Overview

Dataset statistics

Number of variables12
Number of observations29
Missing cells26
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory106.6 B

Variable types

Numeric6
Categorical2
Text4

Dataset

Description경상남도내 수영장 현황입니다. 수영장의 시도, 시군구, 시설명, 소유기관, 관리주체, 부지면적, 건축면적 등에 관한 정보를 제공합니다.
Author경상남도
URLhttps://www.data.go.kr/data/3079741/fileData.do

Alerts

시도 has constant value ""Constant
실내외 구분 has constant value ""Constant
건축면적_제곱미터 is highly overall correlated with 연면적_제곱미터High correlation
연면적_제곱미터 is highly overall correlated with 건축면적_제곱미터High correlation
부지면적_제곱미터 has 5 (17.2%) missing valuesMissing
건축면적_제곱미터 has 1 (3.4%) missing valuesMissing
연면적_제곱미터 has 1 (3.4%) missing valuesMissing
관람석 수용인원_명 has 19 (65.5%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:28:53.536536
Analysis finished2023-12-11 23:28:57.671427
Duration4.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T08:28:57.727187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q18
median15
Q322
95-th percentile27.6
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5146932
Coefficient of variation (CV)0.56764621
Kurtosis-1.2
Mean15
Median Absolute Deviation (MAD)7
Skewness0
Sum435
Variance72.5
MonotonicityStrictly increasing
2023-12-12T08:28:57.860702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 1
 
3.4%
2 1
 
3.4%
29 1
 
3.4%
28 1
 
3.4%
27 1
 
3.4%
26 1
 
3.4%
25 1
 
3.4%
24 1
 
3.4%
23 1
 
3.4%
22 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1 1
3.4%
2 1
3.4%
3 1
3.4%
4 1
3.4%
5 1
3.4%
6 1
3.4%
7 1
3.4%
8 1
3.4%
9 1
3.4%
10 1
3.4%
ValueCountFrequency (%)
29 1
3.4%
28 1
3.4%
27 1
3.4%
26 1
3.4%
25 1
3.4%
24 1
3.4%
23 1
3.4%
22 1
3.4%
21 1
3.4%
20 1
3.4%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
경상남도
29 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 29
100.0%

Length

2023-12-12T08:28:58.028880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:28:58.170610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 29
100.0%

시군
Text

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T08:28:58.334716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters87
Distinct characters28
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

Unique10 ?
Unique (%)34.5%

Sample

1st row창원시
2nd row창원시
3rd row창원시
4th row창원시
5th row창원시
ValueCountFrequency (%)
창원시 11
37.9%
통영시 2
 
6.9%
김해시 2
 
6.9%
남해군 2
 
6.9%
산청군 2
 
6.9%
진주시 1
 
3.4%
사천시 1
 
3.4%
밀양시 1
 
3.4%
거제시 1
 
3.4%
의령군 1
 
3.4%
Other values (5) 5
17.2%
2023-12-12T08:28:58.626997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

시설명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T08:28:58.861932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length11.034483
Min length7

Characters and Unicode

Total characters320
Distinct characters79
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

Unique29 ?
Unique (%)100.0%

Sample

1st row창원 실내수영장
2nd row늘푸른전당수영장
3rd row시민생활체육관수영장
4th row서부스포츠센터수영장
5th row올림픽기념국민생활관체육관 부설수영장
ValueCountFrequency (%)
실내수영장 6
 
14.3%
수영장 5
 
11.9%
창녕군립수영장 1
 
2.4%
시민스포츠센터수영장 1
 
2.4%
김해서부문화센터수영장 1
 
2.4%
밀양스포츠센터수영장 1
 
2.4%
거제시문화예술회관 1
 
2.4%
의령국민체육센터수영장 1
 
2.4%
창원 1
 
2.4%
사천 1
 
2.4%
Other values (23) 23
54.8%
2023-12-12T08:28:59.240670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
영 30
 
9.4%
수 28
 
8.8%
장 28
 
8.8%
센 15
 
4.7%
터 15
 
4.7%
13
 
4.1%
내 12
 
3.8%
체 11
 
3.4%
육 11
 
3.4%
실 10
 
3.1%
Other values (69) 147
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 305
95.3%
Space Separator 13
 
