Overview

Dataset statistics

Number of variables15
Number of observations96
Missing cells267
Missing cells (%)18.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.2 KiB
Average record size in memory130.4 B

Variable types

Numeric9
Categorical5
Text1

Dataset

Description경상남도 테니스장 현황을 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3080569

Alerts

시도 has constant value ""Constant
연번 is highly overall correlated with 시군구 and 2 other fieldsHigh correlation
부지면적(㎡) is highly overall correlated with 건축면적(㎡) and 6 other fieldsHigh correlation
건축면적(㎡) is highly overall correlated with 부지면적(㎡) and 4 other fieldsHigh correlation
연면적(㎡) is highly overall correlated with 부지면적(㎡) and 4 other fieldsHigh correlation
경기장 면적(㎡) is highly overall correlated with 부지면적(㎡) and 3 other fieldsHigh correlation
경기장 코트 면수 is highly overall correlated with 부지면적(㎡) and 5 other fieldsHigh correlation
관람석 좌석수 is highly overall correlated with 부지면적(㎡) and 3 other fieldsHigh correlation
관람석 수용인원(명) is highly overall correlated with 부지면적(㎡) and 3 other fieldsHigh correlation
시군구 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
소유기관 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
관리주체 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
경기장 바닥재료 is highly overall correlated with 부지면적(㎡) and 4 other fieldsHigh correlation
부지면적(㎡) has 10 (10.4%) missing valuesMissing
건축면적(㎡) has 57 (59.4%) missing valuesMissing
연면적(㎡) has 57 (59.4%) missing valuesMissing
경기장 코트 면수 has 1 (1.0%) missing valuesMissing
관람석 좌석수 has 77 (80.2%) missing valuesMissing
관람석 수용인원(명) has 61 (63.5%) missing valuesMissing
준공연도 has 4 (4.2%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique
건축면적(㎡) has 2 (2.1%) zerosZeros
연면적(㎡) has 2 (2.1%) zerosZeros
관람석 좌석수 has 1 (1.0%) zerosZeros

Reproduction

Analysis started2024-04-17 11:51:46.063377
Analysis finished2024-04-17 11:51:53.382530
Duration7.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.5
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:53.458268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.75
Q124.75
median48.5
Q372.25
95-th percentile91.25
Maximum96
Range95
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation27.856777
Coefficient of variation (CV)0.57436653
Kurtosis-1.2
Mean48.5
Median Absolute Deviation (MAD)24
Skewness0
Sum4656
Variance776
MonotonicityStrictly increasing
2024-04-17T20:51:53.597723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
50 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%
87 1
1.0%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
경상남도
96 

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 96
100.0%

Length

2024-04-17T20:51:53.710182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:51:53.788775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 96
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size900.0 B
창원시
24 
거제시
9 
합천군
7 
의령군
6 
산청군
6 
Other values (16)
44 

Length

Max length5
Median length3
Mean length3.15625
Min length3

Unique

Unique5 ?
Unique (%)5.2%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 24
25.0%
거제시 9
 
9.4%
합천군 7
 
7.3%
의령군 6
 
6.2%
산청군 6
 
6.2%
통영시 6
 
6.2%
양산시 6
 
6.2%
진주시 5
 
5.2%
김해시 5
 
5.2%
창녕군 3
 
3.1%
Other values (11) 19
19.8%

Length

2024-04-17T20:51:53.878013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 24
25.0%
거제시 9
 
9.4%
합천군 7
 
7.3%
김해시 7
 
7.3%
의령군 6
 
6.2%
산청군 6
 
6.2%
통영시 6
 
6.2%
양산시 6
 
6.2%
남해군 5
 
5.2%
진주시 5
 
5.2%
Other values (8) 15
15.6%

시설명
Text

UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2024-04-17T20:51:54.096556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length9.6354167
Min length6

Characters and Unicode

Total characters925
Distinct characters135
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

Unique96 ?
Unique (%)100.0%

Sample

1st row창원 종합테니스장
2nd row마산종합운동장 테니스장
3rd row창원덕동시립 테니스장
4th row진해공설운동장 테니스장
5th row동읍운동장 테니스장
ValueCountFrequency (%)
테니스장 69
39.0%
공설테니스장 6
 
