Overview

Dataset statistics

Number of variables17
Number of observations86
Missing cells198
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory146.5 B

Variable types

Categorical8
Text1
Numeric8

Dataset

Description전라남도 시군의 축구장(시설명, 관리주체, 면적, 수용인원, 준공연도 등)에 대한 데이터를 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15037308/fileData.do

Alerts

소유기관 is highly overall correlated with 경기장 폭 and 6 other fieldsHigh correlation
관리주체 is highly overall correlated with 경기장 폭 and 7 other fieldsHigh correlation
시군구 is highly overall correlated with 경기장 폭 and 6 other fieldsHigh correlation
부지면적 is highly overall correlated with 경기장 면적 and 1 other fieldsHigh correlation
건축면적 is highly overall correlated with 연면적 and 3 other fieldsHigh correlation
연면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
경기장 폭 is highly overall correlated with 경기장 길이 and 7 other fieldsHigh correlation
경기장 길이 is highly overall correlated with 경기장 폭 and 7 other fieldsHigh correlation
경기장 면적 is highly overall correlated with 부지면적 and 5 other fieldsHigh correlation
좌석수_명 is highly overall correlated with 건축면적 and 1 other fieldsHigh correlation
수용인원 is highly overall correlated with 부지면적 and 3 other fieldsHigh correlation
경기장 바닥재료 is highly overall correlated with 경기장 폭 and 5 other fieldsHigh correlation
경기장 면수 is highly overall correlated with 경기장 폭 and 6 other fieldsHigh correlation
좌석형태 is highly overall correlated with 건축면적 and 5 other fieldsHigh correlation
건축구조 is highly overall correlated with 경기장 폭 and 2 other fieldsHigh correlation
준공연도 is highly overall correlated with 좌석형태High correlation
경기장 바닥재료 is highly imbalanced (54.0%)Imbalance
경기장 면수 is highly imbalanced (56.2%)Imbalance
부지면적 has 3 (3.5%) missing valuesMissing
건축면적 has 50 (58.1%) missing valuesMissing
연면적 has 49 (57.0%) missing valuesMissing
좌석수_명 has 51 (59.3%) missing valuesMissing
수용인원 has 45 (52.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:38:51.266867
Analysis finished2023-12-12 02:38:59.728498
Duration8.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size820.0 B
신안군
12 
순천시
7 
광양시
6 
담양군
 
5
목포시
 
5
Other values (17)
51 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
신안군 12
 
14.0%
순천시 7
 
8.1%
광양시 6
 
7.0%
담양군 5
 
5.8%
목포시 5
 
5.8%
화순군 4
 
4.7%
무안군 4
 
4.7%
여수시 4
 
4.7%
곡성군 4
 
4.7%
고흥군 4
 
4.7%
Other values (12) 31
36.0%

Length

2023-12-12T11:38:59.791223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신안군 12
 
14.0%
순천시 7
 
8.1%
광양시 6
 
7.0%
담양군 5
 
5.8%
목포시 5
 
5.8%
곡성군 4
 
4.7%
영암군 4
 
4.7%
고흥군 4
 
4.7%
해남군 4
 
4.7%
여수시 4
 
4.7%
Other values (12) 31
36.0%

시설명
Text

Distinct85
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-12T11:39:00.096852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length9.0465116
Min length5

Characters and Unicode

Total characters778
Distinct characters141
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)97.7%

Sample

1st row북항환경관리소축구장
2nd row목포국제축구센터
3rd row목포국제축구센터인조잔디구장 1
4th row목포국제축구센터인조잔디구장 2
5th row부주산 축구 인조잔디구장
ValueCountFrequency (%)
축구장 9
 
6.8%
종합운동장 5
 
3.8%
운동장 4
 
3.0%
고흥 3
 
2.3%
공설운동장 3
 
2.3%
1 3
 
2.3%
보조구장 2
 
1.5%
마동 2
 
1.5%
축구전용 2
 
1.5%
목포국제축구센터인조잔디구장 2
 
1.5%
Other values (92) 97
73.5%
2023-12-12T11:39:00.689634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 88
 
11.3%
구 72
 
9.3%
축 51
 
6.6%
46
 
5.9%
공 25
 
3.2%
동 24
 
3.1%
운 22
 
2.8%
원 19
 
2.4%
조 18
 
2.3%
경 13
 
1.7%
Other values (131) 400
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 698
89.7%
Space Separator 46
 
