Overview

Dataset statistics

Number of variables14
Number of observations46
Missing cells97
Missing cells (%)15.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory123.9 B

Variable types

Categorical3
Text2
Numeric9

Dataset

Description전라남도 시군의 테니스장(시설명, 소유기관, 관리주체, 면적, 수용인원 등)에 관한 데이터를 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15037310/fileData.do

Alerts

소유기관 is highly overall correlated with 시군구High correlation
시군구 is highly overall correlated with 소유기관High correlation
부지면적 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
건축면적 is highly overall correlated with 연면적 and 3 other fieldsHigh correlation
연면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
면적 is highly overall correlated with 부지면적 and 3 other fieldsHigh correlation
코트 면수 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
좌석수 is highly overall correlated with 건축면적 and 4 other fieldsHigh correlation
수용인원 is highly overall correlated with 건축면적 and 4 other fieldsHigh correlation
준공 연도 is highly overall correlated with 바닥재료High correlation
건설사업비 is highly overall correlated with 부지면적 and 2 other fieldsHigh correlation
바닥재료 is highly overall correlated with 준공 연도High correlation
건축면적 has 13 (28.3%) missing valuesMissing
연면적 has 11 (23.9%) missing valuesMissing
좌석수 has 25 (54.3%) missing valuesMissing
수용인원 has 21 (45.7%) missing valuesMissing
건설사업비 has 27 (58.7%) missing valuesMissing

Reproduction

Analysis started2023-12-13 00:52:10.882578
Analysis finished2023-12-13 00:52:17.361330
Duration6.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
목포시
4 
여수시
4 
순천시
3 
고흥군
3 
영암군
3 
Other values (17)
29 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)13.0%

Sample

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

Common Values

ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
장성군 2
 
4.3%
강진군 2
 
4.3%
완도군 2
 
4.3%
보성군 2
 
4.3%
Other values (12) 18
39.1%

Length

2023-12-13T09:52:17.418179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
영광군 2
 
4.3%
나주시 2
 
4.3%
광양시 2
 
4.3%
진도군 2
 
4.3%
Other values (12) 18
39.1%

시설명
Text

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T09:52:17.583870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.1304348
Min length6

Characters and Unicode

Total characters374
Distinct characters95
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)95.7%

Sample

1st row목포 시립테니스장
2nd row목포 시립정구장
3rd row목포 시민테니스장
4th row부주산테니스장
5th row진남종합테니스장
ValueCountFrequency (%)
테니스장 24
29.6%
목포 3
 
3.7%
팔마 3
 
3.7%
강진 2
 
2.5%
정구장 2
 
2.5%
시립테니스장 2
 
2.5%
스포츠파크 1
 
1.2%
장흥 1
 
1.2%
군립테니스장 1
 
1.2%
우슬 1
 
1.2%
Other values (41) 41
50.6%
2023-12-13T09:52:17.852662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 47
 
12.6%
스 44
 
11.8%
테 42
 
11.2%
니 42
 
11.2%
35
 
9.4%
립 8
 
2.1%
구 6
 
1.6%
시 6
 
1.6%
포 5
 
1.3%
마 5
 
1.3%
Other values (85) 134
35.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 334
89.3%
Space Separator 35
 
9.4%
Decimal Number 3
 
0.8%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 47
 
14.1%
스 44
 
13.2%
테 42
 
12.6%
니 42
 
12.6%
립 8
 
2.4%
구 6
 
1.8%
시 6
 
1.8%
포 5
 
1.5%
마 5
 
1.5%
군 5
 
1.5%
Other values (80) 124
37.1%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 334
89.3%
Common 40
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 47
 
14.1%
스 44
 
13.2%
테 42
 
12.6%
니 42
 
12.6%
립 8
 
2.4%
구 6
 
1.8%
시 6
 
1.8%
포 5
 
1.5%
마 5
 
1.5%
군 5
 
1.5%
Other values (80) 124
37.1%
Common
ValueCountFrequency (%)
35
87.5%
1 2
 
5.0%
2 1
 
2.5%
) 1
 
2.5%
( 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 334
89.3%
ASCII 40
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 47
 
