.2%
2500.0 1
4.2%
494.0 1
4.2%
289.0 1
4.2%
217.0 1
4.2%
95.3 1
4.2%

연면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing15
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean4558.1444
Minimum95.3
Maximum18248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:29.358576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.3
5-th percentile141.18
Q1289
median2047
Q34100
95-th percentile16148.8
Maximum18248
Range18152.7
Interquartile range (IQR)3811

Descriptive statistics

Standard deviation6544.7636
Coefficient of variation (CV)1.4358394
Kurtosis1.5030266
Mean4558.1444
Median Absolute Deviation (MAD)1837
Skewness1.6215651
Sum41023.3
Variance42833931
MonotonicityNot monotonic
2023-12-11T09:42:29.451472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
13000.0 1
 
4.2%
18248.0 1
 
4.2%
2500.0 1
 
4.2%
95.3 1
 
4.2%
289.0 1
 
4.2%
210.0 1
 
4.2%
4100.0 1
 
4.2%
2047.0 1
 
4.2%
534.0 1
 
4.2%
(Missing) 15
62.5%
ValueCountFrequency (%)
95.3 1
4.2%
210.0 1
4.2%
289.0 1
4.2%
534.0 1
4.2%
2047.0 1
4.2%
2500.0 1
4.2%
4100.0 1
4.2%
13000.0 1
4.2%
18248.0 1
4.2%
ValueCountFrequency (%)
18248.0 1
4.2%
13000.0 1
4.2%
4100.0 1
4.2%
2500.0 1
4.2%
2047.0 1
4.2%
534.0 1
4.2%
289.0 1
4.2%
210.0 1
4.2%
95.3 1
4.2%

경기장 바닥재료(내야)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
토사
15 
인조잔디
천연잔디
 
1
잔디
 
1

Length

Max length4
Median length2
Mean length2.6666667
Min length2

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row토사
2nd row인조잔디
3rd row토사
4th row토사
5th row토사

Common Values

ValueCountFrequency (%)
토사 15
62.5%
인조잔디 7
29.2%
천연잔디 1
 
4.2%
잔디 1
 
4.2%

Length

2023-12-11T09:42:29.590996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:29.713991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토사 15
62.5%
인조잔디 7
29.2%
천연잔디 1
 
4.2%
잔디 1
 
4.2%

경기장 바닥재료(외야)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
토사
16 
인조잔디
잔디
천연잔디
 
1

Length

Max length4
Median length2
Mean length2.5
Min length2

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row토사
2nd row인조잔디
3rd row토사
4th row토사
5th row토사

Common Values

ValueCountFrequency (%)
토사 16
66.7%
인조잔디 5
 
20.8%
잔디 2
 
8.3%
천연잔디 1
 
4.2%

Length

2023-12-11T09:42:29.826310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:29.930634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토사 16
66.7%
인조잔디 5
 
20.8%
잔디 2
 
8.3%
천연잔디 1
 
4.2%

경기장 중앙 길이
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.79167
Minimum65
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:30.037565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile97
Q1100
median105
Q3112
95-th percentile121.7
Maximum122
Range57
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.462526
Coefficient of variation (CV)0.10834999
Kurtosis6.3340736
Mean105.79167
Median Absolute Deviation (MAD)5
Skewness-1.7764195
Sum2539
Variance131.38949
MonotonicityNot monotonic
2023-12-11T09:42:30.141412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
105 5
20.8%
100 5
20.8%
112 3
12.5%
110 2
 