5.9%
Decimal Number 12
 
1.5%
Close Punctuation 8
 
1.0%
Open Punctuation 8
 
1.0%
Uppercase Letter 5
 
0.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 88
 
12.6%
구 72
 
10.3%
축 51
 
7.3%
공 25
 
3.6%
동 24
 
3.4%
운 22
 
3.2%
원 19
 
2.7%
조 18
 
2.6%
경 13
 
1.9%
체 12
 
1.7%
Other values (122) 354
50.7%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
B 2
40.0%
C 1
20.0%
Decimal Number
ValueCountFrequency (%)
1 7
58.3%
2 5
41.7%
Space Separator
ValueCountFrequency (%)
46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 698
89.7%
Common 75
 
9.6%
Latin 5
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 88
 
12.6%
구 72
 
10.3%
축 51
 
7.3%
공 25
 
3.6%
동 24
 
3.4%
운 22
 
3.2%
원 19
 
2.7%
조 18
 
2.6%
경 13
 
1.9%
체 12
 
1.7%
Other values (122) 354
50.7%
Common
ValueCountFrequency (%)
46
61.3%
) 8
 
10.7%
( 8
 
10.7%
1 7
 
9.3%
2 5
 
6.7%
, 1
 
1.3%
Latin
ValueCountFrequency (%)
A 2
40.0%
B 2
40.0%
C 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 698
89.7%
ASCII 80
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 88
 
12.6%
구 72
 
10.3%
축 51
 
7.3%
공 25
 
3.6%
동 24
 
3.4%
운 22
 
3.2%
원 19
 
2.7%
조 18
 
2.6%
경 13
 
1.9%
체 12
 
1.7%
Other values (122) 354
50.7%
ASCII
ValueCountFrequency (%)
46
57.5%
) 8
 
10.0%
( 8
 
10.0%
1 7
 
8.8%
2 5
 
6.2%
A 2
 
2.5%
B 2
 
2.5%
, 1
 
1.2%
C 1
 
1.2%

소유기관
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size820.0 B
신안군
12 
순천시
7 
광양시
6 
담양군
 
5
목포시
 
5
Other values (17)
51 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
신안군 12
 
14.0%
순천시 7
 
8.1%
광양시 6
 
7.0%
담양군 5
 
5.8%
목포시 5
 
5.8%
화순군 4
 
4.7%
무안군 4
 
4.7%
여수시 4
 
4.7%
곡성군 4
 
4.7%
고흥군 4
 
4.7%
Other values (12) 31
36.0%

Length

2023-12-12T11:39:00.883804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신안군 12
 
14.0%
순천시 7
 
8.1%
광양시 6
 
7.0%
담양군 5
 
5.8%
목포시 5
 
5.8%
곡성군 4
 
4.7%
영암군 4
 
4.7%
고흥군 4
 
4.7%
해남군 4
 
4.7%
여수시 4
 
4.7%
Other values (12) 31
36.0%

관리주체
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Memory size820.0 B
신안군
12 
광양시
6 
담양군
 
5
화순군
 
4
여수시
 
4
Other values (22)
55 

Length

Max length9
Median length3
Mean length3.2790698
Min length3

Unique

Unique6 ?
Unique (%)7.0%

Sample

1st row북항환경관리소
2nd row목포시
3rd row목포시
4th row목포시
5th row체육시설관리과

Common Values

ValueCountFrequency (%)
신안군 12
 
14.0%
광양시 6
 
7.0%
담양군 5
 
5.8%
화순군 4
 
4.7%
여수시 4
 
4.7%
순천시 4
 
4.7%
무안군 4
 
4.7%
곡성군 4
 
4.7%
해남군 4
 
4.7%
고흥군 4
 
4.7%
Other values (17) 35
40.7%

Length

2023-12-12T11:39:01.024173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신안군 12
 
14.0%
광양시 6
 
7.0%
담양군 5
 
5.8%
화순군 4
 
4.7%
여수시 4
 
4.7%
순천시 4
 
4.7%
무안군 4
 
4.7%
곡성군 4
 
4.7%
해남군 4
 
4.7%
고흥군 4
 
4.7%
Other values (17) 35
40.7%

부지면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)96.4%
Missing3
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean34260.373
Minimum2600
Maximum214934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:01.195004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2600
5-th percentile6856.5
Q19128
median17642
Q350192.5
95-th percentile114204.8
Maximum214934
Range212334
Interquartile range (IQR)41064.5