14.1%
스 44
 
13.2%
테 42
 
12.6%
니 42
 
12.6%
립 8
 
2.4%
구 6
 
1.8%
시 6
 
1.8%
포 5
 
1.5%
마 5
 
1.5%
군 5
 
1.5%
Other values (80) 124
37.1%
ASCII
ValueCountFrequency (%)
35
87.5%
1 2
 
5.0%
2 1
 
2.5%
) 1
 
2.5%
( 1
 
2.5%

소유기관
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
목포시
4 
여수시
4 
순천시
3 
고흥군
3 
영암군
3 
Other values (17)
29 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)13.0%

Sample

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

Common Values

ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
장성군 2
 
4.3%
강진군 2
 
4.3%
완도군 2
 
4.3%
보성군 2
 
4.3%
Other values (12) 18
39.1%

Length

2023-12-13T09:52:17.971873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
영광군 2
 
4.3%
나주시 2
 
4.3%
광양시 2
 
4.3%
진도군 2
 
4.3%
Other values (12) 18
39.1%

관리주체
Text

Distinct26
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T09:52:18.109051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.2391304
Min length3

Characters and Unicode

Total characters287
Distinct characters65
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)34.8%

Sample

1st row위탁_테니스클럽
2nd row위탁_소프트테니스클럽
3rd row위탁_테니스클럽
4th row체육시설관리사무소
5th row여수시_체육지원과
ValueCountFrequency (%)
위탁_테니스협회 8
17.0%
체육시설관리사무소 4
 
8.5%
고흥군 3
 
6.4%
무안군 3
 
6.4%
위탁_테니스클럽 3
 
6.4%
나주시 2
 
4.3%
진도군 2
 
4.3%
영광군 2
 
4.3%
보성군 2
 
4.3%
곡성군 2
 
4.3%
Other values (16) 16
34.0%
2023-12-13T09:52:18.343647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 23
 
8.0%
_ 17
 
5.9%
탁 16
 
5.6%
위 16
 
5.6%
) 15
 
5.2%
니 14
 
4.9%
테 14
 
4.9%
스 14
 
4.9%
협 10
 
3.5%
회 10
 
3.5%
Other values (55) 138
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 250
87.1%
Connector Punctuation 17
 
5.9%
Close Punctuation 15
 
5.2%
Open Punctuation 2
 
0.7%
Dash Punctuation 1
 
0.3%
Other Symbol 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 23
 
9.2%
탁 16
 
6.4%
위 16
 
6.4%
니 14
 
5.6%
테 14
 
5.6%
스 14
 
5.6%
협 10
 
4.0%
회 10
 
4.0%
시 9
 
3.6%
소 7
 
2.8%
Other values (49) 117
46.8%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
87.5%
Common 36
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 23
 
9.2%
탁 16
 
6.4%
위 16
 
6.4%
니 14
 
5.6%
테 14
 
5.6%
스 14
 
5.6%
협 10
 
4.0%
회 10
 
4.0%
시 9
 
3.6%
소 7
 
2.8%
Other values (50) 118
47.0%
Common
ValueCountFrequency (%)
_ 17
47.2%
) 15
41.7%
( 2
 
5.6%
- 1
 
2.8%
1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
87.1%
ASCII 36
 
12.5%
None 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 23
 
9.2%
탁 16
 
6.4%
위 16
 
6.4%
니 14
 
5.6%
테 14
 
5.6%
스 14
 
5.6%
협 10
 
4.0%
회 10
 
4.0%
시 9
 
3.6%
소 7
 
2.8%
Other values (49) 117
46.8%
ASCII
ValueCountFrequency (%)
_ 17
47.2%
) 15
41.7%
( 2
 
5.6%
- 1
 
2.8%
1
 
2.8%
None
ValueCountFrequency (%)
㈜ 1
100.0%

부지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11927.196
Minimum1394
Maximum93612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:18.447700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1394
5-th percentile1959
Q13209
median4999
Q37878
95-th percentile61541.25
Maximum93612
Range92218
Interquartile range (IQR)4669