4.1%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 30
 
9.8%
수 28
 
9.2%
장 28
 
9.2%
센 15
 
4.9%
터 15
 
4.9%
내 12
 
3.9%
체 11
 
3.6%
육 11
 
3.6%
실 10
 
3.3%
민 9
 
3.0%
Other values (66) 136
44.6%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 305
95.3%
Common 15
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 30
 
9.8%
수 28
 
9.2%
장 28
 
9.2%
센 15
 
4.9%
터 15
 
4.9%
내 12
 
3.9%
체 11
 
3.6%
육 11
 
3.6%
실 10
 
3.3%
민 9
 
3.0%
Other values (66) 136
44.6%
Common
ValueCountFrequency (%)
13
86.7%
( 1
 
6.7%
) 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 305
95.3%
ASCII 15
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
영 30
 
9.8%
수 28
 
9.2%
장 28
 
9.2%
센 15
 
4.9%
터 15
 
4.9%
내 12
 
3.9%
체 11
 
3.6%
육 11
 
3.6%
실 10
 
3.3%
민 9
 
3.0%
Other values (66) 136
44.6%
ASCII
ValueCountFrequency (%)
13
86.7%
( 1
 
6.7%
) 1
 
6.7%

소유기관
Text

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T08:28:59.414976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters87
Distinct characters28
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

Unique10 ?
Unique (%)34.5%

Sample

1st row창원시
2nd row창원시
3rd row창원시
4th row창원시
5th row창원시
ValueCountFrequency (%)
창원시 11
37.9%
통영시 2
 
6.9%
김해시 2
 
6.9%
남해군 2
 
6.9%
산청군 2
 
6.9%
진주시 1
 
3.4%
사천시 1
 
3.4%
밀양시 1
 
3.4%
거제시 1
 
3.4%
의령군 1
 
3.4%
Other values (5) 5
17.2%
2023-12-12T08:28:59.705767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 19
21.8%
창 12
13.8%
원 11
12.6%
군 10
11.5%
해 4
 
4.6%
양 2
 
2.3%
청 2
 
2.3%
산 2
 
2.3%
천 2
 
2.3%
남 2
 
2.3%
Other values (18) 21
24.1%

관리주체
Text

Distinct19
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T08:28:59.872569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length7.5172414
Min length3

Characters and Unicode

Total characters218
Distinct characters67
Distinct categories5 ?
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 (%)55.2%

Sample

1st row시설관리공단
2nd row시설관리공단
3rd row시설관리공단
4th row시설관리공단
5th row시설관리공단
ValueCountFrequency (%)
시설관리공단 9
28.1%
체육진흥과 2
 
6.2%
민간위탁(통영관광개발공사 2
 
6.2%
김해문화재단 2
 
6.2%
고성군 1
 
3.1%
함양군 1
 
3.1%
산청군 1
 
3.1%
위탁(산청군체육회 1
 
3.1%
하동군 1
 
3.1%
남해군 1
 
3.1%
Other values (11) 11
34.4%
2023-12-12T08:29:00.182705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 15
 
6.9%
관 13
 
6.0%
공 13
 
6.0%
단 13
 
6.0%
설 11
 
5.0%
리 11
 
5.0%
11
 
5.0%
군 9
 
4.1%
위 7
 
3.2%
탁 7
 
3.2%
Other values (57) 108
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
88.1%
Space Separator 11
 
5.0%
Open Punctuation 7
 
3.2%
Close Punctuation 7
 
3.2%
Other Symbol 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 15
 
7.8%
관 13
 
6.8%
공 13
 
6.8%
단 13
 
6.8%
설 11
 
5.7%
리 11
 
5.7%
군 9
 
4.7%
위 7
 
3.6%
탁 7
 
3.6%
체 5
 
2.6%
Other values (53) 88
45.8%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 193
88.5%
Common 25
 
11.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 15
 
7.8%
관 13
 
6.7%
공 13
 
6.7%
단 13
 
6.7%
설 11
 
5.7%
리 11
 
5.7%
군 9
 
4.7%
위 7
 
3.6%
탁 7
 
3.6%
체 5
 
2.6%
Other values (54) 89
46.1%
Common
ValueCountFrequency (%)
11
44.0%
( 7
28.0%
) 7
28.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
88.1%
ASCII 25
 