Descriptive statistics

Standard deviation39128.461
Coefficient of variation (CV)1.1420909
Kurtosis5.966721
Mean34260.373
Median Absolute Deviation (MAD)9502
Skewness2.2470657
Sum2843611
Variance1.5310365 × 109
MonotonicityNot monotonic
2023-12-12T11:39:01.378882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51810 2
 
2.3%
8214 2
 
2.3%
136089 2
 
2.3%
100100 1
 
1.2%
156254 1
 
1.2%
54339 1
 
1.2%
10708 1
 
1.2%
115772 1
 
1.2%
2600 1
 
1.2%
9531 1
 
1.2%
Other values (70) 70
81.4%
(Missing) 3
 
3.5%
ValueCountFrequency (%)
2600 1
1.2%
5880 1
1.2%
6000 1
1.2%
6468 1
1.2%
6825 1
1.2%
7140 1
1.2%
7234 1
1.2%
7350 1
1.2%
7665 1
1.2%
7700 1
1.2%
ValueCountFrequency (%)
214934 1
1.2%
156254 1
1.2%
136089 2
2.3%
115772 1
1.2%
100100 1
1.2%
98955 1
1.2%
93348 1
1.2%
71104 1
1.2%
70000 1
1.2%
69420 1
1.2%

건축면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)94.4%
Missing50
Missing (%)58.1%
Infinite0
Infinite (%)0.0%
Mean1235.8333
Minimum43
Maximum16112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:01.561008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile70.25
Q1101.5
median297.5
Q3646.75
95-th percentile4074
Maximum16112
Range16069
Interquartile range (IQR)545.25

Descriptive statistics

Standard deviation2835.7868
Coefficient of variation (CV)2.2946353
Kurtosis22.570118
Mean1235.8333
Median Absolute Deviation (MAD)202
Skewness4.4469322
Sum44490
Variance8041686.9
MonotonicityNot monotonic
2023-12-12T11:39:01.711260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
102 2
 
2.3%
106 2
 
2.3%
370 1
 
1.2%
1320 1
 
1.2%
4857 1
 
1.2%
850 1
 
1.2%
80 1
 
1.2%
96 1
 
1.2%
579 1
 
1.2%
213 1
 
1.2%
Other values (24) 24
27.9%
(Missing) 50
58.1%
ValueCountFrequency (%)
43 1
1.2%
44 1
1.2%
79 1
1.2%
80 1
1.2%
81 1
1.2%
85 1
1.2%
95 1
1.2%
96 1
1.2%
100 1
1.2%
102 2
2.3%
ValueCountFrequency (%)
16112 1
1.2%
4857 1
1.2%
3813 1
1.2%
3687 1
1.2%
3163 1
1.2%
2684 1
1.2%
1828 1
1.2%
1320 1
1.2%
850 1
1.2%
579 1
1.2%

연면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)94.6%
Missing49
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean2383.2973
Minimum43
Maximum26759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:01.912749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile72.8
Q1106
median358
Q3777
95-th percentile10740.2
Maximum26759
Range26716
Interquartile range (IQR)671

Descriptive statistics

Standard deviation5655.8854
Coefficient of variation (CV)2.3731346
Kurtosis11.87377
Mean2383.2973
Median Absolute Deviation (MAD)256
Skewness3.3986802
Sum88182
Variance31989040
MonotonicityNot monotonic
2023-12-12T11:39:02.305114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
106 2
 
2.3%
102 2
 
2.3%
100 1
 
1.2%
483 1
 
1.2%
97 1
 
1.2%
26759 1
 
1.2%
44 1
 
1.2%
213 1
 
1.2%
450 1
 
1.2%
141 1
 
1.2%
Other values (25) 25
29.1%
(Missing) 49
57.0%
ValueCountFrequency (%)
43 1
1.2%
44 1
1.2%
80 1
1.2%
81 1
1.2%
95 1
1.2%
97 1
1.2%
100 1
1.2%
102 2
2.3%
106 2
2.3%
141 1
1.2%
ValueCountFrequency (%)
26759 1
1.2%
20845 1
1.2%
8214 1
1.2%
7572 1
1.2%
6712 1
1.2%
5067 1
1.2%
3163 1
1.2%
1253 1
1.2%
852 1
1.2%
777 1
1.2%

