Overview

Dataset statistics

Number of variables15
Number of observations32
Missing cells60
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory132.1 B

Variable types

Text5
Numeric8
Categorical2

Dataset

Description전라남도 시군의 야구장(소유기관, 관리주체, 면적, 수용인원, 준공연도 등)에 대한 데이터를 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15037309/fileData.do

Alerts

건축면적 is highly overall correlated with 좌석수 and 1 other fieldsHigh correlation
중앙길이 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
1루_3루 is highly overall correlated with 면적High correlation
면적 is highly overall correlated with 중앙길이 and 2 other fieldsHigh correlation
좌석수 is highly overall correlated with 건축면적 and 1 other fieldsHigh correlation
수용인원 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
경기장 내야 바닥재료 is highly overall correlated with 경기장 외야 바닥재료High correlation
경기장 외야 바닥재료 is highly overall correlated with 경기장 내야 바닥재료High correlation
건축면적 has 15 (46.9%) missing valuesMissing
연면적 has 14 (43.8%) missing valuesMissing
1루_3루 has 1 (3.1%) missing valuesMissing
좌석수 has 15 (46.9%) missing valuesMissing
수용인원 has 15 (46.9%) missing valuesMissing
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:41:13.239509
Analysis finished2023-12-12 22:41:19.920646
Duration6.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Text

Distinct17
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T07:41:20.051069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96
Distinct characters31
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

Unique6 ?
Unique (%)18.8%

Sample

1st row목포시
2nd row목포시
3rd row여수시
4th row여수시
5th row순천시
ValueCountFrequency (%)
순천시 3
 
9.4%
영암군 3
 
9.4%
담양군 3
 
9.4%
함평군 3
 
9.4%
영광군 2
 
6.2%
목포시 2
 
6.2%
무안군 2
 
6.2%
나주시 2
 
6.2%
여수시 2
 
6.2%
장흥군 2
 
6.2%
Other values (7) 8
25.0%
2023-12-13T07:41:20.390979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 23
24.0%
시 9
 
9.4%
영 5
 
5.2%
순 4
 
4.2%
흥 4
 
4.2%
양 3
 
3.1%
천 3
 
3.1%
담 3
 
3.1%
암 3
 
3.1%
평 3
 
3.1%
Other values (21) 36
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 23
24.0%
시 9
 
9.4%
영 5
 
5.2%
순 4
 
4.2%
흥 4
 
4.2%
양 3
 
3.1%
천 3
 
3.1%
담 3
 
3.1%
암 3
 
3.1%
평 3
 
3.1%
Other values (21) 36
37.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 23
24.0%
시 9
 
9.4%
영 5
 
5.2%
순 4
 
4.2%
흥 4
 
4.2%
양 3
 
3.1%
천 3
 
3.1%
담 3
 
3.1%
암 3
 
3.1%
평 3
 
3.1%
Other values (21) 36
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 23
24.0%
시 9
 
9.4%
영 5
 
5.2%
순 4
 
4.2%
흥 4
 
4.2%
양 3
 
3.1%
천 3
 
3.1%
담 3
 
3.1%
암 3
 
3.1%
평 3
 
3.1%
Other values (21) 36
37.5%

시설명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T07:41:20.609760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.21875
Min length5

Characters and Unicode

Total characters263
Distinct characters90
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

Unique32 ?
Unique (%)100.0%

Sample

1st row목포야구장
2nd row목포 리틀야구장
3rd row진남 야구경기장
4th row진남 실내야구연습장
5th row상사호야구장 1
ValueCountFrequency (%)
야구장 6
 
13.0%
함평 2
 
4.3%
리틀야구장 2
 
4.3%
진남 2
 
4.3%
야구경기장 2
 
4.3%
상사호야구장 2
 
4.3%
무안생활야구장 1
 
2.2%
장흥생활체육야구장 1
 
2.2%
해남야구장 1
 
2.2%
마골 1
 
2.2%
Other values (26) 26
56.5%
2023-12-13T07:41:21.038035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 32
 