Descriptive statistics

Standard deviation20592.669
Coefficient of variation (CV)1.7265307
Kurtosis9.7554235
Mean11927.196
Median Absolute Deviation (MAD)2293.5
Skewness3.1921631
Sum548651
Variance4.2405802 × 108
MonotonicityNot monotonic
2023-12-13T09:52:18.546541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5000 2
 
4.3%
3890 1
 
2.2%
3967 1
 
2.2%
7312 1
 
2.2%
5523 1
 
2.2%
93612 1
 
2.2%
3500 1
 
2.2%
71104 1
 
2.2%
6567 1
 
2.2%
29029 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
1394 1
2.2%
1405 1
2.2%
1941 1
2.2%
2013 1
2.2%
2154 1
2.2%
2231 1
2.2%
2551 1
2.2%
2562 1
2.2%
2776 1
2.2%
2937 1
2.2%
ValueCountFrequency (%)
93612 1
2.2%
88618 1
2.2%
71104 1
2.2%
32853 1
2.2%
29029 1
2.2%
20000 1
2.2%
18843 1
2.2%
12000 1
2.2%
11820 1
2.2%
11794 1
2.2%

건축면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)93.9%
Missing13
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean720.60606
Minimum18
Maximum6375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:18.636830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile36.8
Q171
median112
Q3198
95-th percentile4908.4
Maximum6375
Range6357
Interquartile range (IQR)127

Descriptive statistics

Standard deviation1693.6672
Coefficient of variation (CV)2.3503371
Kurtosis6.5174963
Mean720.60606
Median Absolute Deviation (MAD)57
Skewness2.7480162
Sum23780
Variance2868508.6
MonotonicityNot monotonic
2023-12-13T09:52:18.729125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
95 2
 
4.3%
215 2
 
4.3%
103 1
 
2.2%
187 1
 
2.2%
104 1
 
2.2%
3517 1
 
2.2%
507 1
 
2.2%
147 1
 
2.2%
85 1
 
2.2%
4010 1
 
2.2%
Other values (21) 21
45.7%
(Missing) 13
28.3%
ValueCountFrequency (%)
18 1
2.2%
32 1
2.2%
40 1
2.2%
43 1
2.2%
52 1
2.2%
55 1
2.2%
56 1
2.2%
66 1
2.2%
71 1
2.2%
83 1
2.2%
ValueCountFrequency (%)
6375 1
2.2%
6256 1
2.2%
4010 1
2.2%
3517 1
2.2%
507 1
2.2%
222 1
2.2%
215 2
4.3%
198 1
2.2%
187 1
2.2%
181 1
2.2%

연면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)97.1%
Missing11
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean821.25714
Minimum18
Maximum6375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:18.849739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile37.6
Q189.5
median147
Q3223.5
95-th percentile4689.2
Maximum6375
Range6357
Interquartile range (IQR)134

Descriptive statistics

Standard deviation1689.8534
Coefficient of variation (CV)2.0576423
Kurtosis5.4209667
Mean821.25714
Median Absolute Deviation (MAD)68
Skewness2.5028981
Sum28744
Variance2855604.7
MonotonicityNot monotonic
2023-12-13T09:52:18.944343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
95 2
 
4.3%
382 1
 
2.2%
181 1
 
2.2%
126 1
 
2.2%
4010 1
 
2.2%
85 1
 
2.2%
147 1
 
2.2%
1340 1
 
2.2%
135 1
 
2.2%
3387 1
 
2.2%
Other values (24) 24
52.2%
(Missing) 11
23.9%
ValueCountFrequency (%)
18 1
2.2%
32 1
2.2%
40 1
2.2%
43 1
2.2%
52 1
2.2%
56 1
2.2%
66 1
2.2%
83 1
2.2%
85 1
2.2%
94 1
2.2%
ValueCountFrequency (%)
6375 1
2.2%
6274 1
2.2%
4010 1
2.2%
3387 1
2.2%
3230 1
2.2%
1340 1
2.2%
532 1
2.2%
382 1
2.2%
225 1
2.2%
222 1
2.2%

바닥재료
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
클레이
10 
토사
8 
앙투카
5 
하드코트
4 
하드
3 
Other values (14)
16 