3.4%
시립테니스장 2
 
1.1%
창원 1
 
0.6%
의령공설운동장 1
 
0.6%
창녕 1
 
0.6%
환경기초시설 1
 
0.6%
함안군 1
 
0.6%
함안 1
 
0.6%
대의공설운동장 1
 
0.6%
Other values (93) 93
52.5%
2024-04-17T20:51:54.428375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 119
 
12.9%
스 98
 
10.6%
테 94
 
10.2%
니 94
 
10.2%
82
 
8.9%
공 33
 
3.6%
동 28
 
3.0%
원 23
 
2.5%
운 20
 
2.2%
체 19
 
2.1%
Other values (125) 315
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 838
90.6%
Space Separator 82
 
8.9%
Uppercase Letter 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 119
14.2%
스 98
 
11.7%
테 94
 
11.2%
니 94
 
11.2%
공 33
 
3.9%
동 28
 
3.3%
원 23
 
2.7%
운 20
 
2.4%
체 19
 
2.3%
육 19
 
2.3%
Other values (119) 291
34.7%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 838
90.6%
Common 85
 
9.2%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 119
14.2%
스 98
 
11.7%
테 94
 
11.2%
니 94
 
11.2%
공 33
 
3.9%
동 28
 
3.3%
원 23
 
2.7%
운 20
 
2.4%
체 19
 
2.3%
육 19
 
2.3%
Other values (119) 291
34.7%
Common
ValueCountFrequency (%)
82
96.5%
) 1
 
1.2%
( 1
 
1.2%
2 1
 
1.2%
Latin
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 838
90.6%
ASCII 87
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 119
14.2%
스 98
 
11.7%
테 94
 
11.2%
니 94
 
11.2%
공 33
 
3.9%
동 28
 
3.3%
원 23
 
2.7%
운 20
 
2.4%
체 19
 
2.3%
육 19
 
2.3%
Other values (119) 291
34.7%
ASCII
ValueCountFrequency (%)
82
94.3%
C 1
 
1.1%
I 1
 
1.1%
) 1
 
1.1%
( 1
 
1.1%
2 1
 
1.1%

소유기관
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
창원시
23 
거제시
6 
의령군
6 
합천군
6 
통영시
6 
Other values (19)
49 

Length

Max length5
Median length5
Mean length4.5208333
Min length3

Unique

Unique8 ?
Unique (%)8.3%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 23
24.0%
거제시 6
 
6.2%
의령군 6
 
6.2%
합천군 6
 
6.2%
통영시 6
 
6.2%
김해시 6
 
6.2%
양산시 5
 
5.2%
산청군 5
 
5.2%
진주시 5
 
5.2%
남해군 5
 
5.2%
Other values (14) 23
24.0%

Length

2024-04-17T20:51:54.559842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 24
25.0%
거제시 9
 
9.4%
합천군 7
 
7.3%
김해시 7
 
7.3%
의령군 6
 
6.2%
통영시 6
 
6.2%
양산시 6
 
6.2%
산청군 6
 
6.2%
남해군 5
 
5.2%
진주시 5
 
5.2%
Other values (8) 15
15.6%

관리주체
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size900.0 B
위탁(테니스협회)
29 
위탁(테니스협회)
9 
위탁(거제해양관광개발공사)
7 
산청군
6 
김해시테니스협회
6 
Other values (28)
39 

Length

Max length17
Median length13
Mean length9.6041667
Min length2

Unique

Unique20 ?
Unique (%)20.8%

Sample

1st row 시설관리공단
2nd row 시설관리공단
3rd row 시설관리공단
4th row 위탁(테니스협회)
5th row 위탁(테니스협회)

Common Values

ValueCountFrequency (%)
위탁(테니스협회) 29
30.2%
위탁(테니스협회) 9
 
9.4%
위탁(거제해양관광개발공사) 7
 
7.3%
산청군 6
 
6.2%
김해시테니스협회 6
 
6.2%
체육시설사업소 4
 
4.2%
시설관리공단 3
 
3.1%
직영 2
 
2.1%
위탁(통영관광개발공사) 2
 
2.1%
진해구청 문화위생과 2
 
2.1%
Other values (23) 26
27.1%

Length

2024-04-17T20:51:54.688708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
위탁(테니스협회 38
37.3%
김해시테니스협회 7
 