11.5%
None 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 15
 
7.8%
관 13
 
6.8%
공 13
 
6.8%
단 13
 
6.8%
설 11
 
5.7%
리 11
 
5.7%
군 9
 
4.7%
위 7
 
3.6%
탁 7
 
3.6%
체 5
 
2.6%
Other values (53) 88
45.8%
ASCII
ValueCountFrequency (%)
11
44.0%
( 7
28.0%
) 7
28.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

부지면적_제곱미터
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)91.7%
Missing5
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean24685.042
Minimum3489
Maximum96288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T08:29:00.320002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3489
5-th percentile5880.45
Q17641.5
median19020.5
Q331439.5
95-th percentile56323.95
Maximum96288
Range92799
Interquartile range (IQR)23798

Descriptive statistics

Standard deviation22105.431
Coefficient of variation (CV)0.89549905
Kurtosis3.5705603
Mean24685.042
Median Absolute Deviation (MAD)11745.5
Skewness1.7349971
Sum592441
Variance4.886501 × 108
MonotonicityNot monotonic
2023-12-12T08:29:00.444233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
9916 2
 
6.9%
20476 2
 
6.9%
24119 1
 
3.4%
52751 1
 
3.4%
3489 1
 
3.4%
7650 1
 
3.4%
96288 1
 
3.4%
5832 1
 
3.4%
15478 1
 
3.4%
56952 1
 
3.4%
Other values (12) 12
41.4%
(Missing) 5
17.2%
ValueCountFrequency (%)
3489 1
3.4%
5832 1
3.4%
6155 1
3.4%
6314 1
3.4%
6934 1
3.4%
7616 1
3.4%
7650 1
3.4%
9916 2
6.9%
12228 1
3.4%
15478 1
3.4%
ValueCountFrequency (%)
96288 1
3.4%
56952 1
3.4%
52765 1
3.4%
52751 1
3.4%
39626 1
3.4%
31780 1
3.4%
31326 1
3.4%
29908 1
3.4%
26881 1
3.4%
24119 1
3.4%

건축면적_제곱미터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing1
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean2987.1429
Minimum799
Maximum11208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T08:29:00.559269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum799
5-th percentile1056.5
Q11545
median2308
Q33193
95-th percentile7513
Maximum11208
Range10409
Interquartile range (IQR)1648

Descriptive statistics

Standard deviation2330.2329
Coefficient of variation (CV)0.78008752
Kurtosis5.3841608
Mean2987.1429
Median Absolute Deviation (MAD)831
Skewness2.2164323
Sum83640
Variance5429985.2
MonotonicityNot monotonic
2023-12-12T08:29:00.676287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
8206 1
 
3.4%
1740 1
 
3.4%
3077 1
 
3.4%
1771 1
 
3.4%
1571 1
 
3.4%
1772 1
 
3.4%
1358 1
 
3.4%
2197 1
 
3.4%
934 1
 
3.4%
1836 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
799 1
3.4%
934 1
3.4%
1284 1
3.4%
1287 1
3.4%
1354 1
3.4%
1358 1
3.4%
1467 1
3.4%
1571 1
3.4%
1740 1
3.4%
1771 1
3.4%
ValueCountFrequency (%)
11208 1
3.4%
8206 1
3.4%
6226 1
3.4%
5825 1
3.4%
3889 1
3.4%
3424 1
3.4%
3385 1
3.4%
3129 1
3.4%
3077 1
3.4%
2995 1
3.4%

연면적_제곱미터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing1
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean4720.7143
Minimum785
Maximum14329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T08:29:00.814173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum785
5-th percentile1036.2
Q12424.75
median3088.5
Q35638.75
95-th percentile13119.15
Maximum14329
Range13544
Interquartile range (IQR)3214

Descriptive statistics

Standard deviation3938.86
Coefficient of variation (CV)0.83437797
Kurtosis0.77466395
Mean4720.7143
Median Absolute Deviation (MAD)1645.5
Skewness1.348477
Sum132180
Variance15514618
MonotonicityNot monotonic
2023-12-12T08:29:00.958000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
14329 1
 