8.3%
122 2
 
8.3%
97 2
 
8.3%
117 1
 
4.2%
120 1
 
4.2%
107 1
 
4.2%
65 1
 
4.2%
ValueCountFrequency (%)
65 1
 
4.2%
97 2
 
8.3%
100 5
20.8%
105 5
20.8%
107 1
 
4.2%
110 2
 
8.3%
111 1
 
4.2%
112 3
12.5%
117 1
 
4.2%
120 1
 
4.2%
ValueCountFrequency (%)
122 2
 
8.3%
120 1
 
4.2%
117 1
 
4.2%
112 3
12.5%
111 1
 
4.2%
110 2
 
8.3%
107 1
 
4.2%
105 5
20.8%
100 5
20.8%
97 2
 
8.3%

경기장 1,3루 길이
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.416667
Minimum29
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:30.619798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile33.65
Q180
median93
Q396
95-th percentile100
Maximum110
Range81
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.679578
Coefficient of variation (CV)0.24210237
Kurtosis3.2130941
Mean85.416667
Median Absolute Deviation (MAD)4
Skewness-1.8959131
Sum2050
Variance427.64493
MonotonicityNot monotonic
2023-12-11T09:42:30.757251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
96 3
12.5%
100 3
12.5%
80 3
12.5%
90 3
12.5%
95 3
12.5%
93 2
8.3%
29 2
8.3%
98 1
 
4.2%
92 1
 
4.2%
110 1
 
4.2%
Other values (2) 2
8.3%
ValueCountFrequency (%)
29 2
8.3%
60 1
 
4.2%
63 1
 
4.2%
80 3
12.5%
90 3
12.5%
92 1
 
4.2%
93 2
8.3%
95 3
12.5%
96 3
12.5%
98 1
 
4.2%
ValueCountFrequency (%)
110 1
 
4.2%
100 3
12.5%
98 1
 
4.2%
96 3
12.5%
95 3
12.5%
93 2
8.3%
92 1
 
4.2%
90 3
12.5%
80 3
12.5%
63 1
 
4.2%

경기장 면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12755.5
Minimum2047
Maximum36000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:30.889645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2047
5-th percentile4538.9
Q17478
median11538
Q312850
95-th percentile29446.15
Maximum36000
Range33953
Interquartile range (IQR)5372

Descriptive statistics

Standard deviation7713.4675
Coefficient of variation (CV)0.60471699
Kurtosis3.4033086
Mean12755.5
Median Absolute Deviation (MAD)2916.5
Skewness1.7290054
Sum306132
Variance59497581
MonotonicityNot monotonic
2023-12-11T09:42:31.022328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7478 3
 
12.5%
7026 2
 
8.3%
11483 2
 
8.3%
10500 2
 
8.3%
13000 1
 
4.2%
11593 1
 
4.2%
12665 1
 
4.2%
2047 1
 
4.2%
4100 1
 
4.2%
36000 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
2047 1
 
4.2%
4100 1
 
4.2%
7026 2
8.3%
7478 3
12.5%
9167 1
 
4.2%
10500 2
8.3%
11483 2
8.3%
11593 1
 
4.2%
11935 1
 
4.2%
12434 1
 
4.2%
ValueCountFrequency (%)
36000 1
4.2%
30619 1
4.2%
22800 1
4.2%
18740 1
4.2%
15000 1
4.2%
13000 1
4.2%
12800 1
4.2%
12780 1
4.2%
12665 1
4.2%
12434 1
4.2%

관람석 좌석수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)77.8%
Missing15
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean1981.1111
Minimum50
Maximum14164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:31.150351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile65.2
Q1108
median200
Q31200
95-th percentile9146.4
Maximum14164
Range14114
Interquartile range (IQR)1092

Descriptive statistics

Standard deviation4603.0152
Coefficient of variation (CV)2.3234513
Kurtosis8.6143415
Mean1981.1111
Median Absolute Deviation (MAD)112
Skewness2.9170646
Sum17830
Variance21187749
MonotonicityNot monotonic
2023-12-11T09:42:31.269119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
200 3
 
12.5%
14164 1
 
4.2%
1620 1
 
4.2%
50 1
 
4.2%
108 1
 
4.2%
88 1
 
4.2%
1200 1
 
4.2%
(Missing) 15
62.5%
ValueCountFrequency (%)
50 1
 
4.2%
88 1
 
4.2%
108 1
 
4.2%
200 3
12.5%
1200 1
 
4.2%
1620 1
 
4.2%
14164 1
 
4.2%
ValueCountFrequency (%)
14164 1
 
4.2%
1620 1
 
4.2%
1200 1
 
4.2%
200 3
12.5%
108 1
 
4.2%
88 1
 
4.2%
50 1
 
4.2%

관람석 수용인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)87.5%
Missing16
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean2216.25
Minimum88
Maximum14164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:31.385201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile95
Q1139.5
median200
Q31305
95-th percentile9773.6
Maximum14164
Range14076
Interquartile range (IQR)1165.5