경기장 바닥재료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
인조잔디
55 
천연잔디
25 
천연전디
 
1
인조잔디 1천연잔디 2토사 1
 
1
천연잔디1인조잔디2
 
1
Other values (3)
 
3

Length

Max length18
Median length4
Mean length4.4069767
Min length3

Unique

Unique6 ?
Unique (%)7.0%

Sample

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

Common Values

ValueCountFrequency (%)
인조잔디 55
64.0%
천연잔디 25
29.1%
천연전디 1
 
1.2%
인조잔디 1천연잔디 2토사 1 1
 
1.2%
천연잔디1인조잔디2 1
 
1.2%
천연잔디 1인조잔디 2 1
 
1.2%
천연잔디 3인조잔디 4 1
 
1.2%
마사토 1
 
1.2%

Length

2023-12-12T11:39:02.445957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:39:02.565525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인조잔디 56
60.2%
천연잔디 27
29.0%
천연전디 1
 
1.1%
1천연잔디 1
 
1.1%
2토사 1
 
1.1%
1 1
 
1.1%
천연잔디1인조잔디2 1
 
1.1%
1인조잔디 1
 
1.1%
2 1
 
1.1%
3인조잔디 1
 
1.1%
Other values (2) 2
 
2.2%

경기장 폭
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7124.3721
Minimum35
Maximum606568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:02.680400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile57.5
Q168
median68
Q375
95-th percentile106
Maximum606568
Range606533
Interquartile range (IQR)7

Descriptive statistics

Standard deviation65400.137
Coefficient of variation (CV)9.1797756
Kurtosis85.999993
Mean7124.3721
Median Absolute Deviation (MAD)4
Skewness9.2736179
Sum612696
Variance4.2771779 × 109
MonotonicityNot monotonic
2023-12-12T11:39:02.793918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
68 26
30.2%
70 9
 
10.5%
64 7
 
8.1%
78 6
 
7.0%
65 4
 
4.7%
75 3
 
3.5%
72 3
 
3.5%
106 2
 
2.3%
74 2
 
2.3%
110 2
 
2.3%
Other values (22) 22
25.6%
ValueCountFrequency (%)
35 1
 
1.2%
45 1
 
1.2%
50 1
 
1.2%
54 1
 
1.2%
57 1
 
1.2%
59 1
 
1.2%
60 1
 
1.2%
63 1
 
1.2%
64 7
8.1%
65 4
4.7%
ValueCountFrequency (%)
606568 1
1.2%
112 1
1.2%
110 2
2.3%
106 2
2.3%
105 1
1.2%
99 1
1.2%
91 1
1.2%
90 1
1.2%
86 1
1.2%
85 1
1.2%

경기장 길이
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1047836.2
Minimum45
Maximum90105104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:02.906765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile75.5
Q1100
median105
Q3110
95-th percentile119.5
Maximum90105104
Range90105059
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9716272.1
Coefficient of variation (CV)9.2727016
Kurtosis86
Mean1047836.2
Median Absolute Deviation (MAD)5
Skewness9.2736185
Sum90113910
Variance9.4405943 × 1013
MonotonicityNot monotonic
2023-12-12T11:39:03.014754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
105 33
38.4%
100 12
 
14.0%
115 5
 
5.8%
110 5
 
5.8%
109 4
 
4.7%
116 3
 
3.5%
120 3
 
3.5%
96 2
 
2.3%
112 2
 
2.3%
65 1
 
1.2%
Other values (16) 16
18.6%
ValueCountFrequency (%)
45 1
1.2%
60 1
1.2%
65 1
1.2%
70 1
1.2%
74 1
1.2%
80 1
1.2%
90 1
1.2%
91 1
1.2%
94 1
1.2%
96 2
2.3%
ValueCountFrequency (%)
90105104 1
 
1.2%
126 1
 
1.2%
120 3
3.5%
118 1
 
1.2%
117 1
 
1.2%
116 3
3.5%
115 5
5.8%
113 1
 
1.2%
112 2
 
2.3%
111 1
 
1.2%

경기장 면적
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10266.907
Minimum2100
Maximum67200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:03.128639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2100
5-th percentile5455.5
Q17140
median7350
Q38970
95-th percentile26713
Maximum67200
Range65100
Interquartile range (IQR)1830