3.4%
2358 1
 
3.4%
6796 1
 
3.4%
2769 1
 
3.4%
2561 1
 
3.4%
2762 1
 
3.4%
2594 1
 
3.4%
4213 1
 
3.4%
934 1
 
3.4%
2580 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
785 1
3.4%
934 1
3.4%
1226 1
3.4%
1287 1
3.4%
1354 1
3.4%
1532 1
3.4%
2358 1
3.4%
2447 1
3.4%
2561 1
3.4%
2580 1
3.4%
ValueCountFrequency (%)
14329 1
3.4%
13774 1
3.4%
11903 1
3.4%
11515 1
3.4%
8674 1
3.4%
7915 1
3.4%
6796 1
3.4%
5253 1
3.4%
5223 1
3.4%
4213 1
3.4%

실내외 구분
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
실내
29 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
실내 29
100.0%

Length

2023-12-12T08:29:01.107745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:29:01.224075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실내 29
100.0%

관람석 수용인원_명
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)80.0%
Missing19
Missing (%)65.5%
Infinite0
Infinite (%)0.0%
Mean713.9
Minimum91
Maximum3078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T08:29:01.324638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile95.05
Q1112.5
median350
Q3500
95-th percentile2556.9
Maximum3078
Range2987
Interquartile range (IQR)387.5

Descriptive statistics

Standard deviation992.45671
Coefficient of variation (CV)1.3901901
Kurtosis3.2941592
Mean713.9
Median Absolute Deviation (MAD)225
Skewness1.9837535
Sum7139
Variance984970.32
MonotonicityNot monotonic
2023-12-12T08:29:01.439369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
500 2
 
6.9%
100 2
 
6.9%
3078 1
 
3.4%
91 1
 
3.4%
400 1
 
3.4%
300 1
 
3.4%
1920 1
 
3.4%
150 1
 
3.4%
(Missing) 19
65.5%
ValueCountFrequency (%)
91 1
3.4%
100 2
6.9%
150 1
3.4%
300 1
3.4%
400 1
3.4%
500 2
6.9%
1920 1
3.4%
3078 1
3.4%
ValueCountFrequency (%)
3078 1
3.4%
1920 1
3.4%
500 2
6.9%
400 1
3.4%
300 1
3.4%
150 1
3.4%
100 2
6.9%
91 1
3.4%

준공연도
Real number (ℝ)

Distinct21
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.8966
Minimum1989
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T08:29:01.552711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1990.2
Q11999
median2007
Q32013
95-th percentile2018
Maximum2020
Range31
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.0093001
Coefficient of variation (CV)0.0044914081
Kurtosis-0.85209873
Mean2005.8966
Median Absolute Deviation (MAD)7
Skewness-0.31063173
Sum58171
Variance81.167488
MonotonicityNot monotonic
2023-12-12T08:29:01.664153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2003 4
 
13.8%
2008 2
 
6.9%
2018 2
 
6.9%
2011 2
 
6.9%
1997 2
 
6.9%
1989 2
 
6.9%
2002 1
 
3.4%
1998 1
 
3.4%
2014 1
 
3.4%
2020 1
 
3.4%
Other values (11) 11
37.9%
ValueCountFrequency (%)
1989 2
6.9%
1992 1
 
3.4%
1993 1
 
3.4%
1997 2
6.9%
1998 1
 
3.4%
1999 1
 
3.4%
2002 1
 
3.4%
2003 4
13.8%
2006 1
 
3.4%
2007 1
 
3.4%
ValueCountFrequency (%)
2020 1
3.4%
2018 2
6.9%
2017 1
3.4%
2016 1
3.4%
2015 1
3.4%
2014 1
3.4%
2013 1
3.4%
2012 1
3.4%
2011 2
6.9%
2009 1
3.4%