12.2%
야 30
 
11.4%
구 30
 
11.4%
14
 
5.3%
원 7
 
2.7%
공 7
 
2.7%
남 6
 
2.3%
육 5
 
1.9%
체 5
 
1.9%
포 4
 
1.5%
Other values (80) 123
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 245
93.2%
Space Separator 14
 
5.3%
Connector Punctuation 2
 
0.8%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 32
 
13.1%
야 30
 
12.2%
구 30
 
12.2%
원 7
 
2.9%
공 7
 
2.9%
남 6
 
2.4%
육 5
 
2.0%
체 5
 
2.0%
포 4
 
1.6%
생 4
 
1.6%
Other values (76) 115
46.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 245
93.2%
Common 18
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 32
 
13.1%
야 30
 
12.2%
구 30
 
12.2%
원 7
 
2.9%
공 7
 
2.9%
남 6
 
2.4%
육 5
 
2.0%
체 5
 
2.0%
포 4
 
1.6%
생 4
 
1.6%
Other values (76) 115
46.9%
Common
ValueCountFrequency (%)
14
77.8%
_ 2
 
11.1%
1 1
 
5.6%
2 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 245
93.2%
ASCII 18
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 32
 
13.1%
야 30
 
12.2%
구 30
 
12.2%
원 7
 
2.9%
공 7
 
2.9%
남 6
 
2.4%
육 5
 
2.0%
체 5
 
2.0%
포 4
 
1.6%
생 4
 
1.6%
Other values (76) 115
46.9%
ASCII
ValueCountFrequency (%)
14
77.8%
_ 2
 
11.1%
1 1
 
5.6%
2 1
 
5.6%

소유기관
Text

Distinct17
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T07:41:21.246743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.125
Min length3

Characters and Unicode

Total characters100
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)18.8%

Sample

1st row목포시
2nd row목포시
3rd row여수시
4th row여수시
5th row순천시
ValueCountFrequency (%)
순천시 3
 
9.4%
영암군 3
 
9.4%
담양군 3
 
9.4%
함평군 3
 
9.4%
영광군 2
 
6.2%
목포시 2
 
6.2%
무안군 2
 
6.2%
나주시 2
 
6.2%
여수시 2
 
6.2%
장흥군 2
 
6.2%
Other values (7) 8
25.0%
2023-12-13T07:41:21.687838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 22
22.0%
시 9
 
9.0%
영 5
 
5.0%
흥 4
 
4.0%
순 3
 
3.0%
담 3
 
3.0%
도 3
 
3.0%
양 3
 
3.0%
암 3
 
3.0%
평 3
 
3.0%
Other values (25) 42
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 22
22.0%
시 9
 
9.0%
영 5
 
5.0%
흥 4
 
4.0%
순 3
 
3.0%
담 3
 
3.0%
도 3
 
3.0%
양 3
 
3.0%
암 3
 
3.0%
평 3
 
3.0%
Other values (25) 42
42.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 22
22.0%
시 9
 
9.0%
영 5
 
5.0%
흥 4
 
4.0%
순 3
 
3.0%
담 3
 
3.0%
도 3
 
3.0%
양 3
 
3.0%
암 3
 
3.0%
평 3
 
3.0%
Other values (25) 42
42.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 22
22.0%
시 9
 
9.0%
영 5
 
5.0%
흥 4
 
4.0%
순 3
 
3.0%
담 3
 
3.0%
도 3
 
3.0%
양 3
 
3.0%
암 3
 
3.0%
평 3
 
3.0%
Other values (25) 42
42.0%

관리주체
Text

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T07:41:21.913857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.71875
Min length3

Characters and Unicode

Total characters119
Distinct characters55
Distinct categories3 ?
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 (%)28.1%