6.9%
위탁(거제해양관광개발공사 7
 
6.9%
산청군 6
 
5.9%
문화위생과 4
 
3.9%
체육시설사업소 4
 
3.9%
시설관리공단 3
 
2.9%
위탁(사천시테니스협회 2
 
2.0%
위탁(밀양시테니스협회 2
 
2.0%
위탁(창녕군개발공사 2
 
2.0%
Other values (24) 27
26.5%

부지면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)96.5%
Missing10
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean6805.8372
Minimum500
Maximum72775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:54.806401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile730
Q11402.5
median2361
Q34991.5
95-th percentile28288.75
Maximum72775
Range72275
Interquartile range (IQR)3589

Descriptive statistics

Standard deviation12583.137
Coefficient of variation (CV)1.8488742
Kurtosis14.622582
Mean6805.8372
Median Absolute Deviation (MAD)1218
Skewness3.643575
Sum585302
Variance1.5833534 × 108
MonotonicityNot monotonic
2024-04-17T20:51:54.927700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2025 2
 
2.1%
1800 2
 
2.1%
1100 2
 
2.1%
710 1
 
1.0%
1511 1
 
1.0%
5900 1
 
1.0%
2250 1
 
1.0%
12652 1
 
1.0%
708 1
 
1.0%
3609 1
 
1.0%
Other values (73) 73
76.0%
(Missing) 10
 
10.4%
ValueCountFrequency (%)
500 1
1.0%
660 1
1.0%
708 1
1.0%
710 1
1.0%
720 1
1.0%
760 1
1.0%
800 1
1.0%
952 1
1.0%
960 1
1.0%
1000 1
1.0%
ValueCountFrequency (%)
72775 1
1.0%
66871 1
1.0%
45000 1
1.0%
32647 1
1.0%
28986 1
1.0%
26197 1
1.0%
25120 1
1.0%
24396 1
1.0%
19682 1
1.0%
18380 1
1.0%

건축면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)92.3%
Missing57
Missing (%)59.4%
Infinite0
Infinite (%)0.0%
Mean197.94872
Minimum0
Maximum2452
Zeros2
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:55.041527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.1
Q153
median90
Q3144.5
95-th percentile613.8
Maximum2452
Range2452
Interquartile range (IQR)91.5

Descriptive statistics

Standard deviation414.23188
Coefficient of variation (CV)2.0926222
Kurtosis24.4341
Mean197.94872
Median Absolute Deviation (MAD)42
Skewness4.7275472
Sum7720
Variance171588.05
MonotonicityNot monotonic
2024-04-17T20:51:55.157343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
90 2
 
2.1%
125 2
 
2.1%
0 2
 
2.1%
238 1
 
1.0%
81 1
 
1.0%
108 1
 
1.0%
48 1
 
1.0%
72 1
 
1.0%
84 1
 
1.0%
315 1
 
1.0%
Other values (26) 26
27.1%
(Missing) 57
59.4%
ValueCountFrequency (%)
0 2
2.1%
19 1
1.0%
30 1
1.0%
32 1
1.0%
33 1
1.0%
43 1
1.0%
45 1
1.0%
48 1
1.0%
51 1
1.0%
55 1
1.0%
ValueCountFrequency (%)
2452 1
1.0%
1080 1
1.0%
562 1
1.0%
315 1
1.0%
248 1
1.0%
241 1
1.0%
238 1
1.0%
199 1
1.0%
169 1
1.0%
152 1
1.0%

연면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)89.7%
Missing57
Missing (%)59.4%
Infinite0
Infinite (%)0.0%
Mean202.28205
Minimum0
Maximum2452
Zeros2
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:55.275480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.1
Q149.5
median80
Q3153
95-th percentile613.8
Maximum2452
Range2452
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation416.15078
Coefficient of variation (CV)2.0572798
Kurtosis23.714645
Mean202.28205
Median Absolute Deviation (MAD)45
Skewness4.6320223
Sum7889
Variance173181.47
MonotonicityNot monotonic
2024-04-17T20:51:55.392478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
78 3
 