Descriptive statistics

Standard deviation4862.5551
Coefficient of variation (CV)2.1940463
Kurtosis7.6537
Mean2216.25
Median Absolute Deviation (MAD)102
Skewness2.7502078
Sum17730
Variance23644442
MonotonicityNot monotonic
2023-12-11T09:42:31.524910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
200 2
 
8.3%
14164 1
 
4.2%
1620 1
 
4.2%
150 1
 
4.2%
108 1
 
4.2%
88 1
 
4.2%
1200 1
 
4.2%
(Missing) 16
66.7%
ValueCountFrequency (%)
88 1
4.2%
108 1
4.2%
150 1
4.2%
200 2
8.3%
1200 1
4.2%
1620 1
4.2%
14164 1
4.2%
ValueCountFrequency (%)
14164 1
4.2%
1620 1
4.2%
1200 1
4.2%
200 2
8.3%
150 1
4.2%
108 1
4.2%
88 1
4.2%

준공연도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.2083
Minimum1963
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T09:42:31.661992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1963
5-th percentile1984.85
Q12008.75
median2012
Q32014
95-th percentile2015
Maximum2015
Range52
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation11.901696
Coefficient of variation (CV)0.0059265244
Kurtosis9.6976745
Mean2008.2083
Median Absolute Deviation (MAD)2
Skewness-3.0507417
Sum48197
Variance141.65036
MonotonicityNot monotonic
2023-12-11T09:42:31.785246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2012 6
25.0%
2014 5
20.8%
2015 3
12.5%
2013 2
 