Sample

1st row야구소프트볼협회
2nd row목포시
3rd row여수시
4th row여수시
5th row순천시
ValueCountFrequency (%)
순천시 3
 
8.8%
담양군 3
 
8.8%
함평군 3
 
8.8%
영광군 2
 
5.9%
무안군 2
 
5.9%
장흥군 2
 
5.9%
영암군 2
 
5.9%
고흥군 2
 
5.9%
나주시 2
 
5.9%
여수시 2
 
5.9%
Other values (11) 11
32.4%
2023-12-13T07:41:22.265555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 20
 
16.8%
시 8
 
6.7%
순 4
 
3.4%
흥 4
 
3.4%
영 4
 
3.4%
함 3
 
2.5%
평 3
 
2.5%
담 3
 
2.5%
양 3
 
2.5%
천 3
 
2.5%
Other values (45) 64
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 116
97.5%
Space Separator 2
 
1.7%
Connector Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 20
 
17.2%
시 8
 
6.9%
순 4
 
3.4%
흥 4
 
3.4%
영 4
 
3.4%
함 3
 
2.6%
평 3
 
2.6%
담 3
 
2.6%
양 3
 
2.6%
천 3
 
2.6%
Other values (43) 61
52.6%
Space Separator
ValueCountFrequency (%)
2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 116
97.5%
Common 3
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 20
 
17.2%
시 8
 
6.9%
순 4
 
3.4%
흥 4
 
3.4%
영 4
 
3.4%
함 3
 
2.6%
평 3
 
2.6%
담 3
 
2.6%
양 3
 
2.6%
천 3
 
2.6%
Other values (43) 61
52.6%
Common
ValueCountFrequency (%)
2
66.7%
_ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 116
97.5%
ASCII 3
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 20
 
17.2%
시 8
 
6.9%
순 4
 
3.4%
흥 4
 
3.4%
영 4
 
3.4%
함 3
 
2.6%
평 3
 
2.6%
담 3
 
2.6%
양 3
 
2.6%
천 3
 
2.6%
Other values (43) 61
52.6%
ASCII
ValueCountFrequency (%)
2
66.7%
_ 1
33.3%

부지면적
Real number (ℝ)

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45516.125
Minimum874
Maximum390198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:22.399988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum874
5-th percentile6877
Q115750
median28505
Q343001
95-th percentile133741.1
Maximum390198
Range389324
Interquartile range (IQR)27251

Descriptive statistics

Standard deviation70559.764
Coefficient of variation (CV)1.5502147
Kurtosis19.369376
Mean45516.125
Median Absolute Deviation (MAD)13944
Skewness4.1540168
Sum1456516
Variance4.9786803 × 109
MonotonicityNot monotonic
2023-12-13T07:41:22.534051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
43340 2
 
6.2%
37229 2
 
6.2%
24624 1
 
3.1%
19000 1
 
3.1%
12350 1
 
3.1%
38931 1
 
3.1%
48500 1
 
3.1%
39129 1
 
3.1%
15000 1
 
3.1%
23031 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
874 1
3.1%
5260 1
3.1%
8200 1
3.1%
8686 1
3.1%
9808 1
3.1%
12350 1
3.1%
12662 1
3.1%
15000 1
3.1%
16000 1
3.1%
16860 1
3.1%
ValueCountFrequency (%)
390198 1
3.1%
166058 1
3.1%
107300 1
3.1%
71104 1
3.1%
48841 1
3.1%
48500 1
3.1%
43340 2
6.2%
42888 1
3.1%
40434 1
3.1%
39129 1
3.1%

건축면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)94.1%
Missing15
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean710.70588
Minimum63
Maximum3813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:22.676418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile73.4
Q1163
median280
Q3874
95-th percentile1949
Maximum3813
Range3750
Interquartile range (IQR)711

