Overview

Dataset statistics

Number of variables16
Number of observations102
Missing cells254
Missing cells (%)15.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory134.3 B

Variable types

Numeric5
Categorical7
Text4

Dataset

Description경상남도 테니스장 현황을 제공합니다.테니스장의 연번, 시도, 시군구, 시설명, 소유기관, 관리주체 등에 관한 정보를 제공합니다.* 시설에서 확인되지 않는 일부 항목(면적, 관람석)등은 제공되지 못함을 양해 바랍니다.
Author경상남도
URLhttps://www.data.go.kr/data/3080569/fileData.do

Alerts

시도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
시군구 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
소유기관 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 시군구 and 2 other fieldsHigh correlation
부지면적(제곱미터) is highly overall correlated with 경기장 면적(제곱미터) and 2 other fieldsHigh correlation
경기장 면적(제곱미터) is highly overall correlated with 부지면적(제곱미터) and 3 other fieldsHigh correlation
경기장 코트 면수 is highly overall correlated with 부지면적(제곱미터) and 3 other fieldsHigh correlation
관람석 수용인원(명) is highly overall correlated with 부지면적(제곱미터) and 3 other fieldsHigh correlation
관리주체 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
경기장 바닥재료 is highly overall correlated with 경기장 면적(제곱미터) and 4 other fieldsHigh correlation
부지면적(제곱미터) has 5 (4.9%) missing valuesMissing
건축면적(제곱미터) has 55 (53.9%) missing valuesMissing
연면적(제곱미터) has 55 (53.9%) missing valuesMissing
관람석 좌석수 has 77 (75.5%) missing valuesMissing
관람석 수용인원(명) has 62 (60.8%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2024-04-13 13:28:05.504931
Analysis finished2024-04-13 13:28:14.549067
Duration9.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.5
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-13T22:28:14.692309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.05
Q126.25
median51.5
Q376.75
95-th percentile96.95
Maximum102
Range101
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation29.588849
Coefficient of variation (CV)0.57454076
Kurtosis-1.2
Mean51.5
Median Absolute Deviation (MAD)25.5
Skewness0
Sum5253
Variance875.5
MonotonicityStrictly increasing
2024-04-13T22:28:14.952442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
66 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
Other values (92) 92
90.2%
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 (%)
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size944.0 B
경상남도
102 

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 102
100.0%

Length

2024-04-13T22:28:15.182844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T22:28:15.382632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 102
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size944.0 B
창원시
24 
양산시
11 
거제시
8 
합천군
7 
산청군
6 
Other values (16)
46 

Length

Max length5
Median length3
Mean length3.1470588
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 24
23.5%
양산시 11
10.8%
거제시 8
 
7.8%
합천군 7
 
6.9%
산청군 6
 
5.9%
통영시 6
 
5.9%
사천시 5
 
4.9%
김해시 4
 
3.9%
의령군 4
 
3.9%
함양군 3
 
2.9%
Other values (11) 24
23.5%

Length

2024-04-13T22:28:15.728951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 24
23.5%
양산시 11
10.8%
거제시 8
 
7.8%
김해시 8
 
7.8%
합천군 7
 
6.9%
산청군 6
 
5.9%
통영시 6
 
5.9%
사천시 5
 
4.9%
의령군 4
 
3.9%
남해군 4
 
3.9%
Other values (8) 19
18.6%

시설명
Text

UNIQUE 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size944.0 B
2024-04-13T22:28:16.613006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length9.7352941
Min length6

Characters and Unicode

Total characters993
Distinct characters142
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

Unique102 ?
Unique (%)100.0%

Sample

1st row창원 종합테니스장
2nd row마산종합운동장 테니스장
3rd row창원덕동시립 테니스장
4th row진해공설운동장 테니스장
5th row동읍운동장 테니스장
ValueCountFrequency (%)
테니스장 68
36.6%
공설테니스장 5
 
2.7%
고성군 2
 
1.1%
시립테니스장 2
 
1.1%
내 2
 
1.1%
창원 1
 
0.5%
하북지산테니스장 1
 
0.5%
환경기초시설 1
 
0.5%
함안군 1
 
0.5%
함안 1
 
0.5%
Other values (102) 102
54.8%
2024-04-13T22:28:17.886158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 124
 