8.3%
2007 1
 
4.2%
1982 1
 
4.2%
1963 1
 
4.2%
2001 1
 
4.2%
2008 1
 
4.2%
2011 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
1963 1
 
4.2%
1982 1
 
4.2%
2001 1
 
4.2%
2003 1
 
4.2%
2007 1
 
4.2%
2008 1
 
4.2%
2009 1
 
4.2%
2011 1
 
4.2%
2012 6
25.0%
2013 2
 
8.3%
ValueCountFrequency (%)
2015 3
12.5%
2014 5
20.8%
2013 2
 
8.3%
2012 6
25.0%
2011 1
 
4.2%
2009 1
 
4.2%
2008 1
 
4.2%
2007 1
 
4.2%
2003 1
 
4.2%
2001 1
 
4.2%

Interactions

2023-12-11T09:42:25.803323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:17.875427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.667250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.792925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.738541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.501929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.244301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.035802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.858841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.880928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.881515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:17.947754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.754771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.896333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.806854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.592093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.331583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.099454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.145684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.961091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.972690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.045878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.827957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.990397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.871291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.659719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.413369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.167808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.208857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.038626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:26.044241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.137810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.913502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.102504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.959638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.729780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.492109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.239129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.299945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.136008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:26.123547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.212265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.240921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.222605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.035395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.815874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.582725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.333605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.383524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.259531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:26.195938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.289502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.345238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.320377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.119402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.877970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.653521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.429335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.454619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.350928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:26.296487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.365884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.440270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.402026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.217672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.950370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.735769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.523162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.533902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.452303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:26.374089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.438344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.528837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.487903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.288919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.020813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.813702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.594842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.615938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.555986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:26.451790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.520259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.628561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.570347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.359209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.104778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.895244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.686063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.708903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.645122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:26.525939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:18.598274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:19.721010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:20.660807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:21.430727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.182081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:22.967945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:23.775715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:24.805443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:25.727200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:42:31.894821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구시설명소유기관관리주체부지면적(㎡)건축면적(㎡)연면적(㎡)경기장 바닥재료(내야)경기장 바닥재료(외야)경기장 중앙 길이경기장 1,3루 길이경기장 면적(㎡)관람석 좌석수관람석 수용인원(명)준공연도
연번1.0000.7561.0000.8560.9090.8110.0000.0000.6530.0000.8480.7730.8060.0000.0000.402
시군구0.7561.0001.0001.0000.9740.5740.0000.0000.7930.7780.8710.9320.8880.0000.0000.070
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소유기관0.8561.0001.0001.0000.9710.6550.0000.0000.9700.9640.8850.9450.9330.0000.0000.000
관리주체0.9090.9741.0000.9711.0000.8840.0000.5690.8540.7310.9200.9720.9540.0000.0000.886
부지면적(㎡)0.8110.5741.0000.6550.8841.0000.8340.6720.6840.3210.0000.0000.6060.9160.9160.000
건축면적(㎡)0.0000.0001.0000.0000.0000.8341.0001.0000.2420.2420.0000.0000.0001.0001.0001.000
연면적(㎡)0.0000.0001.0000.0000.5690.6721.0001.0000.5170.5170.6820.5420.0001.0001.0000.573
경기장 바닥재료(내야)0.6530.7931.0000.9700.8540.6840.2420.5171.0000.9810.4180.0000.8670.0540.0000.000
경기장 바닥재료(외야)0.0000.7781.0000.9640.7310.3210.2420.5170.9811.0000.0000.3610.7410.0540.0000.000
경기장 중앙 길이0.8480.8711.0000.8850.9200.0000.0000.6820.4180.0001.0000.8570.7870.0000.4230.000
경기장 1,3루 길이0.7730.9321.0000.9450.9720.0000.0000.5420.0000.3610.8571.0000.7310.0000.0000.000
경기장 면적(㎡)0.8060.8881.0000.9330.9540.6060.0000.0000.8670.7410.7870.7311.0000.0000.0000.613
관람석 좌석수0.0000.0001.0000.0000.0000.9161.0001.0000.0540.0540.0000.0000.0001.0001.0001.000
관람석 수용인원(명)0.0000.0001.0000.0000.0000.9161.0001.0000.0000.0000.4230.0000.0001.0001.0001.000
준공연도0.4020.0701.0000.0000.8860.0001.0000.5730.0000.0000.0000.0000.6131.0001.0001.000
2023-12-11T09:42:32.103922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구경기장 바닥재료(내야)경기장 바닥재료(외야)
시군구1.0000.4890.472
경기장 바닥재료(내야)0.4891.0000.809
경기장 바닥재료(외야)0.4720.8091.000
2023-12-11T09:42:32.231696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부지면적(㎡)건축면적(㎡)연면적(㎡)경기장 중앙 길이경기장 1,3루 길이경기장 면적(㎡)관람석 좌석수관람석 수용인원(명)준공연도시군구경기장 바닥재료(내야)경기장 바닥재료(외야)
연번1.0000.101-0.486-0.417-0.514-0.464-0.279-0.458-0.4910.3750.4040.3600.000
부지면적(㎡)0.1011.0000.6000.119-0.0110.3950.7450.7190.695-0.1110.2520.6030.259
건축면적(㎡)-0.4860.6001.0001.0000.0860.058-0.7140.7711.000-0.8860.0000.2170.217
연면적(㎡)-0.4170.1191.0001.000-0.084-0.050-0.4670.4310.464-0.3510.0000.4310.431
경기장 중앙 길이-0.514-0.0110.086-0.0841.0000.5720.2910.5110.777-0.1460.5750.2480.000
경기장 1,3루 길이-0.4640.3950.058-0.0500.5721.0000.7020.6150.794-0.2700.7080.0000.209
경기장 면적(㎡)-0.2790.745-0.714-0.4670.2910.7021.0000.0680.120-0.3320.6050.4540.297
관람석 좌석수-0.4580.7190.7710.4310.5110.6150.0681.0000.928-0.5190.0000.0000.000
관람석 수용인원(명)-0.4910.6951.0000.4640.7770.7940.1200.9281.000-0.7660.0000.0000.000
준공연도0.375-0.111-0.886-0.351-0.146-0.270-0.332-0.519-0.7661.0000.0000.0000.000
시군구0.4040.2520.0000.0000.5750.7080.6050.0000.0000.0001.0000.4890.472
경기장 바닥재료(내야)0.3600.6030.2170.4310.2480.0000.4540.0000.0000.0000.4891.0000.809
경기장 바닥재료(외야)0.0000.2590.2170.4310.0000.2090.2970.0000.0000.0000.4720.8091.000