Descriptive statistics

Standard deviation936.68189
Coefficient of variation (CV)1.31796
Kurtosis7.4992862
Mean710.70588
Median Absolute Deviation (MAD)196
Skewness2.530988
Sum12082
Variance877372.97
MonotonicityNot monotonic
2023-12-13T07:41:22.791663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1483 2
 
6.2%
163 1
 
3.1%
200 1
 
3.1%
198 1
 
3.1%
76 1
 
3.1%
125 1
 
3.1%
280 1
 
3.1%
334 1
 
3.1%
63 1
 
3.1%
262 1
 
3.1%
Other values (6) 6
 
18.8%
(Missing) 15
46.9%
ValueCountFrequency (%)
63 1
3.1%
76 1
3.1%
125 1
3.1%
150 1
3.1%
163 1
3.1%
198 1
3.1%
200 1
3.1%
262 1
3.1%
280 1
3.1%
334 1
3.1%
ValueCountFrequency (%)
3813 1
3.1%
1483 2
6.2%
1245 1
3.1%
874 1
3.1%
857 1
3.1%
476 1
3.1%
334 1
3.1%
280 1
3.1%
262 1
3.1%
200 1
3.1%

연면적
Text

MISSING 

Distinct17
Distinct (%)94.4%
Missing14
Missing (%)43.8%
Memory size388.0 B
2023-12-13T07:41:22.941201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3333333
Min length2

Characters and Unicode

Total characters60
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)88.9%

Sample

1st row334
2nd row63
3rd row476
4th row982
5th row992
ValueCountFrequency (%)
5,483 2
 
11.1%
550 1
 
5.6%
334 1
 
5.6%
1,245 1
 
5.6%
200 1
 
5.6%
198 1
 
5.6%
76 1
 
5.6%
125 1
 
5.6%
415 1
 
5.6%
476 1
 
5.6%
Other values (7) 7
38.9%
2023-12-13T07:41:23.241570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 8
13.3%
4 6
10.0%
3 6
10.0%
2 6
10.0%
0 6
10.0%
1 6
10.0%
8 5
8.3%
7 5
8.3%
, 4
6.7%
6 4
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
93.3%
Other Punctuation 4
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 8
14.3%
4 6
10.7%
3 6
10.7%
2 6
10.7%
0 6
10.7%
1 6
10.7%
8 5
8.9%
7 5
8.9%
6 4
7.1%
9 4
7.1%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 8
13.3%
4 6
10.0%
3 6
10.0%
2 6
10.0%
0 6
10.0%
1 6
10.0%
8 5
8.3%
7 5
8.3%
, 4
6.7%
6 4
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 8
13.3%
4 6
10.0%
3 6
10.0%
2 6
10.0%
0 6
10.0%
1 6
10.0%
8 5
8.3%
7 5
8.3%
, 4
6.7%
6 4
6.7%

경기장 내야 바닥재료
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size388.0 B
마사토
11 
인조잔디
10 
천연잔디
3 
토사
3 
인조
3 
Other values (2)
2 

Length

Max length8
Median length4
Mean length3.40625
Min length2

Unique

Unique2 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
마사토 11
34.4%
인조잔디 10
31.2%
천연잔디 3
 
9.4%
토사 3
 
9.4%
인조 3
 
9.4%
천연잔디(마사) 1
 
3.1%
쳔연잔디 1
 
3.1%

Length

2023-12-13T07:41:23.379955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:23.496077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마사토 11
34.4%
인조잔디 10
31.2%
천연잔디 3
 
9.4%
토사 3
 
9.4%
인조 3
 
9.4%
천연잔디(마사 1
 
3.1%
쳔연잔디 1
 
3.1%

경기장 외야 바닥재료
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size388.0 B
마사토
9 
인조잔디
9 
토사
5 
천연잔디
5 
인조
2 
Other values (2)
2 

Length

Max length8
Median length4
Mean length3.40625
Min length2

Unique

Unique2 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
마사토 9
28.1%
인조잔디 9
28.1%
토사 5
15.6%
천연잔디 5
15.6%
인조 2
 