Interactions

2023-12-12T08:28:56.554383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:53.929113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.436227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.931503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.455289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.049619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.643338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.011739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.513713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.014620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.566187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.149859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.715019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.094927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.596811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.094362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.672630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.234251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.785675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.173562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.672879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.194908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.749023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.320528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.862832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.262665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.750749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.273124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.850772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.399427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.952101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.352263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:54.843995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.370726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:55.947719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:28:56.480414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:29:01.749167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군시설명소유기관관리주체부지면적_제곱미터건축면적_제곱미터연면적_제곱미터관람석 수용인원_명준공연도
연번1.0000.8501.0000.8500.8930.4990.0000.0000.5370.802
시군0.8501.0001.0001.0001.0000.6770.0000.0000.0000.117
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소유기관0.8501.0001.0001.0001.0000.6770.0000.0000.0000.117
관리주체0.8931.0001.0001.0001.0000.5580.0000.0000.0000.000
부지면적_제곱미터0.4990.6771.0000.6770.5581.0000.3550.0000.2340.457
건축면적_제곱미터0.0000.0001.0000.0000.0000.3551.0000.6510.9090.124
연면적_제곱미터0.0000.0001.0000.0000.0000.0000.6511.0000.0000.000
관람석 수용인원_명0.5370.0001.0000.0000.0000.2340.9090.0001.0000.409
준공연도0.8020.1171.0000.1170.0000.4570.1240.0000.4091.000
2023-12-12T08:29:01.877780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부지면적_제곱미터건축면적_제곱미터연면적_제곱미터관람석 수용인원_명준공연도
연번1.000-0.018-0.448-0.320-0.3840.369
부지면적_제곱미터-0.0181.0000.090-0.0260.060-0.233
건축면적_제곱미터-0.4480.0901.0000.6890.128-0.238
연면적_제곱미터-0.320-0.0260.6891.000-0.0180.090
관람석 수용인원_명-0.3840.0600.128-0.0181.000-0.388
준공연도0.369-0.233-0.2380.090-0.3881.000

Missing values

2023-12-12T08:28:57.290561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:28:57.453693image/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.
2023-12-12T08:28:57.602939image/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

연번시도시군시설명소유기관관리주체부지면적_제곱미터건축면적_제곱미터연면적_제곱미터실내외 구분관람석 수용인원_명준공연도
01경상남도창원시창원 실내수영장창원시시설관리공단39626820614329실내30781997
12경상남도창원시늘푸른전당수영장창원시시설관리공단3178012871287실내<NA>1997
23경상남도창원시시민생활체육관수영장창원시시설관리공단761633853385실내<NA>1992
34경상남도창원시서부스포츠센터수영장창원시시설관리공단9916388911903실내<NA>2009
45경상남도창원시올림픽기념국민생활관체육관 부설수영장창원시시설관리공단2047628707915실내<NA>1989
56경상남도창원시우리누리 청소년문화센터실내수영장창원시시설관리공단29908582513774실내912008
67경상남도창원시마산실내체육관내 수영장창원시시설관리공단2047662261532실내<NA>1989
78경상남도창원시진동종합복지관내 실내수영장창원시시설관리공단2688131295223실내5002011
89경상남도창원시곰두리국민체육센터 수영장창원시경남지체장애인협회창원지회631422221226실내<NA>2012
910경상남도창원시성산스포츠센터수영장창원시시설관리공단9916253811515실내<NA>2016
연번시도시군시설명소유기관관리주체부지면적_제곱미터건축면적_제곱미터연면적_제곱미터실내외 구분관람석 수용인원_명준공연도
1920경상남도의령군의령국민체육센터수영장의령군의령군527651467785실내<NA>2007
2021경상남도창녕군창녕군립수영장창녕군위탁(창녕군시설관리공단)56952112085253실내<NA>2018
2122경상남도고성군고성군문화체육센터수영장고성군고성군<NA>18362580실내<NA>2003
2223경상남도남해군남해스포츠파크실내수영장남해군님해군 체육진흥과15478934934실내<NA>2002
2324경상남도남해군남해국민체육센터실내수영장남해군남해군 체육진흥과583221974213실내<NA>2015
2425경상남도하동군하동국민체육센터 수영장하동군하동군9628813582594실내<NA>2013
2526경상남도산청군산청문화예술회관수영장산청군위탁(산청군체육회)765017722762실내<NA>2006
2627경상남도산청군남부문화체육센터산청군산청군348915712561실내<NA>2020
2728경상남도함양군함양국민체육센터(수영장)함양군함양군<NA>17712769실내1002014
2829경상남도합천군합천 실내수영장합천군위탁(개인)5275130776796실내1001998