Descriptive statistics

Standard deviation8534.9251
Coefficient of variation (CV)0.83130442
Kurtosis23.341155
Mean10266.907
Median Absolute Deviation (MAD)864
Skewness4.1999652
Sum882954
Variance72844947
MonotonicityNot monotonic
2023-12-12T11:39:03.257678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7140 18
20.9%
6400 5
 
5.8%
7350 5
 
5.8%
8970 4
 
4.7%
7700 3
 
3.5%
6800 2
 
2.3%
8214 2
 
2.3%
7920 2
 
2.3%
21420 2
 
2.3%
4050 2
 
2.3%
Other values (40) 41
47.7%
ValueCountFrequency (%)
2100 1
 
1.2%
4050 2
 
2.3%
5369 1
 
1.2%
5450 1
 
1.2%
5472 1
 
1.2%
5760 1
 
1.2%
6100 1
 
1.2%
6400 5
5.8%
6468 1
 
1.2%
6500 2
 
2.3%
ValueCountFrequency (%)
67200 1
1.2%
29795 1
1.2%
29523 1
1.2%
27492 1
1.2%
26910 1
1.2%
26122 1
1.2%
24730 1
1.2%
21420 2
2.3%
19500 1
1.2%
17004 1
1.2%

경기장 면수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
1
69 
2
8 
3
7 
4
 
1
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.3%

Sample

1st row1
2nd row3
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 69
80.2%
2 8
 
9.3%
3 7
 
8.1%
4 1
 
1.2%
7 1
 
1.2%

Length

2023-12-12T11:39:03.370621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:39:03.455181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 69
80.2%
2 8
 
9.3%
3 7
 
8.1%
4 1
 
1.2%
7 1
 
1.2%

좌석수_명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)68.6%
Missing51
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean1270.6857
Minimum100
Maximum9132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:03.548463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile114
Q1200
median378
Q3630
95-th percentile7347
Maximum9132
Range9032
Interquartile range (IQR)430

Descriptive statistics

Standard deviation2273.4182
Coefficient of variation (CV)1.7891271
Kurtosis5.9156046
Mean1270.6857
Median Absolute Deviation (MAD)178
Skewness2.5919942
Sum44474
Variance5168430.3
MonotonicityNot monotonic
2023-12-12T11:39:03.645852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
200 5
 
5.8%
500 3
 
3.5%
400 2
 
2.3%
100 2
 
2.3%
430 2
 
2.3%
240 2
 
2.3%
2500 2
 
2.3%
150 1
 
1.2%
314 1
 
1.2%
312 1
 
1.2%
Other values (14) 14
 
16.3%
(Missing) 51
59.3%
ValueCountFrequency (%)
100 2
 
2.3%
120 1
 
1.2%
150 1
 
1.2%
180 1
 
1.2%
200 5
5.8%
240 2
 
2.3%
300 1
 
1.2%
312 1
 
1.2%
314 1
 
1.2%
360 1
 
1.2%
ValueCountFrequency (%)
9132 1
 
1.2%
7900 1
 
1.2%
7110 1
 
1.2%
4046 1
 
1.2%
2500 2
2.3%
1462 1
 
1.2%
1240 1
 
1.2%
680 1
 
1.2%
580 1
 
1.2%
500 3
3.5%

수용인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)53.7%
Missing45
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean2373.7073
Minimum100
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T11:39:03.746772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1400
median1000
Q32800
95-th percentile9132
Maximum20000
Range19900
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation3777.1364
Coefficient of variation (CV)1.5912393
Kurtosis11.540718
Mean2373.7073
Median Absolute Deviation (MAD)600
Skewness3.0787726
Sum97322
Variance14266760
MonotonicityNot monotonic
2023-12-12T11:39:03.870188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
500 7
 
8.1%
1000 5
 
5.8%
200 5
 
5.8%
400 3
 
3.5%
5000 2
 
2.3%
4000 2
 
2.3%
100 2
 
2.3%
10000 1
 
1.2%
1600 1
 
1.2%
580 1
 
1.2%
Other values (12) 12
 
14.0%
(Missing) 45
52.3%
ValueCountFrequency (%)
100 2
 
2.3%
200 5
5.8%
370 1
 
1.2%
400 3
3.5%
500 7
8.1%
580 1
 
1.2%
900 1
 
1.2%
1000 5
5.8%
1240 1
 
1.2%
1400 1
 
1.2%
ValueCountFrequency (%)
20000 1
1.2%
10000 1
1.2%
9132 1
1.2%
7900 1
1.2%
6000 1
1.2%
5000 2
2.3%
4000 2
2.3%
3000 1
1.2%
2800 1
1.2%
2000 1
1.2%