6.2%
<NA> 1
 
3.1%
천연잔디(마사) 1
 
3.1%

Length

2023-12-13T07:41:23.644746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:23.774995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마사토 9
28.1%
인조잔디 9
28.1%
토사 5
15.6%
천연잔디 5
15.6%
인조 2
 
6.2%
na 1
 
3.1%
천연잔디(마사 1
 
3.1%

중앙길이
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.46875
Minimum38
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:23.909250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile66.95
Q199.75
median111
Q3122
95-th percentile124.45
Maximum140
Range102
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation21.428026
Coefficient of variation (CV)0.20316943
Kurtosis2.1390087
Mean105.46875
Median Absolute Deviation (MAD)11
Skewness-1.3697235
Sum3375
Variance459.16028
MonotonicityNot monotonic
2023-12-13T07:41:24.044027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
122 4
 
12.5%
100 4
 
12.5%
110 3
 
9.4%
117 2
 
6.2%
116 2
 
6.2%
123 2
 
6.2%
115 2
 
6.2%
80 1
 
3.1%
140 1
 
3.1%
124 1
 
3.1%
Other values (10) 10
31.2%
ValueCountFrequency (%)
38 1
 
3.1%
62 1
 
3.1%
71 1
 
3.1%
75 1
 
3.1%
80 1
 
3.1%
88 1
 
3.1%
93 1
 
3.1%
99 1
 
3.1%
100 4
12.5%
108 1
 
3.1%
ValueCountFrequency (%)
140 1
 
3.1%
125 1
 
3.1%
124 1
 
3.1%
123 2
6.2%
122 4
12.5%
117 2
6.2%
116 2
6.2%
115 2
6.2%
112 1
 
3.1%
110 3
9.4%

1루_3루
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)51.6%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean73.967742
Minimum19
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:24.165595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile27
Q138
median95
Q399
95-th percentile115
Maximum120
Range101
Interquartile range (IQR)61

Descriptive statistics

Standard deviation33.403078
Coefficient of variation (CV)0.4515898
Kurtosis-1.5563567
Mean73.967742
Median Absolute Deviation (MAD)15
Skewness-0.42548253
Sum2293
Variance1115.7656
MonotonicityNot monotonic
2023-12-13T07:41:24.289130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
99 5
15.6%
95 4
12.5%
38 4
12.5%
98 3
9.4%
27 2
 
6.2%
30 2
 
6.2%
120 2
 
6.2%
110 1
 
3.1%
92 1
 
3.1%
19 1
 
3.1%
Other values (6) 6
18.8%
ValueCountFrequency (%)
19 1
 
3.1%
27 2
6.2%
28 1
 
3.1%
30 2
6.2%
38 4
12.5%
39 1
 
3.1%
60 1
 
3.1%
80 1
 
3.1%
90 1
 
3.1%
92 1
 
3.1%
ValueCountFrequency (%)
120 2
 
6.2%
110 1
 
3.1%
100 1
 
3.1%
99 5
15.6%
98 3
9.4%
95 4
12.5%
92 1
 
3.1%
90 1
 
3.1%
80 1
 
3.1%
60 1
 
3.1%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10611.281
Minimum760
Maximum22000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:24.437323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum760
5-th percentile4500.5
Q18126.5
median11366.5
Q312762.25
95-th percentile16450
Maximum22000
Range21240
Interquartile range (IQR)4635.75

Descriptive statistics

Standard deviation4129.1659
Coefficient of variation (CV)0.38912982
Kurtosis1.2599267
Mean10611.281
Median Absolute Deviation (MAD)2317.5
Skewness0.10578852
Sum339561
Variance17050011
MonotonicityNot monotonic
2023-12-13T07:41:24.563188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
13150 2
 