3.1%
125 2
 
2.1%
0 2
 
2.1%
45 1
 
1.0%
61 1
 
1.0%
30 1
 
1.0%
40 1
 
1.0%
99 1
 
1.0%
315 1
 
1.0%
137 1
 
1.0%
Other values (25) 25
26.0%
(Missing) 57
59.4%
ValueCountFrequency (%)
0 2
2.1%
19 1
1.0%
30 1
1.0%
32 1
1.0%
33 1
1.0%
40 1
1.0%
43 1
1.0%
45 1
1.0%
48 1
1.0%
51 1
1.0%
ValueCountFrequency (%)
2452 1
1.0%
1080 1
1.0%
562 1
1.0%
413 1
1.0%
315 1
1.0%
248 1
1.0%
241 1
1.0%
219 1
1.0%
199 1
1.0%
169 1
1.0%

경기장 바닥재료
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
클레이
32 
마사토
14 
아크릴
8 
아크릴, 케미칼
7 
토사
6 
Other values (20)
29 

Length

Max length18
Median length3
Mean length4.3541667
Min length2

Unique

Unique16 ?
Unique (%)16.7%

Sample

1st row우레탄 12, 마사토 10
2nd row마사토
3rd row케미컬
4th row클레이
5th row클레이

Common Values

ValueCountFrequency (%)
클레이 32
33.3%
마사토 14
14.6%
아크릴 8
 
8.3%
아크릴, 케미칼 7
 
7.3%
토사 6
 
6.2%
인조 5
 
5.2%
하드 3
 
3.1%
앙투카 3
 
3.1%
하드코트 2
 
2.1%
케미칼, 앙투카 1
 
1.0%
Other values (15) 15
15.6%

Length

2024-04-17T20:51:55.525963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
클레이 35
26.5%
아크릴 18
13.6%
마사토 15
11.4%
케미칼 10
 
7.6%
토사 9
 
6.8%
인조 6
 
4.5%
하드 6
 
4.5%
앙투카 4
 
3.0%
8 3
 
2.3%
인조잔디 3
 
2.3%
Other values (16) 23
17.4%

경기장 면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2754.1458
Minimum260
Maximum18345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:55.673289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum260
5-th percentile700.25
Q11194
median1800
Q33146
95-th percentile8184.5
Maximum18345
Range18085
Interquartile range (IQR)1952

Descriptive statistics

Standard deviation2724.6828
Coefficient of variation (CV)0.98930229
Kurtosis11.243305
Mean2754.1458
Median Absolute Deviation (MAD)890
Skewness2.8529502
Sum264398
Variance7423896.3
MonotonicityNot monotonic
2024-04-17T20:51:55.805337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 3
 
3.1%
1800 3
 
3.1%
760 2
 
2.1%
1200 2
 
2.1%
782 2
 
2.1%
2608 2
 
2.1%
1400 2
 
2.1%
2300 2
 
2.1%
528 1
 
1.0%
3544 1
 
1.0%
Other values (76) 76
79.2%
ValueCountFrequency (%)
260 1
 
1.0%
450 1
 
1.0%
528 1
 
1.0%
660 1
 
1.0%
671 1
 
1.0%
710 1
 
1.0%
720 1
 
1.0%
760 2
2.1%
782 2
2.1%
800 3
3.1%
ValueCountFrequency (%)
18345 1
1.0%
9948 1
1.0%
9460 1
1.0%
9306 1
1.0%
9251 1
1.0%
7829 1
1.0%
7660 1
1.0%
7507 1
1.0%
7329 1
1.0%
7000 1
1.0%

경기장 코트 면수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)15.8%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean4.6105263
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:55.923354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile12
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9202006
Coefficient of variation (CV)0.85027183
Kurtosis4.2404332
Mean4.6105263
Median Absolute Deviation (MAD)1
Skewness1.9038189
Sum438
Variance15.367973
MonotonicityNot monotonic
2024-04-17T20:51:56.016644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 21
21.9%
3 17
17.7%
4 13
13.5%
1 13
13.5%
6 7
 
7.3%
12 4
 
4.2%
5 4
 
4.2%
8 4
 
4.2%
7 3
 
3.1%
10 2
 
2.1%
Other values (5) 7
 
7.3%
ValueCountFrequency (%)
1 13
13.5%
2 21
21.9%
3 17
17.7%
4 13
13.5%
5 4
 
4.2%
6 7
 
7.3%
7 3
 
3.1%
8 4
 
4.2%
9 2
 
2.1%
10 2
 
2.1%
ValueCountFrequency (%)
22 1
 
1.0%
16 2
 
2.1%
14 1
 
1.0%
12 4
4.2%
11 1
 
1.0%
10 2
 
2.1%
9 2
 
2.1%
8 4
4.2%
7 3
3.1%
6 7
7.3%

관람석 좌석수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)68.4%
Missing77
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean726.36842
Minimum0
Maximum6401
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:56.357671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.8
Q1100
median200
Q3587.5
95-th percentile1990.1
Maximum6401
Range6401
Interquartile range (IQR)487.5