좌석형태
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
<NA>
40 
계단식
26 
의자식
9 
등받이
 
3
계단식의자식
 
2
Other values (6)
6 

Length

Max length7
Median length6
Mean length3.6162791
Min length1

Unique

Unique6 ?
Unique (%)7.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 40
46.5%
계단식 26
30.2%
의자식 9
 
10.5%
등받이 3
 
3.5%
계단식의자식 2
 
2.3%
돌계단 1
 
1.2%
1
 
1.2%
의자식계단식 1
 
1.2%
계 1
 
1.2%
계단식, 의자 1
 
1.2%

Length

2023-12-12T11:39:04.004538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 40
46.0%
계단식 28
32.2%
의자식 9
 
10.3%
등받이 3
 
3.4%
계단식의자식 2
 
2.3%
의자 2
 
2.3%
돌계단 1
 
1.1%
의자식계단식 1
 
1.1%
계 1
 
1.1%

건축구조
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size820.0 B
<NA>
38 
철근콘크리트
35 
철근콘크리트
6 
천연잔디
 
2
경량철골조
 
1
Other values (4)
4 

Length

Max length9
Median length7
Mean length5.0813953
Min length1

Unique

Unique5 ?
Unique (%)5.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 38
44.2%
철근콘크리트 35
40.7%
철근콘크리트 6
 
7.0%
천연잔디 2
 
2.3%
경량철골조 1
 
1.2%
1
 
1.2%
청근콘크리트 1
 
1.2%
철근콘크리트,토사 1
 
1.2%
콘크리트 1
 
1.2%

Length

2023-12-12T11:39:04.122660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:39:04.232485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
철근콘크리트 41
48.2%
na 38
44.7%
천연잔디 2
 
2.4%
경량철골조 1
 
1.2%
청근콘크리트 1
 
1.2%
철근콘크리트,토사 1
 
1.2%
콘크리트 1
 
1.2%

준공연도
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size820.0 B
2009
11 
2010
9 
2006
8 
2013
5 
2011
5 
Other values (23)
48 

Length

Max length10
Median length4
Mean length4.127907
Min length3

Unique

Unique7 ?
Unique (%)8.1%

Sample

1st row2010
2nd row2009
3rd row<NA>
4th row<NA>
5th row2003

Common Values

ValueCountFrequency (%)
2009 11
 
12.8%
2010 9
 
10.5%
2006 8
 
9.3%
2013 5
 
5.8%
2011 5
 
5.8%
2019 4
 
4.7%
2000 3
 
3.5%
2017 3
 
3.5%
2014 3
 
3.5%
2021 3
 
3.5%
Other values (18) 32
37.2%

Length

2023-12-12T11:39:04.389795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2009 11
 