6.2%
12000 2
 
6.2%
12265 1
 
3.1%
8650 1
 
3.1%
11760 1
 
3.1%
7550 1
 
3.1%
11193 1
 
3.1%
4000 1
 
3.1%
5000 1
 
3.1%
13662 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
760 1
3.1%
4000 1
3.1%
4910 1
3.1%
5000 1
3.1%
5700 1
3.1%
7550 1
3.1%
7900 1
3.1%
7906 1
3.1%
8200 1
3.1%
8650 1
3.1%
ValueCountFrequency (%)
22000 1
3.1%
17000 1
3.1%
16000 1
3.1%
13867 1
3.1%
13662 1
3.1%
13500 1
3.1%
13150 2
6.2%
12633 1
3.1%
12598 1
3.1%
12265 1
3.1%

좌석수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)64.7%
Missing15
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean146.76471
Minimum30
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:24.673188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile38
Q180
median100
Q3200
95-th percentile366.8
Maximum370
Range340
Interquartile range (IQR)120

Descriptive statistics

Standard deviation115.61765
Coefficient of variation (CV)0.78777558
Kurtosis-0.35149113
Mean146.76471
Median Absolute Deviation (MAD)50
Skewness1.055113
Sum2495
Variance13367.441
MonotonicityNot monotonic
2023-12-13T07:41:25.069444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
80 3
 
9.4%
100 3
 
9.4%
300 2
 
6.2%
40 2
 
6.2%
370 1
 
3.1%
366 1
 
3.1%
200 1
 
3.1%
60 1
 
3.1%
30 1
 
3.1%
99 1
 
3.1%
(Missing) 15
46.9%
ValueCountFrequency (%)
30 1
 
3.1%
40 2
6.2%
60 1
 
3.1%
80 3
9.4%
99 1
 
3.1%
100 3
9.4%
150 1
 
3.1%
200 1
 
3.1%
300 2
6.2%
366 1
 
3.1%
ValueCountFrequency (%)
370 1
 
3.1%
366 1
 
3.1%
300 2
6.2%
200 1
 
3.1%
150 1
 
3.1%
100 3
9.4%
99 1
 
3.1%
80 3
9.4%
60 1
 
3.1%
40 2
6.2%

수용인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)52.9%
Missing15
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean176.17647
Minimum30
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:25.172117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q199
median100
Q3300
95-th percentile500
Maximum500
Range470
Interquartile range (IQR)201

Descriptive statistics

Standard deviation155.80606
Coefficient of variation (CV)0.88437498
Kurtosis0.25909699
Mean176.17647
Median Absolute Deviation (MAD)50
Skewness1.2315085
Sum2995
Variance24275.529
MonotonicityNot monotonic
2023-12-13T07:41:25.274772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
100 6
 
18.8%
300 2
 
6.2%
500 2
 
6.2%
30 2
 
6.2%
366 1
 
3.1%
99 1
 
3.1%
150 1
 
3.1%
80 1
 
3.1%
40 1
 
3.1%
(Missing) 15
46.9%
ValueCountFrequency (%)
30 2
 
6.2%
40 1
 
3.1%
80 1
 
3.1%
99 1
 
3.1%
100 6
18.8%
150 1
 
3.1%
300 2
 
6.2%
366 1
 
3.1%
500 2
 
6.2%
ValueCountFrequency (%)
500 2
 
6.2%
366 1
 
3.1%
300 2
 
6.2%
150 1
 
3.1%
100 6
18.8%
99 1
 
3.1%
80 1
 
3.1%
40 1
 
3.1%
30 2
 
6.2%

준공연도
Real number (ℝ)

Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.7188
Minimum1990
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:41:25.389056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile2002.2
Q12012.75
median2014.5
Q32017.25
95-th percentile2020.45
Maximum2022
Range32
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation6.4269311
Coefficient of variation (CV)0.0031915734
Kurtosis5.3294419
Mean2013.7188
Median Absolute Deviation (MAD)2.5
Skewness-1.9631386
Sum64439
Variance41.305444
MonotonicityNot monotonic
2023-12-13T07:41:25.496362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2014 5
15.6%
2019 3
9.4%
2015 3
9.4%
2017 3
9.4%
2013 3
9.4%
2020 2
 
6.2%
2009 2
 
6.2%
2012 2
 
6.2%
2016 2
 
6.2%
2000 1
 
3.1%
Other values (6) 6
18.8%
ValueCountFrequency (%)
1990 1
 
3.1%
2000 1
 
3.1%
2004 1
 
3.1%
2008 1
 
3.1%
2009 2
 
6.2%
2012 2
 
6.2%
2013 3
9.4%
2014 5
15.6%
2015 3
9.4%
2016 2
 
6.2%
ValueCountFrequency (%)
2022 1
 
3.1%
2021 1
 
3.1%
2020 2
 
6.2%
2019 3
9.4%
2018 1
 
3.1%
2017 3
9.4%
2016 2
 
6.2%
2015 3
9.4%
2014 5
15.6%
2013 3
9.4%

Interactions

2023-12-13T07:41:18.580899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:13.870101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.643557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.224631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.753590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.346762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.114403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.788696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.699969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:13.976953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.721053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.291823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.830841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.415065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.175941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.875837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.814898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.086145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.794371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.359207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.909842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.490692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.251330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.985856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.908039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.170324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.868337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.418618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.995219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.554593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.344081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.066538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:19.013113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.268077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.941616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.485730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.061175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.627989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.423570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.157081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:19.094355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.371162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.015819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.551367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.145997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.917083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.507543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.280502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:19.201458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.484594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.092125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.624626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.214442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.986544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.618907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.385839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:19.284721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:14.563012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.156271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:15.685443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:16.283589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.053483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:17.710857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:41:18.472730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:41:25.615578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구시설명소유기관관리주체부지면적건축면적연면적경기장 내야 바닥재료경기장 외야 바닥재료중앙길이1루_3루면적좌석수수용인원준공연도
시군구1.0001.0001.0001.0000.0000.6610.9730.5800.6700.0000.0000.0000.8440.4240.432
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소유기관1.0001.0001.0001.0000.0000.6610.9730.5800.6700.0000.0000.0000.8440.4240.432
관리주체1.0001.0001.0001.0000.6280.0000.9750.4330.6450.0000.0000.2260.8440.4240.000
부지면적0.0001.0000.0000.6281.0000.3370.0000.0000.3610.4650.0000.0000.3310.7190.000
건축면적0.6611.0000.6610.0000.3371.0001.0000.0000.0000.8010.4600.4280.5740.4810.000
연면적0.9731.0000.9730.9750.0001.0001.0001.0001.0000.4171.0000.6561.0001.0001.000
경기장 내야 바닥재료0.5801.0000.5800.4330.0000.0001.0001.0000.9340.0000.0000.0000.7670.3190.596
경기장 외야 바닥재료0.6701.0000.6700.6450.3610.0001.0000.9341.0000.0000.0000.0000.6780.5970.210
중앙길이0.0001.0000.0000.0000.4650.8010.4170.0000.0001.0000.6470.6970.1120.0000.627
1루_3루0.0001.0000.0000.0000.0000.4601.0000.0000.0000.6471.0000.0000.5100.2930.155
면적0.0001.0000.0000.2260.0000.4280.6560.0000.0000.6970.0001.0000.0000.0000.000
좌석수0.8441.0000.8440.8440.3310.5741.0000.7670.6780.1120.5100.0001.0000.9340.000
수용인원0.4241.0000.4240.4240.7190.4811.0000.3190.5970.0000.2930.0000.9341.0000.000
준공연도0.4321.0000.4320.0000.0000.0001.0000.5960.2100.6270.1550.0000.0000.0001.000
2023-12-13T07:41:25.749754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경기장 외야 바닥재료경기장 내야 바닥재료
경기장 외야 바닥재료1.0000.847
경기장 내야 바닥재료0.8471.000
2023-12-13T07:41:25.857742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부지면적건축면적중앙길이1루_3루면적좌석수수용인원준공연도경기장 내야 바닥재료경기장 외야 바닥재료
부지면적1.0000.2350.194-0.2700.0990.0860.3580.0450.0000.237
건축면적0.2351.000-0.031-0.118-0.0220.5950.506-0.3300.0000.000
중앙길이0.194-0.0311.0000.3870.6160.4400.5760.0390.0000.000
1루_3루-0.270-0.1180.3871.0000.6150.2550.275-0.1960.0000.000
면적0.099-0.0220.6160.6151.0000.4440.651-0.0140.0000.000
좌석수0.0860.5950.4400.2550.4441.0000.924-0.1970.3200.439
수용인원0.3580.5060.5760.2750.6510.9241.000-0.0080.0640.409
준공연도0.045-0.3300.039-0.196-0.014-0.197-0.0081.0000.1390.000
경기장 내야 바닥재료0.0000.0000.0000.0000.0000.3200.0640.1391.0000.847
경기장 외야 바닥재료0.2370.0000.0000.0000.0000.4390.4090.0000.8471.000