Descriptive statistics

Standard deviation1459.4844
Coefficient of variation (CV)2.0092894
Kurtosis14.207004
Mean726.36842
Median Absolute Deviation (MAD)100
Skewness3.6185675
Sum13801
Variance2130094.7
MonotonicityNot monotonic
2024-04-17T20:51:56.452985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 4
 
4.2%
200 3
 
3.1%
1500 2
 
2.1%
6401 1
 
1.0%
1343 1
 
1.0%
220 1
 
1.0%
216 1
 
1.0%
750 1
 
1.0%
62 1
 
1.0%
0 1
 
1.0%
Other values (3) 3
 
3.1%
(Missing) 77
80.2%
ValueCountFrequency (%)
0 1
 
1.0%
62 1
 
1.0%
100 4
4.2%
108 1
 
1.0%
200 3
3.1%
216 1
 
1.0%
220 1
 
1.0%
276 1
 
1.0%
425 1
 
1.0%
750 1
 
1.0%
ValueCountFrequency (%)
6401 1
 
1.0%
1500 2
2.1%
1343 1
 
1.0%
750 1
 
1.0%
425 1
 
1.0%
276 1
 
1.0%
220 1
 
1.0%
216 1
 
1.0%
200 3
3.1%
108 1
 
1.0%

관람석 수용인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)48.6%
Missing61
Missing (%)63.5%
Infinite0
Infinite (%)0.0%
Mean519.37143
Minimum20
Maximum6401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:56.551179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile41
Q1100
median200
Q3350
95-th percentile1650
Maximum6401
Range6381
Interquartile range (IQR)250

Descriptive statistics

Standard deviation1115.7342
Coefficient of variation (CV)2.1482395
Kurtosis23.97128
Mean519.37143
Median Absolute Deviation (MAD)100
Skewness4.6461071
Sum18178
Variance1244862.8
MonotonicityNot monotonic
2024-04-17T20:51:56.643372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 7
 
7.3%
200 5
 
5.2%
300 4
 
4.2%
150 3
 
3.1%
20 2
 
2.1%
400 2
 
2.1%
50 2
 
2.1%
1343 1
 
1.0%
1500 1
 
1.0%
220 1
 
1.0%
Other values (7) 7
 
7.3%
(Missing) 61
63.5%
ValueCountFrequency (%)
20 2
 
2.1%
50 2
 
2.1%
100 7
7.3%
108 1
 
1.0%
150 3
3.1%
200 5
5.2%
216 1
 
1.0%
220 1
 
1.0%
300 4
4.2%
400 2
 
2.1%
ValueCountFrequency (%)
6401 1
 
1.0%
2000 1
 
1.0%
1500 1
 
1.0%
1343 1
 
1.0%
1000 1
 
1.0%
600 1
 
1.0%
500 1
 
1.0%
400 2
2.1%
300 4
4.2%
220 1
 
1.0%

준공연도
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)30.4%
Missing4
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean2002.7717
Minimum1964
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-04-17T20:51:56.749941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1964
5-th percentile1989.55
Q11996
median2005
Q32009
95-th percentile2013.45
Maximum2015
Range51
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.8206841
Coefficient of variation (CV)0.0044042383
Kurtosis2.8073862
Mean2002.7717
Median Absolute Deviation (MAD)5
Skewness-1.2328339
Sum184255
Variance77.804467
MonotonicityNot monotonic
2024-04-17T20:51:56.859679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2010 10
 