12.8%
2010 9
 
10.5%
2006 8
 
9.3%
2013 5
 
5.8%
2011 5
 
5.8%
2019 4
 
4.7%
2005 3
 
3.5%
2007 3
 
3.5%
2012 3
 
3.5%
2014 3
 
3.5%
Other values (18) 32
37.2%

Interactions

2023-12-12T11:38:58.187312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:52.316394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.112733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.856308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.644751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:55.880698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.640248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.423804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.293814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:52.380827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.212527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.943076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.761585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:55.989709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.741051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.503972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.428560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:52.456640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.325882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.052592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.887059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.100800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.847819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.606770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.541682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:52.537898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.416234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.140631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:55.010751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.198197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.936817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.680479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.663992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:52.693358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.508878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.241266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:55.121293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.291065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.035159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.770357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.772228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:52.822803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.597058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.325299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:55.213328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.372185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.122020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.852090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.898379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:52.937636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.680347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.415298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:55.341303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.457973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.222481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.992767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.979921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.008910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:53.765708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:54.498370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:55.761192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:56.536969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:57.300091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:58.094460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:39:04.497793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구시설명소유기관관리주체부지면적건축면적연면적경기장 바닥재료경기장 폭경기장 길이경기장 면적경기장 면수좌석수_명수용인원좌석형태건축구조준공연도
시군구1.0000.9681.0001.0000.7380.8020.6400.8791.0001.0000.8180.9190.5390.0000.9580.7850.768
시설명0.9681.0000.9680.9801.0001.0001.0000.9911.0001.0001.0001.0001.0000.8930.0000.0000.000
소유기관1.0000.9681.0001.0000.7380.8020.6400.8791.0001.0000.8180.9190.5390.0000.9580.7850.768
관리주체1.0000.9801.0001.0000.8400.7770.6520.8631.0001.0000.8680.9310.0000.0000.9660.9010.834
부지면적0.7381.0000.7380.8401.0000.8290.7930.271NaNNaN0.7530.5760.7070.8600.4660.5040.674
건축면적0.8021.0000.8020.7770.8291.0000.8180.0000.0000.0000.1880.2820.9360.8020.8740.0000.000
연면적0.6401.0000.6400.6520.7930.8181.0000.0000.0000.0000.0000.0000.8210.9330.6640.5520.000
경기장 바닥재료0.8790.9910.8790.8630.2710.0000.0001.0001.0001.0000.7480.8470.4070.3250.0000.0000.766
경기장 폭1.0001.0001.0001.000NaN0.0000.0001.0001.0000.6900.6051.0000.0000.0000.000NaN0.595
경기장 길이1.0001.0001.0001.000NaN0.0000.0001.0000.6901.0000.6051.0000.0000.0000.000NaN0.595
경기장 면적0.8181.0000.8180.8680.7530.1880.0000.7480.6050.6051.0000.7880.3580.0000.0000.0000.713
경기장 면수0.9191.0000.9190.9310.5760.2820.0000.8471.0001.0000.7881.0000.3290.2660.0000.0000.809
좌석수_명0.5391.0000.5390.0000.7070.9360.8210.4070.0000.0000.3580.3291.0000.8450.4940.0000.748
수용인원0.0000.8930.0000.0000.8600.8020.9330.3250.0000.0000.0000.2660.8451.0000.3530.0000.000
좌석형태0.9580.0000.9580.9660.4660.8740.6640.0000.0000.0000.0000.0000.4940.3531.0000.7120.947
건축구조0.7850.0000.7850.9010.5040.0000.5520.000NaNNaN0.0000.0000.0000.0000.7121.0000.895
준공연도0.7680.0000.7680.8340.6740.0000.0000.7660.5950.5950.7130.8090.7480.0000.9470.8951.000
2023-12-12T11:39:04.671818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공연도좌석형태소유기관경기장 면수건축구조관리주체경기장 바닥재료시군구
준공연도1.0000.5850.2840.4690.4870.2690.3740.284
좌석형태0.5851.0000.5700.0000.4630.6550.0000.570
소유기관0.2840.5701.0000.6740.3980.9600.5541.000
경기장 면수0.4690.0000.6741.0000.0000.6580.7180.674
건축구조0.4870.4630.3980.0001.0000.5410.0000.398
관리주체0.2690.6550.9600.6580.5411.0000.4970.960
경기장 바닥재료0.3740.0000.5540.7180.0000.4971.0000.554
시군구0.2840.5701.0000.6740.3980.9600.5541.000
2023-12-12T11:39:04.828620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부지면적건축면적연면적경기장 폭경기장 길이경기장 면적좌석수_명수용인원시군구소유기관관리주체경기장 바닥재료경기장 면수좌석형태건축구조준공연도
부지면적1.0000.4760.3230.3110.3990.5440.4070.6080.3450.3450.4360.1450.3960.2380.2690.250
건축면적0.4761.0000.9570.3170.2850.1580.5740.7680.4270.4270.3890.0000.1020.7110.0000.000
연면적0.3230.9571.0000.2820.253-0.0170.4620.6750.2490.2490.2490.0000.0000.5090.2100.000
경기장 폭0.3110.3170.2821.0000.6100.6240.3340.2650.8730.8730.8380.9640.9820.0001.0000.428
경기장 길이0.3990.2850.2530.6101.0000.6820.3260.1360.8730.8730.8380.9640.9820.0001.0000.428
경기장 면적0.5440.158-0.0170.6240.6821.0000.3770.1760.4930.4930.5230.5340.6680.0000.0000.346
좌석수_명0.4070.5740.4620.3340.3260.3771.0000.7240.1990.1990.0000.2560.1970.2740.0000.307
수용인원0.6080.7680.6750.2650.1360.1760.7241.0000.0000.0000.0000.2170.1710.2170.0000.000
시군구0.3450.4270.2490.8730.8730.4930.1990.0001.0001.0000.9600.5540.6740.5700.3980.284
소유기관0.3450.4270.2490.8730.8730.4930.1990.0001.0001.0000.9600.5540.6740.5700.3980.284
관리주체0.4360.3890.2490.8380.8380.5230.0000.0000.9600.9601.0000.4970.6580.6550.5410.269
경기장 바닥재료0.1450.0000.0000.9640.9640.5340.2560.2170.5540.5540.4971.0000.7180.0000.0000.374
경기장 면수0.3960.1020.0000.9820.9820.6680.1970.1710.6740.6740.6580.7181.0000.0000.0000.469
좌석형태0.2380.7110.5090.0000.0000.0000.2740.2170.5700.5700.6550.0000.0001.0000.4630.585
건축구조0.2690.0000.2101.0001.0000.0000.0000.0000.3980.3980.5410.0000.0000.4631.0000.487
준공연도0.2500.0000.0000.4280.4280.3460.3070.0000.2840.2840.2690.3740.4690.5850.4871.000