Missing values

2023-12-13T07:41:19.422328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:41:19.634909image/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.
2023-12-13T07:41:19.799148image/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 내야 바닥재료경기장 외야 바닥재료중앙길이1루_3루면적좌석수수용인원준공연도
0목포시목포야구장목포시야구소프트볼협회24624334334마사토마사토12299131503003002018
1목포시목포 리틀야구장목포시목포시172716363인조잔디인조잔디75305700<NA><NA>2022
2여수시진남 야구경기장여수시여수시40434476476인조잔디인조잔디11598160003705002008
3여수시진남 실내야구연습장여수시여수시874874982인조잔디<NA>38<NA>760<NA>302009
4순천시상사호야구장 1순천시순천시43340<NA><NA>마사토마사토80604910<NA><NA>2015
5순천시상사호야구장 2순천시순천시43340<NA><NA>마사토마사토110907906<NA><NA>2015
6순천시팔마야구장순천시순천시25000857992인조잔디인조잔디12299125983663662017
7나주시영산강둔치체육공원 야구장나주시나주시8200<NA>8,200마사토토사100958200<NA><NA>2012
8나주시영산강저류지체육공원나주시나주시107300<NA><NA>마사토마사토1229913150801002015
9담양군백진공원야구장담양군담양군16000<NA><NA>천연잔디천연잔디1229913867<NA><NA>2013
시군구시설명소유기관관리주체부지면적건축면적연면적경기장 내야 바닥재료경기장 외야 바닥재료중앙길이1루_3루면적좌석수수용인원준공연도
22영암군남해포권역야구장영암군남해포권역166058125125인조인조1163810516<NA><NA>2014
23무안군무안생활야구장무안군무안군12662<NA><NA>마사토마사토1001201366240<NA>2014
24무안군유소년야구장무안군무안군52607676마사토마사토7130500030302019
25함평군함평 리틀야구장함평군함평군980814835,483토사토사621940001001002004
26함평군전남 야구경기장함평군함평군23031198198토사토사1001201200099992000
27함평군함평 야구장함평군함평군15000200200천연잔디(마사)천연잔디(마사)12599120001501502014
28영광군대마산단야구장영광군영광군39129<NA><NA>쳔연잔디천연잔디117921119380802013
29영광군백수수변공원간이야구장영광군영광군48500<NA><NA>천연잔디천연잔디8838755040402020
30완도군완도야구장완도군완도군38931163163인조토사11795117601001002009
31진도군아리랑야구장진도군진도군12350<NA><NA>마사토천연잔디123988650<NA>1002019