Length

Max length12
Median length10
Mean length3.7608696
Min length2

Unique

Unique13 ?
Unique (%)28.3%

Sample

1st row클레이
2nd row클레이
3rd row클레이
4th row앙투카
5th row하드

Common Values

ValueCountFrequency (%)
클레이 10
21.7%
토사 8
17.4%
앙투카 5
10.9%
하드코트 4
 
8.7%
하드 3
 
6.5%
마사토 3
 
6.5%
클래이 6캐미칼 4 1
 
2.2%
하드6토사2 1
 
2.2%
마사토,인조잔디 1
 
2.2%
인조잔디 1
 
2.2%
Other values (9) 9
19.6%

Length

2023-12-13T09:52:19.042294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
클레이 10
19.6%
토사 8
15.7%
앙투카 5
9.8%
하드 5
9.8%
하드코트 4
 
7.8%
마사토 3
 
5.9%
4 2
 
3.9%
3 1
 
2.0%
아크릴(2)클레이(3 1
 
2.0%
잔디3 1
 
2.0%
Other values (11) 11
21.6%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3331.413
Minimum141
Maximum12284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.153651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile1046.25
Q11751.5
median2856
Q33500
95-th percentile7289.75
Maximum12284
Range12143
Interquartile range (IQR)1748.5

Descriptive statistics

Standard deviation2283.5642
Coefficient of variation (CV)0.68546414
Kurtosis4.3858511
Mean3331.413
Median Absolute Deviation (MAD)1041.5
Skewness1.7960141
Sum153245
Variance5214665.3
MonotonicityNot monotonic
2023-12-13T09:52:19.255809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3500 4
 
8.7%
1575 1
 
2.2%
1043 1
 
2.2%
2198 1
 
2.2%
7450 1
 
2.2%
1227 1
 
2.2%
2500 1
 
2.2%
2774 1
 
2.2%
2900 1
 
2.2%
2610 1
 
2.2%
Other values (33) 33
71.7%
ValueCountFrequency (%)
141 1
2.2%
782 1
2.2%
1043 1
2.2%
1056 1
2.2%
1155 1
2.2%
1227 1
2.2%
1296 1
2.2%
1394 1
2.2%
1499 1
2.2%
1575 1
2.2%
ValueCountFrequency (%)
12284 1
2.2%
8370 1
2.2%
7450 1
2.2%
6809 1
2.2%
6672 1
2.2%
6472 1
2.2%
6375 1
2.2%
4600 1
2.2%
4455 1
2.2%
4320 1
2.2%

코트 면수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3695652
Minimum2
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.339845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5
Q36
95-th percentile11.5
Maximum17
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9466267
Coefficient of variation (CV)0.54876448
Kurtosis4.8896416
Mean5.3695652
Median Absolute Deviation (MAD)1
Skewness1.9627764
Sum247
Variance8.6826087
MonotonicityNot monotonic
2023-12-13T09:52:19.418995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 11
23.9%
5 9
19.6%
6 9
19.6%
3 6
13.0%
2 4
 
8.7%
12 2
 
4.3%
10 2
 
4.3%
17 1
 
2.2%
9 1
 
2.2%
8 1
 
2.2%
ValueCountFrequency (%)
2 4
 
8.7%
3 6
13.0%
4 11
23.9%
5 9
19.6%
6 9
19.6%
8 1
 
2.2%
9 1
 
2.2%
10 2
 
4.3%
12 2
 
4.3%
17 1
 
2.2%
ValueCountFrequency (%)
17 1
 
2.2%
12 2
 
4.3%
10 2
 
4.3%
9 1
 
2.2%
8 1
 
2.2%
6 9
19.6%
5 9
19.6%
4 11
23.9%
3 6
13.0%
2 4
 
8.7%

좌석수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)57.1%
Missing25
Missing (%)54.3%
Infinite0
Infinite (%)0.0%
Mean448.80952
Minimum100
Maximum1845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.497842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median200
Q3500
95-th percentile1675
Maximum1845
Range1745
Interquartile range (IQR)300

Descriptive statistics

Standard deviation498.72193
Coefficient of variation (CV)1.1112107
Kurtosis3.5391375
Mean448.80952
Median Absolute Deviation (MAD)100
Skewness2.1057871
Sum9425
Variance248723.56
MonotonicityNot monotonic
2023-12-13T09:52:19.574716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
200 7
 