12.5%
스 104
 
10.5%
테 98
 
9.9%
니 98
 
9.9%
84
 
8.5%
공 31
 
3.1%
동 28
 
2.8%
원 25
 
2.5%
체 21
 
2.1%
운 21
 
2.1%
Other values (132) 359
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 903
90.9%
Space Separator 84
 
8.5%
Decimal Number 2
 
0.2%
Uppercase Letter 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 124
 
13.7%
스 104
 
11.5%
테 98
 
10.9%
니 98
 
10.9%
공 31
 
3.4%
동 28
 
3.1%
원 25
 
2.8%
체 21
 
2.3%
운 21
 
2.3%
육 21
 
2.3%
Other values (126) 332
36.8%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
84
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 903
90.9%
Common 88
 
8.9%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 124
 
13.7%
스 104
 
11.5%
테 98
 
10.9%
니 98
 
10.9%
공 31
 
3.4%
동 28
 
3.1%
원 25
 
2.8%
체 21
 
2.3%
운 21
 
2.3%
육 21
 
2.3%
Other values (126) 332
36.8%
Common
ValueCountFrequency (%)
84
95.5%
2 2
 
2.3%
) 1
 
1.1%
( 1
 
1.1%
Latin
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 903
90.9%
ASCII 90
 
9.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 124
 
13.7%
스 104
 
11.5%
테 98
 
10.9%
니 98
 
10.9%
공 31
 
3.4%
동 28
 
3.1%
원 25
 
2.8%
체 21
 
2.3%
운 21
 
2.3%
육 21
 
2.3%
Other values (126) 332
36.8%
ASCII
ValueCountFrequency (%)
84
93.3%
2 2
 
2.2%
) 1
 
1.1%
( 1
 
1.1%
C 1
 
1.1%
I 1
 
1.1%

소유기관
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size944.0 B
창원시
24 
양산시
11 
김해시
8 
거제시
8 
합천군
7 
Other values (13)
44 

Length

Max length5
Median length5
Mean length4.9411765
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 24
23.5%
양산시 11
10.8%
김해시 8
 
7.8%
거제시 8
 
7.8%
합천군 7
 
6.9%
통영시 6
 
5.9%
산청군 6
 
5.9%
사천시 5
 
4.9%
남해군 4
 
3.9%
의령군 4
 
3.9%
Other values (8) 19
18.6%

Length

2024-04-13T22:28:18.333870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 24
23.5%
양산시 11
10.8%
김해시 8
 
7.8%
거제시 8
 
7.8%
합천군 7
 
6.9%
통영시 6
 
5.9%
산청군 6
 
5.9%
사천시 5
 
4.9%
의령군 4
 
3.9%
남해군 4
 
3.9%
Other values (8) 19
18.6%

관리주체
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size944.0 B
위탁(테니스협회)
28 
위탁(테니스협회)
11 
산청군
6 
위탁(거제해양관광개발공사)
5 
김해시테니스협회
 
4
Other values (31)
48 

Length

Max length16
Median length14.5
Mean length9.4607843
Min length2

Unique

Unique19 ?
Unique (%)18.6%

Sample

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

Common Values

ValueCountFrequency (%)
위탁(테니스협회) 28
27.5%
위탁(테니스협회) 11
 
10.8%
산청군 6
 
5.9%
위탁(거제해양관광개발공사) 5
 
4.9%
김해시테니스협회 4
 
3.9%
시설관리공단 3
 
2.9%
함양군 3
 
2.9%
남해군 체육진흥과 3
 
2.9%
양산시 3
 
2.9%
사천시 3
 
2.9%
Other values (26) 33
32.4%

Length

2024-04-13T22:28:18.773645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
위탁(테니스협회 39
35.1%
위탁(거제해양관광개발공사 7
 