Missing values

2023-12-11T09:42:26.683123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:42:26.875309image/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.
2023-12-11T09:42:27.011485image/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

연번시도시군구시설명소유기관관리주체부지면적(㎡)건축면적(㎡)연면적(㎡)경기장 바닥재료(내야)경기장 바닥재료(외야)경기장 중앙 길이경기장 1,3루 길이경기장 면적(㎡)관람석 좌석수관람석 수용인원(명)준공연도
01경상남도창원시88올림픽야구장창원시창원시15000<NA>13000.0토사토사10596130002002002007
12경상남도창원시마산종합운동장야구장창원시시설관리공단2348410327.018248.0인조잔디인조잔디117961278014164141641982
23경상남도창원시진해공설운동장야구장창원시시설관리공단277052500.02500.0토사토사1109815000162016201963
34경상남도창원시대산면 사회인야구장(1)창원시창원시11483<NA><NA>토사토사12210011483<NA><NA>2012
45경상남도창원시대산면 사회인야구장(2)창원시창원시11483<NA><NA>토사토사12210011483<NA><NA>2012
56경상남도창원시대산면 사회인야구장(3)창원시창원시7478<NA><NA>토사토사100807478<NA><NA>2012
67경상남도창원시대산면 사회인야구장(4)창원시창원시7478<NA><NA>토사토사100807478<NA><NA>2012
78경상남도창원시대산면 사회인야구장(5)창원시창원시7478<NA><NA>토사토사100807478<NA><NA>2012
89경상남도통영시통영야구장통영시위탁 (통영관광개발공사)<NA>95.395.3인조잔디인조잔디12010030619200<NA>2015
910경상남도사천시사등야구장사천시사천시9380<NA><NA>토사토사1129291672002002012
연번시도시군구시설명소유기관관리주체부지면적(㎡)건축면적(㎡)연면적(㎡)경기장 바닥재료(내야)경기장 바닥재료(외야)경기장 중앙 길이경기장 1,3루 길이경기장 면적(㎡)관람석 좌석수관람석 수용인원(명)준공연도
1415경상남도김해시상동매리야구장(1)김해시상동면67507<NA><NA>토사토사1059512434<NA><NA>2014
1516경상남도김해시상동매리야구장(2)김해시상동면67507<NA><NA>토사토사1059511935<NA><NA>2015
1617경상남도밀양시가곡야구장밀양시체육시설사업소11593<NA><NA>토사잔디10511011593<NA><NA>2013
1718경상남도거제시하청 야구장거제시위탁 (거제시야구연합회)22580217.0210.0인조잔디인조잔디10093187401081082008
1819경상남도의령군의령친환경야구장의령군의령군126000<NA><NA>잔디잔디1009636000<NA><NA>2011
1920경상남도함안군함안 스포츠파크생활야구장(리틀)함안군문화체육시설사업소4100<NA>4100.0인조잔디인조잔디6560410088882015
2021경상남도창녕군남지체육공원 야구장창녕군창녕군2047<NA>2047.0토사토사111632047<NA><NA>2009
2122경상남도남해군남해스포츠파크인조야구장남해군체육시설사업소15680494.0534.0인조잔디인조잔디1059512665120012002003
2223경상남도합천군합천야구장(A)합천군합천군15600<NA><NA>인조잔디토사97297026<NA><NA>2014
2324경상남도합천군합천야구장(B)합천군합천군15600<NA><NA>인조잔디토사97297026<NA><NA>2014