15.2%
350 2
 
4.3%
500 2
 
4.3%
100 2
 
4.3%
1675 1
 
2.2%
1845 1
 
2.2%
540 1
 
2.2%
1200 1
 
2.2%
159 1
 
2.2%
369 1
 
2.2%
Other values (2) 2
 
4.3%
(Missing) 25
54.3%
ValueCountFrequency (%)
100 2
 
4.3%
159 1
 
2.2%
165 1
 
2.2%
172 1
 
2.2%
200 7
15.2%
350 2
 
4.3%
369 1
 
2.2%
500 2
 
4.3%
540 1
 
2.2%
1200 1
 
2.2%
ValueCountFrequency (%)
1845 1
 
2.2%
1675 1
 
2.2%
1200 1
 
2.2%
540 1
 
2.2%
500 2
 
4.3%
369 1
 
2.2%
350 2
 
4.3%
200 7
15.2%
172 1
 
2.2%
165 1
 
2.2%

수용인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)44.0%
Missing21
Missing (%)45.7%
Infinite0
Infinite (%)0.0%
Mean530.84
Minimum100
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.654032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median200
Q3500
95-th percentile2400
Maximum2500
Range2400
Interquartile range (IQR)300

Descriptive statistics

Standard deviation705.97708
Coefficient of variation (CV)1.3299244
Kurtosis4.1282583
Mean530.84
Median Absolute Deviation (MAD)100
Skewness2.2831073
Sum13271
Variance498403.64
MonotonicityNot monotonic
2023-12-13T09:52:19.971075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
200 8
 
17.4%
100 4
 
8.7%
500 3
 
6.5%
2500 2
 
4.3%
400 2
 
4.3%
540 1
 
2.2%
2000 1
 
2.2%
300 1
 
2.2%
159 1
 
2.2%
800 1
 
2.2%
(Missing) 21
45.7%
ValueCountFrequency (%)
100 4
8.7%
159 1
 
2.2%
172 1
 
2.2%
200 8
17.4%
300 1
 
2.2%
400 2
 
4.3%
500 3
 
6.5%
540 1
 
2.2%
800 1
 
2.2%
2000 1
 
2.2%
ValueCountFrequency (%)
2500 2
 
4.3%
2000 1
 
2.2%
800 1
 
2.2%
540 1
 
2.2%
500 3
 
6.5%
400 2
 
4.3%
300 1
 
2.2%
200 8
17.4%
172 1
 
2.2%
159 1
 
2.2%

준공 연도
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437874.37
Minimum1981
Maximum20052016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:20.058537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1981
5-th percentile1987.5
Q12000.5
median2006
Q32013
95-th percentile2018
Maximum20052016
Range20050035
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation2956212.9
Coefficient of variation (CV)6.751281
Kurtosis46
Mean437874.37
Median Absolute Deviation (MAD)7
Skewness6.78233
Sum20142221
Variance8.7391948 × 1012
MonotonicityNot monotonic
2023-12-13T09:52:20.153797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2003 4
 
8.7%
2006 4
 
8.7%
2002 4
 
8.7%
2016 3
 
6.5%
1981 2
 
4.3%
2018 2
 
4.3%
2005 2
 
4.3%
2010 2
 
4.3%
1995 2
 
4.3%
2009 2
 
4.3%
Other values (16) 19
41.3%
ValueCountFrequency (%)
1981 2
4.3%
1987 1
 
2.2%
1989 2
4.3%
1990 1
 
2.2%
1992 1
 
2.2%
1994 1
 
2.2%
1995 2
4.3%
1999 1
 
2.2%
2000 1
 
2.2%
2002 4
8.7%
ValueCountFrequency (%)
20052016 1
 
2.2%
2021 1
 
2.2%
2018 2
4.3%
2017 2
4.3%
2016 3
6.5%
2015 1
 
2.2%
2014 1
 
2.2%
2013 2
4.3%
2012 1
 
2.2%
2010 2
4.3%

건설사업비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)73.7%
Missing27
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean133.73684
Minimum12
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:20.243867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q157.5
median80
Q3171.5
95-th percentile388.4
Maximum500
Range488
Interquartile range (IQR)114