6.3%
김해시테니스협회 6
 
5.4%
산청군 6
 
5.4%
양산시 4
 
3.6%
문화위생과 4
 
3.6%
시설관리공단 3
 
2.7%
함양군 3
 
2.7%
남해군 3
 
2.7%
체육진흥과 3
 
2.7%
Other values (25) 33
29.7%

부지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct92
Distinct (%)94.8%
Missing5
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean8501.1134
Minimum500
Maximum72775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-13T22:28:19.169320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile798
Q11527
median2700
Q37608
95-th percentile42973.6
Maximum72775
Range72275
Interquartile range (IQR)6081

Descriptive statistics

Standard deviation14597.598
Coefficient of variation (CV)1.7171396
Kurtosis8.4420647
Mean8501.1134
Median Absolute Deviation (MAD)1500
Skewness2.9133433
Sum824608
Variance2.1308987 × 108
MonotonicityNot monotonic
2024-04-13T22:28:19.609736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2025 2
 
2.0%
1100 2
 
2.0%
12280 2
 
2.0%
1520 2
 
2.0%
1800 2
 
2.0%
8823 1
 
1.0%
1580 1
 
1.0%
2250 1
 
1.0%
12652 1
 
1.0%
2822 1
 
1.0%
Other values (82) 82
80.4%
(Missing) 5
 
4.9%
ValueCountFrequency (%)
500 1
1.0%
648 1
1.0%
660 1
1.0%
708 1
1.0%
790 1
1.0%
800 1
1.0%
952 1
1.0%
960 1
1.0%
1000 1
1.0%
1100 2
2.0%
ValueCountFrequency (%)
72775 1
1.0%
66871 1
1.0%
64318 1
1.0%
55339 1
1.0%
45000 1
1.0%
42467 1
1.0%
32647 1
1.0%
28986 1
1.0%
26197 1
1.0%
25120 1
1.0%

건축면적(제곱미터)
Text

MISSING 

Distinct41
Distinct (%)87.2%
Missing55
Missing (%)53.9%
Memory size944.0 B
2024-04-13T22:28:20.408180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.4255319
Min length1

Characters and Unicode

Total characters114
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

Unique35 ?
Unique (%)74.5%

Sample

1st row1080
2nd row137
3rd row199
4th row2452
5th row43
ValueCountFrequency (%)
169 2
 
4.3%
71 2
 
4.3%
90 2
 
4.3%
0 2
 
4.3%
125 2
 
4.3%
2
 
4.3%
1080 1
 
2.1%
65 1
 
2.1%
47 1
 
2.1%
99 1
 
2.1%
Other values (31) 31
66.0%
2024-04-13T22:28:21.601145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
19.3%
2 16
14.0%
9 12
10.5%
0 11
9.6%
5 11
9.6%
6 9
7.9%
8 9
7.9%
7 8
 
7.0%
3 8
 
7.0%
4 6
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112
98.2%
Dash Punctuation 2
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
19.6%
2 16
14.3%
9 12
10.7%
0 11
9.8%
5 11
9.8%
6 9
8.0%
8 9
8.0%
7 8
 
7.1%
3 8
 
7.1%
4 6
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
19.3%
2 16
14.0%
9 12
10.5%
0 11
9.6%
5 11
9.6%
6 9
7.9%
8 9
7.9%
7 8
 
7.0%
3 8
 
7.0%
4 6
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
19.3%
2 16
14.0%
9 12
10.5%
0 11
9.6%
5 11
9.6%
6 9
7.9%
8 9
7.9%
7 8
 
7.0%
3 8
 
7.0%
4 6
 
5.3%

연면적(제곱미터)
Text

MISSING 

Distinct40
Distinct (%)85.1%
Missing55
Missing (%)53.9%
Memory size944.0 B
2024-04-13T22:28:22.320569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.4680851
Min length1

Characters and Unicode

Total characters116
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

Unique34 ?
Unique (%)72.3%

Sample

1st row1080
2nd row137
3rd row199
4th row2452
5th row43
ValueCountFrequency (%)
78 3
 