10.4%
2009 8
 
8.3%
2007 6
 
6.2%
2005 5
 
5.2%
2013 4
 
4.2%
1995 4
 
4.2%
2003 4
 
4.2%
2008 4
 
4.2%
1992 4
 
4.2%
2015 4
 
4.2%
Other values (18) 39
40.6%
ValueCountFrequency (%)
1964 1
 
1.0%
1982 1
 
1.0%
1984 1
 
1.0%
1989 2
2.1%
1990 1
 
1.0%
1991 3
3.1%
1992 4
4.2%
1993 1
 
1.0%
1994 3
3.1%
1995 4
4.2%
ValueCountFrequency (%)
2015 4
 
4.2%
2014 1
 
1.0%
2013 4
 
4.2%
2012 3
 
3.1%
2010 10
10.4%
2009 8
8.3%
2008 4
 
4.2%
2007 6
6.2%
2006 3
 
3.1%
2005 5
5.2%

Interactions

2024-04-17T20:51:52.302276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:46.650055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.292035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.001339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.644986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.294869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.981412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.683225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.623051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.375126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:46.720299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.380208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.074683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.711883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.374319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.046065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.751231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.695553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.452263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:46.799610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.464819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.149353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.789186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.465539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.116663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.824445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.782577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.521971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:46.864433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.538332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.216369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.853134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.535641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.188690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.897055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.861606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.590786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:46.931209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.615478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.283578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.920318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.605818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.266583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.969220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.936522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.670939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.004715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.699402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.352533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.992151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.686395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.342465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.050400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.013306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.733993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.076015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.772431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.433881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.072285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.756617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.423252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.129886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.084413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.802933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.148446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.847874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.503341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.143149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.836514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.516355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.202898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.161084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.875980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.224370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:47.927585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:48.577471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.224130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:49.911595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:50.606692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:51.555104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:51:52.235539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T20:51:56.955875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구시설명소유기관관리주체부지면적(㎡)건축면적(㎡)연면적(㎡)경기장 바닥재료경기장 면적(㎡)경기장 코트 면수관람석 좌석수관람석 수용인원(명)준공연도
연번1.0000.9611.0000.9660.9160.0000.0000.0000.8650.0000.2300.0000.5230.278
시군구0.9611.0001.0000.9920.9620.6810.0000.0000.9380.8750.7280.0000.1160.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소유기관0.9660.9921.0001.0000.9680.0000.0000.0000.9220.5940.7960.0000.4280.000
관리주체0.9160.9621.0000.9681.0000.0000.0000.0000.7060.5300.7250.0000.0000.000
부지면적(㎡)0.0000.6811.0000.0000.0001.0000.6680.7240.8550.8570.7190.8190.7910.000
건축면적(㎡)0.0000.0001.0000.0000.0000.6681.0000.9990.8640.4260.8540.8520.9670.632
연면적(㎡)0.0000.0001.0000.0000.0000.7240.9991.0000.8540.4070.8990.8520.9670.559
경기장 바닥재료0.8650.9381.0000.9220.7060.8550.8640.8541.0000.9410.9330.8640.7780.000
경기장 면적(㎡)0.0000.8751.0000.5940.5300.8570.4260.4070.9411.0000.8650.0000.3580.000
경기장 코트 면수0.2300.7281.0000.7960.7250.7190.8540.8990.9330.8651.0000.8330.7280.373
관람석 좌석수0.0000.0001.0000.0000.0000.8190.8520.8520.8640.0000.8331.0001.0000.627
관람석 수용인원(명)0.5230.1161.0000.4280.0000.7910.9670.9670.7780.3580.7281.0001.0000.213
준공연도0.2780.0001.0000.0000.0000.0000.6320.5590.0000.0000.3730.6270.2131.000
2024-04-17T20:51:57.091208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경기장 바닥재료시군구소유기관관리주체
경기장 바닥재료1.0000.5740.5160.212
시군구0.5741.0000.8800.599
소유기관0.5160.8801.0000.621
관리주체0.2120.5990.6211.000
2024-04-17T20:51:57.180281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부지면적(㎡)건축면적(㎡)연면적(㎡)경기장 면적(㎡)경기장 코트 면수관람석 좌석수관람석 수용인원(명)준공연도시군구소유기관관리주체경기장 바닥재료
연번1.0000.066-0.263-0.236-0.106-0.130-0.344-0.3850.1690.7500.7540.5490.480
부지면적(㎡)0.0661.0000.6580.6610.7370.7630.7110.5670.1690.3510.0000.0000.502
건축면적(㎡)-0.2630.6581.0000.9840.4920.6190.6250.5170.0680.0000.0000.0000.410
연면적(㎡)-0.2360.6610.9841.0000.5140.6400.6100.4820.0650.0000.0000.0000.399
경기장 면적(㎡)-0.1060.7370.4920.5141.0000.9350.2160.469-0.0270.4980.2740.2070.687
경기장 코트 면수-0.1300.7630.6190.6400.9351.0000.3030.513-0.0410.3540.4100.3100.645
관람석 좌석수-0.3440.7110.6250.6100.2160.3031.0000.939-0.1930.0000.0000.0000.324
관람석 수용인원(명)-0.3850.5670.5170.4820.4690.5130.9391.000-0.2880.0000.1380.0000.324
준공연도0.1690.1690.0680.065-0.027-0.041-0.193-0.2881.0000.0000.0000.0000.000
시군구0.7500.3510.0000.0000.4980.3540.0000.0000.0001.0000.8800.5990.574
소유기관0.7540.0000.0000.0000.2740.4100.0000.1380.0000.8801.0000.6210.516
관리주체0.5490.0000.0000.0000.2070.3100.0000.0000.0000.5990.6211.0000.212
경기장 바닥재료0.4800.5020.4100.3990.6870.6450.3240.3240.0000.5740.5160.2121.000