Descriptive statistics

Standard deviation128.14438
Coefficient of variation (CV)0.9581831
Kurtosis2.8935958
Mean133.73684
Median Absolute Deviation (MAD)55
Skewness1.7157161
Sum2541
Variance16420.982
MonotonicityNot monotonic
2023-12-13T09:52:20.330696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
60 4
 
8.7%
12 2
 
4.3%
150 2
 
4.3%
55 1
 
2.2%
193 1
 
2.2%
257 1
 
2.2%
34 1
 
2.2%
376 1
 
2.2%
199 1
 
2.2%
80 1
 
2.2%
Other values (4) 4
 
8.7%
(Missing) 27
58.7%
ValueCountFrequency (%)
12 2
4.3%
34 1
 
2.2%
45 1
 
2.2%
55 1
 
2.2%
60 4
8.7%
80 1
 
2.2%
103 1
 
2.2%
135 1
 
2.2%
150 2
4.3%
193 1
 
2.2%
ValueCountFrequency (%)
500 1
 
2.2%
376 1
 
2.2%
257 1
 
2.2%
199 1
 
2.2%
193 1
 
2.2%
150 2
4.3%
135 1
 
2.2%
103 1
 
2.2%
80 1
 
2.2%
60 4
8.7%

Interactions

2023-12-13T09:52:16.382166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.416726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.012834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.577887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.169193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.788836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.382840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.203322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.772970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.439117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.472431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.072239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.645727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.229970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.854190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.442601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.257441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.831917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.495263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.539743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.132743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.708717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.294057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.920214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.508960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.327191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.898541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.552658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.616538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.195400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.773321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.356792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.984459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.572387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.386875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.969125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.614035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.695043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.260094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.834242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.418542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.052636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.632251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.448944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.043770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.682737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.762709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.325030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.915829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.502891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.126648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.701860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.510685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.116100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.742843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.827086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.389466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.977944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.565402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.188959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.767281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.571032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.183402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.809752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.884431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.448794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.038213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.624835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.248420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.838776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.625129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.248632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.888105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.956372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:12.520608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.110176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:13.712071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.318381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:14.913471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:15.697754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:16.319164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:52:20.401449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구시설명소유기관관리주체부지면적건축면적연면적바닥재료면적코트 면수좌석수수용인원준공 연도건설사업비
시군구1.0001.0001.0000.9740.0000.8960.3600.8510.0000.0000.0850.3310.1840.638
시설명1.0001.0001.0000.9781.0001.0001.0000.8880.9731.0000.9270.9421.0001.000
소유기관1.0001.0001.0000.9740.0000.8960.3600.8510.0000.0000.0850.3310.1840.638
관리주체0.9740.9780.9741.0000.7950.0000.0000.5690.0000.0000.0000.3791.0000.857
부지면적0.0001.0000.0000.7951.0000.3810.1530.7000.5900.4070.0000.0000.0000.534
건축면적0.8961.0000.8960.0000.3811.0001.0000.0000.4830.4510.6450.501NaNNaN
연면적0.3601.0000.3600.0000.1531.0001.0000.0000.4200.4080.6450.501NaNNaN
바닥재료0.8510.8880.8510.5690.7000.0000.0001.0000.7050.6780.0000.4921.0000.636
면적0.0000.9730.0000.0000.5900.4830.4200.7051.0000.9270.5420.6550.0000.408
코트 면수0.0001.0000.0000.0000.4070.4510.4080.6780.9271.0000.5310.2520.0000.000
좌석수0.0850.9270.0850.0000.0000.6450.6450.0000.5420.5311.0000.991NaN0.908
수용인원0.3310.9420.3310.3790.0000.5010.5010.4920.6550.2520.9911.000NaN0.473
준공 연도0.1841.0000.1841.0000.000NaNNaN1.0000.0000.000NaNNaN1.000NaN
건설사업비0.6381.0000.6380.8570.534NaNNaN0.6360.4080.0000.9080.473NaN1.000
2023-12-13T09:52:20.509578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
바닥재료소유기관시군구
바닥재료1.0000.3890.389
소유기관0.3891.0001.000
시군구0.3891.0001.000
2023-12-13T09:52:20.583816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부지면적건축면적연면적면적코트 면수좌석수수용인원준공 연도건설사업비시군구소유기관바닥재료
부지면적1.0000.2950.3650.5660.472-0.0240.2030.2280.7180.0000.0000.343
건축면적0.2951.0000.9650.3960.6030.5760.7150.0770.1260.4120.4120.000
연면적0.3650.9651.0000.2840.3500.5320.6830.1210.1710.0000.0000.000
면적0.5660.3960.2841.0000.8110.4460.5060.1400.7140.0000.0000.317
코트 면수0.4720.6030.3500.8111.0000.5500.6170.0490.4640.0000.0000.391
좌석수-0.0240.5760.5320.4460.5501.0000.978-0.1020.5280.0000.0000.000
수용인원0.2030.7150.6830.5060.6170.9781.000-0.0880.3510.0000.0000.141
준공 연도0.2280.0770.1210.1400.049-0.102-0.0881.0000.2700.1510.1510.783
건설사업비0.7180.1260.1710.7140.4640.5280.3510.2701.0000.2040.2040.243
시군구0.0000.4120.0000.0000.0000.0000.0000.1510.2041.0001.0000.389
소유기관0.0000.4120.0000.0000.0000.0000.0000.1510.2041.0001.0000.389
바닥재료0.3430.0000.0000.3170.3910.0000.1410.7830.2430.3890.3891.000