6.4%
71 2
 
4.3%
2
 
4.3%
0 2
 
4.3%
125 2
 
4.3%
169 2
 
4.3%
315 1
 
2.1%
108 1
 
2.1%
48 1
 
2.1%
2100 1
 
2.1%
Other values (30) 30
63.8%
2024-04-13T22:28:23.446338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23
19.8%
2 16
13.8%
0 13
11.2%
5 11
9.5%
8 10
8.6%
9 10
8.6%
7 9
 
7.8%
3 8
 
6.9%
6 7
 
6.0%
4 7
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114
98.3%
Dash Punctuation 2
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23
20.2%
2 16
14.0%
0 13
11.4%
5 11
9.6%
8 10
8.8%
9 10
8.8%
7 9
 
7.9%
3 8
 
7.0%
6 7
 
6.1%
4 7
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23
19.8%
2 16
13.8%
0 13
11.2%
5 11
9.5%
8 10
8.6%
9 10
8.6%
7 9
 
7.8%
3 8
 
6.9%
6 7
 
6.0%
4 7
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23
19.8%
2 16
13.8%
0 13
11.2%
5 11
9.5%
8 10
8.6%
9 10
8.6%
7 9
 
7.8%
3 8
 
6.9%
6 7
 
6.0%
4 7
 
6.0%

경기장 바닥재료
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size944.0 B
클레이
29 
마사토
11 
아크릴 케미칼
8 
인조
7 
아크릴
6 
Other values (26)
41 

Length

Max length17
Median length3
Mean length4.2941176
Min length2

Unique

Unique21 ?
Unique (%)20.6%

Sample

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

Common Values

ValueCountFrequency (%)
클레이 29
28.4%
마사토 11
 
10.8%
아크릴 케미칼 8
 
7.8%
인조 7
 
6.9%
아크릴 6
 
5.9%
토사 5
 
4.9%
하드 5
 
4.9%
앙투카 4
 
3.9%
하드코트 4
 
3.9%
인조잔디4 2
 
2.0%
Other values (21) 21
20.6%

Length

2024-04-13T22:28:23.895206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
클레이 31
23.3%
아크릴 15
11.3%
마사토 12
 
9.0%
케미칼 11
 
8.3%
인조 8
 
6.0%
하드 8
 
6.0%
토사 5
 
3.8%
앙투카 5
 
3.8%
인조잔디 5
 
3.8%
하드코트 4
 
3.0%
Other values (25) 29
21.8%

경기장 면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2688.3431
Minimum260
Maximum9948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-13T22:28:24.317586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum260
5-th percentile583.4
Q11188
median1800
Q33418.75
95-th percentile7652.35
Maximum9948
Range9688
Interquartile range (IQR)2230.75

Descriptive statistics

Standard deviation2261.9476
Coefficient of variation (CV)0.84139094
Kurtosis1.8314215
Mean2688.3431
Median Absolute Deviation (MAD)992
Skewness1.5327973
Sum274211
Variance5116406.8
MonotonicityNot monotonic
2024-04-13T22:28:24.757025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260 4
 
3.9%
800 3
 
2.9%
1800 3
 
2.9%
1200 3
 
2.9%
782 2
 
2.0%
1520 2
 
2.0%
2975 2
 
2.0%
660 2
 
2.0%
1165 1
 
1.0%
4000 1
 
1.0%
Other values (79) 79
77.5%
ValueCountFrequency (%)
260 4
3.9%
528 1
 
1.0%
580 1
 
1.0%
648 1
 
1.0%
660 2
2.0%
671 1
 
1.0%
782 2
2.0%
790 1
 
1.0%
800 3
2.9%
850 1
 
1.0%
ValueCountFrequency (%)
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%
6100 1
1.0%

경기장 코트 면수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4509804
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-13T22:28:25.127288image/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 deviation3.6141292
Coefficient of variation (CV)0.81198497
Kurtosis5.577087
Mean4.4509804
Median Absolute Deviation (MAD)1
Skewness2.0314118
Sum454
Variance13.06193
MonotonicityNot monotonic
2024-04-13T22:28:25.482150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 23
22.5%
3 19
18.6%
4 14
13.7%
1 13
12.7%
6 7
 