Missing values

2023-12-12T11:38:59.165854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:38:59.409756image/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.
2023-12-12T11:38:59.605851image/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

시군구시설명소유기관관리주체부지면적건축면적연면적경기장 바닥재료경기장 폭경기장 길이경기장 면적경기장 면수좌석수_명수용인원좌석형태건축구조준공연도
0목포시북항환경관리소축구장목포시북항환경관리소7665<NA><NA>인조잔디6410064001<NA><NA><NA><NA>2010
1목포시목포국제축구센터목포시목포시2149341611220845천연잔디7911627492391329132등받이철근콘크리트2009
2목포시목포국제축구센터인조잔디구장 1목포시목포시<NA><NA><NA>인조잔디7811526910312401240등받이철근콘크리트<NA>
3목포시목포국제축구센터인조잔디구장 2목포시목포시<NA><NA><NA>인조잔디6710973031370370등받이철근콘크리트<NA>
4목포시부주산 축구 인조잔디구장목포시체육시설관리과7900<NA><NA>인조잔디6510065001500500<NA><NA>2003
5여수시진남보조경기장여수시여수시14300<NA><NA>인조잔디99118116822<NA>200계단식철근콘크리트2001
6여수시진모 축구장 1여수시여수시51810106106인조잔디106116295233400400의자식철근콘크리트2009
7여수시진모 축구장 2여수시여수시51810106106천연잔디10611698411400400의자식철근콘크리트2009
8여수시진남 복합구장여수시여수시22984<NA><NA>인조잔디904540501<NA><NA><NA><NA>2017
9순천시상하수 축구장 1순천시순천시8214<NA>8214인조잔디6810571401120200의자식경량철골조2004
시군구시설명소유기관관리주체부지면적건축면적연면적경기장 바닥재료경기장 폭경기장 길이경기장 면적경기장 면수좌석수_명수용인원좌석형태건축구조준공연도
76신안군지도 운동장신안군신안군21867<NA><NA>천연잔디7011077001<NA><NA><NA><NA>2006
77신안군자은두모운동장신안군신안군9400<NA><NA>천연잔디7010573501<NA><NA><NA><NA>2006
78신안군안좌 해변운동장신안군신안군15300<NA><NA>천연잔디7010573501<NA><NA><NA><NA>2006
79신안군신안 축구장신안군신안군10400<NA><NA>인조잔디6810571401<NA><NA><NA><NA>2000
80신안군도초 종합운동장신안군신안군7234370358인조잔디6810571401<NA><NA><NA><NA>2013
81신안군하의 종합운동장신안군신안군11688180165인조잔디6410064001<NA><NA><NA><NA>2013
82신안군비금 대광축구장신안군신안군18980<NA><NA>천연잔디6810068001<NA><NA><NA><NA>2009
83신안군자은 둔장 운동장신안군신안군9820<NA><NA>천연잔디7511082501<NA><NA><NA><NA>2010
84신안군팔금운동장신안군신안군5880<NA><NA>천연잔디5010954501<NA><NA><NA><NA>2014
85신안군지도봉황경기장신안군신안군8177<NA><NA>인조잔디6410064001<NA><NA><NA><NA>2019