Missing values

2023-12-13T09:52:16.999088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:52:17.174786image/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.
2023-12-13T09:52:17.296309image/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

시군구시설명소유기관관리주체부지면적건축면적연면적바닥재료면적코트 면수좌석수수용인원준공 연도건설사업비
0목포시목포 시립테니스장목포시위탁_테니스클럽3195112112클레이15755200200198112
1목포시목포 시립정구장목포시위탁_소프트테니스클럽2562103103클레이10434200200198112
2목포시목포 시민테니스장목포시위탁_테니스클럽2154<NA><NA>클레이10564200200199455
3목포시부주산테니스장목포시체육시설관리사무소7273<NA><NA>앙투카837012167525002006<NA>
4여수시진남종합테니스장여수시여수시_체육지원과2000062566274하드1228417184525002014<NA>
5여수시여천시립테니스장여수시위탁_테니스협회)25513232토사25513<NA><NA>1989<NA>
6여수시여문 테니스장여수시위탁_테니스클럽)19411818클레이19303<NA>1001999<NA>
7여수시웅천테니스장여수시위탁 (테니스협회)32004040하드코트1413<NA>1002016<NA>
8순천시팔마 테니스장순천시체육시설관리사무소12000164164하드6672105405402002193
9순천시팔마 정구장 1순천시체육시설관리사무소7530222222마사토46006120020001987257
시군구시설명소유기관관리주체부지면적건축면적연면적바닥재료면적코트 면수좌석수수용인원준공 연도건설사업비
36함평군구룡 테니스장함평군위탁_함평군테니스협회)3890<NA><NA>토사350062002001989<NA>
37영광군종합체육센터테니스장영광군영광군4998507532하드 2앙투카 440136369800200760
38영광군종합체육센터 테니스장영광군영광군2231<NA><NA>하드 316923<NA><NA>201560
39장성군워라밸 돔 경기장장성군장성군_문화시설사업소)-799435173230토사30885165<NA>2018<NA>
40장성군삼계 테니스장장성군장성군3584<NA><NA>인조잔디424634<NA><NA>20052016<NA>
41완도군완도군립테니스장1완도군위탁_테니스협회)6468<NA><NA>잔디328143<NA><NA>2000103
42완도군완도군립테니스장2완도군위탁_테니스협회)11794215215하드96809121721722009<NA>
43진도군가마골 테니스장진도군진도군5233104202앙투카34464<NA><NA>2005135
44진도군진도 테니스장진도군진도군6168187225앙투카34126<NA><NA>2003500
45신안군신안테니스장신안군신안군1394<NA><NA>토사13942<NA><NA>2016<NA>