6.9%
8 7
 
6.9%
7 5
 
4.9%
12 4
 
3.9%
5 4
 
3.9%
9 2
 
2.0%
Other values (4) 4
 
3.9%
ValueCountFrequency (%)
1 13
12.7%
2 23
22.5%
3 19
18.6%
4 14
13.7%
5 4
 
3.9%
6 7
 
6.9%
7 5
 
4.9%
8 7
 
6.9%
9 2
 
2.0%
11 1
 
1.0%
ValueCountFrequency (%)
22 1
 
1.0%
16 1
 
1.0%
14 1
 
1.0%
12 4
3.9%
11 1
 
1.0%
9 2
 
2.0%
8 7
6.9%
7 5
4.9%
6 7
6.9%
5 4
3.9%

관람석 좌석수
Text

MISSING 

Distinct16
Distinct (%)64.0%
Missing77
Missing (%)75.5%
Memory size944.0 B
2024-04-13T22:28:26.002166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.6
Min length1

Characters and Unicode

Total characters65
Distinct characters10
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

Unique9 ?
Unique (%)36.0%

Sample

1st row6401
2nd row1343
3rd row1500
4th row216
5th row750
ValueCountFrequency (%)
100 4
16.0%
0 2
 
8.0%
40 2
 
8.0%
20 2
 
8.0%
200 2
 
8.0%
2
 
8.0%
1500 2
 
8.0%
108 1
 
4.0%
425 1
 
4.0%
50 1
 
4.0%
Other values (6) 6
24.0%
2024-04-13T22:28:26.950321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26
40.0%
1 10
 
15.4%
2 7
 
10.8%
5 7
 
10.8%
4 6
 
9.2%
6 3
 
4.6%
- 2
 
3.1%
3 2
 
3.1%
8 1
 
1.5%
7 1
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63
96.9%
Dash Punctuation 2
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
41.3%
1 10
 
15.9%
2 7
 
11.1%
5 7
 
11.1%
4 6
 
9.5%
6 3
 
4.8%
3 2
 
3.2%
8 1
 
1.6%
7 1
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
40.0%
1 10
 
15.4%
2 7
 
10.8%
5 7
 
10.8%
4 6
 
9.2%
6 3
 
4.6%
- 2
 
3.1%
3 2
 
3.1%
8 1
 
1.5%
7 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
40.0%
1 10
 
15.4%
2 7
 
10.8%
5 7
 
10.8%
4 6
 
9.2%
6 3
 
4.6%
- 2
 
3.1%
3 2
 
3.1%
8 1
 
1.5%
7 1
 
1.5%

관람석 수용인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)50.0%
Missing62
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean456.7
Minimum0
Maximum6401
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-13T22:28:27.311961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q1100
median150
Q3300
95-th percentile1525
Maximum6401
Range6401
Interquartile range (IQR)200

Descriptive statistics

Standard deviation1054.9076
Coefficient of variation (CV)2.3098481
Kurtosis27.102443
Mean456.7
Median Absolute Deviation (MAD)100
Skewness4.9302938
Sum18268
Variance1112830.1
MonotonicityNot monotonic
2024-04-13T22:28:27.685848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
100 7
 
6.9%
200 5
 
4.9%
300 4
 
3.9%
50 3
 
2.9%
20 3
 
2.9%
150 3
 
2.9%
400 2
 
2.0%
1000 1
 
1.0%
216 1
 
1.0%
1500 1
 
1.0%
Other values (10) 10
 
9.8%
(Missing) 62
60.8%
ValueCountFrequency (%)
0 1
 
1.0%
20 3
2.9%
40 1
 
1.0%
50 3
2.9%
80 1
 
1.0%
100 7
6.9%
108 1
 
1.0%
120 1
 
1.0%
150 3
2.9%
200 5
4.9%
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
2.0%
300 4
3.9%
216 1
 
1.0%

준공연도
Categorical

Distinct36
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size944.0 B
2010
9 
2009
8 
2007
 