Missing values

2024-04-17T20:51:52.983331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:51:53.166094image/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.
2024-04-17T20:51:53.288975image/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 바닥재료경기장 면적(㎡)경기장 코트 면수관람석 좌석수관람석 수용인원(명)준공연도
01경상남도창원시창원 종합테니스장창원시시설관리공단6687110801080우레탄 12, 마사토 10930622640164011998
12경상남도창원시마산종합운동장 테니스장창원시시설관리공단4966137137마사토18317<NA>3001989
23경상남도창원시창원덕동시립 테니스장창원시시설관리공단24396199199케미컬925112134313432010
34경상남도창원시진해공설운동장 테니스장창원시위탁(테니스협회)753624522452클레이31646150015001964
45경상남도창원시동읍운동장 테니스장창원시위탁(테니스협회)19734343클레이19303<NA><NA>2012
56경상남도창원시북면공설운동장 테니스장창원시위탁(테니스협회)1410<NA><NA>클레이14102<NA><NA>1995
67경상남도창원시의창스포츠파크 테니스장창원시위탁(테니스협회)1890<NA><NA>클레이18723<NA><NA>2006
78경상남도창원시명서2주민운동장 테니스장창원시위탁(테니스협회)1530<NA><NA>클레이15303<NA><NA>2004
89경상남도창원시도계체육공원 테니스장창원시위탁(테니스협회)1906<NA><NA>클레이19063<NA><NA>2006
910경상남도창원시사림운동장 테니스장창원시위탁(테니스협회)20253333클레이16193<NA><NA>1989
연번시도시군구시설명소유기관관리주체부지면적(㎡)건축면적(㎡)연면적(㎡)경기장 바닥재료경기장 면적(㎡)경기장 코트 면수관람석 좌석수관람석 수용인원(명)준공연도
8687경상남도산청군삼장체육공원 테니스장산청군산청군4636<NA><NA>아크릴, 케미칼13302<NA><NA>2010
8788경상남도함양군함양생활체육공원 테니스장함양군함양군8450238413앙투카545011<NA>1002010
8889경상남도거창군거창군립테니스장거창군위탁(테니스협회)<NA>6666인조잔디 6, 하드 67829121001002005
8990경상남도합천군함벽루체육공원 테니스장합천군위탁(테니스협회)4558<NA><NA>클레이39666<NA><NA>1991
9091경상남도합천군군민생활체육공원 테니스장합천군위탁(테니스협회)45000123123클레이46909150020002004
9192경상남도합천군대병 테니스장합천군대병면4508<NA><NA>클레이9201<NA><NA>2005
9293경상남도합천군봉산 테니스장합천군봉산면960<NA><NA>클레이9601<NA><NA>2005
9394경상남도합천군가야 테니스장합천군가야면1000<NA><NA>클레이8001<NA><NA>1997
9495경상남도합천군야로 테니스장합천군야로면1100<NA><NA>클레이8501<NA><NA>1997
9596경상남도합천군초계 테니스장합천군초계면1300<NA><NA>클레이9001<NA><NA>1998