6
2005
 
6
2013
 
5
Other values (31)
68 

Length

Max length9
Median length4
Mean length4.0490196
Min length4

Unique

Unique11 ?
Unique (%)10.8%

Sample

1st row1998
2nd row1989
3rd row2010
4th row1964
5th row2012

Common Values

ValueCountFrequency (%)
2010 9
 
8.8%
2009 8
 
7.8%
2007 6
 
5.9%
2005 6
 
5.9%
2013 5
 
4.9%
2003 4
 
3.9%
2002 4
 
3.9%
2015 4
 
3.9%
2020 3
 
2.9%
2008 3
 
2.9%
Other values (26) 50
49.0%

Length

2024-04-13T22:28:28.087095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2010 9
 
8.7%
2009 9
 
8.7%
2005 7
 
6.8%
2007 6
 
5.8%
2013 5
 
4.9%
2003 4
 
3.9%
2002 4
 
3.9%
2015 4
 
3.9%
1998 3
 
2.9%
1994 3
 
2.9%
Other values (25) 49
47.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size944.0 B
2024-04-09
102 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-09
2nd row2024-04-09
3rd row2024-04-09
4th row2024-04-09
5th row2024-04-09

Common Values

ValueCountFrequency (%)
2024-04-09 102
100.0%

Length

2024-04-13T22:28:28.458681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T22:28:28.749582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-09 102
100.0%

Interactions

2024-04-13T22:28:12.790643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:09.479924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:10.600536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:11.366734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:12.091686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:12.935596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:09.725048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:10.751028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:11.509874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:12.230622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:13.092316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:09.979180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:10.911948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:11.664596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:12.383527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:13.240758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:10.220793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:11.063176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:11.807805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:12.520076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:13.377964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:10.455849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:11.210426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:11.942900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T22:28:12.649587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T22:28:28.941501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구소유기관관리주체부지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)경기장 바닥재료경기장 면적(제곱미터)경기장 코트 면수관람석 좌석수관람석 수용인원(명)준공연도
연번1.0000.9570.9470.9250.0000.9770.9580.8700.1300.0000.8330.5810.628
시군구0.9571.0001.0000.9600.5020.9100.8750.9460.6390.6300.8710.7210.526
소유기관0.9471.0001.0000.9840.5470.9710.9430.9400.6150.6770.8490.7470.667
관리주체0.9250.9600.9841.0000.7270.7960.5200.7980.6880.8540.8760.0920.000
부지면적(제곱미터)0.0000.5020.5470.7271.0000.7830.7030.8800.6730.6000.7340.8100.000
건축면적(제곱미터)0.9770.9100.9710.7960.7831.0000.9980.0000.0000.9011.0001.0000.971
연면적(제곱미터)0.9580.8750.9430.5200.7030.9981.0000.3040.4290.8711.0001.0000.938
경기장 바닥재료0.8700.9460.9400.7980.8800.0000.3041.0000.9020.9490.8701.0000.000
경기장 면적(제곱미터)0.1300.6390.6150.6880.6730.0000.4290.9021.0000.9000.7090.0000.000
경기장 코트 면수0.0000.6300.6770.8540.6000.9010.8710.9490.9001.0000.8430.8350.000
관람석 좌석수0.8330.8710.8490.8760.7341.0001.0000.8700.7090.8431.0000.9610.000
관람석 수용인원(명)0.5810.7210.7470.0920.8101.0001.0001.0000.0000.8350.9611.0000.330
준공연도0.6280.5260.6670.0000.0000.9710.9380.0000.0000.0000.0000.3301.000
2024-04-13T22:28:29.464203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리주체경기장 바닥재료시군구준공연도소유기관
관리주체1.0000.2630.5780.0000.643
경기장 바닥재료0.2631.0000.5560.0000.555
시군구0.5780.5561.0000.1230.982
준공연도0.0000.0000.1231.0000.201
소유기관0.6430.5550.9820.2011.000
2024-04-13T22:28:29.742353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부지면적(제곱미터)경기장 면적(제곱미터)경기장 코트 면수관람석 수용인원(명)시군구소유기관관리주체경기장 바닥재료준공연도
연번1.0000.131-0.073-0.084-0.3810.7320.7310.5550.4700.221
부지면적(제곱미터)0.1311.0000.6670.7220.5200.1690.2370.2920.4900.000
경기장 면적(제곱미터)-0.0730.6671.0000.8960.5910.2940.2450.2530.5490.000
경기장 코트 면수-0.0840.7220.8961.0000.6880.2780.2740.4010.6550.000
관람석 수용인원(명)-0.3810.5200.5910.6881.0000.3620.4000.0000.6760.000
시군구0.7320.1690.2940.2780.3621.0000.9820.5780.5560.123
소유기관0.7310.2370.2450.2740.4000.9821.0000.6430.5550.201
관리주체0.5550.2920.2530.4010.0000.5780.6431.0000.2630.000
경기장 바닥재료0.4700.4900.5490.6550.6760.5560.5550.2631.0000.000
준공연도0.2210.0000.0000.0000.0000.1230.2010.0000.0001.000

Missing values

2024-04-13T22:28:13.766921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T22:28:14.140832image/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.
2024-04-13T22:28:14.403749image/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(제곱미터)건축면적(제곱미터)연면적(제곱미터)경기장 바닥재료경기장 면적(제곱미터)경기장 코트 면수관람석 좌석수관람석 수용인원(명)준공연도데이터기준일자
01경상남도창원시창원 종합테니스장창원시시설관리공단6687110801080우레탄 12 마사토 109306226401640119982024-04-09
12경상남도창원시마산종합운동장 테니스장창원시시설관리공단4966137137마사토18317<NA>30019892024-04-09
23경상남도창원시창원덕동시립 테니스장창원시시설관리공단24396199199케미컬9251121343134320102024-04-09
34경상남도창원시진해공설운동장 테니스장창원시위탁(테니스협회)753624522452클레이316461500150019642024-04-09
45경상남도창원시동읍운동장 테니스장창원시위탁(테니스협회)19734343클레이19303<NA><NA>20122024-04-09
56경상남도창원시북면공설운동장 테니스장창원시위탁(테니스협회)1410<NA><NA>클레이14102<NA><NA>19952024-04-09
67경상남도창원시의창스포츠파크 테니스장창원시위탁(테니스협회)1890<NA><NA>클레이18723<NA><NA>20062024-04-09
78경상남도창원시명서2주민운동장 테니스장창원시위탁(테니스협회)1530<NA><NA>클레이15303<NA><NA>20042024-04-09
89경상남도창원시도계체육공원 테니스장창원시위탁(테니스협회)1906<NA><NA>클레이19063<NA><NA>20062024-04-09
910경상남도창원시사림운동장 테니스장창원시위탁(테니스협회)20253333클레이16193<NA><NA>19892024-04-09
연번시도시군구시설명소유기관관리주체부지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)경기장 바닥재료경기장 면적(제곱미터)경기장 코트 면수관람석 좌석수관람석 수용인원(명)준공연도데이터기준일자
9293경상남도함양군안의생활체육공원 테니스장함양군함양군2975<NA><NA>앙투카29752<NA>5020182024-04-09
9394경상남도함양군함양생활체육공원 테니스장함양군함양군8450238413앙투카545011<NA>10020102024-04-09
9495경상남도거창군거창군립테니스장거창군위탁(테니스협회)<NA>6666인조잔디 6 하드 67829121001002005 20092024-04-09
9596경상남도합천군함벽루체육공원 테니스장합천군위탁(테니스협회)4558<NA><NA>클레이39666<NA><NA>19912024-04-09
9697경상남도합천군생활체육테니스장합천군위탁(테니스협회)45000123123하드코드469091500200020192024-04-09
9798경상남도합천군대병 테니스장합천군대병면4508<NA><NA>클레이9201<NA><NA>20052024-04-09
9899경상남도합천군봉산 테니스장합천군봉산면960<NA><NA>클레이9601<NA><NA>20052024-04-09
99100경상남도합천군가야 테니스장합천군가야면1000<NA><NA>클레이8001<NA><NA>19972024-04-09
100101경상남도합천군야로 테니스장합천군야로면1100<NA><NA>클레이8501<NA><NA>19972024-04-09
101102경상남도합천군초계 테니스장합천군초계면1300<NA><NA>클레이9001<NA><NA>19